The picture in my office of Dirk's 3 in Game 2 of the 2011 Finals. I stare at it often - especially at the many fans who know the ball is about to break their hearts. @swish41
PICARD: Data, shields up
DATA: Brilliant! Shields can reduce damage we sustain. Not immunity. Not hubris. Just prudence. It's not precaution—it's strategy.
[camera shakes]
WORF: HULL BREACHES ON NINE DECKS
DATA: Here's what happened: you told me to raise shields, and I didn't
A community college professor named Marty Lobdell taught the same study skills lecture for 30 years. The video quietly became one of the most watched educational recordings online, with over 10 million views.
He spent his career watching students fail not because they were lazy, but because no one had taught them how their brain actually works when learning something difficult.
The lecture, “Study Less Study Smart,” contains a powerful framework.
Your brain cannot sustain focus the way most people believe. Studies show the average learner hits a wall between 25 and 30 minutes. After that, efficiency collapses. You’re still sitting there, but almost nothing is being absorbed.
Lobdell told the story of a student who planned to study 6 hours a night, 5 nights a week. Thirty hours total. She failed every class. She was not lacking effort. She was confusing time near books with actual learning. The fix is simple: when focus drops, stop, take a 5 minute rewarding break, then return. That reset makes a massive difference.
He also destroyed the myth of highlighting and re reading. Recognition is not the same as recall. To prove it, he read 13 random letters. Almost no one remembered them. Then he turned them into “Happy Thursday.” The entire room recalled them instantly. The brain stores meaning, not repetition.
This is why elaborative encoding works so well.
Finally, he shared the most important principle: 80 percent of study time should be active recitation. Close the book and explain the material in your own words. Teach it to someone else or an empty chair. Retrieval is where real learning happens.
His closing line stuck with me: If this information does not change your
behaviour, you have not actually learned it.
The best students do not study more hours. They stop confusing the feeling of studying with the reality of learning.
Let’s all watch Touchdown Tony Dorsett do awesome things for a minute. Perhaps the least talked about elite running back of his era. That needs to change.
As contagious as it is to watch Belushi, you just can't take your eyes off of Aykroyd.
The sheer amount of talent on that small stage is staggering.
The Blues Brothers performing 'Soul Man' live on SNL, in 1978.
48 years apart yet it is still incredibly impressive.
Updating a popular tweet from a few years ago...
Can we put "Pistol" Pete Maravich's NCAA Division I men's scoring record in perspective?
He scored 3,667 points in 83 games over 3 years.
If you took the nation's top 83 Division I men's basketball single-game scoring performances in the last 3 years (2023-24, 2024-25 & 2025-26), you'd have a combined 3,529 total points.
One of my favorite cinematic #NFL Films segments
The feverish late-game comeback effort made by the #DallasCowboys in Super Bowl XIII against the #Steelers — appropriately set to the dramatic musical score of Jack Trombey's "War Footing".
January 21, 1979
Barry Sanders’ ratings in Madden were such a joke it allowed him to do crazy shit like this that ruined the realism of the game - hang on, being told this is actual NFL game tape.
New research just dropped: this prompting technique cuts AI hallucinations by 50%.
It's called Model-First Reasoning.
Instead of asking "How do I solve [xxx] problem?"
You first force the AI to list: what's involved, what can change, what actions are possible, and what's not allowed.
THEN you ask it to solve using only what it wrote down.
So what makes this different from Chain-of-Thought?
CoT lets the AI think and solve, but at the same time.
It sounds smart. It flows well. But it makes stuff up along the way.
Model-First Reasoning creates a hard wall instead.
Define first. Solve second. No mixing.
The AI can ONLY use what it wrote down in step one. That's the trick.
The researchers tested it on medical scheduling, route planning, resource allocation, and logic puzzles.
Same pattern everywhere: fewer broken rules, more consistent outputs.
Why it works:
✦ LLMs make things up because they assume stuff you never told them.
✦ When you force them to write everything down first, there's nowhere to hide.
✦ It makes a stronger case for why "Human-in-the-loop" works much better, too: we make sure every step is validated before going to the next.
You can read the paper here: https://t.co/vTuQvsNyCk.