Tutorials, real-world applications, and plain-English explanations — for people who use AI at work but don't want to read a research paper to understand it.
Three ways in, depending on what you're trying to do. Each one gathers the relevant posts and puts them in a sensible order.
How do I use AI safely and usefully at work?
How do I roll AI out to my team?
How do I use AI for X?
78% of managers have used AI on feedback or a review in the past year. Only 16% of employees were told. Research on AI-written messages from supervisors shows exactly where the trust goes, and where it doesn't.
Most hackathon projects get no further work once the event ends. The ones that survive share three things the research can name, and all three are decided before and after the day. A one-day format built around them.
Around 9% of organizations rethink a role before filling it. Waiting for yours is betting on a one-in-eleven event. There is a 25-year research literature on doing it yourself, and it says the method works, modestly.
A nationally representative survey mapped AI use to real occupations and tasks. At least a fifth of workers use it in 80% of occupations, and fewer than 3% of tasks pass 50% adoption. It also corrects a number I published here.
Models score around 43% on simple character-counting tasks. Everyone blames tokenization. A larger benchmark found it barely predicts counting errors at all, and what does predict them is worse news for real work.
In a randomized trial, students using an AI tutor scored 127% better on practice problems and no better on the exam. The ones using plain ChatGPT scored 17% worse. Here is what to do differently.
Microsoft measured 29 factors against reported AI impact across 19,854 people. Culture, manager support and talent practices explained 67% of it. Individual mindset explained 32%.
Every assistant now has a thinking mode. Research found three levels of difficulty, and on the easiest one the ordinary model beats the reasoning one. A plain-English guide to which tasks want it.
77% of workers scrutinize a colleague's work more closely when they know AI was involved. The load-bearing words are 'when they know.' A review method that doesn't depend on being told.
Berkeley researchers spent eight months inside a company using AI. In the moment people felt momentum. Stepping back, they felt busier. Both were true.