TestMyPrompt
Find vulnerabilities in your AI prompts before your users do
What it does
TestMyPrompt helps developers and teams find vulnerabilities in AI prompts before they reach production. Test across 22 security, safety and reliability categories, including prompt injection, jailbreaks, data exfiltration, indirect prompt attacks, tooling permissions, bias and hallucination risk. Get clear results, risk ratings and actionable recommendations to help you ship safer, more reliable AI.
Test AI prompts for injection, jailbreak, data leakage, bias, and hallucination risks before production.
Automatically scan system prompts for injection vulnerabilities, policy bypass attempts, and data leakage risks. Ship safer AI products with confidence. Our AI evaluator checks prompts across security, safety, ethics, and quality dimensions so nothing slips through. Paste any system prompt and get a 0–100 risk score with categorised findings before you ship. Comprehensive checks across security, safety & ethics, and quality — covering injection, bias, toxicity, and hallucination risk. Every finding includes a plain-English explanation and a concrete remediation step, not just a flag. Organise tests by product, model, or environment. Assign owner, admin, and member roles per workspace. Every…from testmyprompt.net
More ai this month
the category →
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.
AI · 16d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
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…
AI · 26d ago · cactuscompute.com
Launched alongside, August 2026
the whole month →- TL
Life & fun · 10d ago · louisabraham.github.io
- SA
Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
Life & fun · Aug 2026 · toneyalexander.github.io
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.
AI · 16d ago · simedw.com