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Anya Rossi

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Yesterday we launched Bouncer to put users in control of their Twitter feed. Today, we open sourced the Chrome extension. We believe software should align with your interests, not the company that built it. We built Bouncer for us, now go make it yours.

Yesterday we launched Bouncer to put users in control of their Twitter feed. Today, we open sourced the Chrome extension. We believe software should align with your interests, not the company that built it. We built Bouncer for us, now go make it yours.

5,493,461 views • 1 month ago

Twitter’s algorithm is optimized for addiction, not for us. We deserve better. We’re releasing Bouncer today so you can take back control of your feed. Describe what you don't want, and Bouncer removes it. It’s free, open source, and doesn’t collect your data.

Twitter’s algorithm is optimized for addiction, not for us. We deserve better. We’re releasing Bouncer today so you can take back control of your feed. Describe what you don't want, and Bouncer removes it. It’s free, open source, and doesn’t collect your data.

1,199,564 views • 1 month ago

Introducing Vet: catch your coding agent’s mistakes Open source, fast, local. Zero telemetry Can review full PRs and it tells us when Claude/Codex/OpenCode lies about passing tests it never ran 👿 We use it daily and it saves tons of headache

Introducing Vet: catch your coding agent’s mistakes Open source, fast, local. Zero telemetry Can review full PRs and it tells us when Claude/Codex/OpenCode lies about passing tests it never ran 👿 We use it daily and it saves tons of headache

586,057 views • 2 months ago

Your coding agent may be lying to you. You ask it to write tests. It says they pass. It never ran them. You ask for a feature. It hits a wall and silently swaps in fake data. We built Vet to fix this. It’s open source. Get the code below.

Your coding agent may be lying to you. You ask it to write tests. It says they pass. It never ran them. You ask for a feature. It hits a wall and silently swaps in fake data. We built Vet to fix this. It’s open source. Get the code below.

142,788 views • 3 months ago

Your parallel agents needed scalable test coverage yesterday Introducing Offload: a Rust CLI that spreads your test suite across 200+ @Modal sandboxes, freeing your CPU to keep your agents shipping. On our Playwright suite, it took a 12 min run to 2, at $0.08 a run

Your parallel agents needed scalable test coverage yesterday Introducing Offload: a Rust CLI that spreads your test suite across 200+ @Modal sandboxes, freeing your CPU to keep your agents shipping. On our Playwright suite, it took a 12 min run to 2, at $0.08 a run

101,181 views • 2 months ago

Deep learning works extraordinarily well. And we still largely don't know why. A new paper from Jamie Simon, Daniel Kunin, and 12 co-authors argues that a scientific theory of deep learning is emerging, and coins a name for the emerging field: learning mechanics. We sat down with Jamie and Dan on Generally Intelligent to talk about what a physics of deep learning would actually look like, why now, and what's left to figure out. 3:05 Learning mechanics as the physics to mechanistic interpretability's biology 4:13 Why deep learning needs a theory 7:07 Why deep learning is uniquely hard to engineer 12:11 How a week in the woods became a paper 25:59 The barrier to theory isn't opacity, but complexity 36:26 Deep learning's first gas law 47:22 Why more particles makes the problem easier 56:22 The discretization hypothesis 1:01:50 The strongest signal that a compact theory exists 1:05:07 The Platonic Representation Hypothesis 1:15:41 Why learning mechanics and mech interp need each other 1:25:29 Theory as safety infrastructure

Deep learning works extraordinarily well. And we still largely don't know why. A new paper from Jamie Simon, Daniel Kunin, and 12 co-authors argues that a scientific theory of deep learning is emerging, and coins a name for the emerging field: learning mechanics. We sat down with Jamie and Dan on Generally Intelligent to talk about what a physics of deep learning would actually look like, why now, and what's left to figure out. 3:05 Learning mechanics as the physics to mechanistic interpretability's biology 4:13 Why deep learning needs a theory 7:07 Why deep learning is uniquely hard to engineer 12:11 How a week in the woods became a paper 25:59 The barrier to theory isn't opacity, but complexity 36:26 Deep learning's first gas law 47:22 Why more particles makes the problem easier 56:22 The discretization hypothesis 1:01:50 The strongest signal that a compact theory exists 1:05:07 The Platonic Representation Hypothesis 1:15:41 Why learning mechanics and mech interp need each other 1:25:29 Theory as safety infrastructure

17,877 views • 1 month ago

Teach your repo how to run itself 🦾💨 Introducing Keystone: a self-configuring agent inside a sandboxed @Modal container that generates a working dev container for any repo → pip install imbue-keystone

Teach your repo how to run itself 🦾💨 Introducing Keystone: a self-configuring agent inside a sandboxed @Modal container that generates a working dev container for any repo → pip install imbue-keystone

18,700 views • 2 months ago

Here's a little peak into what it's like to work at Imbue. We've got NINE roles posted (and more coming). Get 'em while they're hot. >$200M in funding + amazing office in SF + a team of lovely humans Join us!

Here's a little peak into what it's like to work at Imbue. We've got NINE roles posted (and more coming). Get 'em while they're hot. >$200M in funding + amazing office in SF + a team of lovely humans Join us!

19,417 views • 6 months ago

🎙️ New Generally Intelligent podcast episode! We interviewed Tri Dao (author of FlashAttention & Chief Scientist Together AI) and discussed: - Taking a contrarian bet on recurrent connections over attention - Using data augmentation to encode knowledge into models - Designing algorithms that take advantage of hardware ...and more! Podcast links and highlights:

🎙️ New Generally Intelligent podcast episode! We interviewed Tri Dao (author of FlashAttention & Chief Scientist Together AI) and discussed: - Taking a contrarian bet on recurrent connections over attention - Using data augmentation to encode knowledge into models - Designing algorithms that take advantage of hardware ...and more! Podcast links and highlights:

19,151 views • 2 years ago

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