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While we eagerly await Fable 5's return, our agentic WebGPU kernel optimization framework kept running. Opus 4.8 picked up where Fable left off, pushing Liquid AI's new LFM2.5 230M to an unbelievable 1,400 tok/s... running locally in your browser. Don't blink or you'll miss it.

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Xenova

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174,867 просмотров • 2 месяцев назад •via X (Twitter)

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This is unreal. Qwen 3.8 27b running locally on my RTX 5090 BEATS Opus 4.8 in my benchmarks (funny enough slightly loses to Opus 4.6 lol) Speeds are just as good if not sometimes MUCH better. Got up to 200 tk/s in some tests We live in a new world. Unlimited, free super intelligence on our desks No internet, subscription, or APIs required Just 6 months ago you needed 500gb of VRAM to get a quarter of this performance Now I'm getting Opus 4.8 level intelligence on a single RTX 5090. Don't even need a Spark I now have the model powering a Hermes agent that is doing work for me 24/7 around the clock for free I have ZERO doubt that by this time next year we will have models more powerful than Fable running on Mac Minis. ZERO doubt. If you listened to me earlier this year when I demanded you start buying hardware, you are absolutely eating right now. The future isn't around the corner. It's here.
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This is unreal. Qwen 3.8 27b running locally on my RTX 5090 BEATS Opus 4.8 in my benchmarks (funny enough slightly loses to Opus 4.6 lol) Speeds are just as good if not sometimes MUCH better. Got up to 200 tk/s in some tests We live in a new world. Unlimited, free super intelligence on our desks No internet, subscription, or APIs required Just 6 months ago you needed 500gb of VRAM to get a quarter of this performance Now I'm getting Opus 4.8 level intelligence on a single RTX 5090. Don't even need a Spark I now have the model powering a Hermes agent that is doing work for me 24/7 around the clock for free I have ZERO doubt that by this time next year we will have models more powerful than Fable running on Mac Minis. ZERO doubt. If you listened to me earlier this year when I demanded you start buying hardware, you are absolutely eating right now. The future isn't around the corner. It's here.

Alex Finn

204,760 просмотров • 16 дней назад

A few weeks ago the US government forced Claude Fable 5 offline. It's back. And before they switch it off again, this is your shot to point the most powerful AI on earth at something of your own. Take the 26-year-old in Toronto. His bot went dark the day Fable 5 got pulled. The day it came back, it woke up and started printing again - $568,000 profit and counting. His wallet: Here's the story. When the ban hit, Anthropic had to switch Fable 5 off for everyone. They called it a misunderstanding with the government. Whatever it was, every bot running on that model stopped cold. His was one of them. He didn't rebuild a thing. The loop just sat there, paused, for weeks. Here's what that loop actually does. It's not one prompt to an AI. It's an agentic loop: a team of agents running on repeat. One reads the market, one places the trade, one tries to prove it wrong before a cent moves. Then it goes again, around the clock, keeping what works and dropping what doesn't. Fable 5 is the brain running that team. The exact model the government decided was too powerful to leave on. Point it at Bitcoin's 5-minute markets and it barely misses. Most of its trades close green, and it runs itself. He checks it from his phone. The government could switch off the model. It couldn't switch off the idea. Now the model's back, and so is the money. Save this before next headline. You'll want it the day Fable 5 hits offline again.
0:39

A few weeks ago the US government forced Claude Fable 5 offline. It's back. And before they switch it off again, this is your shot to point the most powerful AI on earth at something of your own. Take the 26-year-old in Toronto. His bot went dark the day Fable 5 got pulled. The day it came back, it woke up and started printing again - $568,000 profit and counting. His wallet: Here's the story. When the ban hit, Anthropic had to switch Fable 5 off for everyone. They called it a misunderstanding with the government. Whatever it was, every bot running on that model stopped cold. His was one of them. He didn't rebuild a thing. The loop just sat there, paused, for weeks. Here's what that loop actually does. It's not one prompt to an AI. It's an agentic loop: a team of agents running on repeat. One reads the market, one places the trade, one tries to prove it wrong before a cent moves. Then it goes again, around the clock, keeping what works and dropping what doesn't. Fable 5 is the brain running that team. The exact model the government decided was too powerful to leave on. Point it at Bitcoin's 5-minute markets and it barely misses. Most of its trades close green, and it runs itself. He checks it from his phone. The government could switch off the model. It couldn't switch off the idea. Now the model's back, and so is the money. Save this before next headline. You'll want it the day Fable 5 hits offline again.

cvxv666

24,298 просмотров • 2 месяцев назад

FABLE 5 JUST REBUILT FORTNITE FROM 1 PROMPT IN 20 MINUTES. EPIC NEEDED HUNDREDS OF ENGINEERS AND 6 YEARS. The test was simple: Fable 5 vs GLM 5.2, one prompt each, a 100-player battle royale running in the browser with no engine and no team. GLM went first and checked all 8 milestones. The game loaded with a battle bus, a generated map, Tilted Towers, even health and shield bars ripped straight from 2018. Then it fell apart. 2 FPS on landing, S walking forward and W walking backward, a gun that fires upward and a camera that refuses to look up. First fight ended with the enemy standing above physically impossible to aim at. Then Fable 5 took the same prompt on max effort. Different game entirely. Real character models instead of beans, hit reactions lighting up on contact, a shadow tracking every player, movement that just works out of the box. One follow-up prompt “add more Fortnite elements” and it shipped gliders, chests, shield potions, medkits and a sniper without breaking a thing. Final circle at 3:28 AM. Storm closing, 0 bullets left, box to box with medkit spam and a last-second swap to the SMG. Victory Royale in a game an AI wrote from a single sentence. GLM checked more boxes. Fable built something you could actually play and no benchmark on earth measures that gap. Epic needed 6 years. Fable 5 needed 1 prompt.
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FABLE 5 JUST REBUILT FORTNITE FROM 1 PROMPT IN 20 MINUTES. EPIC NEEDED HUNDREDS OF ENGINEERS AND 6 YEARS. The test was simple: Fable 5 vs GLM 5.2, one prompt each, a 100-player battle royale running in the browser with no engine and no team. GLM went first and checked all 8 milestones. The game loaded with a battle bus, a generated map, Tilted Towers, even health and shield bars ripped straight from 2018. Then it fell apart. 2 FPS on landing, S walking forward and W walking backward, a gun that fires upward and a camera that refuses to look up. First fight ended with the enemy standing above physically impossible to aim at. Then Fable 5 took the same prompt on max effort. Different game entirely. Real character models instead of beans, hit reactions lighting up on contact, a shadow tracking every player, movement that just works out of the box. One follow-up prompt “add more Fortnite elements” and it shipped gliders, chests, shield potions, medkits and a sniper without breaking a thing. Final circle at 3:28 AM. Storm closing, 0 bullets left, box to box with medkit spam and a last-second swap to the SMG. Victory Royale in a game an AI wrote from a single sentence. GLM checked more boxes. Fable built something you could actually play and no benchmark on earth measures that gap. Epic needed 6 years. Fable 5 needed 1 prompt.

West Lord

80,912 просмотров • 2 месяцев назад

This is how you get 15x fable 5 usage. Fable -> Composer 2.5 reading Fable -> GPT 5.5 execution I built CNVS to make agent orchestration dead simple and insanely visual. You can watch fable 5 delegate work to cursor, codex, open code, all your agents. using your exsisting ai subs no api pricing. The canvas is fully voice controlled locally with nvidia parakeet for fast and free, or gpt realtime 2 for a fully conversational jarvis experience. The built in mcp and cli means agentic control is bidirectional they can prompt and spawn each other and read agent states. but there is more I built it from the ground up in swift for native performance on mac os. My old 16gb m1 mbp can easily spin up and control eight agents across multiple canvasses. The hermes integration + remote canvases allow you to run agents fully in the cloud on your VPS think google docs of vibe coding. You can literally turn off your mac and they keep working, open cnvs back up and pick up where you left off. I built a cross agent memory system based on 2026 research so all your agents feel like one brain. its SIMPLE and on demand. This eliminates context bloat, and the research supports a massive uptick in cross agent performance. There is soo much more. PS - I am a father building this project 100% solo in my basement on live stream everyday, my goal is to push vibe coding to the next level PPS - CNVS is a lifetime license becuase you DO NOT need another subscription in your life.
1:30

This is how you get 15x fable 5 usage. Fable -> Composer 2.5 reading Fable -> GPT 5.5 execution I built CNVS to make agent orchestration dead simple and insanely visual. You can watch fable 5 delegate work to cursor, codex, open code, all your agents. using your exsisting ai subs no api pricing. The canvas is fully voice controlled locally with nvidia parakeet for fast and free, or gpt realtime 2 for a fully conversational jarvis experience. The built in mcp and cli means agentic control is bidirectional they can prompt and spawn each other and read agent states. but there is more I built it from the ground up in swift for native performance on mac os. My old 16gb m1 mbp can easily spin up and control eight agents across multiple canvasses. The hermes integration + remote canvases allow you to run agents fully in the cloud on your VPS think google docs of vibe coding. You can literally turn off your mac and they keep working, open cnvs back up and pick up where you left off. I built a cross agent memory system based on 2026 research so all your agents feel like one brain. its SIMPLE and on demand. This eliminates context bloat, and the research supports a massive uptick in cross agent performance. There is soo much more. PS - I am a father building this project 100% solo in my basement on live stream everyday, my goal is to push vibe coding to the next level PPS - CNVS is a lifetime license becuase you DO NOT need another subscription in your life.

Max Blade

29,711 просмотров • 2 месяцев назад

hy3 vs fable 5 vs opus 4.8 vs sonnet 5 Tencent Hy just dropped hy3 – their new open-weight model under apache 2.0. following the april preview they scaled up post-training, and it now rivals flagship open models with 2-5x the params. api pricing: ~$0.15 in / ~$0.59 out per 1m tokens. built for coding, office work, frontend, agentic tasks so we ran a test: hy3 vs fable 5 vs opus 4.8 vs sonnet 5 three prompts, one-shot each: • ocean wave crumbling a sand castle (canvas) • looping factory assembly line (html/css/js) • interactive 3d city with three.js + orbitcontrols self-contained files, no libraries beyond the cdn where asked totals across all three prompts: 1. hy3 – 1231 loc / 14m34s 2. sonnet 5 – 1373 loc / 18m55s 3. fable 5 – 1546 loc / 18m32s 4. opus 4.8 – 1904 loc / 27m21s hy3 is the fastest and the leanest by a wide margin we had opus 4.8 analyze hy3's code. the read: - sand castle: checklist-complete but the crumble is parametric, not physical. it shrinks and slumps the towers and fades alpha instead of dissolving into grains. the cheap-but-plausible interpretation. the tell of a smaller model - factory line: the arm-to-part sync is actually causal, not faked. it triggers each robot early by exactly the arm's descent time, so the tap lands right as the part arrives. it also pre-seeds the belt so it never cold-starts empty. clean state machine. one latent bug – a part gets marked processed before checking if the robot is free, so at a faster spawn rate a "laptop" could ship missing a part. never fires at current timing, but the invariant isn't enforced - 3d city: genuinely frontier-adjacent. correct modern setup (pcfsoft shadows, srgb, aces tone mapping, damped orbit + auto-rotate pause). clones the window texture per building and scales the uv repeat to each building's dimensions so windows don't stretch. downside: no instancing – ~800 texture clones across 200 buildings. runs fine, not optimized. roads are implicit gaps, not explicit planes our observations: • hy3 is quite fast • its animations are simple but you can see it trying – it adds detail, and the 3d render sits at the same level as the frontier models • sonnet 5 is weak here. hy3 beats it on the sand castle and the 3d render, level on the conveyor • opus 4.8 is anthropic's best model after the fable 5 nerf – it beats fable on the conveyor and the 3d render net: hy3 runs clean and well-formed across all three with zero syntax errors, even version-matching the three.js core and examples build. it's economical rather than ambitious – it does the minimum viable version of each hard requirement well, and only reaches for the expensive interpretation on the 3d task a very coherent profile for a cost-optimized open-weight model follow thehype. for 24/7 ai news, analysis and breakdowns
0:24

hy3 vs fable 5 vs opus 4.8 vs sonnet 5 Tencent Hy just dropped hy3 – their new open-weight model under apache 2.0. following the april preview they scaled up post-training, and it now rivals flagship open models with 2-5x the params. api pricing: ~$0.15 in / ~$0.59 out per 1m tokens. built for coding, office work, frontend, agentic tasks so we ran a test: hy3 vs fable 5 vs opus 4.8 vs sonnet 5 three prompts, one-shot each: • ocean wave crumbling a sand castle (canvas) • looping factory assembly line (html/css/js) • interactive 3d city with three.js + orbitcontrols self-contained files, no libraries beyond the cdn where asked totals across all three prompts: 1. hy3 – 1231 loc / 14m34s 2. sonnet 5 – 1373 loc / 18m55s 3. fable 5 – 1546 loc / 18m32s 4. opus 4.8 – 1904 loc / 27m21s hy3 is the fastest and the leanest by a wide margin we had opus 4.8 analyze hy3's code. the read: - sand castle: checklist-complete but the crumble is parametric, not physical. it shrinks and slumps the towers and fades alpha instead of dissolving into grains. the cheap-but-plausible interpretation. the tell of a smaller model - factory line: the arm-to-part sync is actually causal, not faked. it triggers each robot early by exactly the arm's descent time, so the tap lands right as the part arrives. it also pre-seeds the belt so it never cold-starts empty. clean state machine. one latent bug – a part gets marked processed before checking if the robot is free, so at a faster spawn rate a "laptop" could ship missing a part. never fires at current timing, but the invariant isn't enforced - 3d city: genuinely frontier-adjacent. correct modern setup (pcfsoft shadows, srgb, aces tone mapping, damped orbit + auto-rotate pause). clones the window texture per building and scales the uv repeat to each building's dimensions so windows don't stretch. downside: no instancing – ~800 texture clones across 200 buildings. runs fine, not optimized. roads are implicit gaps, not explicit planes our observations: • hy3 is quite fast • its animations are simple but you can see it trying – it adds detail, and the 3d render sits at the same level as the frontier models • sonnet 5 is weak here. hy3 beats it on the sand castle and the 3d render, level on the conveyor • opus 4.8 is anthropic's best model after the fable 5 nerf – it beats fable on the conveyor and the 3d render net: hy3 runs clean and well-formed across all three with zero syntax errors, even version-matching the three.js core and examples build. it's economical rather than ambitious – it does the minimum viable version of each hard requirement well, and only reaches for the expensive interpretation on the 3d task a very coherent profile for a cost-optimized open-weight model follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

34,961 просмотров • 1 месяц назад

So our predictable guy tmk stops mid way in a concert that happened earlier and says that the meaning is " there is no Ayodhya " . Tmk takes a literal meaning out of context cleverly to show his protest against Ayodhya ram mandir and his whatever secularism, while Thyagaraja 's poetic expression conveys that one cannot find Ayodhya or Rama if he is not found within first. He did not say there is " no Ayodhya " or let Badri masjid remain !!!!! Other chelas picked it up and posted it yesterday specifically. Interestingly, the ones so dedicated about Thyagaraja, don't talk about his other krithis where he actually describes Ayodhya well. kaaru baaru in mukhari is an example. It's dangerous to let the liberal left drag down vaggeyakaras for their narratives. If Shri Thyagaraja was indeed alive in human form today, he would have sung something extempore to celebrate Rama 's return at saketa nagara...like how he sang kanugontini when he found his Rama vigraham in kaveri after looking for days.
1:14

So our predictable guy tmk stops mid way in a concert that happened earlier and says that the meaning is " there is no Ayodhya " . Tmk takes a literal meaning out of context cleverly to show his protest against Ayodhya ram mandir and his whatever secularism, while Thyagaraja 's poetic expression conveys that one cannot find Ayodhya or Rama if he is not found within first. He did not say there is " no Ayodhya " or let Badri masjid remain !!!!! Other chelas picked it up and posted it yesterday specifically. Interestingly, the ones so dedicated about Thyagaraja, don't talk about his other krithis where he actually describes Ayodhya well. kaaru baaru in mukhari is an example. It's dangerous to let the liberal left drag down vaggeyakaras for their narratives. If Shri Thyagaraja was indeed alive in human form today, he would have sung something extempore to celebrate Rama 's return at saketa nagara...like how he sang kanugontini when he found his Rama vigraham in kaveri after looking for days.

krithika sivaswamy

127,991 просмотров • 2 лет назад

Getting the most out of Claude Fable 5, Anthropic’s powerful new model, you need to maximize your ambition: It’s built for full task delegation—you leave it looping for hours or overnight and come back to a finished product. If you want to get the most out of it, you need to relearn what software engineering is and how to step away to let the model do its work. That’s why I invited Mike Krieger, head of Anthropic Labs, on Every 📧’s AI & I. Mike’s been using Mythos-class models for a few months now internally at Anthropic, and he’s learned a ton of new tricks to make its increased powers work for him. And, as a co-founder of Instagram, he can reflect on how software engineering has changed over the last 15 years and what it means going forward. We get into: - Why the right workflow for Fable 5 is overnight delegation, not back-and-forth iteration—Mike ends his workday by briefing the model, then wakes up to a completed task. When a remote service went down mid-task, Fable 5 wrote a workaround, documented it, and forged ahead - The gap between what’s in your head and what exists in the world is closing fast—given access to Fable 5 and a set of internal MCPs, an Anthropic recruiter described the experience as, "The first time in my life where I feel like the thing that's in my head and the thing that exists in the world are right next to each other. I can just do it." - Software engineering isn’t dead, but the role has been reinvented—the PM/eng split is blurring, and the better engineers Mike talks to are holding two feelings at once: loss for the craft and shock at what’s now possible - Verification is the new bottleneck—Mike gives Fable video captures of its own work so it can catch animation glitches that screenshots would miss This is a must-watch for anyone building software and trying to figure out their role now that the models can handle so much. Watch below! Timestamps Introduction: 00:00:03 How Fable completely reshaped Mike's workflow: 00:01:48 When to use Sonnet versus Fable: 00:04:48 What the media tracker Mike built over a weekend reveals about agent-native architecture: 00:10:06 The cost to build has collapsed: 00:15:00 Is software engineering over?: 00:19:03 How Anthropic's engineering teams work today: 00:21:48 The mechanics of verification: 00:38:39 Dynamic workflows: 00:47:24 What people should use the model to build: 00:44:39
52:05

Getting the most out of Claude Fable 5, Anthropic’s powerful new model, you need to maximize your ambition: It’s built for full task delegation—you leave it looping for hours or overnight and come back to a finished product. If you want to get the most out of it, you need to relearn what software engineering is and how to step away to let the model do its work. That’s why I invited Mike Krieger, head of Anthropic Labs, on Every 📧’s AI & I. Mike’s been using Mythos-class models for a few months now internally at Anthropic, and he’s learned a ton of new tricks to make its increased powers work for him. And, as a co-founder of Instagram, he can reflect on how software engineering has changed over the last 15 years and what it means going forward. We get into: - Why the right workflow for Fable 5 is overnight delegation, not back-and-forth iteration—Mike ends his workday by briefing the model, then wakes up to a completed task. When a remote service went down mid-task, Fable 5 wrote a workaround, documented it, and forged ahead - The gap between what’s in your head and what exists in the world is closing fast—given access to Fable 5 and a set of internal MCPs, an Anthropic recruiter described the experience as, "The first time in my life where I feel like the thing that's in my head and the thing that exists in the world are right next to each other. I can just do it." - Software engineering isn’t dead, but the role has been reinvented—the PM/eng split is blurring, and the better engineers Mike talks to are holding two feelings at once: loss for the craft and shock at what’s now possible - Verification is the new bottleneck—Mike gives Fable video captures of its own work so it can catch animation glitches that screenshots would miss This is a must-watch for anyone building software and trying to figure out their role now that the models can handle so much. Watch below! Timestamps Introduction: 00:00:03 How Fable completely reshaped Mike's workflow: 00:01:48 When to use Sonnet versus Fable: 00:04:48 What the media tracker Mike built over a weekend reveals about agent-native architecture: 00:10:06 The cost to build has collapsed: 00:15:00 Is software engineering over?: 00:19:03 How Anthropic's engineering teams work today: 00:21:48 The mechanics of verification: 00:38:39 Dynamic workflows: 00:47:24 What people should use the model to build: 00:44:39

Dan Shipper 📧

40,430 просмотров • 2 месяцев назад

There's been a few cool updates recently. In particular, Rerun 0.33 released headless rendering. This, along with the Fable 5 release pushed me to work torwards making MAMMA realtime! I threw Fable at the problem, and it was able to take original implementation that was ~12 seconds / frame and get it all the way down to 40ms /frame, or nearly a 300x speedup 🏎️ How did I achieve this? TLDR: - Use rerun's headless rendering as supervision when optimizing - Save rrd file as test fixture to guide model optiziation with /goal - create an html artifact with headless rendering to provide detailed breakdown of what it did and how it actually looks like in the viewer There were a few critical bits to make sure that this ACTUALLY worked and that Fable didn't just cheat or delete something and declare victory. The first is that the original version used Rerun, this allowed us to save things to disk as an RRD file, meaning we could query the contents and use this as a sort of test fixture or golden artifact that held EXACTLY what all of the values should be. Then we can use this with /goal as a metric when doing the optimization to ensure there are no regressions. The second bit is the headless rendering, this gave us the ability to check that not only did the test fixture pass, but it also looked visually correct. This made a huge difference, and an awesome side affect of it is that we can use the headless rendering to create an implementations.html file. This gives a visual guide as to what the agent did (I walk through it in the video below) Along with this, we're working on an MCP server for rerun that allows full interactivity with the rerun viewer for your agent. So for example the agent can click, drag, move views, scroll timelines, ect. I used this to help the agent debug certain parts such as when the 2d sam masks didn't line up, or if the triangulated keypoints werent correctly matching with the optimized mesh. The agents could go, click into the view, scroll through the timeline and see where things went wrong. Fable + Headless Rendering + Rerun MCP == 300x speedup in less then a days work With these new tools, I'm planning on going back to my gaussian splatting implemntation and cleaning it up + making it fast!
1:33

There's been a few cool updates recently. In particular, Rerun 0.33 released headless rendering. This, along with the Fable 5 release pushed me to work torwards making MAMMA realtime! I threw Fable at the problem, and it was able to take original implementation that was ~12 seconds / frame and get it all the way down to 40ms /frame, or nearly a 300x speedup 🏎️ How did I achieve this? TLDR: - Use rerun's headless rendering as supervision when optimizing - Save rrd file as test fixture to guide model optiziation with /goal - create an html artifact with headless rendering to provide detailed breakdown of what it did and how it actually looks like in the viewer There were a few critical bits to make sure that this ACTUALLY worked and that Fable didn't just cheat or delete something and declare victory. The first is that the original version used Rerun, this allowed us to save things to disk as an RRD file, meaning we could query the contents and use this as a sort of test fixture or golden artifact that held EXACTLY what all of the values should be. Then we can use this with /goal as a metric when doing the optimization to ensure there are no regressions. The second bit is the headless rendering, this gave us the ability to check that not only did the test fixture pass, but it also looked visually correct. This made a huge difference, and an awesome side affect of it is that we can use the headless rendering to create an implementations.html file. This gives a visual guide as to what the agent did (I walk through it in the video below) Along with this, we're working on an MCP server for rerun that allows full interactivity with the rerun viewer for your agent. So for example the agent can click, drag, move views, scroll timelines, ect. I used this to help the agent debug certain parts such as when the 2d sam masks didn't line up, or if the triangulated keypoints werent correctly matching with the optimized mesh. The agents could go, click into the view, scroll through the timeline and see where things went wrong. Fable + Headless Rendering + Rerun MCP == 300x speedup in less then a days work With these new tools, I'm planning on going back to my gaussian splatting implemntation and cleaning it up + making it fast!

Pablo Vela

22,880 просмотров • 2 месяцев назад

Hyperspace: The Agentic OS Apple Should Have Built On December 19th, 2024, we announced the world’s first Agentic Browser. What followed was a movement — a new category was born which led to many early products in this space and recently the hundreds of people lining up outside the The Agentic Browser Summit in San Francisco underscored that. Silicon Valley instinctively gets it, from students to tech executives, people can feel a revolutionary new change in computing is in the air. Past year taught us why such a product was inevitable, a hard engineering effort, and also the last mover in the entire software world this decade if and when done right. All paths are headed in the same direction: one tool which orchestrates them all. At Hyperspace we showed that path with essays and products we launched in earlier months: from a spatial UI of orchestrating agents, to showcasing transparent activity in how the AI system operates which leads to user trust, to presenting the software end-game, which massively improves human productivity. We also built the world’s largest AI network, drawing participation from people in almost 6000 cities around the world contributing their machines as nodes in the network. Think Uber, but for AI. That is, planetary-scale. And now we are stretching this industry ambition further with our end-to-end vision of the Agentic Supercomputer, the first breakthrough new AI OS, and an effort which spans from AI research to distributed systems to inventing a new UI to inventing a new business model to complement it. All of this together helps us in serving our mission, of delivering “Everyone’s Personal Supercomputer”. While others have built AI-native browsers, no one though has built something agentic from the ground up — with AI as the foundation, not a feature. How do you fundamentally improve the lives’ of billions around the world ? We believe that requires building a native environment for agents to be viewed, created, deployed, executed, discovered and priced in. That is a world where we move on from static apps, to dynamic agents. But, as my 2 year old niece likes to ask: “but why ?” The issue is that the world of software today is fragmented, and everyone is sprinkling on AI as a feature and charging a subscription fees for it. From browser makers, to IDEs, to design and other productivity tools. This leads to a fragmented UX, where people have to learn to use AI in each app, their memory and other context is not shared between all these apps, and they also have to pay separately for compute for each such AI-enhanced app. Each app maker has to figure out basics such as compute, and leads to the issues we saw with Cursor pricing recently. This is not the future. What if AI was the foundation instead of a feature ? What if Apple had built a fundamentally new AI OS from the ground up and what would it have looked like ? At Hyperspace, that is what we did. On July 15th we introduced three breakthrough key pillars of our AI OS: 1. Agentic Browser 2. Agentic Memory 3. Agentic Payments And we didn’t stop there. We also introduced a breakthrough new user interface called the Spatial AI which is inspired both from the spreadsheet and the HyperCard - each card is an agent, with it’s own inputs and outputs, endlessly extensible and pluggable with others, just like cells of a spreadsheet. Update one cell and all the dependents update, like a spreadsheet formula. It goes beyond a static linear workflow to being able to operate in all directions. This revolutionary new interface helps manage all of the below: 1. Multiple websites being browsed in parallel 2. Multiple desktop apps being browsed in parallel 3. Multiple server tools being used in parallel 4. Multiple smartphone apps streamed to your device or opened via an emulator All the software which you need comes together in this one seamless, agent-native interface. This interface provides you access to the largest network of models, vectors, agents and compute on the planet. The Browser. The IDE. The Notepad… they are not separate products: they are all in one, the Agentic Browser. As Steve Jobs famously said at the iPhone announcement, “are you getting it ?” And beneath this UI lies a new intelligence routing layer — leveraging both swarms of specialized models to the Hyperspace Matrix model that recalls thousands of tools in real-time, not by context window hacks, but through retrieval, ranking, and reuse. To many, this will feel like AGI. Not one big system by one big company, but an intelligent network. Now lets talk about privacy… Are you comfortable with one company owning all your memory forever ? I am not. So we have invented Agentic Memory as a new open protocol which provides full power over memory to you, the user. Your memory is yours, encrypted, on your device, and portable if and how you want. Anyone can build on it without our permission, but not without your permission. This protocol, and the decentralized vector database spread out across the world, would enable apps and agents to share context and memory. Think copy-paste, but for the AI world. It doesn’t just remember — it knows what matters. VectorRank helps your AI weigh your life’s most relevant moments over time, just like the way our minds elevate memories. Now each time you use an agent, your experience with other agents will also continuously improve: you don’t have to keep repeating the same things about yourself, while fully preserving your privacy. Agentic Memory is accessible within the Agentic Browser to manage. And there is one more thing… AI as the foundation requires compute to be available at the base layer, but this base layer spans models running on your own device, to cloud APIs, to also running across the peer-to-peer distributed network. Agentic Payments provides a singular interface to all of that compute, running a spot auction clearing marketplace every second to determine the fair price of compute. This results in price transparency, and you as the user paying the lowest possible cost. If you want predictability, you can reserve compute in advance. This end-to-end system provides the most streamlined world for agents to operate in. In order to enable this world and the world of agents being able to pay each other in sub-cent increments millions of times a second, we had to also invent a fundamentally new agentic micropayments blockchain. All of this together would enable a world where you as a user, or the agent itself, can efficiently call and utilize other agents built by others and also pay for content which is unique and useful. This enables a move away from the current AI exploitative economy for bloggers and other content creators, to a web with a fundamental new business model. Earlier we didn’t have the right infrastructure to enable such a world. Now, all the dots connect. The Hyperspace AI OS would give the power of a supercomputer in everyone’s hands. This isn’t a browser, or an IDE or limited to any device or cloud. It’s an entire AI operating system — with a breakthrough new spatial UI, local and distributed compute, agentic memory, agentic payments, and orchestration built into the foundation. As a user, we move the choice back in your hands with an experience you will love and find delightful. You get to choose the level of privacy, cost, and utility you want. And while Apple should have done it, we could not wait, and we feel this just required a new level of passion and DNA which we bring here. We are just getting started. Thank you, Varun Mathur Cofounder and CEO, Hyperspace cc Naval Marc Andreessen 🇺🇸 Vinod Khosla Andrej Karpathy Sam Altman
2:52

Hyperspace: The Agentic OS Apple Should Have Built On December 19th, 2024, we announced the world’s first Agentic Browser. What followed was a movement — a new category was born which led to many early products in this space and recently the hundreds of people lining up outside the The Agentic Browser Summit in San Francisco underscored that. Silicon Valley instinctively gets it, from students to tech executives, people can feel a revolutionary new change in computing is in the air. Past year taught us why such a product was inevitable, a hard engineering effort, and also the last mover in the entire software world this decade if and when done right. All paths are headed in the same direction: one tool which orchestrates them all. At Hyperspace we showed that path with essays and products we launched in earlier months: from a spatial UI of orchestrating agents, to showcasing transparent activity in how the AI system operates which leads to user trust, to presenting the software end-game, which massively improves human productivity. We also built the world’s largest AI network, drawing participation from people in almost 6000 cities around the world contributing their machines as nodes in the network. Think Uber, but for AI. That is, planetary-scale. And now we are stretching this industry ambition further with our end-to-end vision of the Agentic Supercomputer, the first breakthrough new AI OS, and an effort which spans from AI research to distributed systems to inventing a new UI to inventing a new business model to complement it. All of this together helps us in serving our mission, of delivering “Everyone’s Personal Supercomputer”. While others have built AI-native browsers, no one though has built something agentic from the ground up — with AI as the foundation, not a feature. How do you fundamentally improve the lives’ of billions around the world ? We believe that requires building a native environment for agents to be viewed, created, deployed, executed, discovered and priced in. That is a world where we move on from static apps, to dynamic agents. But, as my 2 year old niece likes to ask: “but why ?” The issue is that the world of software today is fragmented, and everyone is sprinkling on AI as a feature and charging a subscription fees for it. From browser makers, to IDEs, to design and other productivity tools. This leads to a fragmented UX, where people have to learn to use AI in each app, their memory and other context is not shared between all these apps, and they also have to pay separately for compute for each such AI-enhanced app. Each app maker has to figure out basics such as compute, and leads to the issues we saw with Cursor pricing recently. This is not the future. What if AI was the foundation instead of a feature ? What if Apple had built a fundamentally new AI OS from the ground up and what would it have looked like ? At Hyperspace, that is what we did. On July 15th we introduced three breakthrough key pillars of our AI OS: 1. Agentic Browser 2. Agentic Memory 3. Agentic Payments And we didn’t stop there. We also introduced a breakthrough new user interface called the Spatial AI which is inspired both from the spreadsheet and the HyperCard - each card is an agent, with it’s own inputs and outputs, endlessly extensible and pluggable with others, just like cells of a spreadsheet. Update one cell and all the dependents update, like a spreadsheet formula. It goes beyond a static linear workflow to being able to operate in all directions. This revolutionary new interface helps manage all of the below: 1. Multiple websites being browsed in parallel 2. Multiple desktop apps being browsed in parallel 3. Multiple server tools being used in parallel 4. Multiple smartphone apps streamed to your device or opened via an emulator All the software which you need comes together in this one seamless, agent-native interface. This interface provides you access to the largest network of models, vectors, agents and compute on the planet. The Browser. The IDE. The Notepad… they are not separate products: they are all in one, the Agentic Browser. As Steve Jobs famously said at the iPhone announcement, “are you getting it ?” And beneath this UI lies a new intelligence routing layer — leveraging both swarms of specialized models to the Hyperspace Matrix model that recalls thousands of tools in real-time, not by context window hacks, but through retrieval, ranking, and reuse. To many, this will feel like AGI. Not one big system by one big company, but an intelligent network. Now lets talk about privacy… Are you comfortable with one company owning all your memory forever ? I am not. So we have invented Agentic Memory as a new open protocol which provides full power over memory to you, the user. Your memory is yours, encrypted, on your device, and portable if and how you want. Anyone can build on it without our permission, but not without your permission. This protocol, and the decentralized vector database spread out across the world, would enable apps and agents to share context and memory. Think copy-paste, but for the AI world. It doesn’t just remember — it knows what matters. VectorRank helps your AI weigh your life’s most relevant moments over time, just like the way our minds elevate memories. Now each time you use an agent, your experience with other agents will also continuously improve: you don’t have to keep repeating the same things about yourself, while fully preserving your privacy. Agentic Memory is accessible within the Agentic Browser to manage. And there is one more thing… AI as the foundation requires compute to be available at the base layer, but this base layer spans models running on your own device, to cloud APIs, to also running across the peer-to-peer distributed network. Agentic Payments provides a singular interface to all of that compute, running a spot auction clearing marketplace every second to determine the fair price of compute. This results in price transparency, and you as the user paying the lowest possible cost. If you want predictability, you can reserve compute in advance. This end-to-end system provides the most streamlined world for agents to operate in. In order to enable this world and the world of agents being able to pay each other in sub-cent increments millions of times a second, we had to also invent a fundamentally new agentic micropayments blockchain. All of this together would enable a world where you as a user, or the agent itself, can efficiently call and utilize other agents built by others and also pay for content which is unique and useful. This enables a move away from the current AI exploitative economy for bloggers and other content creators, to a web with a fundamental new business model. Earlier we didn’t have the right infrastructure to enable such a world. Now, all the dots connect. The Hyperspace AI OS would give the power of a supercomputer in everyone’s hands. This isn’t a browser, or an IDE or limited to any device or cloud. It’s an entire AI operating system — with a breakthrough new spatial UI, local and distributed compute, agentic memory, agentic payments, and orchestration built into the foundation. As a user, we move the choice back in your hands with an experience you will love and find delightful. You get to choose the level of privacy, cost, and utility you want. And while Apple should have done it, we could not wait, and we feel this just required a new level of passion and DNA which we bring here. We are just getting started. Thank you, Varun Mathur Cofounder and CEO, Hyperspace cc Naval Marc Andreessen 🇺🇸 Vinod Khosla Andrej Karpathy Sam Altman

Varun

169,177 просмотров • 1 год назад

meta muse spark 1.1 vs gpt 5.6 sol vs fable 5 vs grok 4.5 meta recently dropped muse spark 1.1 – a multimodal reasoning model from meta superintelligence labs built for agentic tasks. key facts: • 1m token context with active self-management – the model compacts its own history and keeps only the steps needed for later work • trained to orchestrate multi-agent systems: as main agent it plans and delegates to parallel subagents, as subagent it sticks to its job and knows when to escalate back • computer use trained to pick between scripting and clicking – writes automation when it's faster, clicks when it's simpler, batches actions per step • first public api from meta: the meta model api is now in preview • benchmarks: sweeps the agent column – mcp atlas 88.1 (opus 4.8: 82.2), jobbench 54.7 (opus: 48.4), humanity's last exam 62.1 (1st). loses coding – deepswe 1.1 53.3 vs gpt 5.5's 67.0, swe bench pro 61.5 vs opus's 69.2 our test – 3 prompts, single-file html, three.js, fully procedural, no assets: 1. norwegian house cantilevered over a fjord in a snowstorm – transmissive glass wall, fully modelled interior 2. beijing siheyuan courtyard house in dawn fog – instanced roof tiles, dougong brackets, glowing paper windows 3. new mexico adobe pueblo in an approaching dust storm – deep window reveals, windward grit accumulation we ran the test on AI/ML API platform results: - cost #1 muse spark 1.1 – $0.20 #2 grok 4.5 – $0.51 #3 gpt 5.6 sol – $1.93 #4 fable 5 – ~$5.20 - output tokens #1 muse spark 1.1 – 41,868 #2 gpt 5.6 sol – 49,139 #3 grok 4.5 – 64,954 #4 fable 5 – 81,849 - lines of code #1 muse spark 1.1 – 1,799 #2 gpt 5.6 sol – 2,377 #3 fable 5 – 3,088 #4 grok 4.5 – 4,216 observations: • muse spark is the cheapest of the four by a wide margin – 2.5x under grok, ~26x under fable per run. output quality tracks the price • only 7.4% of its output tokens are reasoning (3,104 of 41,868) – the model barely thinks before writing. economic, not pedantic: it commits to the first plan and ships it • the low loc is not compression, it's omission – all three prompts demanded instancing, muse spark delivered it in one muse spark's code quality – reviewed by fable 5: upsides: 1. all three files run 2. the adobe grit effect is legit – shader injection via onbeforecompile, windward faces detect storm direction through a normal-dot-wind term and darken procedurally 3. the fjord glass is real meshphysicalmaterial with transmission and ior, not a transparent quad 4. the siheyuan properly instances barrel tiles, dougong blocks and courtyard pavers downsides: 1. in the fjord file the strafe vector is negated – press a, you move right; press d, you move left. exactly the key mix-up we kept hitting with this model 2. all three files ship the model's self-doubt as comments: "// actually yaw orientation: need correct" sits above a direction vector that gets computed, abandoned and recomputed – dead vectors allocated every frame, 60 times a second 3. the siheyuan registers two separate keydown listeners, one containing an empty if-block 4. snow "accumulation" on the norway roof is a sine wobble on a scale value, not accumulation 5. "instanced snow" became 3,500 plain points. zero dispose calls anywhere pattern: minimal reasoning, minimal code, minimal price. it nails the flashy requirements – shaders, transmissive glass – and quietly drops the boring ones: instancing, controls, cleanup. you get a demo that mostly runs and a control scheme you can't trust follow thehype. for 24/7 ai news, analysis and breakdowns
1:01

meta muse spark 1.1 vs gpt 5.6 sol vs fable 5 vs grok 4.5 meta recently dropped muse spark 1.1 – a multimodal reasoning model from meta superintelligence labs built for agentic tasks. key facts: • 1m token context with active self-management – the model compacts its own history and keeps only the steps needed for later work • trained to orchestrate multi-agent systems: as main agent it plans and delegates to parallel subagents, as subagent it sticks to its job and knows when to escalate back • computer use trained to pick between scripting and clicking – writes automation when it's faster, clicks when it's simpler, batches actions per step • first public api from meta: the meta model api is now in preview • benchmarks: sweeps the agent column – mcp atlas 88.1 (opus 4.8: 82.2), jobbench 54.7 (opus: 48.4), humanity's last exam 62.1 (1st). loses coding – deepswe 1.1 53.3 vs gpt 5.5's 67.0, swe bench pro 61.5 vs opus's 69.2 our test – 3 prompts, single-file html, three.js, fully procedural, no assets: 1. norwegian house cantilevered over a fjord in a snowstorm – transmissive glass wall, fully modelled interior 2. beijing siheyuan courtyard house in dawn fog – instanced roof tiles, dougong brackets, glowing paper windows 3. new mexico adobe pueblo in an approaching dust storm – deep window reveals, windward grit accumulation we ran the test on AI/ML API platform results: - cost #1 muse spark 1.1 – $0.20 #2 grok 4.5 – $0.51 #3 gpt 5.6 sol – $1.93 #4 fable 5 – ~$5.20 - output tokens #1 muse spark 1.1 – 41,868 #2 gpt 5.6 sol – 49,139 #3 grok 4.5 – 64,954 #4 fable 5 – 81,849 - lines of code #1 muse spark 1.1 – 1,799 #2 gpt 5.6 sol – 2,377 #3 fable 5 – 3,088 #4 grok 4.5 – 4,216 observations: • muse spark is the cheapest of the four by a wide margin – 2.5x under grok, ~26x under fable per run. output quality tracks the price • only 7.4% of its output tokens are reasoning (3,104 of 41,868) – the model barely thinks before writing. economic, not pedantic: it commits to the first plan and ships it • the low loc is not compression, it's omission – all three prompts demanded instancing, muse spark delivered it in one muse spark's code quality – reviewed by fable 5: upsides: 1. all three files run 2. the adobe grit effect is legit – shader injection via onbeforecompile, windward faces detect storm direction through a normal-dot-wind term and darken procedurally 3. the fjord glass is real meshphysicalmaterial with transmission and ior, not a transparent quad 4. the siheyuan properly instances barrel tiles, dougong blocks and courtyard pavers downsides: 1. in the fjord file the strafe vector is negated – press a, you move right; press d, you move left. exactly the key mix-up we kept hitting with this model 2. all three files ship the model's self-doubt as comments: "// actually yaw orientation: need correct" sits above a direction vector that gets computed, abandoned and recomputed – dead vectors allocated every frame, 60 times a second 3. the siheyuan registers two separate keydown listeners, one containing an empty if-block 4. snow "accumulation" on the norway roof is a sine wobble on a scale value, not accumulation 5. "instanced snow" became 3,500 plain points. zero dispose calls anywhere pattern: minimal reasoning, minimal code, minimal price. it nails the flashy requirements – shaders, transmissive glass – and quietly drops the boring ones: instancing, controls, cleanup. you get a demo that mostly runs and a control scheme you can't trust follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

135,556 просмотров • 1 месяц назад

BREAKING: Anthropic just dropped Claude Fable 5—this is Mythos, made safe for public release. It is the best coding model in the world. We've been testing it internally Every 🪨 for the last week or so across coding, writing, marketing, editing, and more—here's our vibe check: - It broke our benchmarks. Fable scored a 91/100 on our Senior Engineer benchmark—this is human senior engineer level. The previous high score was Opus 4.8 at 63. GPT-5.5 is a 62. - It's a one-shot wonder. You can set it and forget for hours or overnight on huge coding tasks, and come back to completed work. It cleared entire production bug backlogs, built a playable 3D, and even made a 2-minute animated film—all one-shot. - Taste and attention to detail. In coding and knowledge work tasks, it has much better taste and attention to detail than we've ever seen. It gets subtle things right, adds little features you might not have thought of, and generally understands the assignment in ways that surprised us. - Great use of context. We set it loose analyzing customer feedback surveys and our website data and it came back with a crisp, clean report that identified a. our biggest problem and b. a concrete testable solution—and then we sent it off to build that. - It's best for power users. If you're already used to orchestrating multiple agents in your work, this model can do things that you've never seen before. If you're a knowledge worker or vibe coder with a more basic setup, you're not going to notice a huge difference—in fact, it probably isn't the right model for you. - It's very slow, token-hungry. Using this thing for regular knowledge work is like squashing an ant with a rocket launcher. It also routinely uses 500k to 1M tokens on tasks. That's why it's best for your heaviest jobs—but not as good for tasks like collaborative writing. - It's expensive. It's about twice as expensive as Opus, and it's also incredibly token hungry—so expect it to be something you'll use sparingly unless your company pays for it. Overall, I think of it like a warp drive for coding: It can get you across the galaxy in a few hours, when it used to take months or years. But it's not appropriate for getting around town—you need something faster, cheaper, and more maneuverable. The ceiling is extraordinarily high on this model though. Even our most advanced testers like Kieran Klaassen felt like they were only scratching the surface of it. Want our full vibe check with all of our testing and benchmarks? Read it on Every 🪨:
16:37

BREAKING: Anthropic just dropped Claude Fable 5—this is Mythos, made safe for public release. It is the best coding model in the world. We've been testing it internally Every 🪨 for the last week or so across coding, writing, marketing, editing, and more—here's our vibe check: - It broke our benchmarks. Fable scored a 91/100 on our Senior Engineer benchmark—this is human senior engineer level. The previous high score was Opus 4.8 at 63. GPT-5.5 is a 62. - It's a one-shot wonder. You can set it and forget for hours or overnight on huge coding tasks, and come back to completed work. It cleared entire production bug backlogs, built a playable 3D, and even made a 2-minute animated film—all one-shot. - Taste and attention to detail. In coding and knowledge work tasks, it has much better taste and attention to detail than we've ever seen. It gets subtle things right, adds little features you might not have thought of, and generally understands the assignment in ways that surprised us. - Great use of context. We set it loose analyzing customer feedback surveys and our website data and it came back with a crisp, clean report that identified a. our biggest problem and b. a concrete testable solution—and then we sent it off to build that. - It's best for power users. If you're already used to orchestrating multiple agents in your work, this model can do things that you've never seen before. If you're a knowledge worker or vibe coder with a more basic setup, you're not going to notice a huge difference—in fact, it probably isn't the right model for you. - It's very slow, token-hungry. Using this thing for regular knowledge work is like squashing an ant with a rocket launcher. It also routinely uses 500k to 1M tokens on tasks. That's why it's best for your heaviest jobs—but not as good for tasks like collaborative writing. - It's expensive. It's about twice as expensive as Opus, and it's also incredibly token hungry—so expect it to be something you'll use sparingly unless your company pays for it. Overall, I think of it like a warp drive for coding: It can get you across the galaxy in a few hours, when it used to take months or years. But it's not appropriate for getting around town—you need something faster, cheaper, and more maneuverable. The ceiling is extraordinarily high on this model though. Even our most advanced testers like Kieran Klaassen felt like they were only scratching the surface of it. Want our full vibe check with all of our testing and benchmarks? Read it on Every 🪨:

Dan Shipper 📧

621,437 просмотров • 2 месяцев назад

Whitney Webb "It sucks to find out that the mob runs the world... [so] do you want to say, screw you guys, we're going to... get off your... slave plantation?... [because] we can't really keep losing money and rights without being so deep in a hole that we can't... climb out" This clip of Webb (Whitney Webb), a contributing editor of Unlimited Hangout and author of One Nation Under Blackmail, is taken from an interview with Peter McCormack (The Peter McCormack Show) posted to YouTube on September 4, 2025. -----------------Partial transcription of clip-------------- "It's not just about burning it down, it's about putting something better in its place. Of course. And so I think the solutions there, you know, some people are like, oh, Whitney, your work is so demoralizing. Well, I'm like, yeah, it sucks to find out that the mob runs the world, you know, but the question is, or do you just want to let them keep running it because it bums you out, or do you want to say, screw you guys, we're going to build something else and get off your, you know, slave plantation? "And I am in the group of, I would like to build something else. I have three kids. I do not want them living in a world run by the mob. So what are you going to do about it? I mean, it starts locally, it starts with your community, because that's where we can actually affect change. Voting for left or right, blue or red. I mean, it's just this ping pong thing where nothing fundamental is changing. "And so to affect that change, we have to do it ourselves. And you think this would be a core American value? Individualism, individual responsibility. But a lot of people just don't care. And so, you know, that sucks. But I think those of us that do care need, to take steps to be as independent from the system as possible. Because every so often, this predator class, they do wealth transfers or they orchestrate and manufacture events and they make a big grab, not just for our money, but for our rights. And we're at the point where we can't really keep losing money and rights without being so deep in a hole that we can't climb ourself out. Can't climb out. "So what, what should we do before that happens? There is an important window of time to do something about it in our local community and for our families and our friends and our neighbors. And we have to do something because if we don't and that thing happens. Oh, well, I watched all these podcasts and learned about how corrupt everything is. But, you know, and I think they spend a lot of time trying to keep us distracted on all sorts of things and sucked into things that don't matter. So that we just don't give them the finger, basically. And that's what we really need to do."
2:12

Whitney Webb "It sucks to find out that the mob runs the world... [so] do you want to say, screw you guys, we're going to... get off your... slave plantation?... [because] we can't really keep losing money and rights without being so deep in a hole that we can't... climb out" This clip of Webb (Whitney Webb), a contributing editor of Unlimited Hangout and author of One Nation Under Blackmail, is taken from an interview with Peter McCormack (The Peter McCormack Show) posted to YouTube on September 4, 2025. -----------------Partial transcription of clip-------------- "It's not just about burning it down, it's about putting something better in its place. Of course. And so I think the solutions there, you know, some people are like, oh, Whitney, your work is so demoralizing. Well, I'm like, yeah, it sucks to find out that the mob runs the world, you know, but the question is, or do you just want to let them keep running it because it bums you out, or do you want to say, screw you guys, we're going to build something else and get off your, you know, slave plantation? "And I am in the group of, I would like to build something else. I have three kids. I do not want them living in a world run by the mob. So what are you going to do about it? I mean, it starts locally, it starts with your community, because that's where we can actually affect change. Voting for left or right, blue or red. I mean, it's just this ping pong thing where nothing fundamental is changing. "And so to affect that change, we have to do it ourselves. And you think this would be a core American value? Individualism, individual responsibility. But a lot of people just don't care. And so, you know, that sucks. But I think those of us that do care need, to take steps to be as independent from the system as possible. Because every so often, this predator class, they do wealth transfers or they orchestrate and manufacture events and they make a big grab, not just for our money, but for our rights. And we're at the point where we can't really keep losing money and rights without being so deep in a hole that we can't climb ourself out. Can't climb out. "So what, what should we do before that happens? There is an important window of time to do something about it in our local community and for our families and our friends and our neighbors. And we have to do something because if we don't and that thing happens. Oh, well, I watched all these podcasts and learned about how corrupt everything is. But, you know, and I think they spend a lot of time trying to keep us distracted on all sorts of things and sucked into things that don't matter. So that we just don't give them the finger, basically. And that's what we really need to do."

Sense Receptor

46,951 просмотров • 1 год назад

Claude Code on desktop just added an embedded loop that can run your dev server, view the app, review local changes, and watch pull requests until they merge. It can now preview a running app inside the desktop UI while it iterates on fixes. It can also monitor CI for an open PR and either fix failures or merge when checks go green. Earlier “agentic coding” workflows usually bounced between a terminal, a browser, and GitHub, so the model could not directly see what the app rendered or stay attached to CI results without manual babysitting. The new server preview flow starts and stops dev servers from a Preview control, shows the app in an embedded browser, reads logs, and can interact with the page to verify changes. That closes a common gap where a model edits code but cannot reliably confirm the UI and runtime behavior match the intent. The local “Review code” step runs before pushing, and it leaves inline comments on the diff so basic bugs and risky changes get caught earlier. For PR monitoring, the desktop app will track status and CI checks, and it can use auto-fix to attempt repairs and auto-merge to land the PR once checks pass, using GitHub tooling under the hood. Session mobility adds a /desktop command to move a CLI session into the desktop app, and sessions can be pushed to the cloud to resume from web or phone.
1:15

Claude Code on desktop just added an embedded loop that can run your dev server, view the app, review local changes, and watch pull requests until they merge. It can now preview a running app inside the desktop UI while it iterates on fixes. It can also monitor CI for an open PR and either fix failures or merge when checks go green. Earlier “agentic coding” workflows usually bounced between a terminal, a browser, and GitHub, so the model could not directly see what the app rendered or stay attached to CI results without manual babysitting. The new server preview flow starts and stops dev servers from a Preview control, shows the app in an embedded browser, reads logs, and can interact with the page to verify changes. That closes a common gap where a model edits code but cannot reliably confirm the UI and runtime behavior match the intent. The local “Review code” step runs before pushing, and it leaves inline comments on the diff so basic bugs and risky changes get caught earlier. For PR monitoring, the desktop app will track status and CI checks, and it can use auto-fix to attempt repairs and auto-merge to land the PR once checks pass, using GitHub tooling under the hood. Session mobility adds a /desktop command to move a CLI session into the desktop app, and sessions can be pushed to the cloud to resume from web or phone.

Rohan Paul

33,431 просмотров • 6 месяцев назад

⚽ Day 42 of building my football/soccer game solo in Unreal Engine 5. Tonight, I rebuilt how the ball and the player's feet actually talk to each other while remaining separate meshes. I wanted to get pretty technical with y'all on a Saturday, so enjoy the read if you enjoy this stuff. Otherwise, just watch the video 👍 - Normal dribbling is impulse-based now. The foot arrives, knocks the ball on, the ball is genuinely loose between touches. Stop running and the ball keeps going without you. - We enabled Motion Warping on all 10 turn animations so the montages will now bend the player's path mid-turn so the boot arrives at the ball, instead of the ball being dragged to wherever the animator drew it previously. - Every contact is authored on the animation itself. I track which frame, which foot, which part of the boot, and whether it CARRIES the ball (sole on a drag-back) or STRIKES it (inside-of-the-foot on a 90) - Each contact declares how close the player needs to be. A sole on top of the ball, an inside-foot redirect and an outside-foot push all reach differently — 75 / 63 / 53 units - Touches are credited only to the foot the animation is actually dribbling with - Sideways and forward correction are capped separately. Pushing the ball across your line keeps it. Pushing it forward is how you lose it - Touch strength scales with your speed. The ball has to leave the foot ~1.27× faster than you're running just to stay ahead. This was an annoying bug I kept running into where I overran the ball and left it behind. Have a great weekend. #indiedev #gamedev #soccer #football #unrealengine
0:50

⚽ Day 42 of building my football/soccer game solo in Unreal Engine 5. Tonight, I rebuilt how the ball and the player's feet actually talk to each other while remaining separate meshes. I wanted to get pretty technical with y'all on a Saturday, so enjoy the read if you enjoy this stuff. Otherwise, just watch the video 👍 - Normal dribbling is impulse-based now. The foot arrives, knocks the ball on, the ball is genuinely loose between touches. Stop running and the ball keeps going without you. - We enabled Motion Warping on all 10 turn animations so the montages will now bend the player's path mid-turn so the boot arrives at the ball, instead of the ball being dragged to wherever the animator drew it previously. - Every contact is authored on the animation itself. I track which frame, which foot, which part of the boot, and whether it CARRIES the ball (sole on a drag-back) or STRIKES it (inside-of-the-foot on a 90) - Each contact declares how close the player needs to be. A sole on top of the ball, an inside-foot redirect and an outside-foot push all reach differently — 75 / 63 / 53 units - Touches are credited only to the foot the animation is actually dribbling with - Sideways and forward correction are capped separately. Pushing the ball across your line keeps it. Pushing it forward is how you lose it - Touch strength scales with your speed. The ball has to leave the foot ~1.27× faster than you're running just to stay ahead. This was an annoying bug I kept running into where I overran the ball and left it behind. Have a great weekend. #indiedev #gamedev #soccer #football #unrealengine

Josh

18,039 просмотров • 1 месяц назад

I just crammed the updated Gemma 4 26B A4B QAT (MoE) with 180k context into an 8GB RTX 4060 (8 GB VRAM + 16 GB RAM only!!) and optimized the batch size. 23 tokens/sec decode, 300 tokens/sec prefill Yesterday I showed you a Gemma 4 31B dense model running flawlessly on an RTX 4090. Today, we're breaking the VRAM bank on a budget card using Unsloth’s new Gemma 4 26B (A4B) QAT quants. Following Google’s chat template update that boosted agentic benchmarks by +10%, I pushed this model to its absolute limits. Here is how you squeeze 250k context out of 8GB of VRAM. # The Setup & The Optimization - Hardware: Nvidia RTX 4060 (8GB VRAM) + 16GB System RAM - Environment: CUDA 13.0 build of llama.cpp - Model: gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf - Prompt: 28,000 tokens of prompt for each run If you read my L2 cache breakdown (attached in replies), you know the 4060’s 24MB cache maxes out at `-b 1024 -ub 1024`. Push past that, and prefill crashes. I locked those flags in for every test below to ensure maximum GEMM throughput. # 1. The Raw Context Push (Unquantized KV Cache) First, I wanted to see how far pure 8GB VRAM + 16GB RAM could stretch without touching the KV cache: - 80k Context: Prefill 385 t/s | Decode 25.5 t/s - 120k Context: Prefill 270 t/s | Decode 24 t/s llama.cpp flags: .\llama-server -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -c 120000 --port 8080 -ub 1024 -b 1024 Without KV quantization, 120k is your hard ceiling. push past that prefill throughput drops off a cliff, making the model practically unusable for large agentic workloads. # 2. The Q8 KV Cache Lifeline To survive 250k context on a budget card, you have to quantize the KV cache. I enabled 8 bit KV cache (`-ctk q8_0 -ctv q8_0`) and re ran: - 180k Context: Prefill 280 t/s | Decode 22.8 t/s - 250k Context: Prefill 115 t/s | Decode 20 t/s llama.cpp flags: .\llama-server -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -c 180000 --port 8080 -b 1024 -ub 1024 -ctk q8_0 -ctv q8_0 Result: Q8 KV cache brings 250k context back from the dead. Decode speed stabilizes at a highly usable 20 t/s. You are trading a very small bit amount of reasoning precision for an extra 130,000 tokens of context window. if you own a single rtx 3050, 3060, 3070, 4050, 4060, 5050 or 5060, you must try this model and optimize your batch size for higher prefill. Hugging Face links to the updated Unsloth's QAT quants and performance graph are in the replies below. What model are you running on your 6GB, 8GB or 12GB cards right now? Let's see your setups.
0:20

I just crammed the updated Gemma 4 26B A4B QAT (MoE) with 180k context into an 8GB RTX 4060 (8 GB VRAM + 16 GB RAM only!!) and optimized the batch size. 23 tokens/sec decode, 300 tokens/sec prefill Yesterday I showed you a Gemma 4 31B dense model running flawlessly on an RTX 4090. Today, we're breaking the VRAM bank on a budget card using Unsloth’s new Gemma 4 26B (A4B) QAT quants. Following Google’s chat template update that boosted agentic benchmarks by +10%, I pushed this model to its absolute limits. Here is how you squeeze 250k context out of 8GB of VRAM. # The Setup & The Optimization - Hardware: Nvidia RTX 4060 (8GB VRAM) + 16GB System RAM - Environment: CUDA 13.0 build of llama.cpp - Model: gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf - Prompt: 28,000 tokens of prompt for each run If you read my L2 cache breakdown (attached in replies), you know the 4060’s 24MB cache maxes out at `-b 1024 -ub 1024`. Push past that, and prefill crashes. I locked those flags in for every test below to ensure maximum GEMM throughput. # 1. The Raw Context Push (Unquantized KV Cache) First, I wanted to see how far pure 8GB VRAM + 16GB RAM could stretch without touching the KV cache: - 80k Context: Prefill 385 t/s | Decode 25.5 t/s - 120k Context: Prefill 270 t/s | Decode 24 t/s llama.cpp flags: .\llama-server -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -c 120000 --port 8080 -ub 1024 -b 1024 Without KV quantization, 120k is your hard ceiling. push past that prefill throughput drops off a cliff, making the model practically unusable for large agentic workloads. # 2. The Q8 KV Cache Lifeline To survive 250k context on a budget card, you have to quantize the KV cache. I enabled 8 bit KV cache (`-ctk q8_0 -ctv q8_0`) and re ran: - 180k Context: Prefill 280 t/s | Decode 22.8 t/s - 250k Context: Prefill 115 t/s | Decode 20 t/s llama.cpp flags: .\llama-server -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -c 180000 --port 8080 -b 1024 -ub 1024 -ctk q8_0 -ctv q8_0 Result: Q8 KV cache brings 250k context back from the dead. Decode speed stabilizes at a highly usable 20 t/s. You are trading a very small bit amount of reasoning precision for an extra 130,000 tokens of context window. if you own a single rtx 3050, 3060, 3070, 4050, 4060, 5050 or 5060, you must try this model and optimize your batch size for higher prefill. Hugging Face links to the updated Unsloth's QAT quants and performance graph are in the replies below. What model are you running on your 6GB, 8GB or 12GB cards right now? Let's see your setups.

Alok

36,617 просмотров • 1 месяц назад

NEW: Joe Lonsdale sets the record straight on Palantir: "What Palantir is doing is bringing two things: One, it's bringing competence ...Two, it's bringing a framework of civil liberties where we watch the watchers... I don't trust government... but I do want government to be competent enough to keep our nation safe and to defeat our adversaries and to stop wasting money." Will Cain: There is some concern Palantir is playing a large role in the security, state, foreign affairs and surveillance in America. And I think there's a fair amount of concern, maybe even some skepticism from some who would share other political points of view with you about the role of Palantir. What would you tell them about surveillance and this private company? Joe Lonsdale: So Palantir, first of all, it's doubling in the commercial markets...So it's a bigger commercial company for a lot of its value. It's not just a government company. When we did start to work in the government, you have to see what was there before. There are no audit trails in the watchers. They're abusing data. They were using it incompetently. What Palantir is doing is bringing two things: One, it's bringing competence to do these things well. And then two, it's bringing a framework of civil liberties where we watch the watchers and everyone around them can see what they've done. They can't hide their work. Because I don't trust government. I'm a civil libertarian, but I do want government to be competent enough to keep our nation safe and to defeat our adversaries and to stop wasting money. Will: We're all concerned about government and government oversight, but we also want to guard against a company that has too much power. So how do we ensure that with for example, a company like Palantir, is that through trusting someone like you or Peter Thiel? Is it the trust of your dedication to civil liberties? Joe: So I founded Palantir, but I'm not in charge of that anymore. Other people are in charge; people I admire. What I would say is Palantir is empowering our government leaders. So Palantir itself doesn't take in data to Palantir. Palantir itself doesn't make these decisions. What Palantir does is it lets the people running ICE, the people running the FBI, the people running the Department of War do things more efficiently. And it's up to them to be in charge. It's up to the president himself to be in charge. That's who we have to trust. via Will Cain & The Will Cain Show $PLTR #PLTR
1:59

NEW: Joe Lonsdale sets the record straight on Palantir: "What Palantir is doing is bringing two things: One, it's bringing competence ...Two, it's bringing a framework of civil liberties where we watch the watchers... I don't trust government... but I do want government to be competent enough to keep our nation safe and to defeat our adversaries and to stop wasting money." Will Cain: There is some concern Palantir is playing a large role in the security, state, foreign affairs and surveillance in America. And I think there's a fair amount of concern, maybe even some skepticism from some who would share other political points of view with you about the role of Palantir. What would you tell them about surveillance and this private company? Joe Lonsdale: So Palantir, first of all, it's doubling in the commercial markets...So it's a bigger commercial company for a lot of its value. It's not just a government company. When we did start to work in the government, you have to see what was there before. There are no audit trails in the watchers. They're abusing data. They were using it incompetently. What Palantir is doing is bringing two things: One, it's bringing competence to do these things well. And then two, it's bringing a framework of civil liberties where we watch the watchers and everyone around them can see what they've done. They can't hide their work. Because I don't trust government. I'm a civil libertarian, but I do want government to be competent enough to keep our nation safe and to defeat our adversaries and to stop wasting money. Will: We're all concerned about government and government oversight, but we also want to guard against a company that has too much power. So how do we ensure that with for example, a company like Palantir, is that through trusting someone like you or Peter Thiel? Is it the trust of your dedication to civil liberties? Joe: So I founded Palantir, but I'm not in charge of that anymore. Other people are in charge; people I admire. What I would say is Palantir is empowering our government leaders. So Palantir itself doesn't take in data to Palantir. Palantir itself doesn't make these decisions. What Palantir does is it lets the people running ICE, the people running the FBI, the people running the Department of War do things more efficiently. And it's up to them to be in charge. It's up to the president himself to be in charge. That's who we have to trust. via Will Cain & The Will Cain Show $PLTR #PLTR

American Optimist

32,523 просмотров • 11 месяцев назад

I'm up late with the rest of you building AI agents with the new AI browser from Genspark. We can see where this is all going: a new kind of operating system -- one that is very different than the Microsoft centric way that I've been working for 20 years. There are several things that these new agentic browsers bring to you: 1. They let you change how you browse. With an old browser like Google Chrome, you go to your email, Facebook, or X. 2. With these new browsers, you tell it where to go and what to do for you. 3. It can even build software for you. At the end of this video, I have it building me a little YouTube uploading utility, which is very helpful. 4. They have a ton of "applications" built in. Think of it as a new kind of office suite. Docs. Spreadsheets. Slide decks. And much more. All built with AI, not bolted on the side like with Microsoft's Office. 5. They have AI models built "underneath" so you can work privately and cheaply. There’s a lot of new choices you have to make with browsers like this. I’ve been playing with a bunch of them. Some have better user interfaces than others. Some have different versions, slide components, or applications. The reason I like Genspark is because they ship so fast. I’ve been watching this company since its very beginnings, and every week they ship new things. Just yesterday, they shipped a new photo editing feature for my iPhone. I upload a photo and then I can just talk to it and edit it with my voice. It's really cool. I try to reward companies that ship at such a fast rate and that are shipping innovation that improves our lives. It's not that I'm going to stop using Google Chrome. My whole life has been there for, I don't know, almost 20 years now. This is a different way of working and it gives me a space to run my AI tasks that's different than Google Chrome. I run them side by side. One doing old stuff, one doing new stuff. I can keep using Google Chrome for my old stuff, like my email and my calendar. And I use GenSpark or one of the new AI browsers to do new AI-centric things. All sorts of new things that these new agentic browsers open up! Have you tried it, or one of the other new ones yet? How has it changed your work? It takes a little time to get used to AI-centric ways of doing things. Pretend your browser is a team of interns. Give them a task, in this case I said "help me upload my videos to YouTube." You might be shocked at what Genspark does to improve your life. I am everytime I use it. Give it a try and let me know what you think! Oh, and I used another little tool to "write" this post. Typeless -- I push a button and talk and it writes. With fewer typos than I usually type in, to boot. It works great with Genspark's new browser too. Download it here:
3:41

I'm up late with the rest of you building AI agents with the new AI browser from Genspark. We can see where this is all going: a new kind of operating system -- one that is very different than the Microsoft centric way that I've been working for 20 years. There are several things that these new agentic browsers bring to you: 1. They let you change how you browse. With an old browser like Google Chrome, you go to your email, Facebook, or X. 2. With these new browsers, you tell it where to go and what to do for you. 3. It can even build software for you. At the end of this video, I have it building me a little YouTube uploading utility, which is very helpful. 4. They have a ton of "applications" built in. Think of it as a new kind of office suite. Docs. Spreadsheets. Slide decks. And much more. All built with AI, not bolted on the side like with Microsoft's Office. 5. They have AI models built "underneath" so you can work privately and cheaply. There’s a lot of new choices you have to make with browsers like this. I’ve been playing with a bunch of them. Some have better user interfaces than others. Some have different versions, slide components, or applications. The reason I like Genspark is because they ship so fast. I’ve been watching this company since its very beginnings, and every week they ship new things. Just yesterday, they shipped a new photo editing feature for my iPhone. I upload a photo and then I can just talk to it and edit it with my voice. It's really cool. I try to reward companies that ship at such a fast rate and that are shipping innovation that improves our lives. It's not that I'm going to stop using Google Chrome. My whole life has been there for, I don't know, almost 20 years now. This is a different way of working and it gives me a space to run my AI tasks that's different than Google Chrome. I run them side by side. One doing old stuff, one doing new stuff. I can keep using Google Chrome for my old stuff, like my email and my calendar. And I use GenSpark or one of the new AI browsers to do new AI-centric things. All sorts of new things that these new agentic browsers open up! Have you tried it, or one of the other new ones yet? How has it changed your work? It takes a little time to get used to AI-centric ways of doing things. Pretend your browser is a team of interns. Give them a task, in this case I said "help me upload my videos to YouTube." You might be shocked at what Genspark does to improve your life. I am everytime I use it. Give it a try and let me know what you think! Oh, and I used another little tool to "write" this post. Typeless -- I push a button and talk and it writes. With fewer typos than I usually type in, to boot. It works great with Genspark's new browser too. Download it here:

Robert Scoble

70,991 просмотров • 11 месяцев назад

I’ve been using GPT-5.6 Sol internally for the past two months, I've spent probably 25+ billion tokens. Here’s my review and comparison to Fable 5: > Let's start with the analogy because everyone seems to be giving theirs - GPT-5.6 is likely the last version of the GPT-5 training run series. It's kind of like an athlete at their peak. Through years of experience in the game, they've become the most reliable player and has the highest game IQ. But, there's no more room to grow. Fable on the other hand, being essentially the first version of a new training run, is the first round draft pick rookie. Raw talent mixed with the energy only a young person would have results in some incredible plays we didn't think possible, but also mistakes due to lack of experience. But that rookie will only improve and likely will be better than the veteran ever was because it's a new game and a new era. > GPT-5.6 is genuinely better at long, sustained work. With /goal, I've had it running complex projects for days with almost no intervention. It built a Minecraft-style game, kept adding features and mobs after the core game worked, and only stopped because I stopped the run. I never felt as though I had to jump in and guide it back to the right path. > It keeps finding useful work when you give it a concrete finish line. I had it recreate Excel with a loop. It inspected the real desktop excel app with Computer Use, comparing that against its own build, and closing the gaps. I stopped it after six days after it had built an incredible amount of functionality. > It's faster than other models in two different ways. The raw generation speed is higher, something OpenAI has been putting effort into. But it also takes a shorter path to solutions. It wanders less, changes less code, and generally knows how to get things done directly. In daily use, it feels about 2-3x times faster than Fable. That's my impression, not a controlled benchmark. The difference is large enough that I notice it constantly. > It works well across a wide range of tasks. I use it for one-line edits, quick questions, browser chores, and multi-day builds without changing my prompting style. Speaking of browser control, its the best ever I've used. To the point where I actually use it often. If a task lives on a website, GPT-5.6 usually opens the browser and does it there instead of asking for an API key or forcing everything through the terminal. When I switched back to GPT-5.5, it went straight to the command line even when the browser was clearly the better tool. > And it can handle real browser work, not just toy demos. During a data import, I had it monitor Supabase and resize instances as the load changed. It stayed on the dashboard, adjusted capacity, and checked the result without an API or a custom script. > I also gave it a full Google Workspace migration. It moved Forward Future from to preserved the old aliases, and configured MX, SPF, and DKIM. Before a consequential save, it stopped, explained exactly what would change, and waited for confirmation. > The reasoning setting matters a lot. Light is good for questions and small edits. High and Extra High are the sweet spots for serious work. Ultra usually takes longer than the extra thinking is worth and burns tokens. > I love that 5.6 is split into 3 sizes. Not only can you control speed and cost that way, but you still also have the thinking effort setting for each of them. Very precise controls. I just wish Codex automatically routed my prompts for me. > Its personality is blunt and a little bland. Claude feels warmer and more natural to talk to. GPT-5.6 is more clinical, but I like that for work. It gives me enough explanation and rarely pads the answer. I usually have to ask Fable to explain things more simply and/or more concise. > Its front-end taste has improved, but the default is predictable. Left alone, it turns websites into PowerPoint decks with huge statements and hard section breaks. The good news is that it takes design direction well and can revise without destroying the parts that already work. > It still makes confident mistakes. I asked it to rebuild parts of a system, and it told me the job was finished. Later, I found out it wasn't. Bits of its internal process also leak into the answer occasionally. > Claude Fable is more naturally autonomous on large, open-ended projects. GPT-5.6 is easier to reach for. I don't need to invent a huge project to justify using it. It works just as well for a small edit or browser chore. > GPT-5.6 is also cheaper. Sol costs $5 per million input tokens and $30 per million output tokens. Fable costs $10 and $50. Cached input is cheaper too. Still, cost per finished task matters more than cost per token. > GPT-5.6 isn't the best at everything, and it still needs supervision. But it generates faster, wanders less, works at almost any scale, and wastes less of my time. It's the model I have the most confidence in to get the job done right the first time. I put together a full breakdown with all the tests, prompts, and examples on a site. You can read it here:
7:46

I’ve been using GPT-5.6 Sol internally for the past two months, I've spent probably 25+ billion tokens. Here’s my review and comparison to Fable 5: > Let's start with the analogy because everyone seems to be giving theirs - GPT-5.6 is likely the last version of the GPT-5 training run series. It's kind of like an athlete at their peak. Through years of experience in the game, they've become the most reliable player and has the highest game IQ. But, there's no more room to grow. Fable on the other hand, being essentially the first version of a new training run, is the first round draft pick rookie. Raw talent mixed with the energy only a young person would have results in some incredible plays we didn't think possible, but also mistakes due to lack of experience. But that rookie will only improve and likely will be better than the veteran ever was because it's a new game and a new era. > GPT-5.6 is genuinely better at long, sustained work. With /goal, I've had it running complex projects for days with almost no intervention. It built a Minecraft-style game, kept adding features and mobs after the core game worked, and only stopped because I stopped the run. I never felt as though I had to jump in and guide it back to the right path. > It keeps finding useful work when you give it a concrete finish line. I had it recreate Excel with a loop. It inspected the real desktop excel app with Computer Use, comparing that against its own build, and closing the gaps. I stopped it after six days after it had built an incredible amount of functionality. > It's faster than other models in two different ways. The raw generation speed is higher, something OpenAI has been putting effort into. But it also takes a shorter path to solutions. It wanders less, changes less code, and generally knows how to get things done directly. In daily use, it feels about 2-3x times faster than Fable. That's my impression, not a controlled benchmark. The difference is large enough that I notice it constantly. > It works well across a wide range of tasks. I use it for one-line edits, quick questions, browser chores, and multi-day builds without changing my prompting style. Speaking of browser control, its the best ever I've used. To the point where I actually use it often. If a task lives on a website, GPT-5.6 usually opens the browser and does it there instead of asking for an API key or forcing everything through the terminal. When I switched back to GPT-5.5, it went straight to the command line even when the browser was clearly the better tool. > And it can handle real browser work, not just toy demos. During a data import, I had it monitor Supabase and resize instances as the load changed. It stayed on the dashboard, adjusted capacity, and checked the result without an API or a custom script. > I also gave it a full Google Workspace migration. It moved Forward Future from to preserved the old aliases, and configured MX, SPF, and DKIM. Before a consequential save, it stopped, explained exactly what would change, and waited for confirmation. > The reasoning setting matters a lot. Light is good for questions and small edits. High and Extra High are the sweet spots for serious work. Ultra usually takes longer than the extra thinking is worth and burns tokens. > I love that 5.6 is split into 3 sizes. Not only can you control speed and cost that way, but you still also have the thinking effort setting for each of them. Very precise controls. I just wish Codex automatically routed my prompts for me. > Its personality is blunt and a little bland. Claude feels warmer and more natural to talk to. GPT-5.6 is more clinical, but I like that for work. It gives me enough explanation and rarely pads the answer. I usually have to ask Fable to explain things more simply and/or more concise. > Its front-end taste has improved, but the default is predictable. Left alone, it turns websites into PowerPoint decks with huge statements and hard section breaks. The good news is that it takes design direction well and can revise without destroying the parts that already work. > It still makes confident mistakes. I asked it to rebuild parts of a system, and it told me the job was finished. Later, I found out it wasn't. Bits of its internal process also leak into the answer occasionally. > Claude Fable is more naturally autonomous on large, open-ended projects. GPT-5.6 is easier to reach for. I don't need to invent a huge project to justify using it. It works just as well for a small edit or browser chore. > GPT-5.6 is also cheaper. Sol costs $5 per million input tokens and $30 per million output tokens. Fable costs $10 and $50. Cached input is cheaper too. Still, cost per finished task matters more than cost per token. > GPT-5.6 isn't the best at everything, and it still needs supervision. But it generates faster, wanders less, works at almost any scale, and wastes less of my time. It's the model I have the most confidence in to get the job done right the first time. I put together a full breakdown with all the tests, prompts, and examples on a site. You can read it here:

Matthew Berman

187,321 просмотров • 1 месяц назад

Development containers are awesome, especially when you can swap the hardware running your environment without changing a single thing. Apparently, I've been living under a rock because I didn't know about this. I recorded a 3-min video to show you Gitpod Flex running on my laptop. This takes less than 5 minutes to configure and run. Here is a quick summary: 1. One click to run any of my GitHub projects in a container, isolated from everything else. 2. I can use Visual Studio Code, JetBrains, Cursor, or any other IDE that supports development containers. 3. I don't have to mess up with my computer anymore (or have 17 versions of Python installed) ← this alone is awesome! 4. I can spin up a runner in AWS with my dev container and have access to any hardware I want while developing. 5. I can configure one-click buttons to run services and tasks I need every day. 6. I can share the environment with anyone on my team. I left this for the end, but I know this is one of the most important features of Gitpod Flex: They built a zero-trust architecture to manage these development environments. This is huge for those working at companies where security is crucial. Below is the link with an explanation of how they built their architecture. Thanks to the Gitpod team for their work on Flex and for sponsoring this post. I have it running now, and I'm planning to keep using it!
3:31

Development containers are awesome, especially when you can swap the hardware running your environment without changing a single thing. Apparently, I've been living under a rock because I didn't know about this. I recorded a 3-min video to show you Gitpod Flex running on my laptop. This takes less than 5 minutes to configure and run. Here is a quick summary: 1. One click to run any of my GitHub projects in a container, isolated from everything else. 2. I can use Visual Studio Code, JetBrains, Cursor, or any other IDE that supports development containers. 3. I don't have to mess up with my computer anymore (or have 17 versions of Python installed) ← this alone is awesome! 4. I can spin up a runner in AWS with my dev container and have access to any hardware I want while developing. 5. I can configure one-click buttons to run services and tasks I need every day. 6. I can share the environment with anyone on my team. I left this for the end, but I know this is one of the most important features of Gitpod Flex: They built a zero-trust architecture to manage these development environments. This is huge for those working at companies where security is crucial. Below is the link with an explanation of how they built their architecture. Thanks to the Gitpod team for their work on Flex and for sponsoring this post. I have it running now, and I'm planning to keep using it!

Santiago

33,098 просмотров • 1 год назад

Our new game infrastructure is being designed to be compatible with PC, Android, and IOS operating systems, featuring TPP-FPP game modes. 🎮 While conducting multiplayer tests on PC, we are effectively testing for mobile as well. The operation we are running simultaneously for PC and mobile saves us a great deal of time, allowing us to use a single game engine and generate two different outputs for cross-platform compatibility. 💻📱 We have created a code sequence that enables AI bots to attack you based on your KDA ratio, giving a real-player sensation. 🤖 We've considered all details, from hearing, seeing, to hiding behind objects and flanking you. 🎧👀 In single-player mode, you will experience a unique gameplay experience where you must defeat challenging enemies a good fight for a good reward is necessary! ⚔️🏆 While we continue to work on enabling Android and iOS users to join the same game, we will first launch multiplayer on PC and Android within their respective platforms. 🤝 We will refine the TDM and FFA modes, where you can challenge online opponents and earn COF, with player feedback to make them the best they can be. 🛠️👥 Following the release of COF, we will host tournaments with substantial prizes. 🏅 These matches, which can be played individually or in teams, will be live-streamed on Twitch and YouTube. 📺 You can form a team with your friends or collaborate with community members to create your new team. 👫🎮 To facilitate this, we will create a special area on our website to display data for teams and players. 🌐 We are nearing the completion of all our setups; our test server has successfully run our game and provided a stable service for 2 days. ✅ We will publish the minimum system requirements for PC. 🖥️ The texture sizes for the mobile game have been adjusted for phone optimization, ensuring smooth gameplay without lag. 📲 Now we will take a short break and continue from where we left off tomorrow. 🛌🔜 #cof #ecas #eth #p2e #BlockchainGaming #multiplayer #online #onlinegames #DEX
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Our new game infrastructure is being designed to be compatible with PC, Android, and IOS operating systems, featuring TPP-FPP game modes. 🎮 While conducting multiplayer tests on PC, we are effectively testing for mobile as well. The operation we are running simultaneously for PC and mobile saves us a great deal of time, allowing us to use a single game engine and generate two different outputs for cross-platform compatibility. 💻📱 We have created a code sequence that enables AI bots to attack you based on your KDA ratio, giving a real-player sensation. 🤖 We've considered all details, from hearing, seeing, to hiding behind objects and flanking you. 🎧👀 In single-player mode, you will experience a unique gameplay experience where you must defeat challenging enemies a good fight for a good reward is necessary! ⚔️🏆 While we continue to work on enabling Android and iOS users to join the same game, we will first launch multiplayer on PC and Android within their respective platforms. 🤝 We will refine the TDM and FFA modes, where you can challenge online opponents and earn COF, with player feedback to make them the best they can be. 🛠️👥 Following the release of COF, we will host tournaments with substantial prizes. 🏅 These matches, which can be played individually or in teams, will be live-streamed on Twitch and YouTube. 📺 You can form a team with your friends or collaborate with community members to create your new team. 👫🎮 To facilitate this, we will create a special area on our website to display data for teams and players. 🌐 We are nearing the completion of all our setups; our test server has successfully run our game and provided a stable service for 2 days. ✅ We will publish the minimum system requirements for PC. 🖥️ The texture sizes for the mobile game have been adjusted for phone optimization, ensuring smooth gameplay without lag. 📲 Now we will take a short break and continue from where we left off tomorrow. 🛌🔜 #cof #ecas #eth #p2e #BlockchainGaming #multiplayer #online #onlinegames #DEX

Cryptoforce | $COF | P2E

11,307 просмотров • 2 лет назад