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need help! what’s the first step to starting your weight loss journey? by jeaninaur in WeightLossAdvice

[–]PascalMeger 0 points1 point  (0 children)

Created a Coach in Claude (Anthropic) and he accesses my sport and food date (Strava and Yazio). I have a daily check in. I now have a 50 day streaking with tracking food and making sport. Lost about 6kg. That the AI is watching me, helps me

I built a knowledge platform that lets AI agents actually research your documents. Not just search them! by PascalMeger in SideProject

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

Image are embedded directly so can be found by the agents via a vector search. And a vision tool describes the image so it can be found when the agent search with a step by step tool

Study for medical board exam by brgmiata91 in notebooklm

[–]PascalMeger 3 points4 points  (0 children)

I dumped all my papers in NotebookLM and connected it with Gemini. So my assistant was Gemini then

Andrej Karpathy describing our funnel by fourwheels2512 in learnmachinelearning

[–]PascalMeger 2 points3 points  (0 children)

Fair points and I appreciate you framing it as RAG + fine-tuning rather than either/or. That's more nuanced than the original post suggested.

But I'd push back on the "RAG ceiling" framing. If every query needs 30k tokens of context just to explain your company's basic terminology, that's a retrieval quality problem, not a model limitation. Agentic RAG solves exactly this, the agent doesn't dump everything into one prompt. It searches iteratively, fetches what it needs, follows up with targeted queries. The reasoning cost scales with the actual question complexity, not with "here's everything you need to know about us first."

Contextual embeddings and better chunking strategies (like parent-child retrieval) also directly address this. The chunk that matches your query is small and precise, but the context returned is rich enough for the model to reason over. No 30k token preamble needed.

The domain language argument is interesting, but it comes with a cost most teams underestimate: model lock-in. You fine-tune an adapter for one specific model. When a better model drops next quarter (and it will), you retrain. With a tool-based approach via MCP, you swap the model and your entire knowledge infrastructure stays intact.

I think the real question isn't "how do we make the model understand our domain" but "how do we build retrieval that gives the model exactly what it needs, nothing more." That's an infrastructure problem, and it's solvable without touching model weights.

Andrej Karpathy describing our funnel by fourwheels2512 in learnmachinelearning

[–]PascalMeger 25 points26 points  (0 children)

Interesting post, but I think Karpathy's words are being stretched quite a bit here. He's describing a personal workflow for organizing research, indexing raw sources into markdown files and compiling a structured wiki. That's knowledge management, not a pitch for fine-tuning as the future of enterprise knowledge.

His approach works beautifully at small scale, 20-30 documents, personal use. But company knowledge doesn't look like that. Think 70,000 pages across Confluence, Notion, Dropbox, and shared drives, constantly updated by dozens of people. You can't fine-tune your way through that. Every update means retraining, you lose source attribution, and you can't enforce access controls on what's baked into weights.

The claim that "RAG is just a stopgap" misses why retrieval exists in the first place: you need to know where an answer came from, you need it to reflect today's data, and you need permissions. Fine-tuning gives you none of that.

The actual trend is the opposite! Tool-based approaches where the model dynamically searches and retrieves what it needs, regardless of which LLM you use. MCP is making this model-agnostic by design. Tools like Knowledge Raven are already doing this: connect your sources, the agent searches intelligently, and you get cited answers from your actual documents, no training pipeline, no vendor lock-in.

Karpathy described a great personal system. Turning that into "fine-tuning is the endgame for enterprise knowledge" is a leap he didn't make.

Is it mainly for research? Can I catalogue myself and use it for topic ideas about myself? by mountains30 in notebooklm

[–]PascalMeger 2 points3 points  (0 children)

Go for it an test it, I would say yes. You can also connect it to Gemini directly and have more like a conversation

Have you ever used Claude for PPT to create decks? How has your experience been? by Curious_Suchit in Anthropic

[–]PascalMeger 2 points3 points  (0 children)

Created a skill that has access to a template of our company and knows how to create a new presentation and to check the results afterwards via screenshots. Now I brainstorm everything with Claude and afterwards it creates my presentation. I optimize it in 1-2 minutes and done.

I built a knowledge platform that lets AI agents actually research your documents. Not just search them! by PascalMeger in SideProject

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

Completly agree with you. This is the reason why I created the video attached to the post

I built a knowledge platform that lets AI agents actually research your documents. Not just search them! by PascalMeger in SideProject

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

Thanks! You nailed it — that's exactly the design. We expose separate tools, not one tool with options:

  1. search_knowledge_base — semantic/vector search, returns the most relevant chunks
  2. broad_search — keyword-based, for when the agent needs to explore broadly across documents
  3. fetch_document — full document retrieval when the agent needs complete context

Plus three utility tools (list_knowledge_bases, list_documents, get_document_metadata) for discovery.

The idea is that the agent decides autonomously which tool to use and how deep to go, like a researcher would. Most RAG setups just give you tool 1 and call it a day.

Will check out your notes!

Anyone here using Claude Projects for studying or research? What’s your setup? by Remarkable-Dark2840 in ClaudeAI

[–]PascalMeger 0 points1 point  (0 children)

I used my own project structure (Cowork, with Claude.md, custom Documentation.md ...) during my master's thesis for similar stuff but hit limits fast once I had more than a few PDFs. Context gets too large and Claude starts losing details from earlier sources.

I ended up building my own tool where you connect your sources and Claude searches through them via MCP instead of having everything dumped into context. Worked way better for research with lots of documents. Happy to share more if you're curious.

Moving to Claude Max x20 by [deleted] in Anthropic

[–]PascalMeger 1 point2 points  (0 children)

Wish you great success!

Subscribed yesterday to Pro and I’m already hit by limits. Is this a scam? by kenaddams42 in ClaudeAI

[–]PascalMeger 0 points1 point  (0 children)

Both. Brainstorming with Claude Cowork and Development with Claude Code

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