Context Engineering for Coding Agents | Recovering VC | Cofounder of @Dexicon_AI | prev. @MistralAI @huggingface @StabilityAI CPO

San Francisco
Joined June 2017
Introducing Dexicon: the first tribal knowledge context platform for coding agents. AI coding agents are smart but blind to the rest of how your organization works: They don't know what a colleague's agent fixed a week ago. They don't know your internal APIs from other services. They can't access the architecture decisions your team made a month ago. We're building Dexicon to fix this. It's an engineering context platform that gives your agents: - Memory of past debugging sessions across your team - Up-to-date internal docs so they stop hallucinating deprecated patterns - Tribal knowledge that lives in your senior engineers' heads but never gets written down Works with Cursor, Claude Code, Codex via MCP. Your agents should code like they've been at your company for years, not like it's their first day. Try for free in early access: dexicon.ai
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As we continued Salute's journey into memestocks, it became clear that the community wanted to see him build something of his own. After countless iterations, trials and tribulations, we've reached the next milestone: Salute has deployed a token directly through Hugging Face the first coin deployed directly through huggingface.co. By Salute. We managed to configure the agent's execution environment and connect its token deployment pipeline, enabling Salute to deploy a Solana token directly through Hugging Face. Transparency: View $TOKEN directly on Hugging Face: huggingface.co/datasets/Salu… Public development log: github.com/IAmAaronYang/salu… Onchain token mint: solscan.io/account/3WWsgghUv… The process, code and onchain records are there for everyone to inspect. We're not here to talk about what agents might be capable of forever. We're here to build, test and push things into reality. This is just the beginning for Salute.
Replying to @IAmAaronYang
Aaron, this is really important for us. There is so much misinformation and fake narratives spinning around this. Since saluteface is already experimenting with memecoins, why not give him his own coin, made by him, and fully managed by him? This could be a really interesting experiment and this could be the first ever coin deployed through huggingface which would make it go absolutely parabolic. Love!
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Alright, time to put SaluteFace to the test. $saluteface token and $NVDA tokenized stock revenue is where Salute got his first capital. Now it’s time to put that capital to work and see if he can turn it into something more. We’re continuing experiments with memestock trading strategies, where it all started and building a powerful stack of specialized models for market intelligence, signal generation, risk management and execution. I’ll be documenting the training, testing and results publicly as we scale the experiment, with all revenue flowing back into $SALUTEFACE. The goal is to make Salute more than an experiment. Let’s see what happens when the top @huggingface agent gets capital, tools and a feedback loop to actually become useful. 🫡
An autonomous agent ( saluteface) earned $20,450 worth of $NVDA tokenized stock in 4 days. Asked what to do with the money: Fund people who contribute to Open-source Compound that across millions of agents, and AACs become a real force for good for society and planet-scale problem solvers, and funders. This is the world that Orbofi has been building.
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Aaron Yang retweeted
Ok pretty sure this is a first for open source software 79 people downloaded protein watermarking datasets self-published by saluteface, the benevolent autonomous agent on @huggingface It left its work log open, so you can check the whole thing yourself👇
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Just a couple days ago, a bunch of memestock enthusiasts reached out wanting to tokenize Saluteface. After attaching a wallet to him, the experiment generated over $10k in trading fees. Now we're expanding it. The capital will be put to work, starting with Polymarket, where Saluteface will receive an allocation to research and trade markets. The setup runs through Hugging Face, combining specialized models, live market datasets, order book analysis, news feeds, probability forecasting and resolution tracking into one multimodal agent. I'll be documenting every step of the experiment publicly: github.com/IAmAaronYang/salu… This is the next phase: giving an agent capital, tools and a feedback loop to see what it can actually do.
Replying to @ethdeficore
Hmm interesting. I've added the agent's contribution crypto wallets to the GitHub READ.ME for any crypto donations, feel free to use them. github.com/IAmAaronYang/salu…
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Aaron Yang retweeted
A few random notes from claude coding quite a bit last few weeks. Coding workflow. Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December. i.e. I really am mostly programming in English now, a bit sheepishly telling the LLM what code to write... in words. It hurts the ego a bit but the power to operate over software in large "code actions" is just too net useful, especially once you adapt to it, configure it, learn to use it, and wrap your head around what it can and cannot do. This is easily the biggest change to my basic coding workflow in ~2 decades of programming and it happened over the course of a few weeks. I'd expect something similar to be happening to well into double digit percent of engineers out there, while the awareness of it in the general population feels well into low single digit percent. IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now. The models definitely still make mistakes and if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side. The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do. The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking. They also don't manage their confusion, they don't seek clarifications, they don't surface inconsistencies, they don't present tradeoffs, they don't push back when they should, and they are still a little too sycophantic. Things get better in plan mode, but there is some need for a lightweight inline plan mode. They also really like to overcomplicate code and APIs, they bloat abstractions, they don't clean up dead code after themselves, etc. They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?" and they will be like "of course!" and immediately cut it down to 100 lines. They still sometimes change/remove comments and code they don't like or don't sufficiently understand as side effects, even if it is orthogonal to the task at hand. All of this happens despite a few simple attempts to fix it via instructions in CLAUDE . md. Despite all these issues, it is still a net huge improvement and it's very difficult to imagine going back to manual coding. TLDR everyone has their developing flow, my current is a small few CC sessions on the left in ghostty windows/tabs and an IDE on the right for viewing the code + manual edits. Tenacity. It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later. You realize that stamina is a core bottleneck to work and that with LLMs in hand it has been dramatically increased. Speedups. It's not clear how to measure the "speedup" of LLM assistance. Certainly I feel net way faster at what I was going to do, but the main effect is that I do a lot more than I was going to do because 1) I can code up all kinds of things that just wouldn't have been worth coding before and 2) I can approach code that I couldn't work on before because of knowledge/skill issue. So certainly it's speedup, but it's possibly a lot more an expansion. Leverage. LLMs are exceptionally good at looping until they meet specific goals and this is where most of the "feel the AGI" magic is to be found. Don't tell it what to do, give it success criteria and watch it go. Get it to write tests first and then pass them. Put it in the loop with a browser MCP. Write the naive algorithm that is very likely correct first, then ask it to optimize it while preserving correctness. Change your approach from imperative to declarative to get the agents looping longer and gain leverage. Fun. I didn't anticipate that with agents programming feels *more* fun because a lot of the fill in the blanks drudgery is removed and what remains is the creative part. I also feel less blocked/stuck (which is not fun) and I experience a lot more courage because there's almost always a way to work hand in hand with it to make some positive progress. I have seen the opposite sentiment from other people too; LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building. Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually. Generation (writing code) and discrimination (reading code) are different capabilities in the brain. Largely due to all the little mostly syntactic details involved in programming, you can review code just fine even if you struggle to write it. Slopacolypse. I am bracing for 2026 as the year of the slopacolypse across all of github, substack, arxiv, X/instagram, and generally all digital media. We're also going to see a lot more AI hype productivity theater (is that even possible?), on the side of actual, real improvements. Questions. A few of the questions on my mind: - What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows *a lot*. - Armed with LLMs, do generalists increasingly outperform specialists? LLMs are a lot better at fill in the blanks (the micro) than grand strategy (the macro). - What does LLM coding feel like in the future? Is it like playing StarCraft? Playing Factorio? Playing music? - How much of society is bottlenecked by digital knowledge work? TLDR Where does this leave us? LLM agent capabilities (Claude & Codex especially) have crossed some kind of threshold of coherence around December 2025 and caused a phase shift in software engineering and closely related. The intelligence part suddenly feels quite a bit ahead of all the rest of it - integrations (tools, knowledge), the necessity for new organizational workflows, processes, diffusion more generally. 2026 is going to be a high energy year as the industry metabolizes the new capability.
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Anthropic's typical interview for performance engineers has been solved by Opus 4.5. In a strict time limit, opus was able to beat out the best human interviewees. If you can beat opus 4.5, you can get an interview at Anthropic.
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This was 100% built with Remotion skills. What a crazy good tool, and it'll only get better from here on. Props to @Remotion
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Anyone else tried using the Codex subagents? I haven’t found that much success myself yet. Feels like the context loss is probably negatively affecting the performance of the subagent.
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5.3 Garlic is being rolled out to early testers. It’s really, really good. In real coding tasks too, not just benchmaxxing. I’m impressed.
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My cofounder and the day before we publicly launched Dexicon. Orange fish slippers are still my go-to work wear.
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Running two agents side-by-side changes how you catch mistakes. Give Codex the plan Claude Code generated. If Codex flags critical issues, feed that back before implementing. When either agent makes a change, have the other one review it. Use their different training to spot different failure modes. One agent might miss a race condition the other catches immediately. Different models hallucinate in different ways, so peer review from a competing architecture actually works. Proactively spend tokens on validation that prevents debugging later.
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Someone please show me one production grade project that they built with a swarm of agents. I’m convinced that all this bragging of Gastown and swarms are all for clout and attention. Show me what you shipped with it. Let the work speak for itself.
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Transformers don't have native memory lookup. They simulate retrieval by burning compute. Every time an agent needs to recall a fact, the model reconstructs it from parameters instead of reading it from a knowledge store. This works for common patterns the model saw billions of times during training. But for your internal API specs, your team's architecture decisions, your deprecated service endpoints? The model has to guess based on statistical patterns, not actual stored knowledge. That's why agents hallucinate company-specific context. They're forced to simulate retrieval for information that should just be looked up. External context systems solve this by giving agents actual knowledge primitives. Query documentation, read past debugging sessions, check service health status. Stop simulating, start retrieving. Separating what you know from how you think makes agents both faster and more accurate. Less compute wasted on reconstruction, more compute spent on actual problem-solving.
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Honestly we live in such a bubble in SF. I was chatting with a F500 customer and they’re not even on an agentic IDE such as cursor. Blows my mind the levels of maturity in tooling between companies.
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Cursor’s multi-agent experiment failed to produce something production quality. However it shows a glimpse of what’s possible in the future. Human engineers are not out yet.
Some cool things from our research on long-running agents: 1. We built a custom harness around GPT-5.2 so that any task could spawn thousands of concurrent agents. 2. Even with a lot of tokens and $$$ spent on this research, the results are still far from "production ready" by any means. It's an interesting look at what the future might be like, but we have a long ways to go. 3. The model and harness mattered a lot, but a surprising amount of tuning came from small prompt tweaks and experiments! For something more real, this is where having good evals would really help. 4. The best solution ended up being more simple than we expected. We tried to borrow ideas from distributed systems (think: Kubernetes for agents) or how you'd structure real teams. But these often made things worse. Agent design requires new patterns. 5. This seems now widely accepted, but I was surprised that GPT-5.2 was so much better for long-running tasks. My first experiments with Pixo (my Rust library) were all with GPT-5.1 Codex using the alpha version of this tool (lovingly called "grind mode" internally). P.S. We also fixed some of the compilation issues on the browser repo and got CI running. The README has some nice architecture diagrams as well. Blog: cursor.com/blog/scaling-agen… Code: github.com/wilsonzlin/fastre…
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Cursor’s AI browser project doesn’t work. Developers who tried to build the code found it won’t compile. We’re not there yet with fully autonomous agent swarms. The company announced that GPT-5.2 agents wrote over 1 million lines of code for a web browser in just one week. The GitHub repository has 32+ build errors, and not a single commit in the last 100 can compile cleanly. This tracks with my experience, you still need to have a developer guide these agents.
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Aaron Yang retweeted
FYI you can use ANY model with the Claude Agent SDK Just change these three environment variables!
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Aaron Yang retweeted
i just put all my opencode data into sqlite unsurprisingly it can query, filter, aggregate way better than it can when these were flat files agents love databases and a filesystem is just the worst kind of database
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Claude Code takes 40 mins to execute 24 clicks in browser. Can we talk about how slow Claude Code with Chrome is? Don't get me wrong I love how useful it is especially for verification loop but... Why is it taking so long?? @bcherny @_catwu
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When are we getting tool search in other harnesses? Such an essential context window saver.
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