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🚨NEWS: $TSLA DEVELOPING NEW SMALLER, CHEAPER EV SUV • Tesla is developing an all-new compact electric SUV, according to four people familiar with the matter. • The vehicle is not a variant of the Model 3 or Model Y. ✅ Key Details • Length: approximately 4.28 meters (14 feet)... — significantly shorter than the Model Y (about 15.7 feet). • Planned production locations: primarily China (Shanghai factory), with potential expansion to the United States and Europe. • Expected pricing: substantially lower than the entry-level Model 3 (which starts at ~$37,000 in the US and $34,000 in China). ✅ Design Approach • Smaller battery for cost reduction (resulting in shorter range than the Model Y’s 306–327 miles). • Single electric motor instead of dual motors. • Significantly lighter weight target (~1.5 metric tons vs. ~2 tons for Model Y). ✅ Strategic Context • This project comes after Elon Musk scrapped a previous low-cost EV plan in 2024 to focus on robotaxis and Optimus. • The new SUV could be offered in both fully autonomous and human-driven versions to suit different global markets and regulations. • The development is still in early stages, with production unlikely to start in 2026.show more

Tsla Archive

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56,232 次观看 • 4 个月前 •via X (Twitter)

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NEWS: Volvo is discontinuing the EX30 EV in the U.S., just a year after it launched. It will be pulled from the market after the 2026 model year. • Starting price: $40,000 • EPA range: Up to 261 miles • 0-60mph: As low as 3.4s • 69 kWh battery • Up to 422 hp, AWD • 12.3" center display • Peak charging rate: 153 kW • Length: 166", 18" shorter than a Model 3 All current orders in the system will be produced with production winding down for the U.S. after the summer. Dealers have until March 20, 2026 to place EX30 and EX30 Cross Country orders. Volvo said the new EX60 and EX90 electric SUVs will continue in the U.S. with the former set to launch later this year and the latter having just received enhancements for 2026.

NEWS: Volvo is discontinuing the EX30 EV in the U.S., just a year after it launched. It will be pulled from the market after the 2026 model year. • Starting price: $40,000 • EPA range: Up to 261 miles • 0-60mph: As low as 3.4s • 69 kWh battery • Up to 422 hp, AWD • 12.3" center display • Peak charging rate: 153 kW • Length: 166", 18" shorter than a Model 3 All current orders in the system will be produced with production winding down for the U.S. after the summer. Dealers have until March 20, 2026 to place EX30 and EX30 Cross Country orders. Volvo said the new EX60 and EX90 electric SUVs will continue in the U.S. with the former set to launch later this year and the latter having just received enhancements for 2026.

Sawyer Merritt

212,939 次观看 • 5 个月前

🚨 MERCEDES JUST PUT A MOTOR ONLY 8 CM THICK INTO A CAR THAT CAN HIT 62 MPH IN 2.1 SECONDS. Instead of conventional radial flux motors, Mercedes is betting big on axial flux technology. In these motors, the electromagnetic force flows parallel to the axle, allowing two magnetic rotors to sandwich a central stator in a flat, disc-like layout. The result is dramatically smaller and more powerful. The front motor in the new all-electric Mercedes-AMG GT 4-door Coupe is just 9 cm wide. The rear motors are even thinner at roughly 8 cm each. Despite their tiny size, they help launch the heavy performance car from 0-62 mph in just 2.1 seconds, with a top speed of up to 186 mph. Why this matters: • Axial flux motors are significantly more power-dense and can be up to 50% lighter than traditional designs • Their extreme thinness frees up packaging space in the vehicle for better weight distribution, aerodynamics, or interior room • Mercedes acquired YASA in 2021 and has spent years developing the complex manufacturing processes needed to build them at scale • The technology is debuting in a high-performance AMG model, showing Mercedes is serious about using it in its most demanding cars The deeper implication: While most of the EV conversation focuses on batteries and software, the electric motor itself is undergoing a quiet revolution. Axial flux designs have long been seen as theoretically superior but extremely difficult to manufacture at scale. By solving the production challenges and putting these motors into a real high-performance car, Mercedes is pushing the entire industry forward. The next generation of electric performance cars may not just have bigger batteries they may have fundamentally better motors. We’re watching the physical hardware of EVs evolve as dramatically as the software has. How important do you think motor technology (rather than just battery size) will be for the future of electric performance cars? Follow for more frontier automotive engineering and electric vehicle technology.

🚨 MERCEDES JUST PUT A MOTOR ONLY 8 CM THICK INTO A CAR THAT CAN HIT 62 MPH IN 2.1 SECONDS. Instead of conventional radial flux motors, Mercedes is betting big on axial flux technology. In these motors, the electromagnetic force flows parallel to the axle, allowing two magnetic rotors to sandwich a central stator in a flat, disc-like layout. The result is dramatically smaller and more powerful. The front motor in the new all-electric Mercedes-AMG GT 4-door Coupe is just 9 cm wide. The rear motors are even thinner at roughly 8 cm each. Despite their tiny size, they help launch the heavy performance car from 0-62 mph in just 2.1 seconds, with a top speed of up to 186 mph. Why this matters: • Axial flux motors are significantly more power-dense and can be up to 50% lighter than traditional designs • Their extreme thinness frees up packaging space in the vehicle for better weight distribution, aerodynamics, or interior room • Mercedes acquired YASA in 2021 and has spent years developing the complex manufacturing processes needed to build them at scale • The technology is debuting in a high-performance AMG model, showing Mercedes is serious about using it in its most demanding cars The deeper implication: While most of the EV conversation focuses on batteries and software, the electric motor itself is undergoing a quiet revolution. Axial flux designs have long been seen as theoretically superior but extremely difficult to manufacture at scale. By solving the production challenges and putting these motors into a real high-performance car, Mercedes is pushing the entire industry forward. The next generation of electric performance cars may not just have bigger batteries they may have fundamentally better motors. We’re watching the physical hardware of EVs evolve as dramatically as the software has. How important do you think motor technology (rather than just battery size) will be for the future of electric performance cars? Follow for more frontier automotive engineering and electric vehicle technology.

TheNewPhysics

399,616 次观看 • 2 个月前

Tesla's Giga Berlin expansion confirmed: 500,000 to 800,000 annual capacity 🏭 In July 2025, Tesla confirmed an expansion of Giga Berlin that will add 2 million square feet of factory space. This boosts annual production capacity from 500,000 to 800,000 vehicles, with provisions for in-house battery cell production. In December 2025, Tesla announced plans for new local battery cell production by 2027. The rear underbody Gigacasting at Giga Berlin consolidates what were previously 70 individual parts into a single large aluminum casting. By late 2025, the factory is operating on two shifts to meet demand, exporting Model Y vehicles to over 30 markets globally. ❌ Don't leave your insights to chance with the X algorithm ✅ Subscribe for free to my weekly newsletter about all things Gigacasting and magnesium Thixomolding: 📧 📊 The Gigacasting Database:

Tesla's Giga Berlin expansion confirmed: 500,000 to 800,000 annual capacity 🏭 In July 2025, Tesla confirmed an expansion of Giga Berlin that will add 2 million square feet of factory space. This boosts annual production capacity from 500,000 to 800,000 vehicles, with provisions for in-house battery cell production. In December 2025, Tesla announced plans for new local battery cell production by 2027. The rear underbody Gigacasting at Giga Berlin consolidates what were previously 70 individual parts into a single large aluminum casting. By late 2025, the factory is operating on two shifts to meet demand, exporting Model Y vehicles to over 30 markets globally. ❌ Don't leave your insights to chance with the X algorithm ✅ Subscribe for free to my weekly newsletter about all things Gigacasting and magnesium Thixomolding: 📧 📊 The Gigacasting Database:

Luca Greco

98,931 次观看 • 6 个月前

50% cheaper Claude inference with just one line of code change! - Remove → model="claude-opus-4-8" - Add → model="ship-like/claude-opus-4-8" I verified the cost saving in my own terminal by invoking the same Anthropic model with the same prompt. The underlying engineering by Ship is actually interesting, and the patterns can be used in any production LLM stack. Essentially, a trained model is a frozen artifact. Every request performs the same forward-pass, whether it extracts a date or refactors a module, because the compute decision was made at training time, before the request existed. Ship makes that decision at inference time instead. After seeing a request, it searches over executions, involving single models, cascades, ensembles, or harnesses with tools, and serves the cheapest one that will match the reference model's quality. This is not a basic router, because picking a cheaper model per query doesn't ensure the cheaper model preserves the original's behavior, like output shape, tool-call patterns, and refusals. Ship measures this equivalence directly. Outputs stay distributionally indistinguishable from the reference model, not token-identical, since two calls to the same model already differ, but they are indistinguishable in capability and behavior. Of course, some requests execute cheaply and some cost Ship more than the customer pays, but the price per request is still a flat 50% off either way, so the execution-cost variance moves off the application's bill entirely. The video below depicts the cost savings and output in my real invocation, and I partnered with the team to put this together.

50% cheaper Claude inference with just one line of code change! - Remove → model="claude-opus-4-8" - Add → model="ship-like/claude-opus-4-8" I verified the cost saving in my own terminal by invoking the same Anthropic model with the same prompt. The underlying engineering by Ship is actually interesting, and the patterns can be used in any production LLM stack. Essentially, a trained model is a frozen artifact. Every request performs the same forward-pass, whether it extracts a date or refactors a module, because the compute decision was made at training time, before the request existed. Ship makes that decision at inference time instead. After seeing a request, it searches over executions, involving single models, cascades, ensembles, or harnesses with tools, and serves the cheapest one that will match the reference model's quality. This is not a basic router, because picking a cheaper model per query doesn't ensure the cheaper model preserves the original's behavior, like output shape, tool-call patterns, and refusals. Ship measures this equivalence directly. Outputs stay distributionally indistinguishable from the reference model, not token-identical, since two calls to the same model already differ, but they are indistinguishable in capability and behavior. Of course, some requests execute cheaply and some cost Ship more than the customer pays, but the price per request is still a flat 50% off either way, so the execution-cost variance moves off the application's bill entirely. The video below depicts the cost savings and output in my real invocation, and I partnered with the team to put this together.

Akshay 🚀

63,725 次观看 • 25 天前

Model 3 comes with lots of the same improvements that you love about New Model Y And starts at ~40,000 EUR in many countries – Better ride comfort – Improved aerodynamics for maximum efficiency & range – Quieter cabin (~20% reduction in road noise!) – Rear touchscreen with Bluetooth capability – Customizable ambient lighting – Ventilated front seats – Improved connectivity Also, running costs are much lower vs comparable combustion engine vehicles since all Teslas require very little maintenance & electricity is cheaper than gas in many regions

Model 3 comes with lots of the same improvements that you love about New Model Y And starts at ~40,000 EUR in many countries – Better ride comfort – Improved aerodynamics for maximum efficiency & range – Quieter cabin (~20% reduction in road noise!) – Rear touchscreen with Bluetooth capability – Customizable ambient lighting – Ventilated front seats – Improved connectivity Also, running costs are much lower vs comparable combustion engine vehicles since all Teslas require very little maintenance & electricity is cheaper than gas in many regions

Tesla Europe, Middle East & Africa

1,715,643 次观看 • 1 年前

Woah... the Tesla Semi is no joke. The Tesla team really made it the best semi truck in the world. Just check out the main stats shared from Jay Leno's video. • It goes 500-miles for the long-range model, tested fully loaded and also has a a 325-mile standard-range option available - they both handle max payload • It does 1.2 MW charging, which recovers ~60% of the battery (~300 miles of range) in just 30 minutes during mandatory driver breaks • Has a drag coefficient of ~0.4, which is lower than a Bugatti Veyron bc of the bullet-shaped cab and radical center-driver seating (fyi this is a ~7% improvement over previous design) • The battery is engineered to last 1 million miles, has the same cells as the Cybertruck • It's 50% cheaper to operate in California and nearly 20% cheaper per mile nationwide, this includes everything like energy, maintenance, no oil changes • While Jay Leno was hauling 60-70,000 lbs GVW, he said “I don’t even feel like I’m pulling anything…” • The Tesla Semi fleet already logged >13.5 million miles total, with one truck approaching 440,000 miles with 95% uptime • It's ~1,000 lbs lighter overall + 2,000 lb EV weight exemption keeps it at full payload parity with diesel trucks • Regen braking is so strong it descends steep grades at highway speed with almost zero use of service brakes • The truck is proven in Alaska winters with heat pump and minimal range loss • It has a turning radius like a Model 3/Y despite being a full-size Class 8 truck • Tesla has a new dedicated factory near Giga Reno ramping high-volume production this year (tens of thousands annually) with strong customer demand already locked in If I were a business, the Tesla Semi is a no-brainer!

Woah... the Tesla Semi is no joke. The Tesla team really made it the best semi truck in the world. Just check out the main stats shared from Jay Leno's video. • It goes 500-miles for the long-range model, tested fully loaded and also has a a 325-mile standard-range option available - they both handle max payload • It does 1.2 MW charging, which recovers ~60% of the battery (~300 miles of range) in just 30 minutes during mandatory driver breaks • Has a drag coefficient of ~0.4, which is lower than a Bugatti Veyron bc of the bullet-shaped cab and radical center-driver seating (fyi this is a ~7% improvement over previous design) • The battery is engineered to last 1 million miles, has the same cells as the Cybertruck • It's 50% cheaper to operate in California and nearly 20% cheaper per mile nationwide, this includes everything like energy, maintenance, no oil changes • While Jay Leno was hauling 60-70,000 lbs GVW, he said “I don’t even feel like I’m pulling anything…” • The Tesla Semi fleet already logged >13.5 million miles total, with one truck approaching 440,000 miles with 95% uptime • It's ~1,000 lbs lighter overall + 2,000 lb EV weight exemption keeps it at full payload parity with diesel trucks • Regen braking is so strong it descends steep grades at highway speed with almost zero use of service brakes • The truck is proven in Alaska winters with heat pump and minimal range loss • It has a turning radius like a Model 3/Y despite being a full-size Class 8 truck • Tesla has a new dedicated factory near Giga Reno ramping high-volume production this year (tens of thousands annually) with strong customer demand already locked in If I were a business, the Tesla Semi is a no-brainer!

Teslaconomics

129,568 次观看 • 4 个月前

Today, we’re pulling back more of the curtain on the in-house teams and technology that will bring the Ford Universal Electric Vehicle to life. More than half of our aero team came from the Formula 1 world, where air is either your greatest ally or most punishing enemy - and every curve matters. From the sculpted roofline to the underbody, our teams are chasing physics in search of longer range and lower cost for our customers. Our new mid-size electric truck’s aerodynamic efficiency is more than 15% better than any other pickup truck on the market today. “To the air, it’s no longer a truck”. From a zonal architecture and 48V low-voltage system, to the new in-house E-Box, and smaller and lighter wire harnesses, the team is obsessing over every detail with UEV. Our aim is to have a family of vehicles that we expect to compete on price with the best in the world, including gas vehicles. And they will be FUN to drive!

Today, we’re pulling back more of the curtain on the in-house teams and technology that will bring the Ford Universal Electric Vehicle to life. More than half of our aero team came from the Formula 1 world, where air is either your greatest ally or most punishing enemy - and every curve matters. From the sculpted roofline to the underbody, our teams are chasing physics in search of longer range and lower cost for our customers. Our new mid-size electric truck’s aerodynamic efficiency is more than 15% better than any other pickup truck on the market today. “To the air, it’s no longer a truck”. From a zonal architecture and 48V low-voltage system, to the new in-house E-Box, and smaller and lighter wire harnesses, the team is obsessing over every detail with UEV. Our aim is to have a family of vehicles that we expect to compete on price with the best in the world, including gas vehicles. And they will be FUN to drive!

Chris

28,770 次观看 • 5 个月前

NEWS: Volvo has announced US pricing for their all-new EV, the EX30, that will arrive in America this year. • Starting price: $44,900 • EPA range: Up to 253 miles • 0-60mph: As low as 3.4s (on higher trims) • 69kWh battery • Up to 422 hp, AWD • 12.3" center display • Exterior: Around 25% of all aluminum & 17% of all steel and plastics used are recycled • Interior: Around 30% of decor parts use recycled plastic • Peak charging rate: 153 kW • Length: 166", 18" shorter than a Model 3 • Manufactured in Belgium

NEWS: Volvo has announced US pricing for their all-new EV, the EX30, that will arrive in America this year. • Starting price: $44,900 • EPA range: Up to 253 miles • 0-60mph: As low as 3.4s (on higher trims) • 69kWh battery • Up to 422 hp, AWD • 12.3" center display • Exterior: Around 25% of all aluminum & 17% of all steel and plastics used are recycled • Interior: Around 30% of decor parts use recycled plastic • Peak charging rate: 153 kW • Length: 166", 18" shorter than a Model 3 • Manufactured in Belgium

Sawyer Merritt

138,853 次观看 • 1 年前

🇺🇸 The Tesla Semi might be the most underrated story of the year... After years of delays, Tesla has finally started taking orders for its electric heavy truck, and the numbers explain why this matters. The 500-mile version comes in around $290,000, well below the $400,000+ that Daimler and Volvo charge for their electric trucks, with longer range and faster charging on top. The demand is already there. California trucking firms have applied for state subsidies to buy over 1,200 Tesla Semis, more than every other electric truck application in the state combined since 2019. The timing could not be better either. Diesel prices have climbed roughly 50% since the war with Iran began, which means electricity per mile has become dramatically cheaper than the alternative, and the new Sparks, Nevada assembly line is built to produce up to 50,000 trucks a year. Same playbook as the Model S a decade ago. Established players dismissed Tesla, then spent billions trying to catch up. The pattern is repeating in real time. Source: NYT

🇺🇸 The Tesla Semi might be the most underrated story of the year... After years of delays, Tesla has finally started taking orders for its electric heavy truck, and the numbers explain why this matters. The 500-mile version comes in around $290,000, well below the $400,000+ that Daimler and Volvo charge for their electric trucks, with longer range and faster charging on top. The demand is already there. California trucking firms have applied for state subsidies to buy over 1,200 Tesla Semis, more than every other electric truck application in the state combined since 2019. The timing could not be better either. Diesel prices have climbed roughly 50% since the war with Iran began, which means electricity per mile has become dramatically cheaper than the alternative, and the new Sparks, Nevada assembly line is built to produce up to 50,000 trucks a year. Same playbook as the Model S a decade ago. Established players dismissed Tesla, then spent billions trying to catch up. The pattern is repeating in real time. Source: NYT

Mario Nawfal

66,192 次观看 • 2 个月前

Robotics keeps hitting the same wall. Single task RL works, but... it does not scale to hundreds of tasks or new embodiments. This new paper looks like a real step toward fixing that. The team introduces MMBench, a benchmark with 200 tasks across many domains and robots, and Newt, a language conditioned world model trained online across all 200 tasks at once. The simple idea behind Newt: The model learns from demos to get the right priors It trains across many tasks through online interaction It uses language to ground the goal It adapts fast when a new task shows up What stood out to me: ✅ One model trained on 200 tasks at the same time ✅ Language conditioned control for both states and RGB ✅ Better data efficiency than strong baselines ✅ Strong open loop control ✅ Fast adaptation to new tasks and embodiments ✅ Full release of 200 checkpoints, 4000 demos, code, and benchmark This is a good push toward general control instead of one model per task. If you want the full paper: Project page: —- Weekly robotics and AI insights. Subscribe free:

Robotics keeps hitting the same wall. Single task RL works, but... it does not scale to hundreds of tasks or new embodiments. This new paper looks like a real step toward fixing that. The team introduces MMBench, a benchmark with 200 tasks across many domains and robots, and Newt, a language conditioned world model trained online across all 200 tasks at once. The simple idea behind Newt: The model learns from demos to get the right priors It trains across many tasks through online interaction It uses language to ground the goal It adapts fast when a new task shows up What stood out to me: ✅ One model trained on 200 tasks at the same time ✅ Language conditioned control for both states and RGB ✅ Better data efficiency than strong baselines ✅ Strong open loop control ✅ Fast adaptation to new tasks and embodiments ✅ Full release of 200 checkpoints, 4000 demos, code, and benchmark This is a good push toward general control instead of one model per task. If you want the full paper: Project page: —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

70,090 次观看 • 8 个月前

Building on the previous paper, in this study we compare a continuous “smooth return” S2>S1 model with an event-driven one, where long periods of relative calm are punctuated by short, intense episodes of global reorganisation. Both models cover the same time window. Neither uses archaeological data in its construction. When compared against where early humans and early civilizations actually appear and persist, the difference is statistically robust. The smooth model behaves like background noise. The event-driven model lines up in time and space far better than chance allows, even after aggressive temporal and spatial randomization tests. Statistically, the event-driven model lines up with where and when early civilizations appear far better than a smooth, continuous model, even after we randomize both timing and location to test what could arise by chance. The event timeline itself was built independently from well-known late-glacial disruptions - such as Heinrich events, meltwater pulses, and abrupt deglacial transitions - rather than from any archaeological data. Nothing here claims that specific events caused specific cultures. It does suggest that history may not unfold on a smooth clock. Human societies seem to flourish during recovery phases between disruptions, not during the disruptions themselves. The animation contrasts the two return models. Draft paper : Source & Results : (coming soon)

Building on the previous paper, in this study we compare a continuous “smooth return” S2>S1 model with an event-driven one, where long periods of relative calm are punctuated by short, intense episodes of global reorganisation. Both models cover the same time window. Neither uses archaeological data in its construction. When compared against where early humans and early civilizations actually appear and persist, the difference is statistically robust. The smooth model behaves like background noise. The event-driven model lines up in time and space far better than chance allows, even after aggressive temporal and spatial randomization tests. Statistically, the event-driven model lines up with where and when early civilizations appear far better than a smooth, continuous model, even after we randomize both timing and location to test what could arise by chance. The event timeline itself was built independently from well-known late-glacial disruptions - such as Heinrich events, meltwater pulses, and abrupt deglacial transitions - rather than from any archaeological data. Nothing here claims that specific events caused specific cultures. It does suggest that history may not unfold on a smooth clock. Human societies seem to flourish during recovery phases between disruptions, not during the disruptions themselves. The animation contrasts the two return models. Draft paper : Source & Results : (coming soon)

Craig Stone

10,899 次观看 • 6 个月前

I went a little overboard with Codex last week and burned through my entire weekly allowance in two days. Luckily, my quota reset today. Otherwise, I’m not sure what I would’ve done. It got me thinking: instead of asking one large model to handle everything from start to finish, why not let a stronger model plan the project and review the work, while a model built for execution handles the day-to-day implementation? So I tried it. The result was better than I expected. I used GPT-5.6 Sol in Codex as the decision-maker, then ran Ling-3.0-flash from Ant Ling inside OpenCode as the execution engine. Together, they built a small 3D farming game. Before writing any code, I had Codex create four documents: SPEC.md defined the product scope and the lines we couldn’t cross. ARCHITECTURE.md laid out the isometric coordinate system, state machine, and module boundaries. TASKS.md broke the project into small jobs Ling could tackle one at a time. ACCEPTANCE.md explained how each step would be tested and what “done” actually meant. Then I gave Ling a very straightforward role: You are the execution model for this project. Read all four documents before you begin. Work only on the task assigned for this round. When you’re done, run typecheck, test, and build. If anything fails, read the error, fix it, and run the checks again. Do not move on to the next task early. Ling handled dependency installation, project structure, strict TypeScript configuration, test setup, and a production build in 6 minutes and 3 seconds. It ran into issues with the Vite test config, a TS6310 error, and a missing jsdom dependency along the way. Instead of stopping at the first error, it kept reading the logs and fixing the problems until all three checks passed. The speed was honestly hard to believe. If you exclude the time spent waiting on tools, it was producing more than 100 tokens per second. That made the whole development loop feel noticeably faster. After this experiment, I’m planning to keep using the same workflow. If the task is small, there’s no reason to call an expensive planning model for every single step. If the task is large, handing the entire project to a Flash model in one prompt isn’t a great idea either. The setup that makes more sense to me is: Use a more capable model such as Codex to explore the project, make architectural decisions, and break the work down. Put the constraints into specs, schemas, types, and tests instead of leaving them buried in chat history. Give Ling-3.0-flash a steady stream of clear, verifiable implementation tasks. Report bugs with structured context and actual error logs, rather than saying, “It still doesn’t work.” Bring Codex back in for architecture reviews, visual checks, and changes that affect multiple parts of the project. The point of this setup isn’t to give AI a big “build the whole project” button. It’s to turn software development into a pipeline with a much more sensible cost structure: Codex figures out the plan, sets the boundaries, and catches problems. Ling-3.0-flash moves quickly, calls tools reliably, and works through well-defined tasks at scale. For agent workflows that involve lots of repetitive edits, production tasks, and tool calls, this may be a more practical answer than simply using the biggest model for everything.

I went a little overboard with Codex last week and burned through my entire weekly allowance in two days. Luckily, my quota reset today. Otherwise, I’m not sure what I would’ve done. It got me thinking: instead of asking one large model to handle everything from start to finish, why not let a stronger model plan the project and review the work, while a model built for execution handles the day-to-day implementation? So I tried it. The result was better than I expected. I used GPT-5.6 Sol in Codex as the decision-maker, then ran Ling-3.0-flash from Ant Ling inside OpenCode as the execution engine. Together, they built a small 3D farming game. Before writing any code, I had Codex create four documents: SPEC.md defined the product scope and the lines we couldn’t cross. ARCHITECTURE.md laid out the isometric coordinate system, state machine, and module boundaries. TASKS.md broke the project into small jobs Ling could tackle one at a time. ACCEPTANCE.md explained how each step would be tested and what “done” actually meant. Then I gave Ling a very straightforward role: You are the execution model for this project. Read all four documents before you begin. Work only on the task assigned for this round. When you’re done, run typecheck, test, and build. If anything fails, read the error, fix it, and run the checks again. Do not move on to the next task early. Ling handled dependency installation, project structure, strict TypeScript configuration, test setup, and a production build in 6 minutes and 3 seconds. It ran into issues with the Vite test config, a TS6310 error, and a missing jsdom dependency along the way. Instead of stopping at the first error, it kept reading the logs and fixing the problems until all three checks passed. The speed was honestly hard to believe. If you exclude the time spent waiting on tools, it was producing more than 100 tokens per second. That made the whole development loop feel noticeably faster. After this experiment, I’m planning to keep using the same workflow. If the task is small, there’s no reason to call an expensive planning model for every single step. If the task is large, handing the entire project to a Flash model in one prompt isn’t a great idea either. The setup that makes more sense to me is: Use a more capable model such as Codex to explore the project, make architectural decisions, and break the work down. Put the constraints into specs, schemas, types, and tests instead of leaving them buried in chat history. Give Ling-3.0-flash a steady stream of clear, verifiable implementation tasks. Report bugs with structured context and actual error logs, rather than saying, “It still doesn’t work.” Bring Codex back in for architecture reviews, visual checks, and changes that affect multiple parts of the project. The point of this setup isn’t to give AI a big “build the whole project” button. It’s to turn software development into a pipeline with a much more sensible cost structure: Codex figures out the plan, sets the boundaries, and catches problems. Ling-3.0-flash moves quickly, calls tools reliably, and works through well-defined tasks at scale. For agent workflows that involve lots of repetitive edits, production tasks, and tool calls, this may be a more practical answer than simply using the biggest model for everything.

雪踏乌云

23,107 次观看 • 20 天前

🚨 BMW HAS SOLVED ONE OF HYDROGEN’S BIGGEST PACKAGING PROBLEMS. The company has developed a new “Hydrogen Flat Storage” system for the iX5 that uses seven slim hydrogen tanks instead of two large ones. This flat design fits into the same space as the high-voltage battery pack used in the electric iX5. This is significant because it allows BMW to build the hydrogen-powered iX5 on the same production line as petrol, diesel, plug-in hybrid, and fully electric versions without major changes to the factory or vehicle architecture. The system stores 7kg of hydrogen at 700 bar and gives the iX5 an estimated range of 385 miles. BMW plans to start series production of the iX5 Hydrogen in 2028, using a fuel cell developed in partnership with Toyota. Why this matters: • One of the biggest barriers to hydrogen vehicles has been packaging the tanks without sacrificing interior space or requiring completely separate production lines • This modular “flat storage” approach makes hydrogen powertrains much more practical to manufacture at scale • It gives BMW flexibility to produce multiple powertrains on one platform depending on demand and regional infrastructure The deeper implication: While battery electric vehicles currently dominate, BMW is continuing to develop hydrogen as a parallel technology, particularly for larger vehicles and longer-range applications. Being able to build both BEVs and FCEVs on the same line is a pragmatic engineering step that could make hydrogen vehicles more commercially viable in the future if the refuelling infrastructure catches up. Follow for more frontier automotive and energy technology.

🚨 BMW HAS SOLVED ONE OF HYDROGEN’S BIGGEST PACKAGING PROBLEMS. The company has developed a new “Hydrogen Flat Storage” system for the iX5 that uses seven slim hydrogen tanks instead of two large ones. This flat design fits into the same space as the high-voltage battery pack used in the electric iX5. This is significant because it allows BMW to build the hydrogen-powered iX5 on the same production line as petrol, diesel, plug-in hybrid, and fully electric versions without major changes to the factory or vehicle architecture. The system stores 7kg of hydrogen at 700 bar and gives the iX5 an estimated range of 385 miles. BMW plans to start series production of the iX5 Hydrogen in 2028, using a fuel cell developed in partnership with Toyota. Why this matters: • One of the biggest barriers to hydrogen vehicles has been packaging the tanks without sacrificing interior space or requiring completely separate production lines • This modular “flat storage” approach makes hydrogen powertrains much more practical to manufacture at scale • It gives BMW flexibility to produce multiple powertrains on one platform depending on demand and regional infrastructure The deeper implication: While battery electric vehicles currently dominate, BMW is continuing to develop hydrogen as a parallel technology, particularly for larger vehicles and longer-range applications. Being able to build both BEVs and FCEVs on the same line is a pragmatic engineering step that could make hydrogen vehicles more commercially viable in the future if the refuelling infrastructure catches up. Follow for more frontier automotive and energy technology.

TheNewPhysics

2,071,461 次观看 • 2 个月前

Samsung Galaxy S25 Ultra has completely targeted its competitors at the iPhone, and no longer competes with Chinese brand phones. This is because young people in South Korea are almost completely occupied by Apple, and Samsung’s strategy is to pull these young people back, so it strives to make Galaxy look like the Apple iPhone. There are several reasons for not competing with Chinese brands: 1. On the surface, although the Chinese Ultra models have powerful cameras and are suitable for those who pursue the ultimate in images, they are only limited to these people. Overall, the sales of Ultra models are not high, and even the sum of all brands of Ultra models cannot be compared with the sales of S24Ultra. 2. Moreover, these models with powerful cameras are relatively thick and heavy, with serious camera bulges, and the design is not perfect. It cannot be perfect. This design may not be suitable for everyone. Samsung will not easily take the risk to adopt this design in the global market. 3. The infinitely enhanced camera configuration will greatly increase the cost, which is difficult for Samsung to accept, and the production of this frequently updated camera may not be able to support Samsung's sales demand of tens of millions. In short, the Chinese brand Ultra model is more like a special non-popular model suitable for geeks. The Galaxy S25 series is defined as a popular model, and it must consider everyone's feelings and sufficient supply. This is why Samsung will not design the S25 Ultra as a Chinese brand Ultra. It remains consistent with the iPhone 16 Pro Max, but subtly. It is always slightly better than it For example, it is a little thinner (8.2mm vs 8.25mm), a little lighter (219g vs 227g), a little narrower bezel, a little stronger performance, a little more camera (retain 3x), a little more ultra-wide-angle pixels (50MP vs 48MP), a little bigger battery, and a little faster charging. Even the most incredible improvement: One UI 7.1 is a little smoother than iOS18 software. This is happening.

Ice Universe

347,352 次观看 • 1 年前

Tesla’s new Megapack factory in Houston will be their most capable yet, with an annual production capacity of 50 GWh—25% more than their other two Megapack factories. Once fully ramped, Tesla’s three Megafactories will produce a combined 130 GWh of battery energy storage a year, enough to power ~50 million homes. That's nearly 4.2x more battery energy storage than Tesla deployed in all of 2024. The Houston facility will be the first to manufacture the all-new Megapack 3 in late 2026. This product will partly source LFP cells from Tesla's brand new LFP manufacturing plant in Nevada (next to Giga Nevada). The Nevada facility will begin production in the coming months, making it the first time Tesla will produce and source LFP cells domestically — and from its own operations. Tesla Energy is just getting started🔋

Sawyer Merritt

134,971 次观看 • 11 个月前

🚨Models are starting to LOCK IN on where the Heaviest Snow and Strongest Winds will be with our upcoming Bomb Cyclone. ✅ Every Weather Model is now on board with a DUMPING of snow across the Carolinas, with some spots getting several inches and some spots getting up to 1 Foot! ❄️ The highest Snow Totals will fall closest to the center of the Nor’easter in coastal North Carolina and Virginia, where up to a foot of snow is possible. 🎯 Cities like Raleigh, Wilmington, Virginia Beach, and the Outer Banks will see Blizzard conditions for much of the day Saturday as snow piles up and winds really start to CRANK. ⬅️ Further inland, Heavy Snow is also likely in Charlotte, Columbia, Greenville, and Asheville, with totals generally in the 3–6 inch range. 🌨️ Elsewhere in the Northeast, models currently agree this storm largely misses you, with lighter snow of 2–4 inches possible in Delaware, Maryland, and into Nantucket. ➡️⬅️ There is still time for small east or west shifts, but each new model run lowers those odds. ⚠️ We know this will be a big storm for our Carolina brotheren. Winter Storm Watches are already out, and several may be upgraded to Blizzard Warnings by Friday. 🔒 Lock in, my brotheren. A Blizzard is coming. ❄️🌬️

🚨Models are starting to LOCK IN on where the Heaviest Snow and Strongest Winds will be with our upcoming Bomb Cyclone. ✅ Every Weather Model is now on board with a DUMPING of snow across the Carolinas, with some spots getting several inches and some spots getting up to 1 Foot! ❄️ The highest Snow Totals will fall closest to the center of the Nor’easter in coastal North Carolina and Virginia, where up to a foot of snow is possible. 🎯 Cities like Raleigh, Wilmington, Virginia Beach, and the Outer Banks will see Blizzard conditions for much of the day Saturday as snow piles up and winds really start to CRANK. ⬅️ Further inland, Heavy Snow is also likely in Charlotte, Columbia, Greenville, and Asheville, with totals generally in the 3–6 inch range. 🌨️ Elsewhere in the Northeast, models currently agree this storm largely misses you, with lighter snow of 2–4 inches possible in Delaware, Maryland, and into Nantucket. ➡️⬅️ There is still time for small east or west shifts, but each new model run lowers those odds. ⚠️ We know this will be a big storm for our Carolina brotheren. Winter Storm Watches are already out, and several may be upgraded to Blizzard Warnings by Friday. 🔒 Lock in, my brotheren. A Blizzard is coming. ❄️🌬️

Brady Harris

50,135 次观看 • 6 个月前

Tesla cars, especially with FSD, are the safest ever built and nothing else comes close Every Tesla carries a top-notch 5-star NHTSA rating Even the starting model doesn't have any compromises on this part Now Pair that with FSD Supervised and you're looking at a system ~9x safer than the average U.S. driver - 5-Star NHTSA ratings across every model - FSD Supervised: ~9x safer than the avg. U.S. driver (1 crash per 6.36M miles, Q3 2025) - Euro NCAP Best-in-Class 2025 - Model 3 & Model Y - Model Y: Highest Euro NCAP score ever recorded (362 pts) Engineered to protect everyone inside and outside the car There are real-life incidents where people have walked away unharmed from crashes so severe they would have been fatal in any other car. Tesla's safety is not just numbers on paper; it saves lives every day The safest cars ever built and getting safer every day Tesla did not just raise the bar. It inspired the entire industry to follow

Tesla cars, especially with FSD, are the safest ever built and nothing else comes close Every Tesla carries a top-notch 5-star NHTSA rating Even the starting model doesn't have any compromises on this part Now Pair that with FSD Supervised and you're looking at a system ~9x safer than the average U.S. driver - 5-Star NHTSA ratings across every model - FSD Supervised: ~9x safer than the avg. U.S. driver (1 crash per 6.36M miles, Q3 2025) - Euro NCAP Best-in-Class 2025 - Model 3 & Model Y - Model Y: Highest Euro NCAP score ever recorded (362 pts) Engineered to protect everyone inside and outside the car There are real-life incidents where people have walked away unharmed from crashes so severe they would have been fatal in any other car. Tesla's safety is not just numbers on paper; it saves lives every day The safest cars ever built and getting safer every day Tesla did not just raise the bar. It inspired the entire industry to follow

X Freeze

18,612 次观看 • 4 个月前

TESLA SEMI: THE FUTURE OF TRUCKING IS ELECTRIC, EFFICIENT & AUTONOMOUS The Tesla Semi isn’t just another truck—it’s a complete redefinition of long-haul trucking. With production ramping up and deliveries accelerating, it’s already proving why electric Class 8 semis will dominate the industry. Why the Tesla Semi is critically important: •Unmatched efficiency & cost savings ~1.7 kWh/mile energy consumption (real-world data from PepsiCo & early fleets) — far lower than diesel equivalents. Operating costs drop dramatically with electricity vs. diesel, plus massive reductions in maintenance (no oil changes, fewer brake replacements thanks to regenerative braking). •Insane performance 0–60 mph in ~20 seconds fully loaded (500-mile range version), 0–60 in under 10 seconds in lighter configs. Instant torque makes hills and merging effortless—safer and faster than diesel trucks. •500+ mile range on a single charge Real-world highway range exceeds 500 miles even with heavy loads, eliminating most range anxiety for long-haul routes. Megachargers add hundreds of miles in ~30 minutes. •Autonomy-ready platform Built on Tesla’s Full Self-Driving hardware and software stack. Future unsupervised FSD will eliminate driver fatigue, reduce accidents (94% of which involve human error), and enable 24/7 operation—transforming driver economics and safety. •Environmental & regulatory edge Zero tailpipe emissions, helping fleets meet tightening emissions standards (California, EU, etc.). Lower noise pollution for urban deliveries and night operations. •Fleet economics revolution Lower TCO (total cost of ownership) than diesel trucks over the vehicle life. When combined with solar + Megapack charging depots, operating costs approach near-zero marginal energy cost. The Tesla Semi isn’t competing in trucking—it’s redefining the entire category. From PepsiCo’s early fleet to upcoming high-volume production, it’s the truck that makes electric long-haul not just possible, but inevitable 🚛⚡

TESLA SEMI: THE FUTURE OF TRUCKING IS ELECTRIC, EFFICIENT & AUTONOMOUS The Tesla Semi isn’t just another truck—it’s a complete redefinition of long-haul trucking. With production ramping up and deliveries accelerating, it’s already proving why electric Class 8 semis will dominate the industry. Why the Tesla Semi is critically important: •Unmatched efficiency & cost savings ~1.7 kWh/mile energy consumption (real-world data from PepsiCo & early fleets) — far lower than diesel equivalents. Operating costs drop dramatically with electricity vs. diesel, plus massive reductions in maintenance (no oil changes, fewer brake replacements thanks to regenerative braking). •Insane performance 0–60 mph in ~20 seconds fully loaded (500-mile range version), 0–60 in under 10 seconds in lighter configs. Instant torque makes hills and merging effortless—safer and faster than diesel trucks. •500+ mile range on a single charge Real-world highway range exceeds 500 miles even with heavy loads, eliminating most range anxiety for long-haul routes. Megachargers add hundreds of miles in ~30 minutes. •Autonomy-ready platform Built on Tesla’s Full Self-Driving hardware and software stack. Future unsupervised FSD will eliminate driver fatigue, reduce accidents (94% of which involve human error), and enable 24/7 operation—transforming driver economics and safety. •Environmental & regulatory edge Zero tailpipe emissions, helping fleets meet tightening emissions standards (California, EU, etc.). Lower noise pollution for urban deliveries and night operations. •Fleet economics revolution Lower TCO (total cost of ownership) than diesel trucks over the vehicle life. When combined with solar + Megapack charging depots, operating costs approach near-zero marginal energy cost. The Tesla Semi isn’t competing in trucking—it’s redefining the entire category. From PepsiCo’s early fleet to upcoming high-volume production, it’s the truck that makes electric long-haul not just possible, but inevitable 🚛⚡

Tesla Owners Silicon Valley

15,161 次观看 • 6 个月前