Viewing profile — AbhinavX

Not surprised. On many benchmarks (i.e tau), we have seen the same thing. Probably lots of training on every publicly available benchmark

>An interesting observation we made was that even though the 3 optimizers were given tens of thousands of dollars in compute, they found the best solution very early on Honestly, t…

Awesome--let us know what you think!

we're working on that right now, we'd love to hear your opinions(if you're interested you can send us an email at team@lucidic.ai).

LLM as a judge for agent usually has context overload and even if you have a really good prompt for your evaluation, LLMs hallucinate because there is just too much information to …

We're new to the open source scene so we don't have anything published yet but plan to in the future. A basic overview of the way we do clustering is we condense stateful informati…

Colloquially, AI agents are just while loops with LLM calls and tool calls. More specifically, what distinguishes an agent from LLM pipelines is that its next step is determined dy…

Langfuse and Helicone work well for traditional LLM operations, but AI agents are different. We discovered that AI agents require fundamentally different tooling, here are some exa…

the way it is integrated (its explained more in the docs) is by installing the python/typescript sdk and writing "lai.init()" at the top of your code. Then we capture all LLM calls…

Hi HN, we’re Abhinav, Andy, and Jeremy, and we’re building Lucidic AI ( https://dashboard.lucidic.ai ), an AI agent interpretability tool to help observe/debug AI agents. Here is a…