A developer writing about productivity with AI tools like Claude Code describes experiencing what he calls “AI Brain Fry” — the cognitive exhaustion that comes from juggling multiple concurrent AI-generated tasks and their follow-ups across many open threads and contexts.
According to the post, the problem arises from a familiar workflow pattern: starting a task, waiting for an AI response, opening another thread to stay productive, then facing a cascade of replies that each spawn clarifications, follow-ups, and side tasks. “You juggle. And all of a sudden you find yourself hopping between 20 threads, trying to keep all those contexts in your brain RAM,” the author writes.
To address this, the developer implemented a system organizing work around two core issues: sequencing and context management. His approach treats pending work like a priority queue stored entirely in git.
Here’s how the system operates: Each task lives as a markdown file in a repository alongside the relevant code. The queue state is committed as snapshots, ensuring nothing critical depends on memory or chat window history. An AI agent cycles through the board, researching tickets, building work tagged as safe, running tests, and verifying deployments. Critically, the agent never decides — anything requiring human judgment lands in what the author calls an “owner inbox.”
A script reads the entire board and presents a prioritized list of items awaiting his decision: code reviews, questions, or trade-off rulings. The interface for this is a Claude artifact — a single-page document rebuilt each cycle from git data, functioning as a read-only viewer while git remains the source of record.
The key mechanism for fighting context fatigue is deliberate closure: the developer takes one item, loads its context, makes a decision or requests an action, and explicitly closes it by returning it to the queue. “That closing is the whole trick,” he notes. By closing a ticket, its context closes with it, allowing genuine focus when the next item is selected.
The author acknowledges the system remains challenging, particularly around tooling — serving context quickly enough that loading a ticket takes roughly a minute, and preventing tickets from entering endless loops when blocked awaiting human judgment. He describes the system as “a living organism” that continues evolving but represents “a huge step forward in productivity with AI tools.”
Key facts
- The developer experienced cognitive overload from managing 20+ concurrent AI task threads with cascading follow-ups
- His solution stores all work as markdown files in git commits, eliminating reliance on memory or chat history
- An AI agent manages the task board automatically, but a human must review and decide on sensitive items
- A prioritized inbox surfaces only items awaiting human judgment, preventing context switching
- Explicitly closing each task after decision-making is the core technique for reducing mental fatigue