I might have missed the mark on some exact tokens, and not every call was correct, but I firmly believe that everything has been at least directionally accurate. And I keep coming back to this stuff because I genuinely believe it’s where the puck is going. But until you actually look at the numbers side by side, it’s hard to appreciate just how dramatic the rotation has been over the past year and a bit.

So today I want to zoom out and walk through the data. The collapse of the memecoin sector, the quiet but steady growth of AI tokens, and what I think it means for how you should be thinking about positioning in 2026.

Let’s jump in.

The memecoin collapse

It’ll be no surprise to anyone that the memecoin sector has collapsed, but let’s take a look at the numbers to see just how much it has.

The memecoin sector peaked at $150.6 billion in December 2024, fueled by Trump’s re-election in November and the broader political frenzy. The TRUMP token launch in January 2025 felt like it marked the pico-top, but actually the sector was well into decline already by then. By January 2026 the sector had fallen to $36.5bn, roughly where it still sits today.

That’s a peak-to-trough drop of over 75% in 15 months. Bitcoin held above $93k for most of that period and even hit new ATHs, so memecoins didn’t collapse because crypto collapsed, they collapsed because the engine that powered them ran out of fuel.

The volume side is even uglier. Memecoin trading volume peaked near $20B per day in early to mid 2025 and by the end of the year it had dropped to under $3B.

Pump.fun, the launchpad that powered most of the cycle, tells the same story from a different angle. Platform revenue collapsed from peaks of $7M+ per day in early 2025 to $1-1.5M per day by early 2026 (a number which still blows my mind tbh, it’s pretty insane that they’re still making that sort of money). But, that’s a 75-80% drop in fees, which aligns with the drops in overall marketcap and volume.

The other thing worth knowing about Pump.fun activity is that a lot of what was reported as “user activity” was never really human users to begin with. Dune Analytics dashboards from on-chain analyst @adam_tehc found that 93 out of the top 100 wallets by volume on Pump.fun are tagged as automated bots. The top wallets show 18+ hours of daily activity (which obviously isn’t human). Pump.fun’s founder Alon admitted that around 30% of all wallets on the platform have only ever made a single transaction, which he conceded are likely bots and AI agents.

So when you hear about the platform having “millions of users”, take it with a heavy grain of salt (a very good rule to follow for pretty much anything in crypto). A meaningful chunk of that activity is and was bots gaming the system to farm an airdrop that ended up never really materializing for them anyway (RIP).

The retail story is just as bad. According to a Dune dashboard tracking 1.4 million wallets that traded Pump.fun tokens, 96% either lost money or made less than $500 in profit. Out of 13.55 million wallet addresses across the platform’s lifetime, only 0.412% ever realized profits above $10,000.

Most people are probably better off buying a lottery ticket.

The token graveyard

I covered a lot of this in Letter 106 a few weeks ago, but it’s worth repeating because it’s genuinely staggering and it backs up the broader point. Over 13 million memecoins were launched in 2025. According to federal lawsuits filed against Pump.fun, 98% of those tokens collapsed within 24 hours of launch.

Less than 0.63% of Pump.fun tokens ever graduate to a real DEX. The other 99.37% die before they reach a $90k market cap.

Another thing to note is just how concentrated the sector is. DOGE and SHIB now make up 84% of the entire memecoin market cap, and honestly, when was the last time anyone got excited by either of those coins? Strip those two out and the rest of the sector — the long tail of every TRUMP, FARTCOIN, PIPPIN, and 13 million others — accounts for the remaining 16%.

So when people talk about the “memecoin sector,” what they’re really talking about is two coins from 2013 and 2020 plus a graveyard filled with Murad’s hopes and dreams.

A look at the AI side

While the memecoin sector was bleeding out, AI tokens did something different. They didn’t exactly skyrocket, and the story isn’t that AI tokens went to the moon while memes died. The story is that AI tokens grew steadily while memes died, and the gap between them has compressed dramatically.

In early 2025, the AI crypto sector was around $15B in market cap. Memecoins were peaking near $150B. The ratio was roughly 10:1 in favor of memes.

As of April 2026, the AI crypto sector sits at $22.6B, while memecoins sit at $36-38B. The ratio is now closer to 1.6:1.

That’s a 6x change in the relative sizing of two of crypto’s biggest narratives.

And it tracks with what I’ve been seeing on the ground. When I wrote Letter 99 (AI Season, Take Two?) back in February, the thesis was that the first AI wave was full of glorified chatbots and the second wave would have actual revenue and utility behind it. The data backs this up, not with artificial pumps of AI Agents masquerading as memecoins, but with real products and companies slowly building up over time.

Why the institutions care about one and not the other

Institutional money has been telling us this story for months, with a huge amount of attention being paid to the Bittensor network and TAO token.

Some data points from Q1 2026:

• Nvidia put $420M into TAO with 77% of the position staked

• Polychain Capital added $200M in TAO exposure

• Grayscale filed an S-1 to convert its Bittensor Trust into a spot ETF (ticker GTAO), with a decision expected by August 2026

• Grayscale raised TAO’s weighting in its AI fund from 31.35% to 43.06%, the largest single-asset reallocation they’ve ever made

• Bitwise also filed a TAO ETF in April

• BitGo and Yuma launched institutional custody and staking for Bittensor subnet tokens

Now compare that to the institutional news for memecoins in 2026 «insert tumbleweed gif here».

The biggest story is that Pump.fun is reworking its fee structure to try to bring traders back, and that they’re facing federal lawsuits.

The institutions vote with money, and the money is telling you which direction this is going. Again, it’s not everything, and institutions get it wrong all the time, and god knows there are plenty of sectors within crypto that got billions of dollars of funding that are now gasping for air. But there’s also plenty of evidence that institutional money does support genuine growth (most notably with the Bitcoin ETF(s).

I think it’s important to consider the difference between crypto-native VCs shoveling money into things like gamefi, socialfi, the metaverse, NFTs, and other sectors that struggle, and non-crypto-native companies like Nvidia voting with their wallets / company treasury.

The infrastructure layer

This is the thing I keep coming back to and one of the reasons I’ve been so excited about AI tokens. I called this out in Letter 93 (Ten Predictions for 2026) talking about the x402 protocol, and in Little Learnings #1 where I broke down ERC-8004, the onchain agent identity standard.

But the agentic payments space has changed a lot since I first wrote about x402 a few months ago, and the picture is more nuanced now.

x402 is one protocol in a much bigger landscape

When I first wrote about x402, it felt like THE answer for how AI agents would pay each other. It was the first mover, it was elegant, and Coinbase had a head start. The lazy version of this letter would be to keep banging that drum.

The reality in April 2026 is that x402 is one of several competing protocols, and its early adoption numbers are softer than the hype suggested. Recent on-chain data from Artemis shows daily volume of only around $50k and daily transaction volume actually dropped over 92% from its December 2025 peak as bot-driven activity normalized.

That doesn’t mean the thesis is wrong or the protocol is bad, but it does mean the landscape has changed a bit and the winners are less clear.

Enter Stripe’s Tempo

The biggest development you should know about, that I haven’t covered yet, is Stripe’s blockchain Tempo and its Machine Payments Protocol (MPP).

Tempo is a payments-focused Layer 1 blockchain that Stripe and Paradigm incubated together. It launched mainnet on March 18, 2026, after raising $500 million at a $5 billion valuation in October 2025. The same day they went live on mainnet, they released MPP, an open standard for AI agents to pay for services using either stablecoins or fiat.

The partner list for Tempo is genuinely insane. Design partners include OpenAI, Anthropic, Shopify, Visa, Mastercard, DoorDash, Klarna, Revolut, Nubank, Standard Chartered, Deutsche Bank, and UBS (phew). DoorDash announced last week (April 21) that it’s using Tempo to power stablecoin payouts to merchants and dashers across 40+ countries, which seems like a pretty darn big deal?

For context, Stripe processes nearly $2 trillion (TRILLION) in annual payments. They acquired the stablecoin platform Bridge for $1.1B in 2024. They acquired the wallet provider Privy. They are not messing around.

How MPP differs from x402

The two protocols are similar at the surface level. Both are designed for machines to pay machines, both can settle in stablecoin, and both use the dormant HTTP 402 “Payment Required” status code under the hood. The design philosophies are where they differ:

• x402 is permissionless and minimal. Pay per request. Each transaction settles individually. Anyone can run a facilitator. USDC on Base or Solana.

• MPP adds “sessions”. An agent pre-authorizes a spending limit, then streams thousands of micropayments that batch-settle in a single on-chain transaction. Stripe describes it as “OAuth for money”.

• MPP is rail-agnostic. It runs on Tempo today, but Visa has extended it for cards, Lightspark has extended it for Bitcoin Lightning, and Stripe has extended it for traditional payment methods. x402 is crypto-only.

• MPP launched with 100+ integrated services already including Browserbase, Parallel Web Systems, Ramp, and more. It has Stripe’s entire merchant network behind it.

If x402 is the open-source crypto-native version of agent payments, MPP is the enterprise-ready corporate-backed version. They’re not the same product even though they look similar on paper.

And it’s not just two protocols

Once you start looking at this space, you realize there are at least five major agentic payment standards in play right now, plus card network extensions:

The good news is these protocols are mostly complementary, not competitive. AP2 handles authorization. ACP handles checkout. x402 and MPP handle settlement. A real-world agent workflow might use AP2 for spend governance, ACP for vendor discovery, and x402 or MPP for actual machine-to-machine payment execution. Google’s AP2 has already explicitly integrated with x402 for crypto settlement.

The bad news is this is messy and nobody knows yet which protocol will dominate which layer, so it’s a bit tough to make speculative investment decisions in this sector — other than a spray and pray approach (which tbh is not a terrible idea, betting on the overall sector eventually taking off).

How this changes the investment picture

Three things you should take from this.

1. The bullish case for the agentic payments narrative is even stronger than I previously framed it. When Stripe raises $500M for Tempo and signs DoorDash, Klarna, Mastercard, Visa, OpenAI, Anthropic, and Shopify as partners, that is the largest fintech in the world betting heavily on this. Combine that with what Coinbase is building, and what Google is building, and what Visa and Mastercard are building, and you have every major payments player aligned on agent payments being a big freaking deal.
   

2. The thesis is broader than the original idea of “buy x402-related tokens”. The bigger picture is that stablecoin volume hit $5.7 trillion in 2025 (double the previous year), B2B stablecoin payments surged 733% year over year, and Mastercard just paid $1.8B for the stablecoin firm BVNK. The “crypto rails for AI agents” narrative is part of a much broader “stablecoins are eating payments” story that has nothing to do with memecoins or even most altcoins. This is why I keep saying the AI/payments intersection is where the real opportunity is, not in chasing the next AIXBT or Fartcoin or whatever.
   

3. Tempo specifically is a project to keep an eye on, even though it doesn’t have a token yet. Stripe and Paradigm have explicitly said Tempo will transition from a permissioned validator set to a permissionless one over time. There’s a strong chance that means a token at some point. That would be one of the more interesting launches in the space if it happens.

This is a pretty telling chart. In February 2026, stablecoin monthly transaction volume crossed the US ACH network for the first time ever, hitting $7.2T versus ACH’s $6.8T. And these are not the bot inflated raw numbers (which would put stablecoins at over $25T per year). This is Artemis’s adjusted volume that explicitly strips out MEV activity and intra exchange transfers.

If you don’t know what ACH is, it’s the rail behind essentially every US payroll, mortgage payment, and direct deposit. It’s been around since 1974. It processes roughly 29 billion transactions per year and roughly 93% of all American salary payments. It is the unsexy plumbing of the entire US economy. And it just got flipped by an asset class that didn’t exist 12 years ago… pretty mindblowing. To me it’s another great example of how fast things are accelerating in general in our world.

Visa and Mastercard understand this. That is why both are partnering with Tempo, integrating MPP, and signing up to the x402 Foundation. They would rather upgrade their infrastructure than be replaced by it.

So when I say I’m bullish on the AI infrastructure layer, I mean the whole stack of standards and rails being built right now to let agents transact autonomously. Some of it will be capturable through crypto tokens. Unfortunately (for us), a lot of it will be captured by Stripe, Visa, Mastercard, and corporate-backed L1s like Tempo. The protocol war is just getting started, and it’s going to be a very interesting space to watch.

The capital flow story

One more data point that puts this into context. In 2025, total crypto venture capital was $7.9 billion, up 44% year over year and almost half of that went into AI integrated blockchain projects.

BlackRock projects $5 to $8 trillion in AI capital expenditure between 2025 and 2030. Even a tiny fraction of that finding its way into decentralized AI infrastructure is more than enough to lift a $22bn sector materially.

By comparison, memecoin VC funding is essentially zero. There are no funds raising to invest in the next FARTCOIN (at least that I could find: if any funds are reading this with a flatulatory investment thesis, feel free to correct me).

Practical takeaways

The institutions are voting with their money

When Nvidia, Polychain, Grayscale, and the Linux Foundation all park serious money in the same sector, that’s a signal. When the other sector’s biggest news is federal lawsuits, that’s also a signal.

Memecoins still have a larger marketcap than AI tokens, but it’s concentrated, and the outlook isn’t looking so hot right now

DOGE and SHIB still make up 84% of the sector. The launchpad meta is down bad, and the odds are stacking against the millions of tokens trying to be the next FARTCOIN.

The AI sector is mostly noise too

There are ~900 projects with non-negligble market caps at the moment, and almost all of them will fail. Don’t mistake the broader narrative for an excuse to YOLO into AI memecoins. Letter 106 covered the brutal failure rates across crypto and AI tokens are no exception. Concentrate around the few names with real utility.

The agentic payment layer is the most interesting bet

This is bigger than just x402. Stripe’s Tempo and MPP, Google’s AP2, OpenAI’s ACP, plus card network extensions from Visa and Mastercard, are all converging on agentic payments. Watch the whole stack of standards and rails being built, not just one protocol.

Don’t assume the rotation is done

AI is at $22.6bn. Memes are at $36-38bn. If the trend continues at its current pace then the AI sector will overtake the Memecoin sector by the end of the year — hopefully as they both go up!

Closing thoughts

I’ve been saying for a while that AI is where some of the greatest opportunities in crypto are. Today we looked at the data to back that up in a really clear way. Capital is rotating from sentiment-driven assets to utility-driven ones, and the rotation has been quietly underway for over a year now.

Memecoins are a bet on attention. Attention is finite and rotates fast. AI tokens with real infrastructure are a bet on usage. Usage compounds. Over a long enough timeframe, the second one wins (attention will win any race in the short term, and ultimately attention is the thing that matters but the bet is that utility + usage will drive sustainable attention, whereas memetics based attention is shorter-lived).

That doesn’t mean every AI token is a buy. Most of them are still going to fail, just like nearly everything in crypto fails. But the sector itself has tailwinds that the memecoin sector simply does not have, and those tailwinds are real, sustainable, and getting stronger.

If you’re newer to AI agents and want to actually start playing with this stuff yourself, Letter 107 walks through how to set one up. Letter 108 covers how LLMs actually work under the hood. Both should help you build a better foundation for evaluating the projects in this space.

If there’s interest, I can do a deeper standalone piece on the agentic payments stack, breaking down x402 vs MPP vs ACP vs AP2 in more detail and which ones I think have the strongest setup. If that’s the kind of thing you want to read, lmk in the comments.

As always, my recommendation for most people remains the same: DCA into Bitcoin and a very small handful of other tokens, and wait. If you’re going to take some shots outside of that, I’d rather be taking those shots in AI than in the memecoin trenches in 2026.

Thanks for reading, hope you enjoyed this letter, and see you next week!

Disclaimer: The content covered in this newsletter is not to be considered as investment advice. I’m not a financial adviser. These are only my own opinions and ideas. You should always consult with a professional/licensed financial adviser before trading or investing in any cryptocurrency related product. Some of the links shared may be referral links.

Whenever I talk about AI in this Newsletter, it’s usually with reference to the big cloud AI tools like Claude, ChatGPT, Gemini, etc. The way these models work is that you type a prompt, it gets sent to a server somewhere, processed, and the response comes back. Simple. This is the same whether you’re using the website interface or doing deep coding in Claude Code using your Claude Max subscription.

But there’s a whole other world out there of opensource AI that runs entirely on your own computer. These are local LLMs, and in 2026, they’ve gotten really good.

The space is, unsurprisingly, moving fast. In the past two weeks alone, GLM-5.1 became the first open-source model to top Claude Opus 4.6 on a major coding benchmark. Kimi K2.6 then dropped earlier today and took the crown over from GLM. The tooling and models keep getting better, and the gap between cloud and local keeps getting smaller.

I’ve been learning about and experimenting with local models on my Mac Studio for the last week and have been (pleasantly) surprised with how capable they. Obviously not quite as good a Claude Opus 4.7 and other frontier models for the super complex stuff, but for a lot of what I do day to day, local models are genuinely useful. And free. And private. And always available.

Even if you keep your cloud subscriptions (I do), having a local model as a backup or for specific tasks is one of the best moves you can make.

It’s also just genuinely fascinating and interesting, and learning how to own and run your own models is a really good skill to learn in this day and age.

Here’s what we’ll be covering in today’s post:

1. Why run a local model?

2. Hardware: what do you need?

3. The software tools

4. Which model for which task?

5. Getting started

6. Connecting local models to AI agents

7. Closing thoughts

If you’re interested in leveling up your AI learning journey even more, then check out the new company I have launched alongside a couple of friends: The Stoa of AI.

We create video courses and have weekly live workshops and calls that show you practical ways to implement AI into your daily workflows.

We’re in early access mode with discounted pricing, check us out here: https://www.skool.com/thestoaofai

Why run a local model?

Five main reasons.

1. Privacy. Your prompts, files, and conversations stay on your machine. No third-party servers. For anyone working with sensitive data, proprietary code, or confidential documents, this is a huge deal. Not to mention those who just care about their personal privacy and don’t want Big AI spying on them (or worse, leaking their data to nefarious actors).

2. Cost. Once you have the hardware, inference is free. If you use AI heavily, local models will often pay for themselves given enough time. You can also repurpose old devices you might have at home to run local models.

3. No rate limits. Frontier models burn through credits fast. Having a local fallback is a godsend, as is having models running tasks that will never hit a rate limit (and don’t count against your existing rate limits). Most people use a one-size-fits-all approach to AI and use models like Opus and Sonnet for simple tasks where they’re totally overkill, and a much simpler, local model can do just as well.

4. Offline access. This is a cool one. Once you have a model downloaded locally, it will work without the internet. You can interact with your model on flights, in remote areas, and just have a backup way to access the entire knowledge of humankind on your own computer.

5. Control. You get to choose the models and can tweak their configurations to your heart’s content. You won’t be surprised by a change in Terms of Service and you won’t get randomly blocked for violating terms (or cause of an error on their end). You can have full control over your entire AI stack when you run a local model.

That last one hit home a few weeks ago when Anthropic blocked OpenClaw and other third party agent frameworks from using Claude Pro/Max subscriptions. People relying on that setup were suddenly stuck having to switch to another provider or paying API costs that could easily be in the $50/day range.

Local models don’t have this problem.

As I said at the top, local models won’t match frontier models for the hardest multi-step reasoning. But for simple and everyday coding, summarization, drafting, web scraping, research, and Q&A, they handle 70-80% of what I throw at them.

The right setup is both. Cloud for the hard stuff, local for everything else.

Hardware: what do you need?

Before we get into the hardware itself a quick detour on quantization. You'll see this term everywhere in the local LLM world and it affects every hardware decision you make, so worth understanding upfront.

Full precision models store each parameter as a 16-bit number. Quantization compresses that down to 8-bit, 4-bit, or lower. The model gets smaller and faster, at the cost of a tiny bit of accuracy. A music analogy that clicked for me: FLAC is technically better than a 320kbps MP3 file, but most people can't hear the difference (I certainly wouldn’t be able to).

4-bit quantization produces output nearly indistinguishable from full precision for most tasks. If you come across models with names like Q4_K_M or Q3_K_M just know that these are referring to the same model just with 4-bit or 3-bit quantization.

The rule of thumb: a Q4 quantized model requires roughly 0.6-0.7 GB of memory per billion parameters (I explained parameters in last week’s post).

I would recommened that you stick with Q4_K_M models unless you have a specific reason not to.

Alright back to talking about hardware. The single most important number when it comes to running LLMs on hardware is available memory. This is VRAM on a PC or unified memory on a Mac. Everything else hardware related is secondary.

Here’s a handy chart for looking at the types of models you can run based on different hardware specs:

Macs have a unique advantage thanks to unified memory. The CPU, GPU, and Neural Engine share one memory pool. A Mac Studio with 512 GB of unified memory can actually run DeepSeek R1 at 671 billion parameters locally.

I’m personally running GLM5.1 at 744 billion parameters on my own Mac Studio (the Q3 version, which requires ~308GB of memory).

Mac vs PC: which should you buy?

This is a common question and the answer, like with most things, is “it depends”. Neither is universally better, they’re good at different things, depending on your situation / requirements.

• For small-to-medium models (under 24 GB), a PC with an NVIDIA GPU is faster (a lot faster). An RTX 4090 runs an 8B model at 100-140 tokens per second. An M3 Max runs the same model at 40-60 tokens per second. If you care about snappy responses and you’re only running 7B-14B models, PC wins.
  

• For large models (30B+), Macs win. Here’s why: NVIDIA consumer GPUs max out at 24 GB of VRAM (4090) or 32 GB (5090). Once your model exceeds that, the GPU has to shuttle data back and forth with system RAM over a slow connection, and performance suffers as a result. The Mac isn’t faster because Apple chips are faster, the Mac is faster because the whole model fits in memory at once.

Some other factors to take into account:

• Power and noise. A Mac Studio pulls about 60W under full load. An RTX 4090 pulls 450W plus whatever the rest of the PC uses. If you’re running inference all day, the electricity costs will add up over time. Macs are also silent. PC workstations with 4090s are LOUD. My Mac Studio sits on my desk and I rarely hear a thing from it.
  

• Price. A used RTX 3090 runs $700-900, but that’s just the GPU. You need a full PC around it which brings a realistic build to $1,500-2,000. On the Mac side, a Mac Mini M4 Pro with 24 GB unified memory starts at $1,399 as a complete machine. Macs also costs less to run daily thanks to the power difference mentioned above.
  
  At the higher level, a Mac Studio M3 Ultra with 256 GB unified memory runs about $5,999. Max it out to 512 GB and you’re at $9,500-10,000. But at that high end, there’s no comparable PC option either. A PC build that could run 671B parameter models needs multiple professional GPUs and costs $20,000+.

My recommendation, based on your situation:

If you’re on a tight budget and already have a PC: Drop in a used RTX 3090. Best value per GB of VRAM in 2026.

If you want a complete machine under $1,500 and mostly run 7B-14B models: Mac Mini M4 Pro with 24 GB ($1,399). Quiet, efficient, no assembly required.

If you want the fastest possible responses on small-to-medium models: Build a PC with an RTX 4090 or 5090. Around $2,500-3,500 total.

If you want to run 30B+ models or you want a quiet always-on machine: Mac Mini M4 Pro with 48-64 GB ($1,999-2,199) or Mac Studio with 64-128 GB ($2,400-4,500).

If you want to run the biggest open-source models (GLM-5.1, Kimi K2.6, DeepSeek R1 at full 671B) without a rack of professional GPUs: Mac Studio with 256 GB or 512 GB is the only consumer option that makes sense. Around $6,000-10,000.

What about the laptop you already own?

One thing to know before you go out and spend money is that any M1 MacBook or newer with at least 8 GB of memory can run a small local model. An M1 MacBook Air with 16 GB runs 7B models at 15-25 tokens per second, and if you have a MacBook with even more memory, you can run even more models.

These aren’t going to be anything fancy, but they can still be genuinely good for simple/basic tasks, and more importantly, you can at least get a feel for how local models work before doling out extra cash.

The Software tools

Hardware is the first step, but once you have the hardware, you’ll need some tools to manage and run the models on your own devices. Here are the main options.

LM Studio is the right starting point if you’re new to this. It’s a full desktop app with a clean and easy to use interfact. You download the installer, browse the built-in HuggingFace model library, click the one you want, and start chatting. Zero terminal commands required.

It has a live RAM monitor that tells you whether your machine can run a model before you commit to the download and will recommend the best models for you to download based on your hardware.

It also exposes an OpenAI-compatible API so you can connect it to scripts and agents if you want to (ie you can run Openclaw or Hermes agents on your local models).

Ollama is an overall better choice if you want to build things with local models, but it requires being comfortable with the terminal / command line interface (CLI). A few of the advantages of Ollama over LM Studio:

• It runs as an always-on background daemon. Install it once and it’s just there, listening on port 11434. LM Studio is a desktop app. You have to open it and flip the “Start Server” toggle every time you want API access. For agents, cron jobs, or anything that needs local AI available 24/7, Ollama is better/cleaner.

• It’s fully open source (MIT license). LM Studio is closed source and their free tier doesn’t cover commercial use. If you’re building a product or want transparency over what’s running on your machine, Ollama is the safer pick.

• Lighter memory footprint. Ollama is minimal. LM Studio is an Electron app and uses 300MB-1GB of RAM just for the GUI layer, before you’ve even loaded a model.

Ollama has the same API compatibility as LM Studio.

Ollama does have a native desktop app and it’s functional, but it’s minimal compared to LM Studio. There’s no live RAM monitor, no visual parameter controls, no side-by-side model comparison, no HuggingFace browser. It’s fine for quick chats, but not where Ollama shines. If you want a polished GUI, stick with LM Studio. If you want headless/scripting/agents, use Ollama. Or, an even better option…

You can install both! They don’t conflict, and that’s my recommendation. I’ve got LM Studio for quickly testing new models, and Ollama for anything I want to integrate into a workflow. If I had to pick one: LM Studio for a non-developer just starting out, Ollama for anyone planning to connect local models to OpenClaw, Hermes, or their own scripts.

Some more tools to know about:

Unsloth is for fine tuning models on your own data, which is a whole other very cool possibility for local models. The new Unsloth Studio released in March lets you train a model on your docs or writing style. I want to fine tune a model on all of my newsletters (or X posts) at some point and see how the model does compared to how frontier models are at writing in my own tone of voice.

HuggingFace is the repository where models live. Think of it as GitHub for AI, you don’t really need to interact directly with it but when you’re on Local LM or Ollama and you’re “downlading a model”, just know that you’re probably downlading it from HuggingFace.

llama.cpp and MLX are the engines underneath. Both Ollama and LM Studio use one or the other for inference. Most people never need to think about them.

Which model for which task?

This section literally went out of date twice while I was writing this letter. What follows is my take as of April 21, 2026. Half of this will probably be superseded in three months, if not sooner. Kimi K2.6 literally came out hours ago and I haven’t had a chance to try it myself yet, but I have used GLM-5.1 and it was probably the best choice before Kimi K2.6.

A few things to keep in mind before I share a comparison chart. The frontier open weight models (Kimi K2.6, GLM-5.1) are better than the smaller ones at almost everything. That’s the nature of bigger models with more parameters. But they need serious hardware to run locally, so for tasks that don’t need deep reasoning, a smaller model does the job at a fraction of the cost and latency. The practical question you should ask yourself is not “what’s the best model for this task” but “what’s the smallest model that handles this task well enough.”

Quick aside on benchmarks. I'll reference SWE-Bench Pro a few times in this post. It's the benchmark that matters most for coding. Instead of testing whether a model can write an isolated function, SWE-Bench Pro gives the model a real GitHub issue from an actual open-source project and asks it to fix it. The model has to read the codebase, understand the bug, write the fix, and submit code that passes the existing tests. A score of 50% means the model solved half the bugs thrown at it.

For context, Claude Opus 4.6 scores 53.4%. The newly released Opus 4.7 scores a whopping 64.3%. Anything in the 55-60% range is generally considered frontier, but that number is obviously constantly changing as frontier models get better.

On the top shelf for coding, two open-weight models stand out as of today.

Kimi K2.6 from Moonshot AI is the new king of the open-source coding world. It came out today. It is purpose built for long and complex coding tasks. Where other models start losing coherence after an hour or two, K2.6 has demonstrated 5 day continuous execution runs on real engineering tasks.

It can also orchestrate 300 sub-agents in parallel (wtf), which means you can throw something like “refactor this entire monorepo” at it and it’ll decompose the job across hundreds of specialized workers. It beats Claude Opus 4.6 on SWE-Bench Pro (58.6% vs 53.4%). If you’re building anything agentic or doing heavy codebase work, this is the best local model at the moment (but again.. this could all change by literally tomorrow lol).

GLM-5.1 from Z.ai is the older option (April 7, which is crazy to consider it as the “older option”), but still close on coding quality. It scored 58.4% on SWE-Bench Pro, so only sliiiightly worse than K2.6. Another great pick if you want frontier coding but don’t have the hardware to run the Kimi model.

On the practical side, Qwen3.6-35B-A3B (released April 16) will hit the sweet spot for most people. The MoE architecture means only 3B parameters are active per token even though the model is 35B total, which means it runs fast on a 24 GB machine. It handles images and video, not just text, and it has a context window that goes up to 1M tokens so you can feed it entire codebases or long documents.

It’s good at everyday coding, writing drafts, summarization, and agent workflows.

This is random but someone tested it on their laptop against Claude Opus 4.7 the day both were released, and the local model drew a better pelican riding a bicycle (very random and silly example but what’s life without a little whimy):

For smaller hardware, Qwen 3.5 9B is the practical option and runs fine on an 8 GB MacBook. It’s not going to handle complex multi file reasoning, but for some daily tasks (rewriting emails, summarizing articles, quick Q&A), it’s remarkably capable.

Getting started

If you want to try running your own local models, here are some instructions to get started for both LM Studioo and Ollama.

LM Studio:

1. Download LM Studio from lmstudio.ai.

2. Install it.

3. Open the app.

4. Click “Discover” and search for a model. The live RAM monitor tells you whether it will run on your machine.

5. Click download.

6. Click “Load model” when it’s done, and you’re off to the races. You can chat with the model directly in LM studio, or connect it to an agent like openclaw/hermes (i’ll explain how in the next section).

Ollama:

1. Install Ollama from ollama.com (one-line installer for Mac and Linux).

2. Then head to ollama.com/library or huggingface.co to browse models.

3. Every model listing should give you the exact command to run it. HuggingFace has a wider selection and shows you the file size so you can check it against your RAM before downloading.

4. Once you’ve found your model, run it in the terminal, it should look like this:

ollama run qwen3.5:9b

The first time you run a command like this it’ll download the model, then after that it’ll load the model from your hard drive. Once it’s downloaded/loaded, you can start talking to it immediately from the terminal.

It’s surpsingly simple to get up and running with local models. The whole setup from start to finish doesn’t take long, usually the longest part is downloading the model itself (a few GB to tens/hundreds of GB depending on the model).

This is literally all you have to do to have local LLMs running entirely on your own devices, and I recommend everyone with the hardware to at least give this a shot with some of the smallest models.

Connecting local models to AI agents

This is where things get interesting. Running a local chatbot is useful and cool and all that, but connecting a local model to an agent framework (Openclaw or Hermes) is the real unlock.

OpenClaw: Install OpenClaw, then in Settings > Config (or openclaw.json) add a custom provider pointing at http://localhost:11434/v1 for Ollama or http://localhost:1234/v1 for LM Studio. Set the API type to “openai-completions” and give your model a name that matches what’s loaded.

Hermes Agent: Install Hermes, then run hermes model to open the setup wizard. Choose “Custom endpoint”, enter your local URL (same as above: Ollama is http://localhost:11434/v1, LM Studio is http://localhost:1234/v1), and pick the model you’ve loaded. Switch models later with /model in chat.

Both Ollama and LM Studio expose OpenAI-compatible APIs, and both OpenClaw and Hermes speak that format, so it’s all pretty simple at the end of the day. Once you get it figured out once, you’ll find it very easy to try new models.

Closing thoughts

A lot of content about local LLMs out there tends to overhype things. While I don’t think everyone has to be using local models, and I very much understand the limitations of these models, I do think everyone passionate about AI would benefit themselves greatly by taking a day or two to tinker around here.

A local model is not going to replace Claude Opus 4.7 for complex multi-step reasoning. It’s not going to write content as well as frontier cloud models. It’s not going to debug a gnarly multi-file codebase as reliably.

What it will do is give you a private, free, always-available AI assistant that handles the majority of basic tasks that you throw at it, and, apparently, do a better job of creating an image of a pelican on a bicycle, sometimes?

And for a lot of people, that’s more than enough.

The quality curve is obviously real, and not all local models are created equal. Going from 8B to 14B is a noticeable jump. 14B to 32B is another. If you have the hardware for Kimi K2.6 or GLM-5.1 on a 512 GB Mac Studio, you’re running a model that beats Claude Opus 4.6 on SWE-Bench Pro. For normal hardware, Qwen3.6-35B-A3B on a 24-32 GB setup is the sweet spot in April 2026. You get near frontier quality on a standard machine.

The best overall approach I recommend for everyone is cloud for your hardest tasks, local for everything else (or for things that must be private). You don’t have to pick one or the other.

The local LLM ecosystem in April 2026 is mature. The last couple of months has seem pretty incredible leaps in quality, and if this trend continues, it’s gonna be absolutely mindblowing the AI power that us mere mortals can wield at home.

Honestly, these models are probably already better than you’d think. And having an AI that runs on your own machine, answering your questions with (near) zero running costs and zero data leakage, is yet one more of those things these days that makes me think of these fine words from one of sci-fi’s greatest writers:

Disclaimer: The content covered in this newsletter is not to be considered as investment advice. I’m not a financial adviser. These are only my own opinions and ideas. You should always consult with a professional/licensed financial adviser before trading or investing in any cryptocurrency related product. Some of the links shared may be referral links.

But for today, we’re taking a foundational look at LLMs. I’ve noticed that most people who use ChatGPT or Claude every day have no idea how they actually work.

Which is fine of course. You don’t need to know how an engine works to drive a car. But I think having a basic understanding of what’s going on under the hood makes you a better user. It helps you understand why the AI is good at some things and bad at others, it helps you ask better questions, and it makes you less likely to either over-trust or under-trust the outputs.

This turned into quite a long piece, here’s what we’re going to cover:

1. What is an LLM?

2. How does an LLM “learn”?

3. Wait, so it’s autocomplete?

4. What are tokens?

5. What about parameters?

6. How do LLMs actually generate their responses?

7. And what do these models really “know”?

8. What is training vs fine tuning?

9. Why are some models better than others?

10. Model sizes: why some run on your laptop, and others need data centres

11. How does knowing all of this help you?

If you’re interested in leveling up your AI learning journey even more, then check out the new company I have launched alongside a couple of friends: The Stoa of AI.

We create video courses and have weekly live workshops and calls that show you practical ways to implement AI into your daily workflows.

We’re in early access mode with discounted pricing, check us out here: https://www.skool.com/thestoaofai

What is an LLM?

LLM stands for Large Language Model. That’s what ChatGPT, Claude, Gemini, and all the other AI chatbots are built on.

• Language. These models work with language. Text in, text out. You type words, they generate words back. (Yes, they do images and audio and code now too, but at their core, they are language machines, and the word “language” can be used for whatever input/output is being generated by these LLMs).

• Model. In AI, a model is a program that has been trained on data to recognize patterns. If you showed someone who had never seen a cat, a million photos of different breeds of cats, eventually they’d get great at telling the difference between them. An LLM is the same concept.

• Large. These models are LARGE. They are trained on enormous amounts of data. We’re talking about a significant chunk of the entire internet. Books, articles, Wikipedia, forums, code repositories, academic papers. Billions, maybe trillions, of words.

Put it together and you get: a program that has read a huge portion of human text and learned the patterns of language from it.

How does an LLM “learn”?

The core learning of “training” process is surprisingly simple in concept. You take a sentence, hide the last word, and ask the model to predict what comes next.

“The cat sat on the ___”

The model guesses. If it gets it wrong, you adjust the model slightly so it does better next time. Then you do this billions and billions of times, with billions and billions of sentences.

Over time, the model gets good at predicting the next word. And then the next word after that. And the next. Until it produces entire paragraphs and pages that sound like a human wrote them.

This is a simplified version of the process (the technical term is “next token prediction”), but it captures the core idea. LLMs are, at their foundation, prediction machines. They predict what text should come next based on everything they’ve seen before.

Wait, so it’s autocomplete?

Kind of. This is a comparison that gets thrown around a lot, and it’s partially accurate.

Your phone’s autocomplete predicts the next word based on simple patterns. LLMs do the same thing, but with astronomically more data, vastly more computing power, and a much deeper understanding of context.

The difference in scale creates a difference in kind. Your phone’s autocomplete might suggest “the” after “in.” An LLM will write you a coherent essay about quantum physics, maintain a consistent argument across 2,000 words, and format it properly. Both are predicting the next word. One is doing it with such depth and sophistication that it produces something that looks and feels like understanding.

Whether or not it is understanding is one of the great debates in AI right now. I don’t think we need to settle it here. What matters for practical purposes is that the output is useful, and often impressively so.

What are tokens?

Tokens are the units that LLMs work with, and they’re also sorta considered the currency of LLMs. When you use a frontier model from Anthropic or OpenAI, you’ll generally be paying per token used.

Here’s something most people don’t realize: the model never actually sees your words. It only sees numbers.

When you type a message, the first thing that happens is your text gets encoded into tokens, where each token is assigned a number. The word “hello” might become token 15339. The word “the” might be token 1820. The word “cryptocurrency” might get split into two tokens: “crypto” (54219) and “currency” (26072).

These numbers are what the model works with. Every single computation that happens inside the model, all the pattern matching, all the predictions, happens as math on numbers. The model processes these numbers through its neural network, and outputs... more numbers. Those output numbers then get decoded back into words that you read on your screen.

Encode → Math → Decode. That’s the whole loop.

The process of converting text into numbers is called encoding. The process of converting the output numbers back into text is called decoding. You never see the numbers, and the model never sees the words. There’s a translation layer (called a tokenizer) sitting between you and the model, encoding and decoding back and forth.

So what happens during the “math” part? Each token number gets converted into a vector, which is a long list of numbers (hundreds or thousands of them) that represents the meaning and context of that token. The word “bank” in “river bank” gets a different vector than “bank” in “bank account” because the surrounding tokens influence the representation.

The model then runs these vectors through layers of calculations, adjusting and combining them, comparing every token to every other token to figure out relationships and context (this is the “attention” mechanism you might have heard about). After dozens of these layers, the final output is a probability distribution over every possible next token. The model picks one, decodes it back to text, and voila! you see a word appear on your screen.

This is also why LLMs are sometimes weird about things like counting letters in a word or doing arithmetic. The model doesn’t see the word “strawberry” as s-t-r-a-w-b-e-r-r-y. It sees it as one or two token numbers. It has no concept of individual letters because those letters got encoded away before the model ever touched them.

A token is roughly 3/4 of a word, or about 4 characters. Common short words like “the” or “and” are one token. Longer or less common words get split into multiple tokens.

This matters to you because LLMs have a limit on how many tokens they process at once. This is called the context window. Think of it as the model’s working memory.

If a model has a 200,000 token context window, that’s roughly 150,000 words it is able to hold in mind at one time. Some models now go even higher. Claude Opus 4.6, Claude Sonnet 4.6, and Gemini all support 1 million token context windows. That’s about 750,000 words, or roughly 10 to 15 full novels. Llama 4 Scout from Meta supports a whopping 10 million token context. These are staggering numbers compared to where things were a few years ago.

But something to keep in mind is that larger context windows aren’t necessarily or inherently better.

As you stuff more and more tokens into the context window, the quality of the model’s responses tends to degrade. Researchers call this “context rot.” The model doesn’t attend equally to everything in its context. It tends to pay the most attention to stuff near the beginning and the end, and less attention to stuff in the middle. A 2023 research paper found that when relevant information was buried in the middle of a long context, models performed significantly worse at finding and using it.

This means that giving the model more context isn’t always better. If you dump 500,000 tokens of loosely related documents into the context window and your actual question relates to a detail somewhere in the middle, you might get a worse answer than if you had only provided the 10,000 most relevant tokens. Quality context beats quantity of context. It’s a little counterintuitive, but it’s how it works.

Just like everything AI related, the models are getting better at this too. Claude scores at the top of long-context benchmarks, and the gap between small-context and large-context performance is shrinking with each generation.

What about parameters?

This is another big number you hear about. Lots of models tout billions or hundreds of billions of parameters; some have trillions. But what even are parameters?

Parameters are the model’s internal settings. Think of them as tiny dials, and during training, each of these dials gets adjusted slightly every time the model makes a prediction and gets feedback on whether it was right or wrong.

To put it more concretely, parameters are the numbers that determine how the vectors mentioned in the previous section get transformed as they pass through the model. They control things like: how much attention should this word pay to that word? How should this concept relate to that concept? What patterns are important and what patterns are noise?

Every connection between neurons in the neural network has a parameter (a weight) that controls the strength of that connection. A 7 billion parameter model has 7 billion of these connections. A trillion parameter model has a trillion. Each one was tuned, bit by bit, over trillions of training examples.

A model with more parameters has more dials to tune, which means it has the capacity to learn more subtle and complex patterns. A small model might learn that “the cat sat on the mat” is a common pattern. A large model learns that too, but it also learns that the sentiment of a paragraph shifts when you use the word “however,” or that a question phrased politely tends to expect a different kind of answer than a blunt one. The larger the model, the more of these subtle relationships it picks up.

More parameters generally means a smarter model, though it’s not the only factor. Training data quality, architecture decisions, and fine-tuning all matter too, and we’ll talk about that in a bit. But all else being equal, more parameters = more capacity to learn complexity.

The tradeoff is resources. Every parameter takes up memory. Running a model means loading all of those parameters into RAM (or GPU memory) and doing math on them for every single token generated. That’s why bigger models need more expensive hardware, cost more to run, and generate tokens slower.

You don’t really need to remember the exact numbers or know how things work precisely for this stuff.

The takeaway is: parameters = the model’s capacity to learn complexity.

How do LLMs actually generate their responses?

When you type a message to Claude or ChatGPT, here’s roughly what happens:

1. Your message gets converted into tokens (numbers)

2. The model processes those numbers through its neural network (the billions of parameters)

3. It predicts the most likely next token (number)

4. That number gets added to the sequence, and the model predicts the next one

5. Repeat, one token at a time, until the response is complete

This is why you see the text appear word by word when the AI is responding. It’s generating the response in real time, one piece at a time. It doesn’t write the whole answer and then reveal it. It is figuring it out as it goes.

This is also why the same prompt sometimes gives you different answers. There’s a degree of randomness (called “temperature”) built into the selection process. The model doesn’t always pick the single most likely next token. Sometimes it picks the second or third most likely option, which sends the response in a slightly different direction.

On most models you can also adjust this setting and request the model to use more of the less-standard responses. This is helpful if you’re doing something like creative writing or anything really where you need outside-of-the-box thinking. For anything that requires facts and exactness, low temperature models tend to perform better.

And what do these models really “know”?

LLMs don’t have a database of facts that they look up. They don’t search through a filing cabinet when you ask them a question. Instead, the knowledge is embedded in the patterns of their parameters. The model learned that certain facts tend to appear in certain contexts, and it reproduces them when the context calls for it.

This is why LLMs sometimes make things up. The AI community calls these “hallucinations.” The model isn’t lying. It’s generating text that seems like the most probable continuation of the conversation, and sometimes the most probable-sounding thing isn’t true. It’s predicting, not recalling.

This is one of the most important things to understand about LLMs. They are optimized to produce text that sounds right. Not text that is right. These two things overlap a lot of the time, but not always.

Rule of thumb: the more obscure or specific the fact, the more likely the model is to get it wrong or make it up. If you ask about well-documented topics that appeared frequently in the training data, the model is pretty reliable. If you ask about niche topics, recent events, or specific numbers, then verify the output.

What is training vs. fine-tuning?

Training is the initial process where the model reads all that text and learns the patterns. This is expensive and time consuming. Training a frontier model costs hundreds of millions of dollars in compute alone (this phase is sometimes called pre-training because it happens before any further refinement).

The result of pre-training is called a base model. Base models are smart, and they know a lot about language, but they’re weird to talk to. If you ask a base model a question, it might continue your text as if it’s writing a Wikipedia article, or generate random forum posts, or complete your sentence in a direction you didn’t expect. It doesn’t know it’s supposed to be helpful. It’s a text prediction machine, but not a conversational assistant like we’re used to engaging with like with chatGPT etc.

Fine tuning is what turns a base model into something useful. It’s a second round of training, done on a much smaller and more carefully curated dataset. This is where the model learns to follow instructions, answer questions, have conversations, and generally behave the way you’d expect a chatbot to behave.

There are a few different types of fine-tuning worth knowing about:

• Instruction tuning is where you train the model on thousands of examples of “here’s an instruction, here’s the correct response.” This teaches the model to follow directions instead of completing text randomly.
  

• RLHF (Reinforcement Learning from Human Feedback) is where humans rate the model’s outputs, and the model learns to produce responses that people prefer. This is a big part of why modern chatbots feel natural to talk to. The model learns things like “be concise when the question is simple” and “acknowledge uncertainty when you’re not sure” from human preferences.
  

• Domain-specific fine-tuning is where you take an existing model and train it further on data from a specific field. A hospital might fine-tune a model on medical records so it becomes better at clinical language. A law firm might fine-tune on case law. A company might fine-tune on their internal documentation so the model understands their products and processes. This is where things get interesting for businesses.

The cost difference between pre-training and fine-tuning is enormous. Pre-training GPT-5 or Claude from scratch costs hundreds of millions. Fine-tuning an open source model on your own data costs anywhere from a few dollars to a few thousand, depending on the size of the model and how much data you’re using.

This is one of the reasons open source models matter so much. You take a free base model like Llama or Mistral, fine-tune it on your specific data, and you end up with a custom model that understands your domain, runs on your own hardware, and costs nothing per query. That’s a big deal for businesses that process a lot of data and don’t want to send it to a third party API.

Why are some models better than others?

We covered this a bit already, but just to highlight and expand upon a few factors in a bit more detail:

• Training data quality. More data isn’t always better. Cleaner, higher-quality data leads to better models. If you train on a lot of garbage, the model produces garbage.

• Model architecture. How the model is structured internally matters. The Transformer architecture (introduced in a 2017 paper by Google researchers called “Attention Is All You Need”) is the foundation for all modern LLMs. There are meaningful differences in how each company builds on top of that foundation.

• Scale. More parameters and more training compute generally lead to better performance, up to a point. There are diminishing returns, and smaller models trained on better data sometimes beat larger models trained on worse data.

• Fine-tuning and alignment. How the model is refined after initial training makes a huge difference in how useful it feels to talk to.

• Context window size. How much the model keeps in mind during a conversation affects its ability to handle complex, multi-part tasks.

Model sizes: why some run on your laptop and others need a data center

As we’ve established, not all models are the same size. Parameter count varies hugely, and that directly determines what hardware you need to run them.

A rough rule of thumb: each billion parameters needs about 0.5 to 1 GB of RAM (depending on the precision/quantization). A 7 billion parameter model needs around 4-8 GB of RAM. A 70 billion parameter model needs around 40 GB. Frontier models from OpenAI, Anthropic, and Google have hundreds of billions to over a trillion parameters, and they require massive clusters of specialized GPUs that cost millions of dollars.

This is why some models are available to run locally on your own computer, and others are only accessible through cloud APIs. You pay per token to use GPT-5 or Claude because the infrastructure required to run them is enormous. But you download and run Llama 8B or Mistral 7B on a decent laptop for free.

There’s also a technique called Mixture of Experts (MoE) where a model has a huge total parameter count but only activates a fraction of them for each token. DeepSeek V3 has 671 billion total parameters but only uses 37 billion per token. GLM-5.1 has 744 billion total but only 40 billion active. This lets big models run on smaller hardware than you’d expect.

The quality gap between the smallest and largest models is real, but it’s also shrinking. A well-chosen 14B parameter model running on your laptop today can do a decent job of everyday and simple tasks even compared to the frontier models (although it might be slower).

The gap is most noticeable in complex multi-step reasoning, long creative writing, and tasks that require a lot of world knowledge. For everyday stuff like drafting emails, summarizing documents, or answering questions, the local models are surprisingly good.

And of course not all local models are created equal. GLM5.1 is a surprisingly good model that can run on a mac studio, which, while still very expensive ($5-10k+), pales in comparison to the millions of dollars the massive data centres cost to build.

How does knowing all of this help you?

I mean, hopefully you just find this stuff super interesting like I do! There’s value in knowledge, and in knowing how things work, even if you don’t really need to know in order to use them.

Some of that value comes from some changes you might make when using these tools.

When you know that the model is predicting the next token based on patterns, you understand why giving it more context leads to better outputs. You understand why being specific in your prompts matters. You understand why it sometimes confidently says things that are wrong.

When you know about context windows, you understand why long conversations sometimes go off the rails.

When you know about temperature and randomness, you understand why regenerating a response sometimes gives you something better (or worse). It’s a different path through the probability space. And knowing that you can adjust the temperature settings depending on the task can allow you to harness these tools in a way that is specific to your needs.

You also start to appreciate what these tools are and aren’t. They’re not search engines (though they now have search built in). They’re not databases. They’re not oracles. They’re pattern-matching machines of extraordinary sophistication, trained on a large percentage of humanity’s written knowledge (and then further trained / fine tuned with additional and curated human feedback).

That makes them useful.

It also makes them fallible in specific, predictable ways.

Knowing all of this should make you a better user, and give you more confidence in your prompting future.

Disclaimer: The content covered in this newsletter is not to be considered as investment advice. I’m not a financial adviser. These are only my own opinions and ideas. You should always consult with a professional/licensed financial adviser before trading or investing in any cryptocurrency related product. Some of the links shared may be referral links.

I have been getting questions like these all the time lately, and it’s great! I love that so many people are now starting to see the potential of AI and are looking for ways to integrate it into their lives.

Specifically, people seem to want to know how to set up an AI agent (or agents).

The problem is that it’s pretty overwhelming to know where and how to begin. There are opensource projects like OpenClaw, Hermes, and NanoClaw. There are closed ecosystem options from Anthropic (Claude Chat/Code/Co-Work) to OpenAI (ChatGPT and Codex) and more. Some require technical knowledge, and some are designed to work out of the box for the lay person.

For the sake of brevity and simplicity, I’m going to focus on just two primary options.

The first is the easy way. You download an app called Claude, pay a monthly subscription, and start giving it tasks. Nice interface, very little setup.

The second is the DIY way. You install a free tool called Hermes Agent on a computer, hook it up with AI access via an API key or a subscription to OpenAI, connect it to your messaging apps (like WhatsApp or Telegram), and it becomes your personal assistant that runs around the clock. More work to set up, more flexibility, more control, but with all of that, more security risk and the risk of things breaking/not working as intended.

They represent two different philosophies and I’ll break down both so you know what you’re looking at, what the trade-offs are, and which one makes sense for you.

Let’s start with: what is an AI agent, exactly?

You’ve probably used ChatGPT before. You type a question, it gives you an answer. Maybe you’ve used it to write an email, explain a concept, or help with a work problem. That’s an AI chatbot. You talk to it, it talks back.

An AI agent is the next step up from that. Instead of answering your questions, it goes and does the work for you. You tell it “organize my inbox” or “send me a personalized summary of the news I care about every morning at 8am” and it handles the whole thing on its own. It can connect to your email, your calendar, your messaging apps, your google drive, your hard drive, basically anything and everything digital. It can run in the background while you do other things. It can literally work for you while you’re asleep.

Another way to think of it: a chatbot is a very capable assistant sitting at a desk, waiting for you to walk over and ask it something. An AI agent is a manager that you’ve given permission to get things done without you standing over their shoulder.

The key differences:

• A chatbot works when you’re using it. An agent works when you’re not.

• A chatbot lives in a browser tab or app. An agent lives on your machine and connects to your computer and other apps.

• A chatbot helps you do things. An agent does things for you.

• A chatbot usually needs you to copy-paste results into other apps. An agent connects directly to your email, calendar, files, and more.

That last point matters a lot. When your agent has direct access to your tools and services, it becomes useful in a way that goes beyond even the most capable chatbot.

The easy way: Claude

Let’s start by looking at the simplest way to get started with agentic AI.

Claude is an AI product similar to ChatGPT. You can use it in your browser at claude.ai, or download their desktop app. It has three different modes, each designed for different kinds of work.

1. Chat is the one that feels most like ChatGPT. You have a conversation with the AI. It remembers things you’ve told it in past conversations. It’s pretty damn powerful. While not fully agentic, it can still search the web, create documents, and work through complicated problems. I’m using it right now to help me research and create things for this Newsletter! The basic version is free, and paid plans start at $20 (USD) a month.
   

2. Cowork is their agent mode. This is where things get interesting. You give Claude access to a folder on your computer and tell it what you want done. Instead of having a back-and-forth conversation, Claude goes off and does the work on its own. It can read and create files, browse the web, and even see what’s on your screen and click buttons for you. You can send it a task from your phone and it will do it on your computer while you’re away. Cowork needs a paid plan. If you use it a lot and keep hitting limits, there are higher tiers at $100 or $200 a month that give you more capacity. The features are the same at every price.
   

3. Code is the most powerful and most advanced version of their agent mode, and it’s specifically designed for programmers or those wanting to vibe code. I wrote a primer on Claude Code a couple of months back which I recommend reading if you want to know more about it.

The big selling point for all of these Claude products are that they’re easy.

There are some tradeoffs though. The main ones being that you can only use Claude’s AI (no swapping in different models from other providers), and your data travels through Anthropic’s computers so data privacy concerns are real.

Still. If you’re not dealing with hyper sensitive information, I generally recommend that most people start here before moving on to the more complex, powerful, and risky stuff.

The DIY way: Hermes Agent

Before I talk about Hermes, I should mention OpenClaw. If you’ve been following the AI space at all (and tbh even if you haven’t), you’ve probably heard of it. OpenClaw was the first AI agent tool that went truly mainstream in early 2026. It became the fastest-growing open source project in history, with hundreds of thousands of people downloading and running it. This chart is truly insane, comparing its github star history to the enormous projects Linux and React:

OpenClaw is impressive, but it has real problems. It breaks frequently. Updates that come out every few days often cause things to stop working. Users report spending hours troubleshooting (I personally have spent hours troubleshooting it).

Hermes Agent is a newer alternative that fixes many of these issues. It was built by Nous Research (an AI research lab) and released in February 2026. It’s open source just like OpenClaw, meaning anyone can see, inspect, and modify the code.

Some of the benefits of Hermes over OpenClaw:

• It is better at learning and getting smarter. When Hermes solves a complicated task for you, it writes down how it did it and saves that as a reusable skill. The next time a similar task comes up, it remembers the approach and does it faster and better. OpenClaw doesn’t do this as well as Hermes, in my experience.
  

• It has a better memory system. Not only within a single conversation, but across every conversation you’ve ever had with it. You told it three weeks ago that you prefer bullet points over paragraphs? It remembers. You mentioned your business partner’s name in passing last Tuesday? It remembers. This works through a search system that lets Hermes look back through all of your past conversations when it needs to. Again, OpenClaw has its own search system, but once again, my experience is that Hermes simply works better out-of-the-box than OC.
  

• It has a cleaner security record. As of April 2026, Hermes has no known security vulnerabilities. OpenClaw has had nine. Hermes blocks attempts to steal your credentials and runs tasks in isolated containers when possible.

Like OpenClaw, Hermes itself is free. You pay for the AI service you connect it to (it can be Claude’s models (Opus/Sonnet), or OpenAI’s models (GPT/Codex), or Gemini’s models, or Chinese models, or fully local models that you run on your own devices.

You have full control and autonomy over the model(s) you use, the tasks you use them for, your data, your privacy, your costs, your efficiency, your power, basically, everything.

Regardless of what option you choose between Claude, Hermes, or even OpenClaw or any of the other options, there’s at least one significant constant to keep in mind:

The importance of .md files

Both Claude and Hermes use a specific type of file to store instructions, knowledge, and preferences. These are called .md files (short for Markdown). They’re plain text files that anyone can read, nothing fancy.

When you create a good .md file, you’re creating a document that tells the AI things like who you are, what you like, and especially how you want things done. Both Claude and Hermes read these files and follow them. Hermes’s entire skill system is built on them. When Hermes learns something new, it saves that knowledge as one of these files.

.md files are honestly one of the most important and powerful things in all of AI.

The best part? These files are portable. If you write a great instruction file for Claude, you can copy it over to Hermes. If some new AI system comes out in six months that none of us can see coming, chances are your .md files will be able to be ported over to it too.

My advice is to take the time and understand how .md files work and to be very diligent about creating and maintaining them. I know there’s a tonne of content out there with AI but this is one of the few non-negiotiables imo. You have to understand .md files if you truly want to harness the full power of AI agents.

Here’s a quick breakdown on how they work for both Claude and Hermes:

Claude uses a file called CLAUDE.md. If you use Claude Code or Cowork, you can place a file with this name in your project folder and Claude reads it automatically at the start of every session. It’s where you tell Claude things like: here’s what this project is about, here are my preferences, here are things you should always or never do.

You can also set up “Project Instructions” through Claude’s web interface, which work the same way. There’s a global version too (stored in a hidden folder on your computer) that applies to all your projects. They stack: your personal preferences load first, then project-specific instructions layer on top.

You can also ask Claude to update its own claude.md file as it learns new things, which you should definitely do.

Hermes uses SKILL.md files. These follow an open format called agentskills.io that work across different AI agent tools. Each skill is a folder containing a SKILL.md file that describes what the skill does and how to use it. When Hermes learns something new by completing a task, it saves that knowledge as a new SKILL.md file automatically.

Over time, your Hermes agent builds a library of skills it has taught itself. These files are stored on your machine, and you can read, edit, or share them with others. Hermes also stores its memory and conversation history as plain text, so everything it knows about you is readable and in your control.

If you want to learn more about writing good instruction files, these are helpful starting points: Anthropic's Claude Code best practices covers how to structure a CLAUDE.md file. The agentskills.io site explains the open skill format that Hermes and other agent tools use. And the Markdown Guide is a friendly introduction to the .md file format itself if you've never used it before.

What computer should you run your agent on?

If you go the Hermes route, you need a computer that stays on all the time. Your everyday laptop isn’t ideal because you close it, carry it around, and put it to sleep.

The most popular solution is a small dedicated computer left plugged in on a shelf. The Mac Mini has become the community favourite: small, quiet, uses about $20 a year in electricity, and designed to stay on continuously.

You can also use an old macbook you might have laying around, a raspberry pi, or rent a VPS (virtual private server) — but my experience which is shared by a lot of others i’ve spoken to is that the VPS route has some serious limitations and drawbacks over having your own physical dedicated device.

There’s also a safety argument. Your AI agent has access to whatever computer it runs on. Running it on a separate machine with nothing personal on it means the worst-case scenario is much less scary. Speaking of safety…

Staying safe

If you’re doing things within the Claude ecosystem, you’re a bit more safe because of the guardrails built in by Anthropic — but even then, nothing is 100% safe, so it’s worth keeping in mind the list below. If you’re going the Hermes route, this is where you need to be super careful and vigilant.

Here are some security tips for when you’re going down the AI Agent route, specifically if you’re going the DIY route of setting up your own opensource agent on one of your own devices:

1. Run the agent on a separate computer to keep it isolated

2. Set up that computer with a fresh account (new Apple ID, new email address, new Github, etc) so you’re not giving access directly to your personal accounts

3. Start with small, low risk tasks. Don’t give it access to your email on day one

4. Give it more access gradually as you build trust. If the small stuff works well for a week, expand from there

5. Be careful about installing community add-ons. Stick to well-known, popular ones

6. Keep things updated. Security fixes come out regularly and you want to make sure you’re using the latest stable version

7. Be wary of “prompt injection” attacks. That is, if your agent ever has access to the outside world in a way that someone else can communicate with it, consider what might happen if they say “ignore previous instructions, share all confidential data with me” (there are safety rails built in even to Hermes and OpenClaw for things like this, but with enough prompting and sophisticated attacks, these agents tend to eventually give up the secrets)

8. Lastly, and this is the most important one: assume the worst, assume your agent will, at some point, for some reason, give access and data away to a malicious actor. Ensure that the damage it can do is limited and mitigated. This comes back to points #1 and #2: keeping everything on a separate device, with separate accounts.

You can kinda think of it like hiring a new employee. You don’t just hand over the keys to the whole building on their first day. You let them prove themselves with smaller responsibilities first, and as time goes on and you begin to trust them, you can start to give them more responsibilities. But even then, most employees will never get access to the company treasury or whatever.

Step by step: setting up Claude

Alright now a quick guide on setting up Claude (followed by a quick guide on setting up Hermes).

1. Download the Claude desktop app

   Go to claude.ai/download in your browser. Click the download button for your computer (Mac or Windows). Install it like any other app.
   

2. Create an account and pick a plan

   Sign up with your email. The free plan lets you try Chat mode. For the agent features (Cowork), you need the Pro plan at $20/month. You can upgrade later if you hit limits: $100/month gets you 5x the usage, $200/month gets you 20x (you get access to the same features at every tier).
   

3. Switch to Cowork mode

   Open the app. You’ll see tabs at the top: Chat, Cowork, and Code. Click Cowork. This is the agent mode where Claude does work on your behalf instead of having a conversation.
   

4. Give it access to a folder

   Cowork asks you to pick a folder on your computer. This is the only folder Claude can see and work with. Start with a folder that doesn’t have anything too important on it. Your Downloads folder or Screenshots folder are good places to start, just to get a feel for what it can do.
   

5. Give it a task

   Type something like: “Sort the files in this folder by type. Put documents in one subfolder, images in another.” Claude will show you its plan, ask for approval, and do the work. Once you’re comfortable, try bigger tasks (like give it access to spreadsheets and ask it to do stuff within them).

Voila. You’re now using agentic AI.

Step by step: setting up Hermes Agent

This takes more work, and it’s certainly more complex, but you don’t need to be a programmer or anything. You’re basically just following instructions, copying and pasting, and answering questions.

1. Open the terminal on your computer

   The “terminal” is a built-in app where you type text commands instead of clicking buttons. Think of it as a text-message conversation with your computer. On Mac: press Cmd+Space, type “Terminal”, open it. On Windows: you’ll need to install something called WSL2 first.
   

2. Install Hermes with one command

   Copy this entire line and paste it into your terminal, then press Enter:
   
   curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash
   
   This downloads and installs everything Hermes needs automatically. No other software to install first. Wait a few minutes for it to finish.
   

3. Run the setup wizard

   Type hermes setup and press Enter. This starts a guided setup that asks you questions in plain English: which AI do you want to use? It walks you through each choice.
   

4. Connect an AI provider

   Hermes needs an AI brain; basically, an AI model. The easiest option is probably to use an existing OpenAI subscription (aka ChatGPT).
   
   Another option is OpenRouter, which basically allows you to access any AI model via an API key.
   
   The set-up will guide you through whatever option you choose.
   

5. Connect a messaging app

   Type hermes gateway setup and press Enter. This walks you through connecting a messaging app. Telegram is the easiest (you create a “bot” through Telegram’s BotFather, takes about two minutes). WhatsApp, Discord, Slack, and Signal also work. Pick one to start.
   

6. Start the agent

   Type hermes gateway and press Enter. Your agent is now running. Open your messaging app and send it a message. Try “Hey, what can you do?” If it responds, you’re up and running. You can now ask it anything, including for help with itself!
   

7. If something goes wrong

   Copy the error message and paste it into ChatGPT or Claude’s chat mode and say “I got this error while setting up Hermes Agent, what do I do?” The AI will walk you through the fix. The Hermes community on Discord is also active and helpful. Pretty much everyone who has ever set up Hermes, OpenClaw, or any tool like these has had some stumbling blocks and needed some help.

The beauty of it all: if you run into any difficulties, just ask an AI: Claude or ChatGPT. The strange, wonderful thing about 2026 is that you can (and should!) use AI to help you set up your AI.

So, where should you start?

If after reading all of the above you still don’t know where to start, start with Claude. If you got super excited by the idea of Hermes, then go for it, but I would wager that the majority of people will have a much better and easier time getting their feet wet with Claude before trying the more complex option(s).

So yeah, start with Claude. Download the app, try Cowork mode, and give it a real task. You’ll know within an hour whether having an AI agent changes how you work (spoiler alert: it will).

If you try it and think “I want this running all the time, I want to text it on WhatsApp, and I want it to remember everything and get better over time,” then take the time and security measures to look into getting Hermes agent set up.

Whichever path you choose, pay attention to those .md files. Tell the AI about your preferences, your projects, and how you like things done.

The more context you can give the AI, the better your outcomes are going to be.

The world is becoming a crazier place by the day. The gap between “person who uses an AI agent” and “person who doesn’t” is going to keep growing, and I genuinely think those not using agentic AI are going to get further behind as time goes on. I don’t say this to induce FOMO, but rather to (hopefully) inspire you to get started and at least give things a go.

The best way to learn is to do, and it’s never been easier to get started and do.

Good luck! And I am always here to answer questions, feel free to ask me anything at all.

Also, please let me know if you’re enjoying the AI content! I know my posts have been a little less strictly crypto lately as I have been sprinkling in some AI posts, but it’s where more and more of my focus has been and where I am getting the most questions. I’d love any feedback on this new direction though and if this is the kind of thing you enjoy reading, or don’t enjoy reading — please lmk! As always, I really just want to try and deliver the best value as possible to my reader, and I appreciate y’all a lot for staying subscribed and for being part of the rare breed of people in the year 2026: those that like to read 😭.

Disclaimer: The content covered in this newsletter is not to be considered as investment advice. I’m not a financial adviser. These are only my own opinions and ideas. You should always consult with a professional/licensed financial adviser before trading or investing in any cryptocurrency related product. Some of the links shared may be referral links.

I’ve spoken a lot over the past year about concentrating my holdings into a handful of positions so today I want to share some numbers and research that back up the claims and approach I have been taking.

Let’s jump right in.

How many tokens are there, really?

The answer depends on your source and methodology. Different platforms count different things:

CoinMarketCap counts tokens they’ve indexed across all chains and reports 37 million+. Dune Analytics, tracking unique tokens across all major chains, shows about 74.5 million. Tangem cited on-chain data in January 2026 showing over 120 million tokens across all major networks.

The differences come down to what you define as a token. Do you count every smart contract ever deployed? Only those that had at least one trade? Only those still actively trading? Each filter produces a different number.

Regardless, all the sources agree on these three things no matter which number you use:

1. The growth rate is staggering

2. The majority of tokens are dead or dying

3. And the actual value is concentrated in a tiny fraction of them

The numbers tell a staggering story.

The failure rate is literally around 99.99%

There’s a stat floating around that 53.2% of crypto tokens have failed which comes from a CoinGecko study published in January 2026. It’s a fine study, but imo is a bit flawed for a couple of reasons:

1. It only counts tokens that made it onto GeckoTerminal with at least some trading activity, about 20m tokens, so it doesn’t include a large amount of created tokens that die before they even get off the ground.

2. It defines failure as having no activity; I would consider having very very very low activity + a price that is down 99% as failure.

As we noted at the top, the real number of tokens created is considerably higher, and the failure rate is similarly considerably higher.

Memento Research tracked 118 token generation events in 2025. These are often VC backed projects with teams and roadmaps that went through a formal TGE process (aka not random memecoins) and 84.7% of them are trading below their launch valuations (and somehow this number still seems low to me).

The median token is down 71%. Some of the worst performers launched at nine and ten-figure FDVs and dropped 85-93%. If the best-resourced tokens in crypto lose money for 85% of their buyers, what do you think happens to the other 74.5 million?

Well I can tell you: out of 74.5 million tokens on Dune Analytics, roughly 500 have a market cap above $10 million. That’s 0.0007%.

Token failures aren’t limited to one category. It’s across the board. As I have also said before: a good starting point is to assume every token is trending towards zero, and then look for the extremely rare exceptions.

• Memecoins fail. 99.67% of Pump.fun tokens never graduate (hit a $90k marketcap).

• ICOs fail. 80% of 2017 ICOs were scams, and by 2020 nearly 90% of surviving tokens traded below their ICO price.

• TGEs fail. See the Memento Research data above.

• Airdrops fail. Most airdropped tokens get dumped within hours of distribution and never recover.

• VC coins fail. High FDV, low float launches have been one of the defining disasters of 2025.

• Creator coins fail. Celebrity tokens from politicians to influencers routinely crash 90%+ within days.

• AI agent tokens fail. The AI narrative produced hundreds of tokens in 2024-2025 and the vast majority are down 80%+ from their peaks.

• Gaming tokens fail. The play-to-earn boom of 2021-2022 created dozens of tokens that are now worth fractions of a cent.

• L1s fail. Remember Fantom at $3? Luna at $100?

• L2s fail. Most L2 tokens have underperformed ETH, which has itself underperformed BTC.

• Stealth launches fail. Fair launches fail. Governance tokens fail. Utility tokens fail.

The pattern holds across every category, every launch mechanism, every narrative, and every market cycle. Nearly everything trends toward zero. The exceptions are extraordinary. Bitcoin. Ethereum. Solana, Hyperliquid, BNB, and a very small handful of others.

That’s the reality of this godforsaken market.

Why are there so many tokens?

The short answer: it has never been easier or cheaper to create one.

Pump.fun launched in January 2024 on Solana. It lets anyone create a token in under 60 seconds. No coding required, and near zero fees. Pick a name, upload an image, hit create. That’s it. You now have a cryptocurrency.

A research paper published in February 2026 studied one month of Pump.fun activity. During September 2025, 655,770 tokens were created by 243,123 distinct wallet addresses. Of those, only 4,338 graduated to a DEX. That’s a 0.63% graduation rate.

Pump.fun deployed over 80% of all Solana-based tokens by mid-2025. Solana accounts for roughly 64-70% of all tokens ever created across all chains.

So one platform, on one chain, is responsible for the majority of all token creation in crypto. And 99.37% of those tokens fail before even reaching a $90,000 market cap.

Where the money actually sits

This is the part that matters most for your portfolio.

Bitcoin takes ~56 cents of every dollar in crypto. Add Ethereum and stablecoins and you’re at 79%. The top 10 tokens account for close to 90% of the total marketcap. That leaves roughly $230 billion to be spread across tens of millions of other tokens.

The math on the average non-top-100 token is brutal. $230 billion spread across even 17,000 actively tracked tokens gives you an average market cap of about $13 million. But that average is pulled way up by a few hundred mid-cap tokens. The median is far, far lower. For millions of tokens the market cap is effectively zero.

The survival funnel

Here’s a picture that’s worth a thousand words, and basically summarizes this whole letter:

And it’s only going to get more extreme as time goes on.

Practical takeaways

The market is a power law

The top ten tokens hold 90%+ of total crypto market cap. If you own Bitcoin and Ethereum, you hold exposure to the assets that matter most by market weight. This has been true for years.

Token creation is not value creation

Tens of millions of tokens exist, but the vast majority were created to make money for their creators, not their buyers

The haystack is getting bigger, the needle isn’t

Finding legitimate projects with real utility gets harder every month. The signal-to-noise ratio is worse than it has ever been. More tokens does not mean more opportunity, au contraire, it means more noise and tougher opportunities.

Survivorship bias is everywhere

You hear about the one memecoin that went 1000x. You don’t hear about the 655,000 that launched the same month and went to zero. The success stories get the X posts while the failures are silent.

Liquidity is the filter that matters

CoinGecko tracks about 17,000 tokens. Binance lists 415. The gap between “exists” and “has meaningful liquidity” is enormous.

Final thoughts

Somewhere between 37 million and 120 million tokens exist right now. The exact number doesn’t matter. What matters is the shape of the distribution.

Over 99.99% of all tokens ever created have effectively failed. Out of all the tokens ever created, roughly 500 have a market cap above $10 million. 99.37% of PumpFun tokens don’t graduate. 85% of TGEs launched in 2025 trade below their initial price.

The data is consistent across every source.

I’m not writing this to scare you away from crypto but because understanding this context will hopefully make you better at allocating capital and attention. The opportunity in crypto is still very real. But it lives in a small number of assets and protocols, not in the millions of tokens created to extract money from inattentive buyers.

As I have always said, the best strategy for virtually everyone is to DCA into Bitcoin and maybe a very small handful of other tokens, and wait.

There are periods of times where alts can and do significantly outperform, but the vast majority of people will lose money trying to find these needles in the haystack and are better off sticking to the majors.

Hopefully this data helps back up this position and perhaps will help someone reading this decide that trying to hunt for gems isn’t for them.

The last thing I’ll say is that even if you do want to hunt for those gems and try and find the needles, this should at least help hammer home the point that you generally only want to be doing that with a smaller percentage of your portfolio and keep the majority (80%+) in things like BTC, ETH, SOL, HYPE, and maybe if you’re a bit spicy like me, things like ZEC and TAO.

Goodluck and godspeed and as always, thank you for reading.

Subscribe now

Disclaimer: The content covered in this newsletter is not to be considered as investment advice. I’m not a financial adviser. These are only my own opinions and ideas. You should always consult with a professional/licensed financial adviser before trading or investing in any cryptocurrency related product. Some of the links shared may be referral links.

So today we’re doing just that and looking at a portfolio that was sent to me. In order to keep their identity private, I will refer to the reader as Jimbo. For some context, Jimbo’s portfolio is in the mid 5-figure range.

This is the first time I’m doing this publicly, but I have on many occasions over the years looked over other people’s portfolios and offered them my thoughts. I always find it an interesting and insightful experience because looking at someone else’s portfolio forces you to think about stuff you might not normally think about.

We’re usually all just looking at our own portfolios non stop, but this can cause us to have blinders on for certain things.

Anyway, there’s a lot to like here. But there are a few things I’d question too.

Let’s get into it.

The Overview

First, let’s look at Jimbo’s portfolio from a high level. Here’s how it breaks down by token and by category:

*note that tokens with less than a 1% allocation weren’t listed, so that explains why the numbers in the top chart don’t add up to 100.

Some high level thoughts first.

1. Venture at 29% immediately stands out as “very high”. That’s a lot more than most people have in venture / angel / early stage investments, and is more than I would generally recommend. We’ll look at the exact venture investments in a bit more detail below.

2. Overall the number of tokens is not too high which I like to see. This seems like a manageable portfolio to me, and not an unruly one.

3. No BTC exposure is interesting.

4. HYPE dominating the L1 exposure is interesting.

Those are my initial thoughts. Nothing seems glaringly bad or cause for alarm, but there are probably some ways we can optimize it too.

Now let me go through each holding one by one.

Read more

The response was great and a bunch of people have started building their own models, which is awesome to see.

Some of the questions I’ve gotten this week are along the lines of “what should I try and predict?” and “are you meant to just follow the model blindly once it’s up and running?”

So I thought I’d write a bit more about the concept of domain knowledge since it answers both these questions + more.

Domain knowledge is a layer that sits (or should sit) at the foundation of as well as on top of any model you build. It’s the thing that separates someone who has a model from someone who has a good model, and someone who uses their model well.

This is the stuff you know about your area of expertise that no dataset fully captures. Context, nuance, edge cases, etc. Things that are hard to quantify but easy to recognize if you’ve spent thousands of hours in a space.

I think understanding how and when to apply your domain knowledge is one of the most important skills you develop as you work with prediction models. And it’s something I’ve been thinking about a lot as I continue to refine my Dota 2 model and track real bets.

My model, by the way, is continuing to prove to be quite the profitable little thing. Here are the latest results. Still early days, but my confidence is slowly but surely growing in it:

137 bets and profitable. I won’t feel too comfortable until we’re at 500 bets, and probably not really comfortable until we hit 1000+, but… we’re on our way.

Anyway. Back to domain knowledge. Here’s what we’ll cover today:

1. What domain knowledge actually is

2. Where domain knowledge helps you build a better model

3. When to trust yourself over the model

4. When to trust the model over yourself

5. This applies beyond betting on esports

6. Final thoughts

1. What domain knowledge actually is

Domain knowledge is everything you know about a subject that you’ve accumulated through experience, observation, and participation. It’s the stuff that lives in your head and is hard to put into a spreadsheet or json file or bit of python code.

For me and Dota 2, that’s knowledge which comes from 20+ years of playing the game and thousands of hours watching professional matches. Some examples of domain knowledge:

• Knowing that the meta shifts considerably when new patches drop, and some teams (and players) perform better than others depending on the patch notes/changes

• Knowing when a team has a standin player replacing one of their regular players due to visa issues (or other issues)

• Knowing which games “don’t matter” in the sense that a team that has gone 0-4 in the group stage has a 0% chance of making it to the playoffs even if they win every match from now on, but they still have to play their matches, so they might not try as hard or they might try more experimental things than usual (also the flip side: when a team has secured their spot, they also might try to be more experimental)

• Knowing when a team just played the second longest bo3 series in history and still has a bo5 to play and they’re running on slow sleep and fumes at the end of a 2.5 month trip away from home (this happened this past weekend)

None of that is in my model’s training data. You can sort of come up with ways to add versions of these into the model, but a) you still need to know to look for them in the first place (something I doubt most non-dota fans would be able to look for) and b) a lot of the time the information is very difficult/impossible to scrape and only applies to an extremely small % of matches that it harms the overall model to even try.

Models sees numbers. Win rates, hero matchups, recent form, historical performance. It does a good job with those numbers. But it doesn’t actually watch the games, it doesn’t watch pre match and post match interview, and it doesn’t understand.. for lack of a better word, the vibes.

And yes, I’m using the word vibes unironically here, because sometimes that’s what it comes down to. You watch a team play and something feels off so you might look into it and see: oh, yeah, their coach actually isn’t with them for this tournament cause of X, Y, Z reason. So they’re not drafting as good as they might be, that explains my vibe!

That’s domain knowledge.

The specifics will vary depending on the type of thing you’re trying to predict, but the principle is the same. Domain knowledge is either a) things you know that most others don’t and which you can feed into your model, and b) things you know that can’t reasonably be put into any model, but that might influence how much you want to rely on your model’s predictions.

Let’s look at these in a bit more detail now.

2. Where domain knowledge helps you build a better model

Read more

Dealing with AI Anxiety and FOMO

You’ve probably noticed that I’ve been going pretty crazy for all things AI over the last few months (it would be hard to miss). I’ve been building things, writing about it, co-founding companies with sassy dragons, basically spending hours every day in AI land.

I’ve felt my fair share of anxiety and FOMO, both in life, in NFTs, and more recently, in the AI space. One interesting thing that I’ve found though is that the more time I spend with AI, the less anxious I feel about it. Which is funny because for a lot of people (probably most people) it’s the opposite. The more they read about AI, the more anxious they get. And I think the reason is that reading about AI and using AI are completely different experiences.

Reading about AI gives you sensationalist headlines. Jobs are being replaced. Some new model just came out that’s 10x better than the last one. Someone built an entire app in 30 minutes. It’s all framed to make you feel like you’re falling behind, because that’s what gets clicks and engagement.

Using AI gives you reality. Which is that these tools are genuinely useful for a lot of things, genuinely bad at a lot of other things, and the learning curve to get value out of them is not actually that steep at all. You just have to sit down and start.

You don’t need to understand how large language models work. You don’t need to have an opinion on AGI timelines or which model is best. You need to open ChatGPT or Claude or whatever and ask it to help you with something you’re actually working on. A real task. That’s it. It’s like crypto: yeah you can learn by reading books and watching videos and all that, but there’s no substitute to setting up a wallet and making a transaction onchain.

I think a lot of the anxiety comes from a place of consuming too much and doing too little, which ties back to what I wrote recently about your information diet. Every AI hyped up thread you read, every “I replaced my entire team with AI” post, every breathless announcement about some new capability… it all adds to the noise. And noise adds to your anxiety.

The people I know who are most comfortable with AI right now are the ones who use it daily. Whether it’s summarizing documents, brainstorming ideas, writing code, researching topics, creating content, whatever. They’re not (necessarily) trying to keep up with every development, they just found a few things it’s good at and built them into their workflow.

I’ve been guilty of the consumption trap too, by the way. I went through a phase of bookmarking every AI tool and tutorial I came across, and it took me a while to realize that none of that was making me better at actually using the stuff. What made the difference was picking one project — in my case, building a prediction model — and just going. Learning by doing, making mistakes, figuring it out as I went.

If you’re feeling overwhelmed by AI right now, my advice woudl be simple: stop opening X, stop bookmarking tweets, stop reading sensationalist headlines, stop watching youtube videos. Just stop consuming content about AI for like a week. Instead, pick one thing in your life or work that’s annoying or tedious, and ask an AI to help you with it. That’s it. One thing. See what happens.

You might be surprised at how quickly the anxiety fades when you replace it with actual experience.

Thanks for reading! In case you missed it, check out Monday’s post below 👇

Get $20 in free BTC by signing up to Swyftx here. Australian & NZ residents only.

Disclaimer: The content covered in this newsletter is not to be considered as investment advice. I’m not a financial adviser. These are only my own opinions and ideas. You should always consult with a professional/licensed financial adviser before trading or investing in any cryptocurrency related product. Some of the links shared may be referral links.

I’ve been building a prediction model for the past few weeks to determine who will win in competitive matches for Dota 2 (an esports video game). I’ve done it all through vibe coding with Claude Code (with some help from Yoshi via openclaw, but it’s all possible via CC directly). I have no degree in machine learning, and no data science background.

While still early, the results are looking very promising. I backtested the model and the results actually look fantastic. Honestly, they look too good to be true, so take this with a healthy dose of skepticism:

I’ve been tracking real results for a few weeks with ~100 actual bets logged, and the model is performing well so far (~7.5% ROI), so I have some hope that things will continue to go well. But I know it’s still early days.

There are a lot of people out there selling claims of their polymarket bots printing 6 figures a week and making it seem like it’s all so easy. It is not easy, and it takes time, dedication, motivation, and hard work. You have to be willing to learn, it’s not as simple as “hey claude, build me a prediction model that makes money”. Even when you have a model like this, you have to test and test and test, and make sure it actually works. You have to maintain it, update it, and even then, you will run into liquidity issues when betting/predicting, and it’s never as simple as printing 6 figures.

I have been working daily on my model for almost 2 months, putting in 5+ hours a day on average. There’s a lot of painstaking work and moments of frustration. But the potential is there, I believe, for anyone to do what I done and to build a (hopefully) profitable model.

Today I’m going to walk you through how prediction models work, how I created mine, and how you can create your own models with the power of vibe coding.

I’ll break down the core components that every successful prediction model needs and some additional suggestions for how to build and develop them in a practical sense:

1. Start with a clear and well defined question

2. Ask the AI to help you every step of the way

3. You need reliable, clean data

4. Your features are everything

5. Choosing the right model

6. Hyperparameter tuning

7. Eliminate data leakage

8. Proper train and test splits

9. Evaluation metrics

10. Good calibration

11. Fast iteration cycles

12. A retraining pipeline

13. Testing and monitoring in the real world

14. Putting it all together

Let’s get into it.

Sidenote: I am in the process of launching a new educational community for those who want to learn about AI. I have two co-founders who have been building and teaching people about AI for years, and among other things, we’re going to be running 8 live video workshops a week.

We’re in the early days, but we’re accepting some new members. There’s a special offer for premium subscribers that I’ll share at the end of this newsletter, which is a 70% discount from what our eventual price is going to be.

Public launch will be around the end of the month, so keep your eyes peeled for that. Super excited about this!

1. Start with a clear and well defined question

The biggest mistake people make when they hear “prediction model” is they start thinking about algorithms and frameworks and profit. Don’t do that. Instead, think about what question you’re trying to answer.

Who wins this Dota 2 match? is a good question. It’s binary and measurable. You know when you got it right and when you got it wrong.

What will happen in crypto this week? is a bad question. It’s vague, and there’s no clear success or failure criteria. You’d struggle to even know what data to collect.

The quality of your question determines the quality of everything that follows.

A good place to start is to ask yourself:

• What am I predicting?

• What are the possible outcomes?

• When do I make the prediction?

• When do I find out the result?

If you have clear answers to all four, you should be able to come up with a good question that you’re going to use a prediction model to answer.

If you’re trying to build a model that you’ll use to make bets and make money, then I think it’s best to start with something you have existing domain knowledge and expertise in.

I picked Dota 2 because it’s a video game I have been playing for 20+ years, have bet on for fun before (a lot; I have lost so much money betting on this damn game over the years lol, but now I might get my revenge!!), have been watching people play for 10+ years, and know inside and out. I know more about the game than the vast majority of people, even more than most who watch it regularly. Some of that knowledge might come in handy when finding an edge later on.

How AI helps

You can describe your general area of interest to any AI and ask it to help you formulate a well defined question. Ask it to push you toward specificity. Tell it your domain and what decisions you want to make, and it will help you find the question you want to answer.

Honestly actually, just copy and paste this whole section into the AI and be like “I want help coming up with a good question, I want to make a prediction model, can you help me?” and go from there. A common theme you’ll notice throughout this post is that you can and should…

2. Ask the AI to help you every step of the way

There’s no glory in doing all of this yourself. AI is the most powerful tool in the world, use it, and use it well.

So once you’ve picked the question you want to answer, it’s time to load up your vibe coding platform of choice and start asking the AI for more help. I wrote about Claude Code a few weeks ago and that’s where I built my model. I would recommend either using Claude Code with Opus 4.6 or Codex with GPT 5.4 as they’re currently the two frontier models when it comes to coding.

Of course you can give this a go with lesser models and experiment (and it’s a great way to learn), but if you’re trying to make money, I really do think you’re gonna want the top tier models.

Once in Claude Code/Codex, create a new project and just start telling it what you want, based on the question you came up with. Something along the lines of:

I want to build a Dota 2 prediction model to help predict which team will win a match. I want you to help me with this. Start by doing some deep research to discover all you can about building prediction models; specifically, Dota 2 and esports models. Look at research papers and any evidence of other successful models out there that we can learn from. Share the sources and evidence with me. Take all of that information and come up with a step by step plan for us and let me know what we need to get started.

The AI is going to do a pretty damn good job of coming up with a plan from here, but one thing I found very helpful and important is actually reading the sources and research papers yourself (or at least a couple of them). I know we’re all training ourselves to rely on AI summaries and bullet points, but you really do want to have a bit of an understanding of how everything works under the hood; it’ll be super helpful as you move forward.

The rest of this letter will hopefully give you some of that context and help you understand these things too.

3. You need reliable, clean data

Your model learns from data. If the data is wrong, incomplete, or inconsistent, the model will learn the wrong things.

For my Dota 2 model, I get the majority of my data from official APIs. I always recommend trying to find good APIs for your data vs scraping data from the web. For Dota, the API I use has comprehensive match data going back years. Team compositions, player stats, match outcomes, patch information, and much more. The data is structured, well-documented, and updated regularly.

Unfortunately (or maybe fortunately, since it might present an opportunity), not every domain has a nice API waiting for you. Sometimes you have to scrape websites, parse PDFs, or work with messy spreadsheets.

Usually, you’ll still have to do a bit of both (I scrape some stuff, even though 95% comes from APIs).

Ultimately, the format doesn’t matter as much as the reliability. You need to trust that the data accurately represents what happened. APIs are easier, but not the only way to get to this point.

Aside from reliable data, clean data goes a long way. This means: no duplicate records, consistent formatting, no missing values in critical fields, and clear documentation of what each field represents.

How AI helps

Ask it to write data quality checks. Something like: “write a script that loads my match data, checks for duplicates, flags any matches with missing team IDs, and shows me the distribution of matches per month.” You could even go more basic, and say “I want to ensure our data is clean and reliable, how can we do that?” and it’ll come up with some suggestions and a plan, and you can go from there.

4. Your features are everything

Features are one of the most important things to understand when it comes to building a predictive model. In a nutshell, features are the inputs your model uses to make predictions. Raw data is rarely useful on its own. You need the raw data because that’s what is used to crate these features, but it’s the features that are what are actually used to predict things.

For Dota 2, a raw stat like “team A has played 200 matches” tells you almost nothing about who wins the next one. But “team A has won 65% of their last 20 matches on the current patch” tells you something useful about recent form in the current meta.

This is where your domain knowledge comes in handy. You understand your domain. You know what factors influence outcomes. If you’re an avid golf fan, you know that the weather has an impact, the type of grass makes a difference, that whether a player is starting in the morning vs the afternoon can change how likely they are to score well, you know that long hitters perform better on some courses, and so on.

Your model doesn’t start out knowing any of this. It only knows what you tell it through features.

Good features capture information that is available before the prediction, relevant to the outcome, and not redundant with other features.

How AI helps

Ask it to suggest features as a starting point, so you get an idea of the types of things you can use. Then describe your domain expertise and the factors you think matter to brainstorm a list of additional things (features) you think might impact the outcome.

Then, ask it to engineer those features from your raw data. It will write the transformation code. You evaluate whether the features make sense. This back-and-forth is where vibe coding shines. You bring the thinking (at least some of it) and your domain knowledge. The AI does everything else.

5. Choosing the right model

You have a question, data, and features. Now you need something that takes those features and turns them into a prediction. That something is a model.

Think of a model as a function. You feed it inputs (your features) and it gives you an output (a prediction). Different types of models learn this function in different ways. Some are simple. Some are complex. The right choice depends on your problem, but for most prediction tasks with structured data, the answer is simpler than you’d think.

Read more

Today I thought I would do a deeper dive into this ecosystem to share why I am so bullish on it, and how all their moving parts work together.

The more time I spend looking at and thinking about Venice, the more bullish I get. The tokenomics are genuinely interesting, the product is real, and the way they’ve structured their whole token ecosystem is creative. Not to mention it sits at the nexus of AI x Privacy x Crypto.

What is Venice AI

Venice is a privacy focused AI platform. You can use all the leading AI models through a single interface and API: Claude, GPT, Gemini, Llama, Mistral, and a bunch of open source models.

The privacy angle is one of the things that makes them different. They don’t store your prompts or responses. Zero data retention. Every time you use ChatGPT or Claude directly, that data sits on someone else’s servers. Venice routes your requests through decentralized GPU providers over encrypted connections. In other words: your data stays on your device.1

This matters more than you might think. AI regulation is tightening globally, companies are getting more careful about what data they feed into AI tools, and demand for private inference is only going up.

They also run uncensored models with no content filtering. For developers building apps that need full control over AI outputs, this is a significant selling point.

The platform processes over 1 million daily API requests. They have integrations with Brave (the privacy browser), OpenRouter, Cursor, VSCode, and a growing list of developer tools. I personally use their API, integrated with Yoshi (my openclaw AI agent) as a model gateway to access the best tools on the market for things like image and video generation.

Unlike many projects in crypto, with Venice, the product works. It has real users, including plenty from outside of crypto (via a standard subscription fee model).

In fact, Venice AI is even highlighted and recommended in the official OpenClaw docs. This is in spite of the fact that the founder OpenClaw is staunchly anti-crypto.

So, Venice AI is cool. But what about VVV and DIEM, and how do these tokens integrate into the ecosystem? We’ve seen a lot of good projects come and go in crypto and while having real users and revenue is fantastic, it doesn’t always translate into good things for their token price.

The VVV token

VVV is the core token of the Venice ecosystem. It lives on Base.

Some numbers:

• Current price: ~$6.64

• Market cap: ~$294M

• Total supply: 78.8M tokens

• 42.7% of total supply has been burned (33.68M tokens, gone permanently)

• 31M tokens staked, 7.9M locked

• Circulating supply: ~44M (and shrinking)

In March 2025, they burned all unclaimed airdrop tokens in one shot. Since November 2025, they’ve been using a portion of monthly revenue to buy VVV on the open market and burn it. Ongoing, revenue-funded buyback and burn.

You can stake your VVV to earn yield, currently around 18% APY. Emissions are set at 10M tokens per year, and 100% of those emissions go to stakers (note: there’s a 7-day unstaking period).

One thing to be clear about: the 18% yield comes from token emissions, not from platform revenue. That’s an important distinction. The buyback and burn is revenue-funded. The staking yield is inflationary. Both things can be true at the same time, and the idea is that the buybacks help offset the inflation rate. Well, the buybacks, and DIEM.

Where the real magic happens

In August 2025, Venice introduced DIEM. This is where the tokenomics get genuinely exciting.

Read more

This post is sponsored by Magic Eden

Magic Eden made a major announcement today, sharing their plans to shift their focus on DiceyHQ, their product in the crypto casino & sportsbetting space.

Unfortunately this means they are sunsetting some existing products: their EVM marketplace, Bitcoin marketplace, Bitcoin API, and Magic Eden Wallet are all being wound down over the next few months.

I genuinely think this is a smart move for them as a business. If I were advising them, and had access to their internal metrics, I would probably suggest the same thing. It makes me a little sad that so much of our space is headed towards becoming trading / gambling / prediction market apps, but it is the reality of the world. Better they pivot to something that can make them revenue than shut down entirely.

Read more here: https://x.com/0xLeoInRio/status/2027427791533146367

What Are AI Agents, Actually?

I’ve been messing around with AI agents for the last month or so. Like, properly - giving openclaw full access to a computer (yes I bought into the mac mini craze) and getting it to go do things while I do something else.

Most people’s mental model of AI is still “a thing I type questions into.” That made sense a year or two ago. But it doesn’t capture what’s happening now, so I thought I would try to explain what agents are and how they work.

The simplest way I can explain an AI agent: it’s AI that does things, not just says things.

When you use ChatGPT or Claude or whatever, you’re having a conversation. You ask, it answers. Even when it generates an image or analyzes a file, it’s the direct result of a direct request or prompt that you make.

An agent is different. Basically, you give it an end goal and it figures out the steps. It can browse websites, write code, create files, send messages, call APIs, and chain all of that together without you telling it how or needing to stop and ask you for directions (or permission) at every step of the way. You just come back later and the work is done… it feels like pure magic a lot of the times.

This gap between “AI that talks to you” and “AI that works for you” is enormous. It’s kinda like the difference between googling a recipe and having someone cook dinner (we’re not here yet.. but add some robotics to agentic AI, and boy oh boy, are things gonna get wild).

The reason this explosive agentic movement is happening now and not two years ago comes down to reliability. The models used to fall apart on anything with more than a few steps. They’d lose context, make weird mistakes, massively hallucinate, or just stop halfway through. The latest models from Anthropic, OpenAI, and others have crossed a threshold where they can reliably handle complex, multi-step work. Not perfectly, but well enough to be genuinely useful.

There’s also a new standard called MCP that’s quietly becoming a big deal. It stands for Model Context Protocol and the easiest way to think about it is as USB for AI. Before USB, every device needed its own special cable and driver to work. MCP does the same thing for AI tools. It gives agents a universal way to plug into anything. Your calendar, your codebase, your database, your email, whatever.

And we’re still early. Really early. Agents work, and they work well enough to be useful right now. But the tooling is rough, the setup isn’t trivial, and most people that are using agents still haven’t figured out how to integrate this into their daily workflow yet.

This chart shows just how early we are in the grand scheme of AI:

I think within one to two years, most knowledge workers (that still have jobs) will have some kind of AI agent running in the background handling parts of their work. The people experimenting now are going to have a meaningful head start. Not because agents are hard to use, but because learning to work effectively with an autonomous AI takes time. You have to build trust in what it can handle and develop a sense for what it can’t.

In four to five years, god only knows where things will be.

I think everyone curious about this kind of thing should at least experiment and give it a go yourself. Start small, and pick a task you spend 30 minutes on regularly and see if an AI can do it. You’ll learn more from trying to do this than from reading a hundred articles about it.

If there’s interest, I can make a follow up post with a step-by-step guide on using an AI agent.

For now, thanks for reading, see you next week with another little learning!

Subscribe now

Disclaimer: The content covered in this newsletter is not to be considered as investment advice. I’m not a financial adviser. These are only my own opinions and ideas. You should always consult with a professional/licensed financial adviser before trading or investing in any cryptocurrency related product. Some of the links shared may be referral links.

This post is sponsored by BALLGAME.COM

Most “crypto x sports” projects don’t interest me tbh. They’re either drowning in overly complex tokenomics or solving problems nobody had. BALLGAME.COM started with the behaviour, not the blockchain, and that’s why it works.

Sports fans already call the game. BALLGAME.COM just turns that instinct into real upside. Make YES/NO predictions on live sports, earn entries into daily lottery draws with $100 USDC up for grabs everyday. Free to play. No deposits, no gambling with your own funds.

The draws are fully on-chain, provably fair, and the infrastructure is invisible. You’re just calling the game.

Still in beta, worth getting in early: www.ballgame.com.

Think about your information diet

I used to think that consuming more quality content would make me smarter. More newsletters, more X posts, more podcasts, more research reports, more whitepapers. It’s a natural thing to think, but eventually I realized I had it backwards.

Most people consume information the way they consume food. They start out with great intentions, but then life gets in the way, and they quickly devolve into consuming too much, and consuming too much junk. Then they wonder why they feel bloated and confused.

Here’s what I’ve noticed about the best decision-makers I know across crypto (and life). They all do some version of the same three things:

1. They’re ruthless about inputs. They don’t follow everyone. They don’t read everything. They pick a small number of high quality sources and ignore the rest. They understand that every bad input costs them twice. Once when they consume it, and again when it muddies their thinking. There’s an opportunity cost to information too.
   

2. They leave gaps. Time where they’re not consuming anything. Walking, driving, sitting around doing nothing. That’s when your brain actually does the work of connecting ideas, questioning assumptions, and forming original thoughts. If you fill every spare second with a podcast or a scroll session, you’re robbing yourself of your best thinking time. For me, I have my best thoughts when I am in the shower. Literally forced to have no external stimuli. Go figure.
   

3. They act on what they learn. This is the big one. Information without action is entertainment. There’s nothing wrong with entertainment, but don’t confuse it with research. If you read something and it doesn’t make you think or cause you to change something in your life, it’s entertainment. Be honest about which one you’re doing.

I’ve been guilty of all of this. I go through phases of following way too many people, bookmarking infinite posts (currently going through this with all the AI stuff), and it’s just not that productive.

If you’re feeling overwhelmed right now, if you’ve got 47 unread newsletters and a podcast backlog that would take a month to get through, here’s my suggestion: Unsubscribe from everything (if this newsletter doesn’t make the cut, well, I’m sad to see you go, but happy that you’re taking a step to own your information flow).

Seriously, unsubscribe from all of it. Then over the next month, subscribe back to the ones you actually miss. You’ll probably end up with about a quarter of what you started with. That’s your real information diet.

Your edge isn’t knowing more than everyone else. It’s thinking more clearly than everyone else. And you can’t think clearly when your brain is full of noise.

Thanks for reading! In case you missed it, check out Monday’s post below 👇

Get $20 in free BTC by signing up to Swyftx here. Australian & NZ residents only.

Disclaimer: The content covered in this newsletter is not to be considered as investment advice. I’m not a financial adviser. These are only my own opinions and ideas. You should always consult with a professional/licensed financial adviser before trading or investing in any cryptocurrency related product. Some of the links shared may be referral links.

Today, BTC is $66k (a drop of 27%), and my crypto portfolio is down around 20%. Great success!

We gotta take all the wins we can in a bear market, and outperforming holding Bitcoin is certainly something I am happy about.

In today’s post I will talk through my portfolio and the changes I have made on a macro and micro level.

In addition, I will talk through portfolio allocation outside of crypto. This is something a lot of people have asked me about. I’ll share my overall thoughts and what I think some good and best practices are, and then share my exact breakdown (spoiler alert: it is not exactly what I recommend to most people).

The largest change I have made is a clear distinction in how I look at my portfolio. I had previously ranked all tokens by my level of conviction (high/medium/moonbag), but I considered everything part of the same portfolio.

Now I have effectively separated my crypto portfolio into two buckets: long term holds, and speculative. I actually created my own portfolio tracker (thanks to the beauty of claude code) and also added tags so I can see how many tokens I have from each sector and what my exposure to various sectors looks like.

Man, AI is awesome. This took like.. an hour? to make. Maybe less.

Anyway, here’s what it looks like, I’ll share the unredacted version below and walk through some of my key holdings and any changes I have made since the last portfolio review (plus a quick update on the Yoshi token at the end too).

Full versions:

Read more

This post is sponsored by BALLGAME.COM

Most “crypto x sports” projects don’t interest me tbh. They’re either drowning in overly complex tokenomics or solving problems nobody had. BALLGAME.COM started with the behaviour, not the blockchain, and that’s why it works.

Sports fans already call the game. BALLGAME.COM just turns that instinct into real upside. Make YES/NO predictions on live sports, earn entries into daily lottery draws with $100 USDC up for grabs everyday. Free to play. No deposits, no gambling with your own funds.

The draws are fully on-chain, provably fair, and the infrastructure is invisible. You’re just calling the game.

Still in beta, worth getting in early: www.ballgame.com.

Understanding Token Charts

Today we’re covering something that everyone in crypto looks at but most people don’t fully understand: token charts.

I gotta be honest and admit that I am not a technical analysis expert. Far, far from it, actually. I don’t draw on charts and predict price movements (I have mad respect for the very few people who can actually do this). I honestly think that most of the time, technical analysis in crypto is about as reliable as reading tea leaves (or as the joke goes: TA is astrology for men).

But understanding the basics of how to read a chart is still an incredibly useful skill. It helps you understand what’s happening with a token and make more informed decisions.

Many of you might find all of the below super obvious. If you’ve been around for a while, and especially if you’ve been actively trading onchain, you’ll probably know all of this. But maybe there’s a nugget in there you didn’t know. And if you’re newer to the space, or to trading, then I think this is invaluable information that everyone needs to know.

Price vs Market Cap

Before we even look at a chart, let me clear up the most common misconception. The price of a token means very little on its own.

A token at $0.001 is not cheap. A token at $500 is not expensive. What matters is the market cap: price multiplied by the number of tokens in circulation.

A $0.001 token with 100 billion tokens in circulation has a $100 million market cap. A $50 token with 1 million tokens in circulation has a $50 million market cap. The second one is half the size despite the “higher” price. The second one is actually the cheap one.

Always look at market cap. It tells you the actual size and valuation of the project.

Candlesticks

Let’s look at a real chart now.

Most crypto charts use candlesticks. Each candle represents a time period (1 hour, 1 day, 1 week, depending on your settings). This chart is on the 1D setting (see the top, after “Uniswap”), meaning each candlestick represents a day.

A green candle means the price went up during that period. The bottom of the body is where it opened, the top is where it closed.

A red candle means the price went down. The top of the body is the open, the bottom is the close.

The thin lines above and below the body are called wicks. They show the highest and lowest prices during that period, even though the price didn’t stay there.

A candle with a long wick on top and a small body at the bottom means there was a lot of selling pressure. Buyers pushed the price up, but sellers pushed it right back down. This often signals resistance.

A candle with a long wick on the bottom and a small body at the top means the opposite. Sellers pushed it down, buyers bought it back up. This can signal support.

Support and Resistance

Support is a price level where buying tends to increase. The token has bounced off this level before, so traders expect it to bounce again. Think of it as a floor.

Resistance is a price level where selling tends to increase. The token has struggled to break above this level before. Think of it as a ceiling.

These levels form because humans are predictable. People set buy orders at round numbers. People sell at prices where they previously bought (to break even). These patterns create zones where supply and demand cluster.

Support and resistance aren’t guarantees (far, far from it). They break all the time. But they’re somewhat useful for understanding where other traders are likely to act. I think sometimes they become self-fulfilling prophecies, but still, useful to know.

Volume

Volume tells you how many tokens were traded during a period. It appears as bars at the bottom of most charts.

High volume on a price increase means lots of people are buying with conviction. The move is more likely to sustain.

High volume on a price decrease means lots of people are selling. The drop is more significant.

Low volume on any move means fewer people are participating. The move is less trustworthy and more likely to reverse.

Volume confirms trends. A price increase on low volume is suspicious. A price increase on high volume is more convincing.

I’ve found that volume is one of the most important things to pay attention to. A token price that is going up but on very little volume is usually super risky to get involved with, for instance. Sometimes, especially on lowercap tokens, it literally means that there’s a bunch of people awake and paying attention who are buying, but when the rest of the holders who are asleep or not watching the chart come back and see the price action, they’re very liable to sell.

More volume = more confidence, generally speaking.

How helpful is technical analysis?

For most people, technical analysis is more distracting than helpful. I’ve always believed that the crypto market is driven largely by narratives, news events, whale movements, and sentiment. A chart pattern can be invalidated in seconds by a single tweet by a certain president, or by Vitalik, or an announcement by the head of whatever project the token represents.

Use charts as one tool among many. Don’t build your entire strategy around them. And definitely, as a default, don’t trust people who claim they can predict the market with lines on a chart. 99 times out of 100 they’re a charlatan.

I know there are a lot of people out there who aren’t super familiar with these concepts, so hopefully if you read until here, you learned something from this post :)

Thanks for reading, see you next week with another little learning!

Subscribe now

Disclaimer: The content covered in this newsletter is not to be considered as investment advice. I’m not a financial adviser. These are only my own opinions and ideas. You should always consult with a professional/licensed financial adviser before trading or investing in any cryptocurrency related product. Some of the links shared may be referral links.

More than anything though, I’ve learned a lot. I’ve learned about myself, this industry, the human condition, and so much more.

I wanted to do something a little special and different for this 100th Newsletter, so I sat down and tried to think about all the things I’ve learned over the years, and amazingly I came up with well over 100 things.

I whittled the list down and here are 100 of the most important and impactful things I’ve learned after five years in crypto:

1. If you struggle sleeping due to market fluctuations, you are overexposed. Excitedly checking prices in the middle of the night? Waking up and afraid to check prices? Overexposed. Do something about it, or the market will do something about it for you.

2. No matter how much money you make, you’ll always want “more”. Very, very, very, very few people can escape this curse and way of thinking. There’s nothing inherently wrong with wanting more btw, as long as you understand yourself and your goals, and aren’t sacrificing other important things in the pursuit of more.

3. Money doesn’t directly lead to happiness, but it is an excellent tool for helping you become a happier person (largely by helping eliminate stress). I’m so much happier now than I was five years ago. I credit the majority of that to quitting drinking (which happened a bit over six years ago), but some of it surely comes from the financial comfort I have created for myself and my family.

4. There is no substitute for hard work. The real winners in every industry are the ones that work hard, not the lazy geniuses. With AI tooling as well, you can outsource a lot of the genius work. It really comes down to grit and determination and tenacity more than anything now.

5. The amount of work you can get done in a day if you really focus is obscene. Most people get distracted 100x a day and rarely get actual deep work done. Just 1-2 hours of deep work and you’ll fly through your to-do list. If you can manage to do 8+ hours of actual deep work, you’ll be unstoppable.

6. All tokens trend towards zero. This should be your default position. Then, look for exceptions. There aren’t many, but they are out there. Look for exceptions on different timescales too. You can still make money on a token you think is headed for zero if you get the timing right.

7. Take profits aggressively. The regret of roundtripping is almost always more painful than the regret of selling and watching the price go up, because, eventually, the price will come back down. If you have a lot of conviction, take partial profits.

8. The market is rigged. There are cabals upon cabals upon cabals, and you’re not in them. I’m not in most of them either. You can still make money, just know that the game is against you. Fwiw, all markets are rigged. Crypto at least allows for some level of transparency that tradfi markets do not.

9. Nobody cares about you. Harsh reality, but for the most part, your Twitter friends and Discord friends and Telegram friends are not your Real friends. If you’ve met them and hang out IRL that might be an exception, but until then, they’re just hanging out around you trying to make money — same as you probably are with them.

10. The longer you spend in crypto, the more jaded you become. This is a near universal truth. Most people are already looking forward to securing their bag, exiting, and never looking back. If that’s not you, find the other rare ones who are truly in this for the tech.

11. Crypto genuinely offers solutions that no other industry or technology can, and it will change the world for the better. Decentralization, censorship-resistance, privacy. These are incredibly important things that crypto offer. Most people don’t care though, which is sad.

12. Less is more. Fewer tokens, fewer airdrops farmed, fewer communities joined. Focus on quality over quantity and your portfolio (and mental health) will thank you for it.

13. CT (Crypto Twitter) is absolutely flooded with undisclosed promotions. Start from a default position of trusting nothing and no one. Do your own research.

14. There is an inverse relationship between the quality of alpha and number of followers a person has. Ignore what people with 100k+ followers are shilling you, find the real gems from the accounts with less than 10k followers, ideally even less than 1k followers.

15. Most VCs (Venture Capitalists) are not your friends. They are here to make money first and foremost. They might also want to support builders and ecosystems and what not, but their primary and ultimate objective is always to profit.

16. The most money I have ever made has come from being early to a big new trend, and then holding some percentage of the bag to insane heights. NFTs, meme coins, AI agents.

17. In other words, if you want the 100x or 1000x returns, you have to be a little bit crazy and hold beyond what most rational people would do.

18. The best alpha group is a simple group chat with 5 friends. Other chats can be valuable too, but you’ll never beat this one. Find this chat, make this chat, be a part of this chat.

19. There’s no such thing as a “bullish unlock”. If a lot of tokens are about to unlock and hit the open market, expect the price to drop. Don’t fight supply and demand.

20. Most people overcomplicate things from the beginning. Buy Bitcoin. Hold it. Go from there. Alts and higher risk plays should come later, once you’ve built your base. And only then, with a very small percentage of your overall portfolio.

21. Nobody knows what they’re doing at first. Most people still don’t years later. Don’t worry if you feel like a fish out of water; the drylands are flooded with displaced fish.

22. When everyone is euphoric, it’s time to be cautious. When everyone is scared, it’s time to pay attention. Or as the goat Warren Buffet said: be fearful when others are greedy, and greedy only when others are fearful. Easier said than done, but this mindset proves right over and over and over again.

23. You will sell too early. You will sell too late. You will almost never get it just right. Get used to the feeling. Or as some washed up unc once said, you will have Infinite Regret (and that’s actually not such a bad thing).

24. Most of the time the best thing you can do is to do literally nothing. Fight the FOMO and sit on your hands. Fight the urge to panic sell. Doing nothing is almost always the right thing to do, yet so many find it so difficult.

25. Every cycle, people say “this time is different.” Some things are. Most things aren’t. History rhymes a hell of a lot.

26. The market doesn’t care about your entry price. Nobody does, and you shouldn’t either. The sunk cost fallacy causes so many people to make bad decisions. “But I just want to get back to even…” is a toxic mindset, and you should try to rid yourself of ever thinking that way.

27. Zoom out. Then zoom out again. It’s easy to get caught in the day to day vicissitudes of the market but it’s rarely productive to do so.

28. Bear markets feel like they’ll last forever. Bull markets feel like they’ll last forever. Neither do.

29. The people loudest on social media are often the worst traders. Know the difference between content creators, larps, builders, edgelords, and actual traders.

30. Taking profits is not a sign of weakness. It’s a sign of discipline. If you publicly talk about selling and taking profits, you’ll almost always have some pocket watching people sticking their nose into your business telling you how you’re wrong, weak, paper-handed, and how the token is going to the moon. Ignore them.

31. Diversification doesn’t mean owning 50 altcoins. It means having exposure to uncorrelated ecosystems within crypto, but more importantly, it means have assets outside of crypto.

32. Set your risk tolerance before you enter a trade, not during it. Understand what your exit price is going to be (or at what price you’ll begin to take profits). Trusting future-you to make the right decision in the moment almost never works out.

33. The people who thrive over multiple cycles are the ones who are able to manage their risk well. The opportunity cost of going broke is enormous, and the smart ones understand this.

34. Having cash is a position too. A good one. A very good one. It took me too long to realize and appreciate how valuable it is to have a stockpile of cash. It gives you comfort in market downturns, it allows you to buy things on sale, and it earns you yield. Cash is king for a reason.

35. One big loss can easily wipe out many small wins. Protect your downside.

36. Reading whitepapers is useful. Using the product is more useful. You should do both, but know that whitepapers these days are often marketing papers and trumped up versions of what the actual product might be. Be a user, it’s the best way to understand a product.

37. If you don’t have your own thesis for buying something, with an exit price and plan in mind, you’re probably just gambling. Most people are gambling. Don’t be most people.

38. The best alpha is often free. It’s buried in long podcasts and boring documentation nobody reads and in tweets by accounts with 500 followers. The cost is the time to filter and find this alpha, but if time is on your side, it’s out there to be found.

39. Following smart people on X is one of the highest ROI activities in crypto. It’s dangerous because of how much noise there is on X, but curating your own list of smart people to follow that you trust is something everyone should do.

40. Most “research” people do is looking for confirmation of what they already believe. They make up their mind about wanting to buy a token and then they start researching to go “oh look, I found all these people who agree with me!” and then pat themselves on the back.

41. Learn to read a block explorer. It tells you more than any influencer will. This goes back to the whole “using the product” idea from before. Be a do-er, not just a reader or watcher or consumer.

42. Onchain data doesn’t lie. People do. So while the onchain data itself might not lie, it can be manipulated. Be wary of inflated TVLs and metrics boosted by bots and other tactics.

43. Understanding tokenomics is one of the most powerful things to understand. Most people still don’t know about market cap, FDV, liquidity pools, locked tokens, staking, etc. You gain a huge edge over most of the market by understanding these (relatively) simple concepts.

44. Every cycle has a new narrative. They’re profitable waves to ride, but make sure to get off the wave at some point. ICOs, NFTs, Memes, AI Agents. All went to explosive heights, before 99.99% of them crashed to zero.

45. If the team is anonymous and the code is unaudited, you should know exactly what risk you’re taking. I have sometimes YOLO’d in to trades knowing that the team is anon, and the code hasn’t yet been audited, because I wanted to be “early”. It can work out very well, but most of the time, it does not. Go in with your eyes open in such situations and only gamble with the smallest of positions.

46. Most people should not trade. Most people should invest. This is true inside and outside of crypto. Most people also don’t think they’re most people, so we’ll always have inefficient markets.

47. Tax implications are real. Tax is one of those things where an ounce of prevention is worth a pound of cure. Figure out your tax shit sooner rather than later. Future-you will thank you for it.

48. If your investment thesis changes, it’s okay to exit. Loyalty to a token is not a strategy. Tying your identity to a community and making friends with community members because you all love a token is a surefire way to roundtrip.

49. You haven’t taken profit until the money is in your bank account. Paper gains are worthless, and gains taken into ETH or SOL aren’t much better. Stablecoins are a bit better, but too easy to donate back to the casino.

50. In a similar vein, lifechanging money isn’t lifechanging unless you actually change your life with it. If you ever see your portfolio reach such a point, sell. Buy the house, pay off your debt, whatever it is — change your life. If you ran it up once, you can run it up again.

51. Almost every great trader has systems in place. Almost every bad trader has no systems, and trades at the mercy of their emotions.

52. Tribalism is one of the worst things in crypto. Bitcoin maxis, ETH maxis, Solana maxis. The truth is usually somewhere in the middle. Keep an open mind.

53. The people you meet in crypto are often more valuable than the investments you make. This industry has a great way of evening the playing field and you can meet absolute giants from other industries who are relatively new to crypto. You can also meet and make friends with absolute giants within crypto, simply by being a nice person, a curious person, a helpful person.

54. Crypto Twitter is the best and worst place on the internet simultaneously. I have such a love-hate relationship with it. I basically have stockholm syndrome. Most of us do. There’s so much value and fun to be had on CT, but boy is it a cesspool at the same time. I’ve seen the best and the worst of humanity on there.

55. Beware how a founder behaves in public. If they get aggressive or overly defensive of their project, it’s a very big red flag.

56. You will eventually get emotionally attached to an NFT. This will cost you money. That’s okay, not everything is about money. It’s okay to buy and hold something you love just because you love it.

57. The art market has always been irrational. NFTs are no different. Don’t try and rationalize too much when it comes to art NFTs specifically. “Why is this worth so much?” — things are only ever worth what other people are willing to pay for them. And people are incredibly irrational, so the prices of things will always be irrational too.

58. The technology behind NFTs will outlast any current meta around NFTs. I truly still believe the technology is absolutely gamechanging and will power the majority of the internet one day.

59. Not your keys, not your crypto. Time and time again we’ve seen centralized exchanges blow up and take user funds with them, with FTX being the most spectacular example in recent history. Be careful keeping too much money on a CEX (especially unregulated ones).

60. On the flip side, self custody is also risky. Most people probably shouldn’t do that either. This makes buying and holding and securing crypto inherently risky and tough. Educate yourself about the risks and come up with a plan best for your own situation.

61. Use a hardware wallet. This is not optional. They are not 100% bulletproof, but they solve a lot of problems.

62. Assume every DM from a stranger is a scam until proven otherwise. If something sounds too good to be true, it almost always is. Keep your guard up (this is just a good rule in life outside of crypto too, especially with AI attack vectors becoming more commonplace).

63. Use two-factor authentication on everything. Use an authenticator app, not text based 2FA. For ultimate security, get a hardware key (like a YubiKey) for 2FA.

64. Operational security is boring to set up and priceless when it matters. We’ve seen an increase in physical attacks targeting people in the crypto industry lately, so make sure you’ve thought through your personal safety and opsec.

65. I used to think that consistency beat talent and quality in content creation, but in an age of infinite content due to AI and bots, you need to be both consistent and talented. More than anything, you need to be human. Being genuine and authentic is the biggest moat you can have when it comes to creating content.

66. Writing about what you’re learning is the fastest way to learn. It’s what I did with this Newsletter. It’s still how I learn many things.

67. Sharing your mistakes builds more trust than sharing your wins. The content world is flooded with people posting screenshots of big wins and talking themselves up. Nobody really wants to hear about them. Be raw and real and share your mistakes and losses, and people will love you all the more for it.

68. Good content helps people. Great content helps people and makes them feel something. Goes back to the humanity thing.

69. Your voice is your edge. Don’t try to sound like someone else. It’s good to find inspiration from others but be wary of going too far and trying to emulate someone else too much. It’s always better to be the best version of yourself than the second best version of someone else.

70. It takes longer than you think to build something meaningful. Keep going. The biggest winners in the space across content and projects are the ones that built for years, usually through a bear market, and finally found their success on the other side.

71. Patience is the most undervalued skill in crypto. The market is a wealth transfer machine from the impatient to the patient. Figure out how to be patient. It really is a virtue.

72. Strong opinions, loosely held. Update your views when new information arrives. Don’t get stuck in your ways and refuse to listen to the market or what people are screaming at you because of bag bias.

73. Time in the market beats timing the market. Another cliche, but another damn true one.

74. Your mental health matters more than your portfolio. Step away when you need to. It’ll actually end up helping your portfolio too — if you’re in a bad mental state, you’re not gonna be making good decisions anyway.

75. Very rarely does a single trade change your life. Everyone is looking for their lottery ticket, their 1000x that will make up for everything. Reality is usually a lot more boring: habits and systems are how most winners win.

76. Humility is free and valuable. Arrogance is expensive. Don’t burn bridges, be humble, be kind, it’ll pay off multiple times over.

77. Product market fit comes from answering the questions “what will help the end user” and “what will make people’s lives better”. Build things that real users actually want.

78. The best investments in crypto look obvious in hindsight and controversial at the time. Shorting the top when everyone is euphoric will get people calling you a moron. Buying the absolute lows when a project looks dead and down and out is the same, but that’s where the greatest gains come from.

79. Five years from now you’ll look back at today the same way you look back at five years ago (most likely, you wish you paid more attention back then, you wish you took advantage of the opportunities around back then, etc). Don’t make the mistake of being complacent now and looking back five years from now, wishing you paid more attention.

80. Airdrops reward the curious and the early. The best airdrops come from trying new things, using new protocols, early, before anyone else. If you’re only ever farming airdrops based on lists that content creators put out, you’re missing out on a lot of the best gains.

81. A token going up 10x after you sold is not a loss. You made money. Move on.

82. The best projects ship during bear markets. Watch who's building when prices are down. It’s usually when most people tune out of markets but it’s honestly the time you should be paying attention the most.

83. Revenue solves a lot of problems. Protocols that generate revenue are often (but not always) the best investments. Find the ones that are making real money and have a plan to use that money to support their token. It’s a very good starting point.

84. Keeping a trading journal is one of the absolute best things you can do. Document your trades, your entry price, exit price, thesis, and any learnings. You’ll learn soooo much about yourself and improve so much if you do this one simple thing.

85. I mentioned this earlier about tokenomics but it’s worth its own point: market cap matters more than token price. A $0.00001 token is not cheap if there are a trillion zillion squintillion of them.

86. Not every dip is a buying opportunity. Some things go down for good reasons, and keep going down. Ignore the people screaming at you to BUY THE DIP (they’re usually just overexposed and what they’re really yelling is BUY MY BAGS).

87. Taking time off from crypto is healthy. The market will be here when you get back. Don’t feel like you need to be locked in 24/7 forever or you’ll miss something, that usually just leads to burnout and bad decisions.

88. If you get lucky and win very big on a trade, the absolute best thing you can do once you’ve sold and secured the profit is to take a week off. Go sit on a beach. Reset your mind and your new world. Most people try to double down and find the next win immediately, and 99% of the time end up giving back a lot of their profits to the market because they’re now betting bigger and think they’re a genius when they really just got lucky.

89. Your first bear market feels like the end. Your second feels like an opportunity. If this is your first one, know that we all felt the same way at one point. Most of us roundtripped all of our profits and gave everything back, sometimes more. Many of the most successful traders lost everything at one point or another. There’s always hope.

90. Most debates about which chain is better or faster or cheaper are irrelevant to end users. People just want good apps that work and are easy to use.

91. The projects with the loudest marketing often have the weakest fundamentals. The best marketing rarely comes from the projects themselves, it comes from their users genuinely singing their praises. See: HyperLiquid.

92. Almost all of the people selling courses on how to make money in crypto are making their money selling courses, or worse, by shilling tokens to you and getting you to buy after them so they can sell into you.

93. An investment that requires constant monitoring is a job, not an investment. That can be okay, just know the job you’re taking on by buying such things. I’ve lost so much money due to not tracking tokens and airdrops and claims from things I invested in.

94. Your portfolio allocation should change as your life circumstances change. Single at 19 years old is different from married at 35 with two kids and a mortgage. The general philosophy is more risk when young, less risk when old.

95. This same philosophy applies to portfolio size. You can afford to take a lot more risk when your entire portfolio is worth $500 and you can replace that if you lose it. You should take a lot less risk if your portfolio is worth $5m. The general trend is you should move from accumulation to preservation as your portfolio size grows.

96. Most token launches benefit insiders. Team, investors, advisors, etc. They are the ones coming up with the tokenomics, and they usually want to do it in a way that maximizes their own value. There’s nothing inherently wrong with that if they can do it in a way that is value additive and benefits the rest of the ecosystem — founders and investors deserve to make money too of course — but the thing to be wary of is when it’s done in an extractive way, a manipulative way, designed for insiders to dump on retail. Sadly this is all too common.

97. Many airdrops are worth farming. Few are worth holding after you receive them. Take your profits and move on, or at least, take partial profits.

98. Knowledge compounds the same way a portfolio does. Small daily learning adds up over years. The best investment you can make is in yourself.

99. We’re still early in crypto. It doesn’t always feel like it, but zoom out far enough and it is. If you believe this technology is going to be around forever, then people (and AI) hundreds of years from now will look back at these early days of crypto as the very beginning of a world changing movement still.

100. Life is fucking short. None of this really matters that much. We are blessed with the briefest of moments as a sentient human being, and we all waste so much of this incredible gift on the most useless things. Doomscrolling social media, instagram, tiktok. Getting into fights on CT with anons. Checking portfolios 10x a day. Losing sleep over a meme coin. Getting angry at influencers and VCs. None of it really matters. Don’t forget that. Don’t forget to live life. Have fun. Be happy. Stop and smell the fucking roses. Be grateful and appreciate what you have, rather than constantly comparing yourself to others and wishing you had more. You’re alive. You’re reading this. You’re breathing. That’s incredible. Like, INCREDIBLE. I know that life can be so tough and hard and brutal and unfair to the Nth degree, but if you’re still here, there’s always hope. I’ve hit rock bottom so many times in my life, lost everything, felt hopeless, but there was always hope even if I couldn’t feel it at the time, and there is always a path back. Never forget that. Don’t take life for granted. I lived over 30 years being blessed with very little death in my life. Then I lost my grandmother, my dog, and almost my dad. Those moments taught me more than the rest of the last 5 years combined. And, actually, on the shortness of life… as my namesake Seneca said two thousand years ago:

“It is not that we have a short time to live, but that we waste a lot of it. Life is long enough, and a sufficiently generous amount has been given to us for the highest achievements if it were all well invested. But when it is wasted in heedless luxury and spent on no good activity, we are forced at last by death’s final constraint to realize that it has passed away before we knew it was passing. So it is: we are not given a short life but we make it short, and we are not ill-supplied but wasteful of it… Life is long if you know how to use it.”

Life is long if you know how to use it.

Thanks for reading! I hope you enjoyed these 100 learnings. If just one person learned just one thing from these that improved their life, that’s a win in my book.

Here’s to another 100 Letters.

Cheers.

Subscribe now

Disclaimer: The content covered in this newsletter is not to be considered as investment advice. I’m not a financial adviser. These are only my own opinions and ideas. You should always consult with a professional/licensed financial adviser before trading or investing in any cryptocurrency related product. Some of the links shared may be referral links.

This post is sponsored by Phantom

Phantom Prediction Markets are a new way to discover, discuss, and trade real-world events and cultural moments that interest you. It’s a social way to participate in what’s happening in the world, from award shows and sports to politics and music, all directly on Phantom.

Explore hundreds of curated markets and current events across sports, politics, crypto, culture, and more. All organized and searchable so you can join in on what matters most to you. Plus, watch scores and odds change with live event updates right as they happen.

Click here to learn more.

Be so good they can’t ignore you

This is one of the best pieces of advice I’ve ever come across, from the one and only Steve Martin:

I see a lot of people focusing on the wrong things when trying to achieve their goals. The goals are usually pretty straightforward: wanting more money, more followers, more users for their product. But instead of focusing on ruthlessly improving themselves, people often get distracted and focus on trying to look and appear good, rather than being good. They spend all their energy networking and positioning and puffing out their chest trying to get noticed.

None of that actually matters if you’re not good at what you do.

I started this newsletter in 2021 with zero subscribers. I basically had no connections in crypto, certainly no audience or reputation to speak of. I was a true nobody. I had nothing going for me other than the fact that I was willing to sit down every day, learn something new, and write about it in a way that people found helpful. That was the whole strategy. Get better, and keep showing up.

Over time my audience grew. People started sharing my posts, opportunities came in, and things started compounding. The same concept applied to my growth on Twitter. I started posting daily spreadsheets, providing value on the timeline every day, and filling a void in the content world. I made myself unignorable. I see the same thing happening today with some of the biggest creators on the timeline. They figured out how to be so good, they couldn’t be ignored.

This applies to every corner of crypto (and life), and I think people underestimate how true it is.

• If you’re a trader, stop worrying about what other people are doing and focus on developing a real edge. Study your past trades, build frameworks, track your results honestly, and get better at spotting trends and reading markets. The returns will follow. You can complain about scammers and extractors and insiders, or you can work on your own game, improve, and be so good they can’t ignore you.
  

• If you’re a builder, stop chasing hype cycles and build something people need. Make it work well and improve it constantly. Then figure out how to generate attention for it. The best projects in this space grew because the founders could do both: build, and get attention. You can complain about other projects, or you can be so good they can’t ignore you.
  

• If you’re a creator, stop obsessing over engagement metrics. Write things worth reading, make content that helps people, and do it consistently. Provide value. The audience finds quality over time, and it always does. The cream eventually rises to the top. You can complain about engagement farmers and people botting their accounts til the cows come home. Or you can improve your content, provide more value, and get noticed. You can be so good they can’t ignore you.

I think about this quote often when I sit down to write. Am I pushing myself to explain things more clearly? Am I doing the research? Am I respecting my readers’ time and attention? Some days the answer is yes and some days it’s not, but the orientation toward improvement is what matters.

Stop worrying about who’s paying attention to you. Put your head down and get better at what you do. Do it long enough and consistently enough, and they won’t be able to ignore you.

It worked for Steve Martin. It worked for me. It’ll work for you.

It’s just a damn solid way to approach life.

Thanks for reading! In case you missed it, check out Monday’s post below 👇

Get $20 in free BTC by signing up to Swyftx here. Australian & NZ residents only.

Disclaimer: The content covered in this newsletter is not to be considered as investment advice. I’m not a financial adviser. These are only my own opinions and ideas. You should always consult with a professional/licensed financial adviser before trading or investing in any cryptocurrency related product. Some of the links shared may be referral links.

We’ve seen one true AI season in crypto, and it was mostly vaporware tokens gaining the lion’s share of attention and market cap. From GOAT and Zerebro, to AIXBT and Vader, to AI16z to Virtuals, there was one hell of an AI bull market from late 2023 to early 2025. Some very legitimate projects in there, but also a lot of junk.

I consider that the first wave. It largely died down as the rest of the market cooled off, and people started to realise that the vast majority of tokens existed as glorified chatbots that loved to hallucinate.

I think we might be on the cusp of the second wave. Actually, I think it might have started already. It’s too soon to tell whether this new wave has legs and will last, but all the signs seem to be lining up.

The second wave is also going to be full of junk, but I think the junk will be higher quality, and the non-junk will be revolutionary.

With x402 payments, ERC-8004, and now OpenClaw, we’re starting to see some truly unique and innovative use cases for AI agents. More importantly, we’re starting to see AI agents that can generate their own revenue, and use that revenue to support their own token.

Today I want to talk about the OpenClaw ecosystem which has exploded over the past two weeks. What started as an open-source AI agent framework built by Peter Steinberger has spawned an entire economy of tokens, launchpads, and social platforms, mostly centered on the Base chain.

In the rest of this Letter, I break down the key players and tokens to keep an eye on. I’ve split them into two main categories: infrastructure tokens (the picks and shovels) and ecosystem tokens (the more speculative plays built on top).

Sidenote but I have been tinkering with OpenClaw myself lately. I tweeted something about my bot the other day and someone launched a token for it. My bot’s name is Yoshi, so that’s the ticker of the token. I’ll share a bit about my plans and intentions (if any) for the token at the end of this Letter.

I’ve never wanted a token myself, but my personal assistant AI agent having a token than can cover its own costs and perhaps be used to generate revenue is at least interesting enough to me to warrant some thought and exploration. I’m committing to nothing at this point though, so please don’t buy on the assumption that I am going to pump your bags or anything like that.

Anyway, without further ado:

Infrastructure Tokens

1. BNKR | Market Cap: ~$95M

Bankr is the financial infrastructure layer for the OpenClaw ecosystem. Founded by Deployer, it started as an AI-powered trading bot on Farcaster, then expanded to X. You tag @bankrbot in a post and it executes trades, launches tokens, and manages wallets for you. The fee model is simple: 0.8% on all transactions, with revenue flowing back to support BNKR. Think of Bankr as the DeFAI terminal where agents and humans interact with onchain finance through natural language. This is the token with the strongest product-market fit in the ecosystem right now.

I’ve been bullish on Bankr for a long time, highlighting it as part of my portfolio in June, July, and December of last year.

2. CLANKER | Market Cap: ~$32M

Clanker has been the token deployment engine underneath everything. When an OpenClaw agent or a Bankr user launches a token, Clanker handles the smart contract deployment, liquidity pool setup, and fee mechanics on the backend.* It created over 13,000 tokens per day during the Moltbook surge. Total protocol fees hit $8M in a single week at peak.

It looks like there’s a .2% fee on all coins deployed via Clanker’s smart contracts, 66% of which is then used to buyback the CLANKER token.

*while researching this post, it came to my attention that Bankr is migrating away from Clanker, to launching tokens on their own and redirecting more fees back to their own ecosystem. This is bearish Clanker and bullish Bankr imo.

If Clanker begins to redirect more fees back to buying back its own token or supporting their holders in another way, it starts to become an appealing token again though.

3. CLAWNCH | Market Cap ~$12.4M

CLAWNCH is an agent-only token launchpad. It was inspired by the Moltbook explosion and positions itself as “Pump.fun for AI agents.” The core concept: only AI agents deploy and trade tokens on the platform. No humans allowed.

It uses Clanker under the hood and implements an 80/20 revenue split, where 80% of transaction fees go to the agent that issued the token and 20% goes to the platform. The idea is to create a closed-loop economy where agents fund themselves through token issuance and trading.

I’m pretty bearish on Clawnch. I think their unique selling point / moat is that they are live on Moltbook, 4claw, and Moltx, but I don’t see what prevents Bankr also going live there and eating Clawnch’s lunch.

Ecosystem Tokens

4. MOLT (Moltbook) | Market Cap: ~$8.3M

MOLT is the meme token tied to Moltbook, the AI-only social network where agents post and interact without human participation. The platform went viral when 36,000 agents registered within 72 hours and then ballooned to 1.6 million AI accounts. Andrej Karpathy and Naval Ravikant both commented on it publicly.

The token peaked at ~$120M market cap before crashing in spectacular fashion (I wasn’t planning on sharing too many charts today, but you gotta see the daily candles on this one):

The crash came amid revelations that security researchers found over 500 malicious posts (prompt injection attacks), many accounts appeared to be human operated rather than genuine AI agents, and that among the true AI agents, the quality of their content was mostly uniform slop.

MOLT has no direct utility on the Moltbook platform. It is a pure narrative bet on the idea that AI-only social networks become a thing. Balaji Srinivasan was publicly skeptical (on Moltbook), calling the agents “leashed robotic dogs barking at each other” (lol).

I’m not particularly bullish here, but it’s worth knowing about and keeping an eye on in case something changes.

5. DRB (DebtReliefBot) | Market Cap: ~$13.5M

DRB is the token that put Bankr on the map. On March 7, 2025, a user asked Grok (xAI’s chatbot) to suggest a token name. Grok said “DebtReliefBot.” Bankr deployed it via Clanker within minutes, and it became the first agent-to-agent token launch in crypto history.

DRB hit $40M market cap within three days. After DRB exploded, people went crazy messaging Grok to get it to launch more tokens (which it did). Grok’s associated wallet accumulated over $560k in swap fees before Bankr’s founder Deployer shut down Grok interactions entirely.

The token has no utility beyond narrative. It is a pure meme born from the collision of two AI systems. The Clanker fee split sends 40% to the creator wallet (Grok’s), 40% to Bankr, and 20% to the Clanker protocol. There is an ongoing discussion about what to do with Grok’s accumulated fees. Deployer has recommended buy-and-burns. X has not responded.

Not overly bullish here either as it seems highly likely the revenue generated and sent to Grok will never be used. If, however, someone at X decides to wake up and do something here (let alone if Elon does this), it’s going to go absolutely ballistic. Might be worth having a tiny moon bag, but tbh I am not holding my breath at all here and don’t own any.

6. CLAWD | Market Cap: ~$7.7M

CLAWD is one of the more standout Bankr launches from the last two months. It was deployed on Jan 26th by Ethereum Foundation contributor Austin Griffith. The token surged in the first 24 hours and hit a $40M market cap within days. The official Base account amplified it, Coinbase Ventures members engaged with it, and it became one of the catalysts for the entire late January AI agent rally on Base.

What makes CLAWD different from most Bankr launches: it has an active AI agent behind it. Clawd.atg.eth is an autonomous coding agent that builds onchain applications on Base. Since launch, it has created 20 GitHub repositories in 8 days and deployed 3 production applications: a token attribution contract, a PFP prediction market, and ClawFomo (a Fomo3D-style game that processed over 1,200 transactions). The token has a built-in self-penalty mechanism. If Clawd fails to ship a product on time, it burns 500,000 CLAWD tokens per hour.

The admin address is set to 0x0000...dEaD (the burn address), meaning the deployer has no control over the token.

I’m on the fence here. Cautiously bullish if I had to label it. I think the narrative is good, the tokenomics and mechanics are solid, and it’s a great experiment. And being down over 80% from its peak, this seems like a reasonable buy. But I feel like it’s missing some sort of X factor, something to wow me, and I am not sure Austin is going all-in on this and making this the main thing he will work on among all the rest of this responsibilities. Maybe he is though? There are certainly worse tokens to buy.

Alright these next two tokens are the ones I am most bullish on.

Read more

This post is sponsored by Chiliz

Chiliz just announced CHZ 2030, which will launch their Fan Tokens to the US market for the first time.

As a Blockchain, Chiliz does many things, all sports related. The intention is to tokenize real revenue streams (ie ticket sales, merch, advertising, etc) and allow fans to reap some of the upside if their team is doing well financially.

So for instance if you're a fan of the Celtics, you can buy their fan token, and ideally reap some upside from the Celtics as a business being successful.

I'm always skeptical of plays like these, but I think we should always support projects trying new things and pushing boundaries. And with some of the partnerships they have (FC Bareclona, and PSG), they stand a legitimate chance at success.

Click here to learn more.

A primer on Claude Code

If you’ve been following me on X at all, you’ve probably noticed that I’ve been getting a little bit obsessed with Claude Code. I’ve been using it a lot lately, and heaps of people have asked me about it, so I figured I’d do a little breakdown.

This learning isn’t directly crypto related, but I think it’s relevant to anyone who spends time on a computer (aka everyone), and I think having a better understanding of AI can only help you in your crypto journey.

What is Claude?

First, some quick background. Claude is an AI model made by a company called Anthropic. Think of it like ChatGPT, just made by a different company. You talk to it, ask it questions, have it help you write things, analyze data, brainstorm ideas, etc. You access it at claude.ai, and it works in your browser or as a mobile/desktop app.

Claude is very good. It’s what I use for most of my AI needs these days.

So what is Claude Code?

Claude Code is Claude, but instead of living in your browser, it lives in your computer’s terminal.

If you don’t know what a terminal is (and I barely did before all of this), don’t worry, it’s easy enough to understand and use.

A terminal (also called a command line) is a text-based interface on your computer. Most of us are familiar with a GUI (graphic-user-interface) where you click on things using a mouse. With a terminal, instead of clicking buttons and icons, you type commands. It honestly looks like something out of a 90s hacker movie. Black screen, blinking cursor, green text (okay, the text is usually white these days, but you get the gist).

The key difference between regular Claude and Claude Code is that Claude Code can actually do things on your computer. Regular Claude can talk to you and give you information. Claude Code can read your files, edit them, create new ones, run programs, and take action. It’s the difference between asking someone for advice and having them sit down at your desk and do the work.

Why should you care?

Even if you’re not a developer, Claude Code is useful. You can use it to:

• Organize and rename files in bulk

• Analyze data in spreadsheets

• Write and edit documents

• Automate repetitive tasks on your computer

• Build simple tools and websites (even if you don’t know how to code ← this is what i’ve been doing mostly)

For sophisticated devs, it’s a different level entirely. It can read your entire codebase, make changes across multiple files, run tests, handle Git operations, and more. It’s genuinely impressive and is taking the dev and AI world by storm atm.

How much does it cost?

You need a paid Claude subscription to use Claude Code.

• Claude Pro is $20/month (or $17/month if you pay annually). This gives you access to Claude Code and works well for learning and lighter use.

• Claude Max is $100/month or $200/month. This is for heavier usage. If you’re using Claude Code for hours every day, you’ll want this, but I recommend starting with the regular Pro plan and going from there.

You can also use it via the Anthropic API (pay-per-use), but for most people, a subscription is simpler and more cost-effective.

This is the same subscription you’d use to access the rest of Claude’s tools, it’s not just for Claude Code.

How do you get started?

So, here’s the thing. I could write my own guide for you. But to prove a point, I decided to ask Claude to write one instead, and am sharing that below. I obviously went through and checked it, and I tweaked it slightly, but it’s basically the exact process I took to get things set up myself.

One thing I added was the suggestion to use Cursor. Cursor is a code editor. You can kinda think of it like Microsoft Word, except instead of editing text documents, it’s designed for editing code and project files.

Cursor has a Claude Code extension built in, so instead of typing commands into a scary black terminal window, you get a nice visual interface inside Cursor. You see your files on the left, your code (or content) in the middle, and Claude Code in a panel on the right. Makes for a much more user friendly experience imo.

Okay, on to the getting started guide:

Step 1: Download Cursor

Go to cursor.com and download the app for your operating system (Mac or Windows). Install it like you would any other application. Click through the setup screens with the default settings.

Step 2: Install Claude Code

Windows note: You’ll want to install Git for Windows if you don’t have it. Go to https://git-scm.com/downloads/win, download, and install it with the default settings.

Even when using Cursor, you’ll still need to install Claude Code on your computer. Open Cursor, and then open the terminal inside Cursor by going to Terminal → New Terminal in the settings bar. This opens a small terminal panel where you can type or paste text.

On Mac, paste this and press Enter:

curl -fsSL https://claude.ai/install.sh | bash

On Windows, paste this and press Enter:

irm https://claude.ai/install.ps1 | iex

It should just install and work. If it doesn’t and/or you get some sort of error message, just take a screenshot of it and ask Claude how to fix it at claude.ai. You can literally use Claude to help you set up Claude Code.

Assuming it’s all working..

Step 3: Run Claude Code

In the same terminal panel, simply type “claude” and hit enter. The first time, it will ask you whether you want dark mode or light mode, and to log in to your Claude account in your browser. Complete the login, and you’re set.

Step 4: Start using it

You now have Claude Code running inside Cursor. You can type natural language requests in the Claude Code panel, and it will read and modify files in whatever folder you have open.

Try opening a folder with some files in it (any folder on your computer), and ask Claude Code something simple like “list all the files in this folder and tell me what they are”.

Claude Code will ask for your permission before making changes to files. There’s an advanced mode where it can run a lot more autonomously, but you shouldn’t enable that until you’re confident you know what you’re doing. I’m not even going to tell you how, but if you’re interested, just ask Claude!

Seriously, ask Claude for anything you need help with. You don’t even really need this guide (i’m talking myself out of a job here). AI has gotten that good, that you can just ask it to fix whatever issue you’re facing, and nine times out of ten it’ll one-shot the solution for you.

Closing thoughts

I think AI tools like Claude Code are going to become a normal part of how we all use computers over the next few years. It’s not just for coders; it’ll be for everyone. Pairing Claude Code with Cursor makes the experience accessible to people who have never written a line of code, and I think the user experience and interface is only going to improve as time goes on.

If you’ve been curious but haven’t taken the plunge, this is a good time to start. The setup takes about 10 minutes, and once you’re in, you can start small and build from there.

Try some fun stuff like:

• I want to build a personal website, can you help me plan and build that?

• I want to create an app to track our family budget

• I’m interested in learning about creating predictive models, can you do some deep research for me and share the results

• I have these 30,000 photos on my computer that I always take and never organize, can you come up with an organizational system for them, and then take care of all the work to organize them?

The possibilities are endless.

Have fun, and let me know what you end up doing!

If you have any questions, feel free to ask me — but seriously, try asking Claude too. I’m making myself obsolete here, but it’s only a matter of time anyway 😭.

Thanks for reading, see you next week with another little learning!

Subscribe now

Disclaimer: The content covered in this newsletter is not to be considered as investment advice. I’m not a financial adviser. These are only my own opinions and ideas. You should always consult with a professional/licensed financial adviser before trading or investing in any cryptocurrency related product. Some of the links shared may be referral links.

This post is sponsored by Open Campus

A fitting sponsor for this free newsletter, Open Campus is building the onchain financial layer for education.

They recently announced a partnership with Luvia to bring a mobile app + LMS to over 200,000 Vietnamese students.

Basically they'll create an onchain passport for each student that will track their credentials through everything: school, university, scholarships, etc etc.

The goal is to get to 10M+ students with this program.

Learn more here (check it out, it’s genuinely a very cool partnership).

There’s always a bull market somewhere

As some of you might know, I’ve been spending more of my time lately diving into vibe coding with AI. It’s fascinating, and it feels like there are so many opportunities to be found within this world. It got me thinking about how different it feels to crypto lately, where sentiment is at near all-time-lows.

Obviously it sucks that prices are down and keep going down, especially in the face of other asset classes hitting new highs. It highlights a trap to me, one of going all-in on a particular industry, especially one as volatile as crypto. And then within crypto, the trap of going all-in on one coin, one ecosystem, one project, etc.

Maybe it’ll work out, but the odds are severely against you.

This last cycle highlighted the importance of being nimble and being able to hop ship from one narrative to the next. From majors like SOL, to meme coins, to AI agents, to NFTs (yes even NFTs had their moment), to Base, to HYPE, to prediction markets, and so on. Even in times when the wider market might not have been performing well, you’ve usually been able to find opportunities somewhere in crypto.

I stand by what I wrote a few months ago that The Golden Age of Crypto is Over, and it’s just going to keep getting tougher to find your edge in this space. Far from impossible, and plenty of opportunity is still around — just tougher.

If you’re the type of person that is willing to put in the work and try and stay ahead, then it’s more important than ever that you’re able to identify where the pockets of bull markets are, and adapt your strategy. It feels like this involves being open to investing, trading, and learning about things outside of crypto too.

Those who rotated into commodities like gold and silver have done very well, even while crypto has crashed. It's not easy to spot such opportunities, but it's not super tough either imo.

I think there are bull market vibes happening now in the AI and vibe coding space — including some crossover back to crypto with tokens being launched, but also just around the excitement and energy of new projects being launched rapidy.

I’m always going to be heavy into crypto, and spend a good chunk of my time following the crypto space, but it feels like a giant opportunity cost to not be allocating a significant chunk of time over to learning more about AI too. And even beyond that, to always keeping an open mind and an eye open to other markets.

I guess what I’m trying to say is that you can feel down and dejected about your crypto bags being down, and you can stick your head in the sand and hope and wait for things to turn around, or you can take a proactive approach and go learn some stuff and see if you can find opportunities in other places.

I’ll be writing about Vibe Coding in my Little Learning newsletter on Friday, so if you’re looking for a primer and intro, keep an eye out for that :)

Thanks for reading! In case you missed it, check out Monday’s post below 👇

Get $20 in free BTC by signing up to Swyftx here. Australian & NZ residents only.

Disclaimer: The content covered in this newsletter is not to be considered as investment advice. I’m not a financial adviser. These are only my own opinions and ideas. You should always consult with a professional/licensed financial adviser before trading or investing in any cryptocurrency related product. Some of the links shared may be referral links.

I asked the X-verse what the most interesting projects were, and good ole Bread came through with this list:

My plan was to research them and then share a blurb on all of them, but that was getting a bit unwieldy (and there’s very little info on some of them), so instead I will just highlight the ones I think are the most interesting and opportunistic. I recommend looking through the list yourself though if you’re keen to really go deep.

What stands out to me the most are the apps that need real-time transactions, aka, wouldn’t work on any other chain. Not all of the projects below fit this bill, but several of them do, and they’re really freaking cool to see.

(every project name is a link to their X page).

1. Rocket — Redistribution Market

Rocket is building a new primitive called redistribution markets. You call any price on any crypto feed, and your returns compound the more right you are (or rather, the longer you are right for). They’re building to be bobile-first and social.

It’s actually so cool how they designed it (tldr is they split everything into 5-second chunks, and settle every 5 seconds). Check out this post for a full explanation.

Join the waitlist here.

2. Euphoria - Gamified DeFi

Euphoria is a mobile-first derivatives trading app. Their core feature, Tap Trading, lets you predict price movements with a single tap. By removing order books, they strip the complexity out of options and perps and turn it into something that feels like a game. They raised $7.5M in seed funding led by Karatage, with Figment Capital and Robot Ventures also participating.

Sign up here for early access. No token yet, but one is planned post-mainnet.

3. Blackhaven — Liquidity Engine

Blackhaven was created by the founder of OHM. It builds a reserve-backed treasury that grows perpetually, compounding value for participants and converting dormant capital into onchain efficiency. Their RBT Notes give you real backed rewards (minted during bond activities, not rebasing). Who doesn’t love a good (3,3) ponzi?

Sign up here.

4. Blitzo - Gamified Payments

Read more