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Joined May 2021
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foxriverdevops retweeted
An absolutely GOATed example of domain knowledge being important I'm working on my video editor. I know enough about phonetics to know that certain consonants can fiddle around with silence detection. I tell my agent that. The word 'fricative' is enough for it to start busting out full IPA (international phonetic alphabet) and we brainstorm which consonants are the most troublesome. Turns out, knowing things is really fucking valuable.
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foxriverdevops retweeted
agents are fundamentally non-deterministic by nature. you cannot prompt that away. so any agent you give open-ended spending power to will eventually do something you did not intend non-determinism plus open authority is unbounded downside. you do not fix unbounded downside with better intentions. you fix it by making the downside bounded by design that is the entire product thesis of VAA
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foxriverdevops retweeted
I wrote down how my work with agents changed this year. I still break down the work into vertical slices that ship on their own. I rarely read diffs now: independent reviews and a risk check catch most of it, so my time goes into shaping and guardrails. mgratzer.com/posts/reforging…
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foxriverdevops retweeted
you wouldn't hire people you can't trust. so why are we giving root access to agents we can't audit
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foxriverdevops retweeted
AGENT SWARMS ARE HERE *To accelerate your business Today we're launching Agent Swarms @grepdotai to accelerate your business by tackling the backlogs your team can't keep up with. Swarms are teams of agents that work together to automate a task queue in parallel.
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foxriverdevops retweeted
so many gems and great tips in this talk from @yminsky about how to improve your codebase for agents • agents thrive on feedback, and types are one of the best kinds • make illegal states unrepresentable with types, and it enforces global rules across your codebase • property based tests, fuzzing, and even full-system deterministic simulation testing • expect tests: just print stuff out and capture it in the test. makes behavior legible. it's very similar to how compilers have fixture tests, it reduces the review surface area • formal methods used to be very expensive: seL4 is about 8,700 lines of C and took around 25 person-years, with 23 lines of proof for every line of C code, but agents change the roi youtube.com/watch?v=rUYP4C29…
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foxriverdevops retweeted
Agents reinvented how we produce code. It changed our tools and half of our workflow. It’s time for the other half to catch up. Today, we’re shipping Entire: the Git forge for agent-human collaboration. We’ve built @GoEntire using Entire. Now we want to see what you'll build with it. Here’s why and what’s different. 🧵
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foxriverdevops retweeted
Entire is the ONLY company I've invested in the last two years: b/c it's clear that we need an "AI-native" GitHub, and also clear that GitHub cannot disrupt itself. Entire just reinvented the PR for agents with Trails, and offer a Git forge that can handle agent load:
Agents reinvented how we produce code. It changed our tools and half of our workflow. It’s time for the other half to catch up. Today, we’re shipping Entire: the Git forge for agent-human collaboration. We’ve built @GoEntire using Entire. Now we want to see what you'll build with it. Here’s why and what’s different. 🧵
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foxriverdevops retweeted
MUST MUST MUST MUST MUST MUST MUST read if you are a software engineer in big 2026
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#Sui will become the place where agents coordinate and transact at scale
#Sui will become the place where agents coordinate and transact at scale VAA is just the beginning
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foxriverdevops retweeted
#Sui will become the place where agents coordinate and transact at scale VAA is just the beginning
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foxriverdevops retweeted
The best vid explanation of LoRA I've seen. Amazing work @juliarturc 🫶
Over the past few days, I dug into the geometry of LoRA to understand how exactly it constraints the geometry of the search space. There's a beautiful connection to the *intrinsic dimension* of a task. Also learnt I'm saying "difficulty" wrong... 🤡 0:00 MNIST vs the walking humanoid 1:05 Do we still need fine-tuning? 3:05 What is LoRA? 6:55 What does the rank constraint actually do? (geometric intuition) 9:50 Intrinsic dimension: training along random directions 13:30 Intrinsic dimension of pre-trained LLMs 15:10 Demo: fine-tuning a query router with Qwen3.8-27B
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foxriverdevops retweeted
software engineers are going to have to tackle a new set of problems to unlock powerful multiagent systems to solve the world's hardest problems - right now is one of the most interesting moments in history to be an engineer.
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foxriverdevops retweeted
This is the coolest representation of LLM Steering!
if you have a qwen3.5 4b around, please try steering neurons L14 N8949 +1 and L13 N7956 -1 now you have evil qwen recipe: dose × median(‖resid‖ leaving layer L, tokens 1:) / ‖W_down[:, n]‖` at the down_proj at every input
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Replying to @tamashi095
got it🫡 some never learn...
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youtu.be/9iABRJcgVp4 RSI and it's potential consequences
I'll be on MTS in 20 minutes or so to chat about RSI and its potential consequences.
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