You’re three hours into debugging an authentication issue. You open a new chat with your AI assistant and type:
“I’m building a SolidJS app with a Go backend on Cloud Run. We use Firebase Auth with WorkOS for enterprise SSO, Supabase for Postgres, Turso for edge SQLite, and Backblaze B2 for file storage. The issue is…”
You’ve typed some version of this paragraph dozens of times. In a new tool or a new session, you type the same explanation again.
This is common with AI assistants in 2026. ChatGPT, Claude and Gemini often start a new chat without your project context. Some now have memory features, but that memory stays inside the one tool. What you told Claude isn’t available in Cursor or ChatGPT.
Ditto keeps that context in one memory that every tool can use.
The Real Cost of Context Loss
Context loss costs developers in three ways:
Time. A “context setup” message often runs to a few hundred words. You write one for every new chat, every day, in every tool. That time goes on telling the AI things it should already know.
Quality. When you rush the context setup (and you will, because it’s tedious), the AI gives worse answers. It suggests React patterns when you’re using SolidJS. It recommends Express when your backend is Go. It proposes PostgreSQL schemas when your data lives in Turso. Bad context in, bad advice out.
Continuity. You had a productive debugging session last Tuesday. The AI helped you trace a race condition in your SSE streaming pipeline. Today the same bug resurfaced in a different form. Can you find that conversation? Can the AI build on what you found together? Not if that context is gone.
How Ditto Solves This for Developers
Ditto is built around one premise: every conversation should make the next one better.
Here’s what that looks like in a developer’s daily workflow.
Your Stack Is Always in Context
The first time you tell Ditto about your tech stack, it extracts subjects (“SolidJS”, “Go”, “Cloud Run”, “Firebase Auth”, “Supabase”) and links them in your personal knowledge graph. From that point on, every conversation about your project automatically pulls in relevant context.
When you ask “how should I handle the database migration?”, Ditto already knows you mean Supabase Postgres. It knows you’re on Cloud Run, so it accounts for connection pooling. It knows you have Turso for edge reads, so it asks whether the migration affects both stores. You didn’t have to explain any of that.
Architecture Decisions That Stick
Developers make many decisions every week. Why Postgres over Mongo. Why SSE over WebSockets. Why that specific caching strategy. Most of these decisions live only in your head, or in a Notion doc nobody reads.
With Ditto, these decisions become part of your persistent memory. Six months later, when a new team member asks “why are we using Turso for edge reads?”, you can search your knowledge graph and find the exact conversation where you evaluated the trade-offs. The reasoning and the alternatives you considered are saved and searchable.
Debugging Sessions That Build on Each Other
Here’s a scenario every developer recognizes:
Monday: You debug a memory leak in your SolidJS components. Ditto helps you trace it to an onCleanup handler that wasn’t firing during hot module replacement. You fix it, move on.
Thursday: A similar symptom appears: components aren’t cleaning up properly after navigation. You open Ditto and ask about it.
Without memory, you’d start from scratch. With Ditto, the AI says: “This looks similar to the HMR cleanup issue we debugged Monday. That was caused by missing onCleanup in the createEffect inside ChatFeed. This time it might be the same pattern in a different component. Want me to check?”
Persistent memory with semantic search connects today’s problem with the earlier solution.
Per-Project Threads With Pinned Context
Ditto Threads are where developers get the most use out of Ditto. Instead of one long conversation that loses focus, you create a workspace per project:
- “Auth Refactor”: Attach subjects: Firebase Auth, WorkOS, OAuth. Pin the memory from your architecture decision. Add a note: “Must support both Google and SAML SSO.”
- “Performance Sprint”: Attach subjects: Lighthouse, bundle size, lazy loading. Pin your baseline metrics.
- “API v4 Design”: Attach subjects: REST, GraphQL, OpenAPI. Pin the RFC you drafted with Ditto’s help.
Each thread keeps its own context. The AI uses the context that matters for that workstream. When you switch from your auth thread to your performance thread, the context switches with you.
AI project features in other tools often use static files. Ditto threads pull from your conversation history and knowledge graph, so the context updates as you work.
MCP: Your Memory Everywhere
This section is for developers who use more than one AI tool.
Ditto is both an MCP server and an MCP client. That means:
In Cursor or Claude Code, you can connect Ditto’s MCP server and give your coding assistant access to your full development history. When you’re debugging in your editor, the AI can search your Ditto memories for past solutions. It knows your stack, conventions and past decisions without you pasting context.
{
"mcpServers": {
"ditto": {
"url": "https://api.heyditto.ai/mcp",
"headers": { "Authorization": "Bearer YOUR_API_KEY" }
}
}
}In Ditto, you can connect external MCP servers to give the assistant access to your tools. Connect a GitHub MCP server and Ditto can reference your repos. Connect a database MCP server and it can query your schemas directly.
Every AI tool you connect uses the same memory, so you explain your stack once.
Real Developer Workflows
Here are example workflows a developer can run with Ditto.
Code Review Context
You paste a PR diff into Ditto and ask for a review. Because Ditto remembers the architecture decisions behind the code, it checks intent as well as syntax. “This changes the memory retrieval logic, but last week we decided to keep the two-phase fetch pattern for latency reasons. Are you intentionally reverting that?”
Learning New Frameworks
You’re evaluating a new library. Over several sessions, you discuss trade-offs and read docs together. With Ditto, those evaluation sessions become a structured body of knowledge. Your knowledge graph shows how the new library connects to your existing stack. When you make the final decision, the reasoning is preserved.
Onboarding Context
You’ve spent months building with Ditto. Your knowledge graph holds your project’s technical history: stack decisions, debugging sessions, performance work and API design discussions. When a new team member joins, they can explore your knowledge graph to understand why things are built the way they are. (Public sharing features make this even easier.)
Why Your Context Matters More Than the Model
Many AI tools claim to be “the best AI for developers.” Most compete on model quality: which model writes better code or has the longer context window.
Models change with each release. Your context does not change when the model does: what you know about your projects, decisions, preferences and history.
Ditto lets you use any model you want: for example Claude for architecture discussions and GPT for quick code generation. You can swap the model and keep your memory.
The result is an AI that has more of your context with every conversation, whichever model is running.
Try It
Getting started takes five minutes:
- Open assistant.heyditto.ai and sign up
- Tell Ditto about your current project: its stack, goals and constraints
- See it build your knowledge graph from the conversation
- Come back tomorrow and ask a follow-up. Notice you don’t have to re-explain anything
- Connect Ditto via MCP to your editor for persistent context everywhere
If your AI still asks what your stack is, try Ditto.