Co-founder/CEO of @convex. Former Sr. Director/Principal Engineer of Storage & Databases at Dropbox. Shameless startup junkie.

Seattle, WA, USA
we already don't talk to each other enough
This is bad for humankind as a whole X 0head take> "BUT PRIME, THINK ABOUT THE LONELY PEOPLE" This will only serve to create a greater chasm betwixt the lonely and the rest. This will create an entire lonelier generation. AI's super danger is that it creates in you more and more selfish longings whether you realize it or not. This shit is dangerous and evil.
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Okay I finally permanently changed my coding harness. This one has stuck. I'm no longer on opencode. I'm on opencode 2.
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If a @convex optimized fine-tuned model as smart as kimi, but 2.5x as fast and 30x cheaper would be interesting to you, lmk. Maybe these will start appearing in the Convex AI gateway soon.
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I'll be playing with a new set of evals based on what we've learned about successful architectures in the near future. We can fine tune cheap models to be near SOTA with everything we know about how to use Convex's building blocks (primitives, components) to maximize success.
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This is the version of the labs I want. Would love to pay them to create derivative models.
And interesting frontier lab strategy would be to encourage distillation, set price that mass distillation is unit economic positive, and then just focus on building the best models. This results is a downstream ecosystem addicted to you. And with no capability to surpass.
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The vast majority of VCs are downside protecting, not upside seeking. They’re not looking for an outlier, they’re looking for a “consensus outlier” that’s the same “crazy” bet everyone else is making. Then you don’t stand out and get fired in the next downturn when LPs pull their money. Nothing wrong with that except when VCs pretend to be anywhere near as bold or visionary as founders. That’s horseshit. This is a big part of why most of the best VCs are ex founders. They know exactly how crazy that path is. They don’t pretend to know things they don’t, and they take only the appropriate share of credit for portco success. Those VCs are wonderful to work with.
In the summer of 2023, my friend @benspector, who co-founded Anysphere (aka Cursor), reached out to one top venture funds in Silicon Valley, suggesting we meet. They turned down the intro, saying it wasn’t a category they were interested in. Ben insisted that he thought our team was special and that they had to meet us, but the partner still wouldn’t even take a meeting. I drove over to their office on Sandhill Road and left a note at the front desk saying: “I think you couldn’t be more wrong. This is the largest opportunity of the 21st century.” They ignored my note, and I never heard back. My cofounders were still enrolled and left torn as to whether they should drop out with no funding. We ended up taking the leap of faith and building the fastest-growing company in history. It was a valuable lesson in persistence and why not to listen to investors. The best startups are deeply contrarian.
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agents are really good at generating code they're not good at generating the right code they generate too much code and seldom delete code you can fix it with abstractions, feedback, and architecture put them in boxes that make bad code impossible then win-win-win
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I'm sorry, y'all, AI is still so dumb. I just spend all my "development" hours now just telling this dimwit it missed another detail or screwed something up and burned a lot of tokens while doing it. I'm sure this is progress, but it feels very silly sometimes.
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LLMs are completely unafraid and they value their time at zero. If you tell Claude to use this instead of that, it doesn't say no or ask why or wonder if it will be "worth it." Getting a large group of humans to change behavior requires real visceral pain—there has to be a compelling reason or it won't happen. Most people just want today to go like yesterday. But give an LLM a skill file and the behavior changes instantly. That makes innovation much easier.
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Property testing practices and package installs are tracking almost exactly with agentic engineering adoption over the last 18 months. They give your agent more feedback about what your system should do and what success and failure mean.
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Jamie Turner retweeted
accidentally left my duckdb ingest script on an infinite loop now it's foie gras db
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The Ford Model T in 1920 had 15,000 nodes in its supplier dependency graph before you hit dirt. A modern BMW X5 has an order of magnitude more, with chains 6, 7, 8, 10 layers deep. Automation expanded the supply chain. As building got easier, the bar for excellence went up, and you used suppliers to simplify the domain you had to master. Software is going to do the same thing. Many more smaller companies will deliver excellence in niches. Maybe fewer enormous VC-sized opportunities, though...
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confident humans use a model more tests make humans feel confident tests generate tokens tokens make the model owner money
ok it's official... AI-written unit tests and integration tests are empirically proven to be unhelpful. you should tell your agents to stop writing tests by themselves on the deepswe eval set, banning sonnet 5.5 high from writing any tests actually resulted in slightly higher success rate (non stat-sig), with less time and token spent (stat-sig). this is pretty hard empirical evidence across the tests that were written by the "tests allowed" baseline arm, 65% of them were unit tests, 35% were integration tests, neither bucket resulted in any improvement compared to not writing any tests at all i also picked a random sample of 44 tasks subset where i completely disabled executing even existing tests - it also did not affect success rate at all i spot checked many tests written in the baseline arm, and my intuition is that most tests are simply a repetition of the implementation agent-written tests do not add any value because both the implementation and the tests were simply the agent's interpretation of our intent. the tests aren't any more accurate than the implementation itself i suspect we can still extract some value from unit tests and integration tests if we describe them ourselves when we believe we can articulate our intent better through test cases than through requirements. but i have not proven this yet also worth noting, during deepswe eval the agent wrote almost no e2e tests (only 17, compared to 3000+ unit/integration tests written). so this analysis does not prove nor disprove the value of e2e tests - i will do another eval specifically for that
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this is why @mikeysee is insanely good at what he does
Maybe its the british coming through in me but I really think im alergic to hyperbole. In my DevX capacity I know I could probably get more attention, reach a bigger audience if I exaggerated more but I just cant bring myself to it, its not in my nature. Thoughts?
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Our 3-day retention went from ~30% to 55%+ when Opus dropped. And has only risen since. We always knew if you stuck with @convex to minute 30, you'd probably like it. The problem was the leap from minute 5 to minute 30 required you to actually learn something new. Opus removed that requirement entirely. You got value because Opus already knew how to use Convex very well.
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yup. the labs are not indemnifying you for their models' mistakes.
I’ve been asked why I was so passionate to dig into this system. I am myself experimenting with how do I accelerate and maybe read less code but still ship code confidently. This is a big shift for me and many. But terrible AI prompts and no review _today_ will get you fired.
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A fog-of-tools failure mode shows up when a powerful new technology arrives. You start deriving your product surfaces back from the tool's capabilities rather than from the human need. AI is compelling, powerful, and threatening enough to cause exactly this. Human need, though, is the only reason we all get paid to make anything. Losing sight of that because you're excited about a tool is how you end up building something nobody asked for. And the public isn't interested in using your fancy new shit. No matter how clever it is.
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The most surprising thing to me about the Rails keynote is that people are still using Rails.
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We've always called it software engineering, but we've actually been builder...gineers. In civil engineering, aerospace, or architecture, the people who do the engineering and the people who do the building are obviously different. But in software, we collapsed them into the same role. With agents, we're finally separating the jobs. We're going to specify and encode more upfront, add more guardrails, and design architectures that bias agents toward success. In many ways, we're finally starting to look like actual engineers.
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