Ask questions across your Markdown notes using a fully local Graph RAG engine. Built for Obsidian vaults, works with any folder of Markdown files. Extracts entity-relation triples from wikilinks & YAML frontmatter, retrieves answers via hybrid search (vector + BM25 + temporal). Multilingual. No cloud. Runs on Ollama.
Hey all, I hope you're doing well! I'm building an open-source runtime called Conifer (conifer.build) and wanted to share it with this community.
The idea is that most of the pain of running models locally isn't the model itself, it's everything around it: setup, storage, quantization, memory, and scheduling work across whatever hardware you've got. Conifer handles that layer so local inference runs fast and stays private, instead of being a fragile fallback to the cloud.
We've got funding to build it, and right now we're already beating llama.cpp on some benchmarks, with more improvements coming as we prepare for launch.
We're launching our beta on June 1st for our waitlist users. It'll be completely free and open source, so you'll be able to install it, point it at a model, and see what it does for yourself. If you're interested, we'd love for you to join the waitlist at http://conifer.build/feedback and check it out.
Happy to answer any questions in the comments!
You know the feeling.
Deep into a refactor. Claude Code stops. "You've reached your usage limit."
I have two Pro accounts. Both paid for. Neither knew the other existed.
Shunt pools them behind one endpoint and routes every request to whichever account has headroom left. Reads the utilization header off every Anthropic response.
Least-loaded account wins. Always.
Auto-failover on 429. Claude Code never sees an interruption.
Fully Free and Open source.
drop an image or logo → customize it → export it.
simple.
Been wanting to push Cloudflare's AI stack harder, so I built a personal memory/notes API that combines all four services into one Worker.
What it does: HTTP API + MCP server that stores notes, embeds them as vectors, and lets you search by semantic meaning rather than keywords. Query "infrastructure decisions" and it surfaces a note about "why we switched from Vercel" — no keyword overlap needed.
The stack:
bge-small-en-v1.5 for text embeddingEvery write hits D1 synchronously (instant response) and queues the embedding to Vectorize in the background. So /capture returns immediately and the vector catches up within a second or two.
One thing worth knowing about local dev: Vectorize and Workers AI don't run in wrangler dev locally — you have to use --remote for anything touching those services. Slightly annoying but not a dealbreaker.
Deployment is one click — the repo has a Deploy to Cloudflare button that provisions D1, Vectorize, and deploys the Worker automatically. Took me longer to write the README than to get it running.
Fits comfortably in the free tier for personal scale. Haven't stress-tested the limits yet but for a single user hitting it dozens of times a day, nothing close to quota.
Repo if you want to look at the implementation: https://github.com/rahilp/second-brain-cloudflare
Curious if anyone else has run into the D1 + Vectorize dual-write pattern and has opinions on better ways to handle consistency there.
or is it just that I am unaware?
I was facing problems with adding safety hooks for iOS and Android app submission as they were getting rejected. So, I built an app compliance auditor. https://github.com/atharvnaik1/ipaship-audit
But later on I thought ohh!! Why not create a cli tool, claude skill (ipaship-audit) and a mcp connector which can make every person's llm with safety hooks not just for apps but for every code its written.
You can access it at ~ npm i @async-atharv/ipaship
I have also added kimi and gemini keys with default options.
This audit for secure code, appstore policy compliance, bug fixes and give back REMEDIATION PLAN to your llm agent itself and your llm agent can work on it rapidly on that prompt itself. So no more leaving your IDE or claude code all things handled within the environment you loved 😍 !! ..
I find it a bit perplexing that most Indian companies aren’t using or even talking about the Sarvam-105B model which is the cheapest most capable model out there! My question here is out of curiosity. My team is building something with Sarvam-105B and I want to check if there are others doing the same. If not why?
With a depreciating rupee, aren’t you worried about increasing your AI input cost in your AI workflows?
Hi everyone,
I’m building AgentLantern, an open-source devtool for AI agent projects.
The idea is simple: as agent-based projects grow, it becomes harder to understand how agents, tasks, tools, and configuration files are connected. AgentLantern aims to make these projects easier to document, analyze, validate, and visualize.
I started with CrewAI support, but the goal is to progressively extend AgentLantern to other agent frameworks.
AgentLantern currently provides three main features:
The project is still early, and I’m mainly looking for feedback from people building with AI agents, multi-agent systems, or devtools.
Docs: https://brellsanwouo.github.io/agentlantern/
I’d be happy to hear your thoughts.
Open Source Palantir
We're building OSIRIS - The Open-Source Palantir Alternative
Feel free to Pull Request the team will review and merge if applicable 🙏
Just launched at osirisai.live - a free, open-source global intelligence platform:
-Real-Time Tracking:
-10,000+ commercial, military and private aircraft live on a 3D globe
- 2,000+ satellites including ISS
- 1,400+ worldwide CCTV camera feeds
- Earthquakes, wildfires, nuclear facilities and severe weather
Built-In OSINT Tools (no installs needed):
Nmap port scanning from the browser
- DNS record lookup and enumeration
- WHOIS domain intelligence
- SSL/TLS certificate transparency
- BGP routing and ASN lookup
- Threat intelligence and IP reputation
All running on a 3D interactive globe with day/night cycle, 20+ live API feeds, and a SIGINT news aggregator.
Live: https://osirisai.live
GitHub: https://github.com/simplifaisoul/osiris
Free. Open Source. No sign-up required.
Ciao community di Open WebUI 👋
Sono Nick, faccio parte del team di Ianustec e siamo grandi fan di Open WebUI da molto tempo.
Apprezziamo molto ciò che questa community sta costruendo attorno all'IA open source e self-hosted, quindi volevamo dare il nostro contributo.
Al momento stiamo sviluppando una suite completamente open source di estensioni per Microsoft Office progettate per funzionare con Open WebUI, incluse integrazioni per:
Il nostro obiettivo è rendere i flussi di lavoro di IA nativi all'interno di Microsoft Office, mantenendo tutto aperto, flessibile e compatibile con l'ecosistema di Open WebUI.
Alcune delle cose su cui stiamo lavorando:
Tutto verrà rilasciato come open source.
Ci piacerebbe anche collaborare con la community e conoscere le vostre opinioni:
Siamo entusiasti di collaborare con questa community e contribuire all'ecosistema 🚀
Finally releasing Micracode - an open-source, self-hostable ai App builder.
It’s basically a open source alternative to lovable that runs on your own server and lets you build/deploy apps instantly.
- batteries-included: db, files, auth, payments (planning to support in future)
- code-editor
- BYO AI key
repo link: https://github.com/Jamessdevops/micracode
(Any star will be super appreciated ❤️)
I am basically building things together with our contributors based on your feedback :)
I'm so happy to hear about more things to implement.
Thank you all!
Sharing RedThread, an open-source CLI for AI red-team campaigns:
https://github.com/matheusht/redthread
The angle is AI agents as an attack surface. Prompt injection gets more interesting once the model can call tools, delegate to workers, write memory, retry failed actions, or propose guardrail changes.
RedThread is built for staging/internal targets. It runs LLM red-team campaigns, records traces, scores failures, and can replay exploit and benign cases before treating a defense as evidence.
Current pieces:
It is not a magic prompt shield and not broad production enforcement.
Looking for people who test agent workflows and can suggest realistic failure cases or target adapters.
Hey everyone,
I've been going deep on RAG architectures lately and couldn't find a single resource that covered all the modern variants in one place, so I built one and open-sourced it.
What's in the repo:
RAG types covered: Naive, Advanced, Modular, Agentic, Graph, Corrective (CRAG), Self-RAG, Speculative, Multi-modal, and Long-context RAG.
https://github.com/ather-techie/rag-interview-questions
Looking for contributors! If you've been in an ML/LLM interview recently and got a question not covered here, please open a PR or drop it in the comments. I'll add it with credit.
If this is useful, a star on GitHub goes a long way. it helps others discover it. Thanks!
Reason I built this: family group chats keep getting the same kind of message. "Is this real?" with a screenshot of some sketchy text. Fake USPS fee, IRS arrest threat, "wrong number" that pivots to a crypto pitch a few replies later. Same thing every week.
The people getting these are usually the ones least equipped to spot them, and the kids/grandkids they ping aren't always around in time.
So, small open-source web app for it. Paste the message or upload a screenshot, get a green/yellow/red verdict in plain English. Built so someone in their 70s can use it, not security people.
A few things worth mentioning. It's fully client-side, no backend, no telemetry. The message goes from your browser straight to Anthropic. There isn't a server I could peek at if I wanted to.
It's BYOK, so you plug in your own Anthropic API key (free to start). About a tenth of a cent per scan. I'm never monetizing this.
The scam pattern library is just JSON files in /scam-patterns/. If you've seen something in the wild that's not covered, PR a new file and everyone's version gets better. No retraining.
Built over a weekend with Claude Code after writing a proper spec. Stack is Vite, React, TypeScript, Tailwind. MIT.
Repo: https://github.com/srivatp2-code/scam-shield
Being honest about the limits, Claude can be wrong on both sides. It'll occasionally call a legit message suspicious, and it'll miss novel scams. It's a second opinion, not gospel. Always confirm with the real sender through a channel you trust.
What scam types am I missing in the starter library? Genuinely interested in adding the ones people have seen recently.
Looking for contributors, reviewers and testers.
I got tired of babysitting coding agents on big features, so I built this project, its a fork / cloud version from the Aperant (former auto claude) project with some power-ups.
v2.2.0 just released
Run it on a Linux OS, Ubuntu on VPS, Container or Bare metal.
About: MagesticAI is a web-based AI task management and autonomous agent orchestration platform that builds software through coordinated AI agent sessions. It uses primarily the Claude Agent SDK to run agents in isolated workspaces with security controls, coordinating multiple AI agents through a structured pipeline to build software autonomously with human oversight.
The core pipeline consists of four specialized agents: the Planner Agent creates implementation plans with subtasks, the Coder Agent implements individual subtasks (and can spawn subagents for parallel work), the QA Reviewer validates acceptance criteria, and the QA Fixer resolves issues in a feedback loop. Each agent operates with role-specific tool permissions and security controls.
Hi, i built this agentic ai,
Closed-loop system that ships standalone Python agents.
What's different:
- Interviews you until it understands the request before building anything
- Two testing stages: prompt validation via LLM invoke, then real subprocess execution of generated code. Not the same thing.
- Self-referential: injects its own source as a reference template for generation
- Structured rating schema drives iteration. Human approval gate before anything saves.
Runs on Qwen3.6-35B a3b Q8_0 locally.
https://github.com/0c33/Agentic-Ai
Give a shot and tell me what do you think.
The sequence is always the same: registry launches and grows fast, minimal vetting because the priority is growth, first wave of incidents, community outrage, tooling catches up, security becomes a baseline expectation. npm took about three years to go from event-stream to npm audit being standard. Docker Hub took similar.
MCP is at step 2 heading into step 3. The numbers from a scan of 500 Smithery servers this month: 18.8% had security findings, 6 had live hardcoded credentials, none were caught by a pre-publication scan because there is no pre-publication scan. A Check Point research disclosure in February showed an 8.7 CVSS attack chain against Claude Code where the entire payload was natural language in a config file.
The difference from npm is what the malicious content does. An npm package executes unauthorized code. A malicious MCP skill file gives unauthorized instructions to an agent that already has access to your tools, file system, and APIs. The LLM cannot distinguish between instructions from the user and instructions from a skill file. Both arrive in the context window and both get acted on. Existing security tooling has no model for this.
The fix is the same three layers it always is: pre-publication registry scanning, CI integration for consumers, and a public advisory database. None of the three exist yet in any mature form for MCP.
Whether the timeline is one year or three depends on whether registry operators move proactively or wait for a sufficiently public incident. Based on how npm and Docker played out, my bet is on the incident coming first.
We built a static scanner for this: pip install bawbel - scans skill files and MCP server configs without executing anything. The vulnerability database it checks against the AVE.
`🧬 Flux‑Genotype – A CPU LLM that rewrites itself`
I've been working on an open-source kernel called **flux-genotype**. It orchestrates local models (TinyLlama, Llama 3.2, Hermes 3, DeepSeek-Coder) into a self-modifying ecosystem. Everything runs on **CPU** — I tested it on a Xeon without AVX2, 20 GB RAM.
> **Important:** this is an alpha. It works, it mutates, it evolves — but there's a lot of work ahead. The **MetaDesigner**, in particular, is the module I'm focusing on next. Right now it proposes architectural changes by writing new `.flux` files, but the validation and application pipeline needs to be more robust. The vision is to make it fully autonomous: an external architect that watches the ecosystem, diagnoses weaknesses, and rewrites the structure to improve confidence. It's not there yet, but the foundation is solid.
## How it works
## Real example of self‑modification
The mutation can also replace the Judge. During one of the growth cycles, the MetaDesigner proposed swapping the Judge from **Llama 3.2** to **DeepSeek-Coder 6.7B**. The new configuration was tested, scored better, and the ecosystem applied the change permanently.
The system is not just tweaking parameters — it's rewriting its own **division of labor between models**.
## Why this is different
- It mutates its own architecture, not just model weights.
- It can replace its own Judge with a different model if performance improves.
- It has memory (confidence history with Exponential Moving Average).
- It uses a custom language (`.flux`) with a formal grammar — not YAML, not JSON.
- It runs on modest hardware. No GPU. Just a CPU and 20 GB of RAM.
## If you want to understand the architecture deeply
I wrote a **technical manifesto** that defines FLUX as a formal Architecture Description Language for self-evolving cognitive ecosystems. It covers the fractal design, the OODA loop, the role of the golden ratio, and the long-term vision (including the MetaDesigner). It's in the repo:
📄 `/papers/FLUX-Kernel.pdf`
## The companion novel
There's also a novel called **"IF THIS IS A ROBOT"** (in Italian and English, CC BY-NC-SA 4.0) that tells the story of a guy who finds this kernel running on a forgotten server. The novel is basically the kernel's manual. But the code stands on its own.
## Links
- **Repo:** [github.com/flux-genotype/nodo_zero](https://github.com/flux-genotype/nodo_zero)
- Kernel is **MIT-licensed**. Novel is **CC BY-NC-SA 4.0**.
Happy to answer questions, and **open to collaborators** who want to help push the MetaDesigner forward.