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Adaption AutoScientist
Automating the full research loop behind model training
AI can now self-improve models for the first time. In testing, AutoScientist already outperforms human AI researchers by 35% on average, across 8 verticals, dataset sizes from 5k to 100k, and nearly every open-weight model architecture (DeepSeek, Llama, Gemma, etc.) available through Together AI for fine-tuning.
Less than a thousand people in the world know how to shape a frontier model. They sit inside a handful of labs, working on proprietary systems. Everyone else has been relegated to prompt engineering, contorting requests to fit models built for the average use case.
Adaption believes that should change. Intelligence should not arrive preconfigured, and building AI shouldn’t require a PhD. Model training and reinforcement learning are among the most powerful ways to shape a model, and among the hardest to get right outside a frontier lab. Most attempts fail for the same reasons: catastrophic forgetting that erodes general knowledge, overfitting on small or low-quality datasets, and conflicting training signals that fail to teach new behaviors.
The techniques that work are passed researcher-to-researcher, rarely written down. The result is a world where a small group of experts defines what AI can and cannot do, while everyone else is left on the sidelines. That changes when AI can automate this process.
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About Adaption AutoScientist on Product Hunt
“Automating the full research loop behind model training”
Adaption AutoScientist was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #160 on the daily leaderboard. AI can now self-improve models for the first time. In testing, AutoScientist already outperforms human AI researchers by 35% on average, across 8 verticals, dataset sizes from 5k to 100k, and nearly every open-weight model architecture (DeepSeek, Llama, Gemma, etc.) available through Together AI for fine-tuning.
Adaption AutoScientist was featured in Developer Tools (512.4k followers), Artificial Intelligence (468.5k followers) and Tech (623.5k followers) on Product Hunt. Together, these topics include over 324.5k products, making this a competitive space to launch in.
Who hunted Adaption AutoScientist?
Adaption AutoScientist was hunted by Kyle Austin. 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.
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Kyle Austin