Wardstone
Guard your AI from prompt attacks, harmful content, and PII
What it does
Wardstone is an LLM security API that detects prompt injections, jailbreaks, harmful content, and data leakage in a single call with sub-30ms latency. It works as a firewall between your app and any LLM provider, scanning both inputs and outputs. Drop-in REST API with SDKs for TypeScript, Python, Go, Ruby, PHP, Java, and C#. Four detection categories with risk bands out of the box. Free tier with 10,000 calls/month, no credit card needed. Start protecting your AI in minutes.
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EDAMAME SecurityApr 2026 · edamame.tech · ▲68Axios / LiteLLM hacks behavioral detector app for Mac/PC
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Insight IT Solutions PrismGuard Jul 2026 · insightits.com · ▲5Prompt-injection firewall for prod LLM apps
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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…
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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