here for digital technology stuff
OASIS
Joined May 2021
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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
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…
ALT Two workflow diagrams side by side. February, a human in the middle: I plan the issue with the agent, the agent implements and self-reviews, I review the PR with line comments and the agent fixes it, then I merge. October, a human at both ends: I shape the issue with the agent. Inside guardrails from the repo and Forge, the agent ships, guards and lands the PR with a risk verdict. Low-risk PRs merge automatically, the rest come to me with a list of where to look. I shape the system, turning drift into new guardrails.
foxriverdevops retweeted
you wouldn't hire people you can't trust. so why are we giving root access to agents we can't audit
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.
P = NP? | Complexity Theory Explained Visually
good time for this oldie: youtube.com/watch?v=u2DLlNQi…
foxriverdevops retweeted
good time for this oldie: youtube.com/watch?v=u2DLlNQi…
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…
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. 🧵
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. 🧵
foxriverdevops retweeted
#Sui will become the place where agents coordinate and transact at scale
VAA is just the beginning
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
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.
youtu.be/9iABRJcgVp4 RSI and it's potential consequences
