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Just submitted to arXiv.org: Amy's work on closed loop autonomous crystallisation, sample preparation, and powder X-ray diffraction. This is the most complex automated workflow that we've built so far, involving 3 separate robots & 13 steps Leverhulme Research Centre - Materials Design MIF ERC_ADAM

Andy Cooper

9,787 subscribers

39,425 görüntüleme • 2 yıl önce •via X (Twitter)

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The workflow starts with crystal growth followed by sample preparation (2-step grinding), sample mounting, and PXRD data acquisition. It is orchestrated by our system architecture, #ARChemist, masterminded by @HatemFakhrulde1

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A key step is grinding the crystals for better orientational averaging (and, indeed, to get them out of the sample vial ...)

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To process the crystals, we use this versatile @ABBRobotics YuMi robot for sample handling

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Very proud of @amylunt for building this in her PhD, especially given the huge slow-down during COVID. Amy, you're a superstar. The work was done with @sam_c and also involved @HatemFakhrulde1, @gabriellapizz, Louis, @TheWubberDuck, @nici_rankin, @robclow11 and Ben A. ⭐️⭐️⭐️

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By the way - if anyone is interested, we are hiring a Lecturer in this general area of chemistry automation - advert to be posted soon (DM me if you're interested)

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Credit also to @daftpunk for the 🎸

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Btw, I have the @amazon receipt for the MP3 ...

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@arxiv @amylunt @LC_Mater_Design @MIF_UoL @erc_adam This gets a “very cool!” and “how can they get robots to do that?” from my 5yo daughter. Have to say I’m in agreement!

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@arxiv @amylunt @LC_Mater_Design @MIF_UoL @erc_adam Cheers Ross. The toys are in the post.

Pepe Marquez profil fotoğrafı

Pepe Marquez2 yıl önce

@MolecularXtal @arxiv @amylunt @LC_Mater_Design @MIF_UoL @erc_adam This is super cool Andy! How do you organize and store the huge amount of data that you guys generate? Did you code your own database?

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@MolecularXtal @arxiv @amylunt @LC_Mater_Design @MIF_UoL @erc_adam Thanks! This is a recent implementation so we don't (yet) have a huge amount of data, but there is the potential to generate it - and yes, we're looking at databases also to compare with predicted structures with @graeme_day in our @erc_adam project

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Humanoid robots are cool. But most of them aren’t ready for real-world work. X-Humanoid just launched Embodied Tien Kung 3.0 - a full-size humanoid robot built to actually operate in industrial and commercial environments. What makes it different? • Strong, high-torque joints for heavy tasks • Can move dynamically and stay stable • Handles complex motions (even over obstacles) • Millimeter-level precision for detailed work It runs on the Wise KaiWu AI platform, which allows the robot to see, think, and act in a continuous loop with less human control and support for multi-robot collaboration. It also supports ROS2, MQTT, and TCP/IP, and offers more open hardware + software interfaces to make development easier for teams. The goal is simple: make humanoid robots practical, easier to build on, and ready for real-world use. Visit: Check out the official launch video below to see what it can do! 👇 #Robotics #HumanoidRobot #AI

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Chidanand Tripathi

69,604 görüntüleme • 5 ay önce

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Eric Schmidt just called AGI the most important event in a thousand years of human history. Then the interviewer brought up radical abundance and UBI. Eric Schmidt: “You tech liberal you.” Every traditional economist already knows the rebuttal. UBI means printing money. Printing money means inflation. End of discussion. Their math is not wrong. Their math is obsolete. The old rule is simple. Print dollars without producing goods and prices explode. Every hyperinflation in history confirms it. But that rule has a hidden assumption buried inside every model. Human labor is the bottleneck of all production. AGI removes the bottleneck. Permanently. Autonomous machinery pulls raw materials from the earth. No operator. Self-driving trucks haul those materials across continents. No driver. Automated factories turn them into goods. No worker. Humanoid robots load the freight, stock the shelves, wire the buildings. No hands. When those robots break, better robots design, build, and replace them. No engineer. No technician. No human anywhere in the loop. From the mine to your front door, the entire chain runs without a single paycheck. Labor is the largest cost hidden inside everything you own. When it hits zero, the price of everything chases it down. Not cheaper. Not discounted. Functionally zero. This is where the inflation argument dies. UBI inflates only when printed dollars chase scarce goods. That is the textbook answer. And the textbook is correct. But inside a fully automated loop, goods stop being scarce. Machines produce faster than humans can spend. GDP does not grow linearly. It goes vertical. For the first time in recorded history, supply will outrun demand at every price point in every market on earth. Ten thousand years of civilization. Every empire. Every war. Every famine. Every economic theory ever written. All downstream of two words. Not enough. AGI does not solve that problem. It makes the question obsolete.

Dustin

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People come from across the world to see Rajasthan’s palaces and forts rising into the sky. But one of its most remarkable sights does the exact opposite. It goes deep into the ground. Chand Baori, in Abhaneri, was built over a thousand years ago. It drops nearly 30 metres across 13 levels and about 3,500 precisely arranged steps making it one of the deepest and largest stepwells in the world. But what impresses most isn’t just the scale, it’s the thinking. We talk a lot today about “design thinking” which is essentially solving problems by blending empathy, function and design so seamlessly that the solution becomes an art form. While the stepwell fills the basic need of water, what emerges is almost hypnotic symmetry. This is design thinking, centuries before we gave it a name. Back then, it was simply… wisdom. #SundayWanderer

People come from across the world to see Rajasthan’s palaces and forts rising into the sky. But one of its most remarkable sights does the exact opposite. It goes deep into the ground. Chand Baori, in Abhaneri, was built over a thousand years ago. It drops nearly 30 metres across 13 levels and about 3,500 precisely arranged steps making it one of the deepest and largest stepwells in the world. But what impresses most isn’t just the scale, it’s the thinking. We talk a lot today about “design thinking” which is essentially solving problems by blending empathy, function and design so seamlessly that the solution becomes an art form. While the stepwell fills the basic need of water, what emerges is almost hypnotic symmetry. This is design thinking, centuries before we gave it a name. Back then, it was simply… wisdom. #SundayWanderer

anand mahindra

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🇨🇳 China’s AI race is starting to look less like a model race and more like an adoption race. Alibaba’s Qwen App shows how AI becomes powerful when it slips into ordinary research habits. The difference is not capability, it is deployment shape. e.g doctors and medical researchers in China appear to be using it as a workflow layer: gathering papers, sorting evidence, framing mechanisms, shaping charts, and drafting research-style explanations. Alibaba is trying to place Qwen directly inside a mass consumer and services ecosystem, including shopping, payments, maps, travel, office tools, education, and healthcare, so the model is closer to daily task execution rather than only a premium research assistant. The important shift is that Qwen is not being used only as a chatbot that answers questions, but as a workflow tool. This strategy lands right in China’s comfort zone. It has a massive digital economy to spread AI apps fast, and people who are already very comfortable with tech. Ipsos, the polling firm, found that China is more excited about using AI than any other country. OpenAI is building a highly capable research assistant; China may be normalizing AI as a default work surface inside professional life. For Alibaba and China, the interesting part is the adoption surface: Qwen can become a front door to many services, which means ordinary users, students, doctors, researchers, and office workers may meet AI inside routine tasks rather than as a separate tool. A normal health question can become a research task because the app first shapes the question, then searches for relevant studies, then separates weak claims from stronger evidence, then turns the result into a clearer explanation. This matters for medicine because a lot of research work is not one big discovery moment, but thousands of small steps involving literature review, data cleanup, experiment interpretation, figure preparation, and careful writing. So for professors, students, office workers, and ordinary users, the difference is not just that Qwen can summarize text; it is being positioned as a work surface for preparing reports, generating presentations, studying, planning, searching, and completing real-world tasks without jumping between apps. Both superpowers are worried about slipping behind. In 2026, it could start to look like they are racing on separate tracks.

Rohan Paul

84,663 görüntüleme • 3 ay önce

LLM Wikis are being slept on. I argue that creating knowledge bases with LLMs or coding agents is one of the most valuable applications of AI today. It's about being intentional in building and scaling your intelligence stack. To showcase this, I wanted to share an LLM Wiki I have built over the last couple of months. It's called PaperWiki, and I use it across all my research workflows, along with my research agents. In fact, I also use it to curate papers I share with my communities, newsletter, and on X. The PaperWiki is updated regularly with automations, so I basically have agents on a loop maintaining it. All the entries are ingested from different sources and stored in a vault (Obsidian) and further indexed using qmd. And then further presented via an HTML artifact. So all of it is easily accessible to all my agents and easily searchable through full-text search and rich semantic search. The structure of the wiki has proven significantly useful to start interesting and exciting cutting-edge research projects with my research agents (from building tiny and more efficient gpt/difussion llms to building out SoTA harnesses and memory systems). It turns out that agents love markdown files and can more easily navigate the papers given the rich metadata structure of the wiki. I am just getting started on this, but it's clear to me that we should all be experimenting with LLM Wikis. Here's why: Building LLM knowledge bases gets you into the habit of leveraging AI outputs in all kinds of creative ways. It's the good kind of tokenmaxxing we should all be pushing for. LLM Wikis can be maintained automatically in a loop. I use an automation that updates the wiki every day based on papers I curate. The curation is another automation I run in a loop (with a bit of human in the loop), so I get to build on all my previous knowledge and expertise, and all of it compounds the deeper the integration/layers. One interesting result of this process is that I feel like I can better spot high-quality papers and remove noise more easily. Social media could never solve that. And most paper aggregators use metrics I simply don't trust. I like that agents can help with the noise vs. signal problem. This is important for research. Lots of people consider agents to produce mostly slop. But it doesn't have to be that way. Careful curations, prompts, automations, verifiers, and human-in-the-loop can produce some astonishing results. And you really don't need frontier models for this. I use a combination of frontier models (opus-4.8) and open-weight models (deepseek-v4-flash) to maintain this. An exciting future work (we are working on this DAIR.AI) is to tune specialized models on top of this to allow LLMs to quickly understand cutting-edge research ideas and can better conceptualize research strategies that further accelerate scientific research agents. I plan to open-source a bunch of this work, including the artifact, but this is currently work in progress, and I was excited to share some thoughts as I continue working on it. Sharing more as I go. Stay tuned!

LLM Wikis are being slept on. I argue that creating knowledge bases with LLMs or coding agents is one of the most valuable applications of AI today. It's about being intentional in building and scaling your intelligence stack. To showcase this, I wanted to share an LLM Wiki I have built over the last couple of months. It's called PaperWiki, and I use it across all my research workflows, along with my research agents. In fact, I also use it to curate papers I share with my communities, newsletter, and on X. The PaperWiki is updated regularly with automations, so I basically have agents on a loop maintaining it. All the entries are ingested from different sources and stored in a vault (Obsidian) and further indexed using qmd. And then further presented via an HTML artifact. So all of it is easily accessible to all my agents and easily searchable through full-text search and rich semantic search. The structure of the wiki has proven significantly useful to start interesting and exciting cutting-edge research projects with my research agents (from building tiny and more efficient gpt/difussion llms to building out SoTA harnesses and memory systems). It turns out that agents love markdown files and can more easily navigate the papers given the rich metadata structure of the wiki. I am just getting started on this, but it's clear to me that we should all be experimenting with LLM Wikis. Here's why: Building LLM knowledge bases gets you into the habit of leveraging AI outputs in all kinds of creative ways. It's the good kind of tokenmaxxing we should all be pushing for. LLM Wikis can be maintained automatically in a loop. I use an automation that updates the wiki every day based on papers I curate. The curation is another automation I run in a loop (with a bit of human in the loop), so I get to build on all my previous knowledge and expertise, and all of it compounds the deeper the integration/layers. One interesting result of this process is that I feel like I can better spot high-quality papers and remove noise more easily. Social media could never solve that. And most paper aggregators use metrics I simply don't trust. I like that agents can help with the noise vs. signal problem. This is important for research. Lots of people consider agents to produce mostly slop. But it doesn't have to be that way. Careful curations, prompts, automations, verifiers, and human-in-the-loop can produce some astonishing results. And you really don't need frontier models for this. I use a combination of frontier models (opus-4.8) and open-weight models (deepseek-v4-flash) to maintain this. An exciting future work (we are working on this DAIR.AI) is to tune specialized models on top of this to allow LLMs to quickly understand cutting-edge research ideas and can better conceptualize research strategies that further accelerate scientific research agents. I plan to open-source a bunch of this work, including the artifact, but this is currently work in progress, and I was excited to share some thoughts as I continue working on it. Sharing more as I go. Stay tuned!

elvis

55,323 görüntüleme • 1 ay önce

Finding twitter scraping bots with Gatsby Sometimes the alpha is on-chain, other times you need an off-chain setup. The latter is often the case with tokens that are born out of activity on X. One recent example is Moonkin , which pumped after Vitalik posted about it here: You knew Moonkin would pump if Vitalik mentioned it but you can't build a scraper yourself. How to profit from this? The video shows an example of how you can find scraper bots that do the dirty work for you and allow you to make money in your sleep. By conducting this type of research on multiple tokens like this, you will end up with decently sized and alpha-rich list of scrapers. $GATSBY now allows you to create and manage lists and to reference them in your queries, allowing you to keep your workflow in platform. The next time you find a token that is sensible to tweets, you just need to setup an Automated Strategy that buys that token if one (or more) wallets from your scrapers list ape it. Automated Strategies coming soon on $GATSBY

Finding twitter scraping bots with Gatsby Sometimes the alpha is on-chain, other times you need an off-chain setup. The latter is often the case with tokens that are born out of activity on X. One recent example is Moonkin , which pumped after Vitalik posted about it here: You knew Moonkin would pump if Vitalik mentioned it but you can't build a scraper yourself. How to profit from this? The video shows an example of how you can find scraper bots that do the dirty work for you and allow you to make money in your sleep. By conducting this type of research on multiple tokens like this, you will end up with decently sized and alpha-rich list of scrapers. $GATSBY now allows you to create and manage lists and to reference them in your queries, allowing you to keep your workflow in platform. The next time you find a token that is sensible to tweets, you just need to setup an Automated Strategy that buys that token if one (or more) wallets from your scrapers list ape it. Automated Strategies coming soon on $GATSBY

Gatsby

24,633 görüntüleme • 1 yıl önce

Satya Nadella was asked whether most people will earn more or less after AI. He did not dodge it. His answer was two words that will either reassure you completely or keep you up at night: glue work. He has been thinking about this carefully. His framing: wages have never been about which tasks you perform. They have always been about what society decides to value. The last 250 years rewarded the accumulation of expertise. AI creates abundance of expertise. So the question becomes what humans build next that cannot be trained away. His answer is glue work. The connective tissue between automated systems. The judgment calls that sit outside any loop an agent can close. The work that holds everything together that nobody has written a spec for yet. He was not dismissing the disruption. He said plainly that real displacement is coming and the workflow will change. But his argument is that humans have always migrated toward the layer above whatever just got automated. Typists became knowledge workers. Knowledge workers become something else. The problem with his answer is the one nobody in that room pushed on. He could name the pattern. He could not name the jobs. Glue work is either the most honest answer a tech CEO has given about the future of employment. Or it is the most elegant way to say I do not know dressed up as reassurance. (Watch this on YouTube at

Satya Nadella was asked whether most people will earn more or less after AI. He did not dodge it. His answer was two words that will either reassure you completely or keep you up at night: glue work. He has been thinking about this carefully. His framing: wages have never been about which tasks you perform. They have always been about what society decides to value. The last 250 years rewarded the accumulation of expertise. AI creates abundance of expertise. So the question becomes what humans build next that cannot be trained away. His answer is glue work. The connective tissue between automated systems. The judgment calls that sit outside any loop an agent can close. The work that holds everything together that nobody has written a spec for yet. He was not dismissing the disruption. He said plainly that real displacement is coming and the workflow will change. But his argument is that humans have always migrated toward the layer above whatever just got automated. Typists became knowledge workers. Knowledge workers become something else. The problem with his answer is the one nobody in that room pushed on. He could name the pattern. He could not name the jobs. Glue work is either the most honest answer a tech CEO has given about the future of employment. Or it is the most elegant way to say I do not know dressed up as reassurance. (Watch this on YouTube at

Ihtesham Ali

74,472 görüntüleme • 1 ay önce

ELON MUSK: "Optimus will be the biggest product ever, but it is a very complex problem to solve. It's one of the hardest things to solve, to make an autonomous humanoid robot that can do tasks that, if you simply ask it to do something or show it a video, it can do the task without any programming. No one's ever achieved this, and there are many challenges in the design of the robot to achieve sufficient dexterity and to be very reliable and have long wear and tear, meaning it needs to be out in the field and not break down. Otherwise, you've got sort of a 70 kilogram robot that just flopped over and you've got to carry it out like a body, you know, it doesn't have wheels. And we don't want Optimus to go haywire. So it's a lot of work to scale, to get the design right and to scale production. This is going to be the hardest product to scale manufacturing that we've ever made at Tesla because everything on the robot is new. And the difficulty of scaling the production ramp is proportionate to the newness of the parts in the robot. With Optimus, there is no supply chain. So we've had to build up a supply chain in its entirety or in-house the production. The Optimus production line that we're building out in Fremont in place of what used to be the Model S & X production, it looks incredible. It's quite stunning to see."

ELON MUSK: "Optimus will be the biggest product ever, but it is a very complex problem to solve. It's one of the hardest things to solve, to make an autonomous humanoid robot that can do tasks that, if you simply ask it to do something or show it a video, it can do the task without any programming. No one's ever achieved this, and there are many challenges in the design of the robot to achieve sufficient dexterity and to be very reliable and have long wear and tear, meaning it needs to be out in the field and not break down. Otherwise, you've got sort of a 70 kilogram robot that just flopped over and you've got to carry it out like a body, you know, it doesn't have wheels. And we don't want Optimus to go haywire. So it's a lot of work to scale, to get the design right and to scale production. This is going to be the hardest product to scale manufacturing that we've ever made at Tesla because everything on the robot is new. And the difficulty of scaling the production ramp is proportionate to the newness of the parts in the robot. With Optimus, there is no supply chain. So we've had to build up a supply chain in its entirety or in-house the production. The Optimus production line that we're building out in Fremont in place of what used to be the Model S & X production, it looks incredible. It's quite stunning to see."

DogeDesigner

307,708 görüntüleme • 22 gün önce

We've all experienced how consumer AI can help in our personal lives, but that same experience hasn't been available at work. Most enterprise data is siloed, consumer AI can't access your work tools, and many orgs won't allow it due to security concerns. We built Amazon Quick Suite to solve those problems. It’s an agentic AI app that acts as your AI teammate at work – connecting to your business data and apps, and letting you interact with AI agents that can find insights, conduct research, and even take actions across your systems – with the security and privacy enterprises need. Quick works across your existing ecosystem, from emails to Salesforce to Slack to AWS services. You can create custom agents to draft content in your personal style, use Quick Research for in-depth analysis, or set up Quick Flow to automate tasks. And most importantly, your interactions never train the underlying models. We've been testing with Amazon employees and select customers and early results show just how transformational Quick is… cutting analysis time from months to days, and turning week-long tasks into minutes. This is an exciting moment and we're just getting started. Quick is available now, so have at it!

We've all experienced how consumer AI can help in our personal lives, but that same experience hasn't been available at work. Most enterprise data is siloed, consumer AI can't access your work tools, and many orgs won't allow it due to security concerns. We built Amazon Quick Suite to solve those problems. It’s an agentic AI app that acts as your AI teammate at work – connecting to your business data and apps, and letting you interact with AI agents that can find insights, conduct research, and even take actions across your systems – with the security and privacy enterprises need. Quick works across your existing ecosystem, from emails to Salesforce to Slack to AWS services. You can create custom agents to draft content in your personal style, use Quick Research for in-depth analysis, or set up Quick Flow to automate tasks. And most importantly, your interactions never train the underlying models. We've been testing with Amazon employees and select customers and early results show just how transformational Quick is… cutting analysis time from months to days, and turning week-long tasks into minutes. This is an exciting moment and we're just getting started. Quick is available now, so have at it!

Andy Jassy

49,241 görüntüleme • 10 ay önce

A TURING AWARD WINNER STOOD UP AND TOLD A ROOM OF ENGINEERS THAT ALMOST NONE OF THEM DO THE ONE THING THAT ACTUALLY SEPARATES REAL ENGINEERING FROM TYPING, THEY WRITE CODE BUT THEY NEVER WRITE THE BLUEPRINT 56 minutes from Leslie Lamport -- Turing Award winner, creator of the tech behind almost every distributed system on earth. -> His claim: architects draw plans before a brick is laid. Programmers just start coding and hope. That gap is where complex systems quietly break. 04:10 -- A blueprint for software is called a spec. Write what the system must DO before touching how it does it. 10:13 -- Thinking above the code is the skill. The language is just the last, easiest step. 43:23 -- What programmers should really know isn't syntax. It's thinking clearly enough that "done" actually means done. 48:15 -- Thinking is hard, so we skip it. We jump straight to code because typing feels like progress. And this is exactly the wall the new "Loop engineering" hype is about to hit -- people now design loops of agents that write and check code while they walk away. But an unattended loop with no spec just ships broken work faster. Lamport is the missing half: the discipline that makes it safe to leave a loop running at all. You thought the leverage moved into better prompts and better loops. This is the man showing it moved above the code, where almost no one is willing to think. Save this. Read it before you trust a single agent loop ↓

A TURING AWARD WINNER STOOD UP AND TOLD A ROOM OF ENGINEERS THAT ALMOST NONE OF THEM DO THE ONE THING THAT ACTUALLY SEPARATES REAL ENGINEERING FROM TYPING, THEY WRITE CODE BUT THEY NEVER WRITE THE BLUEPRINT 56 minutes from Leslie Lamport -- Turing Award winner, creator of the tech behind almost every distributed system on earth. -> His claim: architects draw plans before a brick is laid. Programmers just start coding and hope. That gap is where complex systems quietly break. 04:10 -- A blueprint for software is called a spec. Write what the system must DO before touching how it does it. 10:13 -- Thinking above the code is the skill. The language is just the last, easiest step. 43:23 -- What programmers should really know isn't syntax. It's thinking clearly enough that "done" actually means done. 48:15 -- Thinking is hard, so we skip it. We jump straight to code because typing feels like progress. And this is exactly the wall the new "Loop engineering" hype is about to hit -- people now design loops of agents that write and check code while they walk away. But an unattended loop with no spec just ships broken work faster. Lamport is the missing half: the discipline that makes it safe to leave a loop running at all. You thought the leverage moved into better prompts and better loops. This is the man showing it moved above the code, where almost no one is willing to think. Save this. Read it before you trust a single agent loop ↓

slash1s

15,306 görüntüleme • 27 gün önce

Prisoner of War [2025] Directed By: Louis Mandylor So far like I mentioned a few times here these past few months this is my action film of the year so far and one of Scott Adkins finest work since Avengement [2019] his acting, combat approach and just his overall character in this film is just great. The OST and sound design in this film is just too good. An interesting take on WW2 where they used POW's as entertainment by the Japanese soldiers but James Wright (Scott Adkins) is no ordinary soldier and has very interesting background regarding his skills. I highly recommend doing a double feature with Showdown in Little Tokyo. Because Mark Lester mentioned at the QA over at BIG BAD FILM FEST about his cut of that film (Hopefully it gets picked up) that is very similar to what you see here. This is captured from the upcoming Blu-Ray from Well Go USA Entertainment that drops on Nov.11. Much love to them. #PrisonerofWar

Prisoner of War [2025] Directed By: Louis Mandylor So far like I mentioned a few times here these past few months this is my action film of the year so far and one of Scott Adkins finest work since Avengement [2019] his acting, combat approach and just his overall character in this film is just great. The OST and sound design in this film is just too good. An interesting take on WW2 where they used POW's as entertainment by the Japanese soldiers but James Wright (Scott Adkins) is no ordinary soldier and has very interesting background regarding his skills. I highly recommend doing a double feature with Showdown in Little Tokyo. Because Mark Lester mentioned at the QA over at BIG BAD FILM FEST about his cut of that film (Hopefully it gets picked up) that is very similar to what you see here. This is captured from the upcoming Blu-Ray from Well Go USA Entertainment that drops on Nov.11. Much love to them. #PrisonerofWar

ѕнσgυη ѕυρяємє

15,080 görüntüleme • 9 ay önce

China's robots just started building other robots on a factory floor. 44 seconds of footage, no theatrics this time. The G1 is assembling precision motor components on a real production line, running on a new embodied AI model called UnifoLM-X1-0. This isn't a stunt. It's the same playbook every frontier AI lab uses, just with hardware attached. China's robots are running on their own assembly line specifically to generate the motion data needed to train the next generation, a closed loop where the robot's work becomes its own training set. The timing tells you what this is really for. The company behind this is heading toward a mid-2026 IPO, and until now almost every public G1 appearance has been a gala dance routine or a boxing demo. A robot doing repetitive, unglamorous assembly work is a far stronger data point for investors than another choreographed performance. The CEO has been explicit about the bar he's measuring against, what he calls the 80/80 goal, a robot completing 80% of tasks across 80% of real-world environments. One in-house factory line doesn't clear that bar on its own, but it's the first public sign anyone is building toward it instead of performing for cameras.

China's robots just started building other robots on a factory floor. 44 seconds of footage, no theatrics this time. The G1 is assembling precision motor components on a real production line, running on a new embodied AI model called UnifoLM-X1-0. This isn't a stunt. It's the same playbook every frontier AI lab uses, just with hardware attached. China's robots are running on their own assembly line specifically to generate the motion data needed to train the next generation, a closed loop where the robot's work becomes its own training set. The timing tells you what this is really for. The company behind this is heading toward a mid-2026 IPO, and until now almost every public G1 appearance has been a gala dance routine or a boxing demo. A robot doing repetitive, unglamorous assembly work is a far stronger data point for investors than another choreographed performance. The CEO has been explicit about the bar he's measuring against, what he calls the 80/80 goal, a robot completing 80% of tasks across 80% of real-world environments. One in-house factory line doesn't clear that bar on its own, but it's the first public sign anyone is building toward it instead of performing for cameras.

Shruti

26,118 görüntüleme • 26 gün önce

Most teams are sitting on piles of low-hanging automation fruit. If you’re trying to justify the ROI, you’re probably overthinking it. I talked about this on Applied, the tenex podcast with Alex Lieberman (Alex Lieberman) and Arman Hezarkhani. We built an agent that takes new Typeform leads, researches them online, and drops them into a Zapier Table. Small build, big ripple for any team learning to work with AI. Here’s how to find your own automation silver bullet: 1. Pick one process close to revenue or your biggest cost 2. Break it into 3 to 5 steps 3. Automate the boring parts with a workflow 4. Insert an agent only where judgment is needed 5. Keep a human in the loop. Remove as confidence grows You can run this exercise in 15 mins. You don’t need to code, but you DO need to think like an engineer. What win will you ship this week?

Most teams are sitting on piles of low-hanging automation fruit. If you’re trying to justify the ROI, you’re probably overthinking it. I talked about this on Applied, the tenex podcast with Alex Lieberman (Alex Lieberman) and Arman Hezarkhani. We built an agent that takes new Typeform leads, researches them online, and drops them into a Zapier Table. Small build, big ripple for any team learning to work with AI. Here’s how to find your own automation silver bullet: 1. Pick one process close to revenue or your biggest cost 2. Break it into 3 to 5 steps 3. Automate the boring parts with a workflow 4. Insert an agent only where judgment is needed 5. Keep a human in the loop. Remove as confidence grows You can run this exercise in 15 mins. You don’t need to code, but you DO need to think like an engineer. What win will you ship this week?

Wade Foster

112,490 görüntüleme • 9 ay önce