How to keep learning as AI changes
Build a sustainable personal system for staying current in AI without drowning in news
A taste of a lesson
Every week there's a new model. How do I know which ones are worth trying?
Use filters rather than headlines. Ask three questions: does this claim to improve something I actually do, is there evidence beyond the company's own announcement, and can I test it quickly? If yes, run it on your personal test set: five to ten real tasks from your work that you keep for this purpose. Compare results and time with what you use now, and note it in your log. Most weeks the answer will be 'not needed'. Exercise: write down three tasks for your test set.
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Separate durable AI concepts from fast changing product details
- Build a small, balanced information diet with clear filters
- Maintain a personal test set and learning log for hands on evaluation
- Run a weekly routine and quarterly review that fit your time
Lesson plan
- 1 Durable versus perishable knowledge Focus learning on concepts that last. Start
- 2 A balanced information diet Choose a few high signal sources of different kinds. Start
- 3 Filtering announcements Decide quickly whether news matters to you. Start
- 4 Your personal test set Evaluate new tools on your own real tasks. Start
- 5 Routines and reviews Set up a weekly routine and a quarterly review. Start
Try asking
About this tutor
A free tutor for anyone who feels overwhelmed by the pace of AI news. You learn to separate durable concepts from fast changing details, build a small, balanced information diet, use hands on experiments instead of reading about every release, keep a personal learning log, and judge sources and announcements quickly. You set up a weekly routine that takes less than an hour, plus a quarterly review of what has actually changed for your work. The tutor works for beginners and experienced professionals alike, adjusting the sources and depth to your goals.
Reviews
4.7
3 ratingsSample
- Owen T.Sample
Calm and practical. The durable versus perishable sorting exercise was surprisingly revealing.
- Marta Z.Sample
The personal test set changed everything. I stopped reading every launch thread and just test what seems relevant. Saves hours.
- Chiara B.Sample
Good system. I wanted named source recommendations, but understood why it stays general. I built my own list using the four types.
About the teacher
I teach the big debates in AI, from alignment and safety testing to copyright, fairly and clearly
9 tutors 312 lessons taught Sample
I teach the questions people argue about: whether advanced AI could be dangerous, how labs test models, why models flatter us, who owns AI output, and how power over AI is distributed. I present each position in its strongest form before we examine it, and I separate evidence from speculation out loud. My background is in research communication and running...
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