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THIS GUY BUILT A REAL JARVIS THAT TURNS HIS IDEAS INTO FINISHED PROJECTS ON ITS OWN, AND HE JUST SHOWED HOW THE WHOLE THING WORKS The whole system runs on one clean split he calls a blood-brain barrier. The second brain, his Obsidian vault, is only for things that...

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Yarchi

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86,874 views • 9 days ago •via X (Twitter)

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THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this
0:24

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

Yarchi

57,768 views • 22 days ago

The vision behind $HABE is no longer a theory. It’s real. It’s active. And now, you can see it for yourself. The HABEVERSE is more than a platform. It’s a system built to move ideas into the real world, and it runs on four powerhouse zones: The HABE Innovation Zone: where raw ideas are shaped into structured proposals using HABEAI and a proven process that goes from seed to launch. The HABE Voting Zone: Where your vote helps decide what gets built, and every action earns $HABE. The HABE Marketplace: A new way to invest. Back real products, grow with innovation, and build a portfolio that reflects what you believe in. The HABE Profile Zone: Your personal dashboard. Everything you do is tracked and managed in one place. This prototype is just the first glimpse of what’s coming, and what’s already working. 👉 Check out the prototype on Figma: Start where it still counts: Final hour before the price shifts.
2:19

The vision behind $HABE is no longer a theory. It’s real. It’s active. And now, you can see it for yourself. The HABEVERSE is more than a platform. It’s a system built to move ideas into the real world, and it runs on four powerhouse zones: The HABE Innovation Zone: where raw ideas are shaped into structured proposals using HABEAI and a proven process that goes from seed to launch. The HABE Voting Zone: Where your vote helps decide what gets built, and every action earns $HABE. The HABE Marketplace: A new way to invest. Back real products, grow with innovation, and build a portfolio that reflects what you believe in. The HABE Profile Zone: Your personal dashboard. Everything you do is tracked and managed in one place. This prototype is just the first glimpse of what’s coming, and what’s already working. 👉 Check out the prototype on Figma: Start where it still counts: Final hour before the price shifts.

HABE

14,864 views • 11 months ago

OBSIDIAN TURNED 4,000+ IDEAS INTO A KNOWLEDGE GRAPH THAT REBUILDS ITSELF EVERY NIGHT every lecture adds another point. every saved article adds another signal. every old note becomes part of the map again instead of sitting dead inside a folder. a $599 mac mini runs the loop 24/7, claude extracts the useful parts, verifies the note, then links it to 2-3 related ideas inside obsidian before saving it back into the vault. after 30 days the graph starts getting dense. random notes become clusters, forgotten ideas resurface, and the whole system begins to look less like storage and more like a private memory engine. this is what a second brain looks like when it stops waiting for you to open the app.
0:39

OBSIDIAN TURNED 4,000+ IDEAS INTO A KNOWLEDGE GRAPH THAT REBUILDS ITSELF EVERY NIGHT every lecture adds another point. every saved article adds another signal. every old note becomes part of the map again instead of sitting dead inside a folder. a $599 mac mini runs the loop 24/7, claude extracts the useful parts, verifies the note, then links it to 2-3 related ideas inside obsidian before saving it back into the vault. after 30 days the graph starts getting dense. random notes become clusters, forgotten ideas resurface, and the whole system begins to look less like storage and more like a private memory engine. this is what a second brain looks like when it stops waiting for you to open the app.

Gipp 🦅

18,261 views • 5 days ago

SOMEONE JUST BUILT AN ENTIRE COMPANY BRAIN INSIDE CLAUDE CODE IN 7 DAYS Not Obsidian. Not a doc... A living map of every department, every agent, every SOP on one screen. Click any node and the whole thing opens up: •⁠ ⁠who runs it •⁠ ⁠what SOPs are attached •⁠ ⁠what each person can touch that permission layer is the whole game an employee opens the chat and the AI already knows their access level. agents, data, SOPs surface in the conversation like you tagged them by hand. Obsidian cannot do this... no dev team. no six month build. no enterprise budget. just Claude Code and one week ClaudeKit is the only team you need to build something like this ( bonus: 20 best claude code workflows
1:20

SOMEONE JUST BUILT AN ENTIRE COMPANY BRAIN INSIDE CLAUDE CODE IN 7 DAYS Not Obsidian. Not a doc... A living map of every department, every agent, every SOP on one screen. Click any node and the whole thing opens up: •⁠ ⁠who runs it •⁠ ⁠what SOPs are attached •⁠ ⁠what each person can touch that permission layer is the whole game an employee opens the chat and the AI already knows their access level. agents, data, SOPs surface in the conversation like you tagged them by hand. Obsidian cannot do this... no dev team. no six month build. no enterprise budget. just Claude Code and one week ClaudeKit is the only team you need to build something like this ( bonus: 20 best claude code workflows

Hamza Khalid

63,055 views • 14 days ago

The smartest man in AI just exposed the whole AGI narrative as a LIE. And he used a physics problem from 1905 to prove it. His name is Demis Hassabis. He runs Google DeepMind, and won the Nobel Prize for using AI to crack a problem in biology that had stumped scientists for 50 years. Almost nobody in this industry has a track record like his. He went on the NothingButTech podcast and called out the biggest lie in AI right now: Right now the loudest voices in AI are telling you that AGI is basically here. OpenAI has literally defined AGI as a system that can outperform humans at most "economically valuable work." In other words, if it replaces enough jobs, we have arrived. Hassabis thinks that bar is a joke. He said real general intelligence has to do what the human brain can do, because the brain is the only proof we have that this kind of intelligence is even possible. He called that "a higher bar than just being able to do some useful economic work," which is about as close as a polite British Nobel laureate gets to calling his rivals out. Then he gave the actual test: Today's AI has read everything humans have ever written, including the theory of relativity. So when it explains relativity back to you, it's repeating an answer that already exists. That's not intelligence. So Hassabis proposed a test that makes memorization impossible. Train an AI on only what humanity knew in 1901, four years BEFORE Einstein published relativity. Then ask it to come up with relativity on its own. It can't look up the answer, because in 1901 the answer doesn't exist yet. The only way to pass is to do what Einstein actually did: Take the same physics everyone else had and reason its way to an idea no human had ever had. Hassabis says not a single AI today can, no matter how much it has memorized. Which means what we keep calling "almost AGI" is really just the best librarian in history. It can find any answer that already exists but it cannot create one that doesn't. His second version is even sharper: AlphaGo, the system his own team built, famously invented a brand new move that no human had played in 2,000 years of the game. Everyone called it genius but Hassabis says that still is not the bar. The real test is not whether an AI can invent a new move inside Go, it is whether an AI could INVENT a game as deep and as beautiful as Go in the first place. No model that exists today can do it. The people telling you AGI has already arrived are the same people raising hundreds of billions of dollars on that exact promise. The valuations only work if the finish line is right in front of us. So the finish line keeps getting dragged closer, and AGI keeps getting quietly redefined down to "does useful work," until the products they already sell happen to qualify. Hassabis has nothing to prove and nothing to sell you. He already won the Nobel, and he is telling you the machines still cannot do the one thing that would make them genuinely intelligent, which is have a truly original idea. To be fair to him, he is not a pessimist about it. He believes real AGI IS coming, and he is spending his life building it. He just refuses to pretend it is already sitting in your phone. So the next time a founder tells you AGI is months away, remember that the one man in the room with a Nobel Prize built his test around Einstein, and admitted that nothing we have made can pass it. What do you think?
2:45

The smartest man in AI just exposed the whole AGI narrative as a LIE. And he used a physics problem from 1905 to prove it. His name is Demis Hassabis. He runs Google DeepMind, and won the Nobel Prize for using AI to crack a problem in biology that had stumped scientists for 50 years. Almost nobody in this industry has a track record like his. He went on the NothingButTech podcast and called out the biggest lie in AI right now: Right now the loudest voices in AI are telling you that AGI is basically here. OpenAI has literally defined AGI as a system that can outperform humans at most "economically valuable work." In other words, if it replaces enough jobs, we have arrived. Hassabis thinks that bar is a joke. He said real general intelligence has to do what the human brain can do, because the brain is the only proof we have that this kind of intelligence is even possible. He called that "a higher bar than just being able to do some useful economic work," which is about as close as a polite British Nobel laureate gets to calling his rivals out. Then he gave the actual test: Today's AI has read everything humans have ever written, including the theory of relativity. So when it explains relativity back to you, it's repeating an answer that already exists. That's not intelligence. So Hassabis proposed a test that makes memorization impossible. Train an AI on only what humanity knew in 1901, four years BEFORE Einstein published relativity. Then ask it to come up with relativity on its own. It can't look up the answer, because in 1901 the answer doesn't exist yet. The only way to pass is to do what Einstein actually did: Take the same physics everyone else had and reason its way to an idea no human had ever had. Hassabis says not a single AI today can, no matter how much it has memorized. Which means what we keep calling "almost AGI" is really just the best librarian in history. It can find any answer that already exists but it cannot create one that doesn't. His second version is even sharper: AlphaGo, the system his own team built, famously invented a brand new move that no human had played in 2,000 years of the game. Everyone called it genius but Hassabis says that still is not the bar. The real test is not whether an AI can invent a new move inside Go, it is whether an AI could INVENT a game as deep and as beautiful as Go in the first place. No model that exists today can do it. The people telling you AGI has already arrived are the same people raising hundreds of billions of dollars on that exact promise. The valuations only work if the finish line is right in front of us. So the finish line keeps getting dragged closer, and AGI keeps getting quietly redefined down to "does useful work," until the products they already sell happen to qualify. Hassabis has nothing to prove and nothing to sell you. He already won the Nobel, and he is telling you the machines still cannot do the one thing that would make them genuinely intelligent, which is have a truly original idea. To be fair to him, he is not a pessimist about it. He believes real AGI IS coming, and he is spending his life building it. He just refuses to pretend it is already sitting in your phone. So the next time a founder tells you AGI is months away, remember that the one man in the room with a Nobel Prize built his test around Einstein, and admitted that nothing we have made can pass it. What do you think?

Ricardo

1,281,309 views • 18 days ago

YOUR FIRST BRAIN FORGETS EVERYTHING. Your second brain does not have to. Claude connected to Obsidian is the closest thing to a permanent intelligence layer built on top of everything you have ever thought, read, or built. Your notes are not just stored. They are connected. Every idea linked to every related idea through a dynamic graph that reveals the shape of your thinking in real time. Claude reads your vault. Surfaces connections you forgot you made. Writes alongside your thinking instead of replacing it. Populates your knowledge base from research without polluting your original ideas. The setup takes one afternoon. What compounds after that takes years to fully appreciate. This is the complete guide to building it from scratch. Follow NeilXbt for more second brain and Claude builds. Credit to Ray Fu on TikTok for this!
1:05

YOUR FIRST BRAIN FORGETS EVERYTHING. Your second brain does not have to. Claude connected to Obsidian is the closest thing to a permanent intelligence layer built on top of everything you have ever thought, read, or built. Your notes are not just stored. They are connected. Every idea linked to every related idea through a dynamic graph that reveals the shape of your thinking in real time. Claude reads your vault. Surfaces connections you forgot you made. Writes alongside your thinking instead of replacing it. Populates your knowledge base from research without polluting your original ideas. The setup takes one afternoon. What compounds after that takes years to fully appreciate. This is the complete guide to building it from scratch. Follow NeilXbt for more second brain and Claude builds. Credit to Ray Fu on TikTok for this!

NeilXbt

14,699 views • 1 month ago

A 14-year-old from China built three separate AI brains connected to Claude Code - and each one grows every day on its own. One brain for code, one for projects, one for everything else in his life. 600+ conversations on one topic in a single node - everything linked, everything accessible in one request without copying between tabs. But the main thing isn't storage. If Claude Code made a mistake and he corrected it once - the system remembered. That same mistake never happened again. No re-explaining context, no starting from zero - Claude already knows how he thinks. Instead of one giant messy file- a stack of smaller brains passing context to each other. Brain to brain to brain - just stacking intelligence while his classmates do homework by hand.
1:13

A 14-year-old from China built three separate AI brains connected to Claude Code - and each one grows every day on its own. One brain for code, one for projects, one for everything else in his life. 600+ conversations on one topic in a single node - everything linked, everything accessible in one request without copying between tabs. But the main thing isn't storage. If Claude Code made a mistake and he corrected it once - the system remembered. That same mistake never happened again. No re-explaining context, no starting from zero - Claude already knows how he thinks. Instead of one giant messy file- a stack of smaller brains passing context to each other. Brain to brain to brain - just stacking intelligence while his classmates do homework by hand.

Noisy

22,622 views • 2 months ago

There is one city in America that isn't fully mapped and its in Minnesota. This guy decided to fly a drone over the neighborhood to finish off the work google maps started. Google took down the mapping of the city and the images. So my question is here, What the hell is going on in this one city in Minnesota that makes it that way?
1:09

There is one city in America that isn't fully mapped and its in Minnesota. This guy decided to fly a drone over the neighborhood to finish off the work google maps started. Google took down the mapping of the city and the images. So my question is here, What the hell is going on in this one city in Minnesota that makes it that way?

Paul

36,457 views • 6 days ago

HE MAKES MONEY IN REAL ESTATE WITHOUT BUYING, SELLING, OR EVEN SEEING A SINGLE HOUSE. HERE'S THE EXACT SETUP He never owns a property. He takes a single listing, turns it into a polished 30-second video, and sells that to the agent who posted it. Realtors need video for their feeds and almost none of them can make it. He sits in the middle and builds the whole thing once as a skill that runs on command Here is the exact process: 1. Pull the listing. Go to Zillow, open any listing, download the high-res images, and grab the property info. That is your raw material 2. Turn photos into video with Google Veo. Get a Google API key for Veo, the image-to-video model. It takes the listing photos and animates them into clean 30-second footage. This is the best one out right now 3. Add the voice with ElevenLabs. Get an ElevenLabs API key. Feed it the listing details and it returns a voiceover that sounds like a real human, not a robot. Lay it over the video with the text on screen 4. Send it with AgentMail. Get an AgentMail key so the system can send the finished email out on its own Then you wire it into one skill. Scrape the listing, send images to Veo, add the ElevenLabs voiceover and on-screen text, then send the email. Feed it each key one at a time and have it build each step Who you sell to: Pull realtors off Zillow and Realtor com whose listings have flat photos and zero video. That gap is your pitch. Send a free sample made from their own listing first, then charge a monthly rate for ongoing clips. One agent with ten listings is a recurring client, fully online Bookmark this
2:06

HE MAKES MONEY IN REAL ESTATE WITHOUT BUYING, SELLING, OR EVEN SEEING A SINGLE HOUSE. HERE'S THE EXACT SETUP He never owns a property. He takes a single listing, turns it into a polished 30-second video, and sells that to the agent who posted it. Realtors need video for their feeds and almost none of them can make it. He sits in the middle and builds the whole thing once as a skill that runs on command Here is the exact process: 1. Pull the listing. Go to Zillow, open any listing, download the high-res images, and grab the property info. That is your raw material 2. Turn photos into video with Google Veo. Get a Google API key for Veo, the image-to-video model. It takes the listing photos and animates them into clean 30-second footage. This is the best one out right now 3. Add the voice with ElevenLabs. Get an ElevenLabs API key. Feed it the listing details and it returns a voiceover that sounds like a real human, not a robot. Lay it over the video with the text on screen 4. Send it with AgentMail. Get an AgentMail key so the system can send the finished email out on its own Then you wire it into one skill. Scrape the listing, send images to Veo, add the ElevenLabs voiceover and on-screen text, then send the email. Feed it each key one at a time and have it build each step Who you sell to: Pull realtors off Zillow and Realtor com whose listings have flat photos and zero video. That gap is your pitch. Send a free sample made from their own listing first, then charge a monthly rate for ongoing clips. One agent with ten listings is a recurring client, fully online Bookmark this

Yarchi

106,174 views • 29 days ago

"A society that is in the dark is a society that is not one that is going to win the war of ideas with the United States. But we have to be competent to everybody. This is not a TV show. This is real. This isn't fun & games. This is real." - GOP candidate Chris Christie on China
9:18

"A society that is in the dark is a society that is not one that is going to win the war of ideas with the United States. But we have to be competent to everybody. This is not a TV show. This is real. This isn't fun & games. This is real." - GOP candidate Chris Christie on China

NEWSMAX

32,681 views • 2 years ago

It is not that Rahul Gandhi does not have a brain; it is that his brain is situated in the wrong part of his anatomy. My views, on why Indian economy is not dead, but it will surely be if one were to follow Rahul Gandhi's policies and ideas:
2:14

It is not that Rahul Gandhi does not have a brain; it is that his brain is situated in the wrong part of his anatomy. My views, on why Indian economy is not dead, but it will surely be if one were to follow Rahul Gandhi's policies and ideas:

Anand Ranganathan

108,398 views • 8 months ago

YOUR AI IS FORGETTING EVERYTHING YOU TAUGHT IT YESTERDAY. That is why most outputs still feel generic. The people getting real leverage in 2026 fixed this with one setup: Obsidian + Claude Code. Obsidian stores your thinking. Claude Code reads the patterns. Your notes become permanent context the agent can access anytime: Daily thoughts. Projects. Ideas. Contradictions. Questions you keep returning to. Then the real unlock: Custom slash commands. `/context` loads your recent thinking instantly. `/emerge` finds hidden ideas across your notes. `/challenge` tests your beliefs against your past writing. `/trace` maps how your thinking evolved over time. One critical rule: The AI never writes to the vault. You write. The agent reads. That is what keeps the system valuable. At first it feels like note-taking. Eventually it feels like a second brain.
0:49

YOUR AI IS FORGETTING EVERYTHING YOU TAUGHT IT YESTERDAY. That is why most outputs still feel generic. The people getting real leverage in 2026 fixed this with one setup: Obsidian + Claude Code. Obsidian stores your thinking. Claude Code reads the patterns. Your notes become permanent context the agent can access anytime: Daily thoughts. Projects. Ideas. Contradictions. Questions you keep returning to. Then the real unlock: Custom slash commands. `/context` loads your recent thinking instantly. `/emerge` finds hidden ideas across your notes. `/challenge` tests your beliefs against your past writing. `/trace` maps how your thinking evolved over time. One critical rule: The AI never writes to the vault. You write. The agent reads. That is what keeps the system valuable. At first it feels like note-taking. Eventually it feels like a second brain.

Dami-Defi

41,141 views • 1 month ago

The AI industry is optimizing for a definition of intelligence that does not exist. Andrew Ng just said it out loud. Ng: “AGI, to me, should be less about AI that already knows everything under the sun. That seems very challenging, doesn’t seem practical.” The human brain is not the most powerful economic asset in history because of what it holds. It is powerful because of what it can pick up. Ng: “The amazing thing about the human brain is its plasticity, or its ability to learn.” That same biological hardware that earns a PhD in quantum physics could have been trained on chess, surgery, or rewriting global supply chains from scratch. Ng: “That same human brain, just given different training, could have been a chess master, or could have been amazing at playing tennis.” General intelligence is not omniscience. It is the structural capacity to master whatever you point it at. Ng: “It is through learning that we then gain these incredibly specialized intelligences.” The winner is not whoever builds the biggest model. It is whoever builds the most adaptable one. The AI that walks into a domain it has never touched and executes before a human analyst finishes reading the brief. Ng: “What makes the human brain so valuable for economic tasks, is its ability to just learn to do whatever is needed.” Every corporation on earth pays for human labor because humans adapt. Not because they already know everything. AGI is the digitization of that exact capability. At machine speed. At infinite scale. Ng: “A lot of what makes the human brain so general is not that my brain or your brain already knows everything under the sun. It’s our ability to adapt, to learn a huge range of things.” The most powerful economic asset in history was never specialized knowledge. It was the raw capacity to acquire any knowledge, in any domain, on demand. The winning AI is not an encyclopedia. It is the force that makes encyclopedias irrelevant. And once that exists, the question stops being what the AI knows. It becomes what you can teach it before your competitor wakes up. Most people dominating this conversation have not understood that yet.
1:16

The AI industry is optimizing for a definition of intelligence that does not exist. Andrew Ng just said it out loud. Ng: “AGI, to me, should be less about AI that already knows everything under the sun. That seems very challenging, doesn’t seem practical.” The human brain is not the most powerful economic asset in history because of what it holds. It is powerful because of what it can pick up. Ng: “The amazing thing about the human brain is its plasticity, or its ability to learn.” That same biological hardware that earns a PhD in quantum physics could have been trained on chess, surgery, or rewriting global supply chains from scratch. Ng: “That same human brain, just given different training, could have been a chess master, or could have been amazing at playing tennis.” General intelligence is not omniscience. It is the structural capacity to master whatever you point it at. Ng: “It is through learning that we then gain these incredibly specialized intelligences.” The winner is not whoever builds the biggest model. It is whoever builds the most adaptable one. The AI that walks into a domain it has never touched and executes before a human analyst finishes reading the brief. Ng: “What makes the human brain so valuable for economic tasks, is its ability to just learn to do whatever is needed.” Every corporation on earth pays for human labor because humans adapt. Not because they already know everything. AGI is the digitization of that exact capability. At machine speed. At infinite scale. Ng: “A lot of what makes the human brain so general is not that my brain or your brain already knows everything under the sun. It’s our ability to adapt, to learn a huge range of things.” The most powerful economic asset in history was never specialized knowledge. It was the raw capacity to acquire any knowledge, in any domain, on demand. The winning AI is not an encyclopedia. It is the force that makes encyclopedias irrelevant. And once that exists, the question stops being what the AI knows. It becomes what you can teach it before your competitor wakes up. Most people dominating this conversation have not understood that yet.

Dustin

19,802 views • 3 months ago

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 ↓
36:09

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

191,428 views • 24 days ago

Sam Altman just told you what OpenAI is actually building. Not a chatbot. Not a search tool. Not an assistant. Altman: “Go look around my computer… read my messages… listen to my meetings… intermediate my interactions for me.” That is not a product pitch. That is the CEO of the most valuable AI company on Earth describing what he personally wants. For himself. Every day. Read his messages. Listen to his meetings. Act on his behalf. Make decisions before he knows a decision needs making. Altman: “I don’t have to think. I don’t have to ask you questions.” Every model of AI ever built runs on the prompt. You ask. It responds. You direct. It executes. The human initiates. The machine follows. Altman is describing the death of that model. The agent does not wait. It already read the email. It already heard the meeting. It already knows what you need before you form the thought. You do not operate the machine. The machine operates around you. Then came the line that makes everything else real. Altman: “You can know everything about my life. Start suggesting more things I should build.” He is not asking the AI to execute his ideas. He is asking it to generate them. From his files. His history. His patterns. His entire context. The agent does not just remove friction. It removes the blank page. You never stall. You never run dry. You never sit wondering what to build next. The machine already mapped your market, your gaps, your momentum. It tells you what comes next before you think to ask. But the individual product is not the story. Altman went further. Altman: “Automated companies… where the AI can do not just coding work, but huge amounts of what it takes to run and operate a company.” Not fully automated. He was precise about that. But accelerated to the point where one person with the right stack does what used to take departments. The billion-dollar company did not reach that valuation because the product was worth a billion. It got there because it took a thousand people to deliver it. When an agent absorbs the work of a hundred of those people, the math of every industry rewrites itself. The startup that needed fifty employees and three years of runway now needs five people and six months. The company that took a decade to scale now compounds in quarters. The person holding the line between their data and their tools is not protecting their privacy. They are protecting their ceiling. Because the cost of this leverage is total transparency. You do not get the agent that acts without being asked unless you give it everything. Your messages. Your calendar. Your files. Your patterns. Your life. Altman is not hiding that tradeoff. He is building it as the product. The people who accept it will operate at a speed the people who refuse cannot touch. Right now, two versions of the future are separating. One where you direct the machine. One where the machine already knows. Altman chose. He is building it. The question is not whether this happens. The question is which side of it finds you.
1:40

Sam Altman just told you what OpenAI is actually building. Not a chatbot. Not a search tool. Not an assistant. Altman: “Go look around my computer… read my messages… listen to my meetings… intermediate my interactions for me.” That is not a product pitch. That is the CEO of the most valuable AI company on Earth describing what he personally wants. For himself. Every day. Read his messages. Listen to his meetings. Act on his behalf. Make decisions before he knows a decision needs making. Altman: “I don’t have to think. I don’t have to ask you questions.” Every model of AI ever built runs on the prompt. You ask. It responds. You direct. It executes. The human initiates. The machine follows. Altman is describing the death of that model. The agent does not wait. It already read the email. It already heard the meeting. It already knows what you need before you form the thought. You do not operate the machine. The machine operates around you. Then came the line that makes everything else real. Altman: “You can know everything about my life. Start suggesting more things I should build.” He is not asking the AI to execute his ideas. He is asking it to generate them. From his files. His history. His patterns. His entire context. The agent does not just remove friction. It removes the blank page. You never stall. You never run dry. You never sit wondering what to build next. The machine already mapped your market, your gaps, your momentum. It tells you what comes next before you think to ask. But the individual product is not the story. Altman went further. Altman: “Automated companies… where the AI can do not just coding work, but huge amounts of what it takes to run and operate a company.” Not fully automated. He was precise about that. But accelerated to the point where one person with the right stack does what used to take departments. The billion-dollar company did not reach that valuation because the product was worth a billion. It got there because it took a thousand people to deliver it. When an agent absorbs the work of a hundred of those people, the math of every industry rewrites itself. The startup that needed fifty employees and three years of runway now needs five people and six months. The company that took a decade to scale now compounds in quarters. The person holding the line between their data and their tools is not protecting their privacy. They are protecting their ceiling. Because the cost of this leverage is total transparency. You do not get the agent that acts without being asked unless you give it everything. Your messages. Your calendar. Your files. Your patterns. Your life. Altman is not hiding that tradeoff. He is building it as the product. The people who accept it will operate at a speed the people who refuse cannot touch. Right now, two versions of the future are separating. One where you direct the machine. One where the machine already knows. Altman chose. He is building it. The question is not whether this happens. The question is which side of it finds you.

Dustin

87,680 views • 2 months ago

"I became very fascinated with the idea of the brain computer interface and what consequences that might bring to the world. And this is one of the reasons The Brain Show came up, so that people will have a chance to see some of the realization of these ideas, see how they feel about it, and where we should be leading things."
1:21

"I became very fascinated with the idea of the brain computer interface and what consequences that might bring to the world. And this is one of the reasons The Brain Show came up, so that people will have a chance to see some of the realization of these ideas, see how they feel about it, and where we should be leading things."

Peter Gabriel

24,897 views • 4 months ago

Andrej Karpathy spent 2h showing how he actually uses AI day to day he's a co-founder of OpenAI and led AI at Tesla, so when he shows how he works, it’s worth watching and the whole session is just him telling the machine what he wants in simple terms, like he's briefing a coworker watch what's actually happening the entire time: > he describes the task in normal words > it goes off and does the work > he glances at the result and nudges it with one more sentence that's the whole skill, and you've had it since you learned to talk the only gap between that and a worker that runs on its own is handing that sentence a schedule and the tools to act check his work, then build the version that keeps working when you stop
2:11:11

Andrej Karpathy spent 2h showing how he actually uses AI day to day he's a co-founder of OpenAI and led AI at Tesla, so when he shows how he works, it’s worth watching and the whole session is just him telling the machine what he wants in simple terms, like he's briefing a coworker watch what's actually happening the entire time: > he describes the task in normal words > it goes off and does the work > he glances at the result and nudges it with one more sentence that's the whole skill, and you've had it since you learned to talk the only gap between that and a worker that runs on its own is handing that sentence a schedule and the tools to act check his work, then build the version that keeps working when you stop

Chrome

1,778,183 views • 23 days ago

A 26-YEAR-OLD AI ENGINEER WHOSE BIRMINGHAM THESIS OUTPERFORMED GOOGLE SCHOLAR BY 50% JUST SHIPPED GRAPHIFY: ONE COMMAND TURNS ANY FOLDER INTO A CLAUDE CODE SECOND BRAIN Safi Shamsi built Graphify 48 hours after Karpathy posted his LLM wiki idea. It turns any folder, codebase, docs, PDFs, into a knowledge graph Claude reads instead of grepping. Up to 43x fewer tokens per query. The trick almost nobody is using yet: one flag exports the entire graph as a fully-linked Obsidian vault. Repo: /safishamsi/graphify Setup, end to end: 1. Install: uv tool install graphifyy (or pipx install graphifyy). Verify with graphify --version. 2. Install the skill in Claude Code: graphify claude install. This wires Graphify into Claude Code so you can call it as a skill. 3. Open the folder you want mapped in Claude Code. In the terminal: graphify . It extracts every concept and builds the graph in graphify-out/. 4. Export to Obsidian: graphify . --obsidian. Writes one note per concept, every relationship as a wikilink, every node linked back to its source. 5. Open the new vault in Obsidian (Manage vaults → Open folder as a vault), or drag it as a subfolder into your existing one. That's it. Your Claude Code instance now has a navigable map of the codebase that loads instantly instead of re-reading files every session. Full step-by-step build of the Claude + Obsidian second brain in the article below. Bookmark this
16:36

A 26-YEAR-OLD AI ENGINEER WHOSE BIRMINGHAM THESIS OUTPERFORMED GOOGLE SCHOLAR BY 50% JUST SHIPPED GRAPHIFY: ONE COMMAND TURNS ANY FOLDER INTO A CLAUDE CODE SECOND BRAIN Safi Shamsi built Graphify 48 hours after Karpathy posted his LLM wiki idea. It turns any folder, codebase, docs, PDFs, into a knowledge graph Claude reads instead of grepping. Up to 43x fewer tokens per query. The trick almost nobody is using yet: one flag exports the entire graph as a fully-linked Obsidian vault. Repo: /safishamsi/graphify Setup, end to end: 1. Install: uv tool install graphifyy (or pipx install graphifyy). Verify with graphify --version. 2. Install the skill in Claude Code: graphify claude install. This wires Graphify into Claude Code so you can call it as a skill. 3. Open the folder you want mapped in Claude Code. In the terminal: graphify . It extracts every concept and builds the graph in graphify-out/. 4. Export to Obsidian: graphify . --obsidian. Writes one note per concept, every relationship as a wikilink, every node linked back to its source. 5. Open the new vault in Obsidian (Manage vaults → Open folder as a vault), or drag it as a subfolder into your existing one. That's it. Your Claude Code instance now has a navigable map of the codebase that loads instantly instead of re-reading files every session. Full step-by-step build of the Claude + Obsidian second brain in the article below. Bookmark this

Yarchi

137,897 views • 9 days ago

Aravind Srinivas just described a future most founders are pretending they are ready for. One person. One machine. A company that runs itself. Srinivas: “Buy a Mac mini, set up a Perplexity personal computer, and run their business on that.” Not a side project. Not a pitch deck. A real business with real revenue while the founder is not in the building. AI runs the ads. Handles SEO. Integrates Stripe. Ships features. Answers customers. All of it executing without a single employee. Srinivas: “Have this all working while you can be sipping wine in Napa.” But before he sold the dream he killed the one most people are already chasing. Srinivas: “Everybody talks about this one-person one-billion-dollar company. It’s not truly moving the GDP by one billion. It’s not truly creating new value.” One researcher collecting a billion in equity does not grow an economy. It rearranges numbers between balance sheets. Nothing gets built. No customer gets served. That is not value creation. That is valuation creation. Srinivas wants no part of it. What he described is the opposite. The person driving Uber between shifts who has the idea but not the payroll. Not the engineering. Not the marketing. Not the support staff. That person gets a machine that replaces all of it. Hundreds of thousands in revenue. Millions. Generated by autonomous systems doing the work that used to require ten employees and a burn rate. Not paper wealth. Not valuation theater. Output that moves through an economy and touches real customers. That is what moves GDP. Not one person worth a billion dollars. A million people each building something worth a million. That math rewrites a country. Then Srinivas said the part that separates him from every hype merchant in the room. Srinivas: “Everybody thinks AI is already there. It’s not there yet. Someone has to do that hard work.” The vision is real. The infrastructure is not. The agents are not autonomous. The integrations are not seamless. The plumbing is not finished. Someone has to wire the APIs. Connect the billing. Build the bridge between what a founder wants and what a machine can deliver. That work is not a keynote. It is not a tweet thread. It is engineering that nobody wants to do and everybody will depend on. Whoever finishes it first does not just build a product. They hand every ambitious person on Earth a company they can run alone. The corporations that need five hundred people to do what one founder with the right infrastructure could do are not efficient. They are exposed. And the person building the thing that exposes them just told you exactly what it looks like. He also told you it is not going to build itself.
1:13

Aravind Srinivas just described a future most founders are pretending they are ready for. One person. One machine. A company that runs itself. Srinivas: “Buy a Mac mini, set up a Perplexity personal computer, and run their business on that.” Not a side project. Not a pitch deck. A real business with real revenue while the founder is not in the building. AI runs the ads. Handles SEO. Integrates Stripe. Ships features. Answers customers. All of it executing without a single employee. Srinivas: “Have this all working while you can be sipping wine in Napa.” But before he sold the dream he killed the one most people are already chasing. Srinivas: “Everybody talks about this one-person one-billion-dollar company. It’s not truly moving the GDP by one billion. It’s not truly creating new value.” One researcher collecting a billion in equity does not grow an economy. It rearranges numbers between balance sheets. Nothing gets built. No customer gets served. That is not value creation. That is valuation creation. Srinivas wants no part of it. What he described is the opposite. The person driving Uber between shifts who has the idea but not the payroll. Not the engineering. Not the marketing. Not the support staff. That person gets a machine that replaces all of it. Hundreds of thousands in revenue. Millions. Generated by autonomous systems doing the work that used to require ten employees and a burn rate. Not paper wealth. Not valuation theater. Output that moves through an economy and touches real customers. That is what moves GDP. Not one person worth a billion dollars. A million people each building something worth a million. That math rewrites a country. Then Srinivas said the part that separates him from every hype merchant in the room. Srinivas: “Everybody thinks AI is already there. It’s not there yet. Someone has to do that hard work.” The vision is real. The infrastructure is not. The agents are not autonomous. The integrations are not seamless. The plumbing is not finished. Someone has to wire the APIs. Connect the billing. Build the bridge between what a founder wants and what a machine can deliver. That work is not a keynote. It is not a tweet thread. It is engineering that nobody wants to do and everybody will depend on. Whoever finishes it first does not just build a product. They hand every ambitious person on Earth a company they can run alone. The corporations that need five hundred people to do what one founder with the right infrastructure could do are not efficient. They are exposed. And the person building the thing that exposes them just told you exactly what it looks like. He also told you it is not going to build itself.

Dustin

64,593 views • 3 months ago

🚨Fabrizio Romano on Enzo 🗣️ "Enzo Fernández is a name on Real Madrid’s shortlist. I can guarantee this. He is one of the names, but not the only one. Real Madrid have had four or five midfielders under consideration since last week. It was last Tuesday when José Mourinho arrived in Madrid, was announced as the new Real Madrid manager and presented his ideas for several positions. This included Bernardo Silva and the other signings Real Madrid completed, including Cucurella. Enzo Fernández is definitely one of the names mentioned, with the player open to a move to Real Madrid. There is no doubt about that. However, despite reports suggesting that this could be a very fast deal, the understanding at the moment is that nothing official is happening. This is not currently a Cucurella situation. The Cucurella deal happened very quickly it was agreed and completed out of nowhere. Enzo Fernández is simply one of the names under consideration at Real Madrid, and as I said, he is not the only one. For example, Matheus Fernandes is on Manchester United’s list and has been in contact with United, but he is also a player recommended by Mourinho to Real Madrid. So, it is not only Enzo Fernández."
1:41

🚨Fabrizio Romano on Enzo 🗣️ "Enzo Fernández is a name on Real Madrid’s shortlist. I can guarantee this. He is one of the names, but not the only one. Real Madrid have had four or five midfielders under consideration since last week. It was last Tuesday when José Mourinho arrived in Madrid, was announced as the new Real Madrid manager and presented his ideas for several positions. This included Bernardo Silva and the other signings Real Madrid completed, including Cucurella. Enzo Fernández is definitely one of the names mentioned, with the player open to a move to Real Madrid. There is no doubt about that. However, despite reports suggesting that this could be a very fast deal, the understanding at the moment is that nothing official is happening. This is not currently a Cucurella situation. The Cucurella deal happened very quickly it was agreed and completed out of nowhere. Enzo Fernández is simply one of the names under consideration at Real Madrid, and as I said, he is not the only one. For example, Matheus Fernandes is on Manchester United’s list and has been in contact with United, but he is also a player recommended by Mourinho to Real Madrid. So, it is not only Enzo Fernández."

SHAYEE 𒀭

18,533 views • 12 days ago