dpiers
Joined 14 years ago
Joined 14 years ago
You can be in the driver seat while delegating implementation to agents. You can use agents to generate documentation and refactor architectures. You can ask an agent to explain a feature and then work with it to plan how to extend it.
You don’t have to type the code to be the architect.
Isn't this the new norm for every modern tech company?
Claude Code can help me write and review code faster and better than a human possibly could. I spend my time drafting requirements, steering agents and reviewing diffs instead of writing code manually.
AI is changing knowledge work. Dressing that up with ragebait (game dev, Disney, layoffs) doesn't change that or the fact that the video game industry has always been brutally fickle and hit-driven. The Epic layoffs earlier this year had far more to do with the decline of Fortnite's popularity than AI.
The JOBS Act and proliferation of double-vesting trigger RSUs effectively negated these forcing functions and 'going public' went from a necessary growing pain to a burden and distraction.
“We used several LLMs throughout: Anthropic’s Claude, OpenAI’s Codex, especially with GPT-5.6 Sol and, more recently, Astra.
The latter was only used for writeups and auditing our arguments. For most of the past year progress was slow. We worked through the literature and upgraded various preliminary results, up to obtaining finite time blow up for the Incompressible Porous Media equation (with smooth forcing).
This was until about a month ago, when we had real progress: on August 15th, we obtained the blow up results, with smooth forcing, for both Boussinesq and Euler. I can say the first LLM generated proof Levent sent me was the most horrendous I have ever read; we verified it on Lean on August 22nd. Since this point, we have been working around the clock to understand this proof and turn it into something readable.”
The OpenAI research post states they began training GPT-6 internally on August 28th, and that user chats are used to train models.
“We (the researchers and the agents) did not see any of their work through any means until they released it publicly — in particular, no specific user data was accessed in order to solve this problem. While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models .”
For an incredibly niche topic like this, I believe it’s extremely likely that Buckmaster/Levant’s work would influence the direction of OpenAI’s agents’ work even as a de-identified drop in the overall bucket of training data.
60-90% of prenatal diagnoses in the US result in an elective termination. The number is nearly 100% in Iceland and some other Nordic countries. Unlike autism or ADHD, we have a very clear understanding of exactly what causes Down Syndrome and now potentially how to correct it. A treatment like this is no different from correcting a congenital heart defect - it gives a baby a chance at normal, healthy development.
CC has a complex scene rendering pipeline with a 16ms/frame budget. That is much closer to a game engine than a print statement.
Most people can only encode/decode a single language but an LLM can move between them fluidly.
It's not that things can't go wrong, it's just that they are much better at handling it than Newegg these days.
If OpenAI is not safe from anticompetitive practices then no YC startup is.
If you can’t get basic facts correct no one is going to take your other claims seriously. A16Z has a diverse portfolio of investments and are as famous as they are because of how successful their companies have been.
Previously: Growth Engineer @ Uber Product Engineer @ BitTorrent Full-stack Dev @ ZeroCater Game Engine Programmer @ Terminal Reality
Email: [email protected] Twitter: danielpiers Github: dpiers Blog: http://da.nielpie.rs Resume: http://goodwith.computer