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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... data model underneath -- the thing that makes every command finally make sense. -> The moment it clicks, git stops being scary magic. You stop memorizing "The incantation that fixed it last time" and start actually knowing what's happening. Most people learn just enough git to not get fired. Four commands, blind faith, and a prayer before every merge. In 2026 that's not enough anymore -> git is the literacy test for being in the room, and "I'll just reclone it" is the fastest way to look junior. An AI agent will branch, commit and rebase faster than you can read. When it tangles the history, untangling it runs on understanding the model MIT teaches in this one hour. Anyone can run git push. The person who understands the graph underneath is the one who saves the repo when it breaks. Bookmark & Watch it ↓show more

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213,523 Aufrufe • vor 1 Monat

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 ↓

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22,107 Aufrufe • vor 19 Tagen

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251,766 Aufrufe • vor 1 Monat

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):

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

65,160 Aufrufe • vor 2 Jahren

Gilbert Strang, MIT professor: "Citadel and OpenAI both pay $400K+ for people fluent in one branch of math. I taught it to the whole world from an MIT classroom, for free." this free MIT lecture is the whole engine under the "AI" everyone suddenly worships. strip the branding off a neural network and this is what's underneath: linear algebra, matrices multiplied over and over. the man teaching it spent fifty years at MIT and gave every lecture away for nothing. and what it does is simple. it lets you work with thousands of numbers at once instead of one at a time. how every asset moves against every other, the hidden factors driving a whole market, the guts of a neural net, all of it is just matrices. it is the grammar every model speaks, and it hasn't changed in decades. which is the entire point of the article above. renaissance was already running on this exact math in 1988. the "AI revolution" didn't build the engine, it just rebranded it. here is what the pitch skips. everyone's model runs on the same linear algebra, so the math was never the edge. the matrices don't care whose data goes in. cleaner data, and the discipline to trust the output, are the only parts anyone was ever really paying for.

Gilbert Strang, MIT professor: "Citadel and OpenAI both pay $400K+ for people fluent in one branch of math. I taught it to the whole world from an MIT classroom, for free." this free MIT lecture is the whole engine under the "AI" everyone suddenly worships. strip the branding off a neural network and this is what's underneath: linear algebra, matrices multiplied over and over. the man teaching it spent fifty years at MIT and gave every lecture away for nothing. and what it does is simple. it lets you work with thousands of numbers at once instead of one at a time. how every asset moves against every other, the hidden factors driving a whole market, the guts of a neural net, all of it is just matrices. it is the grammar every model speaks, and it hasn't changed in decades. which is the entire point of the article above. renaissance was already running on this exact math in 1988. the "AI revolution" didn't build the engine, it just rebranded it. here is what the pitch skips. everyone's model runs on the same linear algebra, so the math was never the edge. the matrices don't care whose data goes in. cleaner data, and the discipline to trust the output, are the only parts anyone was ever really paying for.

Rossst.03

33,926 Aufrufe • vor 13 Tagen

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.

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 Aufrufe • vor 1 Monat

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

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

192,483 Aufrufe • vor 3 Monaten

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.

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 Aufrufe • vor 3 Monaten