World Wire

Cohere has released Parse ( parse-v5.0 ) , a document parsing model aimed at high-volume enterprise ingestion. It is a 2.3B-parameter vision language model with an 8,192-token context window and a ~4.6GB footprint, built on Cohere Labs’ North-Micro-Vision-Instruct architecture. Parse takes a PDF, PPT or JPEG page as a base64-encoded data URI and returns Markdown containing text in reading order, tables rendered as HTML, lists, form key-value pairs, image descriptions and bounding box coordinates. There is no separate OCR stage in front of it. Cohere prices the Parse API at $1.50 per 1,000 pages and positions the model on price-performance rather than peak accuracy — a claim the company supports with a self-reported ParseBench score of 79.2 that, as we detail below, measures three of that benchmark’s five dimensions. Is it deployable? Yes , in production. Parse is generally available through the Cohere Parse API, Microsoft Foundry, AWS SageMaker , and single-ten...

Every agent that writes code needs somewhere to run it. That “somewhere” is now a product category with at least a dozen vendors, four incompatible billing models, and marketing pages that quote cold starts measured under conditions nobody publishes. This comparison fixes the units. It covers the five platforms most teams shortlist — E2B , Daytona , Modal Sandboxes , Cloudflare Sandbox SDK , and Vercel Sandbox — along with Runloop , Fly.io Sprites , and Northflank where they change the answer. The four questions that actually decide this Feature matrices for this category are mostly noise. Four properties change architecture, and everything else is a preference: Cold start under concurrency : An agent loop that creates a sandbox per tool call pays this tax thousands of times a day. Filesystem persistence between turns : Does turn 2 see the pip install from turn 1, or does the agent rebuild its world? Egress policy : Can the sandbox reach the inte...

In this tutorial, we use Anthropic’s claude-protein-binder-design dataset, which contains 1,440 AI-designed miniprotein binders tested against 16 targets. Because the release includes both computational predictions and real wet-lab results from two independent labs, we can go beyond simply studying the designs. We evaluate how well structure predictors identify successful binders, whether combining predictions improves performance, how rankings translate into practical testing budgets, and how much disagreement comes from the assays themselves. Also, we train a target-aware classifier to test whether these signals can reliably predict experimental success. Copy Code Copied Use a different Browser import subprocess, sys, warnings, itertools, math warnings.filterwarnings("ignore") import importlib.util _needed = {"huggingface_hub": "huggingface_hub>=0.24", "pyarrow": "pyarrow", "pandas": "pandas", ...