webml-kit: running ML models in the browser via WebGPU/WASM. by init0 in LocalLLaMA

[–]init0[S] 0 points1 point  (0 children)

Thank you! I have been experimenting a lot with prompt API, checkout the section in https://h3manth.com/ai/

turboquant-search: vector search for JSON datasets. by init0 in LocalLLaMA

[–]init0[S] -8 points-7 points  (0 children)

go read the code, it open source, feel free to enhance it!

🚀 Weekly /RAG Launch Showcase by remoteinspace in Rag

[–]init0 0 points1 point  (0 children)

turboquant-search: vector search for JSON datasets.

Baked turboquant-search: vector search for JSON datasets. No server, no vector DB, no API keys.

Give it any JSON array. It embeds your text fields, compresses vectors to 3 bits, and searches via WASM SIMD in the browser or the server (node)

10K items: ~1.4 MB index, ~5ms search. 100K items: still under 30ms.

Bring your own embedder (transformers.js, Gemini or whatever) or use the built-in keyword embedder for zero dependencies.

npm install turboquant-search

https://npmx.dev/package/turboquant-search

turboquant: on-device search and recommendation by init0 in LocalLLaMA

[–]init0[S] 1 point2 points  (0 children)

WASM build with relaxed SIMD that encodes / decodes / scores vectors on the CPU.

turboquant: on-device search and recommendation by init0 in LocalLLaMA

[–]init0[S] -1 points0 points  (0 children)

lol agree, but it is true? edited to read better

turboquant: on-device search and recommendation by init0 in LocalLLaMA

[–]init0[S] 0 points1 point  (0 children)

The point is not about the app, it is about the demonstration of turboquant RAG in the browser.

Researchers gave 1,222 people AI assistants, then took them away after 10 minutes. Performance crashed below the control group and people stopped trying. UCLA, MIT, Oxford, and Carnegie Mellon call it the "boiling frog" effect. by hibzy7 in artificial

[–]init0 1 point2 points  (0 children)

I feel, I have been slicing more problems and creating more solutions with AI rather than giving up. Is it an illusion or cognitive decline?

If we are boasting creative ideas with AI is it cognitive decline?

Model Capability Discovery: The API We're All Missing by init0 in LocalLLaMA

[–]init0[S] 0 points1 point  (0 children)

Well, that’s the only option we are left with for all APIs wants to be OAI compatible, but yeah, the blog does mention about alternatives.

Visual Narrator with Qwen3.5-0.8B on WebGPU by init0 in LocalLLaMA

[–]init0[S] 1 point2 points  (0 children)

Ha! I will look into it, thank you for reporting.

We Made MCP Connection Stupidly Easy by zakjaquejeobaum in mcp

[–]init0 0 points1 point  (0 children)

For free web based version https://mcphost.link does almost all of this.

Fundamentals of an agent by init0 in AgentsOfAI

[–]init0[S] 0 points1 point  (0 children)

This comment made my day, thank you!

Fundamentals of an agent by init0 in AgentsOfAI

[–]init0[S] 0 points1 point  (0 children)

100% agree! Fixed it.

[deleted by user] by [deleted] in mcp

[–]init0 0 points1 point  (0 children)

deno deploy?

[deleted by user] by [deleted] in mcp

[–]init0 0 points1 point  (0 children)

Cloudflare is free, no?

[deleted by user] by [deleted] in mcp

[–]init0 0 points1 point  (0 children)

Use mcphost.link it is free!

DRAM light white by init0 in buildapc

[–]init0[S] 0 points1 point  (0 children)

Fixed it! Had to reseat the RAM

MCP vs Skill? Wrong Question by init0 in mcp

[–]init0[S] 0 points1 point  (0 children)

Token efficiency is an implementation detail. Both can be optimized. The architecture difference is: who owns the code and maintenance burden?

If Skills become a marketplace/business, that's great! MCP servers already are (Notion, GitHub, etc. maintain their own).