Readit NewsYou could work on a totally new game, but, I think companies are looking to cut costs by reusing content.
Code style is a Pareto-optimal problem space: what one person finds readable may look like complete chaos to someone else. There’s no objective truth, and that’s why I believe that in a project involving multiple people, spending time on this is largely a waste of time.
But if I give it a code example that was written by humans and ask it to explain the code, it gives pretty good explanations.
It's also good for questions like "I'm trying to accomplish complicated task XYZ that I've never done before, what should I do?", and it will give code samples that get me on the right path.
Or it'll help me debug my code and point out things I've missed.
It's like a pair programmer that's good for bouncing ideas, but I wouldn't trust it to write code unsupervised.
> […]
> Or it'll help me debug my code and point out things I've missed.
I made both of these statements myself and later wondered why I had never connected them.
In the beginning, I used AI a lot to help me debug my own code, mostly through ChatGPT.
Later, I started using an AI agent that generated code, but it often didn’t work perfectly. I spent a lot of time trying to steer the AI to improve the output. Sometimes it worked, but other times it was just frustrating and felt like a waste of time.
At some point, I combined these two approaches: I cleared the context, told the AI that there was some code that wasn’t working as expected, and asked it to perform a root cause analysis, starting by trying to reproduce the issue. I was very surprised by how much better the agent became at finding and eventually fixing problems when I framed the task from this different perspective.
Now, I have commands in Claude Code for this and other due diligence tasks, and it’s been a long time since I last felt like I was wasting my time.
I think this sums it up well. Working with LLMs is already confusing and unpredictable. Adding a convoluted RAG pipeline (unless it is truly necessary because of context size limitations) only makes things worse compared to simply emulating what we would normally do.
Deleted Comment
It makes me sad to see LLM slop on the front page.
This is 100% within the responsibility of the LLM vendors.
Beyond the LLM, there is a ton of engineering work that can be put in place to detect this, monitor it, escalate, alert impacted parties, and thwart it. This is literally the impetus for funding an entire team or org within both of these companies to do this work.
Cloud LLMs are not interpreters. They are network connected and can be monitored in real time.
As I see it, this prompt is essentially an "executable script". In your view, should all prompts be analyzed and possibly blocked based on heuristics that flag malicious intent? Should we also prevent the LLM from simply writing an equivalent script in a programming language, even if it is never executed? How is this different from requiring all programming languages (at least from big companies with big engineering teams) to include such security checks before code is compiled?
With a subsidized cost of $200/month for OpenAI it would be cheaper to hirer a part-time minimum wage worker than it would be to contract with OpenAI.
And that is the rosiest estimate OpenAI has.
Most of the time, I end up putting in more work than I get out of it. Onboarding, reviewing, and mentoring all take significant time.
Even with the best students we had, paying around 400 euros a month, I would not say that I saved five hours a week.
And even when they reach the point of being truly productive, they are usually already finished with their studies. If we then hire them full-time, they cost significantly more.