11 Vibe Coding Best Practices
Master building with AI assistants
About 11 Vibe Coding Best Practices
Comprehensive guide covering 11 best practices for vibe coding and building with AI assistants. Teaches how to effectively use AI tools instead of coding from scratch.
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AI Overviews Cut CTR by 23.1% in France
Google launched AI Overviews in France on July 22, 2026. Ahrefs tracked 963 domains in Google Search Console for 28 days before and 9 days after, and the most exposed sites lost 23.1% of their click-through rate. If search is your main acquisition channel, this is the data to read before you plan next quarter: it shows which kinds of pages get answered without the click. Read the study on the Ahrefs blog.
We Pinned Our Model Version. The Provider Deprecated It Anyway.
Pinning a model version does not protect you from deprecation. This Towards Data Science piece argues that the recurring cost of production AI is not inference but re-qualification: the eval reruns, prompt retuning and regression testing you owe every time a model changes under you. It breaks down what that tax covers and how to budget for it before a deprecation notice forces the work on you. Read the guide on Towards Data Science.
An honest side-by-side of two coding agents from someone who actually used both
Pi Agent vs OpenCode after 100+ Hours of Real Use
A comparison written after real sustained use rather than a weekend trial, covering where each agent holds up and where each one falls over. The useful kind of tool comparison: specific about the failure modes, and willing to say when the answer is "it depends on what you are building".
Turn Codex from an interactive assistant into something you can script
Running Codex as a Headless Agent
A practical walkthrough of driving Codex headlessly, so it becomes a programmable step in a pipeline rather than a chat window you sit in front of. The shift from interactive to scripted is where coding agents stop being a productivity toy and start being infrastructure. This covers the mechanics of getting there.
Time to First Token
Time to First Token is a structured 10 week curriculum covering LLM inference serving and optimization, designed around 30 minutes of study a day. It walks through batching, KV caching, quantization, throughput versus latency tradeoffs and the serving stack choices that decide your infrastructure bill. Worth the hour a week the moment self-hosting or inference cost stops being a rounding error.