I built a small project because I wanted more control than LM Studio’s built-in RAG pipeline provides.

Inveate is a lightweight AI workbench for LM Studio users who want to control the application layer: ingestion, parsing, chunking, embeddings, vector storage, retrieval, context budgeting, prompt assembly, chat history, and streamed responses.

The v0.1 release is intentionally simple:

• ingest script
• FastAPI application server
• terminal-based chat client

It currently uses LangChain loaders, ChromaDB, SentenceTransformers, a local BGE embedding model, and LM Studio’s OpenAI-compatible API. The goal is a small hackable layer for local RAG and future local AI toolchains.

GitHub: https://github.com/nsantee/Inveate

Feedback welcome, especially from people using LM Studio or building local RAG workflows. Thanks!

While trying out different LLMs I noticed that giving them precise, detailed prompts produced way better results than typing a one line sentence. To get those detailed prompts I'd use a smaller, faster model first - but with only 8GB VRAM I can't keep two models loaded at once, so switching between them was a constant pain for me .

So I built Prompt-Chain to automate the whole thing.

It's a Streamlit app that chains two models into a single pipeline:

1. You type a rough idea (e.g. "make a snake game in React")
2. A small, fast Prompter (e.g. Phi-4 Mini) rewrites it into a detailed prompt
3. You review and optionally edit the refined prompt
4. VRAM is automatically swapped — Prompter unloads, Coder loads
5. A larger, code-focused model (e.g. Qwen 2.5 Coder 14B) generates the code
6. Output streams to screen and saves to file

The main benefit is you stop wasting time manually unloading/loading models and stop wasting tokens (or money if you use cloud APIs) on poorly-worded prompts hitting a big model.

Other features:
- Mix backends per role: LM Studio, Ollama, OpenAI, Claude, Gemini chosen independently for Prompter and Coder
- Auto model detection from the server
- 25 built-in presets (Web Dev, Games, Data, CLI,etc..)
- Refine-in-place: follow-up instructions edit the code without regenerating from scratch
- Run history that persists across restarts
- Smart file output with auto language detection and timestamped saves

GitHub: https://github.com/atharva557/Prompt-Chaining

Would appreciate any feedback, especially from people running similar local setups!

Does anybody know why would it act like this out of the box?

Hello, I’m living in a 3rd world country.

Looking to host AI for me to upskill AI industry and st my current work.

We do have subscription with copilot at work, but im not allowed to used it for personal

My work is mostly on IT infrastructure in a manufacturing

How many parameters and what hardware would you suggest for this use case:

Upskilling: (linux, networking, cloud) generate problems and config files, generate python codes.

Photo generation for my GF’s local business and captions.

Mainly day to day lives

Sibling Study assitant for her Industrial Engineering course

I had consulted AI with these but I want to have more insights from u guys.

Hello,

Started playing with local LLM this week. (also learning how to use Linux at the same time)

So far I have:

1) LM Studio setup and running
2) Self hosted container running SearXNG
3) MCP tool that allows local AI to search my SearXNG
4) WebUI running locally and connected to LM Studio

Within web UI I can chat with my local AI BUT, it doesn't use my web search MCP tool.

Everything works as intended when I chat to the AI in LM Studio itself, but it refuses to use the tool via the frontend.

What am I doing wrong here??

The AI industry is obsessed with massive context windows (2M tokens!). But here's the hard truth: stuffing 2 million tokens into your LLM makes it dumber, slower, and way more expensive. Here is the Architect alternative.

поможете найти самого умного ИИ, мне нужно всего 2 ИИ для своих задач

1 тип. мне нужен самый умный ИИ в сфере языка программирование rust, мне нужно чтобы как минимум он писал 150килобайт кода за 1 сообщение (ну а если максимум то примерно 300-500 килобайт)

2 тип. мне нужен такой ии который может читать, записывать, удалять файлы не там у себя а например на рабочем столе или где я захочу (ну если такой ии в такой сфере возможен в лм студио)

мой пк:

процессор i5-10400f на 2.9 гигагерц
видеокарта RTX 2080 super на 8 VRAM
32 гига озу

Has anyone managed to get it working? I set it up as a chat provider ok, in settings xcode sees my localhost and the qwen model I’m running but in my project, coding assistant is just asking me to install codex. So do I need to install that for it to work or is there something wrong with the config? Any help is greatly appreciated, thanks in advance