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mmarvin

9 karma · joined June 20, 2014

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mmarvin··on Show HN: Twitch Roulette – Find live streamers who need views the most
why would you stream on Twitch then? just because it‘s „fun“? come on
mmarvin··on No Skill. No Taste
why?
mmarvin··on Show HN: Fine-tuned Qwen2.5-7B on 100 films for probabilistic story graphs
Awesome work. Would be cool if you could publish the list of movies that you chose for finetuning. Just out of curiosity.
mmarvin··on My first year in sales as technical founder
What’s your actual message while reaching out on LinkedIn? Do you send a note while sending a connect request?
mmarvin··on How Meta tries to win back teens for Instagram
https://archive.is/oHRTA
mmarvin··on LangPatrol: A static analyzer for LLM prompts that catches bugs before inference
and just fyi, we also run a hosted version that performs inference based validation and optimization, which the local SDK cannot do (for obvious reasons). The SDK is fully usable on its own, but the hosted service is there for teams who want deeper dynamic checks.
mmarvin··on LangPatrol: A static analyzer for LLM prompts that catches bugs before inference
We built a small SDK that lint prompts before they ever hit an LLM. In practice it behaves like ESLint for prompts. It runs locally, no external calls, and flags issues that usually waste tokens or produce inconsistent outputs: unresolved template variables, missing contextual references, contradictory instructions, schema contamination when you expect structured output, and prompts that risk overrunning model context.

It exposes a single function in code, CLI for CI is in the works. The analyzer is language agnostic and fast enough to sit in any prompt generation pipeline, we aim for <50ms. There is also a small devserver with a React UI for experimenting interactively.

The goal is to treat prompts as first class artifacts and catch structural defects early rather than debugging after the fact. Happy to answer questions about heuristics, false positives, or how we estimate token overage.

All of it is open source under MIT, and we plan to keep expanding the issue set. We are also exploring a complementary prompt optimization layer that builds on top of the static analysis described above.

Happy to discuss details or help anyone experiment with it.

mmarvin··on Building a Toast Component
Yes, exactly that’s the case.
mmarvin··on Collaboration sucks
It‘s basically just another way to describe the working doctrine Amazon follows since 30 years (and which Stripe has also mimicked according to Patrick). It bascially allows every individual to make decisions on their own (within their scope), without appproval. Except decisions that can not be undone easily, those still need „collaboration“.
mmarvin··on Sony PlayStation 2 fixing frenzy
You could also check out https://ongoldscar.com, sometimes there is a PS2 in the current drop (repaired, certified and with warranty)
mmarvin··on OpenAI wants ChatGPT to be a 'super assistant' for every part of your life
I think their form factor is going into the direction of the pendant of friends.com or Humane Pin (without a display). How they manage the feedback of the device interests me the most (if the device constantly talks back just via loudspeaker… that gets pretty annoying especially in public places).