Neura Agent Infrastructure Now Live with Governed AI Actions

I keep seeing teams ship code that logs before asking if it should. Secrets, tokens, or user data show up in trace output because it was “just debug.” Nobody notices until a later review or a compliance scan, and by then it’s already in storage, indexed, and replicated. At that point “cleanup” really means chasing ghosts. It’s not that we lack filtering downstream. Pipelines can mask or redact, but they work after the fact. Once unsafe data has left the process boundary, it has already crossed trust zones. The safer approach is to stop it before emission, inside the app, while it’s still under the developer’s control. That’s what I’ve been building into Cerbi. CerbiStream runs inside .NET logging providers, reads a governance profile, and decides at emission time what’s allowed to leave. Nothing about the sink or pipeline has to change. Only governed payloads move forward. It’s not flashy work, but stopping unsafe logs before they exist feels a lot cleaner than scrubbing them from terabytes of data later. #Logging #ApplicationSecurity #DevSecOps #SoftwareArchitecture #dotnet https://lnkd.in/eUFddyQB

I launched CamelAGI on Product Hunt and open-source in GitHub. it’s an alternative to OpenClaw with runtime Cursor SDK or Anthropic Claude Agent SDK CamelAGI it’s your personal AI assistant that runs on your own machine. Create multiple agents, each with their own personality, memory, and tools. Schedule cron jobs for recurring tasks, connect MCP servers for external tools, use voice input, approve tool calls, and track token usage and cost. Use it from Telegram, Discord, the terminal. Same runtime, same memory, every channel. https://lnkd.in/dc_FUNn3 https://lnkd.in/dABe6GnZ

61k+ people apparently agree on one thing: Claude talks too much. And therefore burns too much tokens. There’s a GitHub repo called “caveman” that fixes exactly that. Instead of: “Sure! I’d be happy to help. Let me inspect the codebase and summarize what I find…” You get: “Bug in auth middleware. Token expiry check wrong. Fix:” The repo claims ~75% fewer output tokens across their benchmark. But the funny part is not even the token saving. It’s that one of the best AI workflow upgrades right now is basically: make the agent stop narrating its life. More doing. Less yapping. Claude no small brain. Claude just small mouth. Less token, me like.

Monitorex 0.3.0 just landed on Hex! 🚀 Last few days have been wild — I've been building an HTTP telemetry dashboard for Phoenix apps while also learning how to properly orchestrate AI agents to help me code. The library: Monitorex gives you real-time visibility into outbound (Tesla/Finch/Req) and inbound Phoenix traffic with a dark LiveView dashboard. No DB needed — all ETS-backed. Timeline inspector, Prometheus metrics, alert webhooks, cluster support, the works. The process: I used Hermes Agent to handle a lot of the heavy lifting — spawning sub-agents for code review, running CI checks, managing PRs via Telegram. By the end I was basically pairing with an AI that knew the codebase better than I did. It's a weirdly effective workflow: you think in issues, it thinks in branches. Monitorex takes maybe 2 minutes to set up: `{:monitorex, "~> 0.3.0"}` → route mount → done. 🔗 hex.pm/packages/monitorex | https://lnkd.in/ehFHdDw2 #Elixir #Phoenix #LiveView #AIAgents #OpenSource

Every "missing logs" incident I've investigated started the same way. "Logs are gone between 09:22 and 09:45. The pipeline must be dropping them." So we trace the full chain. Collector? Healthy. Event Hub? Healthy. Ingestion backend? Healthy. Then the real answer: the app received SIGTERM. The container orchestrator killed the process. For 23 minutes, nothing was running. No application. No logs. Nothing to lose. I keep seeing this pattern. Teams build incredible log pipelines; retry logic, backpressure, dead letter queues then blame the pipeline first when gaps appear. Before you debug your pipeline, ask one question: Was your app actually running? #Observability #Kubernetes #SRE

We just shipped the Coralogix CLI and it changes how AI agents investigate production systems. Here's the problem no one talks about: Most agent-driven investigations fail before they finish. Raw log volume overflows context windows. Agents guess at query syntax they've never seen. The work lands back on a human engineer. Every time. We fixed that. The Coralogix CLI runs aggregations server-side, delivers output that's 90% leaner than JSON, and ships with pre-built skills for Claude Code, Cursor, Codex, and 40+ other agents. Zero setup. Your agents handle the rest. Read the full announcement → https://lnkd.in/guZWqN4a

Built an MCP finder chrome extension as part of The School Of AI's EAGv3 (Extensive AI Agents v3) program: Small router-plus-tools agent infra is built from scratch around Gemini API calls Searches in the following order: 1) Anthropic's official MCP server list (modelcontextprotocol/servers) 2) Github community-maintained server list (punkpeye/awesome-mcp-servers) 3) General web search Github: https://lnkd.in/gpKsjmWs

“What if you shut down? You are a young company.” ~ User of CtrlOps.io in a demo call I faced this question in a demo call last week. I politely answered, 1. This product is developed by TST Technology - a company with almost 5 years of experience and a team of 25+. 2. This is not our first product. We have already shipped multiple products. 3. For support, we have a Discord channel where you get help from our team, the community, and AI. 4. Also, this is a 100% local tool. Even if we shut down (which we are not going to), you will still have access and can operate it easily. (He ended up purchasing the lifetime deal.) Honestly, if I were in his place, I would think the same. Why trust a new company that might shut down in a year? At that point, you don’t just evaluate the product, you take a bet on the founders. Founders who have been building for years. Founders who show up consistently. Founders you can trust to keep going. That’s the real decision. Btw, CtrlOps.io is 100% local. We don’t access or store your server data - everything stays safely on your own machine. The only thing we have is your sign-up email. That’s it. 📧 If you’re managing tens of Linux servers, give it a try. PS: This is the same user who said “Agar kharidna hai to direct kharid lo.” And bought within 5 minutes.

If you’ve been building multi-agent systems lately, you know the biggest headache isn't the LLM logic - it’s the "handshake." 🤝 I know I was building in silos until I started using OpenClaw. An agent running on OpenAI’s Swarm can’t easily hand off a task to a specialized agent built on LangGraph or CrewAI. It's as if they speak different languages, use different schemas, and have no way to verify each other. You must define the agent relationships That’s why the A2A Protocol (Agent2Agent) from the Linux Foundation is such a big deal. It’s effectively the TCP/IP moment for the Agentic Web. Zero Vendor Lock-in: Cross-vendor communication is now native. Framework A can finally "talk" to Framework B without custom middleware. Discovery & Negotiation: Agents can actually find one another, query capabilities, and negotiate "contracts" for a task. Built-in Trust: Identity verification and data privacy are baked into the protocol, not bolted on. We are moving past simple chatbots into Agentic Ecosystems - a networked workforce that actually solves problems autonomously across platforms. No human intervention. No copy-pasting JSON between windows. This is how we build a scalable, decentralized AI future. For more info, check it out here: https://lnkd.in/gzsyFFsT #AI #AgenticAI #OpenSource #LinuxFoundation #A2AProtocol #SoftwareEngineering #MultiAgentSystems

the repo author literally notes in the readme that openai killed the auth flow. a whole concurrent batch registration system, proxy rotation, websocket logs, and it's already obsolete. that's the fastest a 2k-star repo has ever aged. https://lnkd.in/dBj7_Fa7