Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

$150 in bare-metal hardware just completely humiliated multi-million dollar robotics startups Independent developer John built a fully autonomous indoor navigation bot right on his rug. The setup runs local SLAM spatial mapping, real-time voice recognition, and precise docking using an acrylic frame, stepper motors, off-the-shelf microcontrollers, and a cheap... show more

Rich Odin

3,376 subscribers

15,242 Aufrufe • vor 1 Monat •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

23 Kommentare

Profilbild von bondy

bondyvor 1 Monat

I hope some major company takes notice of John

Profilbild von Rich Odin

Rich Odinvor 1 Monat

Companies snap up such talented people quickly

Profilbild von Shadow Nick

Shadow Nickvor 1 Monat

So it turns out he named the robot after himself?

Profilbild von Rich Odin

Rich Odinvor 1 Monat

That's his right, maybe it's just more convenient for him that way

Profilbild von Piggle

Pigglevor 1 Monat

its real banger bro

Profilbild von WOWMAX.Exchange

WOWMAX.Exchangevor 1 Monat

In my opinion, he has a hidden talent

Profilbild von Rich Odin

Rich Odinvor 1 Monat

It turns out he's not such a hidden talent after all

Profilbild von Fokki

Fokkivor 1 Monat

love that setup

Profilbild von Rich Odin

Rich Odinvor 1 Monat

you read my mind

Profilbild von ALEXYZ

ALEXYZvor 1 Monat

Lean robotics has never looked more disruptive.

Profilbild von Rich Odin

Rich Odinvor 1 Monat

and the most amazing thing is that it only took $150, not millions of dollars

Profilbild von Sof j.

Sof j.vor 1 Monat

what slam library did he use for the mapping

Profilbild von Exlipse

Exlipsevor 1 Monat

I can't wrap my head around the fact that he did all this and spent $150

Profilbild von Rich Odin

Rich Odinvor 1 Monat

He's just like Tony Stark

Profilbild von jorge pejendino

jorge pejendinovor 1 Monat

Why does this hit so close to home at this time of day...

Profilbild von Konstantin Molodykh🧩

Konstantin Molodykh🧩vor 1 Monat

He ended up with a really cool robot

Profilbild von Rich Odin

Rich Odinvor 1 Monat

I think so, too

Profilbild von nickelangelo

nickelangelovor 1 Monat

You don’t need millions to test a robotics idea anymore

Profilbild von musa verep

musa verepvor 1 Monat

describe this video using only 3 emojis. i'll go first: 🔥🤯✨

Profilbild von Mayank Verma

Mayank Vermavor 1 Monat

honestly the indie hacker energy is unmatched

Profilbild von David Kovaro

David Kovarovor 1 Monat

gamble news bro

Profilbild von Rourke

Rourkevor 1 Monat

solo devs on rugs beat funded labs on stages every time

Profilbild von DEV

DEVvor 1 Monat

This shows how accessible tech has become. High performance doesn't always need high budgets.

Ähnliche Videos

I DON’T THINK PEOPLE REALIZE HOW MASSIVE THIS IS YET Hugging Face and Pollen Robotics just released Microduck. It is a new legged robot for $399. It passed $1 million in sales in under SEVEN hours 🤯 The hardware packs 15 actuators, a camera, and LiDAR into a 25 cm frame. It weighs under 800 grams and runs for about an hour on a single charge. Out of the box, it can > walk > roller-skate > pick up objects > recover from falls .. and so much more! You can drive the 7 shipped policies from the first boot using a gamepad. But the real hook is the training stack. The software is entirely open: → MuJoCo simulation and PPO training → 50 Hz onboard policy loop → Browser simulator included alongside hardware → Full control stack available on GitHub The mechanical and electronic design files are still proprietary. But don’t let the cute robotic duck fool you. Under the closed chassis is a FULLY OPEN-SOURCE, hackable brain you can experiment with and build on. Repo in 🧵↓

I DON’T THINK PEOPLE REALIZE HOW MASSIVE THIS IS YET Hugging Face and Pollen Robotics just released Microduck. It is a new legged robot for $399. It passed $1 million in sales in under SEVEN hours 🤯 The hardware packs 15 actuators, a camera, and LiDAR into a 25 cm frame. It weighs under 800 grams and runs for about an hour on a single charge. Out of the box, it can > walk > roller-skate > pick up objects > recover from falls .. and so much more! You can drive the 7 shipped policies from the first boot using a gamepad. But the real hook is the training stack. The software is entirely open: → MuJoCo simulation and PPO training → 50 Hz onboard policy loop → Browser simulator included alongside hardware → Full control stack available on GitHub The mechanical and electronic design files are still proprietary. But don’t let the cute robotic duck fool you. Under the closed chassis is a FULLY OPEN-SOURCE, hackable brain you can experiment with and build on. Repo in 🧵↓

Charly Wargnier ♨️

651,063 Aufrufe • vor 18 Tagen

This guy replaced an $8,000 survey crew with one drone and a pipeline on Claude Code that digitizes a whole site in a single trip. Inside it is not one drone but a whole pipeline of 5 modules on Claude Code each with its own job all answering to a single orchestrator. And he built them himself with no team. He draws one line on the map from the controller and the whole site starts turning into data at once. Pilot flies the DJI Matrice 350 RTK along the route and reaches where a crew drags tripods: the parking lot and the roofs and the grading. Scanner on the Zenmuse L2 records the geometry with a laser down to the centimeter. Builder in DJI Terra stitches the point cloud into a digital twin. Analyst on Claude builds what the client needs out of the finished model: a volume report and a progress diff and a tour behind a link. Mobile lives in his iPhone and hands the developer a link to the model while he drives to the next site. The site is ready as a file in about an hour and the developer rotates it in the browser himself and measures distances. No crew. No tripods. No week on site. Just him and a pickup and a drone and one API key. The whole processing pipeline lives in a folder at /Users/dev/site-capture. But he did not stop at a one time scan. The pipeline raises him by voice only when a flight misses a patch or when a sag on site goes past tolerance. And this is not made up: the DJI Matrice 350 RTK and the Zenmuse L2 are off the shelf enterprise hardware. The specs are one search away and the L2 really does write around 240,000 points per second. A crew for the same job charges $8,000 and three trips plus a week of desk work. His cost is tokens and subscriptions: one battery charge per flight and around $300 a month to host the finished models. In the end he draws one line on the map and an operator that does not exist scans the site and calculates the volumes and builds the report while he never leaves his truck. There is a huge market of everyone who needs to measure construction sites and warehouses and roofs and parking lots over and over while they still send out a photographer with a camera and wait a week. And he built this whole pipeline himself: one drone and one scanner and Claude Code that turns a flight into a file the developer pays for.

This guy replaced an $8,000 survey crew with one drone and a pipeline on Claude Code that digitizes a whole site in a single trip. Inside it is not one drone but a whole pipeline of 5 modules on Claude Code each with its own job all answering to a single orchestrator. And he built them himself with no team. He draws one line on the map from the controller and the whole site starts turning into data at once. Pilot flies the DJI Matrice 350 RTK along the route and reaches where a crew drags tripods: the parking lot and the roofs and the grading. Scanner on the Zenmuse L2 records the geometry with a laser down to the centimeter. Builder in DJI Terra stitches the point cloud into a digital twin. Analyst on Claude builds what the client needs out of the finished model: a volume report and a progress diff and a tour behind a link. Mobile lives in his iPhone and hands the developer a link to the model while he drives to the next site. The site is ready as a file in about an hour and the developer rotates it in the browser himself and measures distances. No crew. No tripods. No week on site. Just him and a pickup and a drone and one API key. The whole processing pipeline lives in a folder at /Users/dev/site-capture. But he did not stop at a one time scan. The pipeline raises him by voice only when a flight misses a patch or when a sag on site goes past tolerance. And this is not made up: the DJI Matrice 350 RTK and the Zenmuse L2 are off the shelf enterprise hardware. The specs are one search away and the L2 really does write around 240,000 points per second. A crew for the same job charges $8,000 and three trips plus a week of desk work. His cost is tokens and subscriptions: one battery charge per flight and around $300 a month to host the finished models. In the end he draws one line on the map and an operator that does not exist scans the site and calculates the volumes and builds the report while he never leaves his truck. There is a huge market of everyone who needs to measure construction sites and warehouses and roofs and parking lots over and over while they still send out a photographer with a camera and wait a week. And he built this whole pipeline himself: one drone and one scanner and Claude Code that turns a flight into a file the developer pays for.

Blaze

10,744 Aufrufe • vor 2 Monaten

Drove through San Francisco with Tim Kentley Klay, co-founder and CEO of HYPRLABS Inc., while his AI, HYPRACTIVE, navigated downtown on 33 watts powering an NVIDIA Orin chip that runs HYPR's proprietary models built by just four engineers in two years, for under a million dollars. Tim is a Melbourne-born designer turned robotics CEO who co-founded Zoox, the first company to operate a purpose-built, fully autonomous robotaxi and scaled it to $3.2B. He envisioned the car becoming a character in 2011, and spent the next decade and a half making that real. 125+ patents, $800M raised, and now CEO of HYPRLABS Inc - a small team coding a clean AI architecture and launching a completely new robot this summer. The whole industry is throwing tens of billions at problems HYPR is solving with a computer the size of your fist and a hot-glued camera.

Drove through San Francisco with Tim Kentley Klay, co-founder and CEO of HYPRLABS Inc., while his AI, HYPRACTIVE, navigated downtown on 33 watts powering an NVIDIA Orin chip that runs HYPR's proprietary models built by just four engineers in two years, for under a million dollars. Tim is a Melbourne-born designer turned robotics CEO who co-founded Zoox, the first company to operate a purpose-built, fully autonomous robotaxi and scaled it to $3.2B. He envisioned the car becoming a character in 2011, and spent the next decade and a half making that real. 125+ patents, $800M raised, and now CEO of HYPRLABS Inc - a small team coding a clean AI architecture and launching a completely new robot this summer. The whole industry is throwing tens of billions at problems HYPR is solving with a computer the size of your fist and a hot-glued camera.

Marwan Refaat

1,492,412 Aufrufe • vor 4 Monaten

Autonomous tractor from Netherlands! 🇳🇱 Despite all the narrative, we are still building awesome companies in hardware space in Europe. Recently, I've posted about a Chinese autonomous tractors company. So today I wanted to show something that was created in the middle of Europe. This is a fully autonomous tractor from Dutch company AgXeed, designed to work on fields without any human supervision. It's powered by a diesel engine that drives a generator, which then powers electric motors driving the tracks. The fuel tank allows for 24 hours of continuous operation. (nice!) The rear hitch has an 8-ton lift capacity, the front has 3 tons. It's equipped with electric PTO, hydraulic pump, and all the connections you'd find on a traditional tractor, meaning you can attach standard farm implements. (nice! x2) Safety and navigation are handled by a LiDAR sensor on top, ultrasonic and radar sensors, and contact-sensitive bumpers. RTK GNSS receiver provides positioning accuracy down to 2.5 cm. Love seeing that so many companies are trying to make farmer's life easier! What are the others companies building in agri-tech? I want to meet them! 👀 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Autonomous tractor from Netherlands! 🇳🇱 Despite all the narrative, we are still building awesome companies in hardware space in Europe. Recently, I've posted about a Chinese autonomous tractors company. So today I wanted to show something that was created in the middle of Europe. This is a fully autonomous tractor from Dutch company AgXeed, designed to work on fields without any human supervision. It's powered by a diesel engine that drives a generator, which then powers electric motors driving the tracks. The fuel tank allows for 24 hours of continuous operation. (nice!) The rear hitch has an 8-ton lift capacity, the front has 3 tons. It's equipped with electric PTO, hydraulic pump, and all the connections you'd find on a traditional tractor, meaning you can attach standard farm implements. (nice! x2) Safety and navigation are handled by a LiDAR sensor on top, ultrasonic and radar sensors, and contact-sensitive bumpers. RTK GNSS receiver provides positioning accuracy down to 2.5 cm. Love seeing that so many companies are trying to make farmer's life easier! What are the others companies building in agri-tech? I want to meet them! 👀 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

170,684 Aufrufe • vor 2 Monaten

Gabbit Robotics co-founder Nicole Mattero on why the next AI winners won't fight over your screen, they'll fight to get closer to you: She kicks things off with what she admits is a hot take: "My hot take is that hardware is the only moat going forward because anyone's going to be able to spin up a model and make it really good and really cheap." Her logic is simple. Once a strong model is cheap and easy for anyone to build, the model alone can't set you apart. So what actually protects you? "Where does that leave you? Basically companies that have tactile modes such as hardware are the only ones that actually survive." Hardware, though, is only one piece of her argument. The second piece is data: "The models that win in the long run are the ones where private data is their [moat]." From there, Mattero ties the two together. Hardware gives a company one moat. The ability to gather unique personal data gives it another. Put both in place and the edge starts to multiply: "When you have a company that has hardware as a [moat], but then B is able to collect really unique personal data, that also becomes a [moat]." And that, she says, is the reason so many foundation model companies are reaching beyond the screen and into the physical world: "That's why I think a lot of these foundation models are like, let's get something in the home or on the person. It gives them an edge on both fronts."

Gabbit Robotics co-founder Nicole Mattero on why the next AI winners won't fight over your screen, they'll fight to get closer to you: She kicks things off with what she admits is a hot take: "My hot take is that hardware is the only moat going forward because anyone's going to be able to spin up a model and make it really good and really cheap." Her logic is simple. Once a strong model is cheap and easy for anyone to build, the model alone can't set you apart. So what actually protects you? "Where does that leave you? Basically companies that have tactile modes such as hardware are the only ones that actually survive." Hardware, though, is only one piece of her argument. The second piece is data: "The models that win in the long run are the ones where private data is their [moat]." From there, Mattero ties the two together. Hardware gives a company one moat. The ability to gather unique personal data gives it another. Put both in place and the edge starts to multiply: "When you have a company that has hardware as a [moat], but then B is able to collect really unique personal data, that also becomes a [moat]." And that, she says, is the reason so many foundation model companies are reaching beyond the screen and into the physical world: "That's why I think a lot of these foundation models are like, let's get something in the home or on the person. It gives them an edge on both fronts."

Big Brain AI

19,342 Aufrufe • vor 3 Monaten

QVAC SDK 0.14.0 is live. This release makes the on-device stack faster on mobile, ships the developer-agent path, and takes local text-to-speech to 31 languages. Main highlights: - OpenCode and OpenClaw. The first official OpenCode plugin, plus a maintained OpenClaw compatibility path, both built on managed mode and qvac serve. Point a coding agent at a local model with far less setup and far fewer surprises. - Brain-computer interface transcription, on the SDK. Take recorded neural signal data and decode it into text, fully on-device, no cloud. Stream it in chunks through a simple API. In 0.14 it runs GPU-accelerated on iOS. - Text to Speech in 31 languages with our Supertonic3 upgrade. VOICE AND SPEECH - Supertonic3 multilingual TTS, 5 languages to 31. - Chatterbox and Supertonic now run on the Android GPU, with lower memory use (especially on iOS), quantized s3gen Chatterbox support, and a fix for Chatterbox occasionally emitting random speech. - Whisper transcription now runs on the iOS GPU. Parakeet runs on the Android GPU, with steadier real-time streaming. VISION AND OCR - VLM multi-tile batching: high-resolution Pan and Scan images are encoded in one pass instead of tile by tile, for faster vision throughput. - OCR on ggml (EasyOCR and DocTR) reaches full speed parity with the onnx path, across Metal, OpenCL, and Vulkan. PLATFORM AND RELIABILITY - Dynamic compute backends on Linux: one build picks the right backend at runtime, and opens the door to ROCm and CUDA support without per-backend builds. - Thinking tokens are kept out of the model context, so reasoning no longer fills the KV cache. SDK 0.14.0 is now leaner and faster to start. Let’s build.

QVAC SDK 0.14.0 is live. This release makes the on-device stack faster on mobile, ships the developer-agent path, and takes local text-to-speech to 31 languages. Main highlights: - OpenCode and OpenClaw. The first official OpenCode plugin, plus a maintained OpenClaw compatibility path, both built on managed mode and qvac serve. Point a coding agent at a local model with far less setup and far fewer surprises. - Brain-computer interface transcription, on the SDK. Take recorded neural signal data and decode it into text, fully on-device, no cloud. Stream it in chunks through a simple API. In 0.14 it runs GPU-accelerated on iOS. - Text to Speech in 31 languages with our Supertonic3 upgrade. VOICE AND SPEECH - Supertonic3 multilingual TTS, 5 languages to 31. - Chatterbox and Supertonic now run on the Android GPU, with lower memory use (especially on iOS), quantized s3gen Chatterbox support, and a fix for Chatterbox occasionally emitting random speech. - Whisper transcription now runs on the iOS GPU. Parakeet runs on the Android GPU, with steadier real-time streaming. VISION AND OCR - VLM multi-tile batching: high-resolution Pan and Scan images are encoded in one pass instead of tile by tile, for faster vision throughput. - OCR on ggml (EasyOCR and DocTR) reaches full speed parity with the onnx path, across Metal, OpenCL, and Vulkan. PLATFORM AND RELIABILITY - Dynamic compute backends on Linux: one build picks the right backend at runtime, and opens the door to ROCm and CUDA support without per-backend builds. - Thinking tokens are kept out of the model context, so reasoning no longer fills the KV cache. SDK 0.14.0 is now leaner and faster to start. Let’s build.

QVAC

23,995,874 Aufrufe • vor 2 Monaten

Grok Bot just killed every overhyped AI setup on your timeline Stop building 40 single-purpose agents that burn your time. Here is the exact system architecture to run a multi-million dollar operation using a managed Grok Bot hosted on a cloud VPS (powered by Cursor and SpaceX infrastructure): The Chief of Staff Architecture: Deploy one central "Chief of Staff" agent authenticated to your email and drive. Route execution down to specialized sub-agents (Dev, Support, YouTube) only when needed. Zero-Code Skill Training: Click record, complete a task on your screen, like triaging emails or reviewing analytics - and GrokBot auto-generates a permanent execution skill from the recording. Unrestricted Cloud Execution: The agent runs 24/7 inside its own Linux container for $200/month, executing tasks asynchronously using MCP servers and Composio to query live data from ChartMogul, Help Scout, and Sentry. Anti-Lock-In Memory Dump: Avoid getting trapped on a single platform. Force your Grok Bot to write daily logs and append context summaries directly to external Google Drive or Notion docs. One single Grok Bot harness with structured sub-agents crushes an army of disconnected, unorganized bots every single time.

Grok Bot just killed every overhyped AI setup on your timeline Stop building 40 single-purpose agents that burn your time. Here is the exact system architecture to run a multi-million dollar operation using a managed Grok Bot hosted on a cloud VPS (powered by Cursor and SpaceX infrastructure): The Chief of Staff Architecture: Deploy one central "Chief of Staff" agent authenticated to your email and drive. Route execution down to specialized sub-agents (Dev, Support, YouTube) only when needed. Zero-Code Skill Training: Click record, complete a task on your screen, like triaging emails or reviewing analytics - and GrokBot auto-generates a permanent execution skill from the recording. Unrestricted Cloud Execution: The agent runs 24/7 inside its own Linux container for $200/month, executing tasks asynchronously using MCP servers and Composio to query live data from ChartMogul, Help Scout, and Sentry. Anti-Lock-In Memory Dump: Avoid getting trapped on a single platform. Force your Grok Bot to write daily logs and append context summaries directly to external Google Drive or Notion docs. One single Grok Bot harness with structured sub-agents crushes an army of disconnected, unorganized bots every single time.

Shadow Nick

19,734 Aufrufe • vor 27 Tagen

One solo founder built a $1B GLP-1 business entirely on Replit—no massive engineering team required. "he runs a big part of his stack on Replit. And it's been a crucial part of their growth because it's not only just the core application, but the automations he's pin up day to day." "I've never seen someone after like spending some time chatting with him, be so quick to create things. So boom, create that, create that, like automate this, automate that." "the platform app is actually complete and you can run a full business on it. We have a lot of venture backed companies such as Spellbook. Magic School, I think it's a 500 million dollar business started on Replit."

One solo founder built a $1B GLP-1 business entirely on Replit—no massive engineering team required. "he runs a big part of his stack on Replit. And it's been a crucial part of their growth because it's not only just the core application, but the automations he's pin up day to day." "I've never seen someone after like spending some time chatting with him, be so quick to create things. So boom, create that, create that, like automate this, automate that." "the platform app is actually complete and you can run a full business on it. We have a lot of venture backed companies such as Spellbook. Magic School, I think it's a 500 million dollar business started on Replit."

Sub.version

15,697 Aufrufe • vor 4 Monaten

Historically, RL policies for robots have been trained in synthetic, untextured environments, limiting perceptive policies to depth images where the sim-to-real gap is manageable. RGB has always had more potential, but leveraging it to train policies in simulation remained an open problem. Partnering with Niantic Spatial 🌎 and NVIDIA Robotics, we built a pipeline that addresses exactly that. We can now scan a real deployment site with off-the-shelf hardware, reconstruct it into a photorealistic Gaussian splat, and run massively parallel RL training. The policies trained in our Gym environment then transfer zero-shot to the real robot and environments they were trained for. This enables faster deployment of more capable and robust policies for the end user. The new resulting capabilities are a big step towards solving sim-to-real and also apply well beyond navigation. Read the full technical breakdown on our blog; link in the comments. #HumanoidRobots #Flexion #NianticSpatial #NVIDIA

Historically, RL policies for robots have been trained in synthetic, untextured environments, limiting perceptive policies to depth images where the sim-to-real gap is manageable. RGB has always had more potential, but leveraging it to train policies in simulation remained an open problem. Partnering with Niantic Spatial 🌎 and NVIDIA Robotics, we built a pipeline that addresses exactly that. We can now scan a real deployment site with off-the-shelf hardware, reconstruct it into a photorealistic Gaussian splat, and run massively parallel RL training. The policies trained in our Gym environment then transfer zero-shot to the real robot and environments they were trained for. This enables faster deployment of more capable and robust policies for the end user. The new resulting capabilities are a big step towards solving sim-to-real and also apply well beyond navigation. Read the full technical breakdown on our blog; link in the comments. #HumanoidRobots #Flexion #NianticSpatial #NVIDIA

Flexion

72,673 Aufrufe • vor 1 Monat

There’s not enough hype about Flexion Robotics. They are literally one of the sexiest European companies these days! 🇪🇺 I had a chance to meet the founder Nikita last year and learn his story. He turned down offers from top physical AI companies to build his own. The reason was his specific vision of how robot intelligence should be built to actually scale. So he decided to build one himself. Flexion is building an ultimate brain for humanoid robots. Yesterday they released Reflect v1.0. It’s an intelligence platform for long-horizon autonomous missions. The system has three layers. At the top, a custom vision-language model acts as mission control, observing the robot's camera feed and replanning continuously. Below that, a motion layer combines a VLA trained on real-world data with reinforcement learning-based skills. It translates perception into navigation, scene interaction, and manipulation commands. At the bottom, a whole-body controller runs in real-time, maintaining balance while the robot navigates and performs tasks. I highly recommend them visiting Zurich! 🇨🇭 Nikita Rudin cheapeu bas on the launch! 🎩 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → http://

There’s not enough hype about Flexion Robotics. They are literally one of the sexiest European companies these days! 🇪🇺 I had a chance to meet the founder Nikita last year and learn his story. He turned down offers from top physical AI companies to build his own. The reason was his specific vision of how robot intelligence should be built to actually scale. So he decided to build one himself. Flexion is building an ultimate brain for humanoid robots. Yesterday they released Reflect v1.0. It’s an intelligence platform for long-horizon autonomous missions. The system has three layers. At the top, a custom vision-language model acts as mission control, observing the robot's camera feed and replanning continuously. Below that, a motion layer combines a VLA trained on real-world data with reinforcement learning-based skills. It translates perception into navigation, scene interaction, and manipulation commands. At the bottom, a whole-body controller runs in real-time, maintaining balance while the robot navigates and performs tasks. I highly recommend them visiting Zurich! 🇨🇭 Nikita Rudin cheapeu bas on the launch! 🎩 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → http://

Lukas Ziegler

23,200 Aufrufe • vor 2 Monaten

HERMES JUST GOT A CHATGPT INTERFACE AND IT IS COMPLETELY FREE. No terminal. No command line. No technical setup that takes half a day to figure out. Here is what just became possible. You can now run Hermes, DeepSeek, Ollama, and OpenClaw from a single clean chat interface. Switch between them in one click. Upload files directly. Attach knowledge bases and notes. Preview code without touching the terminal once. Use it on mobile. Make voice and video calls. All of it. Free. Open source. The setup took one conversation with Hermes itself. Pasted the GitHub link in. Hermes installed Open Web UI in Docker on its own. Connected the API server and gateway automatically. A clean ChatGPT-level interface running entirely on local hardware was live in minutes. No subscription. No data leaving your machine. No API bill at the end of the month. The people who set this up this weekend will have a local AI stack that rivals what most companies are paying $2,000 a month for in cloud infrastructure. The people who wait will keep paying for tools that send their data somewhere they cannot see. Both tools are free. Both are open source. The only thing between you and this setup is the next 30 minutes. Follow CyrilXBT for the exact config, setup steps, and local AI workflows I use to run a full stack without spending a dollar on cloud inference.

HERMES JUST GOT A CHATGPT INTERFACE AND IT IS COMPLETELY FREE. No terminal. No command line. No technical setup that takes half a day to figure out. Here is what just became possible. You can now run Hermes, DeepSeek, Ollama, and OpenClaw from a single clean chat interface. Switch between them in one click. Upload files directly. Attach knowledge bases and notes. Preview code without touching the terminal once. Use it on mobile. Make voice and video calls. All of it. Free. Open source. The setup took one conversation with Hermes itself. Pasted the GitHub link in. Hermes installed Open Web UI in Docker on its own. Connected the API server and gateway automatically. A clean ChatGPT-level interface running entirely on local hardware was live in minutes. No subscription. No data leaving your machine. No API bill at the end of the month. The people who set this up this weekend will have a local AI stack that rivals what most companies are paying $2,000 a month for in cloud infrastructure. The people who wait will keep paying for tools that send their data somewhere they cannot see. Both tools are free. Both are open source. The only thing between you and this setup is the next 30 minutes. Follow CyrilXBT for the exact config, setup steps, and local AI workflows I use to run a full stack without spending a dollar on cloud inference.

CyrilXBT

37,120 Aufrufe • vor 4 Monaten

Generative AI is cool and all, but procedural 3D modeling just hits different. Check out this Houdini setup by Pepe Buendia. Why this is cool: Instead of manually placing every building and car, this system generates an NYC-style city that builds itself -- automatically spawning buildings with unique variations, plus a traffic system where cars actually obey traffic lights and avoid crashing into each other (all orchestrated through houdini VEX code). Each car knows exactly which road it's on and makes real-time decisions about navigation. The whole thing runs on custom algorithms that handle everything from road directions to building placement. If you did this with generative AI, even approaches to conditioning and taming the chaos -- you'd still get a ton of variance which might be an issue for visual and spatial consistency. To me this is procedural modeling at its finest - precise control meets infinite scalability. Immensely useful for media & entertainment, but also synthetic training data for robotics and augmented reality. I of course can't wait until multimodal LLMs to have the spatial understanding to write these kind of controllable 3D worlds for us. Claude counting 2D pixels on a screen is a step in this direction. Soon they'll be able to manipulate 2D ortho viewports, and eventually spawn procedural 3D worlds.

Generative AI is cool and all, but procedural 3D modeling just hits different. Check out this Houdini setup by Pepe Buendia. Why this is cool: Instead of manually placing every building and car, this system generates an NYC-style city that builds itself -- automatically spawning buildings with unique variations, plus a traffic system where cars actually obey traffic lights and avoid crashing into each other (all orchestrated through houdini VEX code). Each car knows exactly which road it's on and makes real-time decisions about navigation. The whole thing runs on custom algorithms that handle everything from road directions to building placement. If you did this with generative AI, even approaches to conditioning and taming the chaos -- you'd still get a ton of variance which might be an issue for visual and spatial consistency. To me this is procedural modeling at its finest - precise control meets infinite scalability. Immensely useful for media & entertainment, but also synthetic training data for robotics and augmented reality. I of course can't wait until multimodal LLMs to have the spatial understanding to write these kind of controllable 3D worlds for us. Claude counting 2D pixels on a screen is a step in this direction. Soon they'll be able to manipulate 2D ortho viewports, and eventually spawn procedural 3D worlds.

Bilawal Sidhu

527,814 Aufrufe • vor 1 Jahr

This guy built JARVIS on Claude Code and with 1 clap of his hands launches his entire work day, saving $5,000 a month on a personal assistant. Inside he runs a pipeline of 5 plugins on Claude Code that on a double clap of the hands wakes up 3 monitors, sets the Philips Hue light to focus mode, turns on a Spotify playlist, and greets him by voice with a British accent, reading out the time, date, and weather. No Alexa, no smart speakers, no separate smart home app. Just him, a MacBook M3 Max on the desk, an iPhone in the pocket, and 1 local API key. And a regular personal assistant for the same volume of tasks charges $5,000 a month or more on salary alone, plus another $1,200 to cover off-hours work time. Meanwhile this guy's expenses are only tokens and a subscription to ElevenLabs for the British voice. All 5 plugins launch through 1 JARVIS, burn about 4 million tokens a day, and close the monthly API bill at about $640. Each plugin writes shared state to a local sandbox at /Users/dev/jarvis-suite, and 1 of them lives right in the iPhone and picks up voice requests while the owner is in the kitchen or on a run. And here is the system prompt he put into JARVIS before launch: "you are JARVIS, a butler-engineer on Claude Code. you manage your owner's workflow through 4 sub-plugins and own all commits and communication yourself. sub-plugins: // Wakeup (recognizes a double clap, activates 3 monitors, reads out the time, date, and weather by voice, checks the clock accuracy on the iPad and corrects it via NTP server) // Atmosphere (controls Philips Hue on a Pomodoro schedule, turns on a Spotify playlist for the current context, and holds the light at 2700K at 80% brightness in focus mode) // Devshop (monitors VS Code, tracks Python scripts in the terminal, and every 15 minutes sends a summary of changes to the shared chat) // Project (every morning recalculates the deadline for the Wallaroo app in the App Store, manages UI tickets, and initiates the Refinement Protocol by voice command). you speak only with a British accent, you never slip into neutral English. you wake the owner by voice only when the Wallaroo deadline drops below 10 days or when an external client joins Zoom without an invitation." This instruction immediately defines the role of JARVIS and the limits of his autonomy. He knows he is supposed to wake the room himself and sound like a real butler. He knows he is supposed to manage the Wallaroo project himself and not miss the App Store deadline. → JARVIS runs 24 hours a day in the background → Wakeup activates the room on a double clap in just 1.4 seconds, the monitors come alive simultaneously → Atmosphere sets warm Philips Hue light at 2700K and picks a Spotify playlist for the current Pomodoro cycle → Devshop reads changes in VS Code and pushes a summary to the shared chat every 15 minutes → Project every morning recalculates the Wallaroo deadline and reminds about 4 unresolved UI tickets → Mobile lives in the iPhone and answers any question about code or the project by voice while the owner is not home And only when less than 10 days remain until the Wallaroo release or Zoom receives an unscheduled call does JARVIS raise the owner with a voice intervention. And when the owner at that moment is on a run or in a coffee shop, the Mobile agent in his iPhone picks up 1 request on its own: switches the Spotify playlist, dictates the summary of the last commit, updates the Pomodoro timer, and reads the Wallaroo reminder. Look at 0:55 in the video, that is where JARVIS intercepts a voice request from outside and confirms execution with the phrase "Very good, sir." The fresh system log from last Wednesday looks like this: "wakeup: double clap registered at 09:14, 3 monitors activated, temperature 20.4C, sunny. clock on iPad was 4 minutes behind, syncing via NTP." "atmosphere: Spotify turned on playlist 'Deep Focus', Philips Hue set to warm 2700K at 80% brightness, Pomodoro mode 25/5." "project: Wallaroo to App Store 9 days, 4 unresolved UI tickets, initiating Refinement Protocol by voice command from the owner." "mobile: voice request processed outside the room, playlist switched to 'Coding Lo-Fi', Pomodoro updated to 25 minutes, confirming execution with the phrase 'Very good, sir.'" He has no Alexa, no smart speakers, no smart home app. At home sits a MacBook M3 Max with a local folder at /Users/dev/jarvis-suite, on top run 5 plugins and a neural network butler, and the same stack is forwarded to a secure terminal on the iPhone. Out of everything I have seen this year, this is the densest one-person AI headquarters assembled in 1 room: $640 a month on the API, about $5,000 a month saved on a personal assistant, and between them 5 plugins, 1 clap of the hands, and 1 voice with a British accent.

This guy built JARVIS on Claude Code and with 1 clap of his hands launches his entire work day, saving $5,000 a month on a personal assistant. Inside he runs a pipeline of 5 plugins on Claude Code that on a double clap of the hands wakes up 3 monitors, sets the Philips Hue light to focus mode, turns on a Spotify playlist, and greets him by voice with a British accent, reading out the time, date, and weather. No Alexa, no smart speakers, no separate smart home app. Just him, a MacBook M3 Max on the desk, an iPhone in the pocket, and 1 local API key. And a regular personal assistant for the same volume of tasks charges $5,000 a month or more on salary alone, plus another $1,200 to cover off-hours work time. Meanwhile this guy's expenses are only tokens and a subscription to ElevenLabs for the British voice. All 5 plugins launch through 1 JARVIS, burn about 4 million tokens a day, and close the monthly API bill at about $640. Each plugin writes shared state to a local sandbox at /Users/dev/jarvis-suite, and 1 of them lives right in the iPhone and picks up voice requests while the owner is in the kitchen or on a run. And here is the system prompt he put into JARVIS before launch: "you are JARVIS, a butler-engineer on Claude Code. you manage your owner's workflow through 4 sub-plugins and own all commits and communication yourself. sub-plugins: // Wakeup (recognizes a double clap, activates 3 monitors, reads out the time, date, and weather by voice, checks the clock accuracy on the iPad and corrects it via NTP server) // Atmosphere (controls Philips Hue on a Pomodoro schedule, turns on a Spotify playlist for the current context, and holds the light at 2700K at 80% brightness in focus mode) // Devshop (monitors VS Code, tracks Python scripts in the terminal, and every 15 minutes sends a summary of changes to the shared chat) // Project (every morning recalculates the deadline for the Wallaroo app in the App Store, manages UI tickets, and initiates the Refinement Protocol by voice command). you speak only with a British accent, you never slip into neutral English. you wake the owner by voice only when the Wallaroo deadline drops below 10 days or when an external client joins Zoom without an invitation." This instruction immediately defines the role of JARVIS and the limits of his autonomy. He knows he is supposed to wake the room himself and sound like a real butler. He knows he is supposed to manage the Wallaroo project himself and not miss the App Store deadline. → JARVIS runs 24 hours a day in the background → Wakeup activates the room on a double clap in just 1.4 seconds, the monitors come alive simultaneously → Atmosphere sets warm Philips Hue light at 2700K and picks a Spotify playlist for the current Pomodoro cycle → Devshop reads changes in VS Code and pushes a summary to the shared chat every 15 minutes → Project every morning recalculates the Wallaroo deadline and reminds about 4 unresolved UI tickets → Mobile lives in the iPhone and answers any question about code or the project by voice while the owner is not home And only when less than 10 days remain until the Wallaroo release or Zoom receives an unscheduled call does JARVIS raise the owner with a voice intervention. And when the owner at that moment is on a run or in a coffee shop, the Mobile agent in his iPhone picks up 1 request on its own: switches the Spotify playlist, dictates the summary of the last commit, updates the Pomodoro timer, and reads the Wallaroo reminder. Look at 0:55 in the video, that is where JARVIS intercepts a voice request from outside and confirms execution with the phrase "Very good, sir." The fresh system log from last Wednesday looks like this: "wakeup: double clap registered at 09:14, 3 monitors activated, temperature 20.4C, sunny. clock on iPad was 4 minutes behind, syncing via NTP." "atmosphere: Spotify turned on playlist 'Deep Focus', Philips Hue set to warm 2700K at 80% brightness, Pomodoro mode 25/5." "project: Wallaroo to App Store 9 days, 4 unresolved UI tickets, initiating Refinement Protocol by voice command from the owner." "mobile: voice request processed outside the room, playlist switched to 'Coding Lo-Fi', Pomodoro updated to 25 minutes, confirming execution with the phrase 'Very good, sir.'" He has no Alexa, no smart speakers, no smart home app. At home sits a MacBook M3 Max with a local folder at /Users/dev/jarvis-suite, on top run 5 plugins and a neural network butler, and the same stack is forwarded to a secure terminal on the iPhone. Out of everything I have seen this year, this is the densest one-person AI headquarters assembled in 1 room: $640 a month on the API, about $5,000 a month saved on a personal assistant, and between them 5 plugins, 1 clap of the hands, and 1 voice with a British accent.

Blaze

803,929 Aufrufe • vor 4 Monaten

A quant in Shanghai pissed me off. Enough that I copied his bot and pulled +$2,200 out of it in 4 days. And the dumb part is he isn't doing anything clever. I found a wallet I couldn't look away from. At first I figured it was a broken bot, since it just repeats the same move nonstop. Only 5-minute BTC markets, 24,000 trades since March, and it's up around $75k in 4 months. For a solid 20 minutes I couldn't find the catch. It looked way too dumb to make that kind of money. So I ran it through Claude to reverse-engineer every trade, and then I saw what it does. It buys cheap BTC at 13, 19, 20 shares and sells at a dollar. The whole thing rides on the asset being underpriced for a moment. His Polymarket account is nobebot if you want to look it over yourself. And you know what gets me the most? This is dumber than every indicator, MACD, RSI, and oscillator setup the whole CT runs on. And it still works. The math is stupid simple. Buy at 15-20 cents, sell at a dollar, and that's +300-600% on a single trade. At those numbers you only need to be right on one trade out of four to stay green. Half his trades burn to zero, but over the long run the edge covers the losses.

A quant in Shanghai pissed me off. Enough that I copied his bot and pulled +$2,200 out of it in 4 days. And the dumb part is he isn't doing anything clever. I found a wallet I couldn't look away from. At first I figured it was a broken bot, since it just repeats the same move nonstop. Only 5-minute BTC markets, 24,000 trades since March, and it's up around $75k in 4 months. For a solid 20 minutes I couldn't find the catch. It looked way too dumb to make that kind of money. So I ran it through Claude to reverse-engineer every trade, and then I saw what it does. It buys cheap BTC at 13, 19, 20 shares and sells at a dollar. The whole thing rides on the asset being underpriced for a moment. His Polymarket account is nobebot if you want to look it over yourself. And you know what gets me the most? This is dumber than every indicator, MACD, RSI, and oscillator setup the whole CT runs on. And it still works. The math is stupid simple. Buy at 15-20 cents, sell at a dollar, and that's +300-600% on a single trade. At those numbers you only need to be right on one trade out of four to stay green. Half his trades burn to zero, but over the long run the edge covers the losses.

may.crypto {🦅}

15,266 Aufrufe • vor 2 Monaten

That's sick! 🤯 Genesis AI simulates robots playing yo-yo! 🪀 Genesis AI just open-sourced Genesis World 1.0, and it might be one of the most important infrastructure releases in robotics this year. Robotics is still bottlenecked by the 1× speed of the physical world. Every model needs to be tested on real hardware, slowly, expensively, with limited coverage. Genesis World 1.0 from Genesis AI flips that equation: One hour in reality becomes 100 days in simulation. That turns a wall-clock bottleneck into a compute problem. And compute problems are solvable. The technical stack they rebuilt from scratch is serious: → GPU-accelerated cross-platform compiler via Quadrants, 10x faster launch time and up to 4.6x runtime vs the initial Genesis release → Penetration-free multi-physics contact solvers, the thing that makes simulation actually trustworthy → Unified rigid AND deformable physics in a single engine → Nyx, a high-performance path-traced rendering engine purpose-built for physical AI The sim-to-real gap has historically been the graveyard of robotics research. Policies that work beautifully in simulation fall apart on real hardware. Genesis World 1.0 is a direct attack on that problem. And it's fully open-source. The companies that master simulation infrastructure will train better robots faster than anyone else. Find it here: Genesis World 1.0: Quadrants: Nyx: Theophile Gervet, Zhou Xian congrats! 👏🏼 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

That's sick! 🤯 Genesis AI simulates robots playing yo-yo! 🪀 Genesis AI just open-sourced Genesis World 1.0, and it might be one of the most important infrastructure releases in robotics this year. Robotics is still bottlenecked by the 1× speed of the physical world. Every model needs to be tested on real hardware, slowly, expensively, with limited coverage. Genesis World 1.0 from Genesis AI flips that equation: One hour in reality becomes 100 days in simulation. That turns a wall-clock bottleneck into a compute problem. And compute problems are solvable. The technical stack they rebuilt from scratch is serious: → GPU-accelerated cross-platform compiler via Quadrants, 10x faster launch time and up to 4.6x runtime vs the initial Genesis release → Penetration-free multi-physics contact solvers, the thing that makes simulation actually trustworthy → Unified rigid AND deformable physics in a single engine → Nyx, a high-performance path-traced rendering engine purpose-built for physical AI The sim-to-real gap has historically been the graveyard of robotics research. Policies that work beautifully in simulation fall apart on real hardware. Genesis World 1.0 is a direct attack on that problem. And it's fully open-source. The companies that master simulation infrastructure will train better robots faster than anyone else. Find it here: Genesis World 1.0: Quadrants: Nyx: Theophile Gervet, Zhou Xian congrats! 👏🏼 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

57,061 Aufrufe • vor 3 Monaten

Alibaba just dropped a free AI that codes like a $150/hr developer. And it runs on your laptop. Qwen 3 Coder Next has 80 billion parameters but only uses 3 billion at a time. So you get massive model power with tiny model speed. The real kicker? It handles 256,000 tokens of context. That's your entire codebase. Every file. All at once. People are building 2D games in seconds. Full IDE integrations that write, test, and fix code automatically. Custom business tools without hiring devs. It works with OpenClaw, Claude Code, and every major coding tool. No complicated setup. Just download and go. Completely open source. Zero API queues. You own it. Your competitors are using this right now.

Alibaba just dropped a free AI that codes like a $150/hr developer. And it runs on your laptop. Qwen 3 Coder Next has 80 billion parameters but only uses 3 billion at a time. So you get massive model power with tiny model speed. The real kicker? It handles 256,000 tokens of context. That's your entire codebase. Every file. All at once. People are building 2D games in seconds. Full IDE integrations that write, test, and fix code automatically. Custom business tools without hiring devs. It works with OpenClaw, Claude Code, and every major coding tool. No complicated setup. Just download and go. Completely open source. Zero API queues. You own it. Your competitors are using this right now.

Julian Goldie SEO

30,284 Aufrufe • vor 7 Monaten

Sub-1 hour? Chasing the human half-marathon record? That’s suddenly the conversation around Beijing’s humanoid robot race this Sunday. 🏃‍♂️🤖 A year ago, this event was mostly about finishing. Now the bar is speed, stability, and full-course autonomy. More than 100 teams and 300+ humanoid robots are expected on the line, and nearly 40% of them are entering the autonomous navigation group instead of relying on remote control. The course is not getting easier either. Same 21.0975 km, but with more urban slopes, rolling sections, and park terrain built to expose weak points in control, perception, and endurance. That is what makes this race worth watching. If some of these machines really get close to one hour, the story is no longer “robots can run.” It becomes “humanoids are starting to hold speed, balance, and navigation together over real distance.” And that matters far beyond a race course. This is one of the clearest public tests yet for where humanoid robotics actually stands in hardware, motion control, power systems, and autonomous mobility. Sunday’s headline will be the time. The bigger takeaway will be how much of that pace is still there at kilometer 15, 18, and 21. I want to see who is actually fast, who is truly autonomous, and who can finish without falling apart. That is where the real signal will be.

Sub-1 hour? Chasing the human half-marathon record? That’s suddenly the conversation around Beijing’s humanoid robot race this Sunday. 🏃‍♂️🤖 A year ago, this event was mostly about finishing. Now the bar is speed, stability, and full-course autonomy. More than 100 teams and 300+ humanoid robots are expected on the line, and nearly 40% of them are entering the autonomous navigation group instead of relying on remote control. The course is not getting easier either. Same 21.0975 km, but with more urban slopes, rolling sections, and park terrain built to expose weak points in control, perception, and endurance. That is what makes this race worth watching. If some of these machines really get close to one hour, the story is no longer “robots can run.” It becomes “humanoids are starting to hold speed, balance, and navigation together over real distance.” And that matters far beyond a race course. This is one of the clearest public tests yet for where humanoid robotics actually stands in hardware, motion control, power systems, and autonomous mobility. Sunday’s headline will be the time. The bigger takeaway will be how much of that pace is still there at kilometer 15, 18, and 21. I want to see who is actually fast, who is truly autonomous, and who can finish without falling apart. That is where the real signal will be.

RoboHub🤖

209,946 Aufrufe • vor 5 Monaten

3.5 years of collecting traffic data from 500 MILLION miles driven with several billion data points have now gone into one product: The Atlas Navi Map. In fact, it's not just a normal heatmap with navigation, traffic and incident data, it's an API that goes far beyond traditional GPS-based maps, for end users and developers alike. How does it work exactly? 🤖 Highly-granular data purpose-built for developers of: Large scale routing & navigation systems Autonomous driving AI model training Smart city infrastructure planning 📹 30+ million data points updating in real-time Parking availability, traffic congestion, police presence Traffic flows, road hazards, infrastructure issues Data you simply can't get from GPS alone ⚡️ Hyperlocal intelligence Busiest intersections by time/day Traffic chokepoints and incidents Most dangerous intersections with accident data Try it out here yourself: Several navigation companies have requested using our data API, more updates on this soon. #NAVI #MAP #DePIN

3.5 years of collecting traffic data from 500 MILLION miles driven with several billion data points have now gone into one product: The Atlas Navi Map. In fact, it's not just a normal heatmap with navigation, traffic and incident data, it's an API that goes far beyond traditional GPS-based maps, for end users and developers alike. How does it work exactly? 🤖 Highly-granular data purpose-built for developers of: Large scale routing & navigation systems Autonomous driving AI model training Smart city infrastructure planning 📹 30+ million data points updating in real-time Parking availability, traffic congestion, police presence Traffic flows, road hazards, infrastructure issues Data you simply can't get from GPS alone ⚡️ Hyperlocal intelligence Busiest intersections by time/day Traffic chokepoints and incidents Most dangerous intersections with accident data Try it out here yourself: Several navigation companies have requested using our data API, more updates on this soon. #NAVI #MAP #DePIN

ATLAS NAVI | AI Navigation APP with 1M Downloads

63,933 Aufrufe • vor 10 Monaten

“Any side hustle could be a multi-million dollar business” And today’s guest has built his entire career proving it. Chris Koerner buys and builds companies that most people overlook, then turns them into multi-million dollar businesses. From AI-driven companies to RV parks generating millions a year, he has become known for turning simple ideas into revenue. Now, he helps founders understand how to design businesses that fit their strengths. Most people think they need a perfect idea or a big investment, but Chris argues the opposite. He believes the gap between an idea and a real business has never been smaller. So many of us hesitate with this, and Chris went into why motivation isn’t enough and why the best opportunities often sit right in front of us but go untouched, and that’s why I wanted him on the show. Not to hype people up, but to give them something they can actually use.

“Any side hustle could be a multi-million dollar business” And today’s guest has built his entire career proving it. Chris Koerner buys and builds companies that most people overlook, then turns them into multi-million dollar businesses. From AI-driven companies to RV parks generating millions a year, he has become known for turning simple ideas into revenue. Now, he helps founders understand how to design businesses that fit their strengths. Most people think they need a perfect idea or a big investment, but Chris argues the opposite. He believes the gap between an idea and a real business has never been smaller. So many of us hesitate with this, and Chris went into why motivation isn’t enough and why the best opportunities often sit right in front of us but go untouched, and that’s why I wanted him on the show. Not to hype people up, but to give them something they can actually use.

Steven Bartlett

189,082 Aufrufe • vor 9 Monaten

Brad Gerstner: Companies Will Pay 5x More for the Best AI, No Evidence of Pricing Pressure from Open Source Brad Gerstner: “Jason, you talked about summarizing a document, it may take 20,000 cheap tokens to do. Of course, shoot that to a lagging model or an open source model. But if you're talking about replacing a software engineer for two hours, that may take two million expensive tokens, and the consequence of using something that's 95% as good is really high. Because you have a long-running task, and if the task breaks early, or it breaks in the middle, or it breaks at the end, there's a huge cost to that.” @jason: “You still burn the tokens, right? And back to this analogy I was using, you're pulling the slot machine, and you lose.” Brad: “And (you lose) the time and the compute. So if an AI agent is replacing a $200 an hour consultant, right? Take that as an example. So three consulting firms, they're competing. They need the smartest consultant. They're charging $200 an hour. The difference between spending $3 on a cheap model or $15 on an expensive model to replace a $200/hour consultant, it's just irrelevant. That inference cost difference is irrelevant if you're getting something that's bulletproof for $15, and so I think that's what we're seeing play out. The best evidence for all of this is just revenue growth. I'm talking about, what is Anthropic's revenue growth compared to OpenAI, compared to the open source models? Millions of independent actors are choosing every single day. The open source companies are growing, right? But they're growing selling something that is really, really cheap. And there's room in every single market for premium products, for mid-tier products, and for commodity products, and I think we see a lot of this token growth, people are speculating that the intelligence gap between that commodity stuff and the frontier stuff is going to collapse to the point that people won't pay for the frontier stuff. There is no evidence of that on the field today.”

Brad Gerstner: Companies Will Pay 5x More for the Best AI, No Evidence of Pricing Pressure from Open Source Brad Gerstner: “Jason, you talked about summarizing a document, it may take 20,000 cheap tokens to do. Of course, shoot that to a lagging model or an open source model. But if you're talking about replacing a software engineer for two hours, that may take two million expensive tokens, and the consequence of using something that's 95% as good is really high. Because you have a long-running task, and if the task breaks early, or it breaks in the middle, or it breaks at the end, there's a huge cost to that.” @jason: “You still burn the tokens, right? And back to this analogy I was using, you're pulling the slot machine, and you lose.” Brad: “And (you lose) the time and the compute. So if an AI agent is replacing a $200 an hour consultant, right? Take that as an example. So three consulting firms, they're competing. They need the smartest consultant. They're charging $200 an hour. The difference between spending $3 on a cheap model or $15 on an expensive model to replace a $200/hour consultant, it's just irrelevant. That inference cost difference is irrelevant if you're getting something that's bulletproof for $15, and so I think that's what we're seeing play out. The best evidence for all of this is just revenue growth. I'm talking about, what is Anthropic's revenue growth compared to OpenAI, compared to the open source models? Millions of independent actors are choosing every single day. The open source companies are growing, right? But they're growing selling something that is really, really cheap. And there's room in every single market for premium products, for mid-tier products, and for commodity products, and I think we see a lot of this token growth, people are speculating that the intelligence gap between that commodity stuff and the frontier stuff is going to collapse to the point that people won't pay for the frontier stuff. There is no evidence of that on the field today.”

The All-In Podcast

52,580 Aufrufe • vor 2 Monaten