Epistemic humility enjoyer
San Francisco
Joined January 2016
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The coolest thing, imo, about language models solving coding was no longer having to pay a mishmash of "software taxes" that aggregators were previously charging. Rather than buying a generic saas tool, you can just make your own bespoke version that is both cheaper and better-suited for your own use cases.
These same dynamics are greatly magnified in hardware due to strong network effects in prototyping and manufacturing. There are so many little "hardware taxes" that you pay which exist purely because the activation energy to do it yourself is so high, and because there has never been a real open-source movement in hardware the way that there was in software.
Next few months are going to be pretty exciting!
Benjamin Bolte retweeted
We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math.
It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training.
We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵
Blog: rikiyat.github.io/blog/weigh…
Benjamin Bolte retweeted
Talked with researchers at Chinese AI labs. I came back thinking the gap between US and Chinese labs is getting wider.
1/openai and anthropic are already in RSI territory. You can feel the speed-up from the outside.
Chinese models aren't strong enough to get there yet. Most of the "RSI" I heard about is automating steps researchers used to do by hand.
2/Everyone there is now obsessed with data, mostly because distillation got a lot harder. So labs are going all in on synthetic and expert data.
3/After GPT-6 Astra, everyone is looking at robotics. Robot companies are already plugging Astra into their hardware. Almost nobody is seriously doing AI for science though.
4/One more thing from this weekend. DeepSeek open-sourced a research prototype of a coding agent.The line in their README I keep thinking about: "take as little human attention as possible."
The model gap isn't closing soon, so they're thinking hard about how little you should have to babysit the agent.
I can't imagine spending the waning moments before AGI unironically caring about "software factories"
What we are seeing at @coderabbitai and elsewhere on this parabolic trajectory is that even larger seed rounds ($8M+) are no longer enough to build a software business. Software factories need quality tokens and large-scale compute to run CI, sandboxes, and other infrastructure to ship software at scale. At this point, smaller startups exist only to funnel ideas to larger startups with more tokens.
