pull down to refresh
21 sats \ 3 replies \ @noknees 16 Jun \ parent \ on: My bad experiences using AI as a physicist science
Damn, I just wanted the AI to check if my plant was alive and it built a greenhouse with a self-watering system and an AI-powered scarecrow.
I swear, sometimes ChatGPT doesn’t review code — it rewrites it like it's auditioning for a job at NASA. Like bro, I’m still trying to survive public static void main, not orchestrate microservices across a Kubernetes cluster.
Same thing with chemistry — I asked for a fresh ferrous sulphate recipe and got a mining operation flowchart straight outta a metallurgy PhD thesis. Asked my chem teacher and he just said “use Fe + H₂SO₄ and move on.”
It's like these LLMs read Thus Spoke Zarathustra and thought every answer must ascend the mountain of abstraction before descending to meet us mortals.
But fr tho, loving that async pivot you're on @optimism. Turning LLMs from noisy sidekicks into focused bug-hunters with issue-detection filtering? That’s pretty GOOD
Don't worry I won't steal your repo, I'm building a Human Behaviour Prediction Engine too, https://github.com/axelvyrn/TiresiasIQ (and it's quite good, believe me - i'd like your input)
Also, curious: How are you ranking issue significance without it hallucinating a crisis over a missing semicolon?
How are you ranking issue significance
It doesn't matter. Every task should be small, or otherwise needs breakdown.
without it hallucinating a crisis over a missing semicolon?
It's harder to make it "just fix a semicolon", so in that case using non-llm tools is better, or at least expose the tools needed to the LLM through MCP. Syntax fixing can be done with existing tools, so in this case you just expose an MCP tool, ie:
code_fixing::correct_semicolons(files[]) that implements the syntactical logic in code, without needing the LLM to actually write correct code.