Shai-li Ron

@ryanjdaniels @Lux_Capital @IndexVentures @01Advisors @sequoia @eladgil @BainCapVC Incredible!

📣 Update: we raised our $60m Series B from @Lux_Capital, @IndexVentures and @01Advisors, with participation from @sequoia, @eladgil and @BainCapVC . When we came out of stealth 283 days ago, we had negotiated contracts worth $30m for our clients. As of last month, that number is over $1 billion. We work with the most ambitious companies in the world, including @tryramp, @clay and @RogoAI. Today, we want you to hear from some of them directly. Contracts are the rails of commerce. @crosbylegal is a hybrid AI law firm that gets them signed 80% faster. We’re announcing our Series B to keep scaling the dream law firm.

@annarmonaco @paradigmai So good!!!

Today we’re launching the newest version of @paradigmai When we started Paradigm, the goal was never to tack AI onto existing spreadsheets. It was to build a new type of interface that does the work for you. Now we’re pushing that vision much further. Workflows turn Paradigm into a system that runs research processes for you. Connect your CRM, existing spreadsheets, Slack, email, and internal data, and let Paradigm continuously run the research workflows your team already does. Same intuitive interface. But now a system of action. If you tried Paradigm before, try it again. Manual research is now a competitive liability.

The future economy will be denominated in compute cycles more than in human labor. In a world where AI drives the majority of electricity consumption and GDP, compute would be the natural collateral for money: an open, auditable, AI-native currency, produced directly through inference and training. Since the inception of Bitcoin, an outstanding open problem in distributed systems was whether it is possible to implement Proof-of-Work consensus on top of real-world computation, as opposed to useless random hashing. While long considered impossible, last year we answered this question affirmatively. Pearl’s mathematical breakthrough enables every GPU cycle powering AI systems to simultaneously produce a native digital currency: ¶PRL. What this means is that the hundreds-of-billions (and soon trillions) of dollars of compute being deployed for AI workloads will double--for effectively free--to secure @prlnet's Proof-of-Work chain; All the properties of Bitcoin, but secured as the by-product of AI inference and training, i.e., by the native operation of GPUs: matrix-multiplication (GEMM). Pearl changes the unit economics of LLMs, which are are fundamentally non-fungible, and will shift a portion of the wealth generated by AI back to users – who drive production, model improvement and demand, yet currently capture none of the upside of the AI era. We’ve spent the last year turning this “2-for-1” breakthrough into a working infrastructure, building from the linear algebra down to the CUDA kernels, alongside world-class mathematicians and low-level engineers. Today, we’re excited to announce that @prlnet is ready, and will soon support state-of-the-art LLM serving, through vLLM and SGLang plugins. Running AI workloads on Pearl transforms AI compute from a sunk expense into an AI-native asset, anchored directly to the production of intelligence. If you’re interested/skeptic or ideally both – we’ve published our next tranche of open problems as a collaborative Polymath challenge – containing math, systems and economics questions we’re grappling with next. We invite you to tear it down, prove it or propose better implementations: pearlpolymath.com. #AIMoney #ProofOfInference

@_orcaman You’re hilarious

GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill is the last name your grand-grandchildren are going to have if everyone decides to keep both family names after getting married

Over the last few months we started building our research team at Cognition and we've come a long way! It's been exciting to figure out what it takes to build a large-scale post-training stack from scratch and push towards the frontier. My personal take is it's been easier than expected, e.g. we were surprised to match Opus 4.5 which seemed so far way just 3 months ago. We definitely still got lots to figure out but the slope is high and this model is just the beginning.

Announcing Nimble! The web holds the data to help AI take the next leap, but it isn’t a database. So we built a product that makes it behave like one. We’ve raised $75M from @NorwestVP, @databricks, and leading VCs to build a system that enables anyone to create live datasets from the web, instantly queryable by AI Agents.

Instead of a French or an English garden, the move should be an Israeli garden - grow crops in the desert with completely new infrastructure (drip irrigation, desalinated water etc)

@nimble_data @NorwestVP @databricks Nimble 💪🏼💪🏼💪🏼

@simile_ai Excited for what’s next!!

Spector brothers > Wright brothers

@DecartAI No team like decart

Introducing Lucy 2.0: a World Editing Model running at 1080p, 30FPS in realtime. Read the technical report, API access, and try the live demo 🧵

The 'S' in ClawdBot stands for security 😅

@_orcaman @openwork_ai @awscloud @deepseek_ai @openrouter @LiteLLM Moving fast!!

Boy, have we got news for you! 🧑‍🍳 The @openwork_ai team is happy to announce: - Amazon Bedrock integration, contributed by our friends at @awscloud (thanks guys!) - Native @deepseek_ai integration - Integration with @openrouter and @LiteLLM! Here's how what it looks like >>

@_orcaman @openwork_ai Too good

Today we are launching @openwork_ai, an open-source (MIT-licensed) computer-use agent that’s fast, cheap, and more secure. @openwork_ai  is the result of a short two-day hackathon our team decided to hack, which brings together some of our favorite open source AI modules into one powerful agent, to allow you to: 1. Bring your own model/API key (any provider and model supported by @opencode is supported by Openwork) 2. ~4x faster than Claude for Chrome/Cowork, and much more token-efficient, powered by dev-browser by @sawyerhood (legend) 3. More secure - contrary to Claude for Chrom/Cowork, does not leverage the main browser instance where you are logged into all services already. You login only to the services you need. This significantly reduces the risk of data loss in case of prompt injections, to which computer-use agents are highly exposed. 4. Free and 100% open-source! You can download the DMG (macOS only for now) or fork the github repo via the link in bio (@openwork_ai). Let us know what you think (or better, send a pull request)!

Kalshi recently raised $300M+ at $5B from Sequoia, a16z, Paradigm and others. Since then, we've grown over 3x, hit $50B of annualized volume, and became the largest prediction market in the world. And today…Kalshi goes global. 140+ countries. 1 liquidity pool.

Introducing Axis: the first AI model that generates regulatory DNA elements and predicts their function. Gene therapies suffer from poor efficacy, toxicity & specificity. Models like Axis can help overcome such risks. Axis beats Google DeepMind's AlphaGenome at predicting regulatory element binding activity by 6.7%.

Epoch report projects that AI models of 2030 will be trained with 1,000x more compute than today… wonder if cards will really be that much faster epoch.ai/files/AI_2030.…