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nerfWatch()
Daily AI benchmarks and community votes on model quality
nerfWatch() tests AI models daily through their APIs, against each model's first week of tracking. See scores beside community votes, get alerts for declines and recoveries, and inspect the open-source engine. API tests, not chat-app tests.
Hi Product Hunt, meet nerfWatch().
When an AI model feels worse, one disappointing answer doesn't tell you much. nerfWatch() gives you a place to compare repeated tests with what people are experiencing.
Here's how it works:
- Each tracked model takes the same 100 questions daily, across reasoning, logic, reading code, following instructions, and long documents.
- Answers are graded by code. Each model is compared with its own first 7 days of tracking, with model settings held fixed.
- Community votes sit beside the benchmark. You can vote once per model per day; votes never change the measured score or verdict.
- Email alerts report when a model enters the worse-than-baseline state or recovers.
- The testing engine is open source, so you can inspect it or run your own checks.
Tracking has just started: the initial baselines are still building, so there are no meaningful before-and-after verdicts yet. The dashboard shows "Too soon" while it collects that first week.
A few limits matter. These are API tests, not tests of ChatGPT, Claude.ai, or other chat apps. They cover a specific set of tasks, and a score change doesn't reveal why it happened. We also can't measure changes from before tracking began.
Take a look at https://nerfwatch.lol and share what you think. Which model should be tracked next, and what kind of test would help you trust the result?
About nerfWatch() on Product Hunt
“Daily AI benchmarks and community votes on model quality”
nerfWatch() was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #44 on the daily leaderboard. nerfWatch() tests AI models daily through their APIs, against each model's first week of tracking. See scores beside community votes, get alerts for declines and recoveries, and inspect the open-source engine. API tests, not chat-app tests.
On the analytics side, nerfWatch() competes within Open Source, Artificial Intelligence, GitHub and Tech — topics that collectively have 1.2M followers on Product Hunt. The dashboard above tracks how nerfWatch() performed against the three products that launched closest to it on the same day.
Who hunted nerfWatch()?
nerfWatch() was hunted by Serge. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of nerfWatch() including community comment highlights and product details, visit the product overview.
Serge