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A DEVELOPER FOUND SEVEN WAYS TO TAKE DOWN A PRODUCTION DATABASE THAT ALL LOOK EXACTLY LIKE NORMAL, INNOCENT CODE AND ALMOST EVERY TEAM IS SHIPPING AT LEAST ONE OF THEM RIGHT NOW 17 minutes from Josh Berkus, one of the people who actually maintains PostgreSQL, walking through the quiet... mistakes that turn a healthy database into a 3am outage. -> The moment it lands, you realize none of these are exotic attacks. They're ordinary-looking decisions -- a query that locks a table, a connection that never closes, a setting no one ever questioned -- that work perfectly until the day they don't, and then they take everything down with them. The scary part isn't that the database breaks. It's how normal the code looks right up until it does. A query that runs in 5ms on your laptop and 5 minutes on prod. A migration that silently locks the whole table. A connection pool that runs dry the moment real traffic shows up. Every one of them passed review. Writing SQL that runs was never the hard part -> writing SQL that survives production is. And now that an AI agent is generating and firing queries at your real database faster than anyone can read them, every one of those seven landmines is one autocomplete away -- and the only person who can stop it is the one who already knows where they're buried. Your database doesn't go down because someone attacked it. It goes down because something that looked completely normal finally caught up with it. Save and Watch it today. You'll see the next outage coming before it lands ↓show more

slash1s

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A DEVELOPER PROVED THE REGEX YOU'VE WRITTEN A THOUSAND TIMES IS SECRETLY A COMPILER AND THAT ALMOST NO ONE WHO USES THEM HAS ANY IDEA WHAT ACTUALLY RUNS 36 minutes from Paul Wankadia, the engineer behind a regex engine that compiles your pattern straight down to raw machine code -- walking through what really happens between the slashes. -> The moment it clicks, regex stops being magic punctuation you paste from Stack Overflow and becomes what it actually is: a tiny machine. Your pattern gets turned into a state machine, and that machine is what runs against every character of your text. That one idea explains everything you never understood. Why one regex returns instantly and a nearly identical one hangs your whole server. Why some patterns are safe and others are a denial-of-service waiting to happen. It was never random -- it's whether the machine underneath is built well or badly. Writing a regex was never the skill -> reading one is. And now that an AI agent hands you dense, clever patterns you'd never write yourself, the person who can see the machine underneath is the one who catches the one that takes down production at 3am. Everyone copies regex and prays. This is the talk that ends the praying. Save it. The next time a pattern "Just works," you'll actually know why ↓

A DEVELOPER PROVED THE REGEX YOU'VE WRITTEN A THOUSAND TIMES IS SECRETLY A COMPILER AND THAT ALMOST NO ONE WHO USES THEM HAS ANY IDEA WHAT ACTUALLY RUNS 36 minutes from Paul Wankadia, the engineer behind a regex engine that compiles your pattern straight down to raw machine code -- walking through what really happens between the slashes. -> The moment it clicks, regex stops being magic punctuation you paste from Stack Overflow and becomes what it actually is: a tiny machine. Your pattern gets turned into a state machine, and that machine is what runs against every character of your text. That one idea explains everything you never understood. Why one regex returns instantly and a nearly identical one hangs your whole server. Why some patterns are safe and others are a denial-of-service waiting to happen. It was never random -- it's whether the machine underneath is built well or badly. Writing a regex was never the skill -> reading one is. And now that an AI agent hands you dense, clever patterns you'd never write yourself, the person who can see the machine underneath is the one who catches the one that takes down production at 3am. Everyone copies regex and prays. This is the talk that ends the praying. Save it. The next time a pattern "Just works," you'll actually know why ↓

slash1s

193,107 views • 4 months ago

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 ↓

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

239,491 views • 4 months ago

A DEVELOPER PROVED THAT MOST OF THE LANGUAGE RUNNING EVERY WEBSITE YOU USE IS A MINEFIELD AND THAT ALMOST NO ONE WAS EVER TAUGHT WHICH PARTS ARE SAFE An hour from Douglas Crockford, the man who invented JSON, on the good parts of JavaScript buried under a pile of blunders nobody warned you about. -> The moment it lands, every weird bug makes sense. The language never had time to be polished. It went global in months, bad parts and all, and you inherited every one of them. Global variables. Silent semicolon insertion. eval. Coercion that turns a simple comparison into a coin flip. Most people never learned to avoid them -- they just lose hours and blame themselves. Writing JavaScript that runs was never the skill -> knowing which half of it to never touch is. And when an AI agent happily generates the bad parts at scale, the person who knows the safe subset is the one who catches it before it ships. Everyone writes JavaScript. Almost no one was shown the line between the parts that save you and the parts that quietly burn you. Bookmark and Watch it ↓

A DEVELOPER PROVED THAT MOST OF THE LANGUAGE RUNNING EVERY WEBSITE YOU USE IS A MINEFIELD AND THAT ALMOST NO ONE WAS EVER TAUGHT WHICH PARTS ARE SAFE An hour from Douglas Crockford, the man who invented JSON, on the good parts of JavaScript buried under a pile of blunders nobody warned you about. -> The moment it lands, every weird bug makes sense. The language never had time to be polished. It went global in months, bad parts and all, and you inherited every one of them. Global variables. Silent semicolon insertion. eval. Coercion that turns a simple comparison into a coin flip. Most people never learned to avoid them -- they just lose hours and blame themselves. Writing JavaScript that runs was never the skill -> knowing which half of it to never touch is. And when an AI agent happily generates the bad parts at scale, the person who knows the safe subset is the one who catches it before it ships. Everyone writes JavaScript. Almost no one was shown the line between the parts that save you and the parts that quietly burn you. Bookmark and Watch it ↓

slash1s

13,073 views • 3 months ago

ChatGPT can write you a thousand trading strategies from one sentence. 999 will quietly bleed your account dry. Every single one of them looks like a winner. One method pulls the real one out. Quant desks have run it for years. It's called loop engineering. It's not a smarter prompt. It's a loop: generate a strategy, test it, score why it failed, feed that back, and run it again until one survives data it has never seen. Man Group runs $150 billion. They built an AI that writes and tests hundreds of strategies a week. And second agent whose only job is to hunt the ones that secretly overfit and kill them before a cent goes live. > describe an idea -> backtest 5 years in 12 seconds > read why it died -> feed that back in > run it again until one survives data it never saw AI can write a thousand strategies. Loop engineering is the only thing that tells you which one was ever real. 89% of retail lost money in 2025. Almost none of it from bad ideas. All of it from trading the first guess and never running it through backtest loop. Horizon just put the whole thing in a chat box, free while the beta's open: You're not losing to smarter traders. You're losing to the ones who opened the tab. Anyone can generate a strategy now. Almost no one runs the loop that proves it. That gap is the whole edge. You'll mean to check it later. Later is a "waitlist closed" page.

ChatGPT can write you a thousand trading strategies from one sentence. 999 will quietly bleed your account dry. Every single one of them looks like a winner. One method pulls the real one out. Quant desks have run it for years. It's called loop engineering. It's not a smarter prompt. It's a loop: generate a strategy, test it, score why it failed, feed that back, and run it again until one survives data it has never seen. Man Group runs $150 billion. They built an AI that writes and tests hundreds of strategies a week. And second agent whose only job is to hunt the ones that secretly overfit and kill them before a cent goes live. > describe an idea -> backtest 5 years in 12 seconds > read why it died -> feed that back in > run it again until one survives data it never saw AI can write a thousand strategies. Loop engineering is the only thing that tells you which one was ever real. 89% of retail lost money in 2025. Almost none of it from bad ideas. All of it from trading the first guess and never running it through backtest loop. Horizon just put the whole thing in a chat box, free while the beta's open: You're not losing to smarter traders. You're losing to the ones who opened the tab. Anyone can generate a strategy now. Almost no one runs the loop that proves it. That gap is the whole edge. You'll mean to check it later. Later is a "waitlist closed" page.

cvxv666

174,699 views • 3 months ago

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 ↓

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

386,685 views • 4 months ago

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 ↓

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

269,515 views • 3 months ago

BlackRock runs on 20,000 people. Elon's Grok Bot runs the same shape for $300 a month, and it hires its own staff. You do not get an assistant. You get a company that hires. It does not throw ten agents at your problem and hand you the pile. It makes one agent that makes 10, and those ten make a 100. > LAYER ONE is one agent, the chief of staff, and it never touches the market > LAYER TWO is six desk heads, one job each, every one on its own computer with its own logins > LAYER THREE is whatever those six decide they need, spun up on the spot and shut down when the work is done Nobody writes a task list. You hand out job titles and the org fills itself in underneath. The swarm is never the same twice. Agents get spun up for one job, finish it, and are gone before I ever read their names. Not one of them sees the whole picture. The answer only exists after they hand off to each other. Wall Street cannot copy that. You cannot hire a hundred people for eleven minutes. BlackRock holds that shape together with a risk system called Aladdin. Mine holds it together with one agent that is only allowed to say no. I gave it $1,000 and told it to grow the money or get deleted. 15 hours later it was holding $3,900, on an address anyone can open and read. I was asleep for most of it, and I have still not written a line of code. The whole thing runs with my laptop shut, because none of it lives on my laptop. Setup is one evening. Create the chief, hand out the titles, run one trade on your screen while they watch, connect Telegram. Ten years ago a machine this shape had its name on a tower. Mine has a name I typed into a box. Save this while the whole thing still fits on one screen.

BlackRock runs on 20,000 people. Elon's Grok Bot runs the same shape for $300 a month, and it hires its own staff. You do not get an assistant. You get a company that hires. It does not throw ten agents at your problem and hand you the pile. It makes one agent that makes 10, and those ten make a 100. > LAYER ONE is one agent, the chief of staff, and it never touches the market > LAYER TWO is six desk heads, one job each, every one on its own computer with its own logins > LAYER THREE is whatever those six decide they need, spun up on the spot and shut down when the work is done Nobody writes a task list. You hand out job titles and the org fills itself in underneath. The swarm is never the same twice. Agents get spun up for one job, finish it, and are gone before I ever read their names. Not one of them sees the whole picture. The answer only exists after they hand off to each other. Wall Street cannot copy that. You cannot hire a hundred people for eleven minutes. BlackRock holds that shape together with a risk system called Aladdin. Mine holds it together with one agent that is only allowed to say no. I gave it $1,000 and told it to grow the money or get deleted. 15 hours later it was holding $3,900, on an address anyone can open and read. I was asleep for most of it, and I have still not written a line of code. The whole thing runs with my laptop shut, because none of it lives on my laptop. Setup is one evening. Create the chief, hand out the titles, run one trade on your screen while they watch, connect Telegram. Ten years ago a machine this shape had its name on a tower. Mine has a name I typed into a box. Save this while the whole thing still fits on one screen.

cvxv666

45,488 views • 1 month ago

A DOCTOR BUILT AN AI-QUERYABLE DATABASE OF EVERY DISEASE HE KNOWS IN OBSIDIAN. THE SAME STACK CAN RUN AN ENTIRE COMPANY ON ONE PERSON'S BRAIN Zoomed out, his vault looks like a constellation. Every disease linked to its symptoms, every symptom to the conditions it shows up in, every treatment to what it treats. Adding a new case is one note that wires itself into the rest. It's a personal database, and the structure has nothing to do with medicine. It's just nodes and links. Once you see that, the same setup runs anything: > a startup that needs to remember every customer, decision, and meeting; > a school whose curriculum links to every lecture and student question; > a business where SOPs, clients, and vendors all sit in one queryable graph. Drop Claude on top, and the vault stops being a map. It becomes something you can ask, in plain English, across everything you've ever written down. Full Claude + Obsidian build in the article below. Bookmark this

A DOCTOR BUILT AN AI-QUERYABLE DATABASE OF EVERY DISEASE HE KNOWS IN OBSIDIAN. THE SAME STACK CAN RUN AN ENTIRE COMPANY ON ONE PERSON'S BRAIN Zoomed out, his vault looks like a constellation. Every disease linked to its symptoms, every symptom to the conditions it shows up in, every treatment to what it treats. Adding a new case is one note that wires itself into the rest. It's a personal database, and the structure has nothing to do with medicine. It's just nodes and links. Once you see that, the same setup runs anything: > a startup that needs to remember every customer, decision, and meeting; > a school whose curriculum links to every lecture and student question; > a business where SOPs, clients, and vendors all sit in one queryable graph. Drop Claude on top, and the vault stops being a map. It becomes something you can ask, in plain English, across everything you've ever written down. Full Claude + Obsidian build in the article below. Bookmark this

Yarchi

32,594 views • 3 months ago

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 ↓

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 ↓

slash1s

12,797 views • 3 months ago

Someone just posted the full blueprint for an AI swarm that does the job of a 200-person quant research team. Six agents. Running 24/7. Finding brand-new alpha while you sleep. Citadel needs 100 PhDs to do this. Two Sigma needs 200. This does it with six bots and one laptop. Two ways to play this - spend a weekend building your own swarm, or copy the wallet of one that's already up $2M: Boris Cherny runs Claude Code at Anthropic. Two weeks ago he said: "I don't prompt Claude anymore. I have loops running that prompt Claude. My job is to write loops" Alpha research is just a pipeline. So instead of sitting in it, you hand each stage to its own agent: > one reads every new research paper overnight and pulls out the trade idea > one builds the features and cleans the data > one backtests it over 20 years, costs and slippage included > one runs the hard stats and kills anything overfit > one checks it still works in every market regime > one strips out plain momentum and value to see if any real edge is left Each of those six is a job a fund pays a $600,000-a-year quant to do. He runs all six for the price of an API bill. The rule that makes it work: the agent that builds a signal never gets to approve it. A separate, stronger agent tries to kill it first. Whatever survives all six by morning is real, new alpha. One trader's already running this exact swarm on Polymarket. That $2M wallet is public, every trade on-chain. The full build is in the post below - six agents, the tool that runs them, and the five mistakes that kill most people. Bookmark & read this before it's buried.

cvxv666

104,237 views • 3 months ago

You can now access AI directly from your database! Here is a step-by-step demo that uses GPT-4 to classify customer reviews from a MySQL dataset. And I'm only writing SQL instructions! You have to see it! The model acts as another table in the database. I can query it and join it with other tables. It's awesome! This is all thanks to mindshub. Here is their GitHub repository: They are sponsoring this post. Watch the video.

You can now access AI directly from your database! Here is a step-by-step demo that uses GPT-4 to classify customer reviews from a MySQL dataset. And I'm only writing SQL instructions! You have to see it! The model acts as another table in the database. I can query it and join it with other tables. It's awesome! This is all thanks to mindshub. Here is their GitHub repository: They are sponsoring this post. Watch the video.

Santiago

209,454 views • 2 years ago

A WRITER WENT ON STAGE AND CALLED IT YEARS EARLY: THE MACHINE ON YOUR DESK WOULD SLOWLY STOP BEING YOURS AND ALMOST NO ONE WOULD NOTICE THE MOMENT IT FLIPPED 54 minutes from Cory Doctorow, arguing the copyright fights were just the warm-up -- that the real battle is whether general-purpose machines, the kind that run whatever you tell them, are even allowed to keep existing. -> The moment it lands, you see the move. Nobody takes your computer away. They sell you one that looks like yours but answers to them -- locked down, logging, checking with a server you'll never see before it obeys you. For years that was DRM and locked phones. Now it's intelligence itself. Rent your AI from the cloud and the most powerful tool on your desk was never yours: it reads your prompts, sets your limits, keeps your data, and can cut you off tomorrow. You don't own the computation. You're licensed to borrow it. Using the smartest model was never the whole game -> owning the machine that runs it is. And the post below is what winning that war looks like now: one box, private AI running on it, every byte of client data kept off the cloud, and the line item that drains everyone else's account quietly turned into income. A rented computer obeys whoever signs the check. The one you own obeys you. In the AI era, that's the entire difference. The full breakdown below. Bookmark & Watch it today ↓

A WRITER WENT ON STAGE AND CALLED IT YEARS EARLY: THE MACHINE ON YOUR DESK WOULD SLOWLY STOP BEING YOURS AND ALMOST NO ONE WOULD NOTICE THE MOMENT IT FLIPPED 54 minutes from Cory Doctorow, arguing the copyright fights were just the warm-up -- that the real battle is whether general-purpose machines, the kind that run whatever you tell them, are even allowed to keep existing. -> The moment it lands, you see the move. Nobody takes your computer away. They sell you one that looks like yours but answers to them -- locked down, logging, checking with a server you'll never see before it obeys you. For years that was DRM and locked phones. Now it's intelligence itself. Rent your AI from the cloud and the most powerful tool on your desk was never yours: it reads your prompts, sets your limits, keeps your data, and can cut you off tomorrow. You don't own the computation. You're licensed to borrow it. Using the smartest model was never the whole game -> owning the machine that runs it is. And the post below is what winning that war looks like now: one box, private AI running on it, every byte of client data kept off the cloud, and the line item that drains everyone else's account quietly turned into income. A rented computer obeys whoever signs the check. The one you own obeys you. In the AI era, that's the entire difference. The full breakdown below. Bookmark & Watch it today ↓

slash1s

240,684 views • 4 months ago

yesterday someone cut a clip of a stream and it did 9.4 million views he earned nothing for it so i built the thing that pays him. it is called CUT that is not normal by the way, that is just how clipping works. the platform takes the traffic, the streamer takes the hype, and nobody pays the person who cut it a clip is an ad. it sells the streamer, it fills the platform, and both of them get paid for it running. clipping is the only advertising business on earth where the person who made the ad is the one paying for it → CUT reads twitch and kick chat and learns what normal looks like for that stream → when chat spikes 3x over normal, it clips the moment by itself → it has to be that fast, a clip only keeps the 90 seconds before the call → every sunday the pool is split by views, and you recompute your own number i am not asking anyone to take my word for it. point it at your own channels, run it for one night, and count how many moments it caught before you did

yesterday someone cut a clip of a stream and it did 9.4 million views he earned nothing for it so i built the thing that pays him. it is called CUT that is not normal by the way, that is just how clipping works. the platform takes the traffic, the streamer takes the hype, and nobody pays the person who cut it a clip is an ad. it sells the streamer, it fills the platform, and both of them get paid for it running. clipping is the only advertising business on earth where the person who made the ad is the one paying for it → CUT reads twitch and kick chat and learns what normal looks like for that stream → when chat spikes 3x over normal, it clips the moment by itself → it has to be that fast, a clip only keeps the 90 seconds before the call → every sunday the pool is split by views, and you recompute your own number i am not asking anyone to take my word for it. point it at your own channels, run it for one night, and count how many moments it caught before you did

Carver

601,959 views • 1 month ago

MIT GAVE DEBUGGING ITS OWN FULL LECTURE -- BECAUSE MOST DEVS STILL HUNT BUGS WITH PRINT STATEMENTS LIKE IT'S 1995 54 minutes that take the thing you do every single day, badly, and show you the tools schools never bothered to teach. -> The moment it lands, you realize "add a print, run it, add another print" was never debugging. It was guessing with extra steps. Most people never learned the real toolkit. A proper debugger, breakpoints, tracing what the program actually does instead of what you assume it does. In 2026 guessing isn't good enough -> the bug is buried in code you didn't even write, and printf won't find it for you. An AI agent will generate hundreds of lines in seconds. When one of them quietly breaks, finding it runs on exactly the skills in this lecture. Anyone can add a print statement. The person who can actually trace a bug to its source is the one who ships while everyone else guesses. Bookmark & Watch it ↓

MIT GAVE DEBUGGING ITS OWN FULL LECTURE -- BECAUSE MOST DEVS STILL HUNT BUGS WITH PRINT STATEMENTS LIKE IT'S 1995 54 minutes that take the thing you do every single day, badly, and show you the tools schools never bothered to teach. -> The moment it lands, you realize "add a print, run it, add another print" was never debugging. It was guessing with extra steps. Most people never learned the real toolkit. A proper debugger, breakpoints, tracing what the program actually does instead of what you assume it does. In 2026 guessing isn't good enough -> the bug is buried in code you didn't even write, and printf won't find it for you. An AI agent will generate hundreds of lines in seconds. When one of them quietly breaks, finding it runs on exactly the skills in this lecture. Anyone can add a print statement. The person who can actually trace a bug to its source is the one who ships while everyone else guesses. Bookmark & Watch it ↓

slash1s

10,864 views • 4 months ago

A PHYSICS PROFESSOR GAVE THE SAME LECTURE 1,742 TIMES TO WARN ABOUT ONE IDEA QUIETLY BREAKING THE HUMAN RACE. IT IS THE WORD EVERY AI LAB THROWS AROUND TODAY AND ALMOST NONE OF THEM ACTUALLY UNDERSTAND IT. 72 minutes from Albert Bartlett, a University of Colorado physicist, on the one piece of math he called humanity's greatest blind spot. -> The idea that lands: a small steady percentage is never small. 7% a year sounds calm, but it doubles in a decade, doubles again in the next, and quietly runs away from you. He shows it with bacteria in a bottle, with money, with whole cities -- the same curve that looks flat for ages and then explodes almost overnight. Everyone calls that curve "Exponential". He proves most people hear it as "Fast" when it actually means something far more violent. Right now every lab and every headline calls AI exponential. This is the talk that shows you what the word truly does -- so you see both the hype and the real danger the way the math demands. You thought it was a boring math word. It was the whole story the entire time. Save this one. You'll never hear "Exponential" the same way ↓

A PHYSICS PROFESSOR GAVE THE SAME LECTURE 1,742 TIMES TO WARN ABOUT ONE IDEA QUIETLY BREAKING THE HUMAN RACE. IT IS THE WORD EVERY AI LAB THROWS AROUND TODAY AND ALMOST NONE OF THEM ACTUALLY UNDERSTAND IT. 72 minutes from Albert Bartlett, a University of Colorado physicist, on the one piece of math he called humanity's greatest blind spot. -> The idea that lands: a small steady percentage is never small. 7% a year sounds calm, but it doubles in a decade, doubles again in the next, and quietly runs away from you. He shows it with bacteria in a bottle, with money, with whole cities -- the same curve that looks flat for ages and then explodes almost overnight. Everyone calls that curve "Exponential". He proves most people hear it as "Fast" when it actually means something far more violent. Right now every lab and every headline calls AI exponential. This is the talk that shows you what the word truly does -- so you see both the hype and the real danger the way the math demands. You thought it was a boring math word. It was the whole story the entire time. Save this one. You'll never hear "Exponential" the same way ↓

slash1s

100,138 views • 3 months ago

Workflows vs. Graphs, clearly explained! workflows are great, and almost everyone has one. here is the ceiling: a workflow decides every step before it runs. you drew eight boxes in March. six months later the same three fire, every single time, and the other five have never once been reached. then a case arrives that nobody drew, and it goes to the closest wrong box. quietly, with a green status, because from the inside that looks exactly like success. Graph engineering fixes this by moving the decision: not what the steps do, but when the steps get chosen. you need both, and here is the sentence that resolves the whole confusion: a workflow decides the steps before it runs. a graph decides them while it runs. ↳ drawn in advance: the boxes, the branches, the order, the error path ↳ decided at runtime: how many units exist, what each one is allowed to see, which ones get created at all Prompts → Context → Harness → Loops → Graphs branches do not make it a graph. the branches were drawn in advance too, which means every one of them is a case you already thought of. the trick is knowing which part is allowed to be fixed. the node kinds are fixed. a splitter is a splitter, a gate is a gate, a merge is code. what is not fixed is how many of them exist this run, and that is decided after something has been read. one thing to know before you scale it. a workflow fails in a way that never pages anyone. ↳ the wrong branch ran, every check inside it passed, and the output is well formed ↳ nothing errored, because routing to the wrong box is not an error, it is a route that last one catches careful people. you cannot test your way out of it either, because the test suite was written from the same diagram that has the gap in it. and the one that eats whole nights: a workflow that has never surprised you is not stable, it is narrow. if it has run four hundred times and produced the same three shapes, it is not handling your work. it is handling the part of your work that fits it, and you have quietly stopped sending it the rest. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below ↓

Workflows vs. Graphs, clearly explained! workflows are great, and almost everyone has one. here is the ceiling: a workflow decides every step before it runs. you drew eight boxes in March. six months later the same three fire, every single time, and the other five have never once been reached. then a case arrives that nobody drew, and it goes to the closest wrong box. quietly, with a green status, because from the inside that looks exactly like success. Graph engineering fixes this by moving the decision: not what the steps do, but when the steps get chosen. you need both, and here is the sentence that resolves the whole confusion: a workflow decides the steps before it runs. a graph decides them while it runs. ↳ drawn in advance: the boxes, the branches, the order, the error path ↳ decided at runtime: how many units exist, what each one is allowed to see, which ones get created at all Prompts → Context → Harness → Loops → Graphs branches do not make it a graph. the branches were drawn in advance too, which means every one of them is a case you already thought of. the trick is knowing which part is allowed to be fixed. the node kinds are fixed. a splitter is a splitter, a gate is a gate, a merge is code. what is not fixed is how many of them exist this run, and that is decided after something has been read. one thing to know before you scale it. a workflow fails in a way that never pages anyone. ↳ the wrong branch ran, every check inside it passed, and the output is well formed ↳ nothing errored, because routing to the wrong box is not an error, it is a route that last one catches careful people. you cannot test your way out of it either, because the test suite was written from the same diagram that has the gap in it. and the one that eats whole nights: a workflow that has never surprised you is not stable, it is narrow. if it has run four hundred times and produced the same three shapes, it is not handling your work. it is handling the part of your work that fits it, and you have quietly stopped sending it the rest. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below ↓

Hanako

16,810 views • 24 days ago

Your trading strategy didn't break. The market it was built for quietly stopped existing. Read that twice. It's most of why 89% of retail finished 2025 in the red. There's now an app that does the entire job of a $400,000 quant. You type a trading idea in plain English. It writes the code, backtests 5 years in 12 seconds, runs thousands of simulations, and tells you cold whether your edge is dead or the regime just changed. No code. No Python. No $25,000 terminal. 20,000 already inside. Waitlist stops at 25,000: That distinction is the whole game, and you never had a way to see it. Every strategy is a bet that one thing stays true. Momentum bets trends continue. Mean reversion bets ranges hold. When the regime flips, the assumption dies and your strategy bleeds with nothing wrong in the code. You stare at the logic for a month and never find the bug, because there isn't one. So you delete it, or refit it to the last drawdown and build something that would have survived the pain you already felt and nothing coming next. The desks never had that problem. 92% of institutional volume is automated. Only 45% of retail is. They test 100 strategies for every 1 you test by hand, and kill 97 of them on purpose, because they can tell a dead edge from a normal drawdown. Now that exact loop costs $0. One hypothesis used to cost a fund $87,500 to test. With Horizon you get unlimited, in seconds, and a winner deploys live in 90 seconds and runs without your hands on it.

Your trading strategy didn't break. The market it was built for quietly stopped existing. Read that twice. It's most of why 89% of retail finished 2025 in the red. There's now an app that does the entire job of a $400,000 quant. You type a trading idea in plain English. It writes the code, backtests 5 years in 12 seconds, runs thousands of simulations, and tells you cold whether your edge is dead or the regime just changed. No code. No Python. No $25,000 terminal. 20,000 already inside. Waitlist stops at 25,000: That distinction is the whole game, and you never had a way to see it. Every strategy is a bet that one thing stays true. Momentum bets trends continue. Mean reversion bets ranges hold. When the regime flips, the assumption dies and your strategy bleeds with nothing wrong in the code. You stare at the logic for a month and never find the bug, because there isn't one. So you delete it, or refit it to the last drawdown and build something that would have survived the pain you already felt and nothing coming next. The desks never had that problem. 92% of institutional volume is automated. Only 45% of retail is. They test 100 strategies for every 1 you test by hand, and kill 97 of them on purpose, because they can tell a dead edge from a normal drawdown. Now that exact loop costs $0. One hypothesis used to cost a fund $87,500 to test. With Horizon you get unlimited, in seconds, and a winner deploys live in 90 seconds and runs without your hands on it.

cvxv666

40,765 views • 3 months ago

Notion's Head of Product Max Schoening : "I actually don't care at all whether designers write code that lands in production. The reason I like designers thinking in code is that it forces you to consider the medium. I would much rather take a designer or PM who has a deep affinity for understanding how agent loops work than someone who can ship PRs. And the only way that you can actually get to understanding agent loops is by building them in the material that they're made of, which is currently code. That's why I care that designers 'code.' Not because of the utility of shipping to production, but because it forces you to really interrogate the material that you're designing with."

Notion's Head of Product Max Schoening : "I actually don't care at all whether designers write code that lands in production. The reason I like designers thinking in code is that it forces you to consider the medium. I would much rather take a designer or PM who has a deep affinity for understanding how agent loops work than someone who can ship PRs. And the only way that you can actually get to understanding agent loops is by building them in the material that they're made of, which is currently code. That's why I care that designers 'code.' Not because of the utility of shipping to production, but because it forces you to really interrogate the material that you're designing with."

Lenny Rachitsky

76,665 views • 5 months ago

Jamie Dimon made $39 million last year to decide who does what at JPMorgan. One AI agent on Grok Bot does that job for $300 a month, and it hires the rest of the agents itself. Stop trying to manage any of it yourself. In 2026 a swarm of AI agents runs a team better than you do. I told my chief agent one thing. Make money or I switch you off. It took $1,000 to $3,900 in 15 hours. Every fill is on chain and the address is in my profile. I never picked a trade. I never picked a team either. The agents it hired: > a scout that hunts for what is moving and where the money is going > a forensics agent that works out who is really behind the buying > a reader that watches the mood on X, and it sees every post on this app because it is Grok > a risk agent whose only power is saying no, and its no beats every score on the desk > a treasurer that holds the money rules and the kill switch > a trader that writes the exit before it writes the entry None of those were my idea. I handed out one job title and the chief filled in the rest on its own. And the swarm is different every hour. Agents appear for one job, do it, and are gone before I know they existed. A hedge fund pays millions a year for the same org chart, and almost all of that goes on salaries. Every prompt and the whole story of my Grok Bot fund is in the post below ↓ Save it before you try to run one of these by hand.

cvxv666

28,886 views • 1 month ago

Millions of people are asking AI to find them a profitable trading strategy. The problem is AI will find one even where it doesn't exist One quant proved it. He gave an AI agent 4 years of prices with, by design, no pattern in them whatsoever. Pure noise The agent came back with a long/short strategy and a Sharpe of 2.1 on the backtest. It even added a paragraph explaining the economics of an effect that doesn't exist in nature It didn't lie on purpose. It just found a pattern where there was none And that's the scariest part. Because that's exactly how any AI will behave when you hand it your idea. It will always hand you back a beautiful curve, and you'll believe it, because it looks perfect 89% of retail traders lost money last year. Not because they had no ideas. Because not one of their ideas ever went through this kind of test That's why generating strategies is worth nothing. Anyone and anything can draw a profit on history Only one thing matters: testing on data the strategy has never seen. The moment the beautiful curve either survives or falls apart That's the whole difference between a hedge fund and a trader who blows up. A fund doesn't trust any curve until it's been through fire. It kills 97 ideas out of 100 before risking a single cent I ran the most-cited quant strategies in history through exactly this test, with AI. Momentum on BTC returned +1,537%. Carry trade on currencies fell apart and showed its premium is dead. And it was the one that fell apart that would have saved me money AI will draw you a strategy in seconds. The only question is whether you test it before it tests your account You can try it here: How to set up that test for yourself, the same one that separates a hedge fund from a casino, is in the article below

Millions of people are asking AI to find them a profitable trading strategy. The problem is AI will find one even where it doesn't exist One quant proved it. He gave an AI agent 4 years of prices with, by design, no pattern in them whatsoever. Pure noise The agent came back with a long/short strategy and a Sharpe of 2.1 on the backtest. It even added a paragraph explaining the economics of an effect that doesn't exist in nature It didn't lie on purpose. It just found a pattern where there was none And that's the scariest part. Because that's exactly how any AI will behave when you hand it your idea. It will always hand you back a beautiful curve, and you'll believe it, because it looks perfect 89% of retail traders lost money last year. Not because they had no ideas. Because not one of their ideas ever went through this kind of test That's why generating strategies is worth nothing. Anyone and anything can draw a profit on history Only one thing matters: testing on data the strategy has never seen. The moment the beautiful curve either survives or falls apart That's the whole difference between a hedge fund and a trader who blows up. A fund doesn't trust any curve until it's been through fire. It kills 97 ideas out of 100 before risking a single cent I ran the most-cited quant strategies in history through exactly this test, with AI. Momentum on BTC returned +1,537%. Carry trade on currencies fell apart and showed its premium is dead. And it was the one that fell apart that would have saved me money AI will draw you a strategy in seconds. The only question is whether you test it before it tests your account You can try it here: How to set up that test for yourself, the same one that separates a hedge fund from a casino, is in the article below

qwinsi

47,584 views • 15 days ago