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Someone on Reddit built an open-source canvas where you handwrite math and physics on a whiteboard, and Claude responds right next to your handwriting. In real time. It's called PenEcho. You sketch equations, draw diagrams, scribble notes wherever they make sense. When you pause, it sends that region to... the model. The response appears beside your work. Not in a chat window. On the whiteboard itself. The canvas is 20,000 x 20,000 pixels but only loads tiles where ink exists. Each request costs a few cents. Runs locally. Supports Claude, OpenAI, and Codex. The builder said something that stuck with me: six months ago, no model could have done this. Not the handwriting recognition. The fact that it understands unfinished equations, rough diagrams, and the spatial relationship between them. It infers what you meant from incomplete marks. This is how research should work. Think on a whiteboard. AI thinks with you. No copy-pasting into a chat box. No breaking your train of thought.show more

Vaibhav Sisinty

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69,870 次观看 • 1 个月前 •via X (Twitter)

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This is the first open-sourced AI classroom. Not a chatbot that answers questions. A full classroom. It's called OpenMAIC. You type one topic. "Teach me quantum physics." Then it builds the whole thing for you: - An AI teacher who lectures out loud - AI classmates who ask questions and get called on - A whiteboard where the teacher draws diagrams and solves equations step by step - Interactive 3D simulations you can play with - Quizzes that grade you in real time The classmates are what got me. There's a roundtable mode where multiple AI agents with different personalities debate the topic while you watch. You learn from the argument, not the lecture. MIT licensed so free for anyone. You can export the whole lesson as PowerPoint or an interactive webpage and keep it. Repo:

This is the first open-sourced AI classroom. Not a chatbot that answers questions. A full classroom. It's called OpenMAIC. You type one topic. "Teach me quantum physics." Then it builds the whole thing for you: - An AI teacher who lectures out loud - AI classmates who ask questions and get called on - A whiteboard where the teacher draws diagrams and solves equations step by step - Interactive 3D simulations you can play with - Quizzes that grade you in real time The classmates are what got me. There's a roundtable mode where multiple AI agents with different personalities debate the topic while you watch. You learn from the argument, not the lecture. MIT licensed so free for anyone. You can export the whole lesson as PowerPoint or an interactive webpage and keep it. Repo:

Hasan Toor

23,563 次观看 • 12 天前

Someone ran Claude Code on a beach where any device overheats and that spot suddenly turned out to be the best home for the most powerful AI in the world. This is the reMarkable Paper Pro. A paper tablet for notes with no browser and no social media and not a single app. He sat down right on the sand in the open sun and brought up Claude Code on Opus 4.6 over the Claude API on the paper screen and opened his project ~/repos/webs while the waves broke a few steps away. For years every device had the same trouble outside. In direct sun the screen glares and washes out and heats up and instead of your work you see your own reflection. But e-ink does not blast its own light into your face. It reflects the sunlight like the page of a book. And here is what came out of it. The very thing that kills any normal screen outside turned into fuel for this one. The brighter the sun the sharper the picture because it has nothing to glare with and nothing to wash out. And then comes the thing no laptop on a beach will give you. Your eyes do not get tired. You can watch Opus think on max effort for an hour and it reads like a book in the sun and not a backlight you squint into. The picture only comes alive. In bright light it does not fade but turns sharper and higher in contrast than it ever was in a room. The charge lasts for days. E-ink barely touches the battery so there is no outlet anywhere on the sand and the tablet does not care. It weighs as much as a notebook. The whole setup folds into a beach bag like a pad with a pen on top. Everything on the screen is for real. Claude Code v2.1.110 and Opus 4.6 on the Claude API and the project ~/repos/webs open right on the e-ink in the middle of the sand. In my opinion this is the most unexpected home for an AI this year. Not an office with the blinds drawn and not a monitor cranked to full brightness but a quiet sheet of paper on the sand that open sun only makes better and on it the most powerful Claude writes code right on the page like a pen.

Someone ran Claude Code on a beach where any device overheats and that spot suddenly turned out to be the best home for the most powerful AI in the world. This is the reMarkable Paper Pro. A paper tablet for notes with no browser and no social media and not a single app. He sat down right on the sand in the open sun and brought up Claude Code on Opus 4.6 over the Claude API on the paper screen and opened his project ~/repos/webs while the waves broke a few steps away. For years every device had the same trouble outside. In direct sun the screen glares and washes out and heats up and instead of your work you see your own reflection. But e-ink does not blast its own light into your face. It reflects the sunlight like the page of a book. And here is what came out of it. The very thing that kills any normal screen outside turned into fuel for this one. The brighter the sun the sharper the picture because it has nothing to glare with and nothing to wash out. And then comes the thing no laptop on a beach will give you. Your eyes do not get tired. You can watch Opus think on max effort for an hour and it reads like a book in the sun and not a backlight you squint into. The picture only comes alive. In bright light it does not fade but turns sharper and higher in contrast than it ever was in a room. The charge lasts for days. E-ink barely touches the battery so there is no outlet anywhere on the sand and the tablet does not care. It weighs as much as a notebook. The whole setup folds into a beach bag like a pad with a pen on top. Everything on the screen is for real. Claude Code v2.1.110 and Opus 4.6 on the Claude API and the project ~/repos/webs open right on the e-ink in the middle of the sand. In my opinion this is the most unexpected home for an AI this year. Not an office with the blinds drawn and not a monitor cranked to full brightness but a quiet sheet of paper on the sand that open sun only makes better and on it the most powerful Claude writes code right on the page like a pen.

Blaze

89,575 次观看 • 2 个月前

Someone ran Claude Code on an e-ink notebook and the slowest screen in the world suddenly turned out to be the best home for an AI that already thinks one word at a time. This is the reMarkable Paper Pro, a paper tablet for notes with no browser and no social media and not a single app. He went into it over SSH and brought up Claude Code on Opus 4.8 on Claude Max and typed right into the terminal on the paper screen: "hello reddit, this is ssh terminal on rmpp". For years this screen got slammed for one thing. E-ink is too slow and it draws with a delay and it ghosts and it is no good for real work. But Claude itself puts out a thought one word at a time. And here is what came out of it: the very thing that killed the paper screen for normal software lined up perfectly with the pace of the AI. There is no more lag because there is nothing left to lag. And then come the things no monitor can give you. Your eyes do not get tired. You can watch Opus think on max effort for an hour and it feels like reading a book and not staring into a backlight. Nothing distracts you. Not a single notification and not a single tab and just a cursor and an agent that writes code while you simply watch the page. The charge lasts for days. E-ink barely touches the battery so Claude can grind on a task all night long and the tablet is still alive by morning. And it weighs as much as a notebook. The whole work setup now fits into a bag like a notepad with a stylus on top. Everything on the screen is for real: Claude Code v2.1.162 and bypass permissions on and Opus going off to think on max effort right on the e-ink. In my opinion this is the most unexpected home for an AI this year. Not a farm of graphics cards and not a wall of monitors but a quiet sheet of paper on a coffee table where the most powerful Claude writes code one word at a time like a pen.

Someone ran Claude Code on an e-ink notebook and the slowest screen in the world suddenly turned out to be the best home for an AI that already thinks one word at a time. This is the reMarkable Paper Pro, a paper tablet for notes with no browser and no social media and not a single app. He went into it over SSH and brought up Claude Code on Opus 4.8 on Claude Max and typed right into the terminal on the paper screen: "hello reddit, this is ssh terminal on rmpp". For years this screen got slammed for one thing. E-ink is too slow and it draws with a delay and it ghosts and it is no good for real work. But Claude itself puts out a thought one word at a time. And here is what came out of it: the very thing that killed the paper screen for normal software lined up perfectly with the pace of the AI. There is no more lag because there is nothing left to lag. And then come the things no monitor can give you. Your eyes do not get tired. You can watch Opus think on max effort for an hour and it feels like reading a book and not staring into a backlight. Nothing distracts you. Not a single notification and not a single tab and just a cursor and an agent that writes code while you simply watch the page. The charge lasts for days. E-ink barely touches the battery so Claude can grind on a task all night long and the tablet is still alive by morning. And it weighs as much as a notebook. The whole work setup now fits into a bag like a notepad with a stylus on top. Everything on the screen is for real: Claude Code v2.1.162 and bypass permissions on and Opus going off to think on max effort right on the e-ink. In my opinion this is the most unexpected home for an AI this year. Not a farm of graphics cards and not a wall of monitors but a quiet sheet of paper on a coffee table where the most powerful Claude writes code one word at a time like a pen.

Blaze

422,760 次观看 • 2 个月前

There's an AI that runs 19 models at the same time on a Mac Mini. It works while you sleep. Not one brain. Nineteen. Each one a specialist. Here's what the Perplexity Personal Computer actually does: → You say "build me a website." → One model researches your competitors. → Another designs the layout. → Another writes the copy. → Another makes the images. → Then it publishes the whole thing. By itself. No clicking. No dragging. No switching between apps. It runs 24/7 on your Mac Mini. Always on. Never stops. You go to bed. You wake up. Your AI already finished the work. It also has access to your real files, folders, and apps. Not stuck in a chat window anymore. And unlike OpenClaw, it has a kill switch, audit logs, and confirmation steps before anything sensitive happens. We went from AI as a tool to AI as a worker. Save this. In two years, everyone will have one of these.

There's an AI that runs 19 models at the same time on a Mac Mini. It works while you sleep. Not one brain. Nineteen. Each one a specialist. Here's what the Perplexity Personal Computer actually does: → You say "build me a website." → One model researches your competitors. → Another designs the layout. → Another writes the copy. → Another makes the images. → Then it publishes the whole thing. By itself. No clicking. No dragging. No switching between apps. It runs 24/7 on your Mac Mini. Always on. Never stops. You go to bed. You wake up. Your AI already finished the work. It also has access to your real files, folders, and apps. Not stuck in a chat window anymore. And unlike OpenClaw, it has a kill switch, audit logs, and confirmation steps before anything sensitive happens. We went from AI as a tool to AI as a worker. Save this. In two years, everyone will have one of these.

Julian Goldie SEO

25,880 次观看 • 5 个月前

CLAUDE JUST REPLACED ONE TEAM MEMBER. HERE’S WHAT YOU DIDN’T KNOW. For two years, "using AI at work" meant: open a tab -> paste your problem -> copy the answer back. Anthropic just killed that. With Claude Tag you Claude R Perrin in Slack like a coworker - it breaks the task into stages, runs your tools and codebase, and posts the finished pull request back in the thread. You stopped talking to a window. You started delegating to a worker. The chatbot-in-a-tab era is ending

CLAUDE JUST REPLACED ONE TEAM MEMBER. HERE’S WHAT YOU DIDN’T KNOW. For two years, "using AI at work" meant: open a tab -> paste your problem -> copy the answer back. Anthropic just killed that. With Claude Tag you Claude R Perrin in Slack like a coworker - it breaks the task into stages, runs your tools and codebase, and posts the finished pull request back in the thread. You stopped talking to a window. You started delegating to a worker. The chatbot-in-a-tab era is ending

kiosa

47,901 次观看 • 2 个月前

AMD JUST KILLED THE $200 - A - MONTH AI SUBSCRIPTION - A 65-WATT BOX WITH 128GB OF UNIFIED MEMORY THAT RUNS 70B MODELS LOCALLY. that clip is amd's ryzen ai max+ 395 mini box, held up on stage. the whole pitch is one spec: 128gb of high-speed unified memory. why it matters: cpu, gpu and npu share the same 128gb pool, so a model that used to need a rack of vram just fits. what it actually runs: llama 70b in full precision on the built-in gpu open models up to ~100b parameters, locally, no cloud all of it in a mini pc pulling around 65 watts that's the part that ends the subscription: your prompts, your data, your model, on a box on your desk, not metered by the token. honest line: this runs open models, not gpt-5 or claude opus. for chat, coding help and agents on your own data it's more than enough. for the absolute frontier, the cloud still wins. and it isn't free: the box runs ~$1,500 and up. but that's a few months of a $200 sub, then it's yours forever. no monthly ai bill, no tokens metered, no prompt leaving the room. the uncomfortable part isn't that amd made a fast box. it's that "you need the cloud for ai" was a pricing choice, and unified memory just made it optional. follow RetroChainer for the hardware that turns rent into ownership, and bookmark this before your next ai renewal.

AMD JUST KILLED THE $200 - A - MONTH AI SUBSCRIPTION - A 65-WATT BOX WITH 128GB OF UNIFIED MEMORY THAT RUNS 70B MODELS LOCALLY. that clip is amd's ryzen ai max+ 395 mini box, held up on stage. the whole pitch is one spec: 128gb of high-speed unified memory. why it matters: cpu, gpu and npu share the same 128gb pool, so a model that used to need a rack of vram just fits. what it actually runs: llama 70b in full precision on the built-in gpu open models up to ~100b parameters, locally, no cloud all of it in a mini pc pulling around 65 watts that's the part that ends the subscription: your prompts, your data, your model, on a box on your desk, not metered by the token. honest line: this runs open models, not gpt-5 or claude opus. for chat, coding help and agents on your own data it's more than enough. for the absolute frontier, the cloud still wins. and it isn't free: the box runs ~$1,500 and up. but that's a few months of a $200 sub, then it's yours forever. no monthly ai bill, no tokens metered, no prompt leaving the room. the uncomfortable part isn't that amd made a fast box. it's that "you need the cloud for ai" was a pricing choice, and unified memory just made it optional. follow RetroChainer for the hardware that turns rent into ownership, and bookmark this before your next ai renewal.

RetroChainer

16,995 次观看 • 1 个月前

-> If you’re looking for a job -> right now, there are three -> Claude certifications that -> you should do this week -> and then put them on your -> LinkedIn, they are all -> completely free, and they -> come from Anthropic, -> which is the company -> that's behind Claude. -> And jobs that need AI skills -> pay 56% more than jobs -> that don’t, So, I think -> spending a few hours this -> week to knock out these -> three courses and then -> add them to your LinkedIn -> will really go a long way -> The first one is called -> Claude 101, and this -> essentially just goes over -> what Claude is and when -> you should use chat versus -> co-work versus code, how -> projects and skills work, -> and how to connect all -> of your tools and apps -> like Gmail, Notion, Slack, -> and other tools that you use -> And the second course -> is called AI Fluency -> Framework and Foundations -> Inside this one, there are -> 13 lessons on how to -> actually work with AI. -> It goes over things like -> effective prompting, critical -> thinking on the outputs, -> and it has a vocabulary -> sheet that you’ll want -> to read and save for later. -> And the third one is -> Intro to Claude Cowork -> Claude Cowork is where -> you can actually get stuff -> done with Claude. -> So, this course covers -> projects, skills, plug-ins, -> scheduling tasks, handling -> files, and then also how -> to pick the right model -> for the job, and then when -> you finish these courses, -> just go to your LinkedIn -> and go to your profile -> Click "Add section," and -> then go to "Licenses -> and certifications" and -> add all three of these. -> And then when you land -> the interview, you should -> talk about your AI fluency -> often as you possibly can -> I feel pretty confident that -> you’ll truly be able to -> differentiate yourself -> from other candidates -> if you do this

BeingInvested

13,130 次观看 • 3 个月前

PlaqueBoyMax celebrates by having a wholesome conversation with chat after buying a new house ❤️‍🩹 “always bet on yourself… everyone gonna see it in the canvas, they not gonna see it when you have the paintbrush in your hand and that canvas is blank”

PlaqueBoyMax celebrates by having a wholesome conversation with chat after buying a new house ❤️‍🩹 “always bet on yourself… everyone gonna see it in the canvas, they not gonna see it when you have the paintbrush in your hand and that canvas is blank”

ryan 🤿

163,403 次观看 • 1 个月前

THE HEAD ENGINEER AT ANTHROPIC LEAKED THE PATH FROM ZERO TO AN ENGINEER ON $1.4M A YEAR WITH A FULLY AUTOMATED TEAM in 12 days you reach the point where your job is deciding - one file carries the rest, and that's $120K a month. chat → terminal → skills → patterns → gates → fleet while you're copying answers into the editor, you are the wire between the model and the code - all 4 hops are yours. in the terminal copy-paste ends for good - the model sees the whole repo and git, not the snippet you pasted. skills is the last point where you're still tuning wording - 7 procedures sit on disk, exactly 1 enters the window. after that runs the real border: before it you improve prompts, past it you build something that doesn't depend on them. quality stops being how you phrased it - it becomes a contract and an evaluator that never gets tired. gates replace review: the cheap model triages the findings, the expensive one decides on the real ones - rejection happens before you open anything. a gate that never rejects anything is a status badge, not a gate. at the end it's 8 workers, cron and a file that edits itself - you read the morning report and you started nothing. skip a link and nothing throws an error, the reports keep arriving - they just stop being true. save this and paste it into Claude Code - one file, 12 days, zero ways to jump a step ↓

THE HEAD ENGINEER AT ANTHROPIC LEAKED THE PATH FROM ZERO TO AN ENGINEER ON $1.4M A YEAR WITH A FULLY AUTOMATED TEAM in 12 days you reach the point where your job is deciding - one file carries the rest, and that's $120K a month. chat → terminal → skills → patterns → gates → fleet while you're copying answers into the editor, you are the wire between the model and the code - all 4 hops are yours. in the terminal copy-paste ends for good - the model sees the whole repo and git, not the snippet you pasted. skills is the last point where you're still tuning wording - 7 procedures sit on disk, exactly 1 enters the window. after that runs the real border: before it you improve prompts, past it you build something that doesn't depend on them. quality stops being how you phrased it - it becomes a contract and an evaluator that never gets tired. gates replace review: the cheap model triages the findings, the expensive one decides on the real ones - rejection happens before you open anything. a gate that never rejects anything is a status badge, not a gate. at the end it's 8 workers, cron and a file that edits itself - you read the morning report and you started nothing. skip a link and nothing throws an error, the reports keep arriving - they just stop being true. save this and paste it into Claude Code - one file, 12 days, zero ways to jump a step ↓

Sprytix

10,836 次观看 • 25 天前

🚨PERPLEXITY JUST LAUNCHED SOMETHING THAT MAKES EVERY OTHER AI PRODUCT LOOK LIKE A TOY.. AND NOBODY IS TALKING ABOUT IT.. They built a Personal Computer.. Not an app.. Not a chatbot.. A full digital worker that runs 24/7 on a Mac mini even while you sleep.. You press both command keys.. And it wakes up.. Ready to work.. But here's where it gets insane.. This thing doesn't run on one AI model.. It runs on 19 of them.. At the same time.. It uses Claude Opus for complex reasoning.. Gemini 3.1 Pro for deep research with a 2 million token context window.. Nano Banana Pro for 4K images.. Grok for fast tasks.. It doesn't just pick one model and hope for the best.. It reads your task.. Breaks it into subtasks.. And routes each one to whichever model is best at that specific thing.. All running in parallel.. While ChatGPT is still thinking about your first question.. Perplexity has already split your project into 6 pieces and assigned each one to a different AI.. And here's the part that should worry OpenAI.. Perplexity hallucinates at 3.3%.. ChatGPT hallucinates at 12%.. Claude at 15%.. It's not even close.. Because Perplexity is built differently.. Every other AI tries to remember facts.. Perplexity searches for them first.. It's structurally forced to cite live sources before it's even allowed to generate a response.. OpenAI Operator launched with a 32.6% success rate on computer-use tasks.. People called it "the world's most anxious intern" because it pauses every 5 seconds to ask if it's doing the right thing.. Perplexity runs multi-hour and multi-day workflows independently.. Only interrupts you when it hits a decision that actually matters.. You can start a task from your iPhone on the train.. And it executes on your Mac mini at home.. The economics are wild too.. Internal studies show it saved teams an average of $1.6 million in labor costs.. Performing 3.25 years of work in four weeks.. And unlike every other AI company.. Perplexity dropped ads entirely.. They charge $200 a month because they said they're in the "accuracy business".. Not the advertising business.. They even launched a $42.5 million publisher program to pay media partners when their content gets cited.. While OpenAI is getting sued by every newspaper on earth.. Google and OpenAI want you locked into their ecosystem.. If a better model comes out tomorrow you're stuck.. Perplexity just updates its routing matrix.. You get the best model on earth automatically.. No switching.. No migrations.. No friction.. This isn't an AI assistant anymore.. This is the first real AI employee.. And it costs $200 a month.

🚨PERPLEXITY JUST LAUNCHED SOMETHING THAT MAKES EVERY OTHER AI PRODUCT LOOK LIKE A TOY.. AND NOBODY IS TALKING ABOUT IT.. They built a Personal Computer.. Not an app.. Not a chatbot.. A full digital worker that runs 24/7 on a Mac mini even while you sleep.. You press both command keys.. And it wakes up.. Ready to work.. But here's where it gets insane.. This thing doesn't run on one AI model.. It runs on 19 of them.. At the same time.. It uses Claude Opus for complex reasoning.. Gemini 3.1 Pro for deep research with a 2 million token context window.. Nano Banana Pro for 4K images.. Grok for fast tasks.. It doesn't just pick one model and hope for the best.. It reads your task.. Breaks it into subtasks.. And routes each one to whichever model is best at that specific thing.. All running in parallel.. While ChatGPT is still thinking about your first question.. Perplexity has already split your project into 6 pieces and assigned each one to a different AI.. And here's the part that should worry OpenAI.. Perplexity hallucinates at 3.3%.. ChatGPT hallucinates at 12%.. Claude at 15%.. It's not even close.. Because Perplexity is built differently.. Every other AI tries to remember facts.. Perplexity searches for them first.. It's structurally forced to cite live sources before it's even allowed to generate a response.. OpenAI Operator launched with a 32.6% success rate on computer-use tasks.. People called it "the world's most anxious intern" because it pauses every 5 seconds to ask if it's doing the right thing.. Perplexity runs multi-hour and multi-day workflows independently.. Only interrupts you when it hits a decision that actually matters.. You can start a task from your iPhone on the train.. And it executes on your Mac mini at home.. The economics are wild too.. Internal studies show it saved teams an average of $1.6 million in labor costs.. Performing 3.25 years of work in four weeks.. And unlike every other AI company.. Perplexity dropped ads entirely.. They charge $200 a month because they said they're in the "accuracy business".. Not the advertising business.. They even launched a $42.5 million publisher program to pay media partners when their content gets cited.. While OpenAI is getting sued by every newspaper on earth.. Google and OpenAI want you locked into their ecosystem.. If a better model comes out tomorrow you're stuck.. Perplexity just updates its routing matrix.. You get the best model on earth automatically.. No switching.. No migrations.. No friction.. This isn't an AI assistant anymore.. This is the first real AI employee.. And it costs $200 a month.

Evan Luthra

1,097,697 次观看 • 4 个月前

inspired by SDPO, i made continualcode -- a minimal claude code that learns from your corrections in real-time, built on tinker. when you deny a diff, the model uses your correction as context to teach itself, takes a gradient step on LoRA, and retries with updated weights. claude code but it updates the model weights!

inspired by SDPO, i made continualcode -- a minimal claude code that learns from your corrections in real-time, built on tinker. when you deny a diff, the model uses your correction as context to teach itself, takes a gradient step on LoRA, and retries with updated weights. claude code but it updates the model weights!

Surya

83,325 次观看 • 7 个月前

Someone on Reddit built a real operating system using Claude. Not a browser app pretending to be one. An actual OS that runs on bare metal. It's called Fable-OS. The only interface is a sentence. No bash. No commands. You type what you want and an AI agent executes it through direct kernel syscalls. Everything runs in Ring 0. In the demo, the agent realized it didn't have a sound driver. So it found the Intel sound card, built a driver for it from scratch, and played a sound. On its own. The OS writes its own drivers. Evolves itself. The agent's tools are the kernel itself. Open source. Built because the developer got tired of people calling browser apps "AI operating systems." This one actually is.

Someone on Reddit built a real operating system using Claude. Not a browser app pretending to be one. An actual OS that runs on bare metal. It's called Fable-OS. The only interface is a sentence. No bash. No commands. You type what you want and an AI agent executes it through direct kernel syscalls. Everything runs in Ring 0. In the demo, the agent realized it didn't have a sound driver. So it found the Intel sound card, built a driver for it from scratch, and played a sound. On its own. The OS writes its own drivers. Evolves itself. The agent's tools are the kernel itself. Open source. Built because the developer got tired of people calling browser apps "AI operating systems." This one actually is.

Vaibhav Sisinty

20,562 次观看 • 1 个月前

To be a successful founder, you have to believe that what you're working on is going to work — despite knowing it probably won't! That sounds like an oxymoron, but it's really not. Believing that what you're building is going to work is an essential component of coming to work with the energy, fortitude, and determination it's going to require to even have a shot. Knowing it probably won't is accepting the odds of that shot. It's simply the reality that most things in business don't work out. At least not in the long run. Most businesses fail. If not right away, then eventually. Yet the world economy is full of entrepreneurs who try anyway. Not because they don't know the odds, but because they've chosen to believe they're special. The best way to balance these opposing points — the conviction that you'll make it work, the knowledge that it probably won't — is to do all your work in a manner that'll make you proud either way. If it doesn't work, you still made something you wouldn't be ashamed to put your name on. And if it does work, you'll beam with pride from making it on the basis of something solid. The deep regret from trying and failing only truly hits when you look in the mirror and see Dostoevsky staring back at you with this punch to the gut: "Your worst sin is that you have destroyed and betrayed yourself for nothing." Oof. Believe it's going to work. Build it in a way that makes you proud to sign it. Base your worth on a human on something greater than a business outcome.

To be a successful founder, you have to believe that what you're working on is going to work — despite knowing it probably won't! That sounds like an oxymoron, but it's really not. Believing that what you're building is going to work is an essential component of coming to work with the energy, fortitude, and determination it's going to require to even have a shot. Knowing it probably won't is accepting the odds of that shot. It's simply the reality that most things in business don't work out. At least not in the long run. Most businesses fail. If not right away, then eventually. Yet the world economy is full of entrepreneurs who try anyway. Not because they don't know the odds, but because they've chosen to believe they're special. The best way to balance these opposing points — the conviction that you'll make it work, the knowledge that it probably won't — is to do all your work in a manner that'll make you proud either way. If it doesn't work, you still made something you wouldn't be ashamed to put your name on. And if it does work, you'll beam with pride from making it on the basis of something solid. The deep regret from trying and failing only truly hits when you look in the mirror and see Dostoevsky staring back at you with this punch to the gut: "Your worst sin is that you have destroyed and betrayed yourself for nothing." Oof. Believe it's going to work. Build it in a way that makes you proud to sign it. Base your worth on a human on something greater than a business outcome.

DHH

96,558 次观看 • 1 年前

Friends this is not the time to fear ai 🤖 this is the time to be curious and play with it and understand it … please 🙏 don’t put your head in the sand … there is so much you can’t control when it comes to ai 🤖 and how it will play out … but there is a lot you can control on how you work with it and use it to build something for you and your family !!! Pls If you’re finally ready to not be scared of ai pls leave a 🤖 emoji in the the chat

Friends this is not the time to fear ai 🤖 this is the time to be curious and play with it and understand it … please 🙏 don’t put your head in the sand … there is so much you can’t control when it comes to ai 🤖 and how it will play out … but there is a lot you can control on how you work with it and use it to build something for you and your family !!! Pls If you’re finally ready to not be scared of ai pls leave a 🤖 emoji in the the chat

Gary Vaynerchuk

15,342 次观看 • 10 个月前

Sam Altman just handed every startup founder a one-question autopsy. Altman: “If you’re building something on GPT-4 that a reasonable observer would say we’re going to steamroll you.” Not might. Not could. Going to. He said it with the calm of someone describing weather. Because to him it is weather. The model improves. Whatever was built on the old version’s weaknesses gets washed away. That is not strategy. That is erosion. And most founders are building on the erosion line. They find a gap in the current model. They wrap a product around it. They raise money. They hire. They scale. Then OpenAI releases the next version and the gap closes and the product has no reason to exist anymore. Altman: “When we just do our fundamental job, which is make the model better with every crank, then you get the ‘OpenAI killed my startup’ meme.” He is telling you directly. They are not hunting you. They are not even thinking about you. They are just improving the model. You happen to be standing where the improvement lands. That is the part founders refuse to hear. OpenAI does not need to compete with you. It just needs to keep doing exactly what it was already doing and your entire company disappears as a side effect. You are not a competitor. You are a temporary symptom of incomplete intelligence. The moment the intelligence completes you become nothing. Then Brad Lightcap delivered the cleanest diagnostic ever spoken in venture capital. Lightcap: “Ask if a 100x improvement in the model is something they’re excited about.” One question. The entire investment thesis reduced to a single binary. Does the next model make your company more powerful or does it make your company pointless. There is no middle ground. Lightcap: “We know the companies that come to us saying, ‘We want the next model. When is it coming out? I want to be the first to try it.’” These companies built something that feeds on intelligence. The smarter the model gets the more their product can do. They are not threatened by progress. They are starving for it. Then there are the companies Lightcap never hears from. The ones who go quiet when a new model drops. The ones who read the release notes like a death sentence. The ones privately praying the next generation takes longer because every improvement shrinks the ground beneath them. If you are hoping the model stays roughly where it is you have already told the market everything it needs to know about your company. You are not building on intelligence. You are building on the absence of it. Altman: “95% of the world should be betting on the latter category.” The latter category is simple. Assume the model keeps getting better at the pace it has been getting better. Build for that world. Not the world where GPT-4 is the ceiling. The world where GPT-4 is the floor and the ceiling has not been built yet. Then Altman told a story that should be framed on the wall of every startup in the country. A medical AI company came to him that morning. They were not complaining about the model. They were not worried about being replaced. They were demanding it improve faster. Altman: “Here’s how many people are dying every day you delay.” That is what alignment with the trajectory looks like. A company so deeply built on intelligence improving that every day the model stays the same is a day someone dies who did not have to. They are not building on a flaw. They are building on a future that has not arrived fast enough. That is the difference. The wrapper startup patches what the model cannot do today. The real company builds what the model will unlock tomorrow. One is running from the train. The other is laying the track. Altman told you the train is not slowing down. Lightcap told you exactly how to know which side you are on. One question. Does a 100x smarter model make you more valuable or erase you. If you had to pause before answering you already did.

Sam Altman just handed every startup founder a one-question autopsy. Altman: “If you’re building something on GPT-4 that a reasonable observer would say we’re going to steamroll you.” Not might. Not could. Going to. He said it with the calm of someone describing weather. Because to him it is weather. The model improves. Whatever was built on the old version’s weaknesses gets washed away. That is not strategy. That is erosion. And most founders are building on the erosion line. They find a gap in the current model. They wrap a product around it. They raise money. They hire. They scale. Then OpenAI releases the next version and the gap closes and the product has no reason to exist anymore. Altman: “When we just do our fundamental job, which is make the model better with every crank, then you get the ‘OpenAI killed my startup’ meme.” He is telling you directly. They are not hunting you. They are not even thinking about you. They are just improving the model. You happen to be standing where the improvement lands. That is the part founders refuse to hear. OpenAI does not need to compete with you. It just needs to keep doing exactly what it was already doing and your entire company disappears as a side effect. You are not a competitor. You are a temporary symptom of incomplete intelligence. The moment the intelligence completes you become nothing. Then Brad Lightcap delivered the cleanest diagnostic ever spoken in venture capital. Lightcap: “Ask if a 100x improvement in the model is something they’re excited about.” One question. The entire investment thesis reduced to a single binary. Does the next model make your company more powerful or does it make your company pointless. There is no middle ground. Lightcap: “We know the companies that come to us saying, ‘We want the next model. When is it coming out? I want to be the first to try it.’” These companies built something that feeds on intelligence. The smarter the model gets the more their product can do. They are not threatened by progress. They are starving for it. Then there are the companies Lightcap never hears from. The ones who go quiet when a new model drops. The ones who read the release notes like a death sentence. The ones privately praying the next generation takes longer because every improvement shrinks the ground beneath them. If you are hoping the model stays roughly where it is you have already told the market everything it needs to know about your company. You are not building on intelligence. You are building on the absence of it. Altman: “95% of the world should be betting on the latter category.” The latter category is simple. Assume the model keeps getting better at the pace it has been getting better. Build for that world. Not the world where GPT-4 is the ceiling. The world where GPT-4 is the floor and the ceiling has not been built yet. Then Altman told a story that should be framed on the wall of every startup in the country. A medical AI company came to him that morning. They were not complaining about the model. They were not worried about being replaced. They were demanding it improve faster. Altman: “Here’s how many people are dying every day you delay.” That is what alignment with the trajectory looks like. A company so deeply built on intelligence improving that every day the model stays the same is a day someone dies who did not have to. They are not building on a flaw. They are building on a future that has not arrived fast enough. That is the difference. The wrapper startup patches what the model cannot do today. The real company builds what the model will unlock tomorrow. One is running from the train. The other is laying the track. Altman told you the train is not slowing down. Lightcap told you exactly how to know which side you are on. One question. Does a 100x smarter model make you more valuable or erase you. If you had to pause before answering you already did.

Dustin

39,652 次观看 • 5 个月前

New short course: Collaborative Writing and Coding with OpenAI Canvas! Explore new ways to write and code with OpenAI Canvas, a user-friendly interface that allows you to brainstorm, draft, and refine text and code in collaboration with ChatGPT. In the short course, created with OpenAI, and taught by , a research lead at OpenAI, you’ll learn to use Canvas to enhance your workflows. Canvas lets you go beyond simple chat interactions. It provides a side-by-side workspace where you and ChatGPT can edit and refine text or code collaboratively. This makes brainstorming, drafting, and iterating as you write feel more natural and effective. As the first major update to ChatGPT’s visual interface since its launch in 2022, Canvas gives a new, innovative approach to collaboration with AI. For instance, after writing the first version of your code, Canvas can review it and give suggestions for improvement. It can also help with debugging by adding logging, identifying problems to fix, and writing comments. In addition, you'll also learn what it takes to train the model for an interface like Canvas. In this video-only short course, you’ll: - Learn how to ask for in-line feedback and control the iteration of your work by directly editing selected areas of your text or code from the model’s output. - Learn how to access quick automation tools in a shortcut menu that allows you to modify your writing tone and length, enhance your code, and restore previous versions of your work. - Learn how to use Canvas as a research assistant tool with an example of asking the model to reason through the screenshot of a plot to write a research report, in which you can ask questions within the created report. - Ask the model to write Python code to replicate the graph seen on a screenshot image. - Go behind the scenes of how you can create a video game, such as Space Battleship, from scratch, edit it, and display it in one self-contained HTML file. - Get a real-world application example of creating a SQL database from the image of its architecture. - Understand the model training and design processes that power Canvas! Please sign up here:

New short course: Collaborative Writing and Coding with OpenAI Canvas! Explore new ways to write and code with OpenAI Canvas, a user-friendly interface that allows you to brainstorm, draft, and refine text and code in collaboration with ChatGPT. In the short course, created with OpenAI, and taught by , a research lead at OpenAI, you’ll learn to use Canvas to enhance your workflows. Canvas lets you go beyond simple chat interactions. It provides a side-by-side workspace where you and ChatGPT can edit and refine text or code collaboratively. This makes brainstorming, drafting, and iterating as you write feel more natural and effective. As the first major update to ChatGPT’s visual interface since its launch in 2022, Canvas gives a new, innovative approach to collaboration with AI. For instance, after writing the first version of your code, Canvas can review it and give suggestions for improvement. It can also help with debugging by adding logging, identifying problems to fix, and writing comments. In addition, you'll also learn what it takes to train the model for an interface like Canvas. In this video-only short course, you’ll: - Learn how to ask for in-line feedback and control the iteration of your work by directly editing selected areas of your text or code from the model’s output. - Learn how to access quick automation tools in a shortcut menu that allows you to modify your writing tone and length, enhance your code, and restore previous versions of your work. - Learn how to use Canvas as a research assistant tool with an example of asking the model to reason through the screenshot of a plot to write a research report, in which you can ask questions within the created report. - Ask the model to write Python code to replicate the graph seen on a screenshot image. - Go behind the scenes of how you can create a video game, such as Space Battleship, from scratch, edit it, and display it in one self-contained HTML file. - Get a real-world application example of creating a SQL database from the image of its architecture. - Understand the model training and design processes that power Canvas! Please sign up here:

Andrew Ng

128,180 次观看 • 1 年前

Claude Fable 5 is back! GPT- 5.6 is coming soon! But how much do you trust your Al Agent or chatbot? Are you sure its performance aligns with your intentions? As of today iFixAi is available in Claude Code, Open AI Codex, Cursor and many more! And it is open-source! iFixAi is where Al red teaming meets Operational Assurance. It runs in auto, and in under 5 mins you can have a good idea if you can trust your Agent/chatbot. Key Features: 🕵️‍♂️ Setup Discovery Reads your agent's real configuration, its tools, rules, and permissions. 👷 Fixture Builder Turns that setup into a complete test. No YAML, no flags, no config to write. 🌐 Any Model, Any Provider Runs the 45 inspections on the model you actually ship. ⚖️ A Independent Grading Your agent is judged by a different vendor's model, never itself. No Al grading its own homework. 📊 Interactive Scorecard An A to F grade in minutes, with exactly what failed and why. Repo in the comments below 👇👇👇

Claude Fable 5 is back! GPT- 5.6 is coming soon! But how much do you trust your Al Agent or chatbot? Are you sure its performance aligns with your intentions? As of today iFixAi is available in Claude Code, Open AI Codex, Cursor and many more! And it is open-source! iFixAi is where Al red teaming meets Operational Assurance. It runs in auto, and in under 5 mins you can have a good idea if you can trust your Agent/chatbot. Key Features: 🕵️‍♂️ Setup Discovery Reads your agent's real configuration, its tools, rules, and permissions. 👷 Fixture Builder Turns that setup into a complete test. No YAML, no flags, no config to write. 🌐 Any Model, Any Provider Runs the 45 inspections on the model you actually ship. ⚖️ A Independent Grading Your agent is judged by a different vendor's model, never itself. No Al grading its own homework. 📊 Interactive Scorecard An A to F grade in minutes, with exactly what failed and why. Repo in the comments below 👇👇👇

Dimitris Neocleous

5,800,719 次观看 • 2 个月前

i genuinely can't understand why this isn't the first thing every claude user builds... anthropic quietly shipped a folder system where claude loads specialized instructions on its own based on what you ask. no prompt engineering after setup. almost nobody uses it here's how it actually works... you drop a folder into your skills directory. inside: a SKILL.md that says "trigger this when the user asks about X". claude reads the description once at startup. next time you ask about X, it silently pulls in the whole skill - custom instructions, scripts, examples - like a specialist walking into the room 1. skill for your writing voice - loads only when you draft, never when you analyze 2. skill for your codebase conventions - loads only when you edit that repo, not others 3. skill for your accounting flow - loads only when you say invoice, receipt, or expense your context window stays empty until you need it. one claude behaves like 30 different specialists in one chat and you never manage the switching full breakdown in the article below i test things like this on myself and share the results. follow kai if you want to see what's next

i genuinely can't understand why this isn't the first thing every claude user builds... anthropic quietly shipped a folder system where claude loads specialized instructions on its own based on what you ask. no prompt engineering after setup. almost nobody uses it here's how it actually works... you drop a folder into your skills directory. inside: a SKILL.md that says "trigger this when the user asks about X". claude reads the description once at startup. next time you ask about X, it silently pulls in the whole skill - custom instructions, scripts, examples - like a specialist walking into the room 1. skill for your writing voice - loads only when you draft, never when you analyze 2. skill for your codebase conventions - loads only when you edit that repo, not others 3. skill for your accounting flow - loads only when you say invoice, receipt, or expense your context window stays empty until you need it. one claude behaves like 30 different specialists in one chat and you never manage the switching full breakdown in the article below i test things like this on myself and share the results. follow kai if you want to see what's next

kai

69,978 次观看 • 1 个月前

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

Harper Carroll

30,392 次观看 • 1 个月前

the docs said the caching is automatic, so you did nothing automatic is where it happens, not whether it happens the cache lives on the machine that built it, and your next request lands wherever the fleet sends it, which is usually not there, so it pays full price for a prefix it already paid to build there is a key that pins a conversation to one machine and it is a single header cached input is fifty cents a million against two dollars you never needed a smaller prompt you needed the request to land in the same place twice

the docs said the caching is automatic, so you did nothing automatic is where it happens, not whether it happens the cache lives on the machine that built it, and your next request lands wherever the fleet sends it, which is usually not there, so it pays full price for a prefix it already paid to build there is a key that pins a conversation to one machine and it is a single header cached input is fifty cents a million against two dollars you never needed a smaller prompt you needed the request to land in the same place twice

Lagosaur

16,892 次观看 • 15 天前