William Pearson

By evals I mean literally tell the agent: given what we discussed about what we are doing and why and what happened, use three different frontier models to look at inputs and outputs of your skill file calling the code, and rate it on effectiveness. Why isn’t it a 10? How could it be made to be so? Run this a few times and you will be surprised how fast it gets astonishingly better And since it is in a skill file plus code with evals (LLM as judge) and unit tests, it stays better forever

@SantimentData Trillionaires in the making.

🚨THE FBI CREATED A FAKE CRYPTOCURRENCY.. LISTED IT ON UNISWAP.. HIRED MARKET MAKERS TO PUMP IT.. THEN ARRESTED EVERYONE WHO SAID YES.. THIS IS THE CRAZIEST LAW ENFORCEMENT OPERATION IN CRYPTO HISTORY!!! The FBI built an actual ERC-20 token on Ethereum called NexFundAI.. 100 billion token supply.. A professional website.. Whitepapers promising "passive income through AI-powered investing".. It looked exactly like every other crypto project.. Because that was the point.. Undercover agents posed as the founding team.. Then reached out to professional market-making firms and said "we need you to fake our trading volume".. Every single firm said yes.. Here's what they recorded.. Gotbit.. A firm run by a 26-year-old Russian who publicly bragged in 2019 that he built a business faking trade volumes.. His team kept internal spreadsheets with columns literally labeled "fake volume" vs "market volume".. When asked how fast they could pump NexFundAI's volume to $1 million per day.. They said "6 hours.. It will cost about $200".. $200 to fake $1 million in daily trading volume.. MyTrade.. Run by a guy who called himself "the mastermind".. He explained the exact psychology of the scam on camera.. "We make the chart look like a really nice roller coaster ride.. That's where people jump in.. We have to make them lose money in order to make profit".. He said that on a recorded FBI video call.. CLS Global.. A Dubai-based firm.. Their bots generated 98% of NexFundAI's total trading volume.. When the FBI asked if they could sync fake volume spikes with fake news announcements.. They said absolutely.. ZM Quant.. Bots executing 10 to 20 trades per minute through dozens of wallets to look organic.. All of them knew it was fraud.. All of them did it anyway.. All of it was recorded.. And the clients were even worse.. Saitama.. A meme coin that hit $7.5 billion market cap.. The founders coordinated buys through private Telegram chats.. Sent "pump it" memes while manipulating the price.. Then dumped on retail investors.. $7.5 billion.. Built entirely on fake volume.. Every penny of real money came from retail investors who thought the momentum was organic.. One founder left Saitama and started Robo Inu.. Used Gotbit again.. Another launched VZZN.. Same playbook.. Lillian Finance.. Founder claimed to be a defense contractor who addressed Congress.. Marketed the token as funding children's hospitals.. Pocketed everything.. When the FBI shut it down.. They seized $25 million in one day.. 18 people indicted across the US, UK, and Portugal.. The CEO of Gotbit was arrested in Portugal and extradited.. Sentenced to 8 months plus $23 million forfeiture.. But here's the part that broke my brain.. Real people bought NexFundAI.. The FBI's fake token.. With zero utility.. Zero real developers.. Created solely to catch criminals.. Attracted real retail investors because the fake volume made the chart look bullish.. When the FBI pulled the liquidity to end the operation.. Those people lost real money.. On a government-issued token.. The FBI had to set up a restitution portal to pay them back.. And it gets worse.. Within 24 hours of the DOJ announcing the sting.. Someone cloned the FBI's exact smart contract.. Launched a copycat token.. Rode the viral momentum.. And made $127,000 in a single day.. Using the exact same manipulation tactics the FBI just arrested 18 people for.. Then in 2026.. The FBI did it again.. New token called Lexobit.. 10 more arrests.. Including operators extradited from Singapore.. IRS forensics showed that in one firm's trading.. 1,209 out of 1,221 consecutive transactions went straight back to wallets the firm controlled.. 99% circular.. The FBI proved what everyone in crypto suspected.. The volume is fake.. The charts are painted.. The momentum is manufactured.. And every time you buy a token because "the chart looks bullish".. You might be the exit liquidity.

@gkisokay I asked grok to help massage it, that's exactly what I needed. Keep up the good work, kind sir. You have been extremely helpful on my journey!

@wmpearson6 now you work with it, point out where it went wrong, let it improve itself, and run it again, and repeat. don't stop until you lock in what you need!

William: Please share the exact commands you used to search X for this request. Hermes: Exact query pattern used: Grok OR xAI OR LLM OR "model release" OR launch OR demo since:2026-05-12 With high-engagement filters applied on top (min_faves / min_retweets style limits) and routed through the x_search tool via xai-oauth (SuperGrok). This matches the documented best practices in the x-search skill for recent high-signal searches. No other commands or tools were used for the X portion.

@gkisokay William: What model are you using? Hermes: grok-4.3 via xai-oauth (SuperGrok).

🚨HOLY FUCKING SHIT...THIS $LINK NEWS SWEPT RIGHT THROUGH THE CRACK: @THE_DTCC WEBSITE LABELS THE @CHAINLINK RESERVE "NOVEL FEE MODEL WHERE OFFCHAIN AND ONCHAIN REVENUE FROM ENTERPRISE ADOPTION IS CONVERTED TO LINK TOKENS." THEY ARE SENDING THE MARKET HINTS IN THE DTCC BLOG POSTS🚨

@gkisokay Looking forward to this. My agent BS's me and gives me runaround with x_search. I even added anti hallucination rules to his soul. Doesn't seem to matter.

Hermes literally turned Grok into SuperGrok after I implemented all these changes to my research agent: - made Grok and X_Search the main engine for X research - replaced static X list scraping with Grok watchlists for priority accounts, topics, narratives, and global AI “ground-shaker” searches. - added stronger ranking so low-signal replies, reposts, promo, stale posts, and tiny-engagement noise do not become top recommendations. - added normalized Grok/X post records with citations, timestamps, metrics when available, freshness labels, and verification status. - research now picks high-signal X posts and asks Grok to deeply analyze what happened, why it matters, what claims need verification, and what content/build opportunities come from it. - wired Grok research into content agent, newsletters, research packets, verification queues, and other downstream agents. The result is that the system is now built to find fresher AI/X signals, understand them more deeply, and automatically turn them into better content opportunities. Tomorrow I'll post the guide on how to turbo-charge your research, too.

Anon, do your bags have exposure to DTCC, one of the world's largest and most important financial market infrastructures, launching their own chain (Collateral Appchain) in Q4 of this year? The ticker is $LINK.

@thekevinqi @garrytan @pmarca Unlikely. China is keeping up and actively pushing it. I'm running a very capable open source model under my desk on a relatively dated consumer GPU. In a year's time that open source model will likely be equivalent to the frontier models today.

@garrytan @pmarca There is some possibility in the multiverse that the Luddite backlash actually puts AI back into Pandora’s box.

The NYT is predictably tearing down Reese Witherspoon for encouraging moms to try AI before they ingest the anti-AI pablum as truth Instead of linking to the NYT op-ed, I think you should watch this video and encourage you to follow Reese Witherspoon on Instagram

@KSimback I'll have my Hermes agent, Anton, look into it 😉

Hermes Atlas Newsletter Issue 2 is coming out tomorrow - so much to cover since the last issue! Signup if you want to stay up to date on all things Hermes Agent > releases v0.13.0 and v0.14.0 > multi-agent Kanban > /goal integration > X/Grok search > Codex CLI > Nvidia partnership > 5 new community projects > gbrain feature

When it comes to billionaires my take is the opposite of Leftists: Billionaires SHOULD consume all their wealth: megayachts, English castles, mountains of cocaine, own a dozen homes and 30 cars - do it! Spend it all! The worst thing that can happen is for a billionaire to try to ‘do good in the world’ and shovel money to NGOs for some cause they get excited about. That’s the worst thing that can happen. Charitable donations should be taxed at 500%. A billionaire’s lavish lifestyle poses no threat to me but their misguided ‘good intent’ is absolutely cancerous and potentially civilization ending.

The COVID cover-up goes all the way to the top. Fauci funded the Wuhan lab. Senior intelligence officials hid classified evidence from the president himself. Scientists were silenced. Millions paid the price. The DOJ has until May 11th to prosecute Fauci before the statute of limitations runs out. I am not letting this go. The American people deserve justice.

"The people who outsource their understanding will quietly become passengers" is the obvious and scary conclusion all of this comes to. This discussion is framed around people who are founders. Scale this out to the general public, when their iPhone has an agent and does this all for them as a suggestion. The vast majority will be passengers.

A founding member of OpenAI just told a room full of founders that the entire way they think about building software is about to flip upside down, and most of them are still working in a paradigm that is quietly going extinct. I watched the talk at 1am and finally understood why the people I know who are best at AI all started saying the same thing. His name is Andrej Karpathy. The talk is called From Vibe Coding to Agentic Engineering. Here is the framework he laid out, and why almost nobody outside the frontier labs has fully internalized what it means yet. He started with a confession. He said as recently as last year, he was using agentic coding tools the same way most people still use them. The model would write a chunk of code. Sometimes it worked. Sometimes he had to fix it. It was helpful but inconsistent and you had to babysit it. Then something happened in December 2024 that he says fundamentally broke the old paradigm, and most people experienced AI last year as a ChatGPT-adjacent thing and never went back to look again. What changed was that the chunks just started coming out fine. He kept asking for more and the code kept working. He could not remember the last time he had to correct it. He started trusting the system. And then he was vibe coding for real, and his side projects folder ballooned because suddenly every weird idea he had was something he could actually ship in an afternoon. The reason he kept stressing this point in front of the room was simple. The transition was not gradual. It was a phase change. And if your last serious encounter with AI coding was anywhere before December, your mental model of what is possible is already a year out of date. The core idea he then introduced is the one that should sit with every founder. He calls it software 3.0, and the framing is precise. Software 1.0 is humans writing explicit code. Software 2.0 is humans creating datasets and training neural networks where the weights become the program. Software 3.0 is something nobody has fully wrapped their head around yet. The neural network itself becomes the computer. Your prompt is the program. The context window is the lever you pull to control what the interpreter does. He gave two examples that landed harder than anything else in the talk. The first was the install instructions for OpenClaw. Normally you would expect a shell script. A command you run. Some configuration. Instead, the install instructions are a paragraph of text you copy and paste into your agent. The agent reads it, looks at your machine, figures out the environment, debugs in real time, and installs everything. There is no script. There is no code. There is a piece of text written for an intelligent reader who happens to be made of weights. The second example was the one that made him stop and rebuild his entire mental model. He had built a small app called MenuGen. You photograph a restaurant menu, the app OCRs the items, generates images of each dish, and shows you what the food looks like. He shipped it. People used it. Then someone showed him the software 3.0 version of the same thing. Take a photo of the menu. Hand it to Gemini. Ask Nano Banana to overlay images of each dish directly onto the menu in the photo. The model does it in a single pass. The image comes back exactly like the menu he photographed, except now every dish has a picture rendered into the pixels. He paused and said the line that should haunt every founder in the room. The entire MenuGen app should not exist. He had built something in the old paradigm that the new paradigm just does, in one model call, with no app at all. The deeper insight underneath both examples is the part most people miss. The software 3.0 paradigm does not just make existing apps faster. It dissolves entire categories of apps. The neural network does so much of the work that the scaffolding around it becomes unnecessary. You are not speeding up the old workflow. You are noticing that the old workflow does not need to exist. He extended this further. Most products today are written for humans. Documentation is written for humans. Setup flows are written for humans. He said his pet peeve has become going to a docs page and being told to do something. He does not want to do anything. He wants to know what to copy and paste into his agent. The companies that figure out how to be agent-native first, where every interface, every doc, every setup flow, every API is built for the agent reading on your behalf rather than for you reading directly, are going to make the human-first versions feel as outdated as a website that does not work on a phone. The final part of the talk was about taste, and this is the part that separates the people who will compound from the people who will plateau. He said the agents are still interns. They have remarkable recall. They have superhuman speed. They can fill in any blank you point them at. But they have no aesthetic judgment, no sense of what matters, no understanding of why the system is being built in the first place. He gave an example from MenuGen where his agent tried to match Stripe purchases to Google accounts using email addresses, even though users could obviously sign up with one email and pay with another. The agent had no model of what a user actually is. It just pattern matched fields together. So the human stays in charge of the spec. The architecture. The taste. The thing that has to be true for the system to be worth building at all. The agent fills in everything underneath that. Then he said the line he had read on the internet that he keeps coming back to every other day. You can outsource your thinking. You cannot outsource your understanding. That is the whole talk in one sentence. The agents will write your code. They will draft your emails. They will research your topics. They will execute your plans. But something still has to direct them, and that something has to actually understand what is being built and why. The bottleneck is no longer typing speed. The bottleneck is comprehension. And the people who keep training their own ability to understand things deeply are the ones who will keep getting more leverage out of every model release. The people who outsource the understanding too will quietly become passengers. He ended on the part most founders will skip and the few who do not skip it will quietly compound on for the next decade. The agents are getting cheaper, faster, and more capable every quarter. None of that matters if you have stopped doing the hard work of understanding what is actually worth building. The ceiling on agentic engineering is not the model. It is the human standing at the top of the system, deciding what to point it at. Most founders are still working in software 1.0 with a 3.0 tool sitting on their desk. The ones who flip first are the ones who win the next decade.

@itsolelehmann Not Hotdog.

karpathy just admitted that his own app got oneshotted and he thinks yours is next. he built menu gen. you take a photo of a restaurant menu and it shows you pictures of what the food actually looks like (because 30-50% of menu items you genuinely have no clue what they are) he vibe coded the whole thing: photo upload → ocr extracts item names → image model generates a picture for each dish → app re-renders the menu with photos next to every item → deployed on vercel but then someone showed him the "software 3.0" version: 1. take the same photo. 2. give it to gemini. 3. say "overlay pictures of each dish onto the menu" gemini returned the original menu photo with food images rendered directly into the pixels just 1 prompt and his entire app became entirely unnecessary here's karpathy's way to test if you're still stuck building in old paradigm: 1. take away all the code in your app. 2. give the raw input directly to an llm. is the output roughly the same? if yes, your code is just adding steps between the input and the output. karpathy thinks the apps that survive are the ones where the code does something the model genuinely can't: > persisting state across users > enforcing access controls > processing payments > connecting to hardware he calls anything else outdated "software 1.0 thinking." the question to ask yourself before you build anything right now: is this an app, or is it just a prompt with extra steps? you simply won't win if your answer is the latter

$Link Spoiler alert: He is talking about Chainlink.