World Wire

Most teams treat ‘which model’ as the important decision. The harness engineering literature keeps pointing somewhere else. In LangChain’s Terminal-Bench experiment , changing only the harness—same model throughout—moved a coding agent from roughly 30th place into the top 5. That result reframes the question. If the harness decides quality, then how you run the loop becomes an architecture decision, not a deployment detail. Paul Iusztin’s open-source course Building a Coding Agent From Scratch builds a Python agent called Decode . Published through Decoding AI , it separates three run modes. Each mode has a different latency profile. Each one therefore wants a different inference provider. One headless core, three shapes The center of the system is a headless harness with no interface of its own. Inside it runs the agent loop every harness shares: the LLM picks an action, a tool executes, the observation feeds back. Everything reads from and write...

The ‘neocloud’ label now covers five companies with very different business models. CoreWeave and Nebius are public companies that publish quarterly results and file reports with the SEC (CoreWeave on Form 10-Q, Nebius as a foreign private issuer on Form 6-K). Lambda and Crusoe are private and heading toward IPOs. Groq has run GroqCloud on its LPU architecture since 2024; after licensing that technology to NVIDIA in December 2025, the independent Groq refocused entirely on inference-cloud infrastructure. This article compares them on what actually matters to a buyer: published pricing, deployed and contracted power, hardware roadmap, contract structure, and independent quality ratings. TL;DR CoreWeave is the only Platinum-rated GPU cloud in SemiAnalysis ClusterMAX 2.0 and commands premium list pricing on H100, H200, and B200. Nebius is the only provider here publishing B300 on-demand pricing; Lambda publishes the lowest B200 on-demand rate. Lambda still pub...

In this tutorial, we explore AutoFigure as a practical toolkit for generating scientific figures directly from text descriptions, paper-like content, and structured methodological explanations. In this tutorial, we set up the complete AutoFigure environment, fix dependency issues such as Pillow compatibility, and prepare the required rendering tools for SVG and PNG outputs. We then build a custom reference figure, configure an API-backed generation workflow, and use AutoFigure to convert a detailed agentic document intelligence pipeline into a publication-style scientific diagram. Along the way, we also test offline SVG rendering, inspect the generated files, create a sample paper and PDF, and export the final outputs to a reusable gallery and a zip archive. Copy Code Copied Use a different Browser import os import sys import json import time import glob import shutil import textwrap import subprocess import importlib from pathlib import Path from getpass import getpass REPO_URL ...