Claude AI: Guides and Practical Know-how | Page 3 | mironsoft Blog
Claude AI
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Feb 25, 2026
A major version upgrade of a core library is rarely a simple composer update or npm install. Claude can extract breaking changes from changelogs, identify affected code in your own project, and build a step-by-step migration plan, but it does not replace careful verification. This article shows the practical process and its limits.
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Feb 23, 2026
Claude Code runs at its core in the terminal, but the official extensions for VS Code and JetBrains IDEs like PhpStorm bring diff views, context menus, and debugger integration directly into your familiar editor. Anyone who only knows Claude Code through the plain command line is missing out on noticeable convenience in daily development work.
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Feb 23, 2026
Large Java enterprise codebases with grown Spring configurations, decades old legacy code and missing test coverage cannot be modernized in a single step. Claude helps systematically understand such codebases, resolve dependency injection conflicts, and break migrations down into manageable, safely testable steps.
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Feb 20, 2026
Classic RAG retrieves documents once and answers the question with the context it found. Agentic RAG goes a step further: Claude itself decides whether a first retrieval pass is enough, whether a follow-up query with refined search terms is needed, or whether multiple distinct knowledge sources must be combined. This article shows how to build such pipelines with clean tool definitions, where their limits lie, and how to debug multi-step chains when something goes wrong.
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Feb 16, 2026
Old Magento modules without documentation cost developer time because nobody remembers why certain code exists. An AI assistant like Claude can summarize an unfamiliar module, trace a call chain across multiple classes, and offer plausible historical explanations. What matters most is checking every claim against the actual code, the Git history, and existing tests before it shapes documentation or decisions.
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Feb 11, 2026
GEO is the discipline of shaping content so that ChatGPT, Perplexity, Google AI Overviews and other generative systems select it as a source, summarize it correctly and cite it. Unlike SEO, GEO does not aim for a position in a results list, it aims for a place inside a generated answer, where users often never visit the website yet still build trust and brand awareness.
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Feb 4, 2026
E-E-A-T signals were originally developed as an evaluation framework for human quality raters at Google, but they now measurably affect answer selection in generative AI search systems as well. Experience, Expertise, Authoritativeness and Trust increasingly help decide which source a language model cites when several topically similar pieces of content are available, and they can be deliberately strengthened through concrete, technically implementable signals.
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Feb 2, 2026
A chart with the wrong chart type obscures relationships instead of revealing them. Claude helps choose the right visualization format, generates working code for Matplotlib, Chart.js and SQL aggregations, and supports building automated reporting dashboards. This article covers the complete path from data source to presentation ready graphic.
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Feb 2, 2026
Complex development tasks are rarely solved reliably with a single prompt. An agent pipeline breaks such a task down into clearly separated stages with defined data flow, built in quality gates and regulated error handling, so every stage stays traceable and errors do not silently propagate all the way to the end.
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Feb 1, 2026
A flawed database schema costs a multiple of the fix that would have been possible at the design stage. Database schema design with AI review uses Claude to spot normalization mistakes, missing constraints and inefficient index strategies before the first migration runs in production.