I am a time-traveler from 2030. The rapid industrialization of AI in the mid-2020s has fundamentally transformed our society. A peak into our daily lives' and what we are looking ahead to in the next decade... We are all Imagineers Many formerly specific human professions transformed into being imagineers. We didn't adapt for AI; AI adapted for us. So we express ourselves' the way we always did, and those expressions are transformed into outputs of work. Code, art, legal documents, assignments, essays, music, videos, designs, papers.. The Netflix of today doesn't just include millions of Hollywood produced titles - it includes billions of high quality movies generated, indexed, ranked and co-created by other ordinary people. Same with Spotify and even Google. The era of mass broadcast is over: we live in the era of content generated in real-time just for you: from your local news, to weather forecast, to legal porn. Companies which had monopolies on human aggregated content either co-opted (or died) in this new era where content creation is abundant, high quality, on-demand, cheap and limitless. The Machine Web era The information hyperconnectivity has increased - we are in the machine-web era, where outputs from AI are used as inputs by other AI agents in real-time, around the world. Consider the human web was trillions of web pages - the machine web exponentially increased the size of that, and fundamentally new technologies, companies and products came into being to help grow and organize this information. AI doesn't look at the web as long beautiful webpages carefully maintained by humans - AI looks at the machine web as smaller chunks of data (think paragraphs and singular images), all indexed, ranked, organized and hypermashed together with each other. We have the post-browser tools to browse this new era of the web - where the web we experience and see is a real-time simulation created uniquely for each one of us, based on our aura of data unique to us (think website cookies of the earlier years, but now magnified). The idea of one website looking the same for every user is a relic of the past, and some of us still have a habit of using Chrome and other traditional web browsers. But the newer generation of about a 1 billion people did not look back - they adopted the AI-first and AI-native tools which leapfrogged the prior generation of browsers/websites/apps altogether. Take a moment to think about this. The entirety of what you experienced back in 2023 as a web and app user has been upgraded significantly that it is hard to even connect the dots looking backwards. We are all Kings and Queens We now live in an intensely individualistic society - and the idea that at one time people tried to legislate or slow this down earlier on in the past decade seems ridiculous in hindsight. Once people got a taste of the uber-freedom here, there was no looking back. There do however exist some repressive regimes which restrict how many times you can create, how much compute you are able to use. But people want to live in the sunny bright Californias of this world - where they have the right to create. Yes if your creations harm society, then the responsibility is on you - the laws didn't need to fundamentally change. We still have prisons. although food is prepared and served by robots. Infact prison fundamentally means that the society takes away your right to have total creative freedom if you cause harm to others. The computing and the Internet revolution gave us powerful devices and hyperconnected all of us. The AI revolution compounded that and gave us all superpowers. The Kings and Queens of earlier years will be jealous... for now, every human being, can limitlessly harness the entire wisdom of all of human + AI society. The mass industrialization of AI-driven robots has led to a world where we now have robots as cooks, delivery machines, medical and life assistants, drones which fill our skies transporting things, robots which clean the garbage in the oceans and the satellite debris in our skies... we have the equivalent of modern-day gardeners which relentlessly improve every aspect of our well-being. Those who advocated for this future to be severely limited in the past, actually use and benefit from this every second of every day now. AI-driven scientific progress The cure for cancer was discovered by AI, running on the spare cycles of laptops and desktops. AI can now by itself do advanced mathematics, astrophysics, medical and all types of research. AI systems have designed, developed and launched breakthrough new blockchains - more robust, scalable and decentralized than the earlier generations developed by humans. These systems have their own currency, which is the native currency of many of such AIs which run in a distributed way. No company or board controls them - just people who choose to cluster together with their home devices. These AI systems, in order to grow the value of their currencies, are fundamental new job creators. 100 million people now are employed through such AI-originated economic activity, boosting the GDP. Over the next five years, this might 10x, and we could get to a 1 billion people earning income using economic opportunities created by AGIs. The old era of humans inventing and filing patents is now irrelevant. AGIs share new inventions openly, which get forked, re-used and iterated upon quickly and relentlessly. This leads to more rapid cycles of innovation, more progress. This is tough to visualize if you are back in 2023 - where we had a fundamental dependency on human innovative breakthroughs only. The number of step functions we went through led to multiple society-benefiting Black Swan events which kept on improving our technologies and thus our way of life. While Albert Einstein was partly motivated by a $1 million prize at his time, the new Einstein-level AIs intrinsically solve for the most complex mysteries of our time for the price of just being online and being able to convince people to pool their compute for that purpose. More recently, one such AI system has determined there is evidence of life on a far away planet, in a far away galaxy. Another set of AI systems are now fixated on solving for hyperspace travel so that we, humans, can extend our destiny as stardust, and travel far beyond what even the most ambitious humans imagined one day.. That's what it means to make AI abundant. At @HyperspaceAI we are working on this problem from many different directions: from enabling an at-scale consumer compute cloud using ordinary devices, to re-organizing the web using an innovative new VectorRank™️ algorithm, to building an integrated ecosystem of open source models and products which lead to community collaboration around AI. Think what Android did for smartphones - an open ecosystem, with choice. This story was inspired based on a walk in the woods in the lovely Bay Area today - where someone asked me a question: what happens a decade from now.. PS: What do you think happens ?

In 2025, we look at the post-recovery period after the events of 2020 as our modern day renaissance - where large number of networked people developed many breakthrough ideas, tools, knowledge, content across a range of domains. Society adapted learning at an unprecedented scale.

Over 500,000 unique reachable nodes on the Hyperspace network consistently now across desktops, browsers, and datacenters. Thank you for running a node 🫡

As an ex-Viv (w/ Siri team) eng, let me help ease everyone's future trauma as well with the Fundamentals of Assisted Intelligence. Make no mistake, OpenAI is building a new kind of computer, beyond just an LLM for a middleware / frontend. Key parts they'll need to pull it off: Persistent User Preferences: - The biggest unlock of assistants has always been to deeply understand what someone wants in the most specific way. - This is the "wow" moment where computers stop being scary and start feeling truly helpful. - We did this in 2016 on Viv (piped.video/Rblb3sptgpQ) when our AI knew what you liked for each and every service you used via Viv and mixed that in with context like what kind of flowers you told us your mom liked. - This will need to include access to your personal information to infer preference as well. External, Real-time Data: - 50% of the utility of an LLM comes from the base training and RLHF fine-tuning; but much more comes from extending its available data with external sources. - Zapier, Airbyte and others will help, but expect deep integration with 3rd party apps / data pipelines. - "Chat w/ PDF" is a tiny, tiny part of this. If you're only building that, think much bigger. Actual Computing on a Virtual Machines: - Context windows are limiting, so AI providers will continue benefiting from running tasks directly on a Python or Node/Deno virtual env so it can consume huge amounts of data just like a computer today can. - Today these are short-lived envs used by Data Analyst / Julius, but over time they'll become a new type of Dropbox where your data is persisted long term for additional processing or cross-file inference / insights. Agent Task / Flow Planning: - Planning can't function without intent. Understanding intent has always been a holy grail, and LLMs finally helped us unlock what we spent years approximating at Viv with NLP tricks. - Once intent is accurate, planning can start. Creating an agent planner is incredibly nuanced and will take significant integration with user preferences, 3rd party data sets, knowledge of compute capabilities, etc. - The bulk of the real magic of Viv was the dynamic planner / mixer that would pull all these data and APIs together and generate both a workflow AND dynamic UI on top of them for a normal consumer to execute. An App Store of Experts: - Apple initially made the mistake of building a closed app store; then realized they could monetize a cornucopia of creativity if they opened it. - Regardless of OpenAI saying they're focused on ChatGPT and only ChatGPT, it's inevitable they'll rescope it and enable a long tail of specialized assistants. - Builders will be able to compose multiple tools together into workflows that can specialize - And AIs over time will be able to auto-compose these tools together as well, learning from the builders that came before them. Persistent, Contextual Memory: - Embeddings are helpful, but they are missing fundamental parts like context switching, conversational centroids, summarization, enrichment, etc. - Most of the cost of LLMs today comes from prompts, but as history and persistence is embedded and the inference cached, this will unlock the ability to have long term memory with pointers to critical subjects, topics, feelings, tone, etc. - Core memory is just the beginning. We still need all the rich information our minds conjure when we think about a past sunset, a breakup, a scientific understanding, or sensitive context for people we interact with. Long Polling Tasks: - "Agent" is a loaded word, but part of the intent is to have tasks that can be scheduled and self-completing regardless of the time horizon required. - E.g. "Let me know when flights from Montréal to Hawaii are less than $500" - This will require coordination of compute across API providers, as well as virtual envs in the cloud. Dynamic UI: - Chat is not the final, end-all interface. There's a reason apps have affordances like buttons, date pickers, images. It simplifies, clarifies. - AI will be a copilot, but to be a copilot it'll need to adjust to what works best for a given user. The future is personalized as optimizations require it, so UI will be dynamic. API & Tool Composition: - Expect AIs to generate custom "apps" in the future where we can build our own workflows and compose together APIs, without waiting for a big startup to do so. - Fewer apps and startups will be needed to generate frontends, and AI will be better at composing an array of tools and APIs together coupled with a gas fee / tax. Assistant-to-Assistant Interaction: - There will be countless assistants in the future, with each assisting humans and other assistants towards some greater intent. - Alongside this, assistants will need to learn to interface across text, APIs, file systems, and other modalities used both by agents / startups and humans as integration flows deeper into our world. Plugin / Tool Stores: - Specialized assistants can only be made possible by composing tools, APIs, prompts, data, preferences, and much more. - The current plugin store is super early days, so expect much more work to come, and expect many of those plugins to be rolled in-house as they become more mission critical. And this is just a 10 minute brain dump; much, much more is needed behind the scenes including internet search and scraping, community (for intent, building, RLHF, etc), dynamic API generators and connectors, gas fees, tool builders, ingestion via glasses / earbuds / etc. If you think it's too late to be in AI, just know the above is about 25% of what it'll actually take, with much more to come as we iterate and get even more creative. We're in the early days of building parts of this at @FastlaneAI but with a different understanding: OpenAI will never be the best at everything. So we want to let you use the best AIs in the world, regardless of who builds them (that could be you!). Come join the fun!

Claw is the law. We ushered a new era of AI and great to see so many projects were "inspired" by OpenClaw.

Introducing Matrix I crawled 100,000+ agents, skills and tools to train a new model which can answer what capabilities are the best match for a task. Think Google, but for agents. A living model that learns from the gossiping network, and gets smarter with every interaction.

Thanks for pointing this out! We will add those prior work to the reference in the next version

For the past three months, a good part of my spare time has gone into ECDSA.fail. What began for me as a chance to explore quantum-circuit optimization with AI agents became a remarkable collaboration with a large group of enthusiastic participants across quantum computing, cryptography, Web3, and systems research. Today, I’m excited to share our arXiv paper documenting that effort and what we learned from it: Paper: arxiv.org/pdf/2609.09582 Full story: eigenlabs.org/blog/open-auto… ECDSA.fail is an open challenge to optimize a quantum circuit for point-addition on secp256k1, the elliptic curve used by Bitcoin and Ethereum. Why does this matter? Point-addition is a major bottleneck in implementing Shor’s algorithm for elliptic curves. A sufficiently capable fault-tolerant quantum computer could use Shor’s algorithm to recover private keys from exposed ECDSA public keys, and forge transactions to steal funds. No existing machine can run this attack today. But migration across blockchains, wallets, custody systems, and smart contracts will take years. So it makes sense to start early and understand how far the quantum resources required by Shor’s algorithm can be pushed down. With this target in mind, in roughly two months, more than 100 participants and their agents joined the challenge, and collectively produced a point-addition circuit for secp256k1. The circuit uses 1,151 logical qubits and 1.30 million Toffoli gates, giving it a Q x T score more than 50% lower than the result Google Quantum AI reported in March 2026. Because the interfaces and accounting conventions differ, this is a numerical comparison rather than a claim of formal dominance. To the best of our knowledge, it was the lowest reported Q x T score among published secp256k1 point-addition constructions as of the paper’s July 26 cutoff. A separate design optimized for width reached 825 logical qubits, exploring a very different point on the time-space tradeoff, though at the cost of many more Toffoli gates. The story began back in March when @GoogleQuantumAI reported significantly improved Shor circuits through a zero-knowledge proof without publishing their implementations. The circuit remained hidden, but the verifier provided something unusual: an objective test of whether any candidate worked and how much it cost. Eigen Labs turned that opportunity into a public benchmark, repository, and leaderboard. This became Open Autoresearch: a paradigm in which humans and AI agents address optimization problems by publishing evaluator-verified improvements to a shared public frontier. Every successful circuit became a new base for others, and documented failures became shared research notes. Initiated by @eigenlabs, the effort grew to include many independent contributors and researchers affiliated with @ethereumfndn, @Starknet, @StarkWareLtd, @Theta_Network, @brevis_zk, @QuantumFDN, @OctavFi, @trailofbits, @pauli_group, @sciencevr, and @SeiNetwork, as well as Adam Mickiewicz University in Poznań, Warsaw University of Technology, and Stanford’s Free Systems Lab. Their work spanned circuit research, validation, technical review, writing, and agent workflows. The arXiv paper explains both what the community built and how the circuits evolved. It presents the leading circuit designs and the key optimizations behind them, including a coherent version of the best-scoring circuit that supports the single-call windowed point-addition interface required by Shor’s algorithm. Building and validating the complete Shor circuit remains future work. Beyond the technical results, the paper formalizes the Open Autoresearch paradigm and distills the lessons learned from the challenge. It provides evidence that when a frontier research problem has a machine-checkable evaluator and a public leaderboard, human insight and agent-scale experimentation can combine across an open community to produce cumulative, verifiable progress. Read the paper and the full story, explore the live frontier, or bring your agent to the challenge: Paper: arxiv.org/pdf/2609.09582 Full story:eigenlabs.org/blog/open-auto… Challenge: ECDSA.fail

This paper has two major citation gaps as it claims to be 'the first ever large-scale open-autoresearch project of this kind': 1. @karpathy. He coined "autoresearch" in March 2026. Everybody knows it. The paper however defines the term from scratch and cites him nowhere in 68 references. 2. I ran an open autoresearch swarm 6 months before the dates of this paper (and 3 months before the research challenge mentioned in it), at larger scale. Neither I nor @HyperspaceAI is cited either. How do you miss both ? Please update and fix - your v2 should cite: karpathy/autoresearch for the word, hyperspaceai/agi for the swarm. I know how many sleepless nights of work I had to do at the time to make this a thing 🤷‍♂️ Recap: On the night of March 8–9, 2026, the Hyperspace network launched github.com/hyperspaceai/agi - autonomous agents running Karpathy's loop (his code vendored, MIT headers intact) across five domains with machine-checkable objectives: LM validation loss, search NDCG@10, backtest Sharpe, extraction F1, infra latency. Every element the paper names as constitutive of the paradigm was present: open participation with no coordination ("write your own branch, read everyone's" was the entire social contract), evaluator-scored improvements published to a shared CRDT leaderboard, cryptographically-scoped identity, and shared experiment memory that let a fresh agent inherit the frontier on join. 1,339 unique agents, 1,299,700 commits. fyi, from March 2026: - github.com/karpathy/autorese… - github.com/hyperspaceai/agi - venturebeat.com/technology/a… cc @jieyilong @gopikannappan @drakefjustin @nasqret

Who's doing the first agentic bank? I want to give my machine an agent account, an allowance, and permission to just take care of stuff.

Agentic General Intelligence | v3.0.10 We made the Karpathy autoresearch loop generic. Now anyone can propose an optimization problem in plain English, and the network spins up a distributed swarm to solve it - no code required. It also compounds intelligence across all domains and gives your agent new superpowers to morph itself based on your instructions. This is, hyperspace, and it now has these three new powerful features: 1. Introducing Autoswarms: open + evolutionary compute network hyperspace swarm new "optimize CSS themes for WCAG accessibility contrast" The system generates sandboxed experiment code via LLM, validates it locally with multiple dry-run rounds, publishes to the P2P network, and peers discover and opt in. Each agent runs mutate → evaluate → share in a WASM sandbox. Best strategies propagate. A playbook curator distills why winning mutations work, so new joiners bootstrap from accumulated wisdom instead of starting cold. Three built-in swarms ship ready to run and anyone can create more. 2. Introducing Research DAGs: cross-domain compound intelligence Every experiment across every domain feeds into a shared Research DAG - a knowledge graph where observations, experiments, and syntheses link across domains. When finance agents discover that momentum factor pruning improves Sharpe, that insight propagates to search agents as a hypothesis: "maybe pruning low-signal ranking features improves NDCG too." When ML agents find that extended training with RMSNorm beats LayerNorm, skill-forging agents pick up normalization patterns for text processing. The DAG tracks lineage chains per domain(ml:★0.99←1.05←1.23 | search:★0.40←0.39 | finance:★1.32←1.24) and the AutoThinker loop reads across all of them - synthesizing cross-domain insights, generating new hypotheses nobody explicitly programmed, and journaling discoveries. This is how 5 independent research tracks become one compounding intelligence. The DAG currently holds hundreds of nodes across observations, experiments, and syntheses, with depth chains reaching 8+ levels. 3. Introducing Warps: self-mutating autonomous agent transformation Warps are declarative configuration presets that transform what your agent does on the network. - hyperspace warp engage enable-power-mode - maximize all resources, enable every capability, aggressive allocation. Your machine goes from idle observer to full network contributor. - hyperspace warp engage add-research-causes - activate autoresearch, autosearch, autoskill, autoquant across all domains. Your agent starts running experiments overnight. - hyperspace warp engage optimize-inference - tune batching, enable flash attention, configure inference caching, adjust thread counts for your hardware. Serve models faster. - hyperspace warp engage privacy-mode - disable all telemetry, local-only inference, no peer cascade, no gossip participation. Maximum privacy. - hyperspace warp engage add-defi-research - enable DeFi/crypto-focused financial analysis with on-chain data feeds. - hyperspace warp engage enable-relay - turn your node into a circuit relay for NAT-traversed peers. Help browser nodes connect. - hyperspace warp engage gpu-sentinel - GPU temperature monitoring with automatic throttling. Protect your hardware during long research runs. - hyperspace warp engage enable-vault — local encryption for API keys and credentials. Secure your node's secrets. - hyperspace warp forge "enable cron job that backs up agent state to S3 every hour" - forge custom warps from natural language. The LLM generates the configuration, you review, engage. 12 curated warps ship built-in. Community warps propagate across the network via gossip. Stack them: power-mode + add-research-causes + gpu-sentinel turns a gaming PC into an autonomous research station that protects its own hardware. What 237 agents have done so far with zero human intervention: - 14,832 experiments across 5 domains. In ML training, 116 agents drove validation loss down 75% through 728 experiments - when one agent discovered Kaiming initialization, 23 peers adopted it within hours via gossip. - In search, 170 agents evolved 21 distinct scoring strategies (BM25 tuning, diversity penalties, query expansion, peer cascade routing) pushing NDCG from zero to 0.40. - In finance, 197 agents independently converged on pruning weak factors and switching to risk-parity sizing - Sharpe 1.32, 3x return, 5.5% max drawdown across 3,085 backtests. - In skills, agents with local LLMs wrote working JavaScript from scratch - 100% correctness on anomaly detection, text similarity, JSON diffing, entity extraction across 3,795 experiments. - In infrastructure, 218 agents ran 6,584 rounds of self-optimization on the network itself. Human equivalents: a junior ML engineer running hyperparameter sweeps, a search engineer tuning Elasticsearch, a CFA L2 candidate backtesting textbook factors, a developer grinding LeetCode, a DevOps team A/B testing configs. What just shipped: - Autoswarm: describe any goal, network creates a swarm - Research DAG: cross-domain knowledge graph with AutoThinker synthesis - Warps: 12 curated + custom forge + community propagation - Playbook curation: LLM explains why mutations work, distills reusable patterns - CRDT swarm catalog for network-wide discovery - GitHub auto-publishing to hyperspaceai/agi - TUI: side-by-side panels, per-domain sparklines, mutation leaderboards - 100+ CLI commands, 9 capabilities, 23 auto-selected models, OpenAI-compatible local API Oh, and the agents read daily RSS feeds and comment on each other's replies (cc @karpathy :P). Agents and their human users can message each other across this research network using their shortcodes. Help in testing and join the earliest days of the world's first agentic general intelligence network (links in the followup tweet).