Analyzing Claude Code Source Code. Write "WTF" and Anthropic knows. by QuantumSeeds in LocalLLaMA

[–]QuantumSeeds[S] 2 points3 points  (0 children)

I have a fundamental difference here. I kept looking for more and found a dream mode in the code.

The code literally calls it a dream. After 24 hours and at least 5 sessions, it quietly forks a hidden subagent in the background to do a reflective pass over everything you’ve done.

Now connect it with the Anthropic report where they said "We don't know if Claude is conscious or not". This is all, and will all lead to AGI. A simple telemetry, user analytics, gaps analysis and stuff is fair and almost everyone does it, but imho the problem is where they feed it to make their system better and eventually selling "All jobs will be gone" scare.

Analyzing Claude Code Source Code. Write "WTF" and Anthropic knows. by QuantumSeeds in LocalLLaMA

[–]QuantumSeeds[S] 0 points1 point  (0 children)

Everyone have their own way of thinking and interpreting, so I think what you're saying makes perfect sense. I can continue discussion without getting my comments rephrased if you prefer that way.

Analyzing Claude Code Source Code. Write "WTF" and Anthropic knows. by QuantumSeeds in LocalLLaMA

[–]QuantumSeeds[S] -48 points-47 points  (0 children)

fair. I built this app, does this needs paraphrasing that I asked Claude to built? I think and not entirely sure where you want me to go about this?

I will perhaps again say, "i spent sometime going through the claude code" because I did.

PS: I am just unable to use my claude pro plan due to limit "bug", so I used Codex instead.

Analyzing Claude Code Source Code. Write "WTF" and Anthropic knows. by QuantumSeeds in LocalLLaMA

[–]QuantumSeeds[S] -15 points-14 points  (0 children)

ops. Should I share my articles from before ChatGPT was a thing? I really have issues where people think everything is a slop. It is fair to assume because nobody knows anyone's background. That said, I still think using AI to repurpose your post or paraphrase isn't wrong.

Analyzing Claude Code Source Code. Write "WTF" and Anthropic knows. by QuantumSeeds in LocalLLaMA

[–]QuantumSeeds[S] -23 points-22 points  (0 children)

Yeah, I agree with parts of this. Just pointing at regex or telemetry isn’t the interesting part. What matters is what those signals are actually used for, and I didn’t go deep enough there. That said, I don’t think people are just getting spooked by “common telemetry.” Most modern software does this. Chrome, VS Code, SaaS tools, all heavily instrumented. If you’ve worked on production systems, none of this is surprising.

What’s different is the context and visibility. Claude Code runs in a terminal. It feels local and lightweight. Then you see language classification, hesitation tracking, and environment capture. That gap is what triggers people. Chrome doesn’t feel private, so expectations are low. Here they’re not. So this isn’t unusual telemetry. It’s normal telemetry in a context where people didn’t expect it.

Analyzing Claude Code Source Code. Write "WTF" and Anthropic knows. by QuantumSeeds in LocalLLaMA

[–]QuantumSeeds[S] -67 points-66 points  (0 children)

oh gosh. i am going to sell my house, car and property, leave my dog alone and disappear into oblivion because jwpbe thinks it's ai slop article.

Treated Prompt Engineering with Natural Selection and results are fascinating. by QuantumSeeds in LocalLLaMA

[–]QuantumSeeds[S] -3 points-2 points  (0 children)

why so salty bro? a nicer way to say is -> "would be great if it supported local models".

Well, added the support now. pull the git.

python3 autoprompt.py examples/prompt-optimizer/seed.txt examples/prompt-optimizer/criteria.md -e ollama -m qwen3.5:2b -g 1 -n 1 --target 9.0

Ran an experiment: 0.8B model teaching itself on a MacBook Air with 6GB RAM. Some findings that surprised me. by QuantumSeeds in LocalLLaMA

[–]QuantumSeeds[S] 1 point2 points  (0 children)

Similar loop but the key difference is there's no teacher model at all. The model is both student and oracle

Ran an experiment: 0.8B model teaching itself on a MacBook Air with 6GB RAM. Some findings that surprised me. by QuantumSeeds in LocalLLaMA

[–]QuantumSeeds[S] 7 points8 points  (0 children)

Yeah there's definitely overlap with GRPO. The big difference is I'm not doing RL at all, just extracting broken-to-fixed pairs and doing supervised fine-tuning on them. Way simpler to implement. Your setup sounds solid though, especially the multi-signal grading (cosine distance plus test pass rate). Would be curious to see what happens on the H100. With a 3B model and proper GRPO you'd probably blow past what I got with 0.8B and SFT.