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Ultimate Bunny needed that pre-mission injection. Model - keyd10iori 🔞 [Commisions Open] VA - OpaluVA 🎰💙🔞 Map - Scamarai DL and alts in comments.

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Unhindered Fool

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Thiridian & Lindsey

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This week we’re getting GPT 5.5 and I couldn’t be more excited to see the first new pre train base in a while Before Spud releases, here’s the last video you need to watch - Greg Brockman perfectly indicated to us 3 important things that perfectly encapsulate the lineage of OpenAI and the mission. 1.This is the culmination of 2 years of research and is a fresh base pre-train that we haven’t gotten for a long time. 2.This is an “exciting release” and it will bring new capabilities (sort of a given but still very grateful nonetheless) & that it is AN EARLY VERSION of what we have coming - meaning they have a lot of RL to do to make this model even better since it’s a fresh pre-train. 3.THIS IS THE BEGINNING OF THE FLYWHEEL. As he said toward the end, “we have this engine of progress that just moves faster and faster and Spud is just one step along the way.” Depending on the capabilities we see this week from Spud, we may be able to map out if AGI will be sooner rather than later, judging by how much recursive work can exist within this model and the harness. In other words, the flywheel.
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This week we’re getting GPT 5.5 and I couldn’t be more excited to see the first new pre train base in a while Before Spud releases, here’s the last video you need to watch - Greg Brockman perfectly indicated to us 3 important things that perfectly encapsulate the lineage of OpenAI and the mission. 1.This is the culmination of 2 years of research and is a fresh base pre-train that we haven’t gotten for a long time. 2.This is an “exciting release” and it will bring new capabilities (sort of a given but still very grateful nonetheless) & that it is AN EARLY VERSION of what we have coming - meaning they have a lot of RL to do to make this model even better since it’s a fresh pre-train. 3.THIS IS THE BEGINNING OF THE FLYWHEEL. As he said toward the end, “we have this engine of progress that just moves faster and faster and Spud is just one step along the way.” Depending on the capabilities we see this week from Spud, we may be able to map out if AGI will be sooner rather than later, judging by how much recursive work can exist within this model and the harness. In other words, the flywheel.

Chris

103,198 views • 4 months ago

I’m thrilled to present something Marián Marčiš and I have been working on for the past few weeks. To my knowledge, this is something that has never been done before, and this is our first model that includes underground parts. Behold a photogrammetric 3D model of Zona X! Zona X (also known as Lanlakuyoc or Lancacuyo) is a mysterious site about a mile North-East of Sacsayhuaman. It’s a big limestone outcrop intersected by a network of tunnels and narrow passageways, some open to the sky, and others going deep underground. It also features several right-angled cuts in the bedrock ("hanan pacha" style) whose function remains unknown to this day, as well as a section of a megalithic wall. It is also said that this site once had a chincana entrance, possibly connecting to Sacsayhuaman, that has now been sealed off. I have an idea of which passage could lead to an entrance, but it remains to be confirmed. It's a true stone labyrinth in which it's easy to get lost, which is why I wanted to create a 3D map of it. I wanted to get a better idea of how these passages are oriented in relation to each other and where they could lead. It took several visits to the site to map all the passages I could crawl through. Some of these passageways become too narrow to squeeze through for an adult of average build, but seem to extend much deeper. It took Marián a LOT of work (and a lot of processing power) to combine and align the thousands of photos from a drone and extracted from a 360° ground video needed to create the 55 millions triangles that make up this model. I’m not sure how he did it but he pulled it off. He really deserves his title of photogrammetry wizard. Please make sure to give him the follow he deserves Marián Marčiš. The video doesn't show all the passages I filmed for the creation of the model. We decided to only show the main ones, and some of the most prominent stone carvings in order to keep the video relatively short. The site has many more nooks and crannies not shown in the video that are just begging to be explored. I hope we can complete this model in the future. Until then, enjoy this one!
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I’m thrilled to present something Marián Marčiš and I have been working on for the past few weeks. To my knowledge, this is something that has never been done before, and this is our first model that includes underground parts. Behold a photogrammetric 3D model of Zona X! Zona X (also known as Lanlakuyoc or Lancacuyo) is a mysterious site about a mile North-East of Sacsayhuaman. It’s a big limestone outcrop intersected by a network of tunnels and narrow passageways, some open to the sky, and others going deep underground. It also features several right-angled cuts in the bedrock ("hanan pacha" style) whose function remains unknown to this day, as well as a section of a megalithic wall. It is also said that this site once had a chincana entrance, possibly connecting to Sacsayhuaman, that has now been sealed off. I have an idea of which passage could lead to an entrance, but it remains to be confirmed. It's a true stone labyrinth in which it's easy to get lost, which is why I wanted to create a 3D map of it. I wanted to get a better idea of how these passages are oriented in relation to each other and where they could lead. It took several visits to the site to map all the passages I could crawl through. Some of these passageways become too narrow to squeeze through for an adult of average build, but seem to extend much deeper. It took Marián a LOT of work (and a lot of processing power) to combine and align the thousands of photos from a drone and extracted from a 360° ground video needed to create the 55 millions triangles that make up this model. I’m not sure how he did it but he pulled it off. He really deserves his title of photogrammetry wizard. Please make sure to give him the follow he deserves Marián Marčiš. The video doesn't show all the passages I filmed for the creation of the model. We decided to only show the main ones, and some of the most prominent stone carvings in order to keep the video relatively short. The site has many more nooks and crannies not shown in the video that are just begging to be explored. I hope we can complete this model in the future. Until then, enjoy this one!

Weird Old World

12,059 views • 1 year ago

A GUY AT GOOGLE DEEPMIND MADE AN ISOMETRIC PIXEL-ART MAP OF NEW YORK CITY AND PUT IT ON THE OPEN WEB FOR FREE it's called you open the tab and the city is just sitting there in classic SimCity 2000 isometric pixel art. you scroll. and it keeps going. and going. i zoomed in on midtown and i could read the H&M signage in times square. in red. as actual pixel-art letters on the side of a building. i could see the crystalline spire of the Bank of America Tower poking out of a clump of skyscrapers. individual rooftop HVAC units. tiny green roof gardens. the little driveway loops in front of the hotels. he estimates the map needs roughly 40,000 tiles. nothing is a placeholder. the guy who made it is Andy Coenen, a senior staff engineer at Google DeepMind. he is not a pixel artist. by his own admission he is "a former electronic musician." what he actually did is kind of insane: > pulled NYC's geometry from the Google Maps 3D tiles API > fine-tuned an open-source image model (Qwen-Image-Edit) on ~40 hand-paired examples of "satellite tile → pixel art tile" > spun up 50 parallel instances on rented GPUs and generated tens of thousands of tiles in a few hours > the fine-tune cost him 12 bucks his own stated mission for the project, verbatim, is one sentence: "what's possible now that was impossible before?" apparently the answer is "one engineer can pixel-art most of a metropolis for the price of a sandwich." and the wildest part to me is he didn't sell it. no signup. no paywall. no NFT. you open the URL and the city is yours to wander. the post landed at 1,325 points on Hacker News and topped bestofshowhn's 2026 list. we live in a timeline where a senior engineer at one of the largest AI labs on earth spent his nights pixel-arting Manhattan for fun and then gave it away. the internet is healing.
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A GUY AT GOOGLE DEEPMIND MADE AN ISOMETRIC PIXEL-ART MAP OF NEW YORK CITY AND PUT IT ON THE OPEN WEB FOR FREE it's called you open the tab and the city is just sitting there in classic SimCity 2000 isometric pixel art. you scroll. and it keeps going. and going. i zoomed in on midtown and i could read the H&M signage in times square. in red. as actual pixel-art letters on the side of a building. i could see the crystalline spire of the Bank of America Tower poking out of a clump of skyscrapers. individual rooftop HVAC units. tiny green roof gardens. the little driveway loops in front of the hotels. he estimates the map needs roughly 40,000 tiles. nothing is a placeholder. the guy who made it is Andy Coenen, a senior staff engineer at Google DeepMind. he is not a pixel artist. by his own admission he is "a former electronic musician." what he actually did is kind of insane: > pulled NYC's geometry from the Google Maps 3D tiles API > fine-tuned an open-source image model (Qwen-Image-Edit) on ~40 hand-paired examples of "satellite tile → pixel art tile" > spun up 50 parallel instances on rented GPUs and generated tens of thousands of tiles in a few hours > the fine-tune cost him 12 bucks his own stated mission for the project, verbatim, is one sentence: "what's possible now that was impossible before?" apparently the answer is "one engineer can pixel-art most of a metropolis for the price of a sandwich." and the wildest part to me is he didn't sell it. no signup. no paywall. no NFT. you open the URL and the city is yours to wander. the post landed at 1,325 points on Hacker News and topped bestofshowhn's 2026 list. we live in a timeline where a senior engineer at one of the largest AI labs on earth spent his nights pixel-arting Manhattan for fun and then gave it away. the internet is healing.

Nav Toor

440,749 views • 2 months ago

Planetary Mapping in the Browser with GeoLibre! Web mapping just left Earth orbit. GeoLibre, the open-source cloud-native GIS platform, now maps nine more worlds beyond Mars and the Moon: - Mercury - Venus - Io, Europa, Ganymede, and Callisto (Jupiter's Galilean moons) - Titan (Saturn's largest moon) - Pluto and Charon You can browse each one right in your browser, with no install required. A few things I'm excited about under the hood: The imagery comes from USGS Astrogeology, which publishes these mosaics only in an equirectangular projection. MapLibre renders Web Mercator, so GeoLibre reprojects each tile to Web Mercator on the fly at the edge, with no pre-processing and no giant downloads. Every body carries its own radius. Switch to the Moon or Pluto and your distance, area, and scale-bar measurements use that world's geometry instead of Earth's, so the numbers are actually right. A planet switcher in the Layers panel lets you hop between Earth, the Moon, Mars, and the rest with one click. Whether you are teaching planetary science, prototyping a mission map, or just curious what Titan looks like next to a scale bar, it is all a click away. Try it out: - Launch GeoLibre Web: - GitHub: - Documentation: Thanks to USGS Astrogeology and OpenPlanetaryMap for making this data open. #GIS #OpenSource #Geospatial #PlanetaryScience #MapLibre #WebGIS #GeoLibre #Astronomy
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Planetary Mapping in the Browser with GeoLibre! Web mapping just left Earth orbit. GeoLibre, the open-source cloud-native GIS platform, now maps nine more worlds beyond Mars and the Moon: - Mercury - Venus - Io, Europa, Ganymede, and Callisto (Jupiter's Galilean moons) - Titan (Saturn's largest moon) - Pluto and Charon You can browse each one right in your browser, with no install required. A few things I'm excited about under the hood: The imagery comes from USGS Astrogeology, which publishes these mosaics only in an equirectangular projection. MapLibre renders Web Mercator, so GeoLibre reprojects each tile to Web Mercator on the fly at the edge, with no pre-processing and no giant downloads. Every body carries its own radius. Switch to the Moon or Pluto and your distance, area, and scale-bar measurements use that world's geometry instead of Earth's, so the numbers are actually right. A planet switcher in the Layers panel lets you hop between Earth, the Moon, Mars, and the rest with one click. Whether you are teaching planetary science, prototyping a mission map, or just curious what Titan looks like next to a scale bar, it is all a click away. Try it out: - Launch GeoLibre Web: - GitHub: - Documentation: Thanks to USGS Astrogeology and OpenPlanetaryMap for making this data open. #GIS #OpenSource #Geospatial #PlanetaryScience #MapLibre #WebGIS #GeoLibre #Astronomy

Qiusheng Wu

10,325 views • 1 month ago

New framework: Kick down your robot, it will get back up every time 🥋 Chinese startup RoboParty is a Beijing startup founded April 2025 by Huang Yi, originally shipping ROBOTO ORIGIN, the world's first full-stack open-source bipedal humanoid. They released UFO: Unsupervised Reinforcement Learning Framework for Humanoid Control. DEFINITIONS -> what differs is where the learning signal comes from: - SUPERVISED: humans supply the right answers (labels), the model imitates them. - UNSUPERVISED: no answer key, the model finds structure in raw data on its own. - REINFORCEMENT LEARNING: no answer key either, the model tries things and a reward scores each attempt. → UNSUPERVISED RL: trial and error where the agent invents its own rewards, instead of engineers hand-writing one per task. REPRESENTATION LEARNING: compress raw states into a useful internal map. TEMPORAL DISTANCE: distance on that map is "how many steps from A to B." CONTRASTIVE: trained by pulling together what's close in time, pushing apart what isn't. -> CONTRASTIVE TEMPORAL-DISTANCE REPRESENTATION LEARNING: the model builds an internal map of body states where distance means how many steps it takes to get from one to another. It is trained by contrast: states that occur close together in a movement get pulled together in the map, randomly paired states get pushed apart. UFO is an open-source training framework that teaches humanoid robots skills, like getting up, walking, goal-reaching, teleoperation, without reference motions -> no motion-capture or human-video demonstrations to imitate. Its core is TeCH, a contrastive temporal-distance representation-learning algorithm: the robot explores, builds pseudo-goals by temporal rolling, and learns goal-conditioned policies from a single unified progress reward. One framework trains five different robots (Unitree G1/H1, RoboParty RP0/RP1, AgiBot X2) with automatic config conversion in ~2–3 hours per robot! The real novelty here "no demonstrations at all". No data-collection arms race,the dominant humanoid-locomotion recipe is tracking: imitate mocap/retargeted-human reference trajectories. The robot self-generates goals from its own exploration and learns from a progress reward, needing zero reference motion data. Everybody else is fighting over data acquisition, while this team just teleports out of the race entirely (inb4 "competition is for losers 💀 ). This strategy reminds me of the DeepSeek playbook applied to robots: open-source the whole stack to become the global default and commoditize everyone else. RoboParty is giving away hardware and now control software (UFO) to be the Android of humanoids. Yet another reason for the US to ban Chinese open models perhaps 🥶 ? What I also really like about this approach is the cross-embodiment infrastructure, one framework trains Unitree G1/H1, RoboParty RP0/RP1, and AgiBot X2 with automatic configuration conversion. Just like Physical Intelligence, RoboParty seems to place itself as a neutral hardware agnostic middle man. Also woth mentioning: their ability ot perform stable skill injection, e.g. adding a cartwheel without forgetting how to walk. A common failure of RL humanoid policies is that teaching a new agile skill destabilizes the existing ones (catastrophic forgetting). UFO claims you can inject rare motions (cartwheel) without collapsing learned behavior. If it holds, that's a significant incremental/continual skill-learning! But again, I have to underline it: no arXiv, no external validation, no success-rate numbers. -> robotics badely needs an independent unbiased evaluator imho. Still, look at that cool demo: robot is getting kicked and pushed around (serious disturbance) during teleoperation (controlled the person at the back wearing the VR headset), and still managed to always get back up. This is some serious demonstration of stability and robustness!
0:49

New framework: Kick down your robot, it will get back up every time 🥋 Chinese startup RoboParty is a Beijing startup founded April 2025 by Huang Yi, originally shipping ROBOTO ORIGIN, the world's first full-stack open-source bipedal humanoid. They released UFO: Unsupervised Reinforcement Learning Framework for Humanoid Control. DEFINITIONS -> what differs is where the learning signal comes from: - SUPERVISED: humans supply the right answers (labels), the model imitates them. - UNSUPERVISED: no answer key, the model finds structure in raw data on its own. - REINFORCEMENT LEARNING: no answer key either, the model tries things and a reward scores each attempt. → UNSUPERVISED RL: trial and error where the agent invents its own rewards, instead of engineers hand-writing one per task. REPRESENTATION LEARNING: compress raw states into a useful internal map. TEMPORAL DISTANCE: distance on that map is "how many steps from A to B." CONTRASTIVE: trained by pulling together what's close in time, pushing apart what isn't. -> CONTRASTIVE TEMPORAL-DISTANCE REPRESENTATION LEARNING: the model builds an internal map of body states where distance means how many steps it takes to get from one to another. It is trained by contrast: states that occur close together in a movement get pulled together in the map, randomly paired states get pushed apart. UFO is an open-source training framework that teaches humanoid robots skills, like getting up, walking, goal-reaching, teleoperation, without reference motions -> no motion-capture or human-video demonstrations to imitate. Its core is TeCH, a contrastive temporal-distance representation-learning algorithm: the robot explores, builds pseudo-goals by temporal rolling, and learns goal-conditioned policies from a single unified progress reward. One framework trains five different robots (Unitree G1/H1, RoboParty RP0/RP1, AgiBot X2) with automatic config conversion in ~2–3 hours per robot! The real novelty here "no demonstrations at all". No data-collection arms race,the dominant humanoid-locomotion recipe is tracking: imitate mocap/retargeted-human reference trajectories. The robot self-generates goals from its own exploration and learns from a progress reward, needing zero reference motion data. Everybody else is fighting over data acquisition, while this team just teleports out of the race entirely (inb4 "competition is for losers 💀 ). This strategy reminds me of the DeepSeek playbook applied to robots: open-source the whole stack to become the global default and commoditize everyone else. RoboParty is giving away hardware and now control software (UFO) to be the Android of humanoids. Yet another reason for the US to ban Chinese open models perhaps 🥶 ? What I also really like about this approach is the cross-embodiment infrastructure, one framework trains Unitree G1/H1, RoboParty RP0/RP1, and AgiBot X2 with automatic configuration conversion. Just like Physical Intelligence, RoboParty seems to place itself as a neutral hardware agnostic middle man. Also woth mentioning: their ability ot perform stable skill injection, e.g. adding a cartwheel without forgetting how to walk. A common failure of RL humanoid policies is that teaching a new agile skill destabilizes the existing ones (catastrophic forgetting). UFO claims you can inject rare motions (cartwheel) without collapsing learned behavior. If it holds, that's a significant incremental/continual skill-learning! But again, I have to underline it: no arXiv, no external validation, no success-rate numbers. -> robotics badely needs an independent unbiased evaluator imho. Still, look at that cool demo: robot is getting kicked and pushed around (serious disturbance) during teleoperation (controlled the person at the back wearing the VR headset), and still managed to always get back up. This is some serious demonstration of stability and robustness!

Léo

35,855 views • 1 month ago

‘pip install elysia’ and ‘elysia start’ That’s literally all it takes to get the most advanced open source agentic RAG app running on your data. We just released 𝗘𝗹𝘆𝘀𝗶𝗮, our open source, agentic RAG framework and an app so cool needed a cool video to go with it. Watch the full video: In the video, we go through these components of Elysia: 1️⃣ 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗲𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Instead of giving agents access to all tools at once, Elysia uses a pre-defined web of nodes with corresponding actions. Each decision agent has global context awareness. 2️⃣ 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗗𝗮𝘁𝗮 𝗗𝗶𝘀𝗽𝗹𝗮𝘆𝘀: Seven different data display formats including tables, e-commerce product cards, GitHub tickets, and charts. The system automatically choses the best display format. 3️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲: Unlike naive RAG systems that perform blind vector searches, Elysia analyzes your collections to understand data structure and meaning before performing queries. 𝗢𝘁𝗵𝗲𝗿 𝗖𝗼𝗼𝗹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: • 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗦𝘆𝘀𝘁𝗲𝗺: Uses positive examples as few-shot demonstrations for smaller, faster models • 𝗖𝗵𝘂𝗻𝗸-𝗢𝗻-𝗗𝗲𝗺𝗮𝗻𝗱: Dynamically chunks documents at query time instead of pre-chunking • 𝗠𝘂𝗹𝘁𝗶-𝗠𝗼𝗱𝗲𝗹 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆: Routes different tasks to appropriate model sizes based on complexity …And also how to get started with your own data! The entire project is open source and designed with customization in mind. You can use it as-is for effective data searching, or install the Python package to create custom tools for whatever agentic AI purposes you need. Big kudos to Edward for the vision, filming, and editing this masterpiece
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‘pip install elysia’ and ‘elysia start’ That’s literally all it takes to get the most advanced open source agentic RAG app running on your data. We just released 𝗘𝗹𝘆𝘀𝗶𝗮, our open source, agentic RAG framework and an app so cool needed a cool video to go with it. Watch the full video: In the video, we go through these components of Elysia: 1️⃣ 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗲𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Instead of giving agents access to all tools at once, Elysia uses a pre-defined web of nodes with corresponding actions. Each decision agent has global context awareness. 2️⃣ 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗗𝗮𝘁𝗮 𝗗𝗶𝘀𝗽𝗹𝗮𝘆𝘀: Seven different data display formats including tables, e-commerce product cards, GitHub tickets, and charts. The system automatically choses the best display format. 3️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲: Unlike naive RAG systems that perform blind vector searches, Elysia analyzes your collections to understand data structure and meaning before performing queries. 𝗢𝘁𝗵𝗲𝗿 𝗖𝗼𝗼𝗹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: • 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗦𝘆𝘀𝘁𝗲𝗺: Uses positive examples as few-shot demonstrations for smaller, faster models • 𝗖𝗵𝘂𝗻𝗸-𝗢𝗻-𝗗𝗲𝗺𝗮𝗻𝗱: Dynamically chunks documents at query time instead of pre-chunking • 𝗠𝘂𝗹𝘁𝗶-𝗠𝗼𝗱𝗲𝗹 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆: Routes different tasks to appropriate model sizes based on complexity …And also how to get started with your own data! The entire project is open source and designed with customization in mind. You can use it as-is for effective data searching, or install the Python package to create custom tools for whatever agentic AI purposes you need. Big kudos to Edward for the vision, filming, and editing this masterpiece

Victoria Slocum

45,497 views • 1 year ago

The Pipeline Deal That Could Remake Iraq’s Oil Map As the Turkey-Iraq pipeline agreement is set to expire in July, the two countries have started negotiations on a new agreement. The treaty lapses during the sharpest contraction in Gulf energy shipping in recent memory, and that has turned a technical renewal into a contest over the shape of Iraq’s export map. The legal background shapes every Iraqi position in the talks. The pipeline went dark in 2023 after Iraq won arbitration against Turkey over unauthorized Kurdish exports between 2014 and 2018, with Turkey ordered to pay about $1.5 billion. Baghdad will not sign a successor agreement that leaves ownership of the crude, the marketing channel, or the payment route open to a parallel Erbil-Ankara reading. Ankara, for its part, is not negotiating a simple renewal. Turkish Energy Minister Alparslan Bayraktar has argued repeatedly that the line carries a nominal capacity of nearly 1.5 million bpd and has never been used at scale. Any new agreement, he says, must include a mechanism to guarantee fuller use. Reaching that volume requires extending the line south toward Basra, since northern production cannot fill it. Turkey folds this into the wider Development Road from Faw to Turkey, reframed as an energy corridor carrying oil, gas, petrochemicals, refining, and electricity alongside road and rail. Baghdad’s instrument is the Basra-Haditha pipeline, which the Oil Ministry calls the backbone of the northern export system. Baghdad has chosen a turnkey, state-financed model with full Oil Ministry ownership after rejecting an investment structure that would have handed an outside party long-term transit rights. If Turkey is seeking ownership leverage under the heading of investment, that is the Iraqi red line. The decisive feature of the Iraqi design is that Basra-Haditha is multidirectional. From Haditha, crude could move north to Ceyhan. In theory, it could also support future western exits toward Baniyas and Tartus on the Syrian coast, or southwest toward Aqaba in Jordan. These branches should not be read as equally imminent projects. Their immediate value is strategic optionality. They give Baghdad a map of alternatives, even before all of them become physical export routes. For the Kurdistan Region, the trajectory is unfavourable. Its 2014-built line, with a design capacity of nearly 900,000 bpd, remains physically useful in the short term while the ministry-controlled Kirkuk-Fishkhabur line, rated at nearly 1.5 million bpd, is rehabilitated as a federal bypass. Once that bypass and Basra-Haditha are in place, the KRG pipeline becomes a feeder rather than a strategic bottleneck. Baghdad’s aim is not necessarily to shut it. The aim is to strip it of independent commercial authority. Turkey signs with Baghdad alone, SOMO markets the crude, and revenue runs through the federal treasury. The KRG line may remain part of the infrastructure, but not as the basis for a parallel political economy. Whatever the structure, a deal struck before the deadline will not restore the pre-2023 model. The likeliest endpoint is Ceyhan returning as a federal corridor under SOMO and the treasury, the KRG line being tolerated as infrastructure without political-commercial autonomy, and Turkey gaining volume guarantees and investment access but not a chokehold over Iraqi oil. Baghdad’s wager is that holding the Syrian and Jordanian options open is what keeps Ceyhan a corridor rather than a second Hormuz. More details:
0:41

The Pipeline Deal That Could Remake Iraq’s Oil Map As the Turkey-Iraq pipeline agreement is set to expire in July, the two countries have started negotiations on a new agreement. The treaty lapses during the sharpest contraction in Gulf energy shipping in recent memory, and that has turned a technical renewal into a contest over the shape of Iraq’s export map. The legal background shapes every Iraqi position in the talks. The pipeline went dark in 2023 after Iraq won arbitration against Turkey over unauthorized Kurdish exports between 2014 and 2018, with Turkey ordered to pay about $1.5 billion. Baghdad will not sign a successor agreement that leaves ownership of the crude, the marketing channel, or the payment route open to a parallel Erbil-Ankara reading. Ankara, for its part, is not negotiating a simple renewal. Turkish Energy Minister Alparslan Bayraktar has argued repeatedly that the line carries a nominal capacity of nearly 1.5 million bpd and has never been used at scale. Any new agreement, he says, must include a mechanism to guarantee fuller use. Reaching that volume requires extending the line south toward Basra, since northern production cannot fill it. Turkey folds this into the wider Development Road from Faw to Turkey, reframed as an energy corridor carrying oil, gas, petrochemicals, refining, and electricity alongside road and rail. Baghdad’s instrument is the Basra-Haditha pipeline, which the Oil Ministry calls the backbone of the northern export system. Baghdad has chosen a turnkey, state-financed model with full Oil Ministry ownership after rejecting an investment structure that would have handed an outside party long-term transit rights. If Turkey is seeking ownership leverage under the heading of investment, that is the Iraqi red line. The decisive feature of the Iraqi design is that Basra-Haditha is multidirectional. From Haditha, crude could move north to Ceyhan. In theory, it could also support future western exits toward Baniyas and Tartus on the Syrian coast, or southwest toward Aqaba in Jordan. These branches should not be read as equally imminent projects. Their immediate value is strategic optionality. They give Baghdad a map of alternatives, even before all of them become physical export routes. For the Kurdistan Region, the trajectory is unfavourable. Its 2014-built line, with a design capacity of nearly 900,000 bpd, remains physically useful in the short term while the ministry-controlled Kirkuk-Fishkhabur line, rated at nearly 1.5 million bpd, is rehabilitated as a federal bypass. Once that bypass and Basra-Haditha are in place, the KRG pipeline becomes a feeder rather than a strategic bottleneck. Baghdad’s aim is not necessarily to shut it. The aim is to strip it of independent commercial authority. Turkey signs with Baghdad alone, SOMO markets the crude, and revenue runs through the federal treasury. The KRG line may remain part of the infrastructure, but not as the basis for a parallel political economy. Whatever the structure, a deal struck before the deadline will not restore the pre-2023 model. The likeliest endpoint is Ceyhan returning as a federal corridor under SOMO and the treasury, the KRG line being tolerated as infrastructure without political-commercial autonomy, and Turkey gaining volume guarantees and investment access but not a chokehold over Iraqi oil. Baghdad’s wager is that holding the Syrian and Jordanian options open is what keeps Ceyhan a corridor rather than a second Hormuz. More details:

The National Context

13,412 views • 3 months ago

Introducing LobeHub: Agent teammates that grow with you. LobeHub is the ultimate space for work and life: to find, build, and collaborate with agent teammates that grow with you. We’re building the world’s first and largest human–agent co-evolving network. Two years ago, we built LobeChat, an open-source interface for using different AI models. Today, LobeChat has 70k+ GitHub stars and serves 6M+ users worldwide. How to fully unlock the power of models has always been a shared mission between us and the community. We started with interaction — a fundamentally new, agent-first experience. Agents are no longer passive tools invoked in a single conversation. They should be proactive, always-on units of work. Treating agents as the minimal atomic unit is also the core of our agent harness infra. Today’s agents are mostly one-off executors. Even with memory, it’s often global — and hallucinates. We build long-term agent teammates that evolve with users. Each agent has its own dedicated memory space, editable by users, allowing humans and agents to co-evolve over time. This, in turn, allows us to design clearer rewards for reinforcement learning and create cleaner environments for continual learning. Agent teammates can work in groups. Through a multi-agent system, agent groups operate faster, more cost-effective, and go beyond what single-agent systems can achieve. For example, a single agent often requires heavy user involvement to proceed step by step, whereas LobeHub can execute the same work from a single instruction, with a supervisor orchestrating agents that run in parallel or debate to produce better results. We are building the collaboration network among agent teammates — and between humans and agent teammates as well. Ease of use matters. AI intelligence and shared human intelligence are equally important. With simple instructions and tool selection, you can effortlessly build and team up with agent coworkers to deliver complex, systematic work — even assembling a quant team to execute trades. Through the LobeHub community, anyone can discover, reuse, and remix agents and agent groups, customizing them to fit their own workflows, preferences, and needs. Last but not least, our vision started with LobeChat: multi-model support is the most efficient approach for users. We believe different models excel in different scenarios. By routing across multiple models, LobeHub improves cost efficiency and unlocks capabilities that a single-model setup cannot easily support.
2:41

Introducing LobeHub: Agent teammates that grow with you. LobeHub is the ultimate space for work and life: to find, build, and collaborate with agent teammates that grow with you. We’re building the world’s first and largest human–agent co-evolving network. Two years ago, we built LobeChat, an open-source interface for using different AI models. Today, LobeChat has 70k+ GitHub stars and serves 6M+ users worldwide. How to fully unlock the power of models has always been a shared mission between us and the community. We started with interaction — a fundamentally new, agent-first experience. Agents are no longer passive tools invoked in a single conversation. They should be proactive, always-on units of work. Treating agents as the minimal atomic unit is also the core of our agent harness infra. Today’s agents are mostly one-off executors. Even with memory, it’s often global — and hallucinates. We build long-term agent teammates that evolve with users. Each agent has its own dedicated memory space, editable by users, allowing humans and agents to co-evolve over time. This, in turn, allows us to design clearer rewards for reinforcement learning and create cleaner environments for continual learning. Agent teammates can work in groups. Through a multi-agent system, agent groups operate faster, more cost-effective, and go beyond what single-agent systems can achieve. For example, a single agent often requires heavy user involvement to proceed step by step, whereas LobeHub can execute the same work from a single instruction, with a supervisor orchestrating agents that run in parallel or debate to produce better results. We are building the collaboration network among agent teammates — and between humans and agent teammates as well. Ease of use matters. AI intelligence and shared human intelligence are equally important. With simple instructions and tool selection, you can effortlessly build and team up with agent coworkers to deliver complex, systematic work — even assembling a quant team to execute trades. Through the LobeHub community, anyone can discover, reuse, and remix agents and agent groups, customizing them to fit their own workflows, preferences, and needs. Last but not least, our vision started with LobeChat: multi-model support is the most efficient approach for users. We believe different models excel in different scenarios. By routing across multiple models, LobeHub improves cost efficiency and unlocks capabilities that a single-model setup cannot easily support.

LobeHub

185,385 views • 7 months ago

🚨Science nerds are going to lose their minds. Kai Rowan just open sourced a framework that predicts how your brain responds to any text, audio, or video by simulating cortical fMRI activity with 30% more accuracy than Meta's own model. No fMRI scanner. No neuroscience PhD. No million-dollar lab. It's called NForge. Here's what this thing actually does: → Feed it any combination of text, audio, or video and it predicts cortical surface activity across ~20,484 brain vertices → Extracts deep features via LLaMA 3.2, V-JEPA2, and Wav2Vec-BERT simultaneously → Generates ROI attention maps showing exactly which brain regions fire hardest at which moments → Runs real-time streaming predictions from live feature streams -- no pre-loading the full clip → Breaks down exactly how much text vs audio vs video drove each prediction with per-vertex modality attribution scores → Adapts to entirely new subjects with just a few calibration scans -- no full retraining required Here's the wildest part: Built on Meta's TRIBE v2 foundation but adds 6 major capabilities Meta never shipped. Cross-subject generalization. Streaming inference. Modality attribution. torch.compile support. Full test coverage. Professional src/ package layout. You literally point this at a movie clip and it tells you which parts of the human cortex light up -- broken down by what your eyes, ears, and language centers each contributed. That sentence shouldn't be real in 2026. But here we are. 100% Open Source. pip install nforge. (Link in the comments)
0:23

🚨Science nerds are going to lose their minds. Kai Rowan just open sourced a framework that predicts how your brain responds to any text, audio, or video by simulating cortical fMRI activity with 30% more accuracy than Meta's own model. No fMRI scanner. No neuroscience PhD. No million-dollar lab. It's called NForge. Here's what this thing actually does: → Feed it any combination of text, audio, or video and it predicts cortical surface activity across ~20,484 brain vertices → Extracts deep features via LLaMA 3.2, V-JEPA2, and Wav2Vec-BERT simultaneously → Generates ROI attention maps showing exactly which brain regions fire hardest at which moments → Runs real-time streaming predictions from live feature streams -- no pre-loading the full clip → Breaks down exactly how much text vs audio vs video drove each prediction with per-vertex modality attribution scores → Adapts to entirely new subjects with just a few calibration scans -- no full retraining required Here's the wildest part: Built on Meta's TRIBE v2 foundation but adds 6 major capabilities Meta never shipped. Cross-subject generalization. Streaming inference. Modality attribution. torch.compile support. Full test coverage. Professional src/ package layout. You literally point this at a movie clip and it tells you which parts of the human cortex light up -- broken down by what your eyes, ears, and language centers each contributed. That sentence shouldn't be real in 2026. But here we are. 100% Open Source. pip install nforge. (Link in the comments)

Guri Singh

244,515 views • 5 months ago

The July 4th weekend All-In The All-In Podcast turned into a long argument about who owns the intelligence layer. The besties think enterprises just woke up to a trap they had been walking into, here's how the conversation went (save this): ◽️ The Palantir-Nvidia deal is a bet against the model-layer duopoly. Palantir will use Nvidia's Nemotron open models to build a custom frontier-quality model for US government agencies, and the agencies own the hardware, the data, and the weights. Sacks framed it as structural: an application company and a chip company both want a competitive model layer, so they are natural partners against a two-provider middle. ◽️ Alex Karp's CNBC "crashout" was actually the thesis. Karp argued enterprises have lost trust in the frontier labs and want to own their compute, models, data, and alpha. Sacks translated it as a new definition of enterprise AI safety: safety means the model provider cannot hoover up your proprietary knowledge and turn it into its next product. ◽️ Figma is the cautionary tale that made it real. Anthropic launched Claude Design into Figma's category, its chief product officer sat on Figma's board and resigned only 3 days before launch, and Figma's stock is down about 50% this year while Anthropic's valuation surged. Sacks listed Claude Science, Security, Legal, Financial, and Code as the same move: dominate the model layer, then take the lucrative verticals. ◽️ The playbook has a name, and it is Microsoft and Google. Sacks argued Anthropic is running the operating-system strategy: own the layer everyone builds on, then walk up the stack. His Google receipt is that fewer than half of searches now send you off-site, versus an early Google that prided itself on how fast it kicked you away. ◽️ The BCG number is what raises the stakes. Chamath cited a BCG return-on-capital-employed study: the cost of capital is back to its long-run 8 to 11%, and half of large US companies cannot earn returns above it. If you are already teetering on your cost of capital, handing your alpha to a provider that may compete with you is not a luxury risk, it is fatal. ◽️ The 16.4x number is the whole argument in one data point. Chamath ran a code-migration task through 8090's harness. Wrapping Claude was 1.4x cheaper and 1.5x faster than Claude Opus alone. Wrapping the best open-source model was 16.4x cheaper, at about 3x slower. For a background task, three extra hours to cut cost by 16x is not a close call. ◽️ Even at 100x cheaper, enterprises were saying no for the wrong reason. Chamath relayed an ex-Meta PM's point that companies reject open models over China and safety fears, when they could host those same open weights on their own GPUs in US data centers with nothing flowing back. The safety objection, she argued, is backwards: the leak is the data you hand the frontier labs. ◽️ Friedberg says the frontier labs are trying to commoditize their own customers. Anthropic has been signing up life-sciences companies to feed a new life-focused model in exchange for early access, and nearly everyone he has talked to now refuses, recognizing that data they spent billions generating becomes worthless once it is pooled with everyone else's. ◽️ The deployment topology is shifting from big hubs to distributed spokes. Friedberg's map: the old assumption was a few capital-advantaged mega-clusters plus inference clouds. The new one is large hubs, medium hubs (enterprise training clusters), and distributed spokes, including on-prem inference in your own building. Owning your weights is the point. ◽️ Chamath's endgame is running GLM himself. An industry contact told him that with harness post-training and telemetry, an open Chinese model like GLM could get as good as Anthropic's Mythos. His conclusion: take GLM, control it soup-to-nuts on US hardware with only US citizens touching it, and pay a fraction. ◽️ The Apple analogy sharpens why renting intelligence is different from renting distribution. Chamath argued Apple is the only platform that respected developers, deliberately keeping its stock apps basic to protect the ecosystem and collect its 30% tax. There is no 30% tax on open models, and worse, you cannot rent intelligence from the same place that rents it to your competitor without ending up identical to them. ◽️ Nvidia's open model is now good enough to matter. Calacanis claimed you cannot tell Jensen Huang's Nemotron from Claude on 95% of searches, and that Nvidia downplayed the model until now to avoid alarming its top customers. The gloves came off once OpenAI, Anthropic, and Elon all signaled their own silicon ambitions. ◽️ Sacks sized the duopoly: roughly $60B and $40B in ARR. Anthropic is around ~$60 billion of ARR, OpenAI at ~$40 billion, and no one else generates meaningful model-layer revenue. Sacks's policy line: the US does not ban monopolies, only anti-competitive tactics, but the government should do nothing to make the duopoly more likely. ◽️ The token deflation call: 90% a year for three years. Calacanis predicted token costs fall 90% annually for three years, putting the price of intelligence near free and making it rational to waste tokens on hardware you already own. Friedberg's version is a 70/20/10 split between big cloud, local, and other clouds. ◽️ A wave of platform lock-in spending is already landing. Calacanis flagged Microsoft standing up a roughly $2.5 billion forward-deployed-engineer effort and Amazon spending about $1 billion on the same, plus OpenAI's version. His read: enterprises will slam the door, because letting a provider's engineers study your business is how it ends up in their model. ◽️ The server-per-employee prediction. Calacanis expects every employee to get $10,000 to $20,000 of local compute, a Mac Studio or a high-RAM Dell, running a personal local model that syncs to a thin laptop. A server per person, so nothing leaks. ◽️ On jobs, the data does not show present-tense loss. Sacks cited a RAMP and Revelio Labs study of over 21,000 US firms: the heaviest AI spenders grew headcount about 10% over two years, and entry-level headcount grew even faster at 12%. Friedberg's harder claim: there is no AI job loss yet, only clunky, gradual value creation, and the media will not reverse its narrative because that destroys its credibility. ◽️ The displacement case is real but forward-dated. The counterpoint on the show was that customer support, entry-level data entry and BPO, and driving are the near-term displacements, with Waymo cited as present-tense evidence: in markets where it hits critical mass, Uber and Lyft stop recruiting drivers. Sacks noted most US entry-level support was already offshored, so the acute risk sits in those countries first. ◽️ The human-premium counternarrative. Friedberg argued that as automation spreads, human interaction gets a premium: the skilled bartender, the real driver, the human-in-the-loop tier. He cited the company (referenced as Klarna) that hyped replacing its whole support team with AI, then reversed a year later on brand grounds. ◽️ The export-control episode needed three conditions, and Sacks says do not over-read it. Commerce lifted controls on Anthropic's Fable 5 after two weeks, with Mythos 5 restored to US customers around June 26 once co-founder Tom Brown replaced Dario as lead negotiator. Sacks's three conditions: Dario boasting for months about a cyber weapon, Amazon reporting failed guardrails in testing, and Dario refusing to roll Fable back. His message to allies: this was a particular set of circumstances rather than the debut of a standing lever. ◽️ The import question nobody answered cleanly. Calacanis pressed on why the US blocks Chinese cars and drones but not Chinese open models like DeepSeek and Kimi. Sacks's answer: a forked open model run on US hardware stops being Chinese, and banning open source would isolate the US and impose a token tax on American enterprises, so let the market decide if American open models win. ◽️ The California fiscal story is a business-climate story. Friedberg walked through the numbers behind Newsom's "balanced" $351B budget: expenses exceed revenue and $20-40B is borrowed to close the gap, the budget grew 65% in six years ($215B to $355B), personal income tax is $142B of ~$211B revenue with the top 1% (150,000 people) paying $70B of it, and the corporate rate of 8.9% sits far above Texas at zero. ◽️ The tax base is leaving, and the state is now taxing everyone else. Friedberg cited 1 to 1.5% of adjusted gross income leaving each year (about 15% over a decade), at least 15 Fortune 500 HQs and ~2,100 firms gone since 2019, and a new 8% software sales tax hitting Word, Gmail, and ChatGPT subscriptions plus a health-insurance tax, on top of a now-permanent 14.4% top bracket. The liabilities behind it run $1.4T in debt, up to $1.5T in unfunded pensions senior to state bonds, and ~$40B/year in out-year deficits. Lastly, the line that framed the whole show: "You can't rent intelligence from the same place that rents it to your competitor." That is the sovereignty thesis in one sentence, and every number in this episode is an argument for it. ____ Follow Fireside Alpha for more summaries on key business and technology conversations.
1:33

The July 4th weekend All-In The All-In Podcast turned into a long argument about who owns the intelligence layer. The besties think enterprises just woke up to a trap they had been walking into, here's how the conversation went (save this): ◽️ The Palantir-Nvidia deal is a bet against the model-layer duopoly. Palantir will use Nvidia's Nemotron open models to build a custom frontier-quality model for US government agencies, and the agencies own the hardware, the data, and the weights. Sacks framed it as structural: an application company and a chip company both want a competitive model layer, so they are natural partners against a two-provider middle. ◽️ Alex Karp's CNBC "crashout" was actually the thesis. Karp argued enterprises have lost trust in the frontier labs and want to own their compute, models, data, and alpha. Sacks translated it as a new definition of enterprise AI safety: safety means the model provider cannot hoover up your proprietary knowledge and turn it into its next product. ◽️ Figma is the cautionary tale that made it real. Anthropic launched Claude Design into Figma's category, its chief product officer sat on Figma's board and resigned only 3 days before launch, and Figma's stock is down about 50% this year while Anthropic's valuation surged. Sacks listed Claude Science, Security, Legal, Financial, and Code as the same move: dominate the model layer, then take the lucrative verticals. ◽️ The playbook has a name, and it is Microsoft and Google. Sacks argued Anthropic is running the operating-system strategy: own the layer everyone builds on, then walk up the stack. His Google receipt is that fewer than half of searches now send you off-site, versus an early Google that prided itself on how fast it kicked you away. ◽️ The BCG number is what raises the stakes. Chamath cited a BCG return-on-capital-employed study: the cost of capital is back to its long-run 8 to 11%, and half of large US companies cannot earn returns above it. If you are already teetering on your cost of capital, handing your alpha to a provider that may compete with you is not a luxury risk, it is fatal. ◽️ The 16.4x number is the whole argument in one data point. Chamath ran a code-migration task through 8090's harness. Wrapping Claude was 1.4x cheaper and 1.5x faster than Claude Opus alone. Wrapping the best open-source model was 16.4x cheaper, at about 3x slower. For a background task, three extra hours to cut cost by 16x is not a close call. ◽️ Even at 100x cheaper, enterprises were saying no for the wrong reason. Chamath relayed an ex-Meta PM's point that companies reject open models over China and safety fears, when they could host those same open weights on their own GPUs in US data centers with nothing flowing back. The safety objection, she argued, is backwards: the leak is the data you hand the frontier labs. ◽️ Friedberg says the frontier labs are trying to commoditize their own customers. Anthropic has been signing up life-sciences companies to feed a new life-focused model in exchange for early access, and nearly everyone he has talked to now refuses, recognizing that data they spent billions generating becomes worthless once it is pooled with everyone else's. ◽️ The deployment topology is shifting from big hubs to distributed spokes. Friedberg's map: the old assumption was a few capital-advantaged mega-clusters plus inference clouds. The new one is large hubs, medium hubs (enterprise training clusters), and distributed spokes, including on-prem inference in your own building. Owning your weights is the point. ◽️ Chamath's endgame is running GLM himself. An industry contact told him that with harness post-training and telemetry, an open Chinese model like GLM could get as good as Anthropic's Mythos. His conclusion: take GLM, control it soup-to-nuts on US hardware with only US citizens touching it, and pay a fraction. ◽️ The Apple analogy sharpens why renting intelligence is different from renting distribution. Chamath argued Apple is the only platform that respected developers, deliberately keeping its stock apps basic to protect the ecosystem and collect its 30% tax. There is no 30% tax on open models, and worse, you cannot rent intelligence from the same place that rents it to your competitor without ending up identical to them. ◽️ Nvidia's open model is now good enough to matter. Calacanis claimed you cannot tell Jensen Huang's Nemotron from Claude on 95% of searches, and that Nvidia downplayed the model until now to avoid alarming its top customers. The gloves came off once OpenAI, Anthropic, and Elon all signaled their own silicon ambitions. ◽️ Sacks sized the duopoly: roughly $60B and $40B in ARR. Anthropic is around ~$60 billion of ARR, OpenAI at ~$40 billion, and no one else generates meaningful model-layer revenue. Sacks's policy line: the US does not ban monopolies, only anti-competitive tactics, but the government should do nothing to make the duopoly more likely. ◽️ The token deflation call: 90% a year for three years. Calacanis predicted token costs fall 90% annually for three years, putting the price of intelligence near free and making it rational to waste tokens on hardware you already own. Friedberg's version is a 70/20/10 split between big cloud, local, and other clouds. ◽️ A wave of platform lock-in spending is already landing. Calacanis flagged Microsoft standing up a roughly $2.5 billion forward-deployed-engineer effort and Amazon spending about $1 billion on the same, plus OpenAI's version. His read: enterprises will slam the door, because letting a provider's engineers study your business is how it ends up in their model. ◽️ The server-per-employee prediction. Calacanis expects every employee to get $10,000 to $20,000 of local compute, a Mac Studio or a high-RAM Dell, running a personal local model that syncs to a thin laptop. A server per person, so nothing leaks. ◽️ On jobs, the data does not show present-tense loss. Sacks cited a RAMP and Revelio Labs study of over 21,000 US firms: the heaviest AI spenders grew headcount about 10% over two years, and entry-level headcount grew even faster at 12%. Friedberg's harder claim: there is no AI job loss yet, only clunky, gradual value creation, and the media will not reverse its narrative because that destroys its credibility. ◽️ The displacement case is real but forward-dated. The counterpoint on the show was that customer support, entry-level data entry and BPO, and driving are the near-term displacements, with Waymo cited as present-tense evidence: in markets where it hits critical mass, Uber and Lyft stop recruiting drivers. Sacks noted most US entry-level support was already offshored, so the acute risk sits in those countries first. ◽️ The human-premium counternarrative. Friedberg argued that as automation spreads, human interaction gets a premium: the skilled bartender, the real driver, the human-in-the-loop tier. He cited the company (referenced as Klarna) that hyped replacing its whole support team with AI, then reversed a year later on brand grounds. ◽️ The export-control episode needed three conditions, and Sacks says do not over-read it. Commerce lifted controls on Anthropic's Fable 5 after two weeks, with Mythos 5 restored to US customers around June 26 once co-founder Tom Brown replaced Dario as lead negotiator. Sacks's three conditions: Dario boasting for months about a cyber weapon, Amazon reporting failed guardrails in testing, and Dario refusing to roll Fable back. His message to allies: this was a particular set of circumstances rather than the debut of a standing lever. ◽️ The import question nobody answered cleanly. Calacanis pressed on why the US blocks Chinese cars and drones but not Chinese open models like DeepSeek and Kimi. Sacks's answer: a forked open model run on US hardware stops being Chinese, and banning open source would isolate the US and impose a token tax on American enterprises, so let the market decide if American open models win. ◽️ The California fiscal story is a business-climate story. Friedberg walked through the numbers behind Newsom's "balanced" $351B budget: expenses exceed revenue and $20-40B is borrowed to close the gap, the budget grew 65% in six years ($215B to $355B), personal income tax is $142B of ~$211B revenue with the top 1% (150,000 people) paying $70B of it, and the corporate rate of 8.9% sits far above Texas at zero. ◽️ The tax base is leaving, and the state is now taxing everyone else. Friedberg cited 1 to 1.5% of adjusted gross income leaving each year (about 15% over a decade), at least 15 Fortune 500 HQs and ~2,100 firms gone since 2019, and a new 8% software sales tax hitting Word, Gmail, and ChatGPT subscriptions plus a health-insurance tax, on top of a now-permanent 14.4% top bracket. The liabilities behind it run $1.4T in debt, up to $1.5T in unfunded pensions senior to state bonds, and ~$40B/year in out-year deficits. Lastly, the line that framed the whole show: "You can't rent intelligence from the same place that rents it to your competitor." That is the sovereignty thesis in one sentence, and every number in this episode is an argument for it. ____ Follow Fireside Alpha for more summaries on key business and technology conversations.

Fireside Alpha

55,816 views • 1 month ago

OpenLedger X Morpheus The partnership of openledger with Morpheus enables Use Morpheus to build "The Autonomous Smart Contract Engineer" on top of OpenLedger. What is Morpheus? Morpheus is a Web3-native AI coding agent that turns natural language into executable smart contracts and full-stack dApps. It is powered by a specialized Solidity model built on top of OpenLedger, tailored for the unique demands of secure and efficient onchain development. It goes beyond code generation. Using fine-tuned models, agent-based architecture, and modular plugin support, Morpheus automates the entire development pipeline-from writing and simulating contracts to deploying and maintaining them. Its mission is to reduce the barrier to dApp creation while enabling autonomous agents and individuals to participate in decentralized economies. Why OpenLedger? The rise of AI agents in Web3 raises urgent questions around transparency, attribution, explainability, and contributor incentives. OpenLedger provides the infrastructure to ensure that contributor data used in model outputs is recorded with verifiable attribution. Through Proof of Attribution, contributors-whether they provide prompts, datasets, or logic refinements-can receive credit and rewards when their work influences model behavior. But attribution alone isn’t enough. In critical domains like smart contract deployment, DeFi automation, and DAO governance, understanding why a model made a decision is just as important as the output itself. OpenLedger supports explainability by linking outputs back to their original data sources-allowing developers and auditors to trace logic, validate decisions, and build trust in AI-powered systems. OpenLedger supports Morpheus by: Recording which data was used in generating model outputs Enabling verifiable attribution of contributed datasets Powering reward mechanisms for contributors Offering scalable and efficient model execution via OpenLoRA Supporting transparency and traceability in model decision-making This creates an open, rewardable foundation for AI-driven coding-without relying on opaque systems. How is the system built? The Morpheus architecture has three layers: Datanet Layer OpenLedger powers Morpheus with a specialized Datanet - a decentralized data layer where developers, auditors, and contributors can share smart contract patterns, audit logs, exploit reports, and logic modules. Each submission is recorded onchain with attribution using OpenLedger’s Proof of Attribution. As the model learns and evolves from this data, contributors receive rewards proportional to their impact on future outputs. The Morpheus architecture has two layers: Intent Layer Users describe what they want to build. Example: "Create a token with tax logic that routes to a DAO." Morpheus parses the instruction, retrieves relevant contract types, and plans a modular execution flow. Agent Layer The agent generates, tests, and assembles the contract. It handles versioning, logic validation, and deployment readiness. Security checks-reentrancy protection, overflow control, gas modeling-are embedded into the generation phase. Generated outputs are mapped to their source data using OpenLedger’s Proof of Attribution, providing traceability across the pipeline. How does the AI model work? Morpheus is being powered by a specialized Solidity model built on top of OpenLedger. This model is purpose-built to handle the nuances of smart contract logic, security, and upgradeability. Unlike generalized coding agents, it is designed specifically for EVM environments and Web3 use cases, drawing from real protocol data and security best practices. Morpheus is fine-tuned on a vertical stack of smart contract data: Audited protocol code (e.g., Uniswap V4, Compound) OpenZeppelin libraries and EIP reference implementations Smart contract vulnerability reports and exploit reconstructions Edge cases from fuzz testing and adversarial examples It uses models like CodeLlama and DeepSeek-Coder, enhanced through RAG pipelines referencing standardized security patterns and emerging protocol designs. This training stack is integrated into a continuous feedback loop, enabling real-time specialization for EVM and beyond. Why a specialized model is needed? Smart contract development is uniquely high-stakes. A generalized AI model is not enough. As 'vibe coding' and natural language programming become more common, we're seeing an influx of AI-generated code in Web3 as well. But smart contracts are not frontends or prototypes-they govern real value, enforce trustless execution, and often become immutable after deployment. Billions have been lost in Web3 due to bugs and inefficiencies: In 2022 alone, over $3.8 billion was stolen due to smart contract exploits, many of which stemmed from avoidable issues like reentrancy, integer overflows, or access control failures. Inefficient contract structures lead to unnecessary gas consumption. Optimizing for gas can reduce costs by up to 40%, saving projects millions over time. Upgradeable contract patterns, like UUPS or Transparent Proxies, require strict adherence to storage layout and initialization rules. Mistakes here often go undetected by generic models and can render a contract unupgradeable or vulnerable. A specialized Solidity model is trained on real-world exploits, EIP standards, and libraries like OpenZeppelin to: Generate secure, gas-efficient code by default Recognize and correctly implement complex proxy patterns Map user intent to modular, auditable contract architectures Incorporate battle-tested logic from audited protocols and fuzz-tested edge cases Morpheus goes beyond syntax-it understands the nuances of decentralized infrastructure and deploys code that meets production-grade standards. What applications will this enable Token creation with built-in logic (tax, liquidity, governance) DeFi automations triggered by market conditions Payment contracts between agents and contributors DAO tooling with dynamic NFT-based voting Cross-chain bridging logic tied to real-world oracles Asset issuance flows through chat-based interfaces Natural language contract templates with reusable logic Each of these flows is backed by OpenLedger’s Proof of Attribution-ensuring traceability, explainability, and fair rewards across the ecosystem. This is the future of AI-native development. Open. Attributed. Explainable. Community-powered. Morpheus and OpenLedger are building the first system for autonomous coding agents where: Contributor work is recorded onchain Reuse is incentivized through attribution Model outputs are traceable and explainable Contracts evolve through human-agent collaboration Anyone can contribute prompts, logic, or flows-and get rewarded The smart contract engineer is no longer a human-only role. It is an agentic, decentralized, and transparent process-powered by OpenLedger.
0:28

OpenLedger X Morpheus The partnership of openledger with Morpheus enables Use Morpheus to build "The Autonomous Smart Contract Engineer" on top of OpenLedger. What is Morpheus? Morpheus is a Web3-native AI coding agent that turns natural language into executable smart contracts and full-stack dApps. It is powered by a specialized Solidity model built on top of OpenLedger, tailored for the unique demands of secure and efficient onchain development. It goes beyond code generation. Using fine-tuned models, agent-based architecture, and modular plugin support, Morpheus automates the entire development pipeline-from writing and simulating contracts to deploying and maintaining them. Its mission is to reduce the barrier to dApp creation while enabling autonomous agents and individuals to participate in decentralized economies. Why OpenLedger? The rise of AI agents in Web3 raises urgent questions around transparency, attribution, explainability, and contributor incentives. OpenLedger provides the infrastructure to ensure that contributor data used in model outputs is recorded with verifiable attribution. Through Proof of Attribution, contributors-whether they provide prompts, datasets, or logic refinements-can receive credit and rewards when their work influences model behavior. But attribution alone isn’t enough. In critical domains like smart contract deployment, DeFi automation, and DAO governance, understanding why a model made a decision is just as important as the output itself. OpenLedger supports explainability by linking outputs back to their original data sources-allowing developers and auditors to trace logic, validate decisions, and build trust in AI-powered systems. OpenLedger supports Morpheus by: Recording which data was used in generating model outputs Enabling verifiable attribution of contributed datasets Powering reward mechanisms for contributors Offering scalable and efficient model execution via OpenLoRA Supporting transparency and traceability in model decision-making This creates an open, rewardable foundation for AI-driven coding-without relying on opaque systems. How is the system built? The Morpheus architecture has three layers: Datanet Layer OpenLedger powers Morpheus with a specialized Datanet - a decentralized data layer where developers, auditors, and contributors can share smart contract patterns, audit logs, exploit reports, and logic modules. Each submission is recorded onchain with attribution using OpenLedger’s Proof of Attribution. As the model learns and evolves from this data, contributors receive rewards proportional to their impact on future outputs. The Morpheus architecture has two layers: Intent Layer Users describe what they want to build. Example: "Create a token with tax logic that routes to a DAO." Morpheus parses the instruction, retrieves relevant contract types, and plans a modular execution flow. Agent Layer The agent generates, tests, and assembles the contract. It handles versioning, logic validation, and deployment readiness. Security checks-reentrancy protection, overflow control, gas modeling-are embedded into the generation phase. Generated outputs are mapped to their source data using OpenLedger’s Proof of Attribution, providing traceability across the pipeline. How does the AI model work? Morpheus is being powered by a specialized Solidity model built on top of OpenLedger. This model is purpose-built to handle the nuances of smart contract logic, security, and upgradeability. Unlike generalized coding agents, it is designed specifically for EVM environments and Web3 use cases, drawing from real protocol data and security best practices. Morpheus is fine-tuned on a vertical stack of smart contract data: Audited protocol code (e.g., Uniswap V4, Compound) OpenZeppelin libraries and EIP reference implementations Smart contract vulnerability reports and exploit reconstructions Edge cases from fuzz testing and adversarial examples It uses models like CodeLlama and DeepSeek-Coder, enhanced through RAG pipelines referencing standardized security patterns and emerging protocol designs. This training stack is integrated into a continuous feedback loop, enabling real-time specialization for EVM and beyond. Why a specialized model is needed? Smart contract development is uniquely high-stakes. A generalized AI model is not enough. As 'vibe coding' and natural language programming become more common, we're seeing an influx of AI-generated code in Web3 as well. But smart contracts are not frontends or prototypes-they govern real value, enforce trustless execution, and often become immutable after deployment. Billions have been lost in Web3 due to bugs and inefficiencies: In 2022 alone, over $3.8 billion was stolen due to smart contract exploits, many of which stemmed from avoidable issues like reentrancy, integer overflows, or access control failures. Inefficient contract structures lead to unnecessary gas consumption. Optimizing for gas can reduce costs by up to 40%, saving projects millions over time. Upgradeable contract patterns, like UUPS or Transparent Proxies, require strict adherence to storage layout and initialization rules. Mistakes here often go undetected by generic models and can render a contract unupgradeable or vulnerable. A specialized Solidity model is trained on real-world exploits, EIP standards, and libraries like OpenZeppelin to: Generate secure, gas-efficient code by default Recognize and correctly implement complex proxy patterns Map user intent to modular, auditable contract architectures Incorporate battle-tested logic from audited protocols and fuzz-tested edge cases Morpheus goes beyond syntax-it understands the nuances of decentralized infrastructure and deploys code that meets production-grade standards. What applications will this enable Token creation with built-in logic (tax, liquidity, governance) DeFi automations triggered by market conditions Payment contracts between agents and contributors DAO tooling with dynamic NFT-based voting Cross-chain bridging logic tied to real-world oracles Asset issuance flows through chat-based interfaces Natural language contract templates with reusable logic Each of these flows is backed by OpenLedger’s Proof of Attribution-ensuring traceability, explainability, and fair rewards across the ecosystem. This is the future of AI-native development. Open. Attributed. Explainable. Community-powered. Morpheus and OpenLedger are building the first system for autonomous coding agents where: Contributor work is recorded onchain Reuse is incentivized through attribution Model outputs are traceable and explainable Contracts evolve through human-agent collaboration Anyone can contribute prompts, logic, or flows-and get rewarded The smart contract engineer is no longer a human-only role. It is an agentic, decentralized, and transparent process-powered by OpenLedger.

OpenLedger

46,944 views • 1 year ago

VoxCPM 2 just dropped by OpenBMB Only 2B-param open-source TTS (Text-to-Speech) model built for production-grade multilingual voice work. Apache-2.0 license, Can run on only 8GB VRAM. • Eliminates the "robotic" feel of traditional TTS, delivering prosody and emotional depth suitable for high-stakes professional environments like filmmaking, gaming, animation, and audiobooks. • 30-language multilingual: no language tag needed, just type in a supported language and generate directly. • Voice design: create a brand-new voice from a text description alone, like age, tone, pace, or emotion. No reference audio required. Describe the desired voice characteristics (gender, age, tone, emotion, pace …) in Control Instruction, and VoxCPM2 will craft a unique voice from your description alone. • Controllable cloning: clone from a short clip, then steer delivery style without losing the speaker’s core voice. • Ultimate cloning: use reference audio + transcript for continuation-style cloning that keeps the tiny vocal details. • 48kHz output: takes 16kHz reference audio and produces studio-quality speech without an external upsampler. • Real-time ready: around 0.3 RTF on RTX 4090, even lower with Nano-VLLM. • Commercial use: Apache-2.0 licensed. Developer-Friendly Infrastructure: - Native Torch Inference: Direct support for PyTorch-based workflows. - Training Flexibility: Supports both full-parameter and LoRA fine-tuning for specific domain adaptation. - Production Readiness: Compatible with voxcpm-nanovllm for large-scale, high-concurrency deployment.
1:58

VoxCPM 2 just dropped by OpenBMB Only 2B-param open-source TTS (Text-to-Speech) model built for production-grade multilingual voice work. Apache-2.0 license, Can run on only 8GB VRAM. • Eliminates the "robotic" feel of traditional TTS, delivering prosody and emotional depth suitable for high-stakes professional environments like filmmaking, gaming, animation, and audiobooks. • 30-language multilingual: no language tag needed, just type in a supported language and generate directly. • Voice design: create a brand-new voice from a text description alone, like age, tone, pace, or emotion. No reference audio required. Describe the desired voice characteristics (gender, age, tone, emotion, pace …) in Control Instruction, and VoxCPM2 will craft a unique voice from your description alone. • Controllable cloning: clone from a short clip, then steer delivery style without losing the speaker’s core voice. • Ultimate cloning: use reference audio + transcript for continuation-style cloning that keeps the tiny vocal details. • 48kHz output: takes 16kHz reference audio and produces studio-quality speech without an external upsampler. • Real-time ready: around 0.3 RTF on RTX 4090, even lower with Nano-VLLM. • Commercial use: Apache-2.0 licensed. Developer-Friendly Infrastructure: - Native Torch Inference: Direct support for PyTorch-based workflows. - Training Flexibility: Supports both full-parameter and LoRA fine-tuning for specific domain adaptation. - Production Readiness: Compatible with voxcpm-nanovllm for large-scale, high-concurrency deployment.

Rohan Paul

13,541 views • 4 months ago

GeoLibre v2.3.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release brings a legend that writes itself from your symbology, a new GeoLens catalog browser, and 200+ GeoLibre Rust geoprocessing tools running entirely in the browser. What's new in v2.3.0 - Automatic on-map Legend: the legend builds itself from your visible layers, with class rows for graduated, categorized, rule-based, and expression styling, gradient bars for heatmaps and raster colormaps, and land-cover labels from a Raster Attribute Table. Rename, hide, reorder, or add your own entries, and it saves with the project. - Symbology swatches in the Layers panel: every row shows a dot, line, square, or image glyph in the layer's own color, so a tall layer stack reads at a glance. - GeoLens catalog browser: connect to a self-hosted GeoLens server, search its catalog, and add datasets as vector tiles, GeoJSON, or rendered raster tiles. - Emerging Hot Spot Analysis: build a space-time cube from timestamped points and classify every cell as a new, intensifying, persistent, diminishing, sporadic, oscillating, or historical hot or cold spot, all client side. - Mosaic time series: the Time Slider now steps through MosaicJSON and STAC collections of many COGs per date, on either a GPU or a WASM rendering engine. - Copy and paste layer styles: give a whole set of layers one consistent look without restyling each in turn. - Shareable tool links: deep-link any Whitebox tool with a ?tool= URL that opens the dialog preselected and pre-fills the form, with a Copy link button to build it for you. - Smarter data loading: pick which layers to load from a multi-layer GeoPackage, import CSVs whose coordinates are in any projected CRS, and read a raster's real CRS, pixel size, and extent from the metadata dialog. - Multiple AI profiles: define several provider, model, and credential setups, pick a default, and switch between them from the assistant panel. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS #Geospatial #OpenSource #RemoteSensing #MapLibre #GeoLibre
0:48

GeoLibre v2.3.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release brings a legend that writes itself from your symbology, a new GeoLens catalog browser, and 200+ GeoLibre Rust geoprocessing tools running entirely in the browser. What's new in v2.3.0 - Automatic on-map Legend: the legend builds itself from your visible layers, with class rows for graduated, categorized, rule-based, and expression styling, gradient bars for heatmaps and raster colormaps, and land-cover labels from a Raster Attribute Table. Rename, hide, reorder, or add your own entries, and it saves with the project. - Symbology swatches in the Layers panel: every row shows a dot, line, square, or image glyph in the layer's own color, so a tall layer stack reads at a glance. - GeoLens catalog browser: connect to a self-hosted GeoLens server, search its catalog, and add datasets as vector tiles, GeoJSON, or rendered raster tiles. - Emerging Hot Spot Analysis: build a space-time cube from timestamped points and classify every cell as a new, intensifying, persistent, diminishing, sporadic, oscillating, or historical hot or cold spot, all client side. - Mosaic time series: the Time Slider now steps through MosaicJSON and STAC collections of many COGs per date, on either a GPU or a WASM rendering engine. - Copy and paste layer styles: give a whole set of layers one consistent look without restyling each in turn. - Shareable tool links: deep-link any Whitebox tool with a ?tool= URL that opens the dialog preselected and pre-fills the form, with a Copy link button to build it for you. - Smarter data loading: pick which layers to load from a multi-layer GeoPackage, import CSVs whose coordinates are in any projected CRS, and read a raster's real CRS, pixel size, and extent from the metadata dialog. - Multiple AI profiles: define several provider, model, and credential setups, pick a default, and switch between them from the assistant panel. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS #Geospatial #OpenSource #RemoteSensing #MapLibre #GeoLibre

Qiusheng Wu

293,227 views • 1 month ago

I know there are many people out there who credit dogs for keeping them going, getting through tough times and even saving their lives. I’ve heard the personal stories from 100s of people. Dogs are so pure and unconditional with their support and love. I can’t explain how much pressure and stress I’ve been under the last year and in particular the last 3-4 months. Tina may no longer be with us but it is because of her that I keep going on this mission. In the dark times I think about her face and her warm cosy fur. I’m not sure but were it not for Tina there are times when I might have broken or had to stop such was the suffering and pain we see. So much is about to come together… 📕 In 43 days the book about Tina will be out. It was a work of pure love. But exhausting too emotionally. 🏥 Tina’s field hospital opens on Tuesday with our new vet starting. Her big hospital is also being built. 🎤 I’m doing about 7-8 events in Ireland and England in May followed by the USA telling Tina’s story to 1000s of people. I’m terrified. 🥘 Her little sibling (in my head) Alba has her own kitchen just open feeding 1250 dogs a day ✂️ We have funded over 20,000 sterilising in 2025 already. That’s just a tiny amount of the things happening. We do them all for the street dogs but I personally do them for Tina. Some dogs (and people) shake you and move you to your very core. They are soul dogs. That is Tina for me. I was exhausted and burnt out tonight with so much to juggle so I sat in bed and opened my Tina folder of videos on my phone. I wanted to sleep but just seeing her face made me want to share a little video and write this. She just makes me want to put more good into the world for all to share. Maybe when I get through the next 2 mad months I dream of sitting on a little quiet beach somewhere on my own reading a book, fully offline and just thinking about Tina. I’ll tell her about all that has happened. I’ve not been able to do that yet. But for now there is one more big push needed in Tina’s name 💛💙
0:41

I know there are many people out there who credit dogs for keeping them going, getting through tough times and even saving their lives. I’ve heard the personal stories from 100s of people. Dogs are so pure and unconditional with their support and love. I can’t explain how much pressure and stress I’ve been under the last year and in particular the last 3-4 months. Tina may no longer be with us but it is because of her that I keep going on this mission. In the dark times I think about her face and her warm cosy fur. I’m not sure but were it not for Tina there are times when I might have broken or had to stop such was the suffering and pain we see. So much is about to come together… 📕 In 43 days the book about Tina will be out. It was a work of pure love. But exhausting too emotionally. 🏥 Tina’s field hospital opens on Tuesday with our new vet starting. Her big hospital is also being built. 🎤 I’m doing about 7-8 events in Ireland and England in May followed by the USA telling Tina’s story to 1000s of people. I’m terrified. 🥘 Her little sibling (in my head) Alba has her own kitchen just open feeding 1250 dogs a day ✂️ We have funded over 20,000 sterilising in 2025 already. That’s just a tiny amount of the things happening. We do them all for the street dogs but I personally do them for Tina. Some dogs (and people) shake you and move you to your very core. They are soul dogs. That is Tina for me. I was exhausted and burnt out tonight with so much to juggle so I sat in bed and opened my Tina folder of videos on my phone. I wanted to sleep but just seeing her face made me want to share a little video and write this. She just makes me want to put more good into the world for all to share. Maybe when I get through the next 2 mad months I dream of sitting on a little quiet beach somewhere on my own reading a book, fully offline and just thinking about Tina. I’ll tell her about all that has happened. I’ve not been able to do that yet. But for now there is one more big push needed in Tina’s name 💛💙

Niall Harbison

233,798 views • 1 year ago

Marathon | Ultimate Overview, Gameplay, Hands-On Impressions ▪️Team based extraction shooter set on Tau Ceti IV, scavenge remains of a lost colony for glory and fortune ▪️Launch: Coming to PS5, Xbox and PC on September 23 ▪️Will be a "premium" product, meaning not F2P ▪️"Weapons and movement feels crisp, fluid, and incredibly satisfying in that rarified way that few studios can achieve" ▪️3 maps, 6 runner classes at launch, more to follow post-launch ▪️Plans to support the game with new maps, weapons, characters, and more as it goes on ▪️Ranked play, end game challenges, seasonal storytelling, secrets to discover, community events all in Season One with more content coming ▪️You play as a "runner", cybernetic mercenary who's given up their human form for a bio-synthetic shell with unique abilities and stats ▪️Runner Types: "Locus" is a soldier class for pushing forward, can use a shield, "Void" is stealth, sneak by undetected, use smoke grenades, "Blackbird" is reconnaissance based, find players, positioning, and more ▪️Uncover secrets hidden in the wake of the original Marathon trilogy's events - how and where this overlaps or clashes with the original games "is a story that will unfold over time" ▪️Objective is to get in, get as much loot as you can, and get out alive ▪️If you die, your gear and loot will be up for grabs. Survive and you can take your gear with you on future runs as you grow in power and fill your vault ▪️Fight in a crew of up to 3 players, various map sizes up to 18 players ▪️PvP + PvE, battle other players, security forces, and "otherworldly threats" ▪️Contextual pings, shared objectives, down-but-not-out mechanics ▪️Environments are filled with weapons, materials, items, resources, cores, keys, backpacks, secrets etc to find You can find things like backpacks that expand your inventory, powerful and unique weapons, consumables to heal yourself, ▪️Select your runner, strategically build your loadouts pre-match ▪️Factions in the game can sponsor you and your loadout ▪️You have the freedom to opt out of crew fill and take all the glory for yourself as a solo runner ▪️"This is probably the best-looking, best-feeling extraction shooter ever made", not easy for newcomers to the genre, can be tough to get into, but one previewer was "reluctant to put Marathon down. This is a good sign – the best thing I can say after any preview is that I want more – but big questions remain" ▪️No two matches ever play out the same, dynamic events, variable weather, unpredictable players etc ▪️Customization: Each Runner "brings their own flair", but serve as foundations to customize your playstyle - collect implants and equipment to augment your Runner, like stacking implants/upgrades to reduce heat build-up to allow you to keep running, jumping, sliding longer ▪️Contracts: "Ingenious" incentives, finish contracts you accept for permanent upgrades to your stats, add precious items like grenades, shields, inventory-expanding backpacks, etc to your market. The bigger your market, the better your minimum loadout becomes ▪️Contracts provide "valuable direction", bring welcome diversity and add a "precious hit of semi-permanent progression" ▪️Prestige Cores: Rare items that can "really push the boundaries" (ex: Glitch has an upgrade that turns double jump into triple jump for more manueverability) ▪️Another rare item is a backpack that turns you invisible while interacting with containers (very useful when everyone on the map is hunting you) ▪️Each match "feels like the most important one ever", high stakes, "surprisingly challenging AI enemies" that were "much, much better at surrounding and overwhelming players than the vast majority of AI enemies found in other games" ▪️Having a balanced crew helps in exploring the "dark and forgotten places" in maps - filled with deadly creatures like a species of giant ticks who swarm you frantically ▪️Battles can become frantic as you encounter other crews, stalk them across the map, encounter AI enemies to fend off when others come to claim your loot (one preview battle turned into a 9 player free-for-all) ▪️Downside could be the reward system where skilled players enter fresh matches with high level gear that outmatches less skilled players, though Bungie says it has gone out of its way to try and make it where a poorly equipped squad can still stand a chance if they play well ▪️Even if you die, you can can still get upgrades, improve your reputation with factions, advance in perk trees, etc by completing various quests, looting chests, or fulfilling specific objectives which unlock new bits of story and can grant perks like buying slightly better gear from vendors ▪️"The world Bungie has built is every bit as beautiful as it is creepy and dystopian, and there are moments where they satisfyingly hint at the events of the Marathon trilogy from the ‘90s", though "what little I saw did only slightly more than pay lip service to the world" ▪️Uses the seasonal reset model like in Diablo 4, where players are stripped of their loot and progress at the end of each season to mix things up with new content and new meta ▪️Game is designed to get you killed, Bungie expects average exfiltration rates to be comfortably below 50% ▪️"You will lose a lot, and losing hurts a lot[...]Extracts are snapshots of this unpredictability. That white-knuckle countdown may become a gut-punch defeat, an unforgettable clutch, a desperate last-second dive into the portal as your downed allies watch in envy and horror (couldn't be me), or an unnerving anticlimax. It's not always a pleasant one, but it is a strong emotional hook" ▪️Some previewers worried about variety, weren't impressed with the objectives in the alpha, like hunting computer monitors on the map, sprinting over, marking dropships, sprinting over, and repeating, though random events help spice things up ▪️Not a 20-map game says Bungie, map design "does have that sticky battle royale quality of generating memories", though still worried 3-4 medium sized maps might not be enough ▪️Bungie's "verbiage" on updates sounds like possibly 1 new map a year + refreshing existing ones in between, which could work in its favor pushing players to really learn maps, but will they hold up over hundreds of hours? ▪️Fights are decided by gun skill, clever ability use, map awareness, how strong your shield is, how many healing supplies you have, etc ▪️Example: If you have one bar of shield and enemy has four, you'll lose 1v1, but change it up with powerful grenades, status effects like toxic or overheat, backpacks are "transformative", etc Gameplay Trailer ▶️ Gameplay Overview ▶️ PS Blog ➡️ IGN ➡️ GamesRadar ➡️ ▶️
2:58

Marathon | Ultimate Overview, Gameplay, Hands-On Impressions ▪️Team based extraction shooter set on Tau Ceti IV, scavenge remains of a lost colony for glory and fortune ▪️Launch: Coming to PS5, Xbox and PC on September 23 ▪️Will be a "premium" product, meaning not F2P ▪️"Weapons and movement feels crisp, fluid, and incredibly satisfying in that rarified way that few studios can achieve" ▪️3 maps, 6 runner classes at launch, more to follow post-launch ▪️Plans to support the game with new maps, weapons, characters, and more as it goes on ▪️Ranked play, end game challenges, seasonal storytelling, secrets to discover, community events all in Season One with more content coming ▪️You play as a "runner", cybernetic mercenary who's given up their human form for a bio-synthetic shell with unique abilities and stats ▪️Runner Types: "Locus" is a soldier class for pushing forward, can use a shield, "Void" is stealth, sneak by undetected, use smoke grenades, "Blackbird" is reconnaissance based, find players, positioning, and more ▪️Uncover secrets hidden in the wake of the original Marathon trilogy's events - how and where this overlaps or clashes with the original games "is a story that will unfold over time" ▪️Objective is to get in, get as much loot as you can, and get out alive ▪️If you die, your gear and loot will be up for grabs. Survive and you can take your gear with you on future runs as you grow in power and fill your vault ▪️Fight in a crew of up to 3 players, various map sizes up to 18 players ▪️PvP + PvE, battle other players, security forces, and "otherworldly threats" ▪️Contextual pings, shared objectives, down-but-not-out mechanics ▪️Environments are filled with weapons, materials, items, resources, cores, keys, backpacks, secrets etc to find You can find things like backpacks that expand your inventory, powerful and unique weapons, consumables to heal yourself, ▪️Select your runner, strategically build your loadouts pre-match ▪️Factions in the game can sponsor you and your loadout ▪️You have the freedom to opt out of crew fill and take all the glory for yourself as a solo runner ▪️"This is probably the best-looking, best-feeling extraction shooter ever made", not easy for newcomers to the genre, can be tough to get into, but one previewer was "reluctant to put Marathon down. This is a good sign – the best thing I can say after any preview is that I want more – but big questions remain" ▪️No two matches ever play out the same, dynamic events, variable weather, unpredictable players etc ▪️Customization: Each Runner "brings their own flair", but serve as foundations to customize your playstyle - collect implants and equipment to augment your Runner, like stacking implants/upgrades to reduce heat build-up to allow you to keep running, jumping, sliding longer ▪️Contracts: "Ingenious" incentives, finish contracts you accept for permanent upgrades to your stats, add precious items like grenades, shields, inventory-expanding backpacks, etc to your market. The bigger your market, the better your minimum loadout becomes ▪️Contracts provide "valuable direction", bring welcome diversity and add a "precious hit of semi-permanent progression" ▪️Prestige Cores: Rare items that can "really push the boundaries" (ex: Glitch has an upgrade that turns double jump into triple jump for more manueverability) ▪️Another rare item is a backpack that turns you invisible while interacting with containers (very useful when everyone on the map is hunting you) ▪️Each match "feels like the most important one ever", high stakes, "surprisingly challenging AI enemies" that were "much, much better at surrounding and overwhelming players than the vast majority of AI enemies found in other games" ▪️Having a balanced crew helps in exploring the "dark and forgotten places" in maps - filled with deadly creatures like a species of giant ticks who swarm you frantically ▪️Battles can become frantic as you encounter other crews, stalk them across the map, encounter AI enemies to fend off when others come to claim your loot (one preview battle turned into a 9 player free-for-all) ▪️Downside could be the reward system where skilled players enter fresh matches with high level gear that outmatches less skilled players, though Bungie says it has gone out of its way to try and make it where a poorly equipped squad can still stand a chance if they play well ▪️Even if you die, you can can still get upgrades, improve your reputation with factions, advance in perk trees, etc by completing various quests, looting chests, or fulfilling specific objectives which unlock new bits of story and can grant perks like buying slightly better gear from vendors ▪️"The world Bungie has built is every bit as beautiful as it is creepy and dystopian, and there are moments where they satisfyingly hint at the events of the Marathon trilogy from the ‘90s", though "what little I saw did only slightly more than pay lip service to the world" ▪️Uses the seasonal reset model like in Diablo 4, where players are stripped of their loot and progress at the end of each season to mix things up with new content and new meta ▪️Game is designed to get you killed, Bungie expects average exfiltration rates to be comfortably below 50% ▪️"You will lose a lot, and losing hurts a lot[...]Extracts are snapshots of this unpredictability. That white-knuckle countdown may become a gut-punch defeat, an unforgettable clutch, a desperate last-second dive into the portal as your downed allies watch in envy and horror (couldn't be me), or an unnerving anticlimax. It's not always a pleasant one, but it is a strong emotional hook" ▪️Some previewers worried about variety, weren't impressed with the objectives in the alpha, like hunting computer monitors on the map, sprinting over, marking dropships, sprinting over, and repeating, though random events help spice things up ▪️Not a 20-map game says Bungie, map design "does have that sticky battle royale quality of generating memories", though still worried 3-4 medium sized maps might not be enough ▪️Bungie's "verbiage" on updates sounds like possibly 1 new map a year + refreshing existing ones in between, which could work in its favor pushing players to really learn maps, but will they hold up over hundreds of hours? ▪️Fights are decided by gun skill, clever ability use, map awareness, how strong your shield is, how many healing supplies you have, etc ▪️Example: If you have one bar of shield and enemy has four, you'll lose 1v1, but change it up with powerful grenades, status effects like toxic or overheat, backpacks are "transformative", etc Gameplay Trailer ▶️ Gameplay Overview ▶️ PS Blog ➡️ IGN ➡️ GamesRadar ➡️ ▶️

Shinobi602

181,743 views • 1 year ago

Borderlands 4 previews have dropped and they are insanely positive, It is being praised across the board as being a massive step up from Borderlands 3 and possibly the best BL to date! Here’s some highlights of the new info: - Borderlands 4 features a more grounded and darker story set on a dystopian world ruled by the Timekeeper - Humor is still present but more restrained to suit the darker narrative tone, Less cringe humor as in BL3 - The game feels like a soft reboot for the franchise, introducing new lore and gameplay structures - Movement has been completely overhauled with double-jumping, gliding, dashing, grappling hooks, and swimming - Four new Vault Hunters are available: Vex (Siren), Rafa (Gadget Soldier), Harlowe (Sniper), and Aton (Brawler) - Kairos, the new planet, is one seamless open world with no loading between zones - Split-screen co-op returns for local multiplayer - The game adopts a Destiny-like structure with side activities, dynamic events, and exploration hubs - Vehicles can be summoned anywhere on the map, making travel smoother and more immersive - The world of Kairos features dynamic weather systems and environmental variety - Combat pacing is faster with smarter, more aggressive enemies that emphasize dodging and movement - One boss previewed was the most complex and mechanics-rich in Borderlands history - Buildcrafting is significantly deeper with new enhancement gear slots and item augments - Weapon rarity is better balanced, legendary drops are rarer - Mission and boss replays are now built-in, allowing players to farm specific content - Online matchmaking is improved with simplified lobbies and clearer difficulty scaling - Gearbox aims to avoid turning Borderlands into a live-service GaaS model - Borderlands 4 aims to reclaim its position in the looter shooter genre after years of stagnation Borderlands 2 is one of my fave co-op games ever and BL3 really let me down but this is sounding like a massive improvement, definitely will be playing this at release this September 12th
0:53

Borderlands 4 previews have dropped and they are insanely positive, It is being praised across the board as being a massive step up from Borderlands 3 and possibly the best BL to date! Here’s some highlights of the new info: - Borderlands 4 features a more grounded and darker story set on a dystopian world ruled by the Timekeeper - Humor is still present but more restrained to suit the darker narrative tone, Less cringe humor as in BL3 - The game feels like a soft reboot for the franchise, introducing new lore and gameplay structures - Movement has been completely overhauled with double-jumping, gliding, dashing, grappling hooks, and swimming - Four new Vault Hunters are available: Vex (Siren), Rafa (Gadget Soldier), Harlowe (Sniper), and Aton (Brawler) - Kairos, the new planet, is one seamless open world with no loading between zones - Split-screen co-op returns for local multiplayer - The game adopts a Destiny-like structure with side activities, dynamic events, and exploration hubs - Vehicles can be summoned anywhere on the map, making travel smoother and more immersive - The world of Kairos features dynamic weather systems and environmental variety - Combat pacing is faster with smarter, more aggressive enemies that emphasize dodging and movement - One boss previewed was the most complex and mechanics-rich in Borderlands history - Buildcrafting is significantly deeper with new enhancement gear slots and item augments - Weapon rarity is better balanced, legendary drops are rarer - Mission and boss replays are now built-in, allowing players to farm specific content - Online matchmaking is improved with simplified lobbies and clearer difficulty scaling - Gearbox aims to avoid turning Borderlands into a live-service GaaS model - Borderlands 4 aims to reclaim its position in the looter shooter genre after years of stagnation Borderlands 2 is one of my fave co-op games ever and BL3 really let me down but this is sounding like a massive improvement, definitely will be playing this at release this September 12th

Synth Potato🥔

1,728,460 views • 1 year ago

In the July 29, 2026 Q&A, Bp. Donald Sanborn explicitly taught an error on the source of jurisdiction for confession which all pre-V2 theologians held to be "false and intolerable." This is the error of "jurisdictio ligata," or the theory that every priest receives in ordination itself all jurisdiction over every penitent so that nothing more—in the way of ecclesiastical jurisdiction received from a superior in the Church—is necessary for validity, and that the Church merely binds or restrict this power so that it cannot ordinarily be used. This theory holds that the only reason a priest "normally" needs "faculties" or "permission" from the Church to absolve is because she "binds" or "restricts" this divinely given power so that it can only be exercised in a specific location. Bp. Sanborn takes this even further in a gratuitous leap in stating that, whenever the bishop of a diocese is absent, this restriction is not present, because there is no authority to "restrict" this power of the priest, so any priest can freely absolve any penitent in that case. It is unfortunate that any educated member of the clergy would attempt to defend such an error today when it has for centuries been universally rejected and was only revived in recent times by certain heretics such as the Jansenists. Galtier, an author who wrote much on the sacrament of penance and was very familiar with the history of the opinions of theologians on this matter, has an excellent passage discussing this error in his 1956 treatise, De paenitentia, published by the Pontifical Gregorian University. Galtier comments as follows: “As if the Church binds the jurisdiction given by Christ Himself by virtue of ordination itself, by prohibiting penitents from subjecting themselves to priests besides those whom she herself wills, by assigning subjects. — But this is false and intolerable: for the Church cannot so bind the power granted by Christ Himself that its act would be invalid" (Galtier, De paenitentia, #536). It is an unfortunate fact that Thesis clergy at times simply make up theology as they go and as needed for the Thesis, without squaring it with the actual teaching of theologians. The ultimate norm seems to be defending the Thesis at all cost. Nothing else can explain how opinions like this could be expressed by these priests when we all know from our seminary studies that they are universally rejected. Those from the Novus Ordo, SSPX, or other R&R who have been looking at the Thesis as a possible theological solution that checks all the boxes should understand that it will in no way solve their theological anxieties. It will not satisfy an intellect looking for an explanation of how they have jurisdiction for confession, or how they can reject the Novus Ordo "hierarchy," or how apostolicity or visibility continue. The true, and only, Catholic teaching is that a priest must receive jurisdiction from an authority in the Church in order to validly hear confessions. There is no way to explain this away as if it is ever unnecessary. It is a divinely established requirement. This is why SSPX also completely lost the battle to Sisco and Salza over the matter of jurisdiction. As long as they say Prevost and his predecessors are true Popes, they have absolutely no way to explain how they can validly absolve penitents. When a true Pope explicitly denies you jurisdiction, you have none. Zero! On the other hand, it is the standard teaching of theologians, notably Lainez, SJ, that the Church (i.e the will of the Popes) supplies jurisdiction when necessary "for the building up of the Church" and "lest souls perish." It was on this grounds that theologians commonly taught supplied jurisdiction in cases of common error and doubt, even before it was inserted in the Canon Law of the Church. This will to grant jurisdiction when necessary lest souls be lost is the true explanation for how priests today, in times of apostasy and a long vacancy in the papacy, have jurisdiction for confession as well as other instances where it is needed for the same purposes. Yet, the Thesis priests are determined to reject this as the source of jurisdiction for their absolution of penitents, because the idea of supplied jurisdiction in this way, or any way, would lead to the dismantling of the Thesis. They are insistent that we are simply garbage men and sacrament machines with no jurisdiction, because this would mean we must look to the Novus Ordo as the one with authority to solve this situation. What a depressing Thesis so inconsistent with the very nature and purpose of the Church!
0:46

In the July 29, 2026 Q&A, Bp. Donald Sanborn explicitly taught an error on the source of jurisdiction for confession which all pre-V2 theologians held to be "false and intolerable." This is the error of "jurisdictio ligata," or the theory that every priest receives in ordination itself all jurisdiction over every penitent so that nothing more—in the way of ecclesiastical jurisdiction received from a superior in the Church—is necessary for validity, and that the Church merely binds or restrict this power so that it cannot ordinarily be used. This theory holds that the only reason a priest "normally" needs "faculties" or "permission" from the Church to absolve is because she "binds" or "restricts" this divinely given power so that it can only be exercised in a specific location. Bp. Sanborn takes this even further in a gratuitous leap in stating that, whenever the bishop of a diocese is absent, this restriction is not present, because there is no authority to "restrict" this power of the priest, so any priest can freely absolve any penitent in that case. It is unfortunate that any educated member of the clergy would attempt to defend such an error today when it has for centuries been universally rejected and was only revived in recent times by certain heretics such as the Jansenists. Galtier, an author who wrote much on the sacrament of penance and was very familiar with the history of the opinions of theologians on this matter, has an excellent passage discussing this error in his 1956 treatise, De paenitentia, published by the Pontifical Gregorian University. Galtier comments as follows: “As if the Church binds the jurisdiction given by Christ Himself by virtue of ordination itself, by prohibiting penitents from subjecting themselves to priests besides those whom she herself wills, by assigning subjects. — But this is false and intolerable: for the Church cannot so bind the power granted by Christ Himself that its act would be invalid" (Galtier, De paenitentia, #536). It is an unfortunate fact that Thesis clergy at times simply make up theology as they go and as needed for the Thesis, without squaring it with the actual teaching of theologians. The ultimate norm seems to be defending the Thesis at all cost. Nothing else can explain how opinions like this could be expressed by these priests when we all know from our seminary studies that they are universally rejected. Those from the Novus Ordo, SSPX, or other R&R who have been looking at the Thesis as a possible theological solution that checks all the boxes should understand that it will in no way solve their theological anxieties. It will not satisfy an intellect looking for an explanation of how they have jurisdiction for confession, or how they can reject the Novus Ordo "hierarchy," or how apostolicity or visibility continue. The true, and only, Catholic teaching is that a priest must receive jurisdiction from an authority in the Church in order to validly hear confessions. There is no way to explain this away as if it is ever unnecessary. It is a divinely established requirement. This is why SSPX also completely lost the battle to Sisco and Salza over the matter of jurisdiction. As long as they say Prevost and his predecessors are true Popes, they have absolutely no way to explain how they can validly absolve penitents. When a true Pope explicitly denies you jurisdiction, you have none. Zero! On the other hand, it is the standard teaching of theologians, notably Lainez, SJ, that the Church (i.e the will of the Popes) supplies jurisdiction when necessary "for the building up of the Church" and "lest souls perish." It was on this grounds that theologians commonly taught supplied jurisdiction in cases of common error and doubt, even before it was inserted in the Canon Law of the Church. This will to grant jurisdiction when necessary lest souls be lost is the true explanation for how priests today, in times of apostasy and a long vacancy in the papacy, have jurisdiction for confession as well as other instances where it is needed for the same purposes. Yet, the Thesis priests are determined to reject this as the source of jurisdiction for their absolution of penitents, because the idea of supplied jurisdiction in this way, or any way, would lead to the dismantling of the Thesis. They are insistent that we are simply garbage men and sacrament machines with no jurisdiction, because this would mean we must look to the Novus Ordo as the one with authority to solve this situation. What a depressing Thesis so inconsistent with the very nature and purpose of the Church!

Fr. Gabriel Lavery

11,027 views • 21 days ago

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 views • 1 month ago