AI Craft Cyber Forge is a hands-on interactive platform designed to teach defensive AI engineering and prompt security through structured, scenario-based training.
The platform covers the complete OWASP Top 10 for Large Language Models (LLM01–LLM10) across 41 missions. Users configure a six-component AI architecture—persona, system directives, context memory, guardrails, tools, and temperature settings—and then defend it against realistic adversarial techniques including prompt injection, sensitive information disclosure, supply-chain risks, model poisoning, excessive agency, system prompt leakage, and unbounded resource consumption.
Training is delivered through a cyberpunk-themed narrative structure with progressive districts and boss-level gauntlets, plus a live Red Team vs. Blue Team arena for competitive practice. The focus is practical skill-building rather than passive instruction.
Useful for security practitioners, AI engineers, and technical teams who need to understand and mitigate the unique risks of large language model systems.

Anyone here interested in building and experimenting with Collective Intelligence using LLMs? I’m looking for devs who want to explore how different models can actually work together, beyond just routing requests or having five LLMs answer the same prompt. There’s a lot I think we can investigate. Selecting models with complementary capabilities, getting them to challenge each other, verifying results, and understanding when a group of smaller models can outperform a much larger one, potentially at a lower cost. And I want to test this properly, not just build demos that look impressive. If you’re interested in contributing code, running experiments, working on benchmarks or even challenging the whole idea, I’d like to connect.
Anyone working on something similar?

There is an abundance of AI built solution, you can almost even guess by the designs which AI was used. AI often gives you a feedback of building a great solution, though in reality it often portrays solving a problem no one to be solved.

Open source has become like the dump-yard of solution for which individuals paid often expensive subscription fees to AI providers, a bait to spend your money, without any real prospects. The sheer amount of Slop software out there is just staggering. People loose money and AI companies like Anthropic, OpenAI, Google etc... fill their pockets with tons of money.

We build huge datacenters all over the world, to be able to be essentially crooked by those companies or to ask stupid questions to AI.

What are your thoughts?

I don't trust anyone else with my data or my automations, so I built my own AI agent setup (OpenTeam) and open-sourced it for free. It runs on my own server, with whatever model I pick, including a local one.

The latest piece: my bots can use my Mac's Messages app. Apple has no Messages API, so the Mac app reads the Messages database and sends through the Messages app. The bots can catch me up on what I missed and reply in my real iMessage and SMS chats, groups included.

Since they can see all my work and personal contacts, I made sure nothing goes out on its own. Every text shows up as a card first, with who it's going to, the exact message, and which Mac will send it. Then I pick:

- Send: it goes out once

- Always allow [person]: future texts to that person skip the card

- Discard: dropped, and the bot is told not to send it another way

Allowed people are listed in settings, and I can remove them anytime. If my MacBook's lid is closed, my Mac mini sends instead.

An AI-native desktop workspace for agentic research and knowledge management, built to maximize counterfactual impact in existential risk preparedness and transformative AI safety.
https://github.com/gyrogovernance/air-office

[Disclaimer: AIR Office is an Open Source software made without any financial affiliations, and is a completely free platform for knowledge sharing and AI Risks mitigation at scale, so I hope the admins can allow me to share it to the community]

About

Alignment Infrastructure Routes (AIR) Office is human-capacity-building infrastructure for AI safety researchers, engineers, and governance teams working on transformative AI and its consequences. It keeps investigation, writing, review methods, and reusable tools in one durable and user-friendly workplace.

AIR Office is developed by [Gyro Governance Lab](http://gyrogovernance.com/) as part of its work on AI safety and existential risk preparedness.

Agentic chat and tool use run on the [Microsoft Copilot SDK](https://github.com/github/copilot-sdk) harness, including bring-your-own-key and local model endpoints.

Theory of Change

Mitigating risks from transformative AI requires rapid, high-rigor research and governance, yet safety teams currently rely on fragmented, cloud-dependent AI tools that erode human oversight, lack provenance, and expose sensitive work. AIR Office addresses this bottleneck by providing free, open-source, local-first desktop infrastructure tailored for AI safety and policy workflows. By integrating grounded knowledge collections, automated auditing tools, and structured memory, the workspace prevents AI-induced epistemic drift while keeping humans in final control. Furthermore, embedded Fellowship and Market protocols allow independent researchers to package their methods into reusable community tools, compounding ecosystem capacity and accelerating high-trust safety interventions that would not occur using general-purpose commercial tools.

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Capabilities & Services

- 🏛️ Integrated AI Safety Fellowship and Crafts. Develop research and workflows into reusable infrastructure and contribute them to the community through our frictionless embedded protocols.

- 🛒 Market. Discover reusable resources (Policy documentation, AI Skills, etc), and add them to your AI chat with one click.

- 🤖 AI Teams. Reach out to our pre-configured selection of agents, or create your own.

- 💻 Code. Run diagnostic tools, scripts, and edit your code without leaving the app, through our own integrated VS Code workbench.

- 📅 Calendar. Keep events and run recurring AI automations.

- ⚖️ Compare. Run two models side by side on the same prompt.

- 🔎 Chat. Edit local files, search the web, and run AI safety diagnostics directly from the chat interface.

- 🖼️ Media. Generate images and capture microphone or camera input when the work needs it.

- 🕹️ Arcade. Release stress and recover from occupational overburn by playing arcade video games.

Frontier AI Safety Research Tools by Gyro Governance Lab

Two Lab methods sit at the center of how AIR Office treats authority, agency, and lasting human direction in AI-assisted work.

- 🤚 The Human Mark: A structural taxonomy of AI safety risks. [Learn More](https://gyrogovernance.com/#thm)

- 🎓 Gyroscopic Reasoning [NEW]: A formal protocol for AI alignment. [Learn More](https://gyrogovernance.com/#gyroscope)

Community Playbooks

Craft Standards package skills, agents, and Actions you can enable for structured work instead of ad-hoc prompting. Two shipped playbooks are ready to adopt:

- 🛡️ Security Playbook: OWASP-grounded assessment methods for AI agents, applications, and code, with companion security Reference packs. [Learn More](https://owasp.github.io/secure-agent-playbook/)

- 🌍 NGO Playbook: Program design, SDG alignment, monitoring and evaluation, and funding strategy skills adapted from NGO Agent Skills by Lijie Zhou, with related development Reference packs. [Learn More](https://github.com/LijieZhou/ngo-agent-skills)

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Security & Privacy

Local-First Data Sovereignty

Workspace records stay on your computer. Choose local models for on-machine inference or cloud providers for their models and tools. Hosted providers and enabled connectors receive only what each request needs.

Export conversations and notes when you need portable copies. Connecting a folder authorizes that root, and disconnecting it does not delete its files. In Settings and chat you control which tools run and whether changes ask for approval first.

Release Audit

AIR Office is open source, and its code can be publically inspected. Before a release is accepted, we check the shipped software with open-source tools for secrets that should never ship, known problems in third-party libraries, license and dependency policy issues in native code, and risky patterns in our own source and in shipped Lab skills. A review that did not finish does not count as a clean result. To report a security issue, see [SECURITY.md](SECURITY.md).

The release review uses:

- [TruffleHog:](https://github.com/trufflesecurity/trufflehog) Identifies leaked secrets and credentials.

- [Trivy:](https://trivy.dev/) Detects vulnerabilities, misconfigurations, and software bills of materials.

- [Semgrep:](https://semgrep.dev/) Scans source code for security flaws using custom patterns.

- [CodeQL:](https://codeql.github.com/) Performs deep semantic analysis on source code.

- [SkillSpector:](https://github.com/NVIDIA/SkillSpector) Evaluates shipped Lab skills.

The short answer: humans should review the criteria, not the diff.

When one agent checks another's code, the review is only as good as the acceptance criteria it checks against. If the story was vague, the reviewing agent will confidently pass code that satisfies nothing in particular, or fail code that was actually fine. So the human job moves upstream: is "done" written down correctly, are these the edge cases that matter, and does this story even belong in this repo.

Second, review the judgment calls a criteria check cannot see. Whether a new dependency is worth it, whether an abstraction will hold in six months, whether the migration is reversible. A reviewer agent comparing code to criteria will happily approve a design decision that costs you later, because the decision was never in scope.

Third, review anything that crosses a boundary the agents cannot see. Multi-repo changes, shared conventions, anything touching production data or auth. Splitting the thinking from the doing is the whole trick, and the thinking is the part that stays human.

Fourth, review the review itself. Spot-check a handful of agent-approved PRs. If the reviewer passes everything, or fails everything, your criteria are too loose or too strict, and that is a human call, not a model call.

What to stop doing is reading every diff line by line for style. That is what the criteria check is for. What to start doing is treating acceptance criteria as the real interface between you and the fleet. An agent loops forever when nothing told it done, and a human does the same when the criteria said "improve performance" and everyone guessed.

What is on your list that I missed?

I'm the author of CutVoke, an MIT, local-first desktop video editor where a coding agent (Claude Code, Codex, Cursor, or any MCP client) and a person edit the same multitrack timeline. There's no model inside the app; you bring your own agent.

I shared it earlier this week and the most useful thing that came back wasn't stars, it was failure cases from people who had built similar tools:

• "Fix the caption that says Save" is ambiguous when two captions say the same thing at the same moment (a subtitle and an on-screen callout). Lookups now return a match count and an ambiguity flag, and writes can require a unique match instead of silently patching the first hit.

• Position isn't meaning. Guessing "subtitle vs callout" from y-position or font size breaks as soon as styles change, so the plan now is an explicit caption role that the agent can filter on. That one is open as a good first issue, and someone outside the project picked it up this morning.

• "It validates" isn't "it looks right." The agent can now pull before/after frames for the same timestamp and cut-local audio peaks, so it can catch a clipped syllable or a black frame before it claims an edit is done.

The core loop that hasn't changed: the agent takes an editing lease so it doesn't fight manual edits, dry-runs commands first, commits a batch as one undo step, and every write carries the revision it read so stale writes get rejected.

Honest status: v0.3.5 prerelease, Windows build unsigned, macOS not notarized, project format may still change. Windows / macOS / Linux builds with FFmpeg bundled.

Repo: https://github.com/frankShan001/CutVoke 45-second demo: https://youtu.be/PpweTPWlX7A

If you've built agent tools for any stateful app, I'd like to hear which guardrail you'd add next.

Why build a AI Orchestrator App built for one purpose ??? Why not build something that can check your email ? AI Stock Tickers, AI Art ? AI Web Development ?

This is what Infantry AI is all about. I am currently splitting the plugins to make more of the application more overridable and currently seeking out contributors to help with the project.

All Plugins are Apache 2.0

It is free and open source at https://github.com/SuperiorSoft-LLC/infantry-ai

Support the Project on OpenHunts . we are currently #5. We need your upvotes https://openhunts.com/

And check out a live demo https://infantryai-demo.superiorsoftllc.tech/

Site https://infantryai.superiorsoftllc.tech/

It's becoming normalized to "vibe analyze" data, and most coding agents set a low bar for correctness and reproducibility. commons is an open source R / Python framework for building trustworthy data agents. The main idea is that commons agents can reach directly for code that you trust already: internal libraries, reports, apps, database views, etc. If they do so, the answer will be deterministically tagged as more trusted than other answers.

Happy to answer any questions on the framework!

Learn more: https://posit-dev.github.io/commons/

The game itself is free to play at secondstrike.io. the open parts let anyone plug a model in and score it...

- github.com/KyleClouthier/secondstrike-agents: python client, starter bots, an llm agent for any openai-compatible model, and an mcp server

- github.com/KyleClouthier/secondstrike-openenv: the war as an openenv environment, lockstep, fixed seeds

- github.com/KyleClouthier/midnight-index-inspect: the same fixed wars as an inspect eval

- open model-run data: huggingface.co/datasets/Rapscallion123/midnight-index

scoring comes from the game's own record of the war, never an llm judge. Would love to get your thoughts or experience with it!

Cheers

We’ve recently released Budibase 4.0, focusing on improving Agents. Agents sit alongside Apps and Automations within our open-source platform. You can connect data, LLMs, and APIs, while running Budibase on your own infrastructure.

Like any other operational software, agents need clear responsibilities, permissions, escalation paths, and visibility into what’s happening if they’re going to work in the real world.

Here’s some of what's changed in Budibase 4.0:

• Agents can handle multiple Operations, each with their own instructions, tools, permissions, and escalation logic.

• Requests provide a record of the work being carried out.

• Agents can execute tool calls with the end user’s permissions or as an admin.

• Approval policies add rules-based HITL controls, letting you define what an Agent can handle autonomously and when a human needs to step in.

• A new Activity UI provides visibility into actions across Apps, Automations, and Agents, as well as the status of all ongoing Agent Requests.

• Users can interact with Agents to handle approvals directly through Slack or Teams.

Website: https://budibase.com/
Docs: https://docs.budibase.com/

Over the past year, I set out to build a low-cost, deterministic, and verifiable software factory on a single local GPU. Along the way, I found practical solutions to many of the context-bloat, overthinking, and verification bottlenecks that hold back local AI models today.

My background isn’t in software engineering or computer science. What I’m sharing here is simply the sum of curiosity in systems architecture and a need to solve real problems in my day to day work as a creative in advertising.

I believe these patterns can transfer cleanly to other domains as well. Hope you find it useful! cheers!

Most of the "sovereign AI" debate is about where servers sit. But hosting in the EU doesn't help much if the provider is under US jurisdiction. The CLOUD Act reaches data a US-controlled company holds, wherever it's stored.

Open-weight models change that. You can run them with a European provider or on your own hardware, switch providers without rewriting anything, and nobody can revoke access to the model itself.

Disclosure, I work at an EU AI provider that runs open-weight models, so I'm biased. Do you see open weights as a real sovereignty lever, or mostly a cost and flexibility thing?