MJ
158 posts



early stage funds are betting on companies building prediction market infrastructure because they want a more asymmetric bet that gives them exposure to the vertical most of these founders are just trying to trade their way to a 4 year hiring engagement at large prediction market companies, and the returns from talent acq deals are counter to venture math in that VC funds need huge outlier returns in order to return capital to their LPs and outperform the S&P 500 the real asymmetric bet is @trylimitless, the fastest growing prediction market in the world. we're not coming for 30%, we're coming for everything, but even 10-30% market penetration at maturity is a much larger outcome for investors than any adjacent infrastructure company that sells to a big corporation for $50m


From Apple Pay to stablecoin in seconds







whoever allows employees/vendors to have stablecoin salaries/payments alongside a line of credit that speaks to tradfi credit-scoring systems will end up building a billion dollar company i think the opportunity set here divides into b2b, retail (high income) and nomadic users


Assets added to the roadmap today: MetaDAO (META), Derive (DRV) coinbase.com/blog/increasin…






Early-Stage Projects Spotlight 1) Oroswap | @Oroswap Oroswap is building an AI-powered decentralized exchange DEX natively deployed within the Zigchain ecosystem. The project aims to integrate artificial intelligence directly into the DeFi experience, optimizing automated market making, liquidity provisioning, and user onboarding. By leveraging AI, the protocol attempts to make onchain trading and market discovery more intuitive and efficient for both everyday retail traders and native DeFi builders. 2) HatcherLabs | @HatcherLabs HatcherLabs is building operational infrastructure and management tools for the emerging agentic economy, with a heavy focus on mobile accessibility and modular frameworks. The team has launched solutions like the Hatcher Android app, which allows developers to deploy, monitor, and chat with AI agents built on frameworks like OpenClaw and Hermes directly from mobile devices. Working closely with networks like SKALE, they provide gas-free, high-throughput environments designed specifically for running live crypto trading bots, automated Discord/Twitter agents, and on-the-go coding assistants. 3) Mavora | @mavoraio Mavora is building an AI-powered behavioral simulation engine designed to model the human psychological engine behind real financial markets rather than just forecasting price charts. Moving past simple LLM text outputs and basic Multi-Agent "swarm" simulations which they argue inevitably drift into a generic, unified consensus, Mavora uses an engineered financial cognition layer populated by highly distinct, financially literate agent personas (such as VC analysts, whales, and retail actors). These agents are hardcoded with specific risk profiles, behavioral thresholds, and curated real-world information diets, allowing users to run complex market scenarios where agents independently react, disagree, and challenge one another with citation-grounded reasoning, starting first in the highly narrative-driven crypto market. 4) GM Wallet | @GMWallet GM Wallet is a Web3 native, multi-chain user interface wallet engineered specifically around daily social interactions, trading, and dApp engagement. Rather than focusing solely on deep institutional custody, the wallet emphasizes seamless asset swaps, fast cross-border value transfers, and direct integration with community rewards (such as integrated airdrop portals and incentive tasks). Its core architecture balances ease of use with instant mainnet connectivity, aiming to capture the everyday retail user demographic through a highly streamlined UX. 5) Cacheon | @cacheon_ai Cacheon is a decentralized AI network building specialized inference optimization infrastructure, operating as Subnet 14 (SN14) on the Bittensor network. The architecture utilizes decentralized miners and validators to maximize processing speed, cut latency, and maintain strict quality control for large-scale AI inference workloads. By creating an open-market competition for compute efficiency, the protocol incentivizes miners to continually optimize how data is processed, cached, and served back to machine learning applications. Found this valuable? Follow me & turn on post notifications so you don't miss out on the next project spotlight post. See ya on my next.....adios👋



Euphoria mainnet is now live. It's time to have some fun. euphoria.finance







