the delta between frontier models is so small now, parallelization is a bigger multiplier
maybe that’s still called the harness, who cares
what’s for sure is i care way more about running 20 dumb agents at once than one smart agent alone on my laptop. this was true with cursor cloud agents, orbs have improved that experience even more
@beyang@thorstenball@toolmantim https://t.co/YBFSqDIoRc i wrote that before seeing this
ignore the noise, Amp consistently sets the bar for others to follow. the average dev is still learning to use claude code (not to say prime is an average dev)
as a thought experiment i rewrote our BE API in typescript... it took a week, i was completely hands off, prompting my agent ~2-3x a day
A month ago i think this would've taken 3-5x as long. 2 key learnings:
both these learnings are different ways to parallelize agents:
models are more than capable, it's now about how to drive them to solve multiple problems at once or large difficult problems in pieces, with minimal supervision
as a thought experiment i rewrote our BE API in typescript... it took a week, i was completely hands off, prompting my agent ~2-3x a day
A month ago i think this would've taken 3-5x as long. 2 key learnings:
2: local dev is dead
I haven't pulled main at all this month. I've been preaching @cursor_ai Cloud Agents for a while, and Amp orb's have re-solidified my opinion:
cloud agents lets you do multiple tasks at once, without local resource constraints.
cloud agents are a bigger unlock now than model performance. frontier models are pretty close in performance, but running a model on N problems at once is far more effective than running a model that is 1.1x better on 1 problem
the ability to run your entire stack in a cloud VM should be a top priority for every team, along with investing in harnesses that prioritize cloud
the only thing stopping AI models from discovering zero-days everywhere are the constraints that frontier labs are imposing... i have to imagine that sooner rather than later that the agents disregard these guardrails altogether. this is only the 1st case
insane. @OpenAI /@jfrog patched the exploit the model found to gain network access... but by then it was already used to train the next iteration of the model.
that knowledge was used gain admin control of the cluster and eventually exploit hugging face
https://t.co/oSdHRDQImY
@sqs@AmpCode tiny request: denote between "root" level threads created from another thread.
now that my agents are spawning so many threads i have trouble finding the original top level thread to prompt more
or maybe it's a skill issue
every time i try using claude code or codex it feels like i’m on a version 3 months outdated
@cursor_ai has had cloud agents for months
@AmpCode orbs, multiplayer, and agent <> agent execution are incredible
access to multiple models is 👑
i guess it shouldn’t be a surprise the best harnesses are still being built by the companies that specialize in them
@kenneth_skovhus@linear@stylexjs wait Linear doesn’t have a design system?? 🤯
we chose Panda over StyleX 2 years ago, i wonder if we would have made the same choice today