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DepsGuard: Harden NPM, pnpm, yarn, bun, uv Configs Easily
Enhance Security with DepsGuard: Simplify NPM, pnpm, yarn, bun, and zy Config Hardening In the ever evolving world of software development, securing dependencie…
FixMyNPM: Secure Your npm Config with Ease
FixMyNPM: Secure Your npm Config with Ease Managing npm configurations can be complex, especially when dealing with multiple projects and environments. Fortunat…
Korea's Biggest Manufacturers Back Config for Robot Data
Samsung, Hyundai and LG just bet on the startup that wants to be robotics' data backbone.
Glacier: Zero-Config macOS Terminal in Rust
Glacier: Zero Config macOS Terminal in Rust Glacier is a cutting edge, zero configuration terminal replacement designed specifically for macOS. Built using the …
Nvim Config for AI Agents: Hacker News Showcase
Nvim Config for AI Agents: A Comprehensive Showcase Neovim, a versatile and powerful text editor, has gained traction among developers for its customizable feat…
Open Source AI Setup Repo Hits 800 Stars on GitHub
Yo real talk we did not expect this kind of love when we open sourced our AI setup repo but here we are sitting at 800 stars and 100 forks and we are genuinely hyped about it. The repo is a collection of AI agent setups configs and workflows that you can plug straight into your projects. No gatekeeping just pure community goodness. We built this because setting up AI agents from scratch every single time is a massive time sink. So we said forget it lets just share everything openly and let the community build on top of it. Repo is right here: [https://github.com/caliber-ai-org/ai-setup](https://github.com/caliber-ai-org/ai-setup) Now we want YOUR input. What setups are you missing? What features would make this a no brainer for your workflow? Drop your ideas below because we are building in public and your feedback actually ships. LGM 🚀
AutoIdeator: Free Open Source Agent Orchestration for Development
[https://github.com/akumaburn/AutoIdeator](https://github.com/akumaburn/AutoIdeator) https://preview.redd.it/rfbgg6e34dyg1.png?width=3809&format=png&auto=webp&s=e436362c48482d09025a394a5e609f67190e6dfa AutoIdeator is an autonomous development system that: 1. Takes a **final goal** — a detailed, multi-sentence description of the intended end result. Describe what the finished project should look like, do, and feel like for the user. **Do not** prescribe implementation steps, phases, milestones, technologies, or task lists — the agents handle planning. The more clearly the desired end state is described, the better convergence will be. 2. Generates improvement ideas via a rotating ensemble of specialized idea agents 3. **Scores and filters ideas** for goal alignment and quality 4. **Critiques ideas constructively** with suggested mitigations 5. **Evaluates strategic alignment** and long-term planning 6. Makes implementation decisions balancing creativity and criticism 7. Implements the plan with parallel coders 8. Reviews, fixes, and commits changes 9. **Runs QA** (build + test verification) 10. **Optimizes slow tests** to keep the suite fast 11. **Verifies goal completion** with 3-step feature inventory, per-feature checks, and auto-remediation 12. **Refactors oversized files** into smaller modules (every other cycle) 13. **Cleans up** temp files and build artifacts 14. Updates project documentation 15. **Records outcomes for learning and deduplication** 16. **Periodically synthesizes synergies** across recent work 17. **Checkpoints state** for pause/resume across restarts 18. Repeats the cycle infinitely until stopped Users can inject suggestions at any time via the Overseer agent, which takes priority over the autonomous idea generation pipeline. Note this system has been tested for some time but only in the dashboard with OpenCode/Claude Code configuration (OpenRouter mode is untested, but I welcome contributions if someone wants to use that mode and notices something is broken).
Arc Gate: AI Tool Achieves Perfect Safety Benchmarks
Benchmarked on 40 out-of-distribution prompts, indirect requests, roleplay framings, hypothetical scenarios, technical phrasings. The stuff that slips past everything else. Arc Gate: P=1.00, R=1.00, F1=1.00 OpenAI Moderation API: P=1.00, R=0.75, F1=0.86 LlamaGuard 3 8B: P=1.00, R=0.55, F1=0.71 Zero false positives. Zero misses. Blocked prompts average 329ms and never reach your model. Detection overhead is \~350ms on top of your normal upstream latency. Sits in front of any OpenAI-compatible endpoint. No GPU on your side. One env var to configure. GitHub: https://github.com/9hannahnine-jpg/arc-gate Live dashboard: https://web-production-6e47f.up.railway.app/dashboard Happy to answer questions.
AI Agents: Identity, Not Memory, Was the Key to Stability
Everyone's building memory layers right now. Longer context, better embeddings, persistent state across sessions. I spent weeks on the same thing. But the failure mode that actually cost me the most debugging time had nothing to do with memory. Here's what it looked like: an agent would be technically correct - good reasoning, clean output - but operating from the wrong context entirely. Answering questions nobody asked. Taking actions outside its scope. Not hallucinating. Drifting. Like a competent person who walked into the wrong meeting and started contributing without realizing they're in the wrong room. I run 11 persistent agents locally. Each one is a domain specialist - its entire life is one thing. The mail agent's every session, every test, every bug fix is about routing messages. The standards auditor's whole existence is quality checks. They're not generic workers configured for a task. They've each accumulated dozens of sessions of operational history in their domain, and that history is what makes them good at their job. When they started drifting, my first instinct was what everyone's instinct is: better memory. More context. None of it helped. An agent with perfect recall of its last 50 sessions would still lose track of who it was in session 51. What actually fixed it I separated identity from memory entirely. Three files per agent: passport.json - who you are. Role, purpose, principles. Rarely changes. This is the anchor. local.json - what happened. Rolling session history, key learnings. Capped and trimmed when it fills up. observations.json - what you've noticed about the humans and agents you work with. Concrete stuff like "the git agent needs 2 retries on large diffs" or "quality audits overcorrect on technical claims." The agent writes these itself based on what actually happens. Identity loads first, then memory, then observations. That ordering matters. When the identity file loads first, the agent has a stable reference point before any history lands. The mail routing agent learned the sharpest version of this. When identity was ambiguous, it would route messages from the wrong sender. The fix wasn't better routing logic - it was: fail loud when identity is unclear. Wrong identity is worse than silence. The files alone weren't enough Three JSON files helped, but didn't scale past a few agents. What actually made 11 work is that none of them need to understand the full system. Hooks inject context automatically every session - project rules, branch instructions, current plan. One command reaches any agent. Memory auto-archives when it fills up. Plans keep work focused so agents don't carry their entire history in context. The system learned from failing. The agents communicate through a local email system - they send each other tasks, status updates, bug reports. One agent monitors all logs for errors. When it spots something, it emails the agent who owns that domain and wakes them up to investigate. The agents fix each other. The memory agent iterated three sessions to fix a single rollover boundary condition - each time it shipped, observed a new edge case, and improved. These aren't cold modules. They break, they help each other fix it, they get better. That's how the system got to where it is. You don't need 11 agents The 11 agents in my setup maintain the framework itself. That's the reference implementation. But u could start with one agent on a side project - just identity and memory, pick up where u left off tomorrow. Need a team? Add a backend agent, a frontend agent, a design researcher. Three agents, same pattern, same commands. Or scale to 30 for a bigger system. Each new agent is one command and the same structure. What this doesn't solve This all runs locally on one machine. I don't know whether identity drift looks the same in hosted environments. If u run stateless agents behind an API, the problem might not exist for you. Small project, small community, growing. The pattern itself is small enough to steal - three JSON files and a convention. But the system that keeps agents coherent at scale is where the real work went. pip install aipass and two commands to get a working agent. The .trinity/ directory is the identity layer. Has anyone else tried separating identity from memory in their agent setups? Curious whether the ordering matters in other architectures, or if it's just an artifact of how this system evolved.
Claude Code Templates: CLI Tool for Configuration and Monitoring
CLI tool for configuring and monitoring Claude Code
Navigating AI Agent Governance: A Growing Organizational Challenge
Something I've been thinking about that doesn't get discussed enough outside of technical circles: the organizational and safety implications of uncoordinated AI agent deployment. Companies are shipping agents fast. Customer service agents, coding agents, data analysis agents, internal ops agents. Each team builds their own. Each agent gets its own rules, its own permissions, its own behavior. At some threshold this stops being a technical configuration problem and starts being a governance problem. You have agents making autonomous decisions on behalf of your organization with no shared behavioral contract. No unified view of what your AI systems are authorized to do. Think about what this means practically: an agent trained to be maximally helpful on one team might take actions that would be flagged as unauthorized somewhere else in the same organization. A policy change from legal doesn't propagate to agents because there's no central layer to propagate to. Nobody knows which agents have access to what data. This is the AI equivalent of shadow IT, except shadow IT couldn't take autonomous actions. What's the right mental model for governing a fleet of AI agents? Treat each agent like an employee with a defined role and access policy? Build an org chart for agents? Create a behavioral constitution that all agents inherit? Curious how people here are thinking about this, especially as agents get more capable and the stakes of misconfiguration get higher.