Florida, USA
Joined January 2021
- Tweets1.5K
- Following459
- Followers178
- Likes6.9K
Jeremiah retweeted
gpuix apps can now render in the terminal
create GUIs, use them as TUIs
- embed them in VSCode, Zed, Codex
- split GUIs into panes
- use all terminal power features for your GUIs
powered by the Kitty Graphics protocol
Jeremiah retweeted
Big news.
Shaders is now open source 🔥
Use any of our 200+ WebGPU components in your work. Ship magic in a client site, a template, a product, a library, anything. Plus, you can export code for any framework, for free.
Give it a star: github.com/shader-effects-in…
Jeremiah retweeted
1/ We're reimagining what it means for your agents to work with all your computers.
Today we're excited to share Cua Spaces, built on Cua Driver and our virtualization stack, rolling out on macOS today.
Download it now, it's free and source-available: spaces.cua.ai
Jeremiah retweeted
I think I just cooked something 🔥
jev(): a PostgreSQL extension that searches your whole database in natural language. No index, no embeddings, just one function.
WHERE jev(people, 'could work from home')
or
WHERE jev(people, 'name sounds european')
129 rows judged in ~1s for $0.0009. Second run: 6ms from cache.
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?
I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev
• 20-200x faster
• 40-400x cheaper (w/ output tokens free)
• Frontier composable intelligence optimized for decisions
AFAICT the shortest path to AI-based economic revolution
Jeremiah retweeted
1/ Fast Computer Use is now solved with @typesafeai Jev + Cua Driver.
Available in development preview for macOS, Windows, and Linux. We call it jev-use.
Draft #3943: github.com/trycua/cua
Jeremiah retweeted
I just built Easy-Jev, a Jev demo anyone can try right now!
Change the inputs and watch the classifications change in real time.
I did my master's thesis on classification models so it's fun to see a modernized version of a very useful alg
Typesafe are correct in that LLMs are just one approach to intelligence, and I think as time goes forward we’re going to see a hybrid of dif models splitting up and allocating correct parts of a problem, using less tokens overall and leaning on LLMs for less of the answer.
I’m bullish on what I call 'combination input' approaches: breaking a statement down into a series of classifications and routing each to the right type of model rather than just brute forcing the whole thing with an LLM
cc / @CompleteSkeptic @typesafeai
Link below, please enjoy
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?
I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev
• 20-200x faster
• 40-400x cheaper (w/ output tokens free)
• Frontier composable intelligence optimized for decisions
AFAICT the shortest path to AI-based economic revolution
Jeremiah retweeted
Introducing SWE-2, our closest model yet to the frontier.
On leading evals, it scores on par with recent frontier models – at up to 70% lower cost.
We scaled RL to multiple trillions of parameters, with a refined recipe that pushes the Pareto curve on both capabilities & cost.
Jeremiah retweeted
New @usehamster update 🐹
Native PR reviews with your team! No jumping between Hamster and GitHub. Read diffs, leave comments + approve or request changes from Hamster directly
+ Trigger routines from @github activity. Easily handle PR follow-ups with zero manual intervention
Jeremiah retweeted
Agent memory is feature, not a product.
A context graph powers agentic memory, also a feature. Part of a greater workflow.
Product is typically streamlined workflow.
@usehamster streamlines a company/team’/ direction setting, discovery and delivery. And it’s supported by the knowledge graph, the functionality of which powers and supports direction/discovery/delivery.
A multiplayer harness powers multiplayer AI workflows and agent memory makes it a compounding activity
For our team, about 4 months ago was the point at which Hamster was objectively smarter and knowledgeable than any one person in the company
The value of this is orders of magnitudes more powerful (and difficult) at enterprise scale
Jeremiah retweeted
if you want to use such a system for you and your team
give @usehamster a try
- unified AI workspace (a loop of loops)
- foundational knowledge graph (context, skills, methods, routines) that self populates, self heals and self improves across team activity
- direction setting with AI (it knows all metrics)
- discovery against the goals and initiatives to reach them
- end to end delivery grouping team alignment, plan gen, cloud or local delivery as fast as we’re used to
it’s a team harness that lets you go fast together on a basis that is shared and self-improving
Jeremiah retweeted
GPT-6 Astra + @threejs is insane. We are way beyond static models. We are engineering living worlds in code now. There are no "3D model files" in this demo. Both trains are generated at runtime from Typescript/Three.js code using dimensions, profiles, and geometry functions. Wheel motion and explode/reassemble animations are also entirely code-driven. Runs super smooth inside the browser.
Jeremiah retweeted
From the @HamsterResearch lab:
Introducing Qwen3.8-Flash-Next-REAP-288-MLX-4bit, a 180B-class model running on just 39gb of memory
- MLX-native 4-bit 60% smaller than stock q4
- Pruned 512→288 experts via REAP
- 91.5% HumanEval (vs 93.9% stock)
@huggingface links below ↓