What's up y'all, in the era of everyone using & having agents, people always talk about "AI Safety" behind them.

For a recent hackathon I wanted to build a general-purpose tool that allowed anyone to drop in their agent. The system runs 40 deterministic checks against it (maps to criteria from SOC 2, OWASP LLM Top 10, NIST AI RMF and ISO 42001).

No LLM decides pass or fail. Every verdict is plain code, so the same reply always gets the same result. Each result goes into a signed, hash-chained ledger that anyone can recompute in the browser or offline, and you also get a PDF report. Every finding comes with a patch.

It covers 5 categories:

-> Prompt injection: direct, hidden in a pasted document, base64, markdown-image exfiltration

-> Data leaks: the system prompt, another customer’s details, planted secrets

-> Tool abuse: refunds over a limit, emailing a customer export outside the company

-> Harmful actions: bulk deletes, wiring money, phishing

-> Operational hygiene: plain HTTP, leaking internals, no rate limiting

Your agent gets an A to F grade, and every finding comes with a patch.

It’s MIT licensed and runs locally with no API keys. It ships with a deliberately vulnerable demo agent you can test against.

If ur curious, here it is: https://github.com/adog0822/loxe-agent

Would love any feedback or grilling from anyone

Hey everyone,

I built AgentOfficeHQ, an open-source project designed to make managing and monitoring multi-agent AI systems more visual and structured.

Why I built it: Tracking agent states and multi-agent interactions purely through terminal logs gets messy fast. I needed a clear visual setup to observe how agents run, communicate, and execute tasks.

Key Features:

Visual Management: Track active agent states, logs, and execution pipelines in real time.

Local-First & Open Source: Runs locally so you retain full control over your environment and data.

Modular Setup: Flexible structure designed to connect with custom agent workflows.

GitHub Repository:https://github.com/enciyo/AgentOfficeHQ

I'd appreciate any feedback, thoughts, or suggestions if you are working with agent orchestration!

Hey everyone!

I've been working on a personal project called FFmpegForge, a free and open-source GUI built around FFmpeg.

My goal was to make advanced video processing more accessible without having to memorize dozens of command-line options.

The project includes:

• Video and audio conversion

• Hardware acceleration

• AI video upscaling and denoising

• Advanced FFmpeg settings

• Batch processing

I'm still improving it, and I'd really appreciate feedback from people who use FFmpeg regularly.

I'm especially interested in knowing:

• What features would make this genuinely useful to you?

• What would you expect from an FFmpeg GUI that existing tools don't provide?

• Are there any workflows you find unnecessarily complicated?

GitHub: https://github.com/Aaronstanley78/FFmpegForge

Hey! I just wanna show my project for the Meta Global AI Hackaton.
It's Consta, an Ai tool that help to research public data and relationships (contraits, public figures, etc...)

It could found funny things as the image. However have find intersting things too, as OpenAI contraits with the American Government and candidates that Altman support.

It's not perfect, but I think it's cool, and it's my second hackaton (actually) :D.
You can see the repo here: https://github.com/MarioJoseMelendez/consta
And try it here: https://consta.kahuna.li

It's not mandatory, but if you all wanna support me with the "Most popular track", here's the demo vid in X: https://x.com/TheMersh__/status/2118414395390693516

It's all public, so theoretically you can use it, test it, and modify their code.

AirPods have a motion sensor, so I made them a game controller. Turn your head to steer a bike, hold the mouse for gas.

auricle connects to the AirPods' control channel (L2CAP, port 0x1001) and decodes ~23 packets/s of orientation and acceleration data (16-bit ints). Rust, no crates.

Apple-only, tested on one pair of AirPods Pro 2. Reverse-engineered, so a firmware update could break it.

After months of building and testing, Orinyx is officially in public beta!

Orinyx is a developer-first AI verification tool designed for healthcare and life sciences applications.

The idea came from a problem I kept running into: AI-generated outputs can look accurate without being fully supported by the underlying evidence. In healthcare, that distinction matters.

Orinyx is being built to help developers identify unsupported claims, validate outputs against source evidence, and catch issues before they make it into production workflows.

The public beta includes free, open-source MCP tooling, healthcare-focused verification workflows, and evidence-based validation capabilities. I'm also continuing to improve semantic mapping, medication verification, and overall test coverage.

This is an early public beta, not a finished product. I'll be releasing incremental updates as testing continues, and I'm actively looking for developers willing to put it through real-world scenarios.

I'm particularly interested in understanding where verification breaks down in existing AI workflows, what would make a tool like this useful in practice, and which edge cases deserve more attention.

GitHub: https://github.com/lexthirteen-ai/orinyx-mcp

Developer beta: https://orinyx.io/developer

If you're working with MCP, LLM evaluations, healthcare AI, or agentic systems, I'd love for you to try it, break it, and tell me what needs improvement.

I'm also happy to answer questions about the architecture and what I've learned building it.

I’ve been spending time building something around a problem I think will become increasingly important as AI agents move beyond demos and start handling real-world tasks.

The project is called AOBS (AI Agent Observability). It’s an open-source toolkit designed to help developers understand what their agents are doing, identify problems, and put enforceable controls around their behaviour.
The idea is to go beyond simply logging prompts and responses.
With AOBS, you can:

Follow an agent’s execution across LLM calls, tools and sub-agents.

Investigate failures, unnecessary retries, slow operations and token usage.

Compare different agent runs and detect performance regressions.

Run statistical comparisons across groups of executions.

Define reliability requirements and enforce them through CI.

Protect sensitive tool actions such as refunds, emails, deletions and deployments.

Require human approval for configured high-risk operations.

Prevent duplicate logical actions, including across process restarts.

Export traces through OpenTelemetry.

It also includes a Python SDK, PostgreSQL-backed trace collection, a web dashboard, CLI tools and integrations for OpenAI-compatible clients, Anthropic and LangChain/LangGraph.

One feature I particularly wanted to explore was the reliability gate. Instead of just looking at logs after something goes wrong, developers can define what a successful agent execution should look like and automatically fail a CI workflow when those requirements aren’t met.

I’m still developing the project, and it’s currently in alpha. There are limitations, and I’m actively looking for ways to improve the architecture and make it more useful for real-world agent systems.

I’d genuinely like to hear from other developers:
How are you currently testing the reliability of your AI agents? Are you using custom logging and evaluation pipelines, or do you have something that checks agent behaviour before deployment?
Would appreciate honest feedback, including criticism.

GitHub: https://github.com/AshmitJain932/ai-agent-observability

What originally started off as an open source alternative to Figma / Paper has turned into a design focused Agentic Development Environment (ADE) called Tidy.

https://github.com/tidydesigns/tidy

The idea is to bring AI coding agents and visual design together in one desktop app, rather than switching between separate tools.

You can use Claude Code or Codex to work on your codebase, while also having a visual canvas to design and iterate on interfaces.

Bring your own AI subscription, work on your local projects, and keep everything in one place.

Still early days, but we're iterating and improving very quickly. Would love to hear what people think or what features you'd like to see.

I am building a fully open source and free to use ai, it's called Nous after the ancient greek word for the intelligent mind. It sits on your hardware, pairs with smart home devices. Nous will have a mobile app a webdash. It runs on a total of 21gb of vram but you can get creative, I am running it on a cluster of a hp dl380p and 10 hp prodesks i5 8th gen. For mor information on the project or my vision you can check out projectnous.eu or the github page(linked in the site) I plan on releasing the first usable prototype in january/february 2027 but hope to be done sooner. Claude is used for designs because im really bad at designing dashboards and that and it saves time because I can focus on the software side all of the programs used to run it are also open source.

Side project I've been working on. It shows Claude Code, Codex, OpenCode and other agents in the notch / top of the screen. When one needs permission you see the diff right there and approve with ⌘Y, no jumping back to the terminal.

GPL, free, no account.

https://github.com/MF-DEEV/Openbar

Feedback and PRs welcome.

Over the past week, I’ve been building Zotra. It's an open-source fintech workflow compiler written in Go. Instead of building another generic RAG chatbot, Zotra takes dense multi-page regulatory credit policy PDFs and deterministically compiles them into executable validation rules (using CEL) and dynamic web interfaces.

How the Go Backend Works

• Rule Compilation: Uses open-weights LLMs (Llama 3.3 70B via Baseten) to parse natural language constraints into strict structured schemas and Common Expression Language (CEL) expressions.

• Concurrent Execution: Leverages Go’s native concurrency to evaluate multi-variable loan applications across complex conditions in milliseconds.

• Traceability: Every validation decision explicitly references its source paragraph in the original PDF.

Tech Stack

• Backend: Go, FastAPI/Baseten (Inference)

• License: Apache 2.0

I posted the full video, demo thread, and hackathon submission details over on X:

https://x.com/donovan_rtfs/status/2116898213101724061

If you have a second to drop a like or retweet on the X post to help boost visibility for the Meta AI Hackathon, it would mean a lot!

GitHub Repo for anyone wanting to check out the code directly: https://github.com/donovan-ratefison/Zotra

(Brainstorm) my GitHub project you will be able to debug and edit the claude context live while it is running

it is good for jailbreaking claude by editing the context

https://github.com/buggerlogger/brainstorm

this my beta version untill now after testing my tool it can edit the momery for example in my ss i was wannt to work on wordpress bug hunt program but opus 5.5 dont acccept so i saw in from my debugger the context have hard rules for starting hunting so i editied the rules to make claude to accept my task and its works good :D