ShardStitch Rebuilds AI Coding Work After a Session Fails

A coding session can fail without losing any files. The chat may hit a usage limit, become too large to follow, get deleted, or simply leave behind a summary that sounds more complete than the working tree really is. The result is an awkward recovery task: determine what changed, what was tried, what still fails, and what the next AI tool can safely assume.

ShardStitch is built for that gap. It is a local-first layer for AI coding memory, recovery, verification, and handoff across tools such as Claude, Cursor, Codex, Gemini, and Roo Code. Its central idea is straightforward: the next session should start from project evidence rather than from a transcript or the previous model's confidence.

The problem is not remembering the conversation

A long AI coding chat contains useful decisions, but it also contains guesses, abandoned approaches, repeated context, and claims that were never checked. A model can say that tests passed while the diff still contains an outdated assertion. If that statement is copied into a new session as fact, the next tool inherits the mistake.

ShardStitch's proposed answer is to split the working state into different kinds of information. Files, Git changes, commits, local notes, and other project state become verified disk facts. Statements made by the previous AI remain inferred claims unless the available evidence supports them. Failed attempts, decisions, risks, and the next action are carried forward separately.

That distinction is the product's most important feature. This is not primarily a longer memory for one chatbot. It is a way to make an AI handoff less dependent on what the old session said about itself.

What a recovery looks like

The intended workflow starts after something has gone wrong. A session stalls, dies, reaches a limit, or becomes too cluttered to continue. ShardStitch scans the local project, including the Git diff, changed files, recent commits, notes, and tool logs. It then maps the state into a continuation packet.

The packet is meant to contain the facts that survived the old session, the decisions that still matter, the approaches that failed, and one small next action. It can also identify risk areas, such as a file with a wider dependency impact than its name suggests. The next AI receives a compact working brief rather than the entire old transcript.

The handoff can target a fresh session in the same tool or a different one. The landing page describes pickup files and MCP paths for supported integrations, alongside clipboard-based handoff for web chats. The product lists 31 handoff targets and 49 MCP tools, although those figures describe the developer's current product scope rather than a guarantee that every target offers the same depth of integration.

There is also a capture workflow for a live session. ShardStitch can package the current state before context becomes unusable, rather than waiting for a crash. The practical value is less about preserving every sentence and more about preserving the unfinished task in a form another tool can inspect.

Local state is the organising principle

ShardStitch positions itself outside the individual AI coding tools. Memory, verification, routing signals, recovery state, and the next action are kept with the project context, so a change of tool does not necessarily mean starting from an empty prompt.

The local-first design matters here. The developer says recovery uses information on the machine, including project files and Git state, and that code and conversations are not uploaded by default. License validation still uses a network connection, and optional features such as chat-share import or a configured cloud model can also involve network access. That is a more useful privacy description than simply calling the product offline.

The product runs on macOS, Windows, Linux, and the web. It is paid software. Its intended audience is developers who move between AI coding tools or regularly deal with interrupted sessions, stale context, and untrusted summaries.

Where it fits, and where it does not

The narrow focus is also the main limitation. ShardStitch depends on surviving project evidence. If the important context exists only in a lost conversation and was never reflected in files, commits, notes, or other readable state, it cannot reconstruct that knowledge from nowhere. Its verification model can label a claim as needing checking, but it cannot make an unrecorded decision factual.

It is also not a general project knowledge graph or a universal live index of a codebase. The workflow is centred on recovery and continuation at the point where an AI session has become unreliable. Developers who use one tool, keep short sessions, and already record decisions in project files may have little need for another layer.

For developers working across Claude, Cursor, Codex, Gemini, Roo Code, and similar tools, ShardStitch is best understood as a recovery and routing layer rather than an AI assistant. It is for people who need the next session to know what actually changed, what remains uncertain, and what to check first. It is not for anyone expecting a dead chat to be restored in full, or for a system that can replace disciplined project records.