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A LOT! So I was making this somewhat basic java springboot app and I told ChatGPT to review it.
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
import org.springframework.stereotype.Service;

@App
public class Base {
    public static void main(String[] args) {
        SpringApplication.run(Base.class, args);
    }
}

// Controller Layer
@RestController
class GreetingController {

    private final GreetingService greetingService;

    // Constructor-based Dependency Injection
    public GreetingController(GreetingService greetingService) {
        this.greetingService = greetingService;
    }

    @GetMapping("/greet")
    public String greet(@RequestParam(value = "name", defaultValue = "World") String name) {
        return greetingService.getGreetingMessage(name);
    }
}

// Service Layer
@Service
class GreetingService {
    public String getGreetingMessage(String name) {
        return "Hello, " + name + "! Welcome to the BPE Window";
    }
}
This app is just supposed to send a Hello message as you can see the GreetingService class. and when ChatGPT gave the output, it was a 200+ lines of code, wrapped in error handling blocks, 20+ variables (whereas mine has only 1) and things I didn't even learn, just to make it "ERROR FREE" Like wtf man! I asked you check whether it works or not and you just gave me a whole thesis on it.
Then again one day, I asked him another science question, and it was pretty simple, how to prepare fresh ferrous sulphate because apparantly I could find how to make ferrous sulphate but not "fresh" on the internet. And he replied with a Flowchart of how to create ferrous sulphate through pyrites. So I straightaway asked my chem teacher and he said that it's a reaction of iron and sulphuric acid filtered later and rebuked for not studying basics thoroughly. But it wasnt MY fault! I WAS MISLEAD!
I am working on a paper for Science, which, by definition, goes beyond the state-of-the-art
Can you brief about it? I might understand something :) Or is it top-secret?
Can you brief about it? I might understand something :) Or is it top-secret?
Will submit it to the editor this week or next week, hopefully. I can send you the paper once it's published. For now, I prefer not doxxing myself too much by being detailed about the field that I work in. Even though some people here already more than what is good about me to remain anon~~
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Okayy!
Even though some people here already more than what is good about me to remain anon~~
lol
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Currently I'm pivoting my setup
from:
Synchronous: letting the LLM run unstructured with different models in a pipeline
to:
Asynchronous:
  1. LLM generates code or human writes it - doesn't matter - and uploads to repo
  2. Issue detection:
    • linting logs one issue per error found
    • If none found, LLM can analyze and create issue for the most significant issue - I specifically make the prompt with instruct repeatedly to only report the most significant issue. Works ok with LRM
    • Users can of course add issues too, LLM analyzes if its a one-shot or if it needs breakdown
  3. Coding LLM can ingest issue and fix it with a pull req
  4. Pull req can get reviewed by LRM or human
  5. Human merges
Everything that can be done with code, like linting, does not use LLMs.
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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.
“He who climbs upon the highest mountains laughs at all tragedies, real or imagined." — Nietzsche (Clearly what GPT thinks before it answers a 4-mark question.)
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?
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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.
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like using standard --fix to lint .js files?
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10 sats \ 0 replies \ @optimism 10h
exactly!
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