What is true on Data?

Some of the best micro agencies right now are one very talented person and 2-3 expensive tools. Here are some stacks I've tried and that are worth building around: - @apify: web scraping and data extraction at scale from pretty much any platform. - @clay: outbound personalization at scale. Enrich leads with a ton of data sources and spits out targeting that’s good enough to book meetings. - @useapolloio + @InstantlyAI: list building plus cold email execution. Apollo gets you the map, Instantly runs the machine. Add Clay on top and you can go from “who do we target” to “sent 2,000 emails” in an afternoon. - @heyreach_io: LinkedIn outbound without losing your mind. Multi account, sequences, inbox workflows. - @ahrefs + @dataforseo API: SEO research plus programmable SERP reality. Ahrefs gives you the big picture, DataForSEO lets you pull keywords, SERPs, and competitor snapshots on demand inside your own workflows. That’s how one person runs an SEO engine. - @OpenAI's Codex or @claudeai Max: your second brain for ops. You already know how it works. Which expensive tool are you paying for that actually unlocks growth?

How do you teach a humanoid robot to walk without programming every move? Reinforcement learning step by step 1. Build a simulation (MuJoCo or NVIDIA Isaac Lab) with joints motors sensors and a physics model. 2. Define the task: walk forward while keeping balance. 3. Write a reward function points for moving toward the target staying upright and matching the desired speed. Penalties for falls, wasted energy and unstable movements. 4. A neural network, the policy, reads the robot's state (joint positions, joint velocities, body orientation) and outputs target positions or torques for each joint. 5. An algorithm like PPO updates the policy over many episodes and millions of simulated time steps. 6. Close the sim-to-real gap with domain randomization, then test carefully in the real world.

AI shopping agents just got a front door at Walmart. While Amazon blocks agents, Walmart announced an MCP server called Agent Gateway: → Approved agents can search Walmart and Sam's Club inventory and buy for you → You don't have to share personal info or purchase history with the agent → Next month: a separate profile per agent in your account → Require approval per purchase, or pre-approve up to a spending limit The rules: agents can't see payment details, can't act beyond what you asked, and get blocked if they break the terms. Walmart's returns policy still applies if an agent gets it wrong. It already works with Meta's Muse, ChatGPT and Gemini. https://t.co/DAaJEy2Ytz

GROK IS NOT A REPLY GUY ANYMORE. IT'S A 117M-USER STACK WIRED INTO X.

DeepSeek Search debuts on the Artificial Analysis Search Index at 69, 17th of 27 search products, at the lowest price of any paid Search API on the board @deepseek_ai launched its Search API on 17 October. We benchmarked it in our Stirrup agent harness, where GPT-5.6 Luna (medium) calls DeepSeek Search as its web_search tool to complete a combination of tasks from BrowseComp, DeepSearchQA and AA-Omniscience. At $0.75 per 1k searches, DeepSeek Search costs less per search than any other paid Search API we benchmark, below Octen, Parallel (turbo) and Parallel (fast) at $1. At about $0.032 per task, it is the 5th cheapest of 27 products, cheaper than 20 of the other 25 Search APIs (median $0.066) and level with Nimble (lite). TinyFish, Octen, Parallel (turbo) and Parallel (fast) cost less. Key benchmarking results for DeepSeek Search: ➤ Artificial Analysis Search Index: DeepSeek Search scores 69, a 36-point lift over the same model with no search (33). It sits between TinyFish at 71 and Linkup (standard) at 68 ➤ AA-Omniscience is DeepSeek Search's strongest benchmark: it scores 70% accuracy on our proprietary factual answer benchmark. This ranks 10th of 27, 3 points behind the joint leaders, Exa (auto, dynamic highlights) and Firecrawl (73%). BrowseComp, which needs multi-hop reasoning across several searches, is its weakest benchmark: at 63% it ranks 25th of 27, well behind the Perplexity variants and Octen (85% to 87%) ➤ Large search results: each search returns about 7k tokens, near the top of the board's range. Each result carries about 1.8k characters of passages from the page. This raises model cost: at about $0.023 per task, DeepSeek's model cost is the 2nd highest on the board, behind Linkup (flash, alpha) at $0.039 ➤ 128-character query limit: DeepSeek rejects queries longer than 128 characters. 6% of the agent's searches hit this limit, and for 87% of these, the model's first shorter rewrite worked Key details: ➤ Setup: GPT-5.6 Luna (medium) in Stirrup, DeepSeek Search API with need_content=false (page passages, no full page content) and 10 results per search ➤ Pricing: $0.75 per 1k successful searches. DeepSeek does not bill empty results ➤ Rate limit: 5 searches per second per account

The big news last week was of course the sharp drawdown in high yield data center bonds and what that might mean for the broader AI buildout. I have been eyeing the price analog between the AI boom today and the internet boom-turned-bust in 1998-2000 and have been comforted by the fact that earnings are so strong today that equity valuations are falling instead of rising. We can see that below. 🧵(1/3)

Tesla's Q3 earnings deck: "Optimus production began in Fremont on our high-volume lines. The bots coming off this line are the first production-quality bots that we have manufactured. While we will continue to iterate on the hardware and manufacturing process, we have proven that the current manufacturing process for Optimus is suitable for a commercial product."

Why is nobody talking about this? ToddlerBot is a 0.56 m, 3.4 kg humanoid robot built at Stanford by Haochen Shi, Weizhuo Wang, Shuran Song and C. Karen Liu. Its goal is to make robot learning cheap and easy to reproduce. The full hardware costs under 6,000 USD. Hardware - 30 active degrees of freedom: 7 per arm, 6 per leg, 2 in the neck, 2 in the waist - Dynamixel servos (XC330, XC430, 2XC430, 2XL430 and XM430 in the current open-source configuration) - 3D-printed structure: a full repair takes about 21 minutes of printing and 14 minutes of assembly - Jetson Orin NX 16 GB, two fisheye cameras, an IMU, two microphones and a speaker Sim-to-real - Zero-point calibration and motor system identification produce a digital twin of the robot - Walking: an RL policy trained in MuJoCo MJX and transferred to the real robot with no extra training, up to 0.25 m/s in any direction and 1 rad/s in place - Push-ups and pull-ups: keyframed motions, also transferred with no extra training Manipulation - RGB diffusion policies trained on 60 teleoperated demonstrations per task - Real-time onboard depth estimation at 10 Hz from the stereo fisheye cameras - A policy trained on one robot runs on another, and two robots tidied a room together Measured limits - Payload: 1,484 g, about 40% of its body weight - Endurance: 19 minutes of walking before motor heating affects stability - Durability: up to 7 falls before a part breaks - The cartwheel works, but its success rate is still low

We present OpenTac, an open, full-stack ecosystem for 3D tactile sensing on any robot surface. Anyone can design and fabricate high-density tactile skins for any part of their robot, then read them out, simulate them, and share data across different sensors. https://t.co/9Vagl9nAf9

BOOOM 🚨 $TSLA's Q3 2026 update is out. Non-GAAP EPS came in at $0.48, ahead of the roughly $0.43 to $0.45 analysts expected 🔥 But the most revealing parts of the deck have little to do with EPS. They show a company steadily shifting its weight from cars to robots, robotaxis, and AI 🔥 🧠 FSD is now on more than three out of four new cars in North America. Over 75% of new North American deliveries included FSD in Q3, up nearly 20 percentage points from Q2. Active FSD subscriptions reached 1.74 million, up from 1.48 million last quarter and 1.04 million a year ago. That count includes both upfront purchases and monthly subscriptions, and it leaves out free trials. Europe is opening up too. Croatia, Slovenia, the Czech Republic, and Slovakia have approved FSD (Supervised) since the end of Q2, bringing the total to nine EU countries. More than 100,000 European customers have already driven over 160 million kilometers on it, or nearly 100 million miles. Tesla says FSD subscriptions helped lift revenue and that FSD is driving demand wherever it's available. Put those together, and software is becoming a real revenue stream that can grow across the cars already on the road, not just with new sales. The outlook points the same way, with Tesla expecting hardware profits to be joined by faster growth in SI, software, and fleet-based profits. 🤖 Optimus has moved from prototypes to a real production line. Optimus production has started on Tesla's high-volume lines in Fremont. The bots coming off that line are the first production-quality Optimus units Tesla has ever built, and Tesla says it has proven the current process is ready for a commercial product. The capacity table lists Fremont as "Early Ramp" and shows a second Optimus site already under construction in Texas. Tesla hasn't shared a capacity number for either one yet. This is the jump from prototypes to manufactured units, and that's the step that matters most for bringing costs down. Building a second site before the first one has even ramped suggests Tesla expects demand well beyond what Fremont can handle. The missing capacity figure is a reminder that the ramp is still early. 🚕 Cybercab is now giving rides in Austin, and unsupervised miles are growing fast. Tesla started Robotaxi rides with Cybercab in Austin on September 3. By the end of the quarter, the Robotaxi service had logged nearly 2 million cumulative unsupervised miles, eight times the total from three months earlier. Austin service now runs from 6am to midnight. Texas now shows more than 125,000 units of installed annual Cybercab capacity with "Production" status. Tesla points out that installed capacity isn't the same as the current production rate, and it says battery supply is still the main constraint on vehicle volumes. It's ramping 4680 cells specifically to support Cybercab and Semi. There's also a telling line in the outlook. Tesla says deliveries will depend partly on "allocation decisions between sale to customers and use for our owned and operated fleet." In plain English, Tesla is openly weighing how many cars to keep for its own network instead of selling them. With the tooling listed as in production, batteries look like what will set the pace for Cybercab. Inside the car, riders get Grok with voice access to their inbox, calendar, files, and tasks, and Tesla pitches the cabin as "a second living room." Tesla is clearly thinking about what riders do during the trip, not just the trip itself. ⚡ Tesla plans to grow its AI training compute by about 50% in a single quarter. Cortex 1 and Cortex 2 in Texas are rated at more than 105 MW and 120 MW, and Tesla has another 40 MW of compute on top of that, for roughly 265 MW in total. The goal is 400 MW by the end of the year, and Tesla says this compute trains autonomy software for both its vehicles and its humanoid robots. Tesla is also building its own chip fab in Austin and plans to start commissioning equipment there in 2027. It hasn't said yet which chips the fab will make. All of this shows up in the spending. R&D rose to $2.76 billion, up 69% from a year ago, and capex hit $4.73 billion, more than double last year's $2.25 billion. Even so, free cash flow stayed positive at $1.5 billion, and cash grew to $45.4 billion. Because one compute pool trains both FSD and Optimus, progress on one can feed the other. A chip fab is one of the boldest vertical integration moves Tesla has made, and staying cash-positive while building it makes that bet much easier to fund. 🔭 Tesla is building every piece of its next chapter at the same time. Robots are coming off a production line, driverless Cybercabs are carrying riders, FSD is turning into recurring software revenue, and the compute behind all of it is about to grow by half. Each piece makes the others stronger, and together they point to a Tesla whose future depends less on how many cars it sells and more on what its AI can do.

This might be one of the most minimalist snake robots ever built. • One motor. • No dozens of actuated joints. • Crawls, climbs between walls and swims. It's called SAW (Single Actuator Wave-like robot), developed by Dr. David Zarrouk's Bio-Inspired and Medical Robotics Lab at Ben-Gurion University of the Negev. How it works • A single motor rotates a helix inside the robot's body. • 3D-printed links follow the helix to create a continuous wave. • The links push against the surrounding surface to propel the robot. • Front wheels handle steering. The numbers • 57 cm/s on flat ground • Around 5× faster than similar wave robots • Around 8 cm/s in a tunnel, with contact against both walls • Around 6 cm/s while swimming in its waterproof version One gait, multiple environments • Gravel, sand and grass • Obstacles and narrow passages • Pipes, tunnels and water The same wave-like motion works across these environments without changing the gait. Why use one motor? Traditional snake robots often rely on 10, 20 or 30 motors. SAW uses a single actuator, reducing the number of moving parts and making the design lighter, simpler to manufacture and suitable for 3D printing. Possible applications • Search and rescue in collapsed buildings • Inspection of pipes and tunnels • Medical imaging and biopsies inside the digestive tract, with a miniaturized version around 1 cm wide or less The research was published in Bioinspiration & Biomimetics under the title Single actuator wavelike robot (SAW): design modeling, and experiments. What caught my attention is how the helical mechanism generates the motion with just one motor. The mechanical design does most of the work

Follow Gemini 4 Argon's report, I implemented their robotics harness. Gemini did a zero-shot bimanual handover. Now open source 🤖👇 https://t.co/N6oUOQY6u9

Superhuman Hardware Design Is The Easy Part

Ropedia Super Week, Day 3 | S-Agent 2.0: Give a Coding Agent Spatial Perception, and It Beats GPT-6

Memory buyers are locking in supply for the next DECADE. Google is reportedly in talks to secure memory supply all the way to 2035. HP and Dell have extended their deals with $MU through 2031. Micron now has 26 long term agreements covering about 35% of its revenue through 2030. Customers don't sign 9 year deals when they expect a glut but rather sign them when they are worried about running short. KeyBanc now sees DRAM contract prices rising 15% to 20% and NAND rising more than 20%. And $MU still trades at about 5x next year's earnings.

Asimov 1 is an open-source reference humanoid robot. It ships as a complete DIY kit. $20,000 total, delivered to your door. https://t.co/OzL6JI3ZDB https://t.co/mZt5hidLLw

I turned @asimovinc's open-source Asimov 1 into an interactive 3D robotics workshop. Built from 1,184 real CAD parts, with Menlo's own walking policy running in a live physics simulation in your browser. 🟠 BUILD the robot using Menlo's assembly instructions. 🟠 UNDERSTAND its mechanisms, including the parallel ankle linkage. 🟠 TEST its walking policy, apply a push, and freeze the simulation to inspect every joint. Built with @threejs. Fully open-source. Try it yourself: https://t.co/EeZ4Bn6WQa

Generative Bionics plans a 5,000 m² humanoid training hub in Italy, targeting Q4 2027. Powered by AMD Helios, it would combine real-world GENE.01 robot training with digital-twin simulations—the first of three planned facilities. https://t.co/EO812iStl5

I kneel... training @asimovinc to transfer from lying on the ground (back/belly/side) to a half kneel, with hopes of translating next from a half kneel to full stand. Trying to find the best way to get up off the ground. https://t.co/TPz46juuvu

Last year I trained the advantage-conditioned VLA for π*0.6 during my internship @physical_int. Now, I have a much improved recipe. Introducing ✨Advantage Guidance✨, a performant RL method with stable advantage-conditioned supervised training and contrastive guidance sampling https://t.co/AyIGA7qQqf

We built a robotics data engine with Astra-level performance at 125x lower cost. And we are open-sourcing the weights today! Introducing Flora, a suite of open models to parse the physical world and turn raw footage into rich training signals. Flora already powers our in-house data engine, labeling 200K hours of videos a day on 500 GPUs. We created it to scale the frontier for robot learning. Now you own the weights too. Flora gives you: - High-quality, timestamped captions to curate and filter human egocentric and robot data; - Dense progress rewards for quality control and RL post-training; - Rubrics scoring for real-world benchmarking Try it today, links in thread:

Superhuman Hardware Design Is The Easy Part

Superhuman Hardware Design Is The Easy Part

Introducing Embodied First Exam: One line of code to run coding agents in 1,090 embodied tasks across 31 simulation suites and 44 robots. Can your agent go far beyond pick-and-place: Puzzles with spatial constraints; Olympic sports with multi-robot collab; Real-time eval, where the world won't pause; Dynamic, agile, whole-body control; AutoResearch with sim2sim Try your own agent: harbor run -d embodied-first-exam -a <your_own_harness> -m <your_own_model>

Superhuman Hardware Design Is The Easy Part

Superhuman Hardware Design Is The Easy Part

Superhuman Hardware Design Is The Easy Part

What Is Physical AI and Why Is Everyone Suddenly Talking About It

@asimovinc Amazing. How much of the stack have you actually opened up?

Ropedia Super Week, Day 1 | Ropedia W0: Different Robots, One World Model

🎉 The CoRL 2026 accepted papers are out! 📄 687 papers 🎤 21 orals ✨ 72 spotlights See you in Austin, Nov 9–11. Congrats to all authors 👏 https://t.co/LkzwCR7Pwh #CoRL2026

🚀 We’re thrilled to announce the release of Lucky Engine 2026.3! "The Matrix for Robots" is now 100% Agentic 🤖✨ To train autonomy, you need a "Matrix" a virtual playground where they can learn before touching the physical world. Today, we are dropping Lucky Engine 2026.3, and we think that it redefines how synthetic environments are built. • Real-time ray tracing & path tracing • 100% agentic (AI agents fully control the engine) • Upload a photo → instant data generation for your policies • Graph-based procedural generation, AI generated determinism • No Blender required. Here’s what’s inside 🧵 👇

Hard lighting cut our expert-only π0.5 checkpoint's success roughly in half. The same lights cost the full fine-tune nothing. New from @castorhat: the fine-tuning ablations behind our IROS demo. https://t.co/JciqcCBMYy

The AI infrastructure buildout is about to enter an ENTIRELY different phase in 2027. SemiAnalysis just released a new report forecasting 51.2 GW of new data center capacity in 2027, compared to 25 GW in 2026 and just 15.6 GW in 2025. We're talking about more than DOUBLING annual capacity additions in a single year. To put that into perspective, SemiAnalysis estimates that the industry could add roughly as much data center capacity in 2027 alone as it had built cumulatively through 2024, excluding China. And here's where things get REALLY interesting. While some investors, including Brad Gerstner, have questioned whether the industry can actually deliver this much capacity, SemiAnalysis points to satellite imagery showing more than 24 GW of data centers already in advanced stages of construction, with those facilities expected to be delivered in 2027. On top of that, the firm reports roughly $1 TRILLION in binding, non cancellable data center leases signed in 2026 so far. Now, think about what this means for the ENTIRE AI ecosystem. Every additional gigawatt of data center capacity creates enormous demand for GPUs, HBM memory, networking equipment, cooling systems, electrical infrastructure, and POWER. And remember, adding data center capacity doesn't automatically mean every facility will be fully equipped with GPUs on day one. In fact, SemiAnalysis is already raising the possibility that as power and construction constraints ease, the next major bottleneck could once again shift towards semiconductors and memory. And here's what I think a LOT of investors are missing. More compute capacity doesn't necessarily mean demand suddenly gets saturated. As inference becomes cheaper, AI adoption can accelerate across autonomous agents, enterprise applications, robotics, and eventually physical AI. Lower costs can unlock entirely new use cases that simply weren't economical before, creating another wave of demand for compute. If SemiAnalysis is even remotely close to being right, we're about to witness one of the largest infrastructure expansion cycles in history. And I believe the companies supplying the compute, memory, networking, and power behind this expansion could be some of the biggest beneficiaries over the next several years. If you want to see how I’m investing around this next phase of the AI infrastructure buildout, check out my full portfolio below: https://t.co/Hkywss4Ugh

‣ 20 Predictions That Will Change How You See Robots Forever ➤ 1. More robots than cars ➝ Billions of humanoids in use, often several per household. ➤ 2. Elder care becomes the biggest market ➝ Aging populations make assistance, mobility help and companionship the main use. ➤ 3. Human-level dexterity ➝ Ultra-sensitive hands that can handle an egg or a surgical tool. ➤ 4. Robots as a subscription ➝ People rent "robot labor" by the hour or month instead of buying the machine. ➤ 5. Days of battery life ➝ New batteries and wireless charging floors. ➤ 6. Robot law ➝ Rules on liability, insurance and privacy, plus debate over legal status. ➤ 7. Nearly human robots ➝ Realistic skin and faces in hospitality and care, mandatory mechanical look in industry. ➤ 8. Work and taxes reinvented ➝ Robot taxes, shorter workweeks or universal basic income. ➤ 9. Robots building robots ➝ Factories, mines and construction sites run almost entirely by humanoids. ➤ 10. A space workforce ➝ The first builders on the Moon and Mars. ‣ Part 2: 10 New Predictions ➤ 11. Mind control ➝ Brain-computer interfaces let people pilot a robot remotely, like a second body. ➤ 12. Artificial muscles ➝ Motors give way to soft fibers for silent, fluid movement. ➤ 13. A tutor for every student ➝ Teaching robots adapt to each child's pace and support human teachers. ➤ 14. Robot rescuers and firefighters ➝ First on the scene in fires, earthquakes and hazardous zones. ➤ 15. Precision farming ➝ Fruit-by-fruit harvesting, plant care and fewer pesticides. ➤ 16. Robot sports and entertainment ➝ Sports leagues, stunt doubles and robot actors become common. ➤ 17. Custom personalities ➝ Voice, character and appearance customizable like an avatar. ➤ 18. Cybersecurity becomes critical ➝ Hacking a robot becomes a serious crime, with mandatory security certification. ➤ 19. Repairable, recyclable robots ➝ Modular design: you swap a part, not the whole robot. ➤ 20. Universal translators ➝ Every robot speaks every language, serving as guide and interpreter anywhere. ✓ Key Takeaways • These are educated guesses, not certainties. • In 75 years, unforeseen breakthroughs (or setbacks) could change everything. What do you think of these predictions?

Ropedia Super Week, Day 1 | Ropedia W0: Different Robots, One World Model

How to build a humanoid robot at home

A robotic arm that learns physical tasks by watching you demonstrate them. Meet the SO-101, an open-source robotic arm supported by Hugging Face's LeRobot. ‣ 3D-print the parts and assemble the arm ‣ Demonstrate a task using the leader arm ‣ Record movements and train a neural network with LeRobot ‣ Let the follower arm perform the task on its own What catches my attention is how accessible this makes robot learning. You can experiment with imitation learning on real hardware without building an entire robot from scratch.

Please sign all your trajectories before 12:00 UTC on October 18, when the snapshot will be taken. All signed contributions recorded before the snapshot count toward the pre-TGE airdrop reward pool. All Axis points feed into a single reward pool, including those earned from the Binance Wallet and BitRobot alliance programs. Contributions submitted after the snapshot will count toward the 6% post-TGE community contributor reward pool.

Robotics is still waiting for its "ChatGPT" moment Language models had the internet to learn from, while robots need diverse physical demonstrations that are expensive to collect Bessemer estimated just 300,000 hours of robot manipulation data globally in April, while others believe robotics could require 100M+ training hours Bessemer projects $3B+ in robot-data spending over two years Open networks are using crypto incentives to coordinate contributors across simulation, teleoperation and human video collection Axis reports 7M+ trajectories from 220K+ contributors, with its data lifting task success from 83.9% to 88.8% on the LIBERO-Plus simulation benchmark Our latest report explores the data opportunity through Axis, BitRobot, Eastworlds & THOR Full report in the next post below

This is what makes @Auki so interesting. Multiple robots and phones collaboratively build a shared digital twin of their environment. A robot detects a problem. A human investigates it through AR, leaves a spatial note, and assigns another robot to clean up. One shared spatial layer. Humans + robots working together. A spatial internet for robotics. 👌 https://t.co/LwW1bpOeBr

You don’t need to build a humanoid robot from scratch to start exploring robotics. This open source robot is available on GitHub. You can study the design explore the code and see how the different parts work together. For robotics enthusiasts and developers projects like this are a great way to learn by building. The best part? You can start with what the community has already shared. Would you build your own version?

An agent generates trajectories for carrying a kettle and pouring water. Dyna2-style RL handles low-level control with precision and robustness to disturbances—beyond motion planning alone, as Qia Yuan noted. Agentic planning + conditioned RL is a huge frontier. https://t.co/GzqaeL3iXE

🚀RP1 is officially here. 🌍Putting control of robotics back in developers’ hands. Introducing RP1 (ROBOTO 01) by RoboParty—a high-performance, fully open-source humanoid robotics platform for frontier robotics research and embodied AI model development. 🤖 Open across the full hardware and software stack: • Robot platform: mechanical CAD, bill of materials, and assembly documentation • Actuators: in-house ROMOMO joint designs and related technical resources • Control: electrical systems, firmware, low-level drivers, and motion control implementations • Development: simulation models, SDKs, data processing tools, and training tools • Engineering foundations: technical documentation and research materials Open the black box. Understand the system. Make it your own. A deviation in motion can originate in the model, control policy, actuator response, or mechanical structure. RP1 opens up the design and implementation of these layers so developers can trace problems, modify the system, and validate changes on real hardware. Open-source algorithms for advanced motion control RP1 has demonstrated continuous walking, recovery from disturbances, getting back up after a fall, and human motion tracking. UFO and MimicLite connect motion learning, control policies, and execution on real hardware. The open toolchain connects data collection, simulation training, hardware deployment, and experimental validation—supporting joint iteration across models, control, and hardware. From RPO to RP1, we’re turning one team’s engineering experience into a starting point for many more. Opening the future of embodied AI to everyone ready to explore it. 🔗RP1 on GitHub: https://t.co/kMqFLi3eJ3 #RoboParty #RP1 #OpenSourceRobotics #EmbodiedAI

The future of Physical AI isn't just about building a smarter model. It's about building a system that keeps learning. Better data improves models. Better models improve robot behavior. Real-world experience reveals what needs to improve next. That creates a powerful loop: Robots → Experience → Data → Better Models → Better Robots. This is what makes the @axisrobotics vision interesting to me: not just collecting data, but building a compounding learning loop around robotics. The real question is how far that loop can take us. #PhysicalAI #Robotics