What is OAuth?

What is OAuth?
4 minutes by Blaine Cook

In this post Blaine, creator of OAuth, explains OAuth in plain terms. It is a standard way to let someone else act on your behalf online, with your permission, without sharing your password. Using examples like Twitter and OpenID Connect, he shows why OAuth exists, how it solves real problems, and why its core idea—delegating access securely—is simple, even if the standard itself seems complicated.

Unblocked: Save time and tokens with better context.
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Give Cursor, Claude, and Copilot the organizational knowledge they need to generate code that reflects how your system actually works. Unblocked synthesizes your code, PR history, conversations, docs, and runtime signals to surface the right context for each task.

How I use Claude code
13 minutes by Boris Tane

Boris developed a workflow for AI-assisted coding that separates planning from execution. First, he researched the codebase deeply, then created a detailed plan in a markdown file. Through iterative annotation, he corrected assumptions and added domain knowledge. Once the plan was finalized, the AI implemented it while Boris supervised. This structured approach ensures better results, reduces errors, and keeps him in control of architecture and product decisions.

YAML? That’s Norway problem
16 minutes by lab174

A deep dive into YAML’s Norway problem: why the country code NO gets parsed as false, its history from YAML v1.0 to v1.2, and why popular libraries still exhibit this behavior in 2026.

Hybrid quota-linear rate limiter
6 minutes by Tony Finch

Tony examines rate limiting algorithms, highlighting the limits of linear rate limiters and fixed-window quotas. He introduces a hybrid quota-linear approach that balances bursty and smooth traffic, aiming to enforce request quotas more precisely with minimal storage. He argues that simpler linear limiters, which track long-term averages, often perform better, avoiding wasteful complexity and unhealthy client request patterns.

ASCII characters are not pixels: A deep dive into ASCII rendering
32 minutes by Alex Harri

How to build an image-to-ASCII renderer that produces sharp edges rather than blurry ones. The key idea is treating ASCII characters as shapes, not pixels, by representing each character as a numerical vector describing how much it fills different regions of its cell. Better character picks come from matching these shape vectors to sampled regions of the source image. Contrast enhancement then sharpens boundaries between regions by compressing darker sampling values, making the final output far more readable.

And the most popular article from the last issue was:

• How to make architecture decisions