@aneeshai
iAccount based inAustralia
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Deep Learning for FUN @aneesha@aus.social
Brisbane
Joined March 2007
- Tweets56.2K
- Following2.3K
- Followers1.8K
- Likes62.6K
Aneesha Bakharia retweeted
NEVER thought i would hear this from jeff dean
he thinks we can compress chip design from 2 years to 3 months with RL + new EDA tooling
essentially just by a specialized auto research loop for hardware!
Aneesha Bakharia retweeted
Jev, now with open weights + vision.
Classified 1,697 SF Tech Week events with Gemma 4 26B-A4B.
Zero labels. No fine-tuning.
Jev-ify any open-source model on SimpleJev. – at San Francisco, CA
One of the craziest use cases I’ve found for Jev: verifiers.
I am so excited about this that I at least wanted to share the high-level idea.
I used Jev to build a custom verifier for the /goal feature in my agent harness.
It checks whether the goal is actually complete after every turn, making continuous verification cheap enough to scale.
This means I can run more of these verifiers (previously handled by another expensive reasoning model) more frequently to keep the agents on track.
System One models are perfect for verification.
I think of this as scaling harnesses further by cleverly combining System One and System Two models.
I have a feeling this will enable a new wave of scalable test-time compute methods. Watch this space closely.
I've just started to experiment with this and am already seeing really good results. I need to explore and figure out a way to benchmark it. I will share more once I have more results.
This is an insane unlock for long-horizon agents.
You heard it here first. And you can expect to see more harnesses embracing this new pattern.
Full guide dropping in the next couple of days.
Aneesha Bakharia retweeted
Cloudflare ha lanzado su propia skill completísima de auditorías de seguridad.
Es la que utilizaban internamente en la empresa como sistema de detección de vulnerabilidades.
→ github.com/cloudflare/securi…
Aneesha Bakharia retweeted
This 9.8K-star GitHub repo is basically an entire LLM engineering course hiding inside one codebase.
It starts with raw text and builds the Transformer in plain PyTorch, then keeps going through pretraining, SFT, reward modeling, DPO, PPO and GRPO. No transformers, trl or peft hiding the training loops.
The smallest model is only 13M parameters, so you can run the whole thing on a single GPU and actually see what changes at every stage.
github.com/FareedKhan-dev/tr…
Aneesha Bakharia retweeted
jev is so exciting! so you started using jev, now you need to know
1. what decisions is jev making? (observability)
2. is it making the right ones for my application? (evals)
3. how can i help jev make better decisions? (engine)
and for obs+evals+engine, you need langsmith
langchain.com/
Aneesha Bakharia retweeted
I built a Photoshop-like image editor. It’s called Compositor.
robbietilton.com/compositor
I originally built it for myself to get off my Adobe subscription, but decided to release it for free and make it open source. It has all the essential tools I need for compositing, with none of the BS.
I know this workflow is kind of archaic in 2026, but I’m still using it until AI can actually get pixel-perfect on some of the details.
The entire app is 12MB. Photoshop is 6,455MB on my machine.
Aneesha Bakharia retweeted
cool demo on how to use GEPA to take an off the shelf model like Jev (trained only on synthetic data) and tune it to your exact decision criteria simply by manipulating the prompt and labeling a few examples
you should follow Seth, he’s way ahead of the curve on AI functions
Jev is cool, but like any foundation model it needs to be calibrated to your decision criteria.
We launched jev-align: an open-source CLI to quickly teach Jev what good and bad looks like using GEPA.
Try it out! github.com/sutro-sh/jev-alig…
Aneesha Bakharia retweeted
We were working on the keynote today with @romainhuet and @sama and most of the fun was trying to figure out how to explain it all to you because there is so much good stuff in there that it's a bit ridiculous all in quick succession.
We'll have some things next week already to not keep you waiting so long, but very excited to show you all new things we've been working on and how it will all come together in the coming months.
Aneesha Bakharia retweeted
jev has sparked more internal demos and exploration of how we can use it in our product than any other model launch
its not even close
Aneesha Bakharia retweeted
imo jev has one massive implication for agents - sifting.
your agent can use jev to check if a file is worth reading, a tool is worth calling, a website is worth browsing..
github.com/kbhuw/jev-sift
Aneesha Bakharia retweeted
For IR folks: @xueguang_ma and I quickly tested Jev as a pointwise/pairwise/setwise/listwise reranker on the classic DL19 and DL20, reranking the top 100 BM25 results. It is pretty good and cheap.
Aneesha Bakharia retweeted
with jev, it's nice to see builder energy back on X, reminiscent of the early days of building with LLMs (back when gpt_index/llama_index/langchain first started)
before jev, most of the 2026 AI demo hype has revolved around the end-to-end capabilities of frontier models. The art of how to build something started taking a backseat to having agents automate as much work as possible.
jev is a lego block. it puts the joy of building back in the hands of humans. it forces you to think about how to build something as a system, instead of one-shotting a prompt through astra/fable.
My positivity for this magical moment in computing is not born out of blind optimism for a possible future, but from awe of the present potential. Everything we've ever wanted to do with software is now possible. We can make anything. We can fix everything.
Aneesha Bakharia retweeted
Introducing Jev Model Router for Claude Code
This Claude Code Mod lets you use Jev through its direct @typesafeai API or @vercel AI Gateway
With every request you send to Claude Code, Jev classifies the subagent model, main model (only at session start to avoid breaking the cache), and effort level
Install it with one command:
npx claude-code-templates@latest --mod productivity/jev-model-router
Full Mod details:
aitmpl.com/component/mod/pro…
Aneesha Bakharia retweeted
Opening access for developers to build Muse connectors. You bring the API -- Muse brings the agent, the browser, and the context of what the person actually wants. People reach your service just by asking for it, and their agent takes it from there.
New connectors are live today. Come build with us. muse.ai/platform
Aneesha Bakharia retweeted
AI attribution works better than you think. Shapley for inference, LoRA for post-training, and hierarchy for pre-training do a surprisingly good job.
some demos:
- Inference -> Shapley: shapley.attribution-based-co…
- Post-Training -> LoRA: github.com/iamtrask/abcgpt
- Pre-Training -> Hierarchy: andrewtrask.substack.com/p/b…
i recognize that you have to squint to see how hierarchy helps with pre-training attribution. TLDR: everyone is going to fine-tune an open source model with their own private data, and register that on routers. instead of AGI we're going to get a RGI... routed general intelligence... a general network of narrow intelligences. that's the indirect solution to most of the AI attribution problem... you'll get paid when people want to use your model in their swarm because of the unique things it knows.
Aneesha Bakharia retweeted
Hands down my favourite fun side project I've ever made. An ESP32 powered Mac 128k that fully works!
Aneesha Bakharia retweeted
After our viral demo comparing Jev to GLiNER for browser use, there was a lot of demand for the local open source browser use so here is the GitHub repo github.com/sahibzada-allahya…