Conventional datasets are static, not evolving as the robots exposure increases. 🀄 they only train a model, which is input for a robot to work. but what happens as the robot's exposure increases and it approaches new unexplored states? a dataset that evolves parallel to the robot's exposure ? @axisrobotics presents dataset V1. a growable community-driven simulation dataset. if the robot fails in an unexplored state, feedback is sent upstream, on which the Axis dataset layer acts and provides new training data to help the robot navigate out of this state — and get better in future operations. Continual pretraining with the AXIS dataset at AXIS 100% showed to improve performance of the π0.5 model and the success rate of Libero Plus tasks by 37.3 % over a matched RoboCasa365 dataset. Axis's findings suggests that this performance improvement stemed not just from data volume, but from broader data coverage and task diversity. Broader data coverage and task diversity, @axisrobotics provides. 🀄 Read through, the official paper: arxiv.org/pdf/2607.21588 @chris_anm01 | @0xsexybanana | @0xzagen

This is especially interesting to me. @FeagineRobotics builds bionic flexible embodiments and trains an embodiment-agnostic foundation model — Fi0. Fi0 is designed so that task and world knowledge don’t have to be rewritten for every new robot body. Instead of being locked to specific dimensions or control interfaces, it functions as a shared brain that can operate across different embodiments. This one model works across different robotic embodiments. With the embodiment-agnostic model and Feagine’s Embodiment Graph, the model simply conditions its action generation on the embodiment’s morphology and current state. 🏗️ This is why Axis brought alive here is just fantastic: Axis can deliver the specialized training pipelines, customized simulation tasks, and scaled trajectory data that Feagine needs for both its flexible manipulators and Fi0 — the embodiment-agnostic model. 📦 The result: a smoother path to scaling an embodiment-agnostic model, far less need for full retraining, lower monetary and physical cost, and reduced risk of damaging real robot hardware during a clunky real learning process.

Omg, do I really remember my first step into @GenLayer ? 🎒 I'll try. I had been hearing about GenLayer from friends, saw some really good videos from @_Kenavee too about Internet court and disputes stuff.. I didn't really understand GenLayer though. Disputes? A "court" for AI?? So, on one chill evening, I got in the dc chat. 💬 I was still trying to navigate my way around, and I think one Adewal3 in the chat helped me navigate through.. I stayed around the dc, joined my regional chat too... Can't really remember who n who I met that day.. Started joining weekly poap spaces with ma @stargirl_hills , team members & the community. 🫟🎙️ But fast forward today, I'm back at genlayer and still trying to understand it all. Recently, i asked ai to explain it for me, and I'm gradually learning. Learning that, in simple terms: "GenLayer lets smart contracts understand language, look at the real world, and make judgment calls, then have a bunch of independent AIs agree on the result so everyone can trust it." genM, frens! 🥽

✱ 2.4x over-subscribed community sale. ✱ The Open Axis evaluation benchmark, with @openroboto. 💺 ✱ An expanding, composable library of robotic capabilities. axisrobotics.ai/blogs/blog/b…

✱ Partnership with @FeagineRobotics 🛰️ ✱ 5M data trajectories from over 200k contributors. ✱ Community sale (just concluded). 🗳️ For 13th - 19th Sep, 2026 (last two weeks).

God, I need $4.8k and God with me.

Ritual | \ ri-chə-wəl - an act or series of acts regularly repeated in a set precise manner. 🕊️ this definition is fittingly related to @ritualnet, especially in its reliving onchain agents. just like a ritual is repeated, agents' actions are recurring.🪃 @ritualnet is more than reliving/self-awakening contracts (agents) onchain. it gives autonomous agents an onchain state; where they have a persistent identity, financial sovereignty and recurring self-scheduled immortality just like a ritual. 🕯️ smart applications of defi can emerge off this!

Kudos to every working part behind @axisrobotics! 👏🏼

$AXIS Community Sale: Milestone Update Total commitments have crossed $500,000 USDC (50% of the $1M raise size). Make sure to commit before time runs out. Window Closure: September 28, 13:00 SGT 🔗 Official portal: sale.axisrobotics.ai

✱ Bots & Bounty. 🔖⏰ ✱ Real2Sim2Real: more photorealistic scenarios coming. For 6th - 12th September, 2026.

Not every onchain model needs to be an LLM. The next generation of onchain AI won’t be one giant model doing everything. It’ll be a stack of purpose-built models, primitives, and agents, where each is used where it makes sense.

Ritual Quiz e34 had successfully held last week. Co-hosts: JT(@AbrahamJT9) & Pico It focused on ritual LLM & ONNX. 🫆 Weekly quiz questions would now be released periodically on this platform. 📕 For week 34, here's the quiz questions: docs.google.com/document/d/1…

Trust erodes w/o provenance, because: • no one can verify whether a decision followed approved logic or drifted. 🛟 on a good note however, @ritualnet utilizes TEEs for ai computations. computation for ai decisions are run securely in trusted execution environments. 🫆 and post-computation results return with attestations that prove: - a specific measured workload & model ran. - the model, after computing had produced the returned output. beyond TEE attestations, zkML proofs, probabilistic proofs and primitives like vTune further strengthen trust and provenance of an agent's decisions. 🎚️ This makes it far more reasonable to assume the agent’s reasoning wasn’t tampered with before it acts onchain (moving money, etc).

That's cool! How much would ads normally cost?

Pre. vs. Post-training tasks. 🖱️🔖 I recently onboarded a friend onto the axis robotics hub.. curious, was he to know the difference btw these tasks, so I created an analogy. 🗺️ think of these tasks as: [Pre-training] 👨🏼‍🏫 - a student goes to school completely ignorant of a topic, so the teacher lectures on that topic in class. vs. [Post-training] 👨🏼‍🎓 - the next day, the student comes to class and, meeting the teacher along the way, the teacher asks her: ❔ "okay, tell me everything you know about this topic and.. If you're mistaken, I'll correct your misconceptions/errors". 🔖 on the first day (pre-training), the student knows nothing bout the topic, so the teacher teaches her. while after getting exposure to the topic, its terms, definitions and concepts; The student is now asked "show me what you know about this". it is similar to the learning robot. in pre-training tasks, the learner robot gets to know about how to complete a specific task, move around and execute.🦽 then after training with sufficient data, the robot is left to execute autonomously, based on what it now knows. and corrected if mistaken, reinforcing learning. during post-training tasks, the instructor (teleoperator) monitors the robot's execution path, and corrects it's errors.🦼 @0xsexybanana | @plpiaoliang

✱ the bounty hunt is ongoing.⏱️ ✱ @axisrobotics integration of DreamZero. ✱ daily task release schedule adjusted. 👨🏼‍🔧 ✱ seek the unseen badge. 🔖 Unofficial. for 30 Aug - 5 Sep. | @plpiaoliang

Why use "global" finance? | crypto × ai |🗿 crypto is a permissionless, inclusive and accessible-to-all world. and, although the addition of ai could add an element of centralisation/gated access. 🚧 the overall crypto × ai could still serve a global purpose. if employed in the financial sector, it could serve a global world. 🗾 from timely payments to vaults & portfolio management and yield optimisation/rebalancing, autonomous agents can handle it. and it's even better when this autonomous ai finance stuff with agents working within predefined user limits is accessible to all through the crypto/defi world.🗺️ pivot to permissionless autonomous finance steered by agents onchain on @ritualnet & @ritualfnd !

Ritual Quiz EP 32 and 33 focused on: 🗺️ - ritual onnx and llm. - general ritual network questions. ritual onnx allows onchain ml inference from developers' .onnx models connected to the @ritualnet chain. due to connectivity issues, the last session on onnx and llm couldn't properly hold.☔ so, come next tuesday we would treat the questions on onnx and llm. 🌋 let's stay with @ritualnet, read up from the official docs and get prepared: docs.ritualfoundation.org/#s… docs.ritualfoundation.org/#o…