Lenz
Independent, multi-model fact-checking API for AI workflows
In plain words
Lenz is an API that fact-checks text for AI applications by extracting claims and verifying them across multiple sources and models. It uses debate between opposing viewpoints, independent reviewer panels, and citation tracking to return scored verdicts with full transparency about sources and reasoning. Available as an API and MCP server, it serves teams building AI products where factual accuracy is critical, offering a free tier with 1,000 daily extractions and paid plans starting at $7.99 monthly.
written from the facts on this page · September 2026
From the sources
Lenz is an AI fact-checking API for products that cannot afford to hallucinate. It extracts verifiable claims from any text, then checks each one: searching independent sources, running multi-model debate, and routing through a review panel — returning a scored verdict with every source, argument, and step visible. Most AI tools give you one model's best guess from memory. Lenz ensures no single model's blind spots drive the conclusion. Available as API and MCP. Try it free at lenz.io/ph
Drop-in fact-checking API for AI pipelines. 1 month Developer access free — Product Hunt exclusive.
An independent, multi-model fact-checking API for the teams that can’t afford to ship a wrong claim. Multiple models across five stages, grounded in independent sources. Opposing-side debate, three independent reviewers, full citation trail. ✓ New code sent Didn't get it? 0" x-text="resending ? 'Sending...' : (resendCooldown > 0 ? 'Resend in ' + resendCooldown + 's' : 'Resend')"> A free month of Developer access — $99 value, no card required. Full Developer access is applied the moment you sign up. New accounts only · within 7 days of your first visit Lenz frames it as a precise, falsifiable statement — removing ambiguity before research begins. Multiple queries across…from lenz.io
Pricing, as stated on its site
Free tier, paid plans from $7.99/mo — Free plan with 1,000 extractions/day and 100 credits/month. Paid plans start at $7.99/month (Plus), $99/month (Developer), $399/month (Scale), plus Enterprise option. Annual billing offers discounts.
checked September 2026 · prices change
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 · 9d 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