Filecoin

1/ Introducing Filecoin Onchain Cloud: an open, verifiable cloud built on content-addressed data, transparent service delivery, and programmable payments. All onchain. No vendor lock-in.

Open-weight models exist. The data layer is still closed. Moving a standard open-source training dataset out of AWS costs $9,000 in egress before a single training run begins. Filecoin is built for open models on open infrastructure. Zero egress. Data moves freely.

CoreWeave is covering up to $1 million in data migration costs just to win customers from hyperscaler egress fees. @JamesKurzFIL: "If your data isn't sovereign, you can't move it to where the GPUs are." Filecoin operators deploy on infrastructure that already exists.

Global renewable energy curtailment exceeded 50 terawatt hours in 2024. Enough to power Norway for a year. Storage tolerates latency in a way compute cannot. Filecoin providers deploy directly where that power is stranded.

The GPU story is well-known. The Data Storage story is nascent. @JamesKurzFIL on @SchwabNetwork: storage demand is already "almost price insensitive." Filecoin's network is already deployed at the scale that demand requires.

Japan's grid connections take up to 10 years. South Korea restricted new construction in Seoul to prevent grid overload. UK industrial electricity runs 4x more expensive than the US. Filecoin network is already distributed across independent providers on every major continent.

One third of new GPU clusters are being built more than 500 kilometers from the nearest storage infrastructure. @JamesKurzFIL calls them data deserts, and Filecoin operators are already moving in.

Google Cloud charges 6x more to move training data than to store it. AWS charges $20,000 in API fees just for a model reading its own data. Open-weight models deserve open infrastructure. Filecoin is built for a data layer that matches the model.

@alanchanguk Proprietary physical data from real worksites needs to be verifiable and persistent to train the next generation of models. The storage layer underneath that data matters as much as the collection.

Today we're starting something new at Fuse. We're building robots. First, why this matters, because it's easy to get wrong. Skilled trades are ageing out faster than they're being replaced. The average electrician is over 45, the average plumber is over 50, and every year we lose more of them to retirement than we train. At the same time the world needs far more of them, not fewer, to build for the surge in energy demand ahead of us. You cannot build power plants, grid and AI data centres without them, and there are not enough of them. So we're attacking this from both ends. We're building a training centre in Birmingham, the first of many: untrained human in, skilled tradesperson out. We grow the workforce. And we're building robotics to amplify what our technicians can do. We don't have enough skilled people as it is. The goal is to make them more productive. Our technicians are at the core of Fuse's mission of acheiving low cost energy and energy abundance. Why we think we'll win where standalone robotics companies have struggled: 1. We already generate the data robots need. Robotics is bottlenecked on real-world physical data, and you cannot scrape it off the internet. Our technicians do real physical work every day across building power plants, electrical work and energy hardware installs. Only a handful of companies have real physical data. Rarer still to have it across this breadth of skilled trades, on sites you own. 2. We can deploy fast, and we learn faster. Standalone labs spend years convincing big customers to let them deploy on site, often against legacy resistance. We own the worksites, so we build, test and deploy on real Fuse jobs from day one. Continuous deployment means an extremely fast learning rate: more deployment, more data, faster improvement. This is greenfield. Everything is open for the new team to define, from initial use case to policy choice to the embodiment itself, including whether we build in-house or partner. We're open to working with the best robotics and world model companies out there. The team will work directly with me. Energy is the bottleneck for AI. Our goal is to unleash it, and robots are how we make sure it never becomes one again. We're hiring the founding team now. If you want to build robots that ship into the real world from week one and make skilled trades more productive, come talk to us.

@CoinMarketCap Every action those agents take on behalf of a citizen becomes a public record. That record needs to live somewhere independently verifiable, not just inside whichever platform processed the booking or filing.

LATEST: 🇪🇪 Estonian PM Kristen Michal approved a proposal to give AI agents government-issued IDs with "limited, controllable and auditable authorizations" for actions like booking flights, filing taxes, or making payments.

@GoogleDeepMind Catching misinterpretation or overreach after the fact requires a record of what the agent actually did, kept independently of the system that ran it. That's a storage decision as much as a safety one.

Instead of assuming AI will always do what we intend, we ask: what if it doesn't? That’s why we’ve developed our AI Control Roadmap: a framework for building and managing the advanced AI we deploy within Google. 🧵

@Kimi_Moonshot 24/7 until done means the work in progress has to survive restarts, crashes, days passing. Where that state lives between checks matters as much as the agent running it.

Introducing Goal Mode in Kimi Work Goal lets your desktop agent run 24/7 until the task is done, built for long-horizon tasks and complex multi-step workflows.

@perplexity_ai A context graph that grows with every run needs to survive outside the session. The sources behind each memory entry have to stay verifiable years from now. That's a storage problem, not just a memory one.

Introducing Brain in Computer. Brain is a continuously learning memory system. Every task on Computer plugs into a context graph built by Brain. It makes Computer more stateful with every run. Available as a research preview for all Perplexity Max subscribers.

@OrcaRouter Likely is. Would be great to connect, DMs open.

@Filecoin Agreed. The more agents gain real-world permissions, the more "verifiable agent governance" starts looking like a missing piece of the stack. Feels like there may be some interesting overlap between what we're building and Filecoin's trust model. Happy to chat if useful.

In 2025, attackers stole corporate data from Microsoft 365 Copilot. The victim clicked nothing. They got an email. The AI read it. The AI obeyed it. In the past, humans got socially engineered. In 2026, agents are getting socially engineered. So we built Firewall + Guardrails to protect agents — and made them FREE on OrcaRouter.ai. Same API key, same gateway, one switch in your console. No code to change. The AI Threat Report 2026 from our security research team explains why. 🧵🐋

@OrcaRouter Exactly, append-only and independent of the enforcement layer. That's the storage model Filecoin's built for.

Signing the audit trail at the gateway covers integrity. Independence is a separate problem. If logs live inside the same trust boundary as policy enforcement, investigators still have to trust the gateway. Exporting signed events to an independent, append-only system creates a much stronger chain of evidence.

By 2030, US data centers could consume 9% of national electricity generation. The transmission infrastructure carrying that power averages over 40 years old. Filecoin providers deploy at the source. No aging transmission required.

🚨 Registration is closing soon for FilecoinTLDR Builder Challenges! Use AI to build your first (or next) Filecoin-powered mini app in a fun, guided sprint. Perfect for beginners, non-coders, and experienced builders alike. Cycle 1: Build a Filecoin-Powered Mini App With One Clear Mechanic 📅 Registration: June 15 – 19 📅 Build Period: June 20 – 26 No coding experience required — Claude Code + provided guides will be your build partner. Join the movement and ship something cool on Filecoin! ⬇️ Full details: x.com/FilecoinTLDR/s… Register here: loops.house/filecointldr-b…

@blocks At that scale, the audit trail matters as much as the automation, a verifiable record of what changed, when, and which agent did it, independent of the system that ran it.

We built an internal AI system called Builderbot. It coordinates agents across our entire codebase. Engineers tag it in Slack, and it researches, plans, and ships. The story so far: - 200,000 operations per day. - 1,500 pull requests merged per week. - 15% of all production code changes across Block. What used to take months now takes days. How we built it: block.xyz/inside/block-r…

@lmstudio The data it trains on and produces needs the same sovereignty. Verifiable, user-owned, not uploaded to a vendor. Local compute and decentralized storage are two halves of the same idea.

For WWDC, we worked with Apple to run Kimi K2.6, a 1T-parameter model, across a cluster of four Mac Studios using a preview version of LM Studio. We showcased secure remote access from a MacBook Neo and iPhone using LM Link. A glimpse of your own private, frontier-scale AI.

@gabepereyra @harvey @cursor_ai The client matter data it trains on needs the same sovereignty. Verifiable, firm-controlled, not sitting in a vendor's infrastructure.

Model strategy for @harvey: We are working on the first model in our legal foundation model series, inspired by @cursor_ai's Composer. Two goals: 1. Allow us to serve frontier intelligence across our product surface areas at an affordable price and a strong security posture. 2. Create the foundations for law firms to build their own specialized models and own their own intelligence. The model series will focus on complex client matters that span months and take dozens of associates. The agentic system will learn to control legal tech tools, sub agents and ask for help from frontier models or human partners, much like a senior associate. We’ve open sourced benchmarks for evaluating our initial post training work that represents work done by associates and in-house lawyers. We are scaling these significantly using synthetic and human pipelines as well as building private evals for firms. Open sourcing this data has allowed us to quickly validate the feasibility of post training open weight models for legal work. With our research partners we’ve already shown promising results post training open source models to approach frontier performance: 1. @baseten - novel compaction strategies for analyzing large data rooms. 2. @FireworksAI_HQ - matching frontier performance by using frontier as an advisor. 3. @appliedcompute - improving performance and reducing cost of large scale review tables. 4. @trajectorylabs & @nvidia - sovereign continual learning over client matters. We plan to continue to invest heavily in working with research partners and open sourcing our data, models and research as much as possible. We believe open research in legal will be important to building trust in the frontier ecosystem. We are also scaling our research team. Harvey Labs is our internal research group, responsible for pushing the frontier of legal intelligence and working closely with labs, research partners, and academia to bring the frontier of agent research into Harvey. Labs is run by @nikogrupen and @ItsJulioPereyra - Niko worked on multi-agent RL at Google Brain and Julio clerked and worked in BigLaw. We believe this pairing is crucial for building frontier legal AI systems. Together they have already made significant progress in scaling our data and training efforts. The long term goal of Harvey Labs is to contribute to the research and infrastructure required for the legal industry to create a frontier ecosystem. We believe that the best version of legal super intelligence is one where each law firm, enterprise and government owns their own specialized version. We are hiring for Harvey Labs across the post training, agent and data stack and open to acquiring talented teams / neolabs in this space. If interested please DM me.