Building cognitive loops for agents: https://xitter.cf/t.co/l6ayCLKy0m

Edinburgh, Scotland
Joined October 2008
EMSON retweeted
Claude Mythos is the start of the end. I think this is my psychosis moment.
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Big deal paper here: field experiment on 515 startups, half shown case studies of how startups are successfully using AI. Those firms used AI 44% more, had 1.9x higher revenue, needed 39% less capital: 1) AI accelerates businesses 2) The challenge is understanding how to use it
🚨 Excited to share a new working paper! 🚨 AI can improve individual tasks. But when does it improve firm performance? Our paper proposes one key friction firms face: the "mapping problem" -- discovering where and how AI creates value in a firm's production process. 🧵1/
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CLIs are super exciting precisely because they are a "legacy" technology, which means AI agents can natively and easily use them, combine them, interact with them via the entire terminal toolkit. E.g ask your Claude/Codex agent to install this new Polymarket CLI and ask for any arbitrary dashboards or interfaces or logic. The agents will build it for you. Install the Github CLI too and you can ask them to navigate the repo, see issues, PRs, discussions, even the code itself. Example: Claude built this terminal dashboard in ~3 minutes, of the highest volume polymarkets and the 24hr change. Or you can make it a web app or whatever you want. Even more powerful when you use it as a module of bigger pipelines. If you have any kind of product or service think: can agents access and use them? - are your legacy docs (for humans) at least exportable in markdown? - have you written Skills for your product? - can your product/service be usable via CLI? Or MCP? - ... It's 2026. Build. For. Agents.
introducing polymarket cli - the fastest way for ai agents to access prediction markets built with rust. your agent can query markets, place trades, and pull data - all from the terminal fast, lightweight, no overhead
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Here's my enormous round-up of everything we learned about LLMs in 2025 - the third in my annual series of reviews of the past twelve months simonwillison.net/2025/Dec/3… This year it's divided into 26 sections! This is the table of contents:
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I’m starting to get into a habit of reading everything (blogs, articles, book chapters,…) with LLMs. Usually pass 1 is manual, then pass 2 “explain/summarize”, pass 3 Q&A. I usually end up with a better/deeper understanding than if I moved on. Growing to among top use cases. On the flip side, if you’re a writer trying to explain/communicate something, we may increasingly see less of a mindset of “I’m writing this for another human” and more “I’m writing this for an LLM”. Because once an LLM “gets it”, it can then target, personalize and serve the idea to its user.
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Sharing an interesting recent conversation on AI's impact on the economy. AI has been compared to various historical precedents: electricity, industrial revolution, etc., I think the strongest analogy is that of AI as a new computing paradigm (Software 2.0) because both are fundamentally about the automation of digital information processing. If you were to forecast the impact of computing on the job market in ~1980s, the most predictive feature of a task/job you'd look at is to what extent the algorithm of it is fixed, i.e. are you just mechanically transforming information according to rote, easy to specify rules (e.g. typing, bookkeeping, human calculators, etc.)? Back then, this was the class of programs that the computing capability of that era allowed us to write (by hand, manually). With AI now, we are able to write new programs that we could never hope to write by hand before. We do it by specifying objectives (e.g. classification accuracy, reward functions), and we search the program space via gradient descent to find neural networks that work well against that objective. This is my Software 2.0 blog post from a while ago. In this new programming paradigm then, the new most predictive feature to look at is verifiability. If a task/job is verifiable, then it is optimizable directly or via reinforcement learning, and a neural net can be trained to work extremely well. It's about to what extent an AI can "practice" something. The environment has to be resettable (you can start a new attempt), efficient (a lot attempts can be made), and rewardable (there is some automated process to reward any specific attempt that was made). The more a task/job is verifiable, the more amenable it is to automation in the new programming paradigm. If it is not verifiable, it has to fall out from neural net magic of generalization fingers crossed, or via weaker means like imitation. This is what's driving the "jagged" frontier of progress in LLMs. Tasks that are verifiable progress rapidly, including possibly beyond the ability of top experts (e.g. math, code, amount of time spent watching videos, anything that looks like puzzles with correct answers), while many others lag by comparison (creative, strategic, tasks that combine real-world knowledge, state, context and common sense). Software 1.0 easily automates what you can specify. Software 2.0 easily automates what you can verify.
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My pleasure to come on Dwarkesh last week, I thought the questions and conversation were really good. I re-watched the pod just now too. First of all, yes I know, and I'm sorry that I speak so fast :). It's to my detriment because sometimes my speaking thread out-executes my thinking thread, so I think I botched a few explanations due to that, and sometimes I was also nervous that I'm going too much on a tangent or too deep into something relatively spurious. Anyway, a few notes/pointers: AGI timelines. My comments on AGI timelines looks to be the most trending part of the early response. This is the "decade of agents" is a reference to this earlier tweet xitter.cf/karpathy/status/188254… Basically my AI timelines are about 5-10X pessimistic w.r.t. what you'll find in your neighborhood SF AI house party or on your twitter timeline, but still quite optimistic w.r.t. a rising tide of AI deniers and skeptics. The apparent conflict is not: imo we simultaneously 1) saw a huge amount of progress in recent years with LLMs while 2) there is still a lot of work remaining (grunt work, integration work, sensors and actuators to the physical world, societal work, safety and security work (jailbreaks, poisoning, etc.)) and also research to get done before we have an entity that you'd prefer to hire over a person for an arbitrary job in the world. I think that overall, 10 years should otherwise be a very bullish timeline for AGI, it's only in contrast to present hype that it doesn't feel that way. Animals vs Ghosts. My earlier writeup on Sutton's podcast xitter.cf/karpathy/status/197343… . I am suspicious that there is a single simple algorithm you can let loose on the world and it learns everything from scratch. If someone builds such a thing, I will be wrong and it will be the most incredible breakthrough in AI. In my mind, animals are not an example of this at all - they are prepackaged with a ton of intelligence by evolution and the learning they do is quite minimal overall (example: Zebra at birth). Putting our engineering hats on, we're not going to redo evolution. But with LLMs we have stumbled by an alternative approach to "prepackage" a ton of intelligence in a neural network - not by evolution, but by predicting the next token over the internet. This approach leads to a different kind of entity in the intelligence space. Distinct from animals, more like ghosts or spirits. But we can (and should) make them more animal like over time and in some ways that's what a lot of frontier work is about. On RL. I've critiqued RL a few times already, e.g. xitter.cf/karpathy/status/194443… . First, you're "sucking supervision through a straw", so I think the signal/flop is very bad. RL is also very noisy because a completion might have lots of errors that might get encourages (if you happen to stumble to the right answer), and conversely brilliant insight tokens that might get discouraged (if you happen to screw up later). Process supervision and LLM judges have issues too. I think we'll see alternative learning paradigms. I am long "agentic interaction" but short "reinforcement learning" xitter.cf/karpathy/status/196080…. I've seen a number of papers pop up recently that are imo barking up the right tree along the lines of what I called "system prompt learning" xitter.cf/karpathy/status/192136… , but I think there is also a gap between ideas on arxiv and actual, at scale implementation at an LLM frontier lab that works in a general way. I am overall quite optimistic that we'll see good progress on this dimension of remaining work quite soon, and e.g. I'd even say ChatGPT memory and so on are primordial deployed examples of new learning paradigms. Cognitive core. My earlier post on "cognitive core": xitter.cf/karpathy/status/193862… , the idea of stripping down LLMs, of making it harder for them to memorize, or actively stripping away their memory, to make them better at generalization. Otherwise they lean too hard on what they've memorized. Humans can't memorize so easily, which now looks more like a feature than a bug by contrast. Maybe the inability to memorize is a kind of regularization. Also my post from a while back on how the trend in model size is "backwards" and why "the models have to first get larger before they can get smaller" xitter.cf/karpathy/status/181403… Time travel to Yann LeCun 1989. This is the post that I did a very hasty/bad job of describing on the pod: xitter.cf/karpathy/status/150339… . Basically - how much could you improve Yann LeCun's results with the knowledge of 33 years of algorithmic progress? How constrained were the results by each of algorithms, data, and compute? Case study there of. nanochat. My end-to-end implementation of the ChatGPT training/inference pipeline (the bare essentials) xitter.cf/karpathy/status/197775… On LLM agents. My critique of the industry is more in overshooting the tooling w.r.t. present capability. I live in what I view as an intermediate world where I want to collaborate with LLMs and where our pros/cons are matched up. The industry lives in a future where fully autonomous entities collaborate in parallel to write all the code and humans are useless. For example, I don't want an Agent that goes off for 20 minutes and comes back with 1,000 lines of code. I certainly don't feel ready to supervise a team of 10 of them. I'd like to go in chunks that I can keep in my head, where an LLM explains the code that it is writing. I'd like it to prove to me that what it did is correct, I want it to pull the API docs and show me that it used things correctly. I want it to make fewer assumptions and ask/collaborate with me when not sure about something. I want to learn along the way and become better as a programmer, not just get served mountains of code that I'm told works. I just think the tools should be more realistic w.r.t. their capability and how they fit into the industry today, and I fear that if this isn't done well we might end up with mountains of slop accumulating across software, and an increase in vulnerabilities, security breaches and etc. xitter.cf/karpathy/status/191558… Job automation. How the radiologists are doing great xitter.cf/karpathy/status/197122… and what jobs are more susceptible to automation and why. Physics. Children should learn physics in early education not because they go on to do physics, but because it is the subject that best boots up a brain. Physicists are the intellectual embryonic stem cell xitter.cf/karpathy/status/192969… I have a longer post that has been half-written in my drafts for ~year, which I hope to finish soon. Thanks again Dwarkesh for having me over!
The @karpathy interview 0:00:00 – AGI is still a decade away 0:30:33 – LLM cognitive deficits 0:40:53 – RL is terrible 0:50:26 – How do humans learn? 1:07:13 – AGI will blend into 2% GDP growth 1:18:24 – ASI 1:33:38 – Evolution of intelligence & culture 1:43:43 - Why self driving took so long 1:57:08 - Future of education Look up Dwarkesh Podcast on YouTube, Apple Podcasts, Spotify, etc. Enjoy!
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Claude + n8n + Apollo = AI Deal Factory that replaces $10,500+/month sales teams... (the exact system we've deployed for 8-figure agency operators) - No more 2+ hours daily manually researching each prospect - No more 5% close rates from generic cold outreach - No more $3,400-$8,200 per prospect for analyst teams - No more winging sales calls with just LinkedIn profiles Just one Apollo input → complete company intelligence + solution matching automatically. Perfect for agencies, SaaS teams & B2B operators. Here's how it works: → Autonomous Apollo Scraping (captures prospects continuously) → Perplexity Intelligence Engine (researches company financials, tech, competitors live) → Product-Match Logic (analyzes which solutions fit their needs automatically) → Strategy Brief Assembly (delivers approach recommendations with context) → Pipeline Automation (syncs to Airtable without manual updates) → Opportunity Validation Loop (eliminates duds, focuses on high-value matches) Built for companies with complex product catalogs. 24/7 operation. Fortune 500 research methodology automated. Want the complete system blueprint? Like + comment "DEAL" + repost, I'll DM it. (must be following)
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One of the new skills required to get the most out of AI-assisted coding tools - Claude Code, Codex CLI, etc - is designing agentic loops: carefully selecting tools to run in a loop to achieve a specified goal. Do this well and you can solve many coding problems with brute force.
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Introducing Claude Sonnet 4.5—the best coding model in the world. It's the strongest model for building complex agents. It's the best model at using computers. And it shows substantial gains on tests of reasoning and math.
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Replying to @claudeai
New Claude Sonnet 4.5 model looking like a time traveler from the future! it wasn't supposed to be able to code autonomously for 30 hours for several more years, per @aidan_mclau 's graphic below. 🤯🤯🤯
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Google has changed note-taking forever. NotebookLM just got major upgrades. 6 Powerful new features you can't miss:
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Over the last two weeks, 200+ members of r/ClaudeAI shared products, apps, and projects they’ve built with Claude. Announcing the Built with Claude contest winners:
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New essay: Every marketing channel sucks right now (full thing on my newsletter, just subscribe via link in bio) These days I’m spending a lot of my time with very early stage startups (yes, as part of the program at a16z to invest up to $1M into each, called a16z speedrun -- speedrun.a16z.com) and as part of this, I spend a lot of time talking about launch and marketing strategy for new products. The options for marketing pretty grim right now. Here are my complaints: - SEO: Takes too long, you’re competing against listicles (and Reddit threads), Google might screw you over at any moment, maybe AI one boxing will kill your traffic next year anyway :( - Influencer marketing: Get a big spike of traffic but none of it converts, the spike goes away after a few days, big creators are too expensive and a slew of small creators need to be cobbled together, lots of babysitting :( - PR/comms: Doesn’t actually generate signups, doesn’t scale and not repeatable, expensive retainers for PR experts to grab coffee with journalists, your competitors will get the same article next month, and press is as likely to attack you as to cover you :( - Email marketing: hope you like spam folders, building a good list takes forever, open rate rates are <30% and CTRs are <5% so hope you weren’t expecting a lot of clicks - Viral loops: doh you need an actually great product, all the contacts spamming techniques no longer work (neither email nor SMS!), your UX will be ruined by aggressive popups and onboarding schemes, and it’s nearly impossible to get viral factor >1 - Referral/affiliate/etc: be ready for a crazy amount of fraud, it’s just as expensive as paid marketing (though people fool themselves into thinking it’s not paid), and surprisingly most people don’t care and will get fatigued quickly! :( - Big launch on social: it can only happen once, you’ll have to spam all your friends to share and over time they might come to resent you, the algo is always working against you, and it only lasts for a few hours :( I could keep going… but you get the picture! Unfortunately this is the state of growth marketing. A lot of channels are not working, or are slow, expensive, or one-time only. This is the natural end state for things, and maybe we’re in a bit of a lull due to the technology super cycle as we’re 15+ years into the mobile wave, we’ve had various kinds of paid ads for 20+ years, and so on. All of these aforementioned marketing channels are now fully mature. The Law of Shitty Clickthroughs, Redux This is the logical ecosystem-wide conclusion for the concept I wrote about many years back, The Law of Shitty Clickthoughs which inspired by the efficient market hypothesis. It said the following: "Over time, all marketing strategies result in shitty clickthrough rates." I encourage you to read the full essay if you haven’t, but the tldr; is that when marketing channels work, everyone jumps in on them, and they start to decay like crazy. Why do they decay? Because customers stop responding over time — they start ignoring things as the novelty wears off. The ROI also goes down, as all the intermediaries that charge you for access to their audiences jack up prices. The super slick self-serve, automated auction model for paid marketing offers many pros and cons, first that it lets you aggregate much larger audiences with simple tools, but by making it easier, of course you end up with a ton of competition that then drives up CAC. In other words, as a marketing channel scales and ages over time — and yes, we’ve had SEO as a concept for 25+ years now — consumer engagement goes down, costs go up, competition goes up, ROI drops like crazy. Big channels versus Little channels All the aforementioned marketing channels are what I describe as “Big Channels.” They have scale, can be moved using $ (or labor, which costs $). And if you’re a big successful company, you might be fine because you have a (hopefully) strong brand, you get a bunch of organic traffic from word of mouth, you (hopefully) have a successful product that can cross-sell into new products, etc. You can have badly performing Big Channels because it’s all blended into a longer LTV recovery period, more organic, and all the other advantages that successful companies have. But let’s talk startups, who have none of these positive dynamics? I want to lay out a few thoughts: Don’t focus on Big Channels, focus on Little Channels. first, you should just know that all the aforementioned big, mature channels will suck for you. These channels have mostly all been bid up by bigger companies, and they are mostly stale to consumers, and you don’t have the same LTV and financial strength as an established product. Instead, a new startup has to be asymmetrical — what you can do that they can’t? The natural solution points towards Little Channels, which are all the smaller marketing strategies that are tried in the early days and abandoned over time. Don’t worry about scaling. Let’s say you have a new product with only 100 active users. If your marketing campaign gets you +500 actives, you’re ecstatic. Gain a few hundred users inside an established company, and you should clean up your resume. Thus, little channels can work: Running mini events with cool speakers, organizing a Facebook group, going after a single college/company/town, emailing your ex-colleagues/friends to try a new thing — these can all help in the early days. You won’t have the competition, the response rates will be higher since you’re doing it all by hand, and you can always scale over time by moving onto the next channel. Novelty is in your favor. There are only so many ways to sell a big established product to customers. The “wow” moments have all been used, the value prop is already understood. The response rate on marketing declines as a result. But if you’re a new, bright-eyed and bushy-tailed product on the market, and you have a “wow” product that presents well in a hype video, then you can get a nice big spike when you launch. And maybe even a few more smaller ones over time. One-time is fine, repeatability is what you do later. A lot of marketing tactics that work in the early days only work once — like a social media launch — but that’s OK. If you can prove that a few unscalable tactics work, and it helps you gain momentum via funding/hiring/otherwise, then over time you’ll have more time to try additional strategies. Your product is brand new, so your marketing can be brand new as well. When you build a new product, it’s always smart to take advantage of the new tech wave, so that you can create something different than what’s existed before. You want to be building a mobile app in 2010, not a website, and you want to be building in AI now. But in the same way, you can use these new technologies on the marketing side too. What does AI allow you to do in marketing that previously couldn’t happen? Whether it’s rapidly creating personalized creative, or generating concepts faster, or creating an interactive bot — what can you do different than no one is doing, using the new tech? Take risks with your brand. You can attract people with your brand by being polarizing. Say “this product is not for you, it’s for these other cooler people.” Or hit competitors directly in the face, in your marketing. Be aggressive. These are things that long-term employees of the world’s trustworthy brands can’t do, because they are being protective, and managing the downside. You need to do everything you can to stand out, because being ignored is the worst outcome for any marketing tactic. The point is — yes, today’s marketing channels sucks, but that has everything to do with the level of competition and the rate of customer fatigue. So go innovate! Try reaching people in new ways, say novel things to people they haven’t yet heard, and if you have to trade off anything, just trade off scalability. (You can deal with that later once your product is successful even at a small scale) Product is (unfortunately) King So I have bad news: Your product actually has to be very good. I wish I lived in a world where you could have amazing marketing and growth strategies, have a shitty product, and you would win. Then marketers would run tech, and they do not. It’s the people visionaries that create the products that run tech, and that’s a good thing! The reason is that even if you do a ton of work to acquire a bunch of users, it won’t matter if they leak out of the DAU number. I’ve come to think of great marketing strategy as a multiplier effect on your inherent product quality. If you have a great product, you will multiply that into greatness. If you have a shitty product, you will multiply that into… well, you get it. I think we’re seeing this in the AI wave at the moment. You have great, highly novel products and when one startup makes a hype video, it just goes viral. They don’t know anything about funnels, A/B testing, CAC, etc. The product is just killer, and the output is highly marketable, and as a result social media really works well for them. A year from now, I think the novelty value will have faded a bit — we aren’t as impressed by the output of image generation models compared to when they first came out. If the novelty fades, as I predict, then in a few years we’ll need to really figure out how to market these products. (Sorry AI researchers, you’re going to have to learn marketing and sales!) Every marketing channel sucks, but it’s going to be OK This essay was not meant to be depressing, but rather to just call out the idea what we can all see — that most of the channels we work with are decades old, that the performance is teetering on an edge. Just as products are innovating by adopting new tech — AI, XR, web3, and so on — we in the marketing field need to innovate as well, by asking ourselves, where can I do something new with XYZ new tech? And it’s very clear that we should not copy incumbent products. They have too many features — try something smaller and more targeted. But the same is true in marketing. Don’t copy the established products and try to execute in Big Channels — instead, think asymmetrically. What can you do that they can’t, and going with small channels is always a great place to start.
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how to prompt like a pro 1. first: share your raw idea 2. ask: “what’s unclear, risky, or missing?” 3. then: “make this resonate with [my audience/customer/community]” insert data about them 4. finally: “what would [0.01% top expert in my field] do here?” way better results.
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Cursor is literally the best AI coding tool. But most AI projects break because of 3 core issues: - AI hallucinations - Loop of errors - Context awareness Here’s the system I use to fix all 3. It’s called the Context Boundary Method ↓
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Cursor Pro Tip: Always end your prompts with: “Explain the full approach you’d take to implement this. Just tell, don’t code.” Cursor will map out its entire plan. Review it, tweak if needed, then let it execute. It makes a HUGE difference in how well Cursor executes your instructions.
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This is David Deutsch. Naval calls him the smartest person alive. Here are the 10 best lessons I learned from them:
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I am so sorry that the U.S. is letting down our friends and allies. Broad tariffs, implemented not just against adversaries but also steadfast allies, will damage the livelihoods of billions of people, create inflation, make the world more fragmented, and leave the U.S. and the world poorer. AI isn’t the solution to everything, but even amidst this challenging environment, I hope our community can hold together, keep building friendships across borders, keep sharing ideas, and keep supporting each other. Much has been written about why high, widespread taxes on imports are harmful. In this letter, I’d like to focus on its possible effects on AI. One silver lining of the new tariffs is that they focus on physical imports, rather than digital goods and services, including intellectual property (IP) such as AI research inventions and software. IP is difficult to tax, because each piece of IP is unique and thus hard to value, and it moves across borders with little friction via the internet. Many international AI teams collaborate across borders and timezones, and software, including specifically open source software, is an important mechanism for sharing ideas. I hope that this free flow of ideas remains unhampered, even if the flow of physical goods is. However, AI relies on hardware, and tariffs will slow down AI progress by restricting access to it. Even though a last-minute exception was made for semiconductors, taxing imports of solar panels, wind turbines, and other power-generation and -distribution equipment will diminish the ability to provide power to U.S. data centers. Taxing imports of servers, cooling hardware, networking hardware, and the like will also make it more expensive to build data centers. And taxing consumer electronics, like laptops and phones, will make it harder for citizens to learn and use AI. With regard to data-center buildouts, another silver lining is that, with the rise of generative AI, data gravity has decreased because compute processing costs are much greater than transmission costs, meaning it’s more feasible to place data centers anywhere in the world rather than only in close proximity to end-users. Even though many places do not have enough trained technicians to build and operate data centers, I expect tariffs will encourage data centers to be built around the world, creating more job opportunities globally. Finally, tariffs will create increased pressure for domestic manufacturing, which might create very mild tailwinds for robotics and industrial automation. As U.S. Vice President J.D. Vance pointed out in 2017, the U.S. should focus on automation (and education) rather than on tariffs. But the U.S. does not have the personnel — or know-how, or supply chain — to manufacture many of the goods that it currently counts on allies to make. Robotics can be helpful for addressing a small part of this large set of challenges. Generative AI’s rate of progress in robotics is also significantly slower than in processing text, visual data, audio, and reasoning. So while the tariffs could create tailwinds for AI-enabled robotics, I expect this effect to be small. My 4-year-old son had been complaining for a couple of weeks that his shoes were a tight fit — he was proud that he’s growing! So last Sunday, we went shoe shopping. His new shoes cost $25, and while checking out, I paused and reflected on how lucky I am to be able to afford them. But I also thought about the many families living paycheck-to-paycheck, and for whom tariffs leading to shoes at $40 a pair would mean they let their kids wear ill-fitting shoes longer. I also thought about people I’ve met in clothing manufacturing plants in Asia and Latin America, for whom reduced demand would mean less work and less money to take home to their own kids. I don’t know what will happen next with the U.S. tariffs, and plenty of international trade will happen with or without U.S. involvement. I hope we can return to a world of vibrant global trade with strong, rules-based, U.S. participation. Until then, let’s all of us in AI keep nurturing our international friendships, keep up the digital flow of ideas — including specifically open source software — and keep supporting each other. Let’s all do what we can to keep the world as connected as we are able. [I had written this letter before the 90 day pause on the tariffs, but am sharing this here since many of the points are still relevant depends on what happens next.] Original text: deeplearning.ai/the-batch/is…
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Less than 24 hours ago, Google dropped Firebase Studio. Cursor AI, Lovable, Bolt, v0, deploy, all in the browser. Free. Minds are blown. 10 wild examples: 👇
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