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Figure shows a serious humanoid production ramp. Brett's graph doesn't have a y-axis. Assuming June 2023 = 1 unit and the graph is linear: 2023: 2 units 2024: 26 2025: 79 2026 (till Apr 21st): 312 Annualized run-rate based on the past 21 days of production: 2,589/year.

68,716 просмотров • 4 месяцев назад •via X (Twitter)

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Loops vs. Graphs, clearly explained! loops are great, but they have a ceiling: a loop makes one unit of work better. it cannot decide which units exist. so you end up with a very good agent running the wrong three steps, in the wrong order, one at a time. Graph engineering fixes this by moving the decision up a layer: what runs, what runs at the same time, and what never runs at all. you need both. here's how it works: a graph splits your system into two kinds of decision. ↳ inside a unit: the loop. produce, check, correct, repeat until green ↳ between units: the graph. split, fan out, merge, gate, send back Prompts → Context → Harness → Loops → Graphs you get parallel work, isolated contexts, and steps that stop running when nothing needs them. the trick is being selective about what becomes a node. only spend a model where judgment lives. merging, ranking, deduping and schema checks are edges, and edges are code. free, instant, and they cannot be argued out of a verdict. a graph where every edge is an agent pays rent on its own wiring. one thing to know before you scale it. a graph has two return paths, and almost everyone builds one. ↳ the correction edge is short. a gate rejects one unit back to the step that produced it, and it fixes the run you are in ↳ the learning edge is long. an accepted result goes back to the splitter as a constraint, and it fixes every run after skip the second and you get a graph that is fast and never gets smarter. next week it starts from the same place with the same blind spots. and a smaller one that eats whole nights: when a unit fails, return that unit, not the batch. send back four slices because one failed and you have just rewritten three correct ones. do it twice in a run and the run never converges. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below ↓

Hanako

73,867 просмотров • 16 дней назад

China's humanoid robotics market is on fire. With orders expected to top 30,000 units this year—a tenfold jump from 2024's total of less than 3,000—2025 is officially shaping up to be the "Year of Mass Production." This surge, driven by an expansion into new sectors like industrial manufacturing, logistics, and elder care, is reflected in a wave of new deals across the industry. Here's a look at some of the key commercial progress: Astribot: A 1,000-unit order for industrial and logistics deployment over two years. TianTai Robotics: Signed a major 10,000-unit order for caregiving robots. Noetix Robotics : Received over 2,000 intent orders in one month, valued at over 100 million yuan, with a focus on education and commercial performances. AgiBot: Expects to ship thousands of units this year and tens of thousands in 2026. Unitree Robotics: Has orders for thousands of units and is one of the most visible products in the industry. UBTech: Aims to deliver 500 industrial humanoids in 2025, with educational robot orders already exceeding 300 units. Robot Era: Delivered over 300 units by July 2025 with 500 more on hand. TLIBOT: Has around 1,000 intent orders. Galbot: Secured orders for its supermarket security robot, Galbot, in 100 stores. AI² Robotics: Has nearly 500 orders for its general-purpose robots for industrial and public service scenarios. But here’s the crucial reality check. While the order boom is exciting, it doesn't automatically translate to fulfilled deliveries. Many companies lack the production capacity to keep up. A significant portion of these are "intent orders" or framework agreements, not guaranteed sales. Furthermore, the market is heavily B2B-focused, with consumer demand representing only about 5% of sales. Some orders are even symbolic, for public relations or strategic purposes. This “order frenzy” is a starting point, not the finish line. The true test for China's humanoid robot industry isn't who can secure the biggest order, but who can consistently deliver on it and build a stable market for the future.

RoboHub🤖

199,146 просмотров • 1 год назад

New "superman" Four months later, Shanghai-based Matrix Robotics unveiled the Matrix-3--yet another anthropomorphic humanoid robot situated on the left side of the "Uncanny Valley." Unlike Boston Dynamics' Atlas, which is designed for industrial settings, the Matrix-3 is a general-purpose humanoid robot intended initially for service-oriented environments such as supermarkets, hotels, and office buildings. Standing 1.7 meters tall and weighing 65 kilograms, the adult-sized robot features a full-body covering of biomimetic fabric integrated with tactile sensors, as well as humanoid biomimetic muscles. It employs linear joints rather than the mainstream rotary joints. Matrix claims it is capable of pulling or pushing a load of 200 kg. Optimus makes extensive use of linear joints (linear actuators). It is equipped with dexterous hands offering 27 DOF and boasts a battery life of 4 hours. The robot is powered by a model named WAVE(like an E2E World Action Model) Matrix states that it has already established a manufacturing facility in Shanghai's Zhangjiang with an annual production capacity of 10,000 units. The company plans to deliver 1,000 units this year, with the first batch scheduled for delivery by the end of June. Pricing starts at $99,000. Notably, Matrix founder Zhang Haixing previously served as a leader at Tesla China's Design and Research Center, where he was deeply involved in the early-stage R&D and design of the Optimus robot. This company seems to be delivering on the future envisioned for Optimus.

CyberRobo

40,727 просмотров • 3 месяцев назад

Everything Elon said about Optimus on the Q4 2024 earnings call: ⦿ I see a path for Tesla to be the most valuable company in the world, possibly bigger than the next five companies combined, overwhelmingly due to autonomous vehicles and autonomous humanoid robots. ⦿ The training compute needed for Optimus will ultimately probably be 10× what is needed for cars. Humanoids likely have 1,000× more useS than a car, which doesn't mean training scales by 1,000×, but probably close to 10×. The training compute will scale progressively as Optimus becomes more productive. ⦿ Long-term, Optimus has the potential to generate $10 trillion in revenue. In that scenario, we can support a lot of training compute. Even $500 billion in training compute is a good deal (chuckles). ⦿ There's a lot of uncertainty with timing because several aspects are being iterated simultaneously. The internal plan is for roughly 10,000 robots to be built this year, but we'll more likely produce several thousand. ⦿ I'm confident those several thousand robots will be able to do useful things. ⦿ The lessons from Production V1 will inform the changes in Production V2, which we expect to launch around mid-next year. ⦿ Our goal, aspirationally, is to ramp 10× every year, but perhaps we end up with 5× growth per year. With that kind of growth, it won't be many years before we're making 100 million robots a year. ⦿ The off-the-shelf components didn't work well, so we had to design everything in-house, including the most sophisticated hand ever made. Optimus will be able to play a piano and thread a needle. ⦿ My long-term prediction is that Optimus will overwhelmingly be the value of the company. ⦿ Optimus is not design-locked. It is rapidly evolving in a good direction. Tesla has by far the best humanoid robotics engineers in the world. Tesla also has all the other necessary ingredients: battery pack, power electronics, charging, communications, real-world AI, and the ability to scale production. ⦿ What other companies are missing is real-world AI and the ability to scale to millions of units a year. ⦿ This year, we aim to use Optimus internally at Tesla. We can easily use several thousand robots at Tesla for repetitive tasks, such as loading sheet metal at the welding line. ⦿ The Production V1 line is roughly 1,000 units per month. The Production V2, launching around mid-next year, will be for 10k units per month. The line after that will be for 100k units a month. Of course, it takes time for any given line to reach its maximum potential. ⦿ A very rough guess: we'll start delivering Optimus to companies outside of Tesla in the second half of 2026. The ramp is going to be exponential, and demand will not be a problem. ⦿ Once we're above 1 million units per year, the production cost of Optimus will be less than $20,000. Its total mass and complexity are much lower than a car. At a similar production volume to the Model Y, Optimus should be about half the cost of a Model Y. ⦿ The price is a different matter than cost. The price of Optimus will be set by market demand. [This is by far the longest Elon has ever spent discussing Optimus on an earnings call.]

The Humanoid Hub

96,475 просмотров • 1 год назад

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Nic Cruz Patane

111,451 просмотров • 2 лет назад