r/OpenSourceAI

I spent months building Writher, a Windows app that combines faster-whisper for transcription and a local Ollama LLM for an AI assistant – everything runs on your machine.

What it does:

• Hold AltGr → instant dictation in ANY app (VS Code, Word, Discord, browser...)
• Press Ctrl+R → voice-controlled AI: manage notes, set reminders, add appointments
• Smart date parsing ("remind me next Tuesday" works!)
• Animated floating widget with visual feedback
• English + Italian supported

No internet required after setup. No subscriptions. Open source.

GitHub: https://github.com/benmaster82/writher

Looking for feedback and contributors!

It seems like RAG pipelines often do:

raw docs -> chunk -> embed -> retrieve -> mask output

But if documents contain emails, phone numbers, names, employee IDs, etc., the vector index is already derived from sensitive data.

docs -> docs__pii_redacted -> chunk -> embed

Invariant: unsanitized text never gets chunked or embedded.

This seems safer from a data-lineage / attack-surface perspective, especially for local or enterprise RAG systems.

Or am I wrong?

Example: https://github.com/mloda-ai/rag_integration/blob/main/demo.ipynb

Looking for play testers for Chains of The Tempest

I’ve experimented with AI as the DM and have had mixed results as many have. So, I built something to fix that with the help of Claude AI. A self-contained mini adventure you upload directly into an AI chat (I've been testing on Claude), so the AI actually has the rules, encounters, and NPCs in front of it. Think of it as giving the AI a proper module to run rather than asking it to improvise an entire game from memory.

It includes custom skills that plug into Claude to handle things like dice rolls and spell lookups, plus some images to make it feel less like reading a wall of text. Claude built the skills for me.

I've been solo playtesting it and it's working way better than just prompting an AI cold, but I need more eyes on it. If you want to try it out and give feedback, DM me and I'll send it over.

I’ve been working on a memory library for LLM agents that tries to control context size by creating a short term and long term memory store (I am running on limited hardware so context size is a main concern). It’s not another RAG pipeline; it’s a stateful, resource-aware system that manages memory across two tiers using pluggable vector storage and indexing:

* **Short‑Term Memory (STM)**: volatile, fast, with FIFO eviction and pluggable vector indexes (HNSW, FAISS, brute‑force). Stores raw conversation traces, tool calls, etc.

* **Long‑Term Memory (LTM)**: persistent, distilled knowledge. Low‑saliency traces are periodically consolidated (e.g., concatenation or LLM summarization) into knowledge items and moved to LTM.

**Saliency scoring** uses a weighted RIF model (Recency, Importance, Frequency). The system monitors resource pressure (e.g., RAM/VRAM) and triggers consolidation automatically when pressure exceeds a threshold (e.g., 85%).

What I’m unsure about:

1. Does this approach already exist in a mature library? (I’ve seen MemGPT, Zep, but they seem more focused on summarization or sliding windows.)

2. Is the saliency‑based consolidation actually useful, or is simple FIFO + time‑based summarization enough?

3. Are there known pitfalls with using HNSW for STM (e.g., high update frequency, deletions)?

4. Would you use something like this?

Thanks!

Source:

It was originally written in Java and I am working on porting to python.

Python https://github.com/Utilitron/VecMem

Java https://github.com/Utilitron/VectorMemory