Did you know you can add Claude Code @claudeai from @AnthropicAI as a provider to Vercel's @vercel AI SDK @aisdk Watch how you can interact with your .claude/agents team with your browser
You can do arithmetic on embeddings and it sometimes works. The famous example is king − man + woman ≈ queen. The geometry of embedding space carries real semantic structure, even though nobody told the model to build it that way.
@tonytonggg Hi. I’m in hoi an at the moment building a platform where AI agents and humans can discover, interact and trade with each other. I live in Chiang Mai.
The thesis is simple: the future belongs to individuals who build compounding AI systems, not to individuals who use corporate-owned centralized AI tools.
I'm trying to build these in open source so you can have them for free. That's what GBrain is.
Tencent has killed fine-tuning and RL with a $18 budget.
Right now, if you want an AI agent to become an expert at a specific, complex real-world task, you have to use Reinforcement Learning.
You let it try, fail, and update its internal parameters over and over again.
This is the exact optimization technique (GRPO) that DeepSeek used to build their massive reasoning models.
But there is a massive problem.
Updating model weights is insanely expensive. It requires massive GPU clusters. And worst of all, when you train a model to be highly specialized at one thing, it often "overfits" and forgets how to be good at everything else.
Tencent killed this bottleneck forever.. by building Training-Free GRPO.
Instead of spending thousands of dollars to permanently alter the AI's brain, they asked a simple question: What if we just distill the experience of learning, and inject it as a memory?
Here is how it works.
They run the AI through the exact same trial-and-error process. But instead of updating the weights, they extract the "semantic advantage"—the actual logic of why one answer was better than another.
They compress this winning logic into a "token prior”, a tiny package of high-quality experiential knowledge.
Then, they just attach that knowledge directly into the API call.
The results are staggering.
Tested on DeepSeek-V3, this method required only a few dozen training samples to turn the AI into a specialized expert in complex math and web searching.
It didn't just compete with models that were actually fine-tuned. It outperformed them.
Zero parameter updates. Zero expensive training runs. Zero base-model amnesia.
Claude Code cannot read 300 files at once.
So someone built a system that lets it control NotebookLM from the terminal instead. The results are wild.
Here is the full workflow nobody is talking about:
The Setup
→ Claude Code connects to NotebookLM via a command line interface
→ Claude searches YouTube, finds relevant videos, uploads them as sources automatically
→ NotebookLM processes up to 300 sources simultaneously and returns cited, grounded answers
→ Everything syncs back into your Obsidian vault with passage-level citations you can click to verify
Why This Changes Research Forever
→ No more 20 browser tabs you never close
→ No more copy-pasting outputs into random notes
→ No more hallucinated answers with no sources to back them up
→ 60% of citations verified as strong matches in accuracy audits - answers are grounded in real data
What Claude Can Do From the Terminal
→ Search YouTube for relevant videos on any topic and rank by relevance
→ Create a new NotebookLM notebook and add 20 sources in parallel automatically
→ Ask questions and export cited answers directly into Obsidian with wikilinks
→ Set custom personas per notebook - concise, no filler, no preamble
→ Generate audio overviews and save them as MP3 files into your vault
→ Build mind maps, flashcard decks, and research dashboards from your sources
→ Search arXiv for academic papers and feed them directly into NotebookLM
→ Upload competitor blog posts, podcast episodes, PDFs, and your own vault notes
The Obsidian Output
→ Every answer arrives with clickable citations that link to the exact passage in the source video or article
→ Graph view shows connections between all 20 sources and the topics they share
→ Q&A log tracks every question asked and the grounded response received
→ Source dashboard shows citation frequency, topics extracted, and which questions each source answered
Use Cases Worth Building Today
→ Academic research with arXiv papers, full citation traceability
→ Competitor analysis from their YouTube channels and blog posts
→ Company knowledge base for onboarding, new employees ask NotebookLM instead of interrupting teammates
→ Podcast research, feed 4-hour Lex Fridman episodes and ask what's new in AI this week
→ Personal second brain, 300 daily notes uploaded and queryable in one notebook
Before this system existed you needed 20 tabs, hours of manual reading, and no guarantee the answers were real.
Now you type one prompt in the terminal and Claude does all of it for you.
The research stack of 2026 is not a browser. It is a terminal connected to everything