Jitesh Nayak retweeted
We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math.
It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training.
We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵
Blog: rikiyat.github.io/blog/weigh…
Jitesh Nayak retweeted
Weekend hack: HackerTube. I was annoyed with YouTube's recommendations so I had Claude filter down 43M comments over 20 years of HN to 6,454 videos that keep being recommended. *Significantly* better viewing experience, ~2 hours with Opus 5.5:
hackertube.org/
- Excellent recs without any logins (state is kept in local browser session)
- Thumbnail is a representative frame from middle of the video instead of the super duper A/B test hyper-optimized user uploads
- Underlying data from the comment analysis as one zip file, point your Claude at it and ask for your own version
- HackerTube Longs ✊
A bit more on the process:
I use Claude Code (desktop app for Mac), Opus 5.5 on Medium effort as my default. I only talk to Claude and have it do everything, the only "manual work" I helped with in tandem was in the browser (reminiscent of MenuGen): 1) I had to create some kind of API key from Google Cloud UI to query YouTube for video metadata, 2) I booted up a VPS box for serving and 3) I bought the domain name. Once the box was up I gave Claude the IP and told it update, secure, harden, deploy and optimize the code, and it did the whole thing.
Many awesome projects that would have normally taken weeks/months even can now be few-shotted for breakfast. What else are we not thinking of.
Jitesh Nayak retweeted
In my view, the right response by the mathematical community to OpenAI’s papers is to say:
“We’re not accepting these papers as finished work. It’s not our job to sit down and read your model-generated proofs.
It is your job to present your work through presentations and convince people of the correctness of your work. It’s always the case that whoever makes a claim needs to provide sufficient explanation and evidence to make independent verification feasible.
Until that moment, you just have a claim.”
There’s a difference between a proof being mathematically correct and a proof being accepted as correct by the mathematical community. Mathematical truth doesn't depend on consensus, but scientific recognition requires scrutiny, communication, and reproducibility.
They're still the arbiters of whether anyone's claims, including OpenAI's, are accepted as valid, and they should leverage that to the maximum extent possible.
Jitesh Nayak retweeted
Terence Tao made a very powerful point:
“In most cases in pure mathematics, the problems are posed not because we desperately want the solution to these problems in and of themselves, but because we have seen from past experience that human-directed efforts to solve these problems tend to spur further development of the field through the efforts to solve such problems, and then to digest any partial or complete solutions that emerge for further insights.
Prematurely solving the problem by purely AI-powered methods—particularly without full transparency into the solution process—can contaminate this process to the point where it actually becomes a net negative for the progress of mathematics as a whole.”
I’ve been following Terence Tao for like 20 years, and I have always been impressed by the clarity of his thinking.
This clarity stands out even more in this difficult moment, as AI labs turn mathematics into a battleground in their race for supremacy.
In this race, they risk damaging one of humanity’s highest intellectual endeavours.
Jitesh Nayak retweeted
I've got an agent in a loop optimizing a renderer with the goal to minimize frame times (and tests to measure). It got times down from 88ms to 2ms and allocations down from ~150K to 500. Sounds good, right? Wrong. This is exactly why agent psychosis is a big fucking problem.
As an experiment, I rewrote the Ghostty core render state in Go, with access to identically laid out data structures as Ghostty and the exact same validation tests. I made a purposely naive renderer (simple, correct, but slow). 88ms per frame with 150,000 allocations (horrendous, lol)!
I then kickstarted a Ralph loop to bring the frame times down. I told it it can't modify input data structures or the public API or tests (they're correct), but it can do anything else it wants. It got to work.
It has worked for about 4 hours. I've spent around $350 on this experiment so far. The results?
88ms => 1.5ms
150K allocs => ~500 allocs
Incredible right? Nope.
My hand-written renderer I ported has frame times (same benchmark) of ~20us (0.020ms) and 0 allocations in the update path.
This is the problem with psychosis and lacking systems understanding. If you don't understand the system, you're going to accept that this is an incredible result. If you understand the system, you'll see better solutions immediately and can do roughly 75x better on throughput.
The people who blindly trust agent output are in the former camp. They're sheeple, overdrinking from a fountain of mediocrity.
Standard disclaimer: I use AI all the time. I like AI. The point I'm making is to not blindly accept results. Think. Analyze. Learn.
Jitesh Nayak retweeted
Three minutes of me driving around the GTs, passing P2s, spinning @George_Kurtz, and going three wide at ~300 km/h in turn 1 at the Daytona Rolex 24 with the Shopify #11 LMP2. Pure flow at the edge of adhesion!
Jitesh Nayak retweeted
We are a Small Research Lab based out of india
and we just dropped a one of a kind SoTA multimodal multilingual document retrieval model.
can embed and query documents across 22 langauges :
English, Spanish, French, German, Italian, Hindi, Marathi, Sanskrit, Kannada, Telugu, Tamil, Malayalam, Chinese, Japanese, Korean, Arabic, Bengali, Gujarati, Odia, Punjabi, Russian, and Thai.
It achieves state of the art performance across cross lingual and cross modal tasks.
You can now query a corpus of Japanese documents in Hindi or any of the other language.
check it out 👇
Jitesh Nayak retweeted
Sorry you don't like our codec implementations in VLC. Looking forward to seeing yours.
This quoted post is unavailable.
Jitesh Nayak retweeted
I interned at an IIT and built the entire codebase for a research project. Just saw the paper published in a journal, with zero mention of me as an author or contributor.
I did the work. They took the credit. This is beyond unfair.
:D
Jitesh Nayak retweeted
Meet the all-new Bulbul 🚀
Natural, familiar speech in 11 Indian languages, with authentic accents that sound just like India.
▪️Not robotic. Not rehearsed. Just real.
▪️Lightning-fast performance
▪️Custom voices for your brand
From lower latency and India-first pricing to wider language, Bulbul sets a new benchmark for Speech AI in India.
If you’re building for India, say it with Bulbul!
Watch it in action ↓
Jitesh Nayak retweeted
BREAKING: China made 5nm chips without EUV.
Let that sink in.
No ASML.
No Nikon.
Just brute-force DUV, clever engineering, and sheer industrial will.
Moore’s Law didn’t die.
It moved to Shanghai.
Jitesh Nayak retweeted
Hey everyone! I’m actively looking for AI/ML Engineer intern roles. Passionate about ML, DL, and building impactful AI solutions. If you know of any opportunities or connections, let’s chat!
Blogs: dev.to/prathameshdevadiga
Github: github.com/devadigapratham/
Jitesh Nayak retweeted
I'm going to call it right now. A lot of stuff is going to break on this mission.
By design.
As part of the plan.
Don't get upset. I'm not saying SpaceX plans to fail. I'm pointing out that SpaceX has taken an ultraimportant principle from software engineering, and realized it applies to all engineering.
Feedback beats planning.
And that, you see, is why SpaceX doesn't do things the NASA way. The NASA way was to gold-plate everything, plan and test and plan and test, and generate mountains of paper detailing every contingency, with every scenario prepared for.
SpaceX just shrugs, says "it's unmanned", and sends it.
Half the time it blows up. That's the whole point. They don't actually want it to blow up, of course, but they're anticipating that it might.
That possibility is part of the plan. Because one rocket blowing up, or crashing, in an actual end-to-end test, beats many, many man-years of planning and plotting.
The key realization here is that knowledge only comes from empirical observation. Everything else is just speculative.
The sooner you get into a feedback loop, and the faster you run it, the more iterations you can do in less time. This means, while others are planning and speculating, you actually learn something.
Relevant data is the most precious thing in the universe. And it's worth blowing up any number of rockets to get it.
Because rockets are just stuff. They're just made of stuff. And you can always get more stuff.
You can never get more time.
So expect to see a lot of things go wrong on this, and other SpaceX missions. Anticipate it. Accept it when it happens. Doesn't mean the dream of the stars is dead.
It just means we're doing it cowboy style.
This is a valuable lesson for our own lives. If there's something you want to do, something you want to try, some goal you have, it's easy to dip a toe in the water, test the temperature, and plan. A lot.
Planning makes us feel good if we're afraid. Because it provides us with the illusion of security. Never mind that we don't know which scenarios are actually going to happen, never mind that we're planning for the wrong thing, planning makes us feel safe. And if we're nervous, we can plan forever.
But the difference between the expert and the novice isn't theory or intelligence or plans. It's relevant domain knowledge. Gathered from empirical observation.
So the trick is to get into that feedback loop as soon as possible, and run it as fast as possible. Give yourself the most possible opportunities to learn, per unit time.
We only learn while we are moving.
