Robotics and Hardware have a launchpad now: Anyone can put a machine up, raise capital for it in public, and trade it from day one. Built using @Raydium LaunchLab and supported by @codecopenflow and @peaq.

Hardware founders need money before they have anything to show, so a lot of good machines never get built. peaq is backing Machinefun to fix that: raise in public, put your first machine on peaqOS, let the fleet grow from there. Read more in article below. 👇

When CodecFlow started last year, robotics was barely on crypto’s radar. The goal was to change that by bringing the whole robot development cycle into the crypto-native world, from the first test to the finished machine. Talking to other builders and looking at the problem as a whole, the same conclusion kept coming up: building a robot is brutally hard, for three reasons. I. Nowhere to test it. Labs and hardware cost more than most ideas are worth, so the first thing we built was a way around them. SimArena lets anyone check whether a robot idea works in simulation before spending anything on equipment. This week, around 80 teams at the robo.innovate hackathon in Munich are using it for their challenges. II. Nothing to train it on. Robots learn from examples, and the examples they need either don’t exist yet or aren’t labelled in a way a robot can learn from. It’s one of the quietest bottlenecks in robotics and one of the hardest to fix, but there’s news on it coming soon. III. No money or network to build it. Once a robot works in simulation, getting it into production takes real capital, factory connections, a supply chain and customers waiting to buy it. This is where most robotics startups die. That’s why we teamed up with @machinedotfun and @peaq on a launchpad and accelerator built for exactly this stage. Through partnerships and integrations with @solana, @Raydium, @sunrise and @peaq, the goal is to give machines something they’ve never had: capital markets of their own, where a robot is tradable from day one, and anyone who believes in it can take a position before the rest of the market catches on. For the first time, the whole robotics lifecycle, in one place: design it → train it → build it. We’re excited to open that door to anyone, anywhere, and help them take a machine from prototype to business. @machinedotfun launches in the next 48H.

Scaling Robotics is starting to look a lot like scaling software. Over the past months we went through more iterations than we’d like to admit, all trying to answer one question: how does hardware get funded, get to market, and survive long enough to ship? Software can launch from a laptop. Anything that has to physically exist needs money (and lots of it), specialized talent, lab space, testing, manufacturing, distribution, the right connections, and time before it can prove anything. Early on, one thing was clear: we did not want to build another launchpad. We wanted a home for robotics onchain. That meant finding the right partners, getting the tokenomics right, and giving early-stage builders everything they need to go from idea to production. A few years ago, none of this would have worked. Why now? To answer that, we have to go back to 2020, when @OpenAI released GPT-3. Most people outside tech didn’t care yet, but if you were playing with it, it was hard to ignore, because a language model could suddenly learn a new task from just one or two examples. There was no retraining involved, you just showed it what you wanted and it figured out the rest. It felt familiar for a reason. Humans have a remarkable ability to pick up new skills from very little. Watch someone tie a knot once and you can probably do it. A new barista watches one latte pour, and they’re halfway there. In 2020, for the first time, software started learning the same way. From there, it took off. Within a couple of years, that same ability was writing code, building apps and designing games. Anyone with an idea could ship something on a screen. But it all stayed on the screen. Fast forward to today, and the same shift is now happening in the physical world. Robots are starting to learn the way GPT-3 did. The clearest example is @GeneralistAI’s GEN-1.5. Show it a few seconds of someone opening a jar or unzipping a pouch, and the robot does it. No new code, no weeks of training. It learns a physical skill the way the barista learned the latte. The hardware is getting cheaper just as fast. In 2023 a two-arm research rig cost close to $20,000 USD. Right now, an open-source robot arm costs about $230. That’s why we, along with roboticists across the globe, believe we’re very close to a ChatGPT moment for robotics. Where teaching a machine means showing it, and the parts cost less than a phone, building a robot stops being something only labs can do. So what’s stopping someone from starting a hardware company? Money. A prototype has never been cheaper, but turning it into a scalable product still needs manufacturing, certification, distribution and a team, long before the first unit sells. It’s not that nobody is funding robotics. Robotics startups raised $18.8B in the first half of this year, more than in all of 2025 and more than the 2021 record. But most of it went to a few giant rounds, like @SkildAI’s $1.4B in January. Across venture, over 40% of seed and Series A money now goes into $100M+ rounds. Right as the tools reach everyone, the money is moving further away from them. Nobody is writing a $100M check for a garage prototype. That’s the gap MachineFun is built for. Our vision is that with greater efficiency in hardware scaling, small start ups, prototypes, and passionate entrepreneur now have the ability to create value at the micro level. By providing simulations tooling like SimArena or verifying your robot through peaqOS, founders can now have early backers follow their journey in public while demonstrating evidence through the provided tooling. By giving founders a platform at the foundational level, we see this as a key stepping stone to reaching mass scalability. The raise is only the start. We also support with what comes after it: getting to market, logistics, and introductions to the manufacturers and partners who can help you build at scale. If you have a machine, we want to make it fun.

There is a reason the robotics boom is being built by so few teams. Hardware is harder, more expensive, slower than software, and a single mistake can cost exponentially more. We're here to change that.

MachineFun was designed to remove the speculative friction from what we think will be one of the highest-growth sectors of the coming decade: Robotics. One of the most heavily funded corners of robotics over the past two years has been robot brains, the AI models that let a robot see, decide and move, with roughly $5B raised by its best-known developers since 2024. One company in this field just had a breakthrough. @dreamlabs_ai, a 12-person robotics lab, released Dream-1, a new kind of model they call a World Action Model. It learns how the world moves from video and uses that to control a robot through a simple API, meaning any developer can plug into it the way they'd plug into ChatGPT. That kind of tooling opens doors for hardware companies that were stuck before. @makermodsai, which sells $2k robot arms, says theirs was up and running in a few minutes and "attempting tasks we never programmed." Teaching a robot one task used to take specialists months, and now it's starting to feel as quick as prompting an LLM. Breakthroughs like this are happening almost daily in robotics, but for anyone outside the industry they've been nearly impossible to get involved in. Dream alone raised a $90M first round, and none of it was open to the public. We've seen where this leads. When AI models became something you could call through an API, a wave of smaller startups got built on top of them, and plenty of YC companies got funded that way. Robotics is starting down the same road. @physical_int, another lab building robot brains, recently opened its own API to outside teams. Almond, a company that builds a robot called Axol, used it to teach Axol to smooth rough edges off metal parts for its own assembly line, and it took only 4 days. Examples like these are why we built MachineFun. As bottlenecks unlock and hardware gets more efficient, businesses that didn't make economic sense before can turn into real revenue almost overnight. Letting teams raise publicly for their ideas is the first step toward an open source robotics hub. We know funding alone isn't enough, though, so we've built a foundry network with @peaq and @codecopenflow to back high-agency teams taking their ideas into the real world with peaqOS and SimArena. As foundation model, tooling, benchmarks and onchain economies keep scaling, MachineFun wants to sit at the center of it, with developers building in public and traders getting in early through speculation.

Launch your robotics token against the industry you're building A robotics project depends on a long chain of companies: chips, memory, cloud compute, software and manufacturing. On you can launch your token against one of them, instead of being limited to SOL or a stablecoin. The creator's share of trading fees builds up in that quote asset. Launch against $NVDAx and the fees from people trading your token become NVIDIA exposure. A team building memory-heavy systems could pick $MU, and a humanoid project could pick $TSLAx. Equity pairs on are available through and powered by @Sunrise. Equities: - $BOT (RoboStrategy): Humanoids Index - $MU (Micron): memory and storage for AI and autonomous systems - $NBIS (Nebius): AI cloud infrastructure - $NVDAx (NVIDIA): GPUs, simulation and physical AI - $SPCX (SpaceX): engineering, manufacturing and autonomous systems - $SPYx (S&P 500): broad exposure to the US economy - $TSLAx (Tesla): automation and humanoid robotics through Optimus Ecosystem tokens: - $PEAQ (peaq): Machine Money Markets & robots as financial actors - $CODEC (Codec Flow): Robotics Execution Layer SOL, USDC and USDT are also supported. Not every founder wants equity exposure, so the choice belongs to the project. Doubling down on your industry 1. Your project benefits from the trading activity around its own token. 2. The assets you collect through trading fees may appreciate as the robotics industry grows. Those assets can also lose value, so this is aligned exposure, not guaranteed returns. Build the machine, launch the token, and collect exposure to the industry you're helping grow.

on the ground at TOKEN2049 Singapore. Live at gm AI v2 with our partner @codecopenflow, connecting with teams building robots, physical AI, and simulations. We’re here to bring more machines onchain.

Monday update. Since last week, we've reworked the launch setup on Raydium's LaunchLab and fixed the remaining issues we found in testing. The fee system is now working and has been tested on-chain. We've put a lot of thought into how it's designed, and we'll share a full breakdown after launch. Holding it until then keeps the launch clean and fair for everyone. We're very close. You'll get at least 12 hours' notice before going live. Appreciate everyone sticking with us through this.

On September 30, the launch configuration didn’t work as expected, so we’ve made further changes and are running additional testing before moving forward. We committed to a launch timeline and didn’t meet it. We understand that the lack of clarity around the delay has been frustrating. We won’t set another launch date until we’re confident we can deliver. Once confirmed, we’ll give at least 12 hours’ notice before going live. Our focus now is on getting the configuration right, testing it properly, and making sure everything is ready before we go live. The next status update will be shared on Monday at 16:00 UTC, with further updates as we make progress. Thank you to everyone who’s been waiting with us.

We're delaying the launch. We found a stronger way to structure rewards, and we want it properly tested before anyone builds on it and starts trading. Project registrations and trading open soon. Sorry for the wait, it will be worth it.