Best of AI-Assisted Development — March 2026

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    Article
    Avatar of devblogsDevBlogs·10w

    Awesome GitHub Copilot just got a website, and a learning hub, and plugins!

    The Awesome GitHub Copilot community repo has grown to 600+ resources including 175+ agents, 208+ skills, 176+ instructions, and 48+ plugins. Microsoft is launching a dedicated website at awesome-copilot.github.com with full-text search, modal previews, and one-click VS Code installs. A new Learning Hub explains core customization concepts like skills, plugins, and hooks. Awesome Copilot is now a default plugin marketplace for both GitHub Copilot CLI and VS Code, allowing installs via a simple CLI command. New resource types include Agentic Workflows (natural-language GitHub Actions running AI agents autonomously) and Hooks (event-triggered automations during Copilot coding sessions).

  2. 2
    Article
    Avatar of collectionsCollections·12w

    Cursor AI Coding Assistant Now Available in JetBrains IDEs

    Cursor AI coding assistant is now available inside JetBrains IDEs (IntelliJ IDEA, PyCharm, WebStorm) via the Agent Client Protocol (ACP). The integration gives developers access to frontier models from OpenAI, Anthropic, Google, and Cursor, along with secure codebase indexing and semantic search. It is free for paid Cursor plan subscribers and requires JetBrains IDE version 2025.3.2 or later with the AI Assistant plugin. The move is part of JetBrains' open ecosystem strategy to avoid vendor lock-in by supporting multiple AI agents.

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    Article
    Avatar of nodelandAdventures in Nodeland·10w

    Software Engineering Splits in Three

    AI-assisted coding is reshaping enterprise software development by shifting the bottleneck from implementation to judgment. The author argues that software engineering is splitting into three tiers: tech companies where AI multiplies senior engineers, large enterprises that need platform guardrails and fractional senior expertise, and small businesses now able to afford custom software via 'software plumbers.' Key implications for enterprise leaders include investing in platform engineering, evolving SRE roles, rethinking the talent pipeline for future senior engineers, and shifting consulting models from body-shopping to fractional senior expertise. The buy-vs-build calculus also changes as custom software becomes cheaper, but judgment remains the scarce resource.

  4. 4
    Video
    Avatar of vscodeVisual Studio Code·12w

    Copilot CLI in VS Code

    GitHub Copilot CLI now automatically detects open VS Code instances and connects to them, allowing developers to share the same session context across both environments. This means you can start a Copilot session in the CLI, then continue it in VS Code and switch back and forth while maintaining full context, making it easy to view and edit files in the IDE while still using the CLI workflow.

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    Article
    Avatar of phProduct Hunt·10w

    OpenAdapter: Every open-source SOTA model in your editor

    OpenAdapter is a single subscription service providing access to multiple open-source SOTA AI coding models (Minimax, GLM, Qwen, Mistral, Kimi, DeepSeek) through one OpenAI-compatible endpoint. It eliminates the need for multiple AI coding subscriptions by letting developers configure their editor once and switch models from a dashboard. Compatible with Cursor, Claude Code, Windsurf, Cline, Aider, and other IDEs.

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    Article
    Avatar of hnHacker News·8w

    We Rewrote JSONata with AI in a Day, Saved $500K/Year | Reco

    Reco's principal data engineer used AI to rewrite JSONata (a JavaScript-based JSON query language) as a pure-Go library called gnata in about 7 hours, spending $400 in AI tokens. The original setup ran jsonata-js pods on Kubernetes, costing ~$300K/year and adding ~150 microsecond RPC overhead per evaluation across billions of events. gnata uses a two-tier evaluation architecture: a fast path for simple expressions that operates directly on raw JSON bytes with zero heap allocations, and a full path with complete JSONata 2.x semantics. A streaming layer batches N expressions against each event, reading raw bytes only once. After a week of shadow-mode validation with 1,778 test cases and 2,107 integration tests, gnata replaced the RPC fleet entirely, delivering 25-1000x speedups. Combined with a rule engine refactor enabled by gnata's batch evaluation capabilities, the total savings reached $500K/year in under two weeks of work.

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    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·9w

    Anatomy of the .claude/ Folder

    A comprehensive guide to the .claude/ folder structure used by Claude Code. Covers CLAUDE.md (the main instruction file), CLAUDE.local.md for personal overrides, the rules/ folder for modular scoped instructions, commands/ for custom slash commands with shell injection, skills/ for auto-invoked reusable workflows, agents/ for specialized subagent personas with isolated context, and settings.json for permission control. Also explains the global ~/.claude/ directory for cross-project preferences and session memory. Includes a practical step-by-step setup progression and a full folder structure reference.

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    Article
    Avatar of syncfusionSyncfusion·9w

    What Developers Say AI Is Actually Good For in Software Development

    AI in software development hasn't replaced developers — it has reduced friction. Developers report AI is most useful for automating repetitive tasks like boilerplate code, documentation, test writing, and project scaffolding. Junior developers get unblocked faster, while senior developers use AI as a thinking partner to review logic and spot edge cases. AI falls short on architectural decisions, business context, and security-critical logic. The post concludes with a promotion of Syncfusion Code Studio as a practical example of AI-assisted tooling for enterprise development.

  9. 9
    Article
    Avatar of freecodecampfreeCodeCamp·9w

    The Claude Code Handbook: A Professional Introduction to Building with AI-Assisted Development

    A comprehensive handbook covering Claude Code, Anthropic's AI-powered software development agent. It walks through installation, VS Code setup, subscription tiers, prompt discipline, Plan Mode, feature-by-feature development, token economics, and the internal agent loop mechanics. The guide targets both experienced developers looking to multiply their output and non-technical builders wanting to create software without prior coding experience. Key practices covered include using Plan Mode for 80% of sessions, writing Product Requirements Documents, building incrementally, managing context windows, and understanding how Claude reads codebases selectively via tool calls.

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    Article
    Avatar of vigetViget·10w

    Using Claude Code More Intentionally

    A practical guide to setting up Claude Code as a proper development collaborator rather than an ad-hoc chat tool. Key strategies include writing a thorough CLAUDE.md as an onboarding document, using .claudeignore to keep context clean, externalizing plans and artifacts to disk for persistence across sessions, building reusable 'skills' (markdown-defined repeatable processes stored in .claude/skills/), integrating CLI tools and MCP servers for structured external access, and using hooks to automatically run tests or commits after Claude finishes tasks. Also covers remote control via the Claude app for supervising long-running jobs and model switching (Haiku/Sonnet/Opus) to balance cost and capability. The core thesis: invest in the environment and infrastructure around Claude Code, not just the prompts.

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    Article
    Avatar of avalonia-blogAvalonia UI Blog·10w

    Accelerate Showcase: Built with AI, Designed with Stitch

    A product manager at Avalonia UI built a polished desktop showcase app without writing any code manually, using Claude Code, Google Stitch for UI design, and Avalonia's DevTools and Parcel MCP servers. The workflow involved generating component skills from documentation, prompting Claude to design and implement an MVVM app targeting .NET 10 for Windows/macOS/Linux, and using the Parcel MCP server to handle cross-platform packaging including macOS code signing and notarization. The result was a shippable app produced in roughly an hour, compared to an estimated week of designer-plus-engineer effort. The post highlights where AI is genuinely useful for Avalonia: building applications with it rather than building the framework itself.

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