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Problem/Solution

Why AI Coding Context Gets Lost (And How to Fix It)

You've spent an hour with Claude Code, making architectural decisions, discussing trade-offs, and building features. The session ends. Two weeks later, a teammate asks: "Why did we use JWT instead of sessions?"

You have no idea. The context is gone.

The Problem: Ephemeral Context

AI coding assistants like Claude Code, Cursor, and Aider operate in sessions. Each session builds rich context:

  • Decisions - "We chose PostgreSQL for its JSON support"
  • Constraints - "Must support SSO for enterprise customers"
  • Trade-offs - "Went with bcrypt over Argon2 for compatibility"
  • Rejected approaches - "Considered Redis sessions, too complex for MVP"

But when the session ends or compacts, this context evaporates. The AI doesn't remember. Your future self doesn't remember. New team members never knew.

Why This Matters

  1. Repeated discussions - Same questions answered multiple times
  2. Lost rationale - Code exists without documented "why"
  3. Onboarding friction - New team members lack historical context
  4. PR review gaps - Reviewers ask questions already answered in development

The Technical Cause

AI models have a context window - a maximum number of tokens they can process at once. For Claude, this is typically 100K-200K tokens. Sounds like a lot, right?

But in a coding session:

  • Each file you read consumes tokens
  • Each response generates tokens
  • Each tool call adds tokens

When the context fills up, older content gets compacted or dropped. That architectural discussion from 30 minutes ago? Gone. That decision about authentication? Compressed beyond recognition.

Existing Solutions (and Their Limitations)

1. CLAUDE.md Files

Claude Code supports project-level CLAUDE.md files that get injected at session start. Great for static rules, but:

  • Requires manual maintenance
  • Doesn't capture session-specific decisions
  • Gets stale quickly

2. Manual Documentation

You could write everything down in Notion, Confluence, or README files. But:

  • Adds friction to the workflow
  • Often gets skipped under time pressure
  • Separates documentation from where decisions are made

3. Git Commit Messages

Detailed commit messages capture intent, but:

  • Only capture what was done, not what was considered
  • Miss pre-implementation discussions
  • Hard to search and navigate

The Solution: Automatic Context Capture

What if context capture happened automatically, in the background, without adding friction to your workflow?

That's the idea behind Ninho. It runs as a Claude Code plugin, monitoring your sessions for:

  • Requirements ("we need to support...")
  • Decisions ("let's use JWT for...")
  • Constraints ("must be under 100ms")
  • Learnings ("actually, don't use git add .")

It organizes this into PRDs (Product Requirements Documents) that live in your repository, versioned with your code.

How It Works

  1. Session Start: Ninho injects relevant PRD context so Claude remembers previous decisions
  2. During Session: Background monitoring captures requirements, decisions, and constraints
  3. File Editing: When you edit related files, relevant decisions are surfaced
  4. PR Creation: Branches are auto-linked to PRDs with context for reviewers
  5. Session End: Learnings extracted and saved for future reference

Getting Started

Install Ninho with a single command:

curl -fsSL https://raw.githubusercontent.com/ninho-ai/ninho/main/install.sh | bash

Then just code normally. Ninho works in the background. No configuration needed.

Conclusion

AI coding assistants are powerful, but their ephemeral nature creates a documentation gap. Every decision made in a session is valuable institutional knowledge that shouldn't be lost.

Automatic context capture closes this gap. Your future self, your teammates, and your code reviewers will thank you.

Try Ninho →