LLMs also deserve fast RL!

Nous Research launches Atropos - an LLM RL learning framework for collecting and evaluating trajectories.

Codeflash optimizes a crucial part it by 17x speeding up LLM RL. Such a classic algorithmic optimization.

Here's how Langflow, a leading AI-agent framework, achieved a 300× speedup in finding the last node in a graph.

The optimization, automatically discovered by Codeflash, eliminated an unnecessary loop and removed duplicate comparisons, making this operation blazing fast.

We’ve all been there…

I was so sure that I could write better code than what I saw on the screen. Four hours of work later, the code ran slower than it had that morning 😂

I found a cracked dev today with a github history like this.

This dev setup a cron to auto-commit to update the timestamp on a file 88 times a day.

Anyone looking to hire a 100x dev? 😆

Ever heard someone say “I’ll know when I know”??? Turns out it applies to software development as well.

After solving a performance bug, I always find myself wondering why I wrote such slow code in the first place.

Usually, it’s because…

My favorite tool for Profiling in Python - PyInstrument.

PyInstrument is a powerful statistical profiler that periodically interrupts and samples the call stack during execution. Pyinstrument minimizing performance overhead and seamlessly integrates into complex projects.

Autonomous AI Agents for code optimization have arrived!

Codeflash ranks as the 7th leading contributor to Pydantic over the past year, a library that sees 300 million downloads every month.

The potential for efficiency is boundless in every project.

An engineer recently told me that his team spends 1.5 days and $6,000 on a single training run. When workloads take multiple days, performance optimization shifts from nice-to-have to mission-critical. This is exactly why we're building Codeflash—to eliminate these painful, time-consuming processes.

You can right-size your infrastructure and negotiate better rates but at the end of the day, reducing cloud costs requires having highly efficient underlying code.

I’ve spoken to a many engineering leaders - after security, code efficiency is the #1 issue that keeps engineering teams up at night.

After numerous recent conversations with engineering leaders, it's clearer than ever to me that when it comes to software development, we're caught in a reactive app performance management cycle. Teams prioritize rapid feature shipping, inadvertently accumulating performance debt with each release.