getbygenius retweeted
Replying to @MrBye32
I will test it on my ELY Gpui components
elygpui.com
It has around 1300 different components LoL 🤣
getbygenius retweeted
i have a competiton that is due here in 11 days gonna build on top of this here but make it so that you’re able to get your website a backlink too! Sick job Jason and team
Introducing OpenSource AEO Toolkit
- Find every source AI cites, and the author behind it
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Try it at treg.to/aeo
Partner with @cloroai
Repo link below 👇
getbygenius retweeted
It’s important for your development as a person to have a solid 6 months where the only person who cares if you live or die is the gas station guy
getbygenius retweeted
Non-deterministic rules via decision models are about to radically improve AI codegen.
Brace yourself for global desloppification.
getbygenius retweeted
Another ~25% CI speedup (for Node projects)
Have Bun run the tests.
Bun JUST shipped vitest support in 1.4+
Every AI will miss this one too 😵
So I found you can instantly ~2x* your GitHub CI speed for free
GH added parallel actions.
But AI usually misses it since it's too new 😱
* Depends on what you're doing, just ask AI to benchmark
github.blog/changelog/2026-0…
getbygenius retweeted
Replying to @jrysana
I’ve been thinking about a solution for this. A local first rework of act… the core written in rust, deliver as wasm with wasix or wasip3, and thus extensible with any programming language.
Github.com/ectorial/wsr
getbygenius retweeted
people will do anything but use fucking precommit hooks or self host their ci
the biggest bottleneck in AI development is actually waiting for CI
bottleneck #2 is waiting for AI PR review bots
For decades, researchers have sought materials that sort electrons by spin while their magnetism cancels.
In 3 days, 90+ Opus 5.5 agents helped us uncover two room-temperature magnetic semiconductor candidates in simulations: YBaMnFeO₅ and KV[Cr(CN)₆].
KV[Cr(CN)₆] was synthesized back in 1999. Its predicted ability to sort electrons by spin appears to have been hiding in plain sight for 27 years.
getbygenius retweeted
Rust also has its own original sin.
Slow compilation and bloated target/ directories are symptoms. The disease is a development process so slow that obvious improvements spend years waiting for permission to exist. People were proposing solutions like these before GPT-3.5 even existed.
Cargo still can’t garbage-collect stale build artifacts. The solution is straightforward: track when artifacts are last used, then delete the ones nobody needs. This is engineering, not a research problem. And yet it still hasn’t shipped.
Cranelift could make debug builds much faster. It’s still a nightly side quest. Cargo Script could let an agent run a one-off Rust program, pull in dependencies, and test an idea quickly. It’s been sitting on nightly for years.
Every feature gets buried under fear of breaking something. Editions come every three years, and most changes are deliberately kept shallow. Rust has turned backward compatibility from a strength into a veto on progress.
Agents don’t need a language dumbed down for them. Every agent is a coding genius. Give it a steep learning curve and it’ll climb it. What it can’t do is make a slow toolchain iterate faster.
Rust has caught the C++ disease: so terrified of breaking the past that it can’t move fast enough to build the future. That’s Rust’s real original sin.
In the Agent era, maybe the better choice is to move fast and make breaking changes across the ecosystem, so we can rethink these problems from first principles and reach better solutions sooner.
getbygenius retweeted
Yielded Auth's core is now zero-dep (other than Effect, of course). Last update shipped a new crypto, jose, and oauth package. E2E type safety included on the jose signings.
github.com/yielded-dev/auth
getbygenius retweeted
Been seeing a lot of Astra for real2sim, but what you need for robotics: physics and affordance.
Our SOTA recipe specifically solves for:
• Affordance: model object details that convey functionality.
• Physics: calibrate simulation dynamics against real-world observations.
Our agent has the action space of a “full-stack simulation engineer” and jointly considers geometry, materials, colliders, articulation, and physics. It automatically calibrates physics against real trajectories and matches task-specific dynamics.
Simulation environments is useful only if it can provide meaningful policy level signals. Methods, comparisons, and results below.
Everyone's been saying software is solved, robotics is next.
Robotics is getting there... this demo is insane.
Been seeing a lot of Astra for real2sim, but what you need for robotics: physics and affordance.
Our SOTA recipe specifically solves for:
• Affordance: model object details that convey functionality.
• Physics: calibrate simulation dynamics against real-world observations.
Our agent has the action space of a “full-stack simulation engineer” and jointly considers geometry, materials, colliders, articulation, and physics. It automatically calibrates physics against real trajectories and matches task-specific dynamics.
Simulation environments is useful only if it can provide meaningful policy level signals. Methods, comparisons, and results below.





























