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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...

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22,107 görüntüleme • 13 gün önce •via X (Twitter)

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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 ↓
42:24

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 ↓

slash1s

382,693 görüntüleme • 1 ay önce

A DEVELOPER WALKED ON STAGE DRESSED AS A 1973 ENGINEER AND "PREDICTED" THE FUTURE OF PROGRAMMING. THE TWIST: EVERYTHING HE DESCRIBED WAS ALREADY INVENTED 40 YEARS EARLIER AND WE STILL REFUSE TO USE IT. 32 minutes from Bret Victor, doing the most quietly savage talk on our entire industry. -> The idea that lands: we write code as step-by-step text instructions and call that "Just how programming is". He shows four better ways -- all discovered in the 60s and 70s, all abandoned. Manipulate the data directly instead of typing blind code. Tell the machine your goal instead of every tiny step. We saw all this, then walked away. Why? The moment you're sure you know what programming is, you stop seeing anything better. That certainty is the cage. And now AI is dragging us back to exactly what he begged for -- you describe the goal in plain words, the machine works out the how. The future he mourned is arriving anyway. You thought text files were just how code works. This is the talk that shows it was a choice, and maybe the wrong one. Watch this one. It'll ruin how you see your job ↓
32:29

A DEVELOPER WALKED ON STAGE DRESSED AS A 1973 ENGINEER AND "PREDICTED" THE FUTURE OF PROGRAMMING. THE TWIST: EVERYTHING HE DESCRIBED WAS ALREADY INVENTED 40 YEARS EARLIER AND WE STILL REFUSE TO USE IT. 32 minutes from Bret Victor, doing the most quietly savage talk on our entire industry. -> The idea that lands: we write code as step-by-step text instructions and call that "Just how programming is". He shows four better ways -- all discovered in the 60s and 70s, all abandoned. Manipulate the data directly instead of typing blind code. Tell the machine your goal instead of every tiny step. We saw all this, then walked away. Why? The moment you're sure you know what programming is, you stop seeing anything better. That certainty is the cage. And now AI is dragging us back to exactly what he begged for -- you describe the goal in plain words, the machine works out the how. The future he mourned is arriving anyway. You thought text files were just how code works. This is the talk that shows it was a choice, and maybe the wrong one. Watch this one. It'll ruin how you see your job ↓

slash1s

678,451 görüntüleme • 6 gün önce

“Math is code. And code is math.” Carina Hong CEO Axiom on why that idea matters for AI: “For centuries, mathematicians reasoned in natural language. But because of the Curry–Howard correspondence, a proof can also be a program.” “You can translate natural-language math into formal code using systems like Lean. Lean is both a theorem-proving language and a programming language.” “That means you can generate solutions as code and then formally verify them. You can even use the same system to verify programs themselves.” “This creates a flywheel of generation and verification, where AI can both write solutions and prove that they’re correct.”
2:25

“Math is code. And code is math.” Carina Hong CEO Axiom on why that idea matters for AI: “For centuries, mathematicians reasoned in natural language. But because of the Curry–Howard correspondence, a proof can also be a program.” “You can translate natural-language math into formal code using systems like Lean. Lean is both a theorem-proving language and a programming language.” “That means you can generate solutions as code and then formally verify them. You can even use the same system to verify programs themselves.” “This creates a flywheel of generation and verification, where AI can both write solutions and prove that they’re correct.”

Forward Future

24,729 görüntüleme • 4 ay önce

IN 1986 MIT FILMED A LECTURE THAT OPENS BY TELLING YOU COMPUTER SCIENCE IS NOT A SCIENCE AND HAS ALMOST NOTHING TO DO WITH COMPUTERS 72 minutes from Hal Abelson and Gerald Sussman, the lecture an entire generation of engineers calls the one that rewired how they think. -> The line that lands: computer science is about computers the way astronomy is about telescopes. The tool was never the point. The real subject was always one thing -- controlling complexity. Everything else is detail. Forty years later it reads like a prophecy. AI writes the syntax now. What's left is exactly what they taught: taming complexity nobody can hold in their head. The language was never the skill -> the thinking was. This is where you learn it. Most people chase the newest framework. The ones who watched this think on a level frameworks can't touch. Bookmark & Watch today it, this one's a legend ↓
1:12:24

IN 1986 MIT FILMED A LECTURE THAT OPENS BY TELLING YOU COMPUTER SCIENCE IS NOT A SCIENCE AND HAS ALMOST NOTHING TO DO WITH COMPUTERS 72 minutes from Hal Abelson and Gerald Sussman, the lecture an entire generation of engineers calls the one that rewired how they think. -> The line that lands: computer science is about computers the way astronomy is about telescopes. The tool was never the point. The real subject was always one thing -- controlling complexity. Everything else is detail. Forty years later it reads like a prophecy. AI writes the syntax now. What's left is exactly what they taught: taming complexity nobody can hold in their head. The language was never the skill -> the thinking was. This is where you learn it. Most people chase the newest framework. The ones who watched this think on a level frameworks can't touch. Bookmark & Watch today it, this one's a legend ↓

slash1s

358,395 görüntüleme • 18 gün önce

We've built a massive verification bottleneck: • 72% of developers use AI to generate code every day • 96% of them don't fully trust it Writing code is now free. All the work now is verifying that the code does what it's supposed to. According to a new State of Code Survey, almost half of the respondents believe the number one skill that will matter most in the coming years is "reviewing and validating AI-generated code for quality and security". Not prompt engineering or vibe-coding, but checking AI's work.
3:44

We've built a massive verification bottleneck: • 72% of developers use AI to generate code every day • 96% of them don't fully trust it Writing code is now free. All the work now is verifying that the code does what it's supposed to. According to a new State of Code Survey, almost half of the respondents believe the number one skill that will matter most in the coming years is "reviewing and validating AI-generated code for quality and security". Not prompt engineering or vibe-coding, but checking AI's work.

Santiago

16,299 görüntüleme • 5 ay önce

"The one thing that's going to be truly future proof is judgment. What is the biggest challenge when you have a thousand AI engineers writing code? AI slop. In an era when you can do everything, the question is which of these things matter. On the engineer side, it is evaluating the code. Which of this code is even valuable? You can have AI engineers writing beautiful code that could be wrong, that could have bugs in it, that could be vulnerable. Someone needs to review it. Make sure to have a human review at some point, especially critical code that is in the core of your system. On the product side is judgment around what needs to be built and evaluating. And similarly in design, you have to have judgment around, does it make sense in the broader design system. I think this judgment is the number one thing that humans are going to bring in an era of infinite productivity."
1:12

"The one thing that's going to be truly future proof is judgment. What is the biggest challenge when you have a thousand AI engineers writing code? AI slop. In an era when you can do everything, the question is which of these things matter. On the engineer side, it is evaluating the code. Which of this code is even valuable? You can have AI engineers writing beautiful code that could be wrong, that could have bugs in it, that could be vulnerable. Someone needs to review it. Make sure to have a human review at some point, especially critical code that is in the core of your system. On the product side is judgment around what needs to be built and evaluating. And similarly in design, you have to have judgment around, does it make sense in the broader design system. I think this judgment is the number one thing that humans are going to bring in an era of infinite productivity."

Patrick OShaughnessy

24,818 görüntüleme • 5 ay önce

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 ↓
39:31

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 ↓

slash1s

429,948 görüntüleme • 17 gün önce

A DEVELOPER PROVED THAT MOST OF THE CLASSES YOU WRITE SHOULD NEVER HAVE BEEN CLASSES AT ALL 27 minutes from Jack Diederich, a Python core developer, on how much of your code is just a function wearing a costume. -> The moment it lands, the rule is brutal: if a class has two methods and one is __init__, you meant to write a function. Most people add structure to feel like real engineers. It just adds weight no one ever needed. Writing more code was never the skill -> writing less of it is. And when an AI agent happily generates ten classes for a job that needed three lines, the person who knows what to delete is the one who ships something clean. Anyone can add abstraction. Knowing when not to is the whole job. Bookmark it and Watch ↓
27:10

A DEVELOPER PROVED THAT MOST OF THE CLASSES YOU WRITE SHOULD NEVER HAVE BEEN CLASSES AT ALL 27 minutes from Jack Diederich, a Python core developer, on how much of your code is just a function wearing a costume. -> The moment it lands, the rule is brutal: if a class has two methods and one is __init__, you meant to write a function. Most people add structure to feel like real engineers. It just adds weight no one ever needed. Writing more code was never the skill -> writing less of it is. And when an AI agent happily generates ten classes for a job that needed three lines, the person who knows what to delete is the one who ships something clean. Anyone can add abstraction. Knowing when not to is the whole job. Bookmark it and Watch ↓

slash1s

266,884 görüntüleme • 21 gün önce

Reviewing AI-generated code is the new bottleneck. Writing code was slow before. Now, in a few minutes, you can generate virtually unlimited lines of code using AI. But developers are now spending 70% of their time checking that code. That's the new bottleneck. I've been using Cline, and they have a feature that actually helps with this: You can click a button and have Cline generate an inline explanation for every single line of AI-generated code. Take a look at the attached video. What it does: 1. You can read the code and the explanation together 2. You don't lose context while reviewing code 3. You understand the "why" behind each change And here is the best part: It works on any git diff. You can use it to review PRs, understand commits from other people, or catch up on a codebase you haven't touched in a while.
3:25

Reviewing AI-generated code is the new bottleneck. Writing code was slow before. Now, in a few minutes, you can generate virtually unlimited lines of code using AI. But developers are now spending 70% of their time checking that code. That's the new bottleneck. I've been using Cline, and they have a feature that actually helps with this: You can click a button and have Cline generate an inline explanation for every single line of AI-generated code. Take a look at the attached video. What it does: 1. You can read the code and the explanation together 2. You don't lose context while reviewing code 3. You understand the "why" behind each change And here is the best part: It works on any git diff. You can use it to review PRs, understand commits from other people, or catch up on a codebase you haven't touched in a while.

Santiago

64,399 görüntüleme • 7 ay önce

AI is changing the software engineering craft. Anders Hejlsberg (Anders Hejlsberg) - creator of C#, TypeScript and industry legend - on why code review needs to get more enjoyable in response: #1 - AI is shifting the craft from writing code, to reviewing code: "In a sense, we're all turning into project managers. We can have an army of junior programmers, called agents, that will just spit out reams of code but someone's got to have the big picture and review all of that. And so, increasingly, our craft is going from one of writing the code, to one of reviewing the code and building the architecture of the code and overseeing the work. It's a different kind of craft. It's a different kind of enjoyment. I've always liked writing the code. To me that was the fulfilling part, seeing it work. In a way, AI robs a little bit of that, because I am less interested in reviewing code." #2 - The code review experience should be improved: "I think we could also make the process of reviewing code much more interesting than it is today. I mean, today, you see a list of diffs in alphabetical order and now it's up to you to make heads or tails of it. There are more pedagogical ways of presenting that. And you could have commentary generated by the AI that tells you what the changes are and whatever, and then tries to guide you along. So that symbiotic relationship, I think we need to work on that more and to keep the enjoyment in there."
2:04

AI is changing the software engineering craft. Anders Hejlsberg (Anders Hejlsberg) - creator of C#, TypeScript and industry legend - on why code review needs to get more enjoyable in response: #1 - AI is shifting the craft from writing code, to reviewing code: "In a sense, we're all turning into project managers. We can have an army of junior programmers, called agents, that will just spit out reams of code but someone's got to have the big picture and review all of that. And so, increasingly, our craft is going from one of writing the code, to one of reviewing the code and building the architecture of the code and overseeing the work. It's a different kind of craft. It's a different kind of enjoyment. I've always liked writing the code. To me that was the fulfilling part, seeing it work. In a way, AI robs a little bit of that, because I am less interested in reviewing code." #2 - The code review experience should be improved: "I think we could also make the process of reviewing code much more interesting than it is today. I mean, today, you see a list of diffs in alphabetical order and now it's up to you to make heads or tails of it. There are more pedagogical ways of presenting that. And you could have commentary generated by the AI that tells you what the changes are and whatever, and then tries to guide you along. So that symbiotic relationship, I think we need to work on that more and to keep the enjoyment in there."

The Pragmatic Engineer

38,988 görüntüleme • 1 ay önce

Bash is all you need! Which is why I'm introducing my holiday project: just-bash just-bash is a pretty complete implementation of bash in TypeScript designed to be used as a bash tool by AI agents. Because it turns out agents love exploring data via shell scripts, even beyond coding. It comes with grep, sed, awk and the 99th percentile features that an agent like Claude Code or Cursor would use. In fact, Claude Code can use it for secure bash execution. In the package - A bash-tool for AI SDK - A binary for use by yourself or your coding agents - An overlay filesystem to feed files to your agent securely - A Vercel Sandbox compatible API, so you can quickly upgrade to a real VM if you need to run binaries - An example AI agent that explores the just-bash code base using just-bash - I imported the Oils shell bash compatibility suite and just-bash passes a very good chunk What is interesting about this codebase: It was essentially entirely written by Opus 4.5. Coding agents love bash and they are good at reproducing it. They are also great at text-book recursive descent parsers and AST tweet-walk interpreters. That said, it is, like, a lot of code and I didn't read it all 😅. This is very much a hack, but it also seems to be _really_ useful. I haven't really found anything agents want to use that it doesn't support and it's fast and secure (caveats apply). It doesn't have write access to your computer and the filesystem is given a root that the agent cannot escape from. Find it at Related: Our recent blog post how we migrated our data analysis agent to bash tools and achieved incredible quality improvements The video shows the example agent investigating the just-bash code base
1:47

Bash is all you need! Which is why I'm introducing my holiday project: just-bash just-bash is a pretty complete implementation of bash in TypeScript designed to be used as a bash tool by AI agents. Because it turns out agents love exploring data via shell scripts, even beyond coding. It comes with grep, sed, awk and the 99th percentile features that an agent like Claude Code or Cursor would use. In fact, Claude Code can use it for secure bash execution. In the package - A bash-tool for AI SDK - A binary for use by yourself or your coding agents - An overlay filesystem to feed files to your agent securely - A Vercel Sandbox compatible API, so you can quickly upgrade to a real VM if you need to run binaries - An example AI agent that explores the just-bash code base using just-bash - I imported the Oils shell bash compatibility suite and just-bash passes a very good chunk What is interesting about this codebase: It was essentially entirely written by Opus 4.5. Coding agents love bash and they are good at reproducing it. They are also great at text-book recursive descent parsers and AST tweet-walk interpreters. That said, it is, like, a lot of code and I didn't read it all 😅. This is very much a hack, but it also seems to be _really_ useful. I haven't really found anything agents want to use that it doesn't support and it's fast and secure (caveats apply). It doesn't have write access to your computer and the filesystem is given a root that the agent cannot escape from. Find it at Related: Our recent blog post how we migrated our data analysis agent to bash tools and achieved incredible quality improvements The video shows the example agent investigating the just-bash code base

Malte Ubl

124,713 görüntüleme • 6 ay önce

Boris Cherny (Creator of Claude Code): "The one technical book I would recommend to everyone that has had the greatest impact on me as an engineer is functional programming in Scala. You're probably never going to use Scala day today, but the way it teaches you to think about coding problems is just such a change from the way that most people were in coding, either practically or in school. It's just. It's incredible. It's going to completely change the way that you code now. I think in types, when I code, the thing that matters in your code the most is the type signatures. This is more important than the code itself." Boris Cherny
0:26

Boris Cherny (Creator of Claude Code): "The one technical book I would recommend to everyone that has had the greatest impact on me as an engineer is functional programming in Scala. You're probably never going to use Scala day today, but the way it teaches you to think about coding problems is just such a change from the way that most people were in coding, either practically or in school. It's just. It's incredible. It's going to completely change the way that you code now. I think in types, when I code, the thing that matters in your code the most is the type signatures. This is more important than the code itself." Boris Cherny

Ryan Peterman

364,456 görüntüleme • 2 ay önce

I love the game of Boggle. This demo showcases our Gemini 2.0 Pro model’s coding abilities in AI Studio. It is mind boggling to think that it can write the full piece of code, including all the right data structures and search algorithms to find all valid words on a Boggle board from a relatively simple prompt asking it to do so. As a computer scientist, I'm also happy that it got its data structures right on the first trie. Discombobulating!
0:41

I love the game of Boggle. This demo showcases our Gemini 2.0 Pro model’s coding abilities in AI Studio. It is mind boggling to think that it can write the full piece of code, including all the right data structures and search algorithms to find all valid words on a Boggle board from a relatively simple prompt asking it to do so. As a computer scientist, I'm also happy that it got its data structures right on the first trie. Discombobulating!

Jeff Dean

122,224 görüntüleme • 1 yıl önce

Introducing "codebase to course", a skill that turns any codebase into an interactive coding course So that you can learn coding through your own projects, complete with visualization, plain-English code translations, metaphors, even quizzes... I vibe code a lot but have no idea how the code works under the hood. This is how I think "learning to code" should be in the AI age: Build first, learn later Link below
2:22

Introducing "codebase to course", a skill that turns any codebase into an interactive coding course So that you can learn coding through your own projects, complete with visualization, plain-English code translations, metaphors, even quizzes... I vibe code a lot but have no idea how the code works under the hood. This is how I think "learning to code" should be in the AI age: Build first, learn later Link below

Zara Zhang

202,530 görüntüleme • 3 ay önce

"Claude Code is the first AI tool that can actually recreate how a data scientist thinks." Here's my new episode with Sumeet Marwaha (Head of Data at Brex) on exactly how to build an AI analyst with Claude Code. We covered: ✅ 3 queries to build a data analyst MCP ✅ How to give the analyst both data + context ✅ Exclusive Brex data on which AI tools are crushing it in startups and enterprise Some quotes from Sumeet: "I've set up dashboards many times in my career. They end up getting ignored. Claude will always read it. Claude will always ask the right questions." "You have to set up Claude Code to not just look at the data. It should be able to search Slack, find the incident, and understand why your metrics look wrong." "This tool is absolutely crushing it based on our data. It's both the startup AND enterprise coding tool of choice." 📌 Watch now:
0:46

"Claude Code is the first AI tool that can actually recreate how a data scientist thinks." Here's my new episode with Sumeet Marwaha (Head of Data at Brex) on exactly how to build an AI analyst with Claude Code. We covered: ✅ 3 queries to build a data analyst MCP ✅ How to give the analyst both data + context ✅ Exclusive Brex data on which AI tools are crushing it in startups and enterprise Some quotes from Sumeet: "I've set up dashboards many times in my career. They end up getting ignored. Claude will always read it. Claude will always ask the right questions." "You have to set up Claude Code to not just look at the data. It should be able to search Slack, find the incident, and understand why your metrics look wrong." "This tool is absolutely crushing it based on our data. It's both the startup AND enterprise coding tool of choice." 📌 Watch now:

Peter Yang

39,600 görüntüleme • 5 ay önce

Amjad Masad on AI as the next programming abstraction: “A hundred years ago people were coding in machine code. Not many people in the world can do machine code. And then Grace Hopper said, ‘I want to invent a compiler so that people can program in English.’” Replit ⠕ CEO Amjad Masad at the Cerebral Valley AI Summit
1:10

Amjad Masad on AI as the next programming abstraction: “A hundred years ago people were coding in machine code. Not many people in the world can do machine code. And then Grace Hopper said, ‘I want to invent a compiler so that people can program in English.’” Replit ⠕ CEO Amjad Masad at the Cerebral Valley AI Summit

a16z

27,953 görüntüleme • 5 ay önce

After reaching AI-native terminal velocity we are swamped in code review. The core insight is that as long as you can write code in parallel you will continue to be stuck until you can review it in parallel. I was surprised by how quickly this became my daily driver for not just review code, but writing it as well.
0:30

After reaching AI-native terminal velocity we are swamped in code review. The core insight is that as long as you can write code in parallel you will continue to be stuck until you can review it in parallel. I was surprised by how quickly this became my daily driver for not just review code, but writing it as well.

Evan Boyle

23,954 görüntüleme • 5 ay önce

I built an AI data visualization Agent. The AI Agent can automatically generate the code and visualize the data using code interpreter sandbox. 100% Opensource Code with step-by-step tutorial.
1:40

I built an AI data visualization Agent. The AI Agent can automatically generate the code and visualize the data using code interpreter sandbox. 100% Opensource Code with step-by-step tutorial.

Shubham Saboo

67,845 görüntüleme • 1 yıl önce

IN 1982 GRACE HOPPER WALKED INTO THE NSA AND EXPLAINED THE NEXT 40 YEARS OF COMPUTING 48 minutes from the woman who invented the compiler -- the first person who made a machine understand human words. -> The idea that lands: stop bending humans to the machine. Bend the machine to humans. She built the first compiler when the experts swore computers could only crunch numbers. She wanted you to write in plain words and let the machine handle the translation. Forty years later you barely write code at all -- you tell the model what you want in plain English and it writes it for you. The compiler was step one. The LLM is the same idea, finished. Memorizing syntax was never the skill -- saying clearly what you actually want is. This is where that idea was born. Most people think talking to your computer in plain English started in 2023. The ones who watch this know a Navy admiral called it in 1982. Bookmark & Watch it today ↓
47:50

IN 1982 GRACE HOPPER WALKED INTO THE NSA AND EXPLAINED THE NEXT 40 YEARS OF COMPUTING 48 minutes from the woman who invented the compiler -- the first person who made a machine understand human words. -> The idea that lands: stop bending humans to the machine. Bend the machine to humans. She built the first compiler when the experts swore computers could only crunch numbers. She wanted you to write in plain words and let the machine handle the translation. Forty years later you barely write code at all -- you tell the model what you want in plain English and it writes it for you. The compiler was step one. The LLM is the same idea, finished. Memorizing syntax was never the skill -- saying clearly what you actually want is. This is where that idea was born. Most people think talking to your computer in plain English started in 2023. The ones who watch this know a Navy admiral called it in 1982. Bookmark & Watch it today ↓

slash1s

60,118 görüntüleme • 13 gün önce