Sumeet Chougule

A ‘hello world’ milestone for Project Suncatcher 🚀 After launching our prototype satellite a few weeks ago, we’ve now successfully sent a command up to orbit, booted up the TPU, ran the prompt, and received our first complete response back on earth - a haiku!

We are announcing three new discoveries in mathematics, computer science, and physics about harmonic analysis, complexity theory, and quantum field theory. openai.com/index/building-sc…

Scan your smart contracts before attackers do. We are opening beta access to QuillShield, our multi-agent auditor for EVM smart contracts, for the first 50 teams. October has made it clear that waiting is no longer an option. In the last few days alone: • PHP: pre-auth remote code execution, CVSS 9.2 • GitLab: CVSS 10, self-hosted servers back to 2014 • Node.js: 10 CVEs in one release • Railway: customer secrets accessed • LiteLLM: credential stealer shipped on PyPI AI helped find some of these bugs, and attackers have the same tools. A bug in a deployed smart contract can drain funds in a single transaction, so we built QuillShield to find them first. Here's what it has done so far: > 25 Critical or High vulnerabilities caught across 155 projects > 497 findings, with 85% of auditor-reviewed ones confirmed valid > 350,000+ lines of Solidity reviewed > 83.8% on EVMBench, against 45.6% for the best published model Here's the beta link: shield.quillaudits.com/

Turns out the future does have flying cars

An agent session ends. The work should carry forward. Worldline turns Claude Code and Codex sessions into a record of actions, changes, costs and open questions then links that record to the next run. Keep the context. Build on the work. getworldline.com

I really felt the AGI profoundly this time

What should the next person inherit from an agent run? More than a transcript: the evidence, decisions and unfinished work. An early look at what we’re building with Worldline.

Introducing Tenet, our first model post-trained for legal. Tenet is a Kimi K3 base that we post-trained with @FireworksAI_HQ on a corpus of publicly available legal data, synthetic data, and human expert data simulating long-horizon legal work. Training increases Tenet's all-pass rate by 82% on LAB and 22% on LAB Contracts relative to the Kimi K3 base model. It achieves state-of-the-art performance on LAB Contracts and places second on LAB. These gains generalize to other leading agentic benchmarks including @mercor's Apex Agents - Corporate Law, @crosbylegal's Redline Bench, and @scale_AI's Professional Reasoning Bench. Tenet is also optimized for token efficiency, operating at less than a fourth the cost of leading foundation models. We additionally post-trained three specialist models for Tenet to use as subagents: 1) M&A Diligence: post-trained with @baseten on our LAB Diligence environment in an RLM harness, this model is optimized for high-scale, long-horizon tasks. 2) Review Tables: trained with @appliedcompute on our Review Table environment, this model is state-of-the-art and cost-effective at high-volume document review and structured data extraction. 3) Firm Knowledge: trained with @EngramLab on our synthetic law firm environment, this model is optimized to learn and search over a firm's knowledge via memory and structured notes. More details on model training, environment design, benchmarking, results, and more in the article by @gabepereyra below. What's next for Harvey’s research? - Scaling LAB to more jurisdictions, practice areas and workflows - Scaling compute to bring new generalist models and capabilities to Harvey More to come soon.

Palantir CEO Alex Karp predicts the future business landscape will be defined by companies that got sovereignty right competing against the frontier labs: “I am quite certain that the companies that get this right will just be the dominant companies, because de facto, it'll be you competing against a frontier lab.” “And you can win because you have access to things that are outside the spectrum, that are outside the model, that are proprietary, that only you understand.” “Or you can selectively use their models, but make sure that alpha is not being migrated out by using an application layer or what is commonly now called an ontology.”