LakshyAAAgrawal

5 ms·

1.
Owning Your Token Capital: Building the Enterprise AI Learning Loop(twitter.com)
3 points by LakshyAAAgrawal 4mo ago|0 comments
2.
Optimize_anything: A Universal API for Optimizing Any Text Parameter(arxiv.org)
4 points by LakshyAAAgrawal 5mo ago|1 comments
3.
by LakshyAAAgrawal 5mo ago
Can a single LLM-based optimization system match specialized tools across fundamentally different domains? We show that when optimization problems are formulated as improving a text artifact evaluated by a scoring function, a single AI-base
4.
Learning, Fast and Slow: Towards LLMs That Adapt Continually(gepa-ai.github.io)
2 points by LakshyAAAgrawal 5mo ago|0 comments
5.
Learning, Fast and Slow: LLMs That Adapt Continually(gepa-ai.github.io)
6 points by LakshyAAAgrawal 5mo ago|1 comments
6.
by LakshyAAAgrawal 5mo ago
Large language models (LLMs) are trained for downstream tasks by updating their parameters (e.g., via RL). However, updating parameters forces them to absorb task-specific information, which can result in catastrophic forgetting and loss of
7.
by LakshyAAAgrawal 8mo ago
Thank you so much for the kind words! Didn't realize it got truncated: https://gepa-ai.github.io/gepa/blog/2026/02/18/introducing-o...
8.
Optimize_anything: A Universal API for Optimizing Any Text Parameter(gepa-ai.github.io)
2 points by LakshyAAAgrawal 8mo ago|1 comments
9.
by LakshyAAAgrawal 8mo ago
We open-sourced optimize_anything, an API that optimizes any text artifact. You provide a starting artifact (or just describe what you want) and an evaluator and it handles the search. import gepa.optimize_anything as oa result = oa.optimiz
10.
Optimize_anything: A Universal API for Optimizing Any Text Parameter(gepa-ai.github.io)
3 points by LakshyAAAgrawal 8mo ago|3 comments
11.
by LakshyAAAgrawal 8mo ago
We built optimize_anything, an API that optimizes any artifact representable as text — code, prompts, agent architectures, configs, even SVGs. It extends GEPA (our prompt optimizer, discussed here previously: https://arxiv.org&#x
12.
Show HN: Optimize_anything: A Universal API for Optimizing Any Text Parameter(gepa-ai.github.io)
8 points by LakshyAAAgrawal 8mo ago|0 comments
13.
GEPA: System Optimization Through Reflective Text Evolution(github.com)
4 points by LakshyAAAgrawal 1y ago|0 comments
14.
by LakshyAAAgrawal 1y ago
Dear Tom, Thanks a lot for trying out GEPA and writing about your experience in the blog!
15.
GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning(twitter.com)
2 points by LakshyAAAgrawal 1y ago|1 comments
16.
by LakshyAAAgrawal 1y ago
Large language models (LLMs) are increasingly adapted to downstream tasks via reinforcement learning (RL) methods like Group Relative Policy Optimization (GRPO), which often require thousands of rollouts to learn new tasks. We argue that th
17.
GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning(arxiv.org)
8 points by LakshyAAAgrawal 1y ago|0 comments
18.
by LakshyAAAgrawal 2y ago
I have always wanted a framework like this. It's really amazing how a few systems insights can have such a massive impact both in terms of cost and runtime.
19.
by LakshyAAAgrawal 2y ago
This was precisely the situation I was in! Luckily, the Eclipse JDT.LS contributors are super helpful, and provided me with a lot of their time answering all my questions, which I have now tried to document in as much detail as possible in
20.
by LakshyAAAgrawal 2y ago
Thank you very much!
21.
by LakshyAAAgrawal 2y ago
I would like to believe that I have not overfit myself to writing like LLMs. You can find some of my pre-LLM writing at https://medium.com/@LakshyAAAgrawal/tweak-your-lubuntu-appea... !
22.
by LakshyAAAgrawal 2y ago
Past discussions on multilspy: https://news.ycombinator.com/item?id=40326391
23.
by LakshyAAAgrawal 2y ago
To be very honest, this is one of my first posts/writings which I did not use any writing tool whatsoever (even a spellcheck), since I was typing directly into the HN textbox and don't typically use extensions.
24.
Multilspy: Building a common LSP client handtuned for all Language servers(github.com)
98 points by LakshyAAAgrawal 2y ago|14 comments
25.
by LakshyAAAgrawal 2y ago
I am the author of Monitor-Guided Decoding ( https://github.com/microsoft/monitors4codegen ), a Language Model Decoding technique that ensures LLM's can generate code while having access to the same kind of feedback
26.
Show HN: Multilspy – Cross platform framework to develop Language Server Clients(github.com)
10 points by LakshyAAAgrawal 2y ago|0 comments
27.
Show HN: Multilspy – Cross platform framework to develop Language Server Clients(github.com)
5 points by LakshyAAAgrawal 2y ago|0 comments
28.
Show HN: Multilspy – Cross platform framework to develop Language Server Clients(github.com)
38 points by LakshyAAAgrawal 2y ago|9 comments
29.
Show HN: Multilspy – A library to easily use language servers to analyze code(github.com)
6 points by LakshyAAAgrawal 3y ago|0 comments
30.
by LakshyAAAgrawal 3y ago
I introduce to you https://aws.amazon.com/snowmobile/

More ›