Everything I'd send you, in one place

jev-router

Open-source tool that routes each Claude Code task to the lowest-cost model suited to it, to reduce token spending.

PyTorch

The most widely used open-source framework for building and training AI models, with free tutorials, documentation and pre-trained models. Most AI research papers today are built on it.

Why I recommend it: Free and open source, now governed by the Linux Foundation's PyTorch Foundation — but Meta created it and remains its biggest contributor, so its direction still reflects big-tech priorities.

TensorFlow

Google's open-source framework for building and training AI models, with free guides and pre-trained models. Once the industry standard, it's now used less in new research than PyTorch but still powers many production systems.

Why I recommend it: Free and open source, but it's a Google project — its development priorities follow Google's. For a new learner, PyTorch is the more common starting point in 2026.

NVIDIA CUDA Toolkit

The official free download and documentation for CUDA, Nvidia's platform for running AI and scientific computing on its graphics chips. Includes compilers, libraries and learning guides.

Why I recommend it: The toolkit itself is free, but it only runs on Nvidia's own chips — learning CUDA ties your skills to one company's hardware. Nvidia dominates AI chips, so that trade-off is real but worth knowing about.

Print attractive ASCII tables in Python with PrettyTable

PrettyTable is a free, open-source Python library for drawing formatted ASCII tables in a terminal or text output. This post from Open-source Projects shows what it does and links to the GitHub repo.

Why I recommend it: Free open-source library (MIT license). The link goes to a blog post on opensourceprojects.dev; the project itself lives at github.com/prettytable/prettytable and pypi.org/project/prettytable. The blog runs on ads and sponsorships.

AWS re:Post

AWS's question-and-answer community with expert-reviewed answers and knowledge-center articles on AWS services.

Antithesis Documentation

The documentation for Antithesis, an autonomous testing platform that continuously explores a simulated copy of a software system to find bugs. The docs cover setup with Docker Compose or Kubernetes, writing test templates, fault injection, the "multiverse" view of execution branches, and using Antithesis alongside AI coding tools.

WebMCP — Chrome for Developers

Chrome's documentation for WebMCP, a proposed web standard that lets websites expose structured tools to AI agents and assistants directly from the browser, so agents can act on a site through defined interfaces instead of simulating clicks.

Good Architecture Is a Function of Time

Essay arguing that software architecture is about making good decisions easier on a timeframe you can reason about — startups, banks, open source projects and large companies each face different time horizons and constraints, and many architecture disagreements are really disagreements about what timeframe is reasonable.

Review GitHub Pull Requests with Codex

OpenAI's official guide to configuring Codex code and security reviews for GitHub pull requests, including automatic reviews and custom repository instructions.

Theorem — Verified Software Stack

A research and engineering effort working toward a shared, formally verified software stack built up from its smallest pieces, starting with verified sandboxes that AI agents cannot escape or game.

AWS Builder Center

Amazon Web Services community hub with builder articles, projects and learning content for developers.

Mouse — Coding Agent for Your Phone

A coding agent that runs your code project in the cloud from your phone and brings back changes for you to review.

Why I recommend it: Company product page. Check what the free plan covers; giving any agent access to your code carries risk.