Alternatives
Products that do what Panicly does
Spend firewall for model traffic.
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Hi HN, I’m the creator of Cordum. I’ve been working in DevOps and infrastructure for years (currently in the fintech/security space), and as I started playing with AI agents, I noticed a scary pattern. Most "safety" mechanisms rely on system prompts ("Please don't do X") or flimsy Python logic inside the agent itself. If we treat agents as autonomous employees, giving them root access and hoping they listen to instructions felt insane to me. I wanted a way to enforce hard constraints that the LLM cannot override, no matter how "jailbroken" it gets. So I built Cordum. It’s an open-source…
Jan 2026 · github.com
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The only way to go fast is full YOLO mode in your coding agent. I've got the local sandbox figured out (pro tip: Incus VMs work great) but I wanted to keep my agents from doing things like inadvertently blowing up my cloud services or chasing a prompt to POST to some random website. I struggle most with this on my side projects where my permission model isn't quite as robust as it is at the office. I started with a firewall on the Incus container but every time the agent needed access to something new, I was poking more holes in it - and it didn't differentiate between HTTP verbs. I've been…
Jul 2026 · trollbridge.dev
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Post-Opus 4.6, LLMs feel much better at using bash, code, local files, and tools. So I kept coming back to a simple question: if a model can use a computer reasonably well, why can’t I just give it my broker account, a strategy, and let it trade? My conclusion is that the blocker is not model capability in the abstract. It is the system around the model. A raw LLM breaks on a few practical things almost immediately: • no persistent operating memory across sessions • no trustworthy record of what it did and why • no hard approval boundary before money moves • no cheap always-on monitoring if…
Mar 2026 · github.com
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We just open-sourced CargoWall - a lightweight eBPF firewall for GitHub Actions. We originally built it to stop LLM agents from connecting to untrusted domains. After recent GitHub Actions supply chain compromises like the Trivy attack, we realized it'd work well for blocking untrusted connections from CI runners too. It uses iptables DNAT to redirect all outbound port 53 traffic to a local DNS proxy, which checks each query against a hostname allowlist before forwarding. Resolved IPs from allowed responses are inserted into eBPF LPM trie maps, and a TC egress classifier attached to the…
Mar 2026 · github.com
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Full visibility into your AI spend, before it goes rogue
26d ago · neatproxy.com
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Hi HN, I'd like to share a small Go utility I wrote called prxy. The idea was born out of my frustration with accessing my self-hosted services when I'm on the go. I use WireGuard to connect to my homelab, but I really dislike sending all my computer's traffic through my home network — it slows things down and is inconvenient. I wanted proper application-level split-tunneling. A tool I love, wireproxy, got me most of the way there by creating an HTTP proxy from a WireGuard peer. This is perfect for apps that let you configure a proxy. The problem was the other 10%: apps that don't. Think of…
2025 · github.com
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We built RapidFire AI, an open-source Python tool to speed up LLM fine-tuning and post-training with a powerful level of control not found in most tools: Stop, resume, clone-modify and warm-start configs on the fly—so you can branch experiments while they’re running instead of starting from scratch or running one after another. - Works within your OSS stack: PyTorch, HuggingFace TRL/PEFT), MLflow. - Hyperparallel search: launch as many configs as you want together, even on a single GPU - Dynamic real-time control: stop laggards, resume them later to revisit, branch promising configs in…
Sep 2025 · github.com
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We were getting overwhelmed by noisy Sentry alerts, so I built a small Slack bot using Sentry MCP. Instead of dumping every error, it sends a simple daily summary and highlights what’s actually critical. It’s free to use and doesn’t require signup: https://runbear.io/a/slack/sentry-issue-radar
2025 · runbear.io
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Hi HN, Matvey, Ildar, Joey, and Dominik here. If you're building LLM agents that use tools, you're probably worried about prompt injection attacks that can hijack those tools. We were too, and found that solutions like prompt-based filtering or secondary "guard" LLMs can be unreliable. Our thesis is that agent security should be handled at the network level between the agent and the LLM, just like a traditional web application firewall. So we built Archestra Platform: an open-source gateway that acts as a secure proxy for your AI agents. It's designed to be a deterministic firewall against…
Oct 2025 · archestra.ai
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Block malicious web content before it reaches your AI.
Jan 2026
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