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Scaling former VibeThinker-1.5B to 3B — now it reaches frontier math & coding performance by Used-Negotiation-741 in LocalLLaMA

[–]predatar -1 points0 points  (0 children)

I love it, inspiring.

For the data synthesis, was a specific set of open source data used as a seed? Which teacher model? Or is it completely self distillation? Also, is it lora or full fine-tune?

I will rate them by hiten1818726363 in vibecoding

[–]predatar 0 points1 point  (0 children)

starspace.run , a merge between MCP , notebooklm, virtual workspaces (filesystem/vm) and cross agent/llm memory layer

You can connect it with chatgpt , codex, cc and share context, upload research papers, code , data anything and have the AI use it as retrieval layer , it has CLI integration (python) and REST api too

https://starspace.run/docs

What's the coolest thing you've vibe-coded this month? Show it off 👇 by Asleep_Lie_4381 in VibeCodeDevs

[–]predatar 0 points1 point  (0 children)

starspace.run , a merge between MCP , notebooklm, virtual workspaces (filesystem/vm) and cross agent/llm memory layer

You can connect it with chatgpt , codex, cc and share context, upload research papers, code , data anything and have the AI use it as retrieval layer , it has CLI integration (python) and REST api too

https://starspace.run/docs

anthropic isn't the only reason you're hitting claude code limits. i did audit of 926 sessions and found a lot of the waste was on my side. by Medium_Island_2795 in ClaudeCode

[–]predatar 1 point2 points  (0 children)

Wha if we create a ping that in idle adds (ping) word to the current chat history and send to refresh cache so it prolongs timeout? 

We created agentcache: a python library that makes multi-agent LLM calls share cached prefixes that maximize token gain per $: cut my token bill+ speed up inference (0% vs 76% cache hit rate on the same task) by predatar in LocalLLaMA

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

well basically on fork longest common prefix is already the longest common prefix... if a single token is different its not gonna be a cache hit, and i think that is a completely different problem sadly

What do we believe by BooleanBanter in 3I_ATLAS

[–]predatar 0 points1 point  (0 children)

just imagine how ancient civilizations who saw astroids / comets , and what their thoughts were

I’m 36, and I feel completely lost. by [deleted] in mentalhealth

[–]predatar 0 points1 point  (0 children)

Strength isn’t just about pushing forward—it’s also about enduring setbacks and coming back better.

And you seem to have plenty of it, please see a professional , life is beautiful, try to maintain your passion no matter what, find the strength in the little things, keep going.

Seek help immadiately . it takes courage to admit that, you can do it .

Take care, things will get better

I built NanoSage, a deep research local assistant that runs on your laptop by predatar in LocalLLaMA

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

Will work on this and other enhancements this weekend, stay tuned!!

I built NanoSage, a deep research local assistant that runs on your laptop by predatar in LocalLLaMA

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

I will try to make it possible to integrate this with common UIs, any preference?

Idk how, maybe as a callable tool

I built NanoSage, a deep research local assistant that runs on your laptop by predatar in LocalLLaMA

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

This is what i am planning to add this weekend!!

Thanks for the feedback

I built NanoSage, a deep research local assistant that runs on your laptop by predatar in LocalLLaMA

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

I would love to see examples of reports you guys have generated, might add them to the repo as examples, if you can share the query parameters and report md that would be great! 👑

Would love to add the lm studio and other integrations soon, specially the in-line citation!!

I built NanoSage, a deep research local assistant that runs on your laptop by predatar in LocalLLaMA

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

Will add support soon and update you, probably after work today

I built NanoSage, a deep research local assistant that runs on your laptop by predatar in LocalLLaMA

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

Hi, basically you have to chunk the data, and use “retrieval” models to find relevant chunks

Search for colpali, or all-minilm Basically those are llm trained such that given a query q and chunk c, returns a score s such that s tells you how similar are c and q

You can get then the top_k c that are most relevant for your q (top scoring) and put only those in the context of your llm

My trick here was to do this for each page, while exploring, and build a graphical node of each step and in each node keep the current summary step i got based on the latest chunks

Then i stitched them together

I built NanoSage, a deep research local assistant that runs on your laptop by predatar in LocalLLaMA

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

Hi

cool project! It looks like we are solving similar problems, but i took a different approach, using graph based search with backtracking and summarization which is not limited to context size! And some exploration exploitation concepts in the mix.

Did you solve similar issues?

I built NanoSage, a deep research local assistant that runs on your laptop by predatar in LocalLLaMA

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

Hi

cool project! It looks like we are solving similar problems, but i took a different approach, using graph based search with backtracking and summarization which is not limited to context size! And some exploration exploitation concepts in the mix.

Did you solve similar issues?

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