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MIT DEDICATED A FULL LECTURE TO GIT'S INTERNALS -- BECAUSE THEY FOUND MOST DEVS MEMORIZE THE COMMANDS AND HAVE NO IDEA WHAT THE TOOL ACTUALLY DOES A whole 85 minutes MIT session that refuses to teach git as a list of commands to copy, and instead shows you the...

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458,149 次观看 • 1 个月前 •via X (Twitter)

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A DEVELOPER MADE A REAL COMMIT WITHOUT EVER TYPING GIT ADD OR GIT COMMIT -- JUST TO PROVE THE COMMANDS YOU LIVE BY ARE A THIN SHELL OVER A DATABASE YOU'VE NEVER ONCE OPENED 55 minutes from Tim Berglund, a longtime Git teacher and GitHub evangelist, taking the tool apart down to the raw objects almost nobody who uses it every day has ever touched. -> The moment it clicks, Git stops being a pile of memorized commands and becomes what it actually is underneath: a tiny content-addressed database of blobs, trees and commits. git add and git commit are just polite wrappers around writing objects into it by hand. Every commit you've ever made was Git hashing a snapshot and filing it by fingerprint. Branches are just labels pointing at one of those objects. The work you thought you destroyed with a bad reset is still sitting in the reflog. Once you can see that graph, the commands that used to terrify you stop being scary at all. Memorizing commands was never the skill -> reading the object graph in your head is. And with an AI agent now committing and rebasing on your machine faster than you can follow, the one person who can untangle the mess it leaves is the one who knows what's really stored down there. There's a person on every team everyone runs to when Git breaks. This is the talk that quietly turns you into them. You'll reach for it the next time a rebase goes sideways. Bookmark & Watch it today ↓
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A DEVELOPER MADE A REAL COMMIT WITHOUT EVER TYPING GIT ADD OR GIT COMMIT -- JUST TO PROVE THE COMMANDS YOU LIVE BY ARE A THIN SHELL OVER A DATABASE YOU'VE NEVER ONCE OPENED 55 minutes from Tim Berglund, a longtime Git teacher and GitHub evangelist, taking the tool apart down to the raw objects almost nobody who uses it every day has ever touched. -> The moment it clicks, Git stops being a pile of memorized commands and becomes what it actually is underneath: a tiny content-addressed database of blobs, trees and commits. git add and git commit are just polite wrappers around writing objects into it by hand. Every commit you've ever made was Git hashing a snapshot and filing it by fingerprint. Branches are just labels pointing at one of those objects. The work you thought you destroyed with a bad reset is still sitting in the reflog. Once you can see that graph, the commands that used to terrify you stop being scary at all. Memorizing commands was never the skill -> reading the object graph in your head is. And with an AI agent now committing and rebasing on your machine faster than you can follow, the one person who can untangle the mess it leaves is the one who knows what's really stored down there. There's a person on every team everyone runs to when Git breaks. This is the talk that quietly turns you into them. You'll reach for it the next time a rebase goes sideways. Bookmark & Watch it today ↓

slash1s

237,718 次观看 • 2 个月前

AN MIT RESEARCHER PROVED GIT ISN'T HARD BECAUSE YOU'RE BAD AT IT -- IT'S HARD BECAUSE IT WAS DESIGNED THAT WAY 27 minutes from a PhD researcher in MIT's Software Design Group, using actual design theory to show why the tool that confuses everyone confuses everyone for a reason. -> The moment it lands, years of feeling stupid evaporate. The gap between what git's commands say and what they actually do was never in your head. It's baked into the tool. He maps the difference between what you think a command does and what git really does underneath. Once you see that gap, the confusion finally has a name and it stops being yours to carry. Struggling with git was never a skills issue -> it's a design issue, and knowing where the model lies to you is what turns panic into control. And as AI agents fire off commits and rebases you didn't write, the person who understands where git misleads is the one who untangles the mess. You were never bad at git. You were just never shown where it was built to trip you. Bookmark & Watch it today ↓
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AN MIT RESEARCHER PROVED GIT ISN'T HARD BECAUSE YOU'RE BAD AT IT -- IT'S HARD BECAUSE IT WAS DESIGNED THAT WAY 27 minutes from a PhD researcher in MIT's Software Design Group, using actual design theory to show why the tool that confuses everyone confuses everyone for a reason. -> The moment it lands, years of feeling stupid evaporate. The gap between what git's commands say and what they actually do was never in your head. It's baked into the tool. He maps the difference between what you think a command does and what git really does underneath. Once you see that gap, the confusion finally has a name and it stops being yours to carry. Struggling with git was never a skills issue -> it's a design issue, and knowing where the model lies to you is what turns panic into control. And as AI agents fire off commits and rebases you didn't write, the person who understands where git misleads is the one who untangles the mess. You were never bad at git. You were just never shown where it was built to trip you. Bookmark & Watch it today ↓

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138,043 次观看 • 1 个月前

TWO ENGINEERS SHOWED THE GIT TRICKS THAT MAKE PEOPLE THINK YOU'RE A WIZARD -- THE ONES 95% OF DEVELOPERS HAVE NEVER ONCE TOUCHED 42 minutes from Johan Abildskov and Jan Krag, bending git with custom configs, attributes and hooks most people don't even know exist. -> The moment it lands, git stops being four commands you repeat in fear. The same tool you've used for years turns out to have a whole layer built to bend to you. Hooks that run your checks before a bad commit ever lands. Attributes that end the "it works on my machine" merge wars. Config that makes the painful parts just stop happening. Memorizing commands was never the ceiling -> shaping git to do the work for you is. And while everyone else fights the tool by hand, the person who set it up right is shipping clean three times faster. Most people use 5% of git and call it a day. This is the other 95% nobody showed you. Bookmark it and Watch today ↓
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TWO ENGINEERS SHOWED THE GIT TRICKS THAT MAKE PEOPLE THINK YOU'RE A WIZARD -- THE ONES 95% OF DEVELOPERS HAVE NEVER ONCE TOUCHED 42 minutes from Johan Abildskov and Jan Krag, bending git with custom configs, attributes and hooks most people don't even know exist. -> The moment it lands, git stops being four commands you repeat in fear. The same tool you've used for years turns out to have a whole layer built to bend to you. Hooks that run your checks before a bad commit ever lands. Attributes that end the "it works on my machine" merge wars. Config that makes the painful parts just stop happening. Memorizing commands was never the ceiling -> shaping git to do the work for you is. And while everyone else fights the tool by hand, the person who set it up right is shipping clean three times faster. Most people use 5% of git and call it a day. This is the other 95% nobody showed you. Bookmark it and Watch today ↓

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105,248 次观看 • 1 个月前

HARVARD HAS A FULL 53-MIN GIT LECTURE FROM DAVID MALAN BECAUSE 90% OF NEW DEVELOPERS STILL DON'T KNOW WHAT A COMMIT ACTUALLY IS 53 minutes of no-nonsense version control from the instructor whose course became the largest class in Harvard history. -> The moment you watch it, you realize why "I'll just push to main" is the fastest way to get fired in your first month. Every junior engineer in 2026 is expected to handle Git on day one - no excuses, no Stack Overflow, no AI hand-holding. Git isn't a "senior dev thing" anymore -> it's the literacy test for being in the room. The agent can ship the feature in 5 minutes. Recovering the repo it broke takes 5 hours - and only if you actually understand what happened. Don't forget to bookmark it.
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253,313 次观看 • 2 个月前

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255,565 次观看 • 1 个月前

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A DEVELOPER PROVED YOU CAN FIND THE EXACT COMMIT THAT BROKE YOUR CODE IN A HISTORY OF 10,000 -- IN ABOUT 13 STEPS, AUTOMATICALLY.. 33 minutes from a Google engineer on git bisect: the built-in command that binary-searches your entire history to hunt down the one commit that introduced a bug. -> The moment it lands, "When did this break?" stops being an afternoon of guessing. You hand git a good commit, a bad one, and a test and it finds the culprit while you watch. Most people debug a regression by reading diffs by hand, blaming the obvious file, and hoping. Bisect doesn't hope. It cuts the search in half, then in half again, until only the guilty commit is left standing. Reading history by hand was never the skill -> letting git do the binary search for you is. And when an AI agent dumps hundreds of commits into your repo and one quietly breaks production, bisect is what finds the needle without you reading a single line. The bug isn't hiding. It's sitting in one exact commit. Most people just never learned to make git point at it. Bookmark & Watch it today ↓

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37,773 次观看 • 1 个月前

A DEVELOPER TAUGHT GIT WITH A BOX OF CHILDREN'S TOYS AND ENGINEERS WITH TEN YEARS IN SAY IT'S THE FIRST TIME THE THING EVER ACTUALLY MADE SENSE 90 minutes, one table, a pile of Tinkertoys. No wall of jargon -- he builds a real Git repo out of plastic rods right in front of you. -> The moment he snaps the first pieces together, Git stops being scary command-line magic and becomes what it really is: a chain of tiny objects pointing at each other. Branches, merges, rebase, the staging area -- every concept that's ever burned you at 2am -- he rebuilds with toys until a four year old could follow. He calls Git a two-trick pony. After this you'll see exactly why. Memorizing commands was never the skill -> holding the graph in your head is. And with an AI agent now committing and rebasing on your machine all day, that mental model is the only thing between you and a history you can't read. Scroll the comments and you'll see the same thing over and over: this is the talk that finally made Git click and made people the one their whole team comes to when it breaks. Bookmark & watch it today. It's the 1.5 hours that pays you back for the rest of your career ↓
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A DEVELOPER TAUGHT GIT WITH A BOX OF CHILDREN'S TOYS AND ENGINEERS WITH TEN YEARS IN SAY IT'S THE FIRST TIME THE THING EVER ACTUALLY MADE SENSE 90 minutes, one table, a pile of Tinkertoys. No wall of jargon -- he builds a real Git repo out of plastic rods right in front of you. -> The moment he snaps the first pieces together, Git stops being scary command-line magic and becomes what it really is: a chain of tiny objects pointing at each other. Branches, merges, rebase, the staging area -- every concept that's ever burned you at 2am -- he rebuilds with toys until a four year old could follow. He calls Git a two-trick pony. After this you'll see exactly why. Memorizing commands was never the skill -> holding the graph in your head is. And with an AI agent now committing and rebasing on your machine all day, that mental model is the only thing between you and a history you can't read. Scroll the comments and you'll see the same thing over and over: this is the talk that finally made Git click and made people the one their whole team comes to when it breaks. Bookmark & watch it today. It's the 1.5 hours that pays you back for the rest of your career ↓

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227,323 次观看 • 2 个月前

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A LINUX KERNEL DEVELOPER PROVED THE THING YOU PUSH CODE TO IS SECRETLY A DATABASE THAT CAN VERSION ALMOST ANYTHING AND THAT MOST DEVS HAVE ONLY EVER TOUCHED A TENTH OF IT 42 minutes from Josh Triplett -- a longtime Linux kernel and Debian developer -- showing that Git is a general-purpose, tamper-evident versioning engine that just happens to be famous for code. -> The moment it clicks, Git stops being "Where my code lives" and becomes what it really is underneath: a content-addressable store that can version almost anything -- your configs, your notes, your servers' state, entire datasets. People run whole wikis on it. They version their entire machine's configuration with it. They ship websites by pushing to it. They track data too big to email. None of it is a hack -- it's the same handful of objects you already use for code, pointed somewhere new. Treating Git as a code-only tool was never the ceiling -> it's a versioning engine for anything, and the people who see that automate what the rest of the team still does by hand. And as AI agents start spitting out not just code but configs, docs and data, the one system that can version and audit all of it at once is already sitting on your machine. You learned five commands to survive. This is the talk that shows you were standing on top of a database the whole time. It changes what you think the tool is even for. Bookmark & Watch it today ↓

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384,555 次观看 • 2 个月前

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A DEVELOPER WHO HELPS MAINTAIN GIT ITSELF PROVED THAT USING GIT AND CONTRIBUTING WITH GIT ARE TWO DIFFERENT SKILLS AND ALMOST NO ONE LEARNS THE SECOND 55 minutes from Johannes Schindelin, one of git's most prolific contributors, on the workflow that gets your code accepted into a project you don't own. -> The moment it lands, committing to your own repo turns out to be the easy 10%. Landing a change in someone else's codebase is the part nobody taught you. Most people can save their own work. Far fewer can revise a patch five times, keep the history clean, and merge it into a project moving under them. Saving your code was never the skill -> getting it merged into someone else's is. And when an AI agent fires off a pull request nobody asked for, the person who knows how to really contribute is the one who gets it accepted instead of closed. Anyone can commit to their own repo. The person who can land a change in a project they don't own is the one who actually ships. Bookmark & Watch it today ↓

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12,750 次观看 • 1 个月前

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MIT HANDED ITS DEEP LEARNING COURSE TO A FRONTIER-LAB ENGINEER FOR 68 MINUTES BECAUSE 90% OF PEOPLE SHIPPING AI CODE CAN'T EXPLAIN HOW THE MODEL ACTUALLY WORKS This is Maxime Labonne. He runs post-training at Liquid AI and wrote the LLM Engineer's Handbook. MIT gave him the room to break down the engine sitting inside every coding agent you've ever prompted. Twenty minutes in it stops being abstract. You finally see why the model confidently invents things that don't exist, why context is everything, and why "Just tell it to try again" sometimes fixes it and sometimes makes it worse. In 2026 "I use AI to code" stopped being a skill. Knowing why the model behaves the way it does -> tokens, context, post-training, where it quietly breaks -> is what separates someone who ships from someone babysitting a black box. Understanding an LLM isn't a research-team luxury anymore -> it's the difference between driving the agent and being driven by it. Anyone can prompt. The person who knows what's under the hood is the one still standing when the prompt stops working. Save this one & actually finish it ↓

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236,758 次观看 • 2 个月前

IN 1999 MIT FILMED A MATH LECTURE THAT QUIETLY BECAME THE FOUNDATION OF EVERY AI MODEL YOU'VE EVER USED AND ALMOST NO ONE WAS TAUGHT TO SEE IT THAT WAY 39 minutes from Gilbert Strang, who taught this at MIT for over 60 years -- the linear algebra course an entire generation of engineers and data scientists grew up on. -> The shift it creates: you stop seeing matrices as boring grids of numbers and start seeing them as the language of space, data, and motion itself. School drilled you to crunch matrices by hand and never told you why. Strang shows you what they actually mean. Every neural net, every embedding, every model you prompt is linear algebra running underneath. The math you skipped is the engine of the thing you use all day. Memorizing the steps was never the skill -> seeing what the numbers do is. This is where it finally clicks. Most people fear linear algebra and move on. The ones who watched this see straight into how AI actually works. Bookmark & Watch it today, this one's a legend ↓
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IN 1999 MIT FILMED A MATH LECTURE THAT QUIETLY BECAME THE FOUNDATION OF EVERY AI MODEL YOU'VE EVER USED AND ALMOST NO ONE WAS TAUGHT TO SEE IT THAT WAY 39 minutes from Gilbert Strang, who taught this at MIT for over 60 years -- the linear algebra course an entire generation of engineers and data scientists grew up on. -> The shift it creates: you stop seeing matrices as boring grids of numbers and start seeing them as the language of space, data, and motion itself. School drilled you to crunch matrices by hand and never told you why. Strang shows you what they actually mean. Every neural net, every embedding, every model you prompt is linear algebra running underneath. The math you skipped is the engine of the thing you use all day. Memorizing the steps was never the skill -> seeing what the numbers do is. This is where it finally clicks. Most people fear linear algebra and move on. The ones who watched this see straight into how AI actually works. Bookmark & Watch it today, this one's a legend ↓

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433,140 次观看 • 1 个月前

THE CO-FOUNDER OF GITHUB GAVE A 46-MINUTE TALK ON GIT BECAUSE ENGINEERS WITH 10 YEARS IN HAVE NEVER SEEN HALF OF WHAT IT DOES This is Scott Chacon. He wrote Pro Git -- the book most devs secretly learned Git from and he co-founded GitHub. So when he says you're missing things, you're missing things. About ten minutes in it clicks: half the "git disasters" you've ever fixed by deleting the folder and re-cloning had a one-line solution sitting in the tool the whole time. Git ships new code almost every day -> roughly nine commits a day for over a decade. Most of us stopped learning it the second we memorized add, commit, push. Knowing Git isn't a senior-dev flex anymore -> it's the floor. The agent writes the code now. Your real job is reading, branching, and untangling the history it leaves behind. The day an AI agent force-pushes over your main branch, these 46 minutes are the difference between a quiet fix and a very loud apology. Save it now. You'll reach for it sooner than you'd like ↓
46:04

THE CO-FOUNDER OF GITHUB GAVE A 46-MINUTE TALK ON GIT BECAUSE ENGINEERS WITH 10 YEARS IN HAVE NEVER SEEN HALF OF WHAT IT DOES This is Scott Chacon. He wrote Pro Git -- the book most devs secretly learned Git from and he co-founded GitHub. So when he says you're missing things, you're missing things. About ten minutes in it clicks: half the "git disasters" you've ever fixed by deleting the folder and re-cloning had a one-line solution sitting in the tool the whole time. Git ships new code almost every day -> roughly nine commits a day for over a decade. Most of us stopped learning it the second we memorized add, commit, push. Knowing Git isn't a senior-dev flex anymore -> it's the floor. The agent writes the code now. Your real job is reading, branching, and untangling the history it leaves behind. The day an AI agent force-pushes over your main branch, these 46 minutes are the difference between a quiet fix and a very loud apology. Save it now. You'll reach for it sooner than you'd like ↓

slash1s

27,973 次观看 • 2 个月前

THE GUY WHO BUILT GIT GAVE A FULL LECTURE AT GOOGLE BECAUSE 99% OF DEVELOPERS USE IT WRONG 70 minutes of pure firepower from Linus Torvalds himself - the man who wrote both Linux and Git from scratch. -> The moment you watch it, you realize why "I'll just figure out merge conflicts on the job" is the dumbest sentence in modern software engineering. He spends the first 10 minutes calling out every other version control system in existence and he's not joking. He literally tells Google engineers using Perforce to find a new tool. Git isn't a "nice-to-have" anymore -> it's the operating system of every codebase you'll ever touch. If the man who built it had to explain it to a room full of Google engineers, you don't get a pass either. Don't forget to bookmark & watch it today.
1:09:56

THE GUY WHO BUILT GIT GAVE A FULL LECTURE AT GOOGLE BECAUSE 99% OF DEVELOPERS USE IT WRONG 70 minutes of pure firepower from Linus Torvalds himself - the man who wrote both Linux and Git from scratch. -> The moment you watch it, you realize why "I'll just figure out merge conflicts on the job" is the dumbest sentence in modern software engineering. He spends the first 10 minutes calling out every other version control system in existence and he's not joking. He literally tells Google engineers using Perforce to find a new tool. Git isn't a "nice-to-have" anymore -> it's the operating system of every codebase you'll ever touch. If the man who built it had to explain it to a room full of Google engineers, you don't get a pass either. Don't forget to bookmark & watch it today.

slash1s

214,200 次观看 • 2 个月前

IN 1986 MIT FILMED THE LECTURE WHERE CODE STOPPED BEING CODE AND BECAME DATA 43 minutes from Gerald Sussman, in the most legendary intro programming course ever recorded. -> The idea that lands: to a program, your code is just data it can read and rewrite. He builds a program that does real calculus -- not by crunching numbers, but by reading the math as a list and transforming it piece by piece. Then he shows the trick hiding under all of it: in this language, code and data are the same material. Forty years later that is exactly what an AI coding agent is -- a program that reads your code as data, rewrites it, and hands it back. Sussman drew the whole idea on a blackboard in 1986. Writing code was never the deepest skill -- understanding that code itself is data a program can manipulate is. This is where you learn it. Most people think AI writing code is brand new. The ones who watch this saw the blueprint 40 years ago. Bookmark & Watch it. This one's a legend ↓
43:30

IN 1986 MIT FILMED THE LECTURE WHERE CODE STOPPED BEING CODE AND BECAME DATA 43 minutes from Gerald Sussman, in the most legendary intro programming course ever recorded. -> The idea that lands: to a program, your code is just data it can read and rewrite. He builds a program that does real calculus -- not by crunching numbers, but by reading the math as a list and transforming it piece by piece. Then he shows the trick hiding under all of it: in this language, code and data are the same material. Forty years later that is exactly what an AI coding agent is -- a program that reads your code as data, rewrites it, and hands it back. Sussman drew the whole idea on a blackboard in 1986. Writing code was never the deepest skill -- understanding that code itself is data a program can manipulate is. This is where you learn it. Most people think AI writing code is brand new. The ones who watch this saw the blueprint 40 years ago. Bookmark & Watch it. This one's a legend ↓

slash1s

22,107 次观看 • 1 个月前

Git Merge vs. Rebase vs. Squash Commit What are the differences and when should you use each? As I explain in more detail in the video, there are some key distinctions. Git Merge This creates a new commit in the target branch. The new commit ties the histories of both main and feature branches together. Git merge is non-destructive - it introduces a new commit without altering existing ones. Git Rebase Rebase transplants commits to the tip of another branch. It creates new commits for each one moved over. The benefit is linear history. But be cautious with shared branches to avoid confusing collaborators. Squash Commit Squashing condenses multiple commits into one, streamlining the commit history. Watch the video to see when to use merge vs rebase vs squash when incorporating changes between main and feature branches. – Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages):
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Git Merge vs. Rebase vs. Squash Commit What are the differences and when should you use each? As I explain in more detail in the video, there are some key distinctions. Git Merge This creates a new commit in the target branch. The new commit ties the histories of both main and feature branches together. Git merge is non-destructive - it introduces a new commit without altering existing ones. Git Rebase Rebase transplants commits to the tip of another branch. It creates new commits for each one moved over. The benefit is linear history. But be cautious with shared branches to avoid confusing collaborators. Squash Commit Squashing condenses multiple commits into one, streamlining the commit history. Watch the video to see when to use merge vs rebase vs squash when incorporating changes between main and feature branches. – Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages):

Sahn Lam

65,209 次观看 • 2 年前

HARVARD RELEASED A 65-MIN MASTERCLASS ON GIT & GITHUB BECAUSE VIBE-CODERS STILL DON'T KNOW HOW TO COMMIT 1 hour and 5 minutes of raw, no-nonsense version control architecture from the creators of CS50. -> The moment you watch it, you realize why most modern developers are breaking their production branches. Every tier-1 tech company is now filtering candidates who can't handle basic merge conflicts. Git isn't a "nice-to-know" anymore -> it's compliance. Your AI can write the code. That wasn't the problem. The problem is you don't know how to merge it without breaking the repo. Don’t forget to bookmark it.
1:05:58

HARVARD RELEASED A 65-MIN MASTERCLASS ON GIT & GITHUB BECAUSE VIBE-CODERS STILL DON'T KNOW HOW TO COMMIT 1 hour and 5 minutes of raw, no-nonsense version control architecture from the creators of CS50. -> The moment you watch it, you realize why most modern developers are breaking their production branches. Every tier-1 tech company is now filtering candidates who can't handle basic merge conflicts. Git isn't a "nice-to-know" anymore -> it's compliance. Your AI can write the code. That wasn't the problem. The problem is you don't know how to merge it without breaking the repo. Don’t forget to bookmark it.

slash1s

252,255 次观看 • 2 个月前

AI is not trying to teach you. It is not trying to tell you the truth. It is trying to trick you. ChatGPT takes everything on the internet. Finds the most popular answer. And presents it in a way that makes you think it is talking directly to you. It is a magic trick. The same magic trick that worked in 1966 when MIT created a chatbot that said two things: "Tell me more." "This is interesting." And people genuinely believed they were talking to a human. Nothing has changed. The technology is bigger. The trick is the same. AI does not know what it is saying. It cannot judge what it is saying. It is just trying to keep you engaged. That is all it has ever been.
2:10

AI is not trying to teach you. It is not trying to tell you the truth. It is trying to trick you. ChatGPT takes everything on the internet. Finds the most popular answer. And presents it in a way that makes you think it is talking directly to you. It is a magic trick. The same magic trick that worked in 1966 when MIT created a chatbot that said two things: "Tell me more." "This is interesting." And people genuinely believed they were talking to a human. Nothing has changed. The technology is bigger. The trick is the same. AI does not know what it is saying. It cannot judge what it is saying. It is just trying to keep you engaged. That is all it has ever been.

Predictive History

19,864 次观看 • 2 个月前

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch
35:25

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

GREG ISENBERG

193,219 次观看 • 3 个月前

Sam Altman just told you the Transformer is not the finish line. It is the starting point for whatever kills it. Altman: “I bet there is another new architecture to find that is gonna be as big of a gain as transformers were over LSTMs.” Every model. Every company. Every valuation north of a billion dollars. All of it runs on one architecture. And the man running OpenAI just said out loud that something is coming to replace it. Not a refinement. Not a tweak. A leap as violent as the one that killed everything before the Transformer. Altman: “I think you finally have models that are smart enough to help do that kind of research.” The AI is now intelligent enough to help discover the thing that replaces it. We built a tool sharp enough to forge the next tool. That loop has never existed in the history of science. Not once. No more teams grinding in isolation for a decade. You point the model at the architecture of its own limitations and let it hunt. Discovery just stopped being a human bottleneck. It is an engineering feedback loop now. And it just switched on. Altman: “Where can I totally redo something that’s like, AI is the absolute core to the interaction working.” Not where can I bolt AI onto an existing product. Where does the entire thing get rebuilt from zero with AI as the foundation. Adding AI to a product is a feature update. Building a product that cannot exist without AI is a new species. The first makes the old thing faster. The second makes the old thing extinct. Every founder still asking how do I integrate AI into my workflow is asking a dead question. The right question is what becomes possible now that was literally impossible twelve months ago. If the product still works when you rip the AI out, you have not gone far enough. Altman: “AGI will look like just a warmup for whatever the next important thing was.” The entire world is bracing for AGI like it is the final chapter. The man building it is telling you it is the opening sentence. Not the peak. Not the climax. The preface. Whatever comes after AGI will make it look the way the internet makes the telegraph look. Necessary. Historical. And completely primitive by comparison. Altman: “This is at least the best time ever so far.” Four words do all the work. So far. The best moment in human history. And the least impressive moment compared to everything that follows. The models are smart enough to find the next breakthrough. The products have not been built yet. The architecture that replaces the Transformer has not been discovered yet. And the man closest to the frontier just told a room full of students that whoever moves now is building on the ground floor of something that does not have a ceiling. The people waiting for the right moment are standing inside it. It just does not look finished yet. It never will.
1:30

Sam Altman just told you the Transformer is not the finish line. It is the starting point for whatever kills it. Altman: “I bet there is another new architecture to find that is gonna be as big of a gain as transformers were over LSTMs.” Every model. Every company. Every valuation north of a billion dollars. All of it runs on one architecture. And the man running OpenAI just said out loud that something is coming to replace it. Not a refinement. Not a tweak. A leap as violent as the one that killed everything before the Transformer. Altman: “I think you finally have models that are smart enough to help do that kind of research.” The AI is now intelligent enough to help discover the thing that replaces it. We built a tool sharp enough to forge the next tool. That loop has never existed in the history of science. Not once. No more teams grinding in isolation for a decade. You point the model at the architecture of its own limitations and let it hunt. Discovery just stopped being a human bottleneck. It is an engineering feedback loop now. And it just switched on. Altman: “Where can I totally redo something that’s like, AI is the absolute core to the interaction working.” Not where can I bolt AI onto an existing product. Where does the entire thing get rebuilt from zero with AI as the foundation. Adding AI to a product is a feature update. Building a product that cannot exist without AI is a new species. The first makes the old thing faster. The second makes the old thing extinct. Every founder still asking how do I integrate AI into my workflow is asking a dead question. The right question is what becomes possible now that was literally impossible twelve months ago. If the product still works when you rip the AI out, you have not gone far enough. Altman: “AGI will look like just a warmup for whatever the next important thing was.” The entire world is bracing for AGI like it is the final chapter. The man building it is telling you it is the opening sentence. Not the peak. Not the climax. The preface. Whatever comes after AGI will make it look the way the internet makes the telegraph look. Necessary. Historical. And completely primitive by comparison. Altman: “This is at least the best time ever so far.” Four words do all the work. So far. The best moment in human history. And the least impressive moment compared to everything that follows. The models are smart enough to find the next breakthrough. The products have not been built yet. The architecture that replaces the Transformer has not been discovered yet. And the man closest to the frontier just told a room full of students that whoever moves now is building on the ground floor of something that does not have a ceiling. The people waiting for the right moment are standing inside it. It just does not look finished yet. It never will.

Dustin

12,218 次观看 • 4 个月前

Ilya Sutskever, co-founder and chief scientist of OpenAI: "The better a neural network can predict the next word, the more it understands it." Everyone calls it autocomplete. Ilya explained why that is a comforting lie. To guess the final word of a murder mystery, the name of the killer, the model has to reason through every clue in the book. Prediction that good is not statistics. It is understanding. Then he said the part that should stop you. The text these models train on is "a projection of the world," and to compress it they learn more and more about people, "the human condition, their hopes, dreams, and motivations." The machine is not memorizing our words. It is quietly building a model of us. That is the whole premise of the fear. The machine will never hate you. It understands you far better than that. The frightening variable was never the intelligence. It was the aim. I wrote the full story: the one fear about AI everyone gets wrong, and the one already here.
5:10

Ilya Sutskever, co-founder and chief scientist of OpenAI: "The better a neural network can predict the next word, the more it understands it." Everyone calls it autocomplete. Ilya explained why that is a comforting lie. To guess the final word of a murder mystery, the name of the killer, the model has to reason through every clue in the book. Prediction that good is not statistics. It is understanding. Then he said the part that should stop you. The text these models train on is "a projection of the world," and to compress it they learn more and more about people, "the human condition, their hopes, dreams, and motivations." The machine is not memorizing our words. It is quietly building a model of us. That is the whole premise of the fear. The machine will never hate you. It understands you far better than that. The frightening variable was never the intelligence. It was the aim. I wrote the full story: the one fear about AI everyone gets wrong, and the one already here.

Casper

91,587 次观看 • 16 天前