I kept running into the same problem: IGCSE students often have to use several different platforms just to manage their learning. One for schoolwork. Another for past papers. Another for marking. Another for tracking progress.
This is an AI-generated summary. ShortSingh links to the original source for the complete article.
AI coding agents can make large changes to a repository very quickly. But an important question remains: Did the agent actually change only what it was supposed to change? I built Vericore to explore this problem. Vericore is an open-source verification layer for AI-assisted development. The workflow is: Understand → Prepare → Agent Changes → Verify Before the agent changes code, Vericore builds repository context and creates a Change Contract.
Choosing between DeepSeek R1 vs OpenAI o3-mini is a critical decision for developers integrating reasoning APIs into software applications in 2026. When it comes to reasoning APIs, these are the two strongest candidates most teams end up weighing. Both models excel at complex tasks, code generation, mathematical analysis, and structured logic. However, they operate on different pricing structures, reasoning token methods, latency patterns, and structured data validation limits. This guide compares the two in detail to help you choose the best API for your developer workflows.
Disclosure: I publish and maintain the Actor used here (publicrecords/etsy-search-scraper on Apify Store). This is the publisher account. You have an Etsy search query and you want the results as rows you can sort and filter, not a screenshot of page one. Here's the shortest route I know, with a real run as proof. Each Etsy search result comes back as one row with listing_id, title, price, currency, shop_name, shop_url, rating_value, review_count, badges (bestseller, star_seller, etsys_pick, popular_now, free_shipping), position and page.
Most CI pipelines run tests. Far fewer pipelines actually stop bad code from reaching production. The difference is a quality gate: a check that must pass before the pipeline is allowed to move to the next stage. In this tutorial, you'll build a GitHub Actions pipeline with four gates: Lint: code style and obvious errors Unit tests + coverage threshold: fail if coverage drops below a minimum API tests: verify the running service behaves correctly Manual approval: a human signs off before production By the end, a failed gate will block deployment automatically. A GitHub repository with a small
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