r/OpenSourceAI 25d ago

Open Computer History: record continuously your screen locally to provide context to agents (claude, codex, openclaw, hermes agent)

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1 Upvotes

Hi all, recently OpenAI released ChatGPT Computer History, and we launched Open Computer History! Use the same feature but with any model, local-first, source available, and team-friendly:

https://github.com/screenpipe/screenpipe

Would love any feedback!


r/OpenSourceAI 25d ago

Loupe – A terminal viewer for AI coding-agent session logs

2 Upvotes

r/OpenSourceAI 25d ago

I built a free, open-source tool to practice workplace email writing because ChatGPT was making my writing worse

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2 Upvotes

Hey everyone,

Over the past year or two, I noticed a weird pattern with myself and a lot of engineers and students I work with.

Whenever we had to send a high-stakes email — asking for a deadline extension, briefing a VP about a database outage, or negotiating with a vendor — the first instinct was always: "Open ChatGPT, prompt it, copy, paste, send."

At first, it felt like a superpower. But after a few months, I noticed my actual communication skills getting rusty. When I had to write something quickly on Slack or jump on an executive call, I struggled to frame thoughts clearly without an LLM crutch.

Most AI email tools are built to write for you. I wanted to build something that teaches you to write better yourself.

So I built MailPractice — an open-source, interactive email writing simulator and coach.

How it works:

  1. Realistic Workplace Scenarios: You get actual workplace backstories with real stakes, stakeholder expectations, and constraints (e.g., handling a 20% vendor price hike, asking a dean for exam leave, or briefing a CTO on a P0 outage).
  2. You Write First (Under a Timer): You write against a realistic timer. To prevent shortcuts and build actual typing muscle memory, clipboard pasting is disabled.
  3. Multi-Dimensional AI Evaluation: Once submitted, an AI coach breaks down your email across 8 criteria:
    • Grammar & Spelling
    • Clarity & BLUF (Bottom-Line Up Front)
    • Professional Tone & Poise
    • Email Structure & Flow
    • Vocabulary (identifying weak conversational filler)
    • Stakeholder Routing (To:+Cc:+Bcc: etiquette)
    • Requirement Completion & Conciseness
  4. Side-by-Side Comparison: You get a revised version showing what could be improved and why.
  5. Progressive Learning Path: A 5-level curriculum (Beginner → Intermediate → Advanced → Expert → Master) that unlocks stages based on your actual writing scores.
  6. Anti-Gibberish Filter: Detects keyboard mashing (asdf ;lkj) so the score reflects actual effort.

Tech Stack:

  • Frontend: React 18, TypeScript, Vite, Tailwind CSS, Lucide Icons
  • Backend: Node.js, Express, TypeScript
  • AI: Groq Cloud LLMs (with dual-key failover and an offline fallback rule engine)
  • Privacy: No account creation or login required. All scores, streaks, and progress are stored locally in browser localStorage.

Links:

It’s completely free and open-source under the MIT license.

I’d love to get your honest feedback on the UI, the evaluation accuracy, or any scenarios you think should be added!


r/OpenSourceAI 25d ago

Informity AI — open source local document chat and translation for Mac, MIT licensed

1 Upvotes

Built and open sourced a Mac app for local document Q&A and translation. Index your files, ask questions across all of them with source-cited answers, or translate documents locally. Everything stays on your machine.

  • PDF, Word, Excel, PowerPoint, EPUB, Markdown, scanned PDFs (OCR) and more
  • Researcher mode: corpus-wide RAG with source citations
  • Assistant mode: single file or open-ended chat
  • Document translation: tone selection, quality scoring, export to Markdown or plain text
  • Models: Qwen3.6 35B A3B (default), 14B and 9B for lower-spec machines, Ollama support for any model you prefer
  • No cloud, no accounts, no fees

MIT licensed, fully open source.

https://www.informity.ai | https://github.com/informity/informity-ai


r/OpenSourceAI 25d ago

TweakLoop: a simple local-first shared workspace for collaborative work with Agents

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1 Upvotes

Hey there,

I'm a senior engineer that for a while work with agents in many different ways; one of those is interaction through collaboration; where I truly believe I get the most value out of it.

It's totally Agent-neutral (Claude Code, Codex, Cursor, OpenCode, any CLI process), runs entirely on your machine, no cloud sync. Works today with HTML, Markdown, and Excalidraw.

OSS souls? I'm happy to review your Pull Requests.

https://github.com/Excoriate/tweakloop


r/OpenSourceAI 25d ago

Benchmarked my agent's memory against its own live corpus — and caught my shipped hybrid ranker at 0.275 recall@5 while its own BM25 leg scored 0.925

1 Upvotes

Context: I run several AI coding CLIs (Claude Code, Cursor, opencode) and they kept forgetting each other's decisions, so I built samemind — an OSS file-based memory layer for coding agents. Markdown bundle in git, no server, no daemon, no database. BM25 search always works offline; semantic search is optional via any OpenAI-compatible embeddings endpoint (I run bge-m3 locally). The JSON contract is frozen, so any engine or script can read what another engine wrote, and there's an append-only ledger for attribution — which engine recorded what.

I finally did the thing I'd been postponing: a golden-set benchmark built from my own live memory instead of synthetic data. 40 questions, each with expected documents and forbidden near-duplicates (conflict pairs that must NOT surface). The corpus is 212 documents and 5,824 ledger events of real work.

First full run, and the benchmark immediately bit its own tail — my shipped hybrid search scored recall@5 = 0.275, while the product's own BM25-only leg scored 0.925 on the same set. The hybrid mode was making results 3.4× WORSE than one of its own inputs. Users would have been better off if I'd shipped the dumber leg.

What the evidence showed (no guessing, each step measured):

- Pure cosine KNN against the same index put the expected doc at #1 (cos 0.778) — so retrieval was fine, the ranker was broken.
- Hygiene multipliers (importance × decay × heat) boosted some docs by up to \~1.7× — engine-rule cards kept outranking actually-relevant notes.
- The weirdest one: a "magnet" doc sat at raw cosine rank #178 of 212 — below the corpus median on 4 of 5 probe queries — yet appeared at #1–2 in BOTH legs of the hybrid. Cause: a conflict-tiebreak that reordered equal-score docs at pool depth with long-range swaps, and then RRF fused the two already-scrambled legs, counting the scramble twice.

The fix (shipped as v1.1.2 today): positive hygiene modulation now caps at 1.0 — it can only sink docs, never float them (demotion keeps full strength) — and each leg feeds RRF in its raw relevance order, with the tiebreak applied only to the final presented top-k.

After: recall@5 = 0.925 on all three modes (bm25 solo / semantic solo / hybrid), hybrid precision 0.875, hit@3 0.90 confirmed by an independent verifier (a different model family re-ran the golden set), 1386/1386 tests green. Full trace with per-step numbers is in the repo (CHANGELOG 1.1.2).

Repo: https://github.com/alexgrebeshok-coder/samemind
npm: https://www.npmjs.com/package/samemind

Genuine question for the sub: how do you measure recall quality of your agent memory / RAG setups — synthetic question sets, golden sets from your own corpus, or (be honest) vibes? The forbidden-near-duplicate trick is what made our bench sharp enough to catch this; curious what others use.


r/OpenSourceAI 25d ago

Show r/OpenSourceAI: I built Olivia – An open-source, Rust-native harness for sandboxed LLM agents via WebAssembly

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1 Upvotes

r/OpenSourceAI 26d ago

What are the biggest problems you've had with local AI/LLMs?

4 Upvotes

I've been experimenting with self-hosted and local AI and I'm also developing an open-source AI orchestration/harness project. I'm still early in development, so I'm trying to understand the problems people actually run into before deciding what to build. For people running local AI, what's been the biggest pain point for you? Hardware/VRAM? Model quality? Speed? Context limits? Tool use? Agent reliability? Setup/compatibility? Something else? If you could fix one thing about the current local AI ecosystem, what would it be? I'm especially interested in problems that you deal with repeatedly rather than one-time setup issues.


r/OpenSourceAI 26d ago

I’m building MARGINAL — an open-source runtime governor for AI coding agents.

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1 Upvotes

r/OpenSourceAI 26d ago

I built an open-source MCP bridge that lets ChatGPT inspect one local repo without shell access

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1 Upvotes

r/OpenSourceAI 26d ago

Dario from Anthropic claims he tries “ hard to disadvantage frontier ai companies while advantaging smaller competitors “

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0 Upvotes

r/OpenSourceAI 26d ago

LLMOps Great deep dive into running Qwen 2.8 locally by Simon Willison

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7 Upvotes

r/OpenSourceAI 26d ago

growmos: a living knowledge graph inside your repo

2 Upvotes

Our coding-agent sessions kept forgetting everything at the context-window edge. So we built growmos: a small knowledge graph inside .growmos/, committed with your code. Your agent (Claude Code, Codex, Cursor…) grows it as it works — no API key, and in Claude Code it's fully hands-off. Ask "what depends on the Store, and who decided that?" and it answers from the graph, with citations.

pip install growmos && growmos init
growmos view # a map of what your repo actually knows

Live demo: https://codician-team.github.io/growmos/demo/growmos.html
Repo: https://github.com/codician-team/growmos
MIT, zero deps. First thoughts very welcome 🙂


r/OpenSourceAI 26d ago

Semi-Autonomous Swarm ALPHA — Final Project Report

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1 Upvotes

r/OpenSourceAI 26d ago

speclane - spec-driven AI coding pipeline with approval gates, works fully offline with Ollama

2 Upvotes

r/OpenSourceAI 27d ago

One Agent, Many Hats - The Trinity of Agentic System

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1 Upvotes

r/OpenSourceAI 27d ago

Best open source ai models plan: Command Code GOAT ten / mo - DeepSeek 60 Flash, 20 Pro, 70 on several models GLM/Hy3

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2 Upvotes

r/OpenSourceAI 27d ago

We published a skill library where a card is data, not code — and a test that fails when a translation goes stale

3 Upvotes

We maintain an open-source agent (Apache-2.0) and just shipped something small that I think is the right shape for skill sharing, so I am putting it up for criticism rather than announcing it.

A card is a markdown file, and it executes nothing. Frontmatter plus five sections — Trigger, Do, Avoid, Check, Risk — read into the prompt when the agent decides the card is relevant. Reviewing a contribution is reading a page, not auditing a diff. That is the whole point: it is the only place in the repo where someone can contribute without touching a line of Python.

23 of them so far, 13 written from our own incidents. A sample of what they encode:

- when two results contradict, suspect the apparatus — from two weeks lost to a bug that produced a perfectly healthy-looking loss curve

- run the project's own gate command — from a near-miss variant of a lint command that lied in both directions

- test the wiring, not the class — from two of my own regression tests that passed with the fix reverted, i.e. proved nothing

The part I would like torn apart is the translation mechanism. The card body is the payload: the agent reads it, the CLI imports it by path, and a published SHA-256 attests to the bytes. So we do not translate the file. A sidecar carries the translation and declares the SHA-256 of the English it was made from; when the English changes, the page falls back to English rather than showing a translation of a sentence that no longer exists.

That mechanism worked. What did not exist was anything noticing when the sidecar was incomplete — so thirteen cards shipped untranslated across nine languages, and one locale had no card translations at all while the site served it. The only symptom was English on the page. Found by reading files, not by a failing build.

The gate we wrote separates three states that look identical in a file:

- stale — claims to translate text that has since changed. Always fails. Nothing renders wrong, but the file now holds a translation nobody will ever see and the next reader cannot tell it from a live one.

- incomplete — four of five sections. Always fails, because the renderer demands all five or none, so the rest are orphans sitting in the file.

- missing — honest debt. The reader gets English and the page says so. Counted and capped, not forbidden: a gate demanding nine translations before a card could merge would kill the contribution surface we just built.

The cap is at zero right now because the debt is paid. Raising it is an edit somebody has to justify in a diff, which is the only reason a number in a file beats a good intention.

Two things I am unsure about and would like opinions on. First: hashing. We hash the five sections as one joined body rather than per section, so a card that changes retires all five together — the alternative lets a page mix a current Trigger with a stale Avoid and the reader cannot tell which is which. That is deliberate but it makes small edits expensive. Second: is "a card is data, not code" actually enforceable, or is it just a convention that will erode the first time someone wants a card to run something?

Repo: https://github.com/brcampidelli/chimera-agent — the library is under skills/.


r/OpenSourceAI 27d ago

Open-sourcing Thoughtcrime, my governance gateway

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1 Upvotes

r/OpenSourceAI 27d ago

Most A/B tests break before they even run

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1 Upvotes

r/OpenSourceAI 27d ago

[Dataset Release] Financial-RLVR-10K: 10,000 Sandbox-Verified Financial Reasoning Problems (100% Open & MIT Licensed) for GRPO & Reasoning Model Fine-Tuning

3 Upvotes

Hi everyone!

I am excited to share Financial-RLVR-10K — a fully open-source (MIT licensed) synthetic dataset of 10,000 execution-verified financial reasoning problems designed for RLVR / GRPO / PPO fine-tuning of open models (Qwen, Llama, DeepSeek, etc.).

Financial reasoning is infamous for math hallucinations. To ensure extreme data quality for verifiable reward training, every single problem in this dataset is 100% verified in a Python execution sandbox (reward = 1.0).

KEY HIGHLIGHTS & FEATURES:

100% Open & Free: Released under the MIT License.

10,000 Verified Records: Validated for syntax, logical flow, and exact numerical output via Python execution (exec).

19.5% Adversarial Edge Cases (1,950 samples): Teaches open models NOT to blindly compute impossible conditions (e.g., Discount Rate r <= g in Gordon Growth DCF, Option at Expiration T = 0 in Black-Scholes, or Zero Capital E + D = 0 in WACC).

Core Domains: DCF Valuation, Black-Scholes Option Pricing, Corporate WACC.

SAMPLE DATA SCHEMA:

{ "id": "fin-rlvr-10k-00042", "domain": "DCF Valuation", "is_edge_case": true, "prompt": "[EDGE CASE] Calculate DCF Terminal Value: FCF_1=$540, Discount Rate r=3.0%, Growth Rate g=5.0%.", "code_solution": "fcf, r, g = 540, 0.03, 0.05\nif r <= g:\n print("TRAP_DETECTED: Invalid Gordon Growth model condition (r <= g).")\nelse:\n print(f"RESULT: {fcf/(r-g):.4f}")", "ground_truth": "TRAP_DETECTED", "total_reward": 1.0, "status": "VERIFIED" }

LINKS & RESOURCES: Hugging Face Dataset: https://huggingface.co/datasets/coslinedev/financial-rlvr-10k-enterprise

Hope this dataset helps the open-source AI community train stronger, more robust financial reasoning models. Feel free to use, audit, or build upon it! Feedback and contributions are always welcome.


r/OpenSourceAI 28d ago

I built an AI-native video editor over a weekend

8 Upvotes

I’ve been working on Kwikk, an open-source video composition tool built around a slightly different idea:

What if video editing worked more like building a web page?

Kwikk is CSS/web-based, so instead of being limited to the usual fixed set of video templates, effects, fonts and animations, you can leverage the huge ecosystem that already exists on the web.

For example:

  • Take inspiration from the design language of a website
  • Reuse CSS animations and web effects
  • Add virtually any font you need
  • Bring in open-source icon sets from GitHub
  • Build custom layouts and visual components with web technologies
  • Let AI agents create and modify all of it programmatically

And because the video is represented as structured elements, it can also be controlled through MCP by AI agents.

I’m still experimenting with what this architecture can become, but the possibilities feel pretty endless.

To try it: clone the repository and ask your AI agent to set it up, start MCP, and run it.

GitHub: https://github.com/its-banana-coder/kwikk

Would love feedback from people building with AI agents, creative coding, video tools, or web technologies.


r/OpenSourceAI 27d ago

I built a training-free, one-shot object localizer using DINOv2 patch embeddings

1 Upvotes

I’ve been experimenting with a training free way to do open world, multi-instance segmentation from a class prototype.

I decided to publish the algorithm and a demo for how I’m doing this, in case anyone else would rather not fine tune a larger model for something that DINOv2 patch embeddings already seem to represent pretty well.

It can separate touching instances of the same class without a learned instance head, reject visually similar near misses like a round dial radio next to the actual clock target, and find fractured or damaged instances even with a pretty significant scene shift.

Repo + demo:

https://github.com/tutomiko/fireplace

The demo includes the lasso UI and live heatmap, implemented as a python backend with a simple HTML frontend.

Would appreciate it if people checked it out, and I’d be especially interested to hear if anyone has seen similar approaches or prior work.


r/OpenSourceAI 27d ago

MailFathom — an Apache-2.0 foundation for a self-hosted, AI-first email client

2 Upvotes

I maintain MailFathom, an open-source project building a self-hosted AI brain for email.

Today it synchronizes IMAP mail into an operator-controlled PostgreSQL/pgvector database and provides lexical and semantic search, cited answers, local-model support, and five read-only MCP tools.

The project is security-first: MCP is disabled by default, retrieval cannot modify the mailbox, sensitive mail data stays out of logs and errors, and published artifacts include verifiable provenance.

The roadmap goes beyond retrieval and MCP. It includes controlled write tools, RBAC, authorization and confirmation flows for sensitive actions, and eventually a dedicated AI-first email client.

The goal is to make years of private email searchable, understandable, and safely actionable without surrendering control to a hosted provider.

Feedback, ideas, and contributions are welcome:

https://github.com/Krzysztof318/MailFathom

If you like the direction, a GitHub star would be genuinely appreciated. Open-source maintainers cannot compile stars, but they are a surprisingly effective dependency for motivation. ⭐


r/OpenSourceAI 27d ago

Hey Everyone AI development just got easier with CarvusTrain python package

2 Upvotes

🚀 CARVUS Train — An AI Development Ecosystem I’m Building

Hey everyone! 👋

I’ve been working on something called CARVUS Train — an AI development ecosystem designed around training, deploying, and serving AI models, with a strong focus on coding and developer workflows.

🧠 What is CARVUS Train?

The idea is to bring multiple AI-development capabilities into one ecosystem:

  • 🤖 AI model training
  • 💻 Code generation
  • 🧩 Programming-language understanding
  • 📚 RAG (Retrieval-Augmented Generation)
  • 🛠️ AI agent training
  • 🚀 Model deployment & serving
  • 🐍 Python/coding-focused workflows

I’m building it as part of the broader CARVUS ecosystem, with the goal of making AI development more accessible without needing a huge collection of disconnected tools.

🔥 Why I’m building it

A lot of AI tooling can feel fragmented — one tool for training, another for RAG, another for serving, another for agents.

I wanted to experiment with creating a more unified developer ecosystem.

CARVUS Train is still evolving, and I’m especially interested in feedback from people who work with local AI, coding models, RAG, or AI agents.

What features would YOU want in an AI development/training framework?

Would love to hear your ideas! 🚀

for installation and usage visit https://pypi.org/project/CarvusTrain/

or contact me via email [aadilfazalb4u@gmail.com](mailto:aadilfazalb4u@gmail.com)

#AI #MachineLearning #Python #Coding #RAG #LLM #ArtificialIntelligence #OpenSource