Here is a real use case: you are are responsible for some alerting channel. You have datadog/ cloud logging/ github all connected. You see a bunch of alerts come through while you are out and about and you prompt CC to investigate - Claude triages and says “all of the sudden you are getting time outs from this bank API your company partners with, this started an hour ago. It’s happening on ~15% of requests”. So you ping the guy at your company who does vendor relationships and go back to your weekend.
This is a non hypothetical example. Obviously it would be better if your job had a real on call rotation and more robust alerting and you wouldn’t be getting slack alerts on the weekend… but I take the approach this job affords me a lot of nice flexibility so it’s ok
But yeah it’s kinda a zone where most weekends there’s no problems so it’s not a huge priority… until it is
I've been watching "How it's made" on Hulu to fall asleep at night.
I’m constantly surprised by how many things are made with human hands, despite the ability to automate.
- Fuzzing with the goal for it to apply domain-specific and source-informed knowledge to choose specific fuzzing approaches.
- More generally, any optimization problem that benefits from domain-specific or source informed knowledge.
- Running Microsoft's SkillOpt [0].
How is Claude monitoring them for hours? Claude runs out of context and extremely long sessions are prohibitively expensive even according to Anthropic (after they dispense with the marketing bullshit of long running tasks)?
Yes, surprisingly, this is something Google cannot do yet.
1/ Using GUI software. My agents are using headful Google Chrome and Figma. It helps a lot to have separate environment, which is not interfering my main machine.
2/ Running long processes (1h+), so I can leave main machine closed.
3/ Running intensive processes. I use Gemma, Whisper and Qwen, which could burn main machine CPU and resources.
I don't value my travel time at all, but it used to be wasted on travelling.
Which tests and optimizations do you propose to run after a night of supervised work when one of main things that all agents keep doing is "load all records from db , and filter them in memory"? It's now become so bad, I had to literally vibecode a separate linter for this. And that's just one of the problems.
So I dunno what to say, except it’s possible to write really solid code with LLMs.
but we do have sufficient AI to make a great product out of a great prompt.
garbage in -> garbage out hasn't gone anywhere.
so: much like to anyone that blindly complains that their compiler hates them : if you actually want help, provide information. If you just want to complain that the compiler is mean, scream at the sky.
plenty of people have figured out how to get this to work; more than enough to confirm that a straight <gambling-machine>/<hallucinatory-psychopath>/<random-number-generator> analogy is too simplistic to explain what we're working with.
You see, there's your problem right there. You're vibe coding, which by definition literally means you're unwilling to look at the generated code. That's not what successful ai assisted software developers are doing. YOU HAVE TO READ THE CODE. Refusing to do that means you're not a serious programmer, you're outsourcing your thought and design and implementation, trying to get something for nothing by taking the easy way out, and you're going to get terrible results no matter what prompts you "engineer". There ain't no such thing as a free lunch (yet).
And while we're at it, to elaborate on what serf said: people mindlessly parroting terms like "stochastic parrot" to criticize llms without having read the actual paper that coined the term and understanding what it really claimed and how other papers responded to it means you're just a human stochastic parrot no better than what you're criticizing -- at least the llm has read all those papers and understands what "stochastic parrot" actually means in context. Ask it, it will be glad to explain!
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Margaret Mitchell. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? (FAccT 2021)
(I wish I was joking)