AbhinavX

6 ms·

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by AbhinavX 1mo ago
Not surprised. On many benchmarks (i.e tau), we have seen the same thing. Probably lots of training on every publicly available benchmark
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by AbhinavX 1mo ago
>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, this confirms some bias I already have from coding — mod
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by AbhinavX 1y ago
Awesome--let us know what you think!
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by AbhinavX 1y ago
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).
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by AbhinavX 1y ago
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 ingest. So we created an agentic pipeline to basically do ev
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by AbhinavX 1y ago
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 information -> create a state embedding -> create tag
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by AbhinavX 1y ago
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 dynamically (based on the output of the previous one) so the e
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by AbhinavX 1y ago
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 examples. First, while LLMs simply respond to prompts, agents o
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by AbhinavX 1y ago
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 and tools with integrated providers (similar
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Launch HN: Lucidic (YC W25) – Debug, test, and evaluate AI agents in production
116 points by AbhinavX 1y ago|39 comments