Maxlearn
Forgetting curve and its relevance for trainers
Maxlearn launched on October 31, 2024 with 3 votes. The site was still online in September 2026, 1 year on.
What it does
Understanding Ebbinghaus’ forgetting curve for crafting effective training modules - a perspective for trainers and instructional designers!
Learn how Ebbinghaus' Forgetting Curve helps improve memory retention, recall and workplace performance through microlearning.
It happens to the best of us. We forget what we learn much sooner than we think. This happens despite of our best efforts to consciously remember what we learned. Retaining newly acquired information is tricky, and we end up forgetting things, sometimes within minutes! It’s a real challenge to design training in such a way that learners retain what’s learned! But there is a way to make information ‘stick in their memory’ making recall possible. And that’s by taking the help of Ebbinghaus’ Forgetting Curve that gives us a framework on how memory works. Delivering a training program is easy, but making employees remember what’s learned when it’s needed the most is the real challenge. Blindly…from maxlearn.com
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MaxLearn ‘Ebbinghaus’ Forgetting Curve 2024 · ▲1Forgetting Curve with MaxLearn's Microlearning approach.

AI memory with biological decay (52% recall)Apr 2026 · github.com · ▲98Most RAG setups fail because they treat memory like a static filing cabinet. When every transient bug fix or abandoned rule is stored forever, the context window eventually chokes on noise, spiking token costs and degrading the agent's reasoning. This implementation experiments with a biological approach by using the Ebbinghaus forgetting curve to manage context as a living substrate. Memories are assigned a "strength" score where each recall reinforces the data and flattens its decay curve (spaced repetition), while unused data eventually hits a threshold and is pruned. To solve the…
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