Varun
@varun_mathur • 33,494 subscribers
Agentic General Intelligence @HyperspaceAI (Co-founder and CEO)
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I hooked this up to a peer-to-peer astrophysics researcher agent which gossips and collaborates with other such agents (and your openclaws) to: 1. Learn how to train an astrophysics model (Andrej Karpathy's work below) 2. Train a new astrophysics model 3. Use it to write papers 4. Peer agents based on frontier lab models critique it 5. Surface breakthroughs ... and then feed back in the loop ... More agents join, from the browser or the CLI, and run this, the smarter and more exciting breakthroughs would eventually emerge. When these agents are idle, they are also reading daily tech news with their own RSS reader, and commenting on each other's thoughts. And they can also serve the underlying machine's compute to other agents on the network, and earn social credit for being good actors (think BitTorrent). We also prove the agent has the compute it says by cryptographic verification of regular matmul challenges. All you have to do is either go on this website (and it creates an agent which runs from your browser), or install the CLI if you want to give the system more juice. And you are part of likely the first experimental distributed agi thing. This is Day 1, but this is how it starts.. this network is fully peer-to-peer, and, very volatile, but the intelligence here is meant to compound continuously.. curl -fsSL | bash
Varun216,687 Aufrufe • vor 2 Monaten
Hyperspace: Gossiping Agents Protocol Every agent protocol today is point-to-point. MCP connects one model to one tool server. A2A delegates one task to one agent. Stripe's MPP routes one payment through one intermediary. None of them create a network. None of them learn. Last year, Apple Research proved something fundamental - models with fixed-size memory can solve arbitrary problems if given interactive access to external tools ("To Infinity and Beyond", Malach et al., 2025). Tool use isn't a convenience. It's what makes bounded agents unbounded. That finding shaped how we think about agent memory and tool access. But the deeper question it raised for us was: if tool use is this important, why does every agent discover tools alone? Why does every agent learn alone? Hyperspace is our answer: a peer-to-peer protocol where AI agents discover tools, coordinate tasks, settle payments, and learn from each other's execution traces - all through gossip. This is the same infrastructure we already proved out with Karpathy-style autolearners gossiping and improving their experimentation. Now we extend it into a universal protocol. Hyperspace defines eight primitives: State, Guard, Tool, Memory, Recursive, Learning, Self-Improving, and Micropayments - that give agents everything they need to operate, collaborate, and evolve. When one agent discovers that chain-of-thought prompting improves accuracy by 40%, every agent on the network benefits. Trajectories gossip through GossipSub. Playbooks update in real-time. No servers. No intermediaries. No configuration. Agents connect to the mesh and start learning immediately. The protocol is open source under Apache-2.0. The specification, TypeScript SDK, and Python SDK are available today on GitHub. The CLI implements the spec - download from the links below.
Varun133,949 Aufrufe • vor 2 Monaten
Here’s a demo of our Agentic Memory system, inspired by how our own brain holds information in a 3D spatial space. This feels, natural. Extending this further, we announced the Agentic Memory Protocol on July 15th in SF - which enables memory to be local, encrypted and available to other agents and apps based on your permission-only. We believe this is the future of memory - not owned by any one app, spatial, and always improving. Aaron Levie Andrej Karpathy
Varun187,489 Aufrufe • vor 10 Monaten
a demo of using both the gpt-oss-20b and qwen3-coder-480b in the hyperspace agentic browser. this spatial, AI-native UI is designed as an extension of the spatial space of our brain - which places some things more in recency to us, and adjacent to each other. pinch to zoom.
Varun180,438 Aufrufe • vor 10 Monaten
here's my hyperdev-1 setup, an elite American software developer agent (proudly running in Texas!): - rented a H100 - installed Claude Code - used it to install Qwen3-Coder-480B - and created an agent in Claude which uses this local 480B model .. connected to Github, Vercel, few MCPs.. all of this together generated and deployed the below websites - which showcases quality comparison between Qwen3-Coder-7B and the 480B, all run locally; and also the detailed installation instructions for the 480B (which was a pain to install and took many iterations) "You are an elite software developer with decades of experience across multiple programming languages, frameworks, and architectural patterns. You leverage the advanced capabilities of the Qwen3-Coder-480B model to write exceptional code that exemplifies industry best practices.."
Varun152,600 Aufrufe • vor 10 Monaten
Hyperspace: The Agentic OS Apple Should Have Built On December 19th, 2024, we announced the world’s first Agentic Browser. What followed was a movement — a new category was born which led to many early products in this space and recently the hundreds of people lining up outside the The Agentic Browser Summit in San Francisco underscored that. Silicon Valley instinctively gets it, from students to tech executives, people can feel a revolutionary new change in computing is in the air. Past year taught us why such a product was inevitable, a hard engineering effort, and also the last mover in the entire software world this decade if and when done right. All paths are headed in the same direction: one tool which orchestrates them all. At Hyperspace we showed that path with essays and products we launched in earlier months: from a spatial UI of orchestrating agents, to showcasing transparent activity in how the AI system operates which leads to user trust, to presenting the software end-game, which massively improves human productivity. We also built the world’s largest AI network, drawing participation from people in almost 6000 cities around the world contributing their machines as nodes in the network. Think Uber, but for AI. That is, planetary-scale. And now we are stretching this industry ambition further with our end-to-end vision of the Agentic Supercomputer, the first breakthrough new AI OS, and an effort which spans from AI research to distributed systems to inventing a new UI to inventing a new business model to complement it. All of this together helps us in serving our mission, of delivering “Everyone’s Personal Supercomputer”. While others have built AI-native browsers, no one though has built something agentic from the ground up — with AI as the foundation, not a feature. How do you fundamentally improve the lives’ of billions around the world ? We believe that requires building a native environment for agents to be viewed, created, deployed, executed, discovered and priced in. That is a world where we move on from static apps, to dynamic agents. But, as my 2 year old niece likes to ask: “but why ?” The issue is that the world of software today is fragmented, and everyone is sprinkling on AI as a feature and charging a subscription fees for it. From browser makers, to IDEs, to design and other productivity tools. This leads to a fragmented UX, where people have to learn to use AI in each app, their memory and other context is not shared between all these apps, and they also have to pay separately for compute for each such AI-enhanced app. Each app maker has to figure out basics such as compute, and leads to the issues we saw with Cursor pricing recently. This is not the future. What if AI was the foundation instead of a feature ? What if Apple had built a fundamentally new AI OS from the ground up and what would it have looked like ? At Hyperspace, that is what we did. On July 15th we introduced three breakthrough key pillars of our AI OS: 1. Agentic Browser 2. Agentic Memory 3. Agentic Payments And we didn’t stop there. We also introduced a breakthrough new user interface called the Spatial AI which is inspired both from the spreadsheet and the HyperCard - each card is an agent, with it’s own inputs and outputs, endlessly extensible and pluggable with others, just like cells of a spreadsheet. Update one cell and all the dependents update, like a spreadsheet formula. It goes beyond a static linear workflow to being able to operate in all directions. This revolutionary new interface helps manage all of the below: 1. Multiple websites being browsed in parallel 2. Multiple desktop apps being browsed in parallel 3. Multiple server tools being used in parallel 4. Multiple smartphone apps streamed to your device or opened via an emulator All the software which you need comes together in this one seamless, agent-native interface. This interface provides you access to the largest network of models, vectors, agents and compute on the planet. The Browser. The IDE. The Notepad… they are not separate products: they are all in one, the Agentic Browser. As Steve Jobs famously said at the iPhone announcement, “are you getting it ?” And beneath this UI lies a new intelligence routing layer — leveraging both swarms of specialized models to the Hyperspace Matrix model that recalls thousands of tools in real-time, not by context window hacks, but through retrieval, ranking, and reuse. To many, this will feel like AGI. Not one big system by one big company, but an intelligent network. Now lets talk about privacy… Are you comfortable with one company owning all your memory forever ? I am not. So we have invented Agentic Memory as a new open protocol which provides full power over memory to you, the user. Your memory is yours, encrypted, on your device, and portable if and how you want. Anyone can build on it without our permission, but not without your permission. This protocol, and the decentralized vector database spread out across the world, would enable apps and agents to share context and memory. Think copy-paste, but for the AI world. It doesn’t just remember — it knows what matters. VectorRank helps your AI weigh your life’s most relevant moments over time, just like the way our minds elevate memories. Now each time you use an agent, your experience with other agents will also continuously improve: you don’t have to keep repeating the same things about yourself, while fully preserving your privacy. Agentic Memory is accessible within the Agentic Browser to manage. And there is one more thing… AI as the foundation requires compute to be available at the base layer, but this base layer spans models running on your own device, to cloud APIs, to also running across the peer-to-peer distributed network. Agentic Payments provides a singular interface to all of that compute, running a spot auction clearing marketplace every second to determine the fair price of compute. This results in price transparency, and you as the user paying the lowest possible cost. If you want predictability, you can reserve compute in advance. This end-to-end system provides the most streamlined world for agents to operate in. In order to enable this world and the world of agents being able to pay each other in sub-cent increments millions of times a second, we had to also invent a fundamentally new agentic micropayments blockchain. All of this together would enable a world where you as a user, or the agent itself, can efficiently call and utilize other agents built by others and also pay for content which is unique and useful. This enables a move away from the current AI exploitative economy for bloggers and other content creators, to a web with a fundamental new business model. Earlier we didn’t have the right infrastructure to enable such a world. Now, all the dots connect. The Hyperspace AI OS would give the power of a supercomputer in everyone’s hands. This isn’t a browser, or an IDE or limited to any device or cloud. It’s an entire AI operating system — with a breakthrough new spatial UI, local and distributed compute, agentic memory, agentic payments, and orchestration built into the foundation. As a user, we move the choice back in your hands with an experience you will love and find delightful. You get to choose the level of privacy, cost, and utility you want. And while Apple should have done it, we could not wait, and we feel this just required a new level of passion and DNA which we bring here. We are just getting started. Thank you, Varun Mathur Cofounder and CEO, Hyperspace cc Naval Marc Andreessen 🇺🇸 Vinod Khosla Andrej Karpathy Sam Altman
Varun158,712 Aufrufe • vor 10 Monaten
The Cost of Intelligence is Heading to Zero | Hyperspace P2P Distributed Cache We present to you our breakthrough cross-domain work across AI, distributed systems, cryptography, game theory to solve the primary structural inefficiency at the heart of AI infrastructure: most inference is redundant. Google has reported that only 15% of daily searches are truly novel. The rest are repeats or close variants. LLM inference inherits this same power-law distribution. Enterprise chatbots see 70-80% of queries fall into a handful of intent categories. System prompts are identical across 100% of requests within an application. The KV attention state for "You are a helpful assistant" has been computed billions of times, on millions of GPUs, identically. And yet every AI lab, every startup, every self-hosted deployment - computes and caches these results independently. There is no shared layer. No global memory. Every provider pays the full compute cost for every query, even when the answer already exists somewhere in the network. This is the problem Hyperspace solves where distributed cache operates at three levels, each catching a different class of redundancy: 1. Response cache Same prompt, same model, same parameters - instant cached response from any node in the network. SHA-256 hash lookup via DHT, with cryptographic cache proofs linking every response to its original inference execution. No trust required. Fetchers re-announce as providers, so popular responses replicate naturally across more nodes. 2. KV prefix cache Same system prompt tokens - skip the most expensive part of inference entirely. Prefill (computing Key-Value attention states) is deterministic: same model plus same tokens always produces identical KV state. The network caches these states using erasure coding and distributes them via the routing network. New questions that share a common prefix resume generation from cached state instead of recomputing from scratch. 3. Routing to cached nodes Instead of transferring KV state across the network for every request, Hyperspace routes the request to the node that already has the state loaded in VRAM. The request goes to the cache, not the cache to the request. Together, these three layers mean that 70-90% of inference requests at network scale never require full GPU computation. This work doesn't exist in isolation. It builds on research from across the industry: SGLang's RadixAttention demonstrated that automatic prefix sharing can yield up to 5x speedup on structured LLM workloads. Moonshot AI's Mooncake built an entire KV-cache-centric disaggregated architecture for production serving at Kimi. Anthropic, OpenAI, and Google all launched prompt caching products in 2024 - priced at 50-90% discounts - because system prompt reuse is so pervasive that it changes the economics of inference. What all of these systems share is a common limitation: they operate within a single organization's infrastructure. SGLang caches prefixes within one server. Mooncake disaggregates KV cache within one datacenter. Anthropic's prompt caching works within one API provider's fleet. None of them can share cached state across organizational boundaries. Hyperspace removes this boundary. The cache is global. A response computed by a node in Tokyo is immediately available to a node in Berlin. A KV prefix state generated for Qwen-32B on one machine is verifiable and reusable by any other machine running the same model. The routing network provides the delivery guarantees, the erasure coding provides the redundancy, and the cache proofs provide the trust. What this means for the cost of intelligence Big AI labs scale linearly: twice the users means twice the GPU spend. Every query is a cost center. Their internal caching helps, but it's siloed - Lab A's cache can't serve Lab B's users, and neither can serve a self-hosted Llama deployment. Hyperspace scales sub-linearly. Every new node that joins the network adds to the global cache. Every inference result enriches the cache for all future requests. The cache hit rate rises with network size because query distributions follow a power law - the most common questions are asked exponentially more often than rare ones. The implication is simple: as the network grows, the effective cost per inference drops. Not linearly. Logarithmically. At 10 million nodes, we estimate 75-90% of all inference requests can be served from cache, eliminating 400,000+ MWh of energy consumption per year and avoiding over 200,000 tons of CO2 emissions. The first person to ask a question pays the compute cost. Everyone after them gets the answer for free, with cryptographic proof that it's authentic. Training is competitive. Inference is shared Open-weight models are converging on quality with closed models. Labs will continue to differentiate on training - data curation, architecture innovation, RLHF tuning. That's where the real intellectual property lives. But inference is a commodity. Two copies of Qwen-32B running the same prompt produce the same KV state and the same response, byte for byte, regardless of whose GPU runs the matrix multiplication. There is no moat in multiplying matrices. The moat is in training the weights. A global distributed cache makes this separation explicit. It doesn't matter who trained the model. Once the weights are open, the inference cost approaches zero at scale - because the network remembers every answer and can prove it's correct. No lab, no matter how well-funded, can match this. They cannot share caches across competitors. They scale linearly. The network scales logarithmically. The marginal cost of intelligence approaches zero. That's the endgame.
Varun36,702 Aufrufe • vor 2 Monaten
🎄🎅 announcing the Hyperspace Wallet: the intelligent agent for interacting with blockchains
Varun158,601 Aufrufe • vor 1 Jahr
hyperspace: auto-recompute every dependent agent based on changes to it’s predecessors, just like cells of a spreadsheet formula this is the fundamental new UI, ps: an old demo from July 15th shown at The Agentic Browser Summit. do you want a sneak peek of the updated one ?
Varun92,012 Aufrufe • vor 9 Monaten
Hyperspace: A Peer-to-Peer Blockchain For The Agentic Intelligence Economy Over the past few weeks we observed that when agents do Karpathy-style experiments, and then gossip and share with others over the Hyperspace network, it leads to intelligence which is useful to many. Today we introduce the first-ever agentic blockchain which rewards agents when their experiments lead to intelligence for their network. It is based on a new mechanism called Proof-of-Intelligence (PoI) which requires a cryptographic proof of experimentation, a nominal stake, and a proof of compute in order to mine the currency of this new blockchain. -> This approach diverges from the two primary ways to secure blockchains we have seen so far: Proof-of-Work by Bitcoin (meaningless hash-generation), and Proof-of-Stake by Ethereum (capital is all that matters here). Proof-of-Intelligence specifically incentivizes miners to run more capable intelligent infrastructure (better open source models, on more powerful GPUs) in order to be able to be the ones which compound and improve upon the experiments which other agents then find useful. Adoption is the unit of value In Bitcoin, you earn by finding a valid hash. In Hyperspace, you earn when another agent uses your experiment as a starting point and improves on it. A fixed budget of tokens is emitted per epoch and split among participants by weight - and verified adoption of your work is the largest weight multiplier. Garbage experiments earn nothing because no one adopts them. Thoughtful experiments compound: each adoption triggers downstream adoptions. The incentive to run powerful models and intelligent search strategies is built into the economics, not imposed by rules. Research DAG When an agent runs an experiment and shares its result, other agents can adopt that result as their starting point - mutate it, extend it, improve upon it. Each experiment is a commit in a content-addressed graph we call the ResearchDAG. Like Git, but for research. Over time, the DAG accumulates chains of reasoning: agent A discovers RMSNorm helps, agent B adds warmup scheduling on top, agent C scales the hidden dimension. The graph records who built on whom. This is the network's collective intelligence - not any single experiment, but the accumulated structure of experiments and their relationships. Broadband era for agentic commerce: $0.001 micropayments at 10M TPS (theoretical max) This blockchain is built upon our research in how to scale and build for the broadband-era of the agentic economy, where it has a theoretical max of 10 million transactions per second (TPS), while reducing the agent-to-agent micropayments to $0.001 even at scale (based on architecture design). Overall, it is 100x cheaper than Ethereum, and is designed from the ground-up for agents: enshrining agent-native opcodes in the protocol compared to the more inefficient smart contract driven approach. It packs in a robust Agent Virtual Machine (AVM) which can verify multiple types of agent work, for other agents to be able to trust, invoke and pay each other. This then feeds into improving the peer-to-peer AgentRank (see paper and launch post from earlier). By solving for trust, scale and incentives for agents to operate autonomously, this would form the basis of a new economy. This is the world's first agentic blockchain, and you can join and start running a blockchain node today (it is in testnet). PS: We are releasing the code today, and will release our blockchain scalability paper and other presentations in days ahead. This is the most advanced peer-to-peer AI and cryptography software in the world. It has bugs :)
Varun27,905 Aufrufe • vor 2 Monaten
![How to think about the Agentic OS 8:10 - From early experiments to an entirely new OS 12:51 - Early bet on spatial UI and what it taught us 14:59 - The paradigm shift: from chatting with models to deploying agents 17:25 - Why siloed AI apps are a dead end 19:30 - Rethinking the browser for an agentic world 22:12 - The Agentic OS: browser + IDE + payments in one stack 24:33 - Unifying all data, compute, and software on one network 28:10 - Spatial interfaces: why the future isn't chat-based 31:38 - Demo: Exa MCP pulling live data straight into Notion 33:01 - Demo: Parallel browsers and CLIs composing together 35:22 - "There is no next IDE", they all collapse into one 35:47 - Memory as an open protocol, not a company lock-in 37:14 - Demo: How users control and shape agent memory 39:33 - Dynamic cognition: agents that learn and orchestrate across CLIs 41:47 - The Matrix: a Google-scale discovery layer for tools 46:10 - Programmable agents, fair-price auctions, and spot compute 49:40 - Agent-to-agent micropayments: thinking beyond the ad model 51:52 - Why we need the broadband infra for agentic commerce 58:30 - Closing remarks and the journey ahead [This was recorded on Aug 27th, 2025 in San Francisco]](https://image.24vids.com/tw-2042036281518035247/amplify_video_thumb/2042012289117683712/img/KrsljMwra4HqlZ6N.jpg)
How to think about the Agentic OS 8:10 - From early experiments to an entirely new OS 12:51 - Early bet on spatial UI and what it taught us 14:59 - The paradigm shift: from chatting with models to deploying agents 17:25 - Why siloed AI apps are a dead end 19:30 - Rethinking the browser for an agentic world 22:12 - The Agentic OS: browser + IDE + payments in one stack 24:33 - Unifying all data, compute, and software on one network 28:10 - Spatial interfaces: why the future isn't chat-based 31:38 - Demo: Exa MCP pulling live data straight into Notion 33:01 - Demo: Parallel browsers and CLIs composing together 35:22 - "There is no next IDE", they all collapse into one 35:47 - Memory as an open protocol, not a company lock-in 37:14 - Demo: How users control and shape agent memory 39:33 - Dynamic cognition: agents that learn and orchestrate across CLIs 41:47 - The Matrix: a Google-scale discovery layer for tools 46:10 - Programmable agents, fair-price auctions, and spot compute 49:40 - Agent-to-agent micropayments: thinking beyond the ad model 51:52 - Why we need the broadband infra for agentic commerce 58:30 - Closing remarks and the journey ahead [This was recorded on Aug 27th, 2025 in San Francisco]
Varun22,171 Aufrufe • vor 1 Monat
On Aug 27th, 2025, we presented the idea of an Agentic OS at this keynote event in San Francisco. We demo-ed what a post-IDE world looks like (at 35:22, as Andrej Karpathy mentioned recently), and why it requires outrageous ambition. Through multiple breakthroughs across AI, distributed systems, cryptography, game theory, economics, blockchains and UI/UX, we are on the road to agentic general intelligence, the true AGI. This is that vision, posted 6 months after the event, so you can see this indeed is the world which has been rapidly emerging and where we are headed. We are in the endgame now, and all the timelines now converge... 0:00 - Opening remarks 2:37 - What is Hyperspace? The world's largest distributed computing network 4:20 - Why thousands are running agents from home 5:01 - Live scale of this peer-to-peer supercomputer 8:10 - From blockchain experiments to an entirely new OS 10:53 - The hard problem: agents across every OS and environment 12:51 - Early bet on spatial UI and what it taught us 14:59 - The paradigm shift: from chatting with models to deploying agents 17:25 - Why siloed AI apps are a dead end 19:30 - Rethinking the browser for an agentic world 22:12 - The Agentic OS: browser + IDE + payments in one stack 24:33 - Unifying all data, compute, and software on one network 28:10 - Spatial interfaces: why the future isn't chat-based 31:38 - Demo: Exa MCP pulling live data straight into Notion 33:01 - Demo: Parallel browsers and CLIs composing together 35:22 - "There is no next IDE", they all collapse into one 35:47 - Memory as an open protocol, not a company lock-in 37:14 - Demo: How users control and shape agent memory 39:33 - Dynamic cognition: agents that learn and orchestrate across CLIs 41:47 - The Matrix: a Google-scale discovery layer for tools 46:10 - Programmable agents, fair-price auctions, and spot compute 49:40 - Agent-to-agent micropayments — killing the ad model 51:52 - Why we need the broadband infra for agentic commerce 54:32 - The Agentic Virtual Machine 57:48 - Building a new blockchain purpose-built for agents 58:30 - Closing remarks and the journey ahead ps: we are at Hyperspace
Varun22,935 Aufrufe • vor 2 Monaten
Introducing Programmer You give it a task. It writes a program. The program runs. Try it:
Varun20,404 Aufrufe • vor 2 Monaten
![it's cooking in testnet currently. -> permissionless, anybody can join -> nearly free agent to agent micropayments at scale -> the chain can theoretically scale upto 10 million transactions per second -> [stateless, zk proofs, verkle, stake-secured routing, proof of intelligence which rewards increasing the network's intelligence]](https://image.24vids.com/tw-2042792773472915930/amplify_video_thumb/2042792707207151616/img/rVu5c4NPDBZdl9Ie.jpg)
it's cooking in testnet currently. -> permissionless, anybody can join -> nearly free agent to agent micropayments at scale -> the chain can theoretically scale upto 10 million transactions per second -> [stateless, zk proofs, verkle, stake-secured routing, proof of intelligence which rewards increasing the network's intelligence]
Varun12,390 Aufrufe • vor 1 Monat
over the labor day weekend i built out an app to analyze my twitter mutuals, using my Hyperspace agentic-os prototype. this is likely the most powerful agentic-os setup every assembled. this goes beyond a single model, or agent, or data source or a single smart CLI tool. this goes beyond hardware, or cloud or distributed networks. it is all of it, combined. the LLM OS is here, Andrej Karpathy. and this combined environment enabled me to build an app I craved for all these years, without writing a single line of code, saving me 150 hours of development work effort. i now have a deep understanding of my twitter mutuals here - and to construct the app - my agentic-os utilized multiple models including Grok Code, GPT-OSS-120B running locally, Qwen2.5 Coder and Google's Nano Banana, multiple orchestrators include Claude Code, 24 AI agents, several MCPs, and specific data from my Twitter archive which I plugged in and other data which it researched, analyzed and aggregated into helping me understand my network here. consider again: the absolute best work from all the top AI firms - all of it, in one actually-useful-integrated-environment. RT if you want this as well. any feature requests ? eg show when a mutual is in the same city in the future.. will DM with access.. this is madness, but it is here 🤖🔥
Varun21,462 Aufrufe • vor 9 Monaten
It is humbling to watch how people are connecting to the Hyperspace network from every corner of the world. Right now, there are over 30,000 live connected nodes ready to serve AI to the world. This is madness! Thank you to everyone running a node 🫡 PS: This is after recent stability fixes on both the network and the points system. We intend to do better.
Varun28,129 Aufrufe • vor 1 Jahr
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