Recent submissions

Comment · 27 days ago
They just merged my PR for the current tightest bound on the integer multiplication problem:

https://github.com/CrocSwap/integer-mult-bounds/pull/37#even...

Something interesting happened last night: Researchers started proposing tighter conditional bounds for integer multiplication, submitted by OpenAI yesterday, then a swarm of collaborators online pushed it even further. Its so interesting that human researchers provided the insights to push to the out-of distribution solutions that no one model reached alone.

I just submitted the current new best conditional bound, conditional κ = 4.123863984e-5, and I have a very useful system setup for auto-research that requires human intuition to reach out-of-distribution solutions.

I have a list of ideas to test so we may not be done yet :) Original paper from OpenAI: https://github.com/openai/math/blob/main/preprints/Integer-m...

Comment · 4 months ago
So hilarious I love this space.

We were working on this cache for years, but mostly in the last few months actually started using it to build 'impossible' products.

Itself, it has no moat so I open sourced it, but I'm not sure that really matters the market is huge.

Comment · 6 months ago
World's first software-only iphone automation(no hardware, jailbreak, or cloud)
Comment · 1 year ago
Introducing Iterative Code Generation: increase the success rate of your vibe coding!

One big problem with AI code generation today is it ‘throws the baby out with the bathwater’ and doesn’t work like software engineers actually write code.

Iterative Code Generation actually increases the success rate of AI code generation/Vibe coding, and mitigates the "generate and pray" problem.

## Why current AI code generation falls short:

Most LLM coding assistants today generate complete solutions in one pass and hope they work. But programming isn't a one-shot process — it's iterative debugging and refinement.

When code inevitably fails, users face a frustrating cycle of: 1. Getting an error message 2. Manually interpreting what went wrong 3. Asking the AI for a fix 4. Repeating until it works... or giving up

## The iterative execution approach:

Instead, this new approach writes code iteratively like real software engineers:

1. Generates a complete solution first 2. Visually executes it line-by-line (like a debugger) 3. Intelligently identifies real errors vs. expected partial execution artifacts 4. Automatically feeds errors back to the AI for targeted fixes 5. Shows the reasoning behind each fix

The results? More robust, reliable code solutions with dramatically fewer iterations.

## Why this matters:

*For developers/Vibe coders:* This approach mirrors how we actually write code — incrementally with constant feedback loops.

*For AI systems:* It creates a tighter execution/feedback loop for models to learn from their mistakes in context. Go from 1 bit resolution in data to N bit, which should also improve training data and code generation models themselves with more valuable data.

This plus the idea of direct 'latent space languages' should unlock previously impossible software across the solution space that we couldn't have imagined before.

Comment · 1 year ago
Tweet text: Introducing Automated AI Optimization We noticed AgentsBase.ai triggers organic growth in google search keywords and models, so we added automated tracking for the AI models. The world's first ahrefs for AI models that automates generating content? Every day it monitors your brand mentions across O1, perplexity, and Gemini so you can track how agents are growing your brand in the next generation of answers. This loop gives agents more signals to iterate better content. AgentsBase downloads and transcribes all of your videos extract+analyze unstructured demographic data no marketing team on the planet currently tracks, just like the models that will soon be in everyone's pocket for nearly free.

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