Observyze
Real-time LLM observability & guardrails — 100% free
What it does
Since our beta, we've rebuilt Observyze for production scale. What's new in Early Access: ⚡ Sub-10ms Ingestion: Non-blocking telemetry proxy with zero latency drag. 🔒 Client-Side PII Scrubbing: In-memory masking before network transmission. 🛑 Active Circuit Breakers: Halts hallucinations before reaching users. 💰 40+ Model Cost Tracking: Exact token-level spend attribution. 🎉 100% Free: Claim 90 days of full Pro tier (100k traces/mo) with no credit card needed!
Trace production AI agents, monitor LLM cost and latency, enforce execution budgets, runtime policies and circuit breakers. Explore Observyze free during Early Access.
Trace every LLM call, enforce runtime limits, and investigate failures before they become expensive production incidents. Instrument in-process with the lightweight SDK without being in the provider request path, or route traffic through the proxy gateway to actively block prompt injections and enforce circuit breakers. Point a supported OpenAI-compatible client at the Observyze proxy, provide your project ID, and send a test request using the documented authentication headers. import os from openai import OpenAI # Authenticate with an Observyze project API key client = OpenAI( api_key =os.environ[ "OBSERVYZE_API_KEY" ], base_url = "https://api.observyze.com/api/v1/proxy/openai/v1" ,…from observyze.com
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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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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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