some improvements and next directions - feedback is appreciated
Hey Product Hunt!
We launched ChatRecall.dev last week to stop AI context from evaporating between sessions. Since then, we’ve realised the chat logs themselves hold the exact architectural blueprint developers and teams are constantly losing.
Whenever an agent builds software, it makes dozens of micro-decisions and cuts corners ("we'll fix this later") that immediately get lost once the prompt session ends.
Today, we’re restructuring ChatRecall around two major upgrades: Decisions and Autonomous Tasks.
1. Persistent Architectural Decisions
Auto-Extracted: As you and your team chat with AI, ChatRecall automatically extracts architectural choices, rejected alternatives, and tech selections.
Context Guardrails via MCP: When an agent attempts to pick a library or steer the architecture, it queries your decision log over MCP. If a decision was already made (e.g., "we use PostgreSQL, not Mongo"), the agent is automatically corrected.
Editable Anywhere: Update decisions directly in the dashboard or tell your AI during a session to update a decision node.
2. Autonomous Tech-Debt Kanban
Instant Task Extraction: Tell an agent "just hardcode this for now to unblock the demo", and ChatRecall automatically generates a follow-up task on your Kanban board.
Closed-Loop Agent Resolution: Tasks are exposed over MCP. When your AI agent has idle cycles (or when you trigger auto-fix), it can pick the task, execute the changes, document its reasoning, and close the ticket—taking the human out of the routine cleanup loop.
3. Streamlined Dashboard & MCP-First Focus
Cleaned-up UI focused on the decision tree and task pipeline.
Full MCP sync across your entire team so everyone’s agents stay on the same page.
The Loop: A missed decision triggers a task --> AI agent picks and resolves it --> the decision board updates --> clean codebase without micro-managing prompts.
It's live and free to explore at chatrecall.dev.
Would love your thoughts: how is your team currently tracking architectural decisions and prompt-created tech debt across AI sessions?
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