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1:48 Heinkel He 111 scale model — built as a historical aircraft replica. Focused on modeling accuracy, paintwork, and engineering detail. Commission builds available. #ScaleModel #ModelAircraft #WW2History #AviationModel #PlasticModel #ProBuiltModels

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12,124 Aufrufe • vor 6 Monaten •via X (Twitter)

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Star Citizen

24,476 Aufrufe • vor 9 Monaten

$He's 26. He built a scale model of the Burj Khalifa so detailed the developer flew him to Dubai - on a used resin printer he runs in a Chicago apartment for a fraction of the $80,000 model studios charge The printer is a large-format resin machine he pulled from a shuttered Chicago prototype shop for $1,400. He models every building in Blender from the developer's own CAD files, splits it into hundreds of printable sections, and resin-prints them over week-long cycles - every balcony, every window mullion, every setback on a 6-foot tower, accurate to the millimeter. He wires fiber-optic lighting through the floors so the model glows like the real building at dusk. Total material cost per model: $600 in resin and $90 in LEDs. Model studios quoted the same developer $80,000 and a four-month wait. He delivered in six weeks for $9,000 He posted a time-lapse to Reddit r/architecture in October showing a 6-foot Burj Khalifa replica rising layer by layer, then lighting up floor by floor. The video hit 2.4 million views in nine days. By January he had built scale models for six developers - a Miami condo tower, a Chicago mixed-use block, a Riyadh masterplan - and one architecture firm that now subcontracts every presentation model to his apartment. $180,000 in his account. His father, a retired union electrician, wires the fiber-optic lighting harnesses on weekends Architectural model studios run on the premise that presentation-grade scale models require their workshops, their staff of twelve, and their $80,000 commissions. Autodesk sells the rendering software on the same premise at $2,400 a seat. He builds the same models on a resin printer in a Chicago apartment that could pack a full luxury tower into a padded crate and ship it to a developer's sales gallery across the country by Tuesday$

He's 26. He built a scale model of the Burj Khalifa so detailed the developer flew him to Dubai - on a used resin printer he runs in a Chicago apartment for a fraction of the $80,000 model studios charge The printer is a large-format resin machine he pulled from a shuttered Chicago prototype shop for $1,400. He models every building in Blender from the developer's own CAD files, splits it into hundreds of printable sections, and resin-prints them over week-long cycles - every balcony, every window mullion, every setback on a 6-foot tower, accurate to the millimeter. He wires fiber-optic lighting through the floors so the model glows like the real building at dusk. Total material cost per model: $600 in resin and $90 in LEDs. Model studios quoted the same developer $80,000 and a four-month wait. He delivered in six weeks for $9,000 He posted a time-lapse to Reddit r/architecture in October showing a 6-foot Burj Khalifa replica rising layer by layer, then lighting up floor by floor. The video hit 2.4 million views in nine days. By January he had built scale models for six developers - a Miami condo tower, a Chicago mixed-use block, a Riyadh masterplan - and one architecture firm that now subcontracts every presentation model to his apartment. $180,000 in his account. His father, a retired union electrician, wires the fiber-optic lighting harnesses on weekends Architectural model studios run on the premise that presentation-grade scale models require their workshops, their staff of twelve, and their $80,000 commissions. Autodesk sells the rendering software on the same premise at $2,400 a seat. He builds the same models on a resin printer in a Chicago apartment that could pack a full luxury tower into a padded crate and ship it to a developer's sales gallery across the country by Tuesday

Carat

135,339 Aufrufe • vor 15 Tagen

He built a trading bot on Polymarket with Claude Result: +$77,123 in 1 month His bot trades the microstructure of short-term crypto Up/Down markets using a hybrid high-frequency strategy with 3 core elements: 1. After a market opens, it chooses a side based on its own probability model and builds the position with limit orders 2. When the underlying asset’s movement changes, it buys the opposite outcome as a hedge or as a reversal 3. Holds the position until the market ends, capturing a consistent edge from the final directional skew His Polymarket account: Using this strategy, he captures a consistent edge and keeps growing his capital

He built a trading bot on Polymarket with Claude Result: +$77,123 in 1 month His bot trades the microstructure of short-term crypto Up/Down markets using a hybrid high-frequency strategy with 3 core elements: 1. After a market opens, it chooses a side based on its own probability model and builds the position with limit orders 2. When the underlying asset’s movement changes, it buys the opposite outcome as a hedge or as a reversal 3. Holds the position until the market ends, capturing a consistent edge from the final directional skew His Polymarket account: Using this strategy, he captures a consistent edge and keeps growing his capital

VALIX

10,765 Aufrufe • vor 19 Tagen

He built a trading bot on Polymarket with Claude Result: +$810,028 in 3 months His bot trades the microstructure of crypto Up/Down markets using a hybrid strategy with 3 stages: 1. After a market opens, it chooses one side based on its probability model and starts building a position 2. If the asset’s movement changes, it buys the opposite outcome as a hedge 3. If it sees arbitrage, it builds a full set of Up and Down. Otherwise, it keeps the directional skew His Polymarket account: Using this strategy and clean execution, he turned $100k into $810k

He built a trading bot on Polymarket with Claude Result: +$810,028 in 3 months His bot trades the microstructure of crypto Up/Down markets using a hybrid strategy with 3 stages: 1. After a market opens, it chooses one side based on its probability model and starts building a position 2. If the asset’s movement changes, it buys the opposite outcome as a hedge 3. If it sees arbitrage, it builds a full set of Up and Down. Otherwise, it keeps the directional skew His Polymarket account: Using this strategy and clean execution, he turned $100k into $810k

VALIX

18,729 Aufrufe • vor 18 Tagen

He built a trading bot on Polymarket with Claude Result: +$198,045 in 4 months His bot trades the microstructure of short-term crypto Up/Down markets using a hybrid high-frequency strategy with 3 core elements: 1. Builds a directional position by choosing the Up or Down outcome that is currently undervalued relative to his model 2. If the market movement changes, it starts buying the opposite side as a hedge or to flip the position 3. If it sees an arbitrage opportunity, it builds a full set of Up and Down His Polymarket account: Using this strategy, he captures a consistent edge and repeats it thousands of times That’s how he keeps growing his capital

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VALIX

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Introducing FLUX-mimic, a next-generation Video-Action Model for general purpose dexterity, developed in partnership with Black Forest Labs. Late last year we published mimic-video and introduced Video-Action Models (VAM): a new family of robotics foundation models built on top of video generation models. We showed that robot control reduces to visual prediction, and that robot capability is downstream of improvements in video modeling accuracy. The obvious implication was that advances in the video modeling frontier would directly translate to increased capabilities in end-to-end robot learning. FLUX-mimic is that thesis at frontier scale: We've applied our VAM architecture to the strongest video backbone available today, FLUX 3 from Black Forest Labs, and trained it on data from our own robots and wearables. General-purpose dexterity, running on a single GPU on premises. Because the model already understands world dynamics, it needs far fewer demonstrations to learn a new task. This is game-changing for our mission to deploy robots to factory floors, where industrial robot data is scarce and expensive to collect. We're now testing and deploying FLUX-mimic with manufacturing leaders like Audi USA, on complex, multi-step manipulation long considered impossible for conventional automation.

Introducing FLUX-mimic, a next-generation Video-Action Model for general purpose dexterity, developed in partnership with Black Forest Labs. Late last year we published mimic-video and introduced Video-Action Models (VAM): a new family of robotics foundation models built on top of video generation models. We showed that robot control reduces to visual prediction, and that robot capability is downstream of improvements in video modeling accuracy. The obvious implication was that advances in the video modeling frontier would directly translate to increased capabilities in end-to-end robot learning. FLUX-mimic is that thesis at frontier scale: We've applied our VAM architecture to the strongest video backbone available today, FLUX 3 from Black Forest Labs, and trained it on data from our own robots and wearables. General-purpose dexterity, running on a single GPU on premises. Because the model already understands world dynamics, it needs far fewer demonstrations to learn a new task. This is game-changing for our mission to deploy robots to factory floors, where industrial robot data is scarce and expensive to collect. We're now testing and deploying FLUX-mimic with manufacturing leaders like Audi USA, on complex, multi-step manipulation long considered impossible for conventional automation.

mimic

117,541 Aufrufe • vor 1 Monat

This 22-year-old American developer showed how he makes $14,200 a month using just a dual-GPU setup and Cohere’s new open-weights model. He doesn't have a powerhouse dev team. No massive VC funding. No expensive enterprise infrastructure. He built a "localized AI workflow" powered by the newly dropped Command A+ model → an ultra-efficient system he calls “algorithmic mining.” The system works like this: 1 open-weights model → Apache 2.0 license integration → Command A+'s Mixture of Experts (MoE) processes complex data at lightning speed → Quantized architecture runs smoothly on just 2 GPUs → Automated agents handle enterprise-level tasks while he sleeps. 3 recurring revenue streams from a 72-hour implementation: > $49/month Essential API access > $99/month Scale tier for small teams > $199/month Custom enterprise pipeline > Delivered with near-zero hosting latency > Total setup time: 3 days He didn't wait for massive cloud budget approval. He took a highly practical, open-source-friendly model, spun it up locally, and built the exact kind of agents usually reserved for Fortune 500 companies. As the developer put it perfectly: In the open-weight era, efficiency and raw speed are the ultimate leverage.

This 22-year-old American developer showed how he makes $14,200 a month using just a dual-GPU setup and Cohere’s new open-weights model. He doesn't have a powerhouse dev team. No massive VC funding. No expensive enterprise infrastructure. He built a "localized AI workflow" powered by the newly dropped Command A+ model → an ultra-efficient system he calls “algorithmic mining.” The system works like this: 1 open-weights model → Apache 2.0 license integration → Command A+'s Mixture of Experts (MoE) processes complex data at lightning speed → Quantized architecture runs smoothly on just 2 GPUs → Automated agents handle enterprise-level tasks while he sleeps. 3 recurring revenue streams from a 72-hour implementation: > $49/month Essential API access > $99/month Scale tier for small teams > $199/month Custom enterprise pipeline > Delivered with near-zero hosting latency > Total setup time: 3 days He didn't wait for massive cloud budget approval. He took a highly practical, open-source-friendly model, spun it up locally, and built the exact kind of agents usually reserved for Fortune 500 companies. As the developer put it perfectly: In the open-weight era, efficiency and raw speed are the ultimate leverage.

Ridark

213,727 Aufrufe • vor 3 Monaten

The biggest driver of AI compute now is real-world deployment. At NVIDIA GTC, IREN's Kent Draper, alongside Lenovo's Vlad Rozanovich, shared what they’re actually seeing on the ground from AI infrastructure demand, and the signal is coming from enterprise use. He pointed to agents, internal enterprise models, and real-world deployments as the workloads now driving demand, and explained that the challenge isn't finding customers, it’s how quickly they can build capacity to keep up. Their strategy? A vertically integrated model focused entirely on power-dense computing, which allows them to scale faster, control the environment, and deliver infrastructure purpose-built for AI rather than multi-tenant data centers trying to support dozens of different workloads.

The biggest driver of AI compute now is real-world deployment. At NVIDIA GTC, IREN's Kent Draper, alongside Lenovo's Vlad Rozanovich, shared what they’re actually seeing on the ground from AI infrastructure demand, and the signal is coming from enterprise use. He pointed to agents, internal enterprise models, and real-world deployments as the workloads now driving demand, and explained that the challenge isn't finding customers, it’s how quickly they can build capacity to keep up. Their strategy? A vertically integrated model focused entirely on power-dense computing, which allows them to scale faster, control the environment, and deliver infrastructure purpose-built for AI rather than multi-tenant data centers trying to support dozens of different workloads.

Six Five Media

7,422,478 Aufrufe • vor 5 Monaten

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How well do egocentric human videos scale? Apparently, really well. Dyna Robotics has introduced Dyna-2, a world-action model (WAM) pre-trained on more than 1 million hours of real egocentric human video. It learns by jointly predicting the next video frames and the actions, and that video prediction (world modeling) is what carries learning from humans to robots. • More human video predictably improves action prediction. • Task accuracy improves across embodiments (bimanual arms and a humanoid) with zero robot data in pre-training. • Post-trained on just a few hours of robot data, mean task performance rose 20% to 53% as human pre-training scaled 1k to 1M hours. WAM vs VLA, matched apple-to-apple: the WAM hit 1.55x the VLA's success rate and won 65% of head-to-heads. Deployed zero-shot at unseen customer sites, it passed 87% vs the VLA's 46%.

The Humanoid Hub

15,034 Aufrufe • vor 17 Tagen

"I think that xAI is severely underrated" "It just kind of blows my mind that people don’t understand how fast the company is moving." "xAI is gonna shock people with the scale that it gets to quickly." "With xAI, they’ve been building the biggest coherent training clusters in the world, & they’re just not really focused on monetizing them right now." Shaun Maguire (Shaun Maguire), Partner Sequoia Capital Elon Musk (Elon Musk) . . . "I think that xAI is severely underrated and it makes sense—their revenue scale compared to OpenAI, Anthropic, Google is obviously much smaller—but it just kind of blows my mind that people don’t understand how fast the company is moving. Yeah. And you just have to look at the derivatives of progress, and then also just look at the bottlenecks. I believe the bottleneck at least is gonna be power. And even if you have the best model in the world, if you can only deliver it to 1% of the market or 2% of the market demand, you’re heavily limited in your growth rate and expansion opportunities. I just think xAI is gonna—like Elon’s the best in the world at atoms, and I think atoms are gonna be a decisive factor in the AI race. And the way Elon builds companies is different than other people. He builds the way I describe it is: he builds up potential energy, and then he converts that potential energy into kinetic energy. With xAI, they’ve been building the biggest coherent training clusters in the world, and they’re just not really focused on monetizing them right now. And it’s kind of crazy to me that people don’t understand the pattern yet. And it’s not to say that I’m bearish on the other foundation model companies. I just think xAI is gonna shock people with the scale that it gets to quickly. But I’m bullish on all of them. Like, I’m bullish on The Boring Company, I’m bullish on Neuralink. Tesla’s a public company so I have no comment on Tesla. But literally insanely bullish on all of them."

"I think that xAI is severely underrated" "It just kind of blows my mind that people don’t understand how fast the company is moving." "xAI is gonna shock people with the scale that it gets to quickly." "With xAI, they’ve been building the biggest coherent training clusters in the world, & they’re just not really focused on monetizing them right now." Shaun Maguire (Shaun Maguire), Partner Sequoia Capital Elon Musk (Elon Musk) . . . "I think that xAI is severely underrated and it makes sense—their revenue scale compared to OpenAI, Anthropic, Google is obviously much smaller—but it just kind of blows my mind that people don’t understand how fast the company is moving. Yeah. And you just have to look at the derivatives of progress, and then also just look at the bottlenecks. I believe the bottleneck at least is gonna be power. And even if you have the best model in the world, if you can only deliver it to 1% of the market or 2% of the market demand, you’re heavily limited in your growth rate and expansion opportunities. I just think xAI is gonna—like Elon’s the best in the world at atoms, and I think atoms are gonna be a decisive factor in the AI race. And the way Elon builds companies is different than other people. He builds the way I describe it is: he builds up potential energy, and then he converts that potential energy into kinetic energy. With xAI, they’ve been building the biggest coherent training clusters in the world, and they’re just not really focused on monetizing them right now. And it’s kind of crazy to me that people don’t understand the pattern yet. And it’s not to say that I’m bearish on the other foundation model companies. I just think xAI is gonna shock people with the scale that it gets to quickly. But I’m bullish on all of them. Like, I’m bullish on The Boring Company, I’m bullish on Neuralink. Tesla’s a public company so I have no comment on Tesla. But literally insanely bullish on all of them."

Molly O’Shea

197,995 Aufrufe • vor 6 Monaten

Stratosphere was our biggest and heaviest character on #Transformers ROTB. He was a real challenge to deal with on the Modeling, Texturing, and Rendering side. He had 1434 UDIMs due to his immense scale and was made up of tens of thousands of objects. His level of detail was truly something else. His vehicle form was also a behemoth. We had also built a mortar for his robot form and a cannon for his vehicle form but neither of these made it into the film as his part in the final battle was cut out. He was originally supposed to help out in the final battle, using both of these weapons against the Sweepers and Predacons. The decision to cut him from the final act came very late as all of his assets were final at that point. We were never told the reason why his role was cut from the film. I had to re-design my texturing system in Mari when working on him to ensure that it could handle such a complex and large character. I had to simplify a lot of the procedural systems and even had to branch off some of them into their own files to ensure that artists could work with somewhat decent performance. We ran into a lot of issues pushing him through our pipeline, both as a model as well as rendering him due to the sheer amount of objects and textures that had to be processed. For shots, it was requested by Lighting that we reduce his memory footprint as Google had complained he was taking up too much memory and their I/O for rendering on the cloud was being affected. The leadership team and I decided that the best way to do this was to half the texel density on the parts that wouldn't be visible in our approved shots and to also half the resolution of the textures in the parts not visible to camera. This allowed us to optimize his memory footprint both in shots as well as storage and I/O. It was massive technical undertaking working on Stratosphere, so much so that we had to delay getting him into shots as we simply couldn't push him through with our normal tooling. I really enjoyed the challenge that he posed for us as a team. CREDITS: Primary Modeling by Oscar Lowe Support Modeling by Arthur Grandjean and his team Final Texturing & Lookdev by Yaz Raji Video credits: Breakdown shot by MPC Film footage by Paramount

Stratosphere was our biggest and heaviest character on #Transformers ROTB. He was a real challenge to deal with on the Modeling, Texturing, and Rendering side. He had 1434 UDIMs due to his immense scale and was made up of tens of thousands of objects. His level of detail was truly something else. His vehicle form was also a behemoth. We had also built a mortar for his robot form and a cannon for his vehicle form but neither of these made it into the film as his part in the final battle was cut out. He was originally supposed to help out in the final battle, using both of these weapons against the Sweepers and Predacons. The decision to cut him from the final act came very late as all of his assets were final at that point. We were never told the reason why his role was cut from the film. I had to re-design my texturing system in Mari when working on him to ensure that it could handle such a complex and large character. I had to simplify a lot of the procedural systems and even had to branch off some of them into their own files to ensure that artists could work with somewhat decent performance. We ran into a lot of issues pushing him through our pipeline, both as a model as well as rendering him due to the sheer amount of objects and textures that had to be processed. For shots, it was requested by Lighting that we reduce his memory footprint as Google had complained he was taking up too much memory and their I/O for rendering on the cloud was being affected. The leadership team and I decided that the best way to do this was to half the texel density on the parts that wouldn't be visible in our approved shots and to also half the resolution of the textures in the parts not visible to camera. This allowed us to optimize his memory footprint both in shots as well as storage and I/O. It was massive technical undertaking working on Stratosphere, so much so that we had to delay getting him into shots as we simply couldn't push him through with our normal tooling. I really enjoyed the challenge that he posed for us as a team. CREDITS: Primary Modeling by Oscar Lowe Support Modeling by Arthur Grandjean and his team Final Texturing & Lookdev by Yaz Raji Video credits: Breakdown shot by MPC Film footage by Paramount

Rassoul Edji

268,448 Aufrufe • vor 1 Jahr

This is Gajesh at 10, building a chatbot for a startup before anyone cared about AI. He recently created Darkbloom, which enables anyone with a Mac on their desk or couch to run open-weight models and earn. The whole arc: > Be Gajesh > Born in Goa, India > Starts coding at 7 with FreeCodeCamp and Code(.)org > No background, no one in tech he could call > Builds chatbot for a startup at 10 when ai wasn't cool > COVID hits. bro has all the time in the world > Starts a YT channel grows it to 15k subs and 1m views in 4 months > Builds his own app (Gaj Finance), gets $7 million AUM > Everyone finds out he's 13. Becomes a sensation on X > Balaji tweets about him > Finishes High School at 16 > Joins Eigen Labs at 15; joins as an Engineer > Gets O1 Visa at 16, Moves to Seattle > Helps build EigenLayer to $10B+ AUM > Accelerates engineering and growth functions. Builds 10+ projects and experiments > Makes a boring product like data availability 2M+ views > Lands customer with 800k MAU with just cold DMs > Featured in Forbes, TheBlock, Decrypt "the 13-year-old who built a $7M money manager" > Launches Darkbloom on Day 1 of his vacation. #1 on Hacker News 48 hours later > Gets 100 million to 5 billion tokens/day in 2 months > Darkbloom has over 350 macs in people's homes, each earning $120-200/mo > Story just getting started. The video is him at 10. He is 18 now. Lives in the SF Bay Area. Gajesh keeps building. Darkbloom is what he’s building now at Eigen Labs.

This is Gajesh at 10, building a chatbot for a startup before anyone cared about AI. He recently created Darkbloom, which enables anyone with a Mac on their desk or couch to run open-weight models and earn. The whole arc: > Be Gajesh > Born in Goa, India > Starts coding at 7 with FreeCodeCamp and Code(.)org > No background, no one in tech he could call > Builds chatbot for a startup at 10 when ai wasn't cool > COVID hits. bro has all the time in the world > Starts a YT channel grows it to 15k subs and 1m views in 4 months > Builds his own app (Gaj Finance), gets $7 million AUM > Everyone finds out he's 13. Becomes a sensation on X > Balaji tweets about him > Finishes High School at 16 > Joins Eigen Labs at 15; joins as an Engineer > Gets O1 Visa at 16, Moves to Seattle > Helps build EigenLayer to $10B+ AUM > Accelerates engineering and growth functions. Builds 10+ projects and experiments > Makes a boring product like data availability 2M+ views > Lands customer with 800k MAU with just cold DMs > Featured in Forbes, TheBlock, Decrypt "the 13-year-old who built a $7M money manager" > Launches Darkbloom on Day 1 of his vacation. #1 on Hacker News 48 hours later > Gets 100 million to 5 billion tokens/day in 2 months > Darkbloom has over 350 macs in people's homes, each earning $120-200/mo > Story just getting started. The video is him at 10. He is 18 now. Lives in the SF Bay Area. Gajesh keeps building. Darkbloom is what he’s building now at Eigen Labs.

Pratik Gandhi

49,350 Aufrufe • vor 3 Tagen

Chinese robotics company Astribot released their latest World-Action Model (WAM), Lumo-2. Technical breakdown: - based on a frozen 🥶 Qwen-3.5 4B VLM - trained in 3 progressive stages: 1. Action is aligned with latent world dynamics (an abstract representation of action). Real-world actions are anchored to physical constraints, while the latent space is guided to focus on motion-relevant changes. This bidirectional relationship makes the model physically grounded -> critical for a world model. 2. Action is aligned with vision and language. Reusing the vision backbone and action encoder from the frozen VLM, the authors add a custom vocabulary (for new actions), a semantic module, an action decoder, and an action projector. This aligns the (new) action representations with the (existing) vision-language semantic space. Most importantly: it builds a direct mapping from natural-language instructions to motor execution. 3. End-to-end training on language, video, and robot data. Only the new modules (everything outside the frozen backbone) are trained end-to-end across temporal reasoning, physical understanding, long-horizon, and dexterous manipulation. At the end of the day, Lumo-2 is not the best on benchmarks, but that's not the point. What's genuinely new: - a way to combine latent world modeling and action generation through progressive alignment - a physically-grounded latent dynamics space - it lifts performance on unseen objects using un-annotated human egocentric video + Vision Pro captures, no special transfer algorithm needed Why it matters: - the whole model is thin trainable adapters (semantic module, action decoder/projector) on a frozen 4B backbone (cheap) - that scale is suited for real-time embedded inference (~2.71× decode speedup, no accuracy loss) - its real moat is long-horizon execution, where the added temporal memory pays off far more than on any other task As a result, this robot can now make your latte (5x sped up video):

Chinese robotics company Astribot released their latest World-Action Model (WAM), Lumo-2. Technical breakdown: - based on a frozen 🥶 Qwen-3.5 4B VLM - trained in 3 progressive stages: 1. Action is aligned with latent world dynamics (an abstract representation of action). Real-world actions are anchored to physical constraints, while the latent space is guided to focus on motion-relevant changes. This bidirectional relationship makes the model physically grounded -> critical for a world model. 2. Action is aligned with vision and language. Reusing the vision backbone and action encoder from the frozen VLM, the authors add a custom vocabulary (for new actions), a semantic module, an action decoder, and an action projector. This aligns the (new) action representations with the (existing) vision-language semantic space. Most importantly: it builds a direct mapping from natural-language instructions to motor execution. 3. End-to-end training on language, video, and robot data. Only the new modules (everything outside the frozen backbone) are trained end-to-end across temporal reasoning, physical understanding, long-horizon, and dexterous manipulation. At the end of the day, Lumo-2 is not the best on benchmarks, but that's not the point. What's genuinely new: - a way to combine latent world modeling and action generation through progressive alignment - a physically-grounded latent dynamics space - it lifts performance on unseen objects using un-annotated human egocentric video + Vision Pro captures, no special transfer algorithm needed Why it matters: - the whole model is thin trainable adapters (semantic module, action decoder/projector) on a frozen 4B backbone (cheap) - that scale is suited for real-time embedded inference (~2.71× decode speedup, no accuracy loss) - its real moat is long-horizon execution, where the added temporal memory pays off far more than on any other task As a result, this robot can now make your latte (5x sped up video):

Léo

32,296 Aufrufe • vor 1 Monat

🚨🇨🇳 TRUMP IN TEARS: China builds air logistics the US can’t match China just changed how logistics works. On February 1, the Caihong YH-1000S, a hybrid fixed-wing transport drone developed by CASC’s 11th Institute, completed its first flight near Chongqing. The aircraft uses a hybrid powerplant developed together with major players from China’s automotive industry. As a result, takeoff and landing distances are shorter, payload and range are higher, and operating costs are lower. The maiden flight focused on validating these performance parameters. What makes this project stand out is how it was built. Aerospace and automotive supply chains were integrated from the start. Modular production and shared components helped reduce development time and manufacturing costs. The system was conceived with serial production in mind. The mission profile is deliberately broad: international cargo transport, emergency deliveries, disaster response, maritime monitoring, coastal control, even weather modification. One airframe, many roles. Once transport aircraft no longer require pilots, long runways, or complex deployment cycles, logistics become faster and harder to disrupt. Airlift stops being a bottleneck and starts looking like infrastructure. Does Trump have any response beyond barking in public and launching another bloated program?

NewRulesGeopolitics

69,216 Aufrufe • vor 6 Monaten

Knowing how LLM contexts work and how to work around context limitations – aka “context engineering” – is becoming so important. No better person to explain than dex Timestamps: 00:00 Intro 01:33 Dex’s path into tech 03:34 Early work in platform engineering 05:28 Replicated 11:24 Metalytics 12:36 12-factor agents 18:27 Context engineering 23:38 Harness engineering 26:11 Context overload 30:45 Loop engineering 44:34 Software factories before and after AI 50:33 Automation limits 55:18 Three options for automating 59:00 RPI framework 1:04:16 Intentional compaction 1:11:48 Token harder vs. token smarter 1:16:44 AI slop 1:19:15 HumanLayer 1:29:09 Book recommendation Brought to you by: • Antithesis — with Antithesis, you can use AI agents to work on critical systems without worrying about correctness. Teams like Jane Street, and the etcd community use Antithesis to ship better code, faster. • Buildkite — the CI orchestration platform built for reliable scale. Used by OpenAI, Anthropic, Cursor, Meta, Uber, Ramp, Nvidia, Airbnb and many more. • Sentry — application monitoring software built by developers, for developers. Check out their AI agent, Seer AI, and Sentry MCP. Three interesting learnings from this episode: 1. Lesson learned: Shipping unread code spells disaster within months. Dex experimented with having the model write the code and humans not reviewing anything in July 2025. Four months later, they shut things down and threw the whole system out. Production broke, and no matter how much the team prompted Opus 4.1, the model could not find the root cause. Once fixed, it took three weeks (!!) to re-onboard to a codebase no human had ever read 2. Context engineering 101: figure out where the “dumb zone” begins. As a rule of thumb, the less of the context window that is used, the better the outcomes are. This is because the attention mechanism is quadratic: the more that goes into the context window, the more compute is required to process it all. 3. “You’re completely right!” or “you’re right to push back on that” are phrases that mean it’s time to start a new session. These responses mean the LLM session is trajectory-poisoned, and you’re wasting time and tokens to continue. This is because models are autoregressive.

Knowing how LLM contexts work and how to work around context limitations – aka “context engineering” – is becoming so important. No better person to explain than dex Timestamps: 00:00 Intro 01:33 Dex’s path into tech 03:34 Early work in platform engineering 05:28 Replicated 11:24 Metalytics 12:36 12-factor agents 18:27 Context engineering 23:38 Harness engineering 26:11 Context overload 30:45 Loop engineering 44:34 Software factories before and after AI 50:33 Automation limits 55:18 Three options for automating 59:00 RPI framework 1:04:16 Intentional compaction 1:11:48 Token harder vs. token smarter 1:16:44 AI slop 1:19:15 HumanLayer 1:29:09 Book recommendation Brought to you by: • Antithesis — with Antithesis, you can use AI agents to work on critical systems without worrying about correctness. Teams like Jane Street, and the etcd community use Antithesis to ship better code, faster. • Buildkite — the CI orchestration platform built for reliable scale. Used by OpenAI, Anthropic, Cursor, Meta, Uber, Ramp, Nvidia, Airbnb and many more. • Sentry — application monitoring software built by developers, for developers. Check out their AI agent, Seer AI, and Sentry MCP. Three interesting learnings from this episode: 1. Lesson learned: Shipping unread code spells disaster within months. Dex experimented with having the model write the code and humans not reviewing anything in July 2025. Four months later, they shut things down and threw the whole system out. Production broke, and no matter how much the team prompted Opus 4.1, the model could not find the root cause. Once fixed, it took three weeks (!!) to re-onboard to a codebase no human had ever read 2. Context engineering 101: figure out where the “dumb zone” begins. As a rule of thumb, the less of the context window that is used, the better the outcomes are. This is because the attention mechanism is quadratic: the more that goes into the context window, the more compute is required to process it all. 3. “You’re completely right!” or “you’re right to push back on that” are phrases that mean it’s time to start a new session. These responses mean the LLM session is trajectory-poisoned, and you’re wasting time and tokens to continue. This is because models are autoregressive.

Gergely Orosz

63,174 Aufrufe • vor 1 Monat

This guy built a trading bot on Polymarket with Claude Result: +$160,092 in 1.5 months His bot trades crypto Up/Down markets using temporal arbitrage, directional skew, and extreme-price buying His process looks like this: 1. Places limit orders on both sides 2. Buys cheap shares during sharp price swings 3. Builds an equal amount of Up and Down with a combined cost below $1 4. Then keeps a directional skew on the side his model sees as more likely 5. Near the end of the market, it can buy the almost-settled outcome at 95-99.9c or the cheap opposite side His Polymarket username: mo-money Using this strategy, he turned $20k into $160k and continues to grow his capital consistently His best trades: $175.84 -> $1,269.90 (+622.18%) $198.55 -> $1,140.35 (+474.33%) $293.46 -> $1,639.80 (+458.78%)

This guy built a trading bot on Polymarket with Claude Result: +$160,092 in 1.5 months His bot trades crypto Up/Down markets using temporal arbitrage, directional skew, and extreme-price buying His process looks like this: 1. Places limit orders on both sides 2. Buys cheap shares during sharp price swings 3. Builds an equal amount of Up and Down with a combined cost below $1 4. Then keeps a directional skew on the side his model sees as more likely 5. Near the end of the market, it can buy the almost-settled outcome at 95-99.9c or the cheap opposite side His Polymarket username: mo-money Using this strategy, he turned $20k into $160k and continues to grow his capital consistently His best trades: $175.84 -> $1,269.90 (+622.18%) $198.55 -> $1,140.35 (+474.33%) $293.46 -> $1,639.80 (+458.78%)

VALIX

23,439 Aufrufe • vor 1 Monat

Thanks for coming on Max Levchin! We talked about how he first invested in Brex, crypto and stablecoins, U.S. credit card debt, and more. Watch now. (12:28) The PayPal Story (32:30) Founder CEOs vs. Hired CEOs (43:49) Entrepreneurship (48:47) The Media Industry (55:53) Credit Card Debt in the US (1:06:03) Affirm's Business Model (1:09:01) Challenges and Ethics in Lending (1:24:31) AI and Underwriting Models (1:32:51) Crypto and Stablecoins We’re proudly sponsored by Brex—a brand I co-founded, now supporting over 30,000 businesses like Anthropic, DoorDash, and Scale AI, helping them make every dollar count. I’m grateful for their continued support as I bring you all conversations with some of the most exceptional founders of our generation. For more information, please go to:

Henrique Dubugras

29,442 Aufrufe • vor 9 Monaten

“Elon’s the best in the world at atoms, & I think atoms are gonna be a decisive factor in the AI race. The way Elon Musk builds companies is different than other people He builds up potential energy, & then he converts that potential energy into kinetic energy ..it’s kind of crazy to me that people don’t understand the pattern yet.” - Tesla: builds gigafactories for ~5 years with no output → suddenly millions of cars - SpaceX: nearly a decade from Falcon 9 to reusability → Starlink everywhere - Starship: ~$0 revenue today → “about to be an unbelievable amount” - xAI: building the biggest coherent training clusters in the world → monetization later Shaun Maguire (Shaun Maguire), Partner at Sequoia Capital Potential = building factories, rockets, compute, & infrastructure before monetization Kinetic = converting that built capacity into massive real-world output & revenue Shaun & Sequoia have backed 5 of Elon's companies: SpaceX, xAI, Neuralink, The Boring Company, & X. . . . "I just—I think xAI is gonna— Elon’s the best in the world at atoms, and I think atoms are gonna be a decisive factor in the AI race. And the way Elon builds companies is different than other people. He builds the way I describe it is: he builds up potential energy, and then he converts that potential energy into kinetic energy. Whereas a lot of other companies, whenever they have potential energy, they’re just immediately converting it into kinetic energy. Elon builds gigafactories that are not producing any cars or revenue for like five years, and then immediately start making, you know, a million cars. And they’ve spent almost a decade getting from Falcon 9 to Falcon 9 reusable, and then very quickly they get Starlink going. Now they’ve been spending almost a decade on Starship, and it’s like zero dollars of revenue from Starship today—but it’s about to be an unbelievable amount. With xAI, they’ve been building the biggest coherent training clusters in the world, and they’re just not really focused on monetizing them right now. And it’s kind of crazy to me that people don’t understand the pattern yet. And it’s not to say that I’m bearish on the other foundation model companies— I just think xAI is gonna shock people with the scale that it gets to quickly. But I’m bullish on all of them."

“Elon’s the best in the world at atoms, & I think atoms are gonna be a decisive factor in the AI race. The way Elon Musk builds companies is different than other people He builds up potential energy, & then he converts that potential energy into kinetic energy ..it’s kind of crazy to me that people don’t understand the pattern yet.” - Tesla: builds gigafactories for ~5 years with no output → suddenly millions of cars - SpaceX: nearly a decade from Falcon 9 to reusability → Starlink everywhere - Starship: ~$0 revenue today → “about to be an unbelievable amount” - xAI: building the biggest coherent training clusters in the world → monetization later Shaun Maguire (Shaun Maguire), Partner at Sequoia Capital Potential = building factories, rockets, compute, & infrastructure before monetization Kinetic = converting that built capacity into massive real-world output & revenue Shaun & Sequoia have backed 5 of Elon's companies: SpaceX, xAI, Neuralink, The Boring Company, & X. . . . "I just—I think xAI is gonna— Elon’s the best in the world at atoms, and I think atoms are gonna be a decisive factor in the AI race. And the way Elon builds companies is different than other people. He builds the way I describe it is: he builds up potential energy, and then he converts that potential energy into kinetic energy. Whereas a lot of other companies, whenever they have potential energy, they’re just immediately converting it into kinetic energy. Elon builds gigafactories that are not producing any cars or revenue for like five years, and then immediately start making, you know, a million cars. And they’ve spent almost a decade getting from Falcon 9 to Falcon 9 reusable, and then very quickly they get Starlink going. Now they’ve been spending almost a decade on Starship, and it’s like zero dollars of revenue from Starship today—but it’s about to be an unbelievable amount. With xAI, they’ve been building the biggest coherent training clusters in the world, and they’re just not really focused on monetizing them right now. And it’s kind of crazy to me that people don’t understand the pattern yet. And it’s not to say that I’m bearish on the other foundation model companies— I just think xAI is gonna shock people with the scale that it gets to quickly. But I’m bullish on all of them."

Molly O’Shea

134,420 Aufrufe • vor 7 Monaten