Back in Flow in 30 Seconds: The ginko start Experience

You know that feeling when you sit down to code and everything just clicks? Your mental model of the codebase is sharp, you remember exactly where you left off, and the next steps are crystal clear. That's flow state—and it's when developers do their best work.

The problem? Getting into flow is hard. Getting back into flow after an interruption is even harder.

Research suggests it takes 15-25 minutes to reach deep focus after a context switch. If you're switching between AI chat sessions, that time compounds. Every new session means rebuilding your mental model, re-explaining your codebase, and hoping your AI assistant catches up before you lose momentum.

What if you could skip all that?

The 30-Second Session Start

Here's what happens when you run ginko start:

$ ginko start

Ready | Hot (10/10) | Think & Build mode
Last session: Completed auth refactor, fixed token refresh bug
Next up: TASK-4 - Add rate limiting to API endpoints (continue)

Sprint: API Hardening 65%
  Follow: ADR-002, ADR-015
  Apply: retry-pattern ★, circuit-breaker ◐
  Avoid: 💡 timeout-gotcha
Branch: feature/rate-limiting (3 uncommitted files)

In under 30 seconds, you have:

Where you left off: Last session's accomplishments
What's next: The specific task to continue
Cognitive scaffolding: Patterns to apply, gotchas to avoid
Git context: Branch status and uncommitted work

No re-explaining. No "let me remind you about the architecture." Just instant context and a clear path forward.

How It Works

Ginko maintains a lightweight event stream that captures the meaningful moments of your development sessions:

Decisions you made: "Chose JWT over sessions for stateless auth"

Patterns you discovered: "Retry with exponential backoff works well for flaky API"

Gotchas you hit: "Timer callbacks need .unref() or Node process hangs"

Work completed: "Finished auth middleware, all tests passing"

When you start a new session, ginko loads the relevant context—not everything, just what matters for your current work. It's like having a colleague who took perfect notes while you were away.

The Anatomy of a Session Start

Let's break down what you see:

Flow State Indicator

Ready | Hot (10/10) | Think & Build mode

The flow score reflects your session continuity. "Hot" means you're picking up where you left off with full context. "Cold" means it's been a while or you're starting something new.

Work modes adapt the AI's behavior:

Hack & Ship: Fast iteration, less ceremony
Think & Build: Balanced approach (default)
Full Planning: Maximum rigor for complex work

Session Context

Last session: Completed auth refactor, fixed token refresh bug
Next up: TASK-4 - Add rate limiting to API endpoints (continue)

"Last session" tells you what you accomplished—useful for morning standups or just jogging your memory.

"Next up" shows your current task from the sprint, with a hint about whether you're starting fresh or continuing work.

Cognitive Scaffolding

Sprint: API Hardening 65%
  Follow: ADR-002, ADR-015
  Apply: retry-pattern ★, circuit-breaker ◐
  Avoid: 💡 timeout-gotcha

This is where ginko shines for teams. Your sprint progress is visible at a glance. But more importantly:

Follow: Architecture decisions that apply to this task
Apply: Patterns from your codebase that work well here
Avoid: Gotchas your team has documented (so you don't repeat mistakes)

The icons indicate confidence levels (★ high, ◐ medium, ○ low) and severity (🚨 critical, ⚠️ high, 💡 medium).

Git Context

Branch: feature/rate-limiting (3 uncommitted files)

Simple but essential—you immediately know what branch you're on and whether you have work to commit.

Why This Matters

The traditional AI coding workflow looks like this:

Open new chat session
Paste context about your project
Explain what you're working on
Remind AI about previous decisions
Finally start coding
Repeat tomorrow

With ginko, it's:

Run ginko start
Start coding

That's not a small difference. Over a week, those 15-20 minutes of context-loading per session add up to hours of lost productivity. Over a month? You're losing a full day of deep work just getting your AI assistant up to speed.

Beyond Individual Sessions

The real power emerges with teams. When your colleague runs ginko start, they see:

Decisions you made while they were offline
Patterns you discovered and documented
Gotchas you hit (so they don't have to)
The sprint context everyone shares

It's like having a persistent team memory that surfaces exactly when needed.

Getting Started

# Install ginko
npm install -g @ginkoai/cli

# Initialize in your project
ginko init

# Start your session
ginko start

The first ginko start will be a cold start—you're building context from scratch. But from the second session onward, you'll feel the difference. Context persists. Flow returns faster. Your AI assistant actually remembers.

Context rot is real, but it doesn't have to be permanent. If you're tired of re-explaining your codebase every session, try ginko and get back to flow in 30 seconds.