❌ Operator gives up after 4 minutes.
✔️ Proxy completes it twice before Operator finishes.
✔️ Not only that, Proxy also delivers complete results.
3/
Task 2: Get the latest basketball news.
❌ Operator gets stuck on a CAPTCHA and needs human help.
✔️ Proxy bypasses it instantly with no manual input.
❌ Operator provides just a link with minimal info.
✔️ Proxy delivers full details in under a minute.
4/
Task 3: Find top-rated @Tripadvisor stays.
❌ Operator takes over 2 minutes and delivers less information.
✔️ Proxy finds 5 top-rated stays in 56 seconds with prices, ratings, and reviews.
5/
Task 4: Get the latest US economy news.
❌ Operator is slow and less informative.
✔️ Proxy provides a detailed, high-quality summary faster.
6/
What’s more, Proxy is ranked #1 globally via the WebVoyager benchmark which assesses agentic capabilities across 600 web based tasks.
7/
.... and when it comes to cost, it’s not even a question.
→ Operator costs $200.
→ Proxy is Free, with a $20/month Pro option.
→ Schedule automations to run on repeat - your own AI agent on autopilot. → Instantly share automations on X/Twitter so anyone can run them... with one click! 🤯
9/
That’s a wrap!
Proxy beats Operator where it matters most: speed, autonomy, features, and cost.
🚨 Karpathy’s new set-up is the ultimate self-improving second brain, and it takes zero manual editing 🤯
It acts as a living AI knowledge base that actually heals itself.
Let me break it down.
Instead of relying on complex RAG, the LLM pulls raw research directly into an @Obsidian Markdown wiki. It completely takes over:
✦ Index creation
✦ System linting
✦ Native Q&A routing
The core process is beautifully simple:
→ You dump raw sources into a folder
→ The LLM auto-compiles an indexed .md wiki
→ You ask complex questions
→ It generates outputs (Marp slides, matplotlib plots) and files them back in
The big-picture implication of this is just wild.
When agents maintain their own memory layer, they don’t need massive, expensive context limits.
They really just need two things:
→ Clean file organization
→ The ability to query their own indexes
Forget stuffing everything into one giant prompt.
This approach is way cheaper, highly scalable... and 100% inspectable!
Wow. Insanely fast turnaround from @himanshustwts!
A full breakdown of @karpathy’s self-improving wiki framework,
walking through every stage from ingestion to what comes next 👀
@himanshustwts @karpathy Omar took a v. similar approach with @Obsidian
THIS is the wildest open-source project I’ve seen this month.
We were all hyped about @karpathy's autoresearch project automating the experiment loop a few weeks ago.
(ICYMI → github.com/karpathy/autor…)
But a bunch of folks just took it ten steps further and automated the entire scientific method end-to-end.
It's called AutoResearchClaw, and it's fully open-source.
You pass it a single CLI command with a raw idea, and it completely takes over 🤯
The 23-stage loop they designed is insane:
✦ First, it handles the literature review.
- It searches arXiv and Semantic Scholar for real papers
- Cross-references them against DataCite and CrossRef.
- No fake papers make it through.
✦ Second, it runs the sandbox.
- It generates the code from scratch.
- If the code breaks, it self-heals.
- You don't have to step in.
✦ Finally, it writes the paper.
- It structures 5,000+ words into Introduction, Related Work, Method, and Experiments.
- Formats the math, generates the comparison charts,
- Then wraps the whole thing in official ICML or ICLR LaTeX templates.
You can set it to pause for human approval, or you can just pass the --auto-approve flag and walk away.
What it spits out at the end:
→ Full academic paper draft
→ Conference-grade .tex files
→ Verified, hallucination-free citations
→ All experiment scripts and sandbox results
This is what autonomous AI agents actually look like in 2026.