Stop Rebuilding AI Context Every Session

ran li — Wed, 27 May 2026 14:16:12 +0000

I built a stateful workspace system for AI coding workflows

After months of heavy AI-assisted development, I kept running into the same workflow problem:

Every AI session felt like a partial reset.

Even with good models,
the workflow itself was fundamentally stateless.

Whenever I:

restarted Cursor
switched chats
changed models
reopened VS Code
resumed work the next day

the AI would lose track of:

which files were actively evolving
what feature I was implementing
recent debugging progress
relevant Git changes
architectural direction
current workspace focus

So I kept rebuilding context manually.

Over and over.

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The Real Problem Isn’t Just Memory

At first I thought this was only a memory problem.

But eventually I realized:

The deeper issue is missing workspace state.

Most AI tools only understand isolated prompts or chat history.

Real software development depends on constantly evolving state:

active files
recent edits
changing priorities
debugging flow
Git activity
unfinished implementation context

That state changes continuously during development.

And most AI systems lose it between sessions.

⸻

Building Contorium

So I started building Contorium:
a persistent workspace state system for AI coding.

Instead of relying entirely on prompts,
Contorium continuously tracks workspace evolution and generates structured development context.

It tracks things like:

active editors
recently modified files
Git changes
current focus
workspace summaries
event history
compressed context memory
evolving file priority

All stored locally inside:

.contora/

The goal is simple:

Help AI understand the current state of development.

Not just remember old conversations.

⸻

Stateful AI Coding

One of the biggest shifts for me was realizing that AI coding workflows should probably behave more like state machines than chat sessions.

The system continuously updates:

what is actively changing
what matters most
where development is currently focused
which files are unstable
what recently evolved

That state can survive:

IDE restarts
session changes
model switches
multi-day workflows

This makes AI interactions feel far less disposable.

⸻

Git-aware Context Tracking

Git turned out to be one of the strongest context signals.

Contorium automatically tracks:

modified files
staged files
working tree changes
recent activity

Instead of dumping entire repositories into prompts,
the system prioritizes what is actively evolving.

This becomes especially useful in:

monorepos
long refactors
large AI-assisted codebases

⸻

Reducing Context Noise

Another issue I kept seeing:

Most AI workflows waste tokens on irrelevant context.

So Contorium also focuses heavily on:

file ranking
context compression
semantic summaries
noisy path filtering
compact workspace exports

The idea is not simply “more context.”

It’s:
better context prioritization.

⸻

Local-first Architecture

I intentionally designed the system as local-first.

No cloud workspace sync.
No hidden telemetry.
No chat scraping.

Optional BYOK providers currently include:

OpenAI
Claude
Gemini
DeepSeek

But the workspace state itself belongs to the developer.

⸻

Current Stack

Current stack includes:

TypeScript
VS Code Extension API
Cursor support
Claude Code / MCP support
local workspace scanners
structured context builders
Git-aware ranking systems

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Current Direction

Right now I’m exploring:

stateful AI coding workflows
persistent development sessions
long-running agent systems
token-efficient context generation
AI-native development environments

I increasingly think persistent workspace state will become a standard layer in future AI IDEs.

Still early.
But already extremely useful in my own workflow.

Website:
https://www.contorium.dev

DEV Community: ran li

Stop Rebuilding AI Context Every Session