
Perplexity launched Computer for Counsel. It is an agentic AI system built for legal teams. The product extends Perplexity Computer, the company’s LLM-agnostic agentic system. It is available now to Perplexity Enterprise and Max subscribers. Lawyers lose hours to administrative work. Computer for Counsel targets that work directly. Nearly 75% of lawyers call administrative tasks a major time challenge, a Thomson Reuters survey found. The story is mostly architectural. It is an orchestr
Will Perplexity's Computer for Counsel product page be live on perplexity.ai before July 4, 2026?
Resolves by Jul 4, 2026
Perplexity launched Computer for Counsel, an agentic AI system designed to automate administrative workflows for legal teams. The product sits as a research, drafting, and workflow layer that breaks legal tasks into subtasks and routes each to different AI models and data sources, then assembles results with citations linked back to their sources for attorney verification. Nearly 75 percent of lawyers report administrative tasks as a major time challenge, and the system addresses this by connecting to over 400 legal and business tools through an open standard called the Model Context Protocol, as well as premium legal sources like case law databases and contract templates. Unlike traditional legal research platforms, it functions as an orchestration layer built on top of tools lawyers already use rather than attempting to replace existing legal research databases.

When confronted with cancer, Connor Christou fed everything tied tied to his regime — blood results, scan data, wearable output, journal entries — into Claude.

Meta released Astryx this week. It is an open-source design system, currently in Beta. The project grew inside Meta’s monorepo over eight years. Astryx is built on React and StyleX. StyleX is Meta’s compile-time CSS engine. TL;DR Astryx is Meta’s open-source, agent-ready React design system, now in Beta. It pairs StyleX styling with a CSS-variable theme cascade and ten themes. A CLI and MCP server lets AI agents scaffold and document UIs. It is production-teste

In this tutorial, we explore the Open-SWE-Traces dataset as a practical resource for studying and preparing agentic software-engineering trajectories for fine-tuning. We stream the dataset directly from Hugging Face, so we can work with a large dataset efficiently in Google Colab without downloading everything locally. We inspect individual records, normalize multi-turn agent conversations, parse final code patches, extract useful metadata, and build an analysis DataFrame to understand trajecto
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