Justin Angel at @aiDotEngineer @JustinAngel
AI/ML. Worked at Meta, Uber, Amazon, Apple, and Microsoft building apps, developer platforms, and hardware. Tweeting about LLM psychotherapy. JustinAngel.net San Francisco, CA Joined August 2008-
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Very cool results by @eliebakouch. Suggesting a new autoResearch benchmark for NanoGPT speedrun: w/ arxiv, w/internet, and w/o anything. 1. Claude leads on refusals and token use. 2. Codex doesn’t refuse and gets similar results 3. kimi K2.5 code uses the least tokens Surprisingly, all advances very reusing existing techniques, and none came up with novel techniques. Some novel adaptations.
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Smart. LinkedIn created a repo with 500 skills for runbooks to work with different services. There’s an MCP that loads the correct runbook skills. The skill contains instructions to update itself as needed. That’s really the path for enterprise coding agents. @Ajay_prakash_mi mentioned they expended their skills repo to 8k daily users beyond engineers including PMs. @aiDotEngineer
Loved @RichardSocher’s demo for his new AutoResearch platform crushing becharks on ML training speed, accuracy, and kernel writing. Very cool SoTA results in kernel writing:
Amazing keynote from DeepMind VP @benoitschilling at @aiDotEngineer: Software engineering isn’t writing code, it’s joining a company with 35B python codebase you need to change. LLMs are good at writing new code. The path to superhuman coding skills goes through self-play. Lock a programmer in a room for two years, tell them to get get better at coding. Same, have LLMs improve with self-play. Developers won’t check their code, like they don’t check assembly code. Given that, the 2026 challenge of coding agents is getting them to write correct code from the start, like secure code, good architecture, evaluated through strict evals. Emitting code as tokens is the problem. Coding agents need to have spatial-dynamic representatio during planning. We need a new programming language for coding agents. Python was created for humans that struggle with writing code. We need stronger burdens of correctness in the language. Chemistry and biology are going to be the next domains are after coding agents. Models will generalize planning capabilities from chemistry and biology back to coding.
The newspaper at @aiDotEngineer is more current than twitter.
Watching @headinthebox’s keynote at @aiDotEngineer
@0xblacklight @aiDotEngineer Loved the talk! Any chance for a link to that skill you mentioned at the end?
Very cool @fewshotlearner trained a 3B VLM on 22 Indian languages for OCR and character recognition getting to SoTA performance. Very cool to see low resource language focus improve performance on all languages. They established their own evals and RL on those languages, which looks like it generalized to performance on English OCR. @aiDotEngineer
Watching @cbmenefee talk about building an ML model to move cows across pastures
Watching @zachlloydtweets on software factories at @aiDotEngineer
Vibe-coded PRs have less reverts than humans, are roughly the same complexity and size as human-authored PRs, and get less code review comments. @dakshgup at @aiDotEngineer
Vision language models and apps by @mervenoyann @aiDotEngineer
Even state of the art vision models don’t do visual reasoning well: length, overlaps, composition, counting. And the numbers are non deterministic. @josephofiowa @aiDotEngineer
Vision AI is three+ years behind usefulness of LLM scaling laws
“The state of Visual AI” @josephofiowa @aiDotEngineer
It’s nice to see @swyx uses cloudflare like the rest of us. loopcraft.swyxio.workers.dev/human
At @aiDotEngineer today @swyx keynote
@LinkBechtel Commenting here to help with the algo. Me me me
@mervenoyann @aiDotEngineer 100%. Big fan of your work. What's the time slot? In the CV track?
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