ShardStitch
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Codex by OpenAI
ShardStitch is a local-first recovery and continuity layer for AI-assisted software development.
It preserves project state, changed files, verified facts, failed attempts, and next steps when an AI coding session gets stuck, reaches a limit, loses context, or needs to move to another tool.
ShardStitch supports cross-tool workflows across Claude Code, Codex, Cursor, Gemini, Roo Code, and other AI coding tools. It helps developers recover work safely, verify what actually changed, and continue without restarting from an unreliable session summary.
Trained on billions of lines of public code, GitHub Copilot puts the knowledge you need at your fingertips, saving you time and helping you stay focused.
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ShardStitch's answer
ShardStitch focuses on recovering the actual project state, not just preserving conversation history. It records verified files, git changes, failed attempts, known constraints, and next steps so coding work can continue after an AI session fails or moves to another tool.
ShardStitch's answer
ShardStitch is local-first, tool-agnostic, and designed for recovery. It helps developers continue from verified project state across multiple AI coding tools instead of relying on one provider’s chat history or memory system.
ShardStitch's answer
AI-assisted developers, software engineers, technical leads, and small development teams who use coding agents and need reliable recovery when sessions get stuck, lose context, reach limits, or switch tools.
ShardStitch's answer
ShardStitch was created around a practical problem: AI coding sessions can make useful progress but still leave developers unsure what changed, what failed, and how to continue. ShardStitch turns that uncertain handoff into a structured, verifiable continuation process.
ShardStitch's answer
ShardStitch is built around local filesystem and git-state inspection, structured Markdown and JSON recovery packets, deterministic verification, and adapters for supported AI coding tools.
ShardStitch's answer
ShardStitch is currently used primarily by individual developers, technical practitioners, and small teams experimenting with AI-assisted software development. No enterprise customers are publicly named at this time.
It definitely increases my productivity.
Based on our record, GitHub Copilot seems to be more popular. It has been mentiond 388 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Snagly is a free, MIT-licensed set of 30 skills for AI coding agents — GitHub Copilot, Claude Code, Cursor, Codex and 70+ others — that turn "an AI that can drive a browser" into "an AI that tests like a QA professional." A skill, if you haven't met them yet, is a reusable instruction set that teaches the agent a specific working method — when to use it, what rigor it requires, what evidence to capture, and what... - Source: dev.to / 30 days ago
Where llms.txt genuinely gets read is a different layer: coding and agent tooling — Cursor, Claude Code, GitHub Copilot, Windsurf — pulling a documentation site's pages with less token waste, plus emerging agent protocols like OpenAI's Agents SDK. That's real, and it's growing fast. - Source: dev.to / 2 months ago
You need an active GitHub Copilot subscription. Plans are available at individual, business, and enterprise tiers at github.com/features/copilot. Once active, all tools use your GitHub account credentials. - Source: dev.to / 3 months ago
For over a decade PhpStorm (starting in my WordPress era) and later WebStorm have been my main IDEs for web development. So when GitHub Copilot launched, it was a natural choice to try it out in WebStorm. It was one of the first AI coding tools I used, and it had a big impact on how I thought about AI-assisted coding. - Source: dev.to / 3 months ago
Before we get into it, there are some things about AI usage worth addressing. I've had my fair share of scepticism in the past, but recent model releases have made it increasingly difficult to argue that AI isn't a viable tool for the majority of workstreams, including building user interfaces. Most large language models are trained on public data scraped from the internet, which means your internal design system... - Source: dev.to / 3 months ago
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