Axis Robotics × Feagine why this partnership is actually bullish for physical ai ? let's try to understand... Most people still think robot intelligence is just "better models" but It's not, the real bottleneck is high-quality, diverse, embodiment-specific training data. Every new robot body usually means starting data collection almost from scratch and that's slow, expensive and why physical ai hasn't scaled like LLMs this is exactly what @axisrobotics is built to fix Axis is compounding data engine for Physical AI, through axis suite, they build customized end to end data pipelines for hardware companies: simulation environments ➤ structured tasks ➤ scaled trajectory generation contributors around the world generate data in the browser, it gets cleaned, augmented, and turned into training-ready datasets so data compounds instead of resetting now they've partnered commercially with @FeagineRobotics feagine builds soft embodied intelligence: - tendon-driven flexible/bionic robot arms (A01/A02/A03) that move more naturally and safely than rigid industrial arms - embodiment free data capture - Fi0, their cross embodiment foundation model - designed so task knowledge survives when the robot's body changes (different length, segments, degrees of freedom) what @axisrobotics is doing in this partnership: - tailoring simulation task design and trajectory generation specifically for Feagine's flexible manipulators (fine manipulation is much harder on soft arms) - supplying key training data that feeds Fi0 this is important because: - It validates axis suite as a real product for hardware OEMs, not just a research dataset play. - soft/flexible robots historically lacked the standardized sim + trajectory data that rigid arms already have. Axis is filling that gap. - combined with Fi0, skills can transfer across different embodiments instead of being locked to one body, intelligence starts compounding across hardware instead of fragmenting real world implication: new robot designs (especially novel or compliant ones) can get useful policies faster, with fewer real-world demos, that's how physical ai actually gets deployed in factories, homes and unstructured environments instead of staying in labs. axis already has other commercial partners (Booster Robotics, Geely, Lotus, etc.) and has shown that their sim data lifts models like π0.5 on tough generalization benchmarks so this Feagine deal is another proof point that the data engine works for real hardware companies, including ones building next generation of bodies. data is the flywheel hardware + models + compounding data is how Physical AI actually scales $AXIS | physical ai is being built in public

I keep seeing same community sale tweet copied with different line breaks Fine, here is the only cut that matters for @axisrobotic They did not wake up and invent a $0.10 token, they spent months making people produce robot runs that survive replay - then anchoring the ones that count on @base - 5M trajectories and 200K contributors is the inventory - top 2 on Base and $12M @hack_vc seed is just how loud that inventory got The sale is a small window on top of that: - $1M USDC - 1% of supply - $100M FDV - $100-$100K tickets - Sonar by @echodotxyz anyway vesting is not a flex..10% at TGE, six months dead, then the rest drips, tge is later, not "right after you ape" Note: if you cannot pass kyc/kyb or you sit in a blocked region, you are not in the room,commitments only move one way I care whether the next million trajectories stay clean more than I care about the raise size GL and check properly again from your end which we call DYOR 🫶

⚡️ $DGAI is now live on @binance Alpha! A decentralized #AI smart network for cost-effective, reliable & verifiable AI.🚀

More slots only matter if work is real @axisrobotics moving challenger testing off public slots is a simple change with a clear effect - more people can actually finish tasks instead of watching them fill up, regular tasks now cap tester usage too. That keeps the queue open for contributors who are here to produce data, not just test the pipeline If you have been waiting for a cleaner entry point, this is it Join @axisrobotics, pick an open task and ship quality trajectories. Repeating again ship trajectories GG

GM traders! A jobs print is a day A share path is a schedule. Realstocks fixed Investment on @mexc is recurring buys of eligible us stocks with usdt, the session still exists, a dividend can exist, you are not leaving a perp on overnight Stock futures is the print tool, I already used that tab too much and 0 fee on eligible products cheapens the scheduled buy, it does not turn one payroll number into a portfolio #MEXC0808

13k WAU at #2 on @base only matters because the work is not off chain vibes @axisrobotics put task creation, verification, scoring and rewards on @base so every valid run can carry provenance, that's Proof of quality work for physical ai: a trajectory you can audit, not a clip you can fake Two things separate this from a normal leaderboard dump: 1. the product loop is real - sim demos + policy takeovers + data that already lifted π0.5 (83.9 → 88.8 on LIBERO-Plus) and hit a physical Franka 2. the chain step is the filter. unsigned tasks don't count. referrals don't count The flywheel doesn't move. #2 is the surface metric Signed trajectories are the asset. GG to the team 🫶

Thinking beyond one data print? 👀 MEXC RealStocks Fixed Investment lets you set recurring purchases for eligible U.S. stocks. mexc.com/support/article/how…