Is AI Dumber Today? An index of AI model experience from user's opinion
Track how AI models feel in everyday use through public community feedback, 7-day experience scores and trends. This is not a capability benchmark.
In plain words
Is AI Dumber Today tracks how AI language models perform in real-world use by collecting community feedback and ratings. The platform aggregates seven-day experience scores and trend data from power users who document model behavior, output quality, and issues like hallucinations. Rather than measuring technical capabilities, it captures subjective user experiences with different AI models and versions, helping people understand how various systems actually feel in everyday applications.
written from the facts on this page · September 2026
From the sources
Power users document autonomous execution, dense output, and surging hallucinations as Anthropic's latest model lands a 66 AA index on scientific benchmarks. Top rated requires at least 30 comments in 7 days. Trending counts version-specific comments in the communities we track. “Qwen3.8 27B have very similar behavior to Minimax M3 (thinks a lot, check everything, and like to continue working on its own for long session until it's done). It's like a slightly worse M3.” “I thought Gemini 3.8 Flash was already impressive enough, didn't expect there would be an even stronger player” “Deepseek v4 flash from day one went into loops so was unusable for tasks that were long, so I always used pro.…from isaidumber.today
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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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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.
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