#claude code

Users will be able to create a podcast from Codex or Claude Code and import it to Spotify

Writing code has always been the most time- and resource-intensive task in software development. AI is changing that, and faster than most engineering organizations are prepared for. Tools like Claude Code and Cursor are already handling significant parts of code construction, freeing developers to spend more time on requirements, architecture, and design. But that shift creates a new challenge nobody is talking about enough. As AI takes on the heavy lifting, the skills that matter most are moving upstream: how to provide the right context for a prompt, how to evaluate what the model produces, and how to understand a problem deeply enough that you can’t be fooled by a confident but wrong answer. This piece explores those three skills and why developers who master them will have a significant edge over those who don’t. Beyond coding: Mastering the art of the prompt Software translation tools such as compilers and assemblers map a high-level description of code to a lower-level represent

Also, rate limits will double for Pro and Max users of tools like Claude Code.

Learn how to connect Claude Code to Discord locally, pair your account, control access, and keep the bot running reliably.

Improve Claude Code performance by having it validate its own work The post How to Make Claude Code Validate its own Work appeared first on Towards Data Science.

Turn Claude Code into your AI coding partner with these 5 hands-on projects, from beginner-friendly builds to advanced agent workflows.

Claude Code token costs usually come from bloated context, not just long prompts. These 7 practical tactics help reduce waste without hurting quality.

Anthropic, of all companies, just shipped three quality regressions in Claude Code that its own evals didn’t catch. Think about that. Three regressions over a short six weeks, by the most sophisticated eval shop in AI. If this can happen to Anthropic, it most definitely can happen to you, and it likely will. In a refreshingly candid postmorten, Anthropic walked through what went wrong. On March 4, the team flipped Claude Code’s default reasoning effort from high to medium because internal evals showed only “slightly lower intelligence with significantly less latency for the majority of tasks.” On March 26, a caching optimization meant to clear stale thinking once an idle hour passed shipped with a bug that cleared it on every turn instead. On April 16, two innocuous-looking lines of system prompt asking Claude to be more concise turned out to cost 3% on coding quality, but only on a wider ablation suite that wasn’t part of the standard release gate. From inside the org, none of it trip

Anthropic, of all companies, just shipped three quality regressions in Claude Code that its own evals didn’t catch. Think about that. Three regressions over a short six weeks, by the most sophisticated eval shop in AI. If this can happen to Anthropic, it most definitely can happen to you, and it likely will. In a refreshingly candid postmorten, Anthropic walked through what went wrong. On March 4, the team flipped Claude Code’s default reasoning effort from high to medium because internal evals showed only “slightly lower intelligence with significantly less latency for the majority of tasks.” On March 26, a caching optimization meant to clear stale thinking once an idle hour passed shipped with a bug that cleared it on every turn instead. On April 16, two innocuous-looking lines of system prompt asking Claude to be more concise turned out to cost 3% on coding quality, but only on a wider ablation suite that wasn’t part of the standard release gate. From inside the org, none of it trip