Ask

Key Features

• •Knowledge Cards: Automatically save solved problems and their solutions as structured cards. When any agent later encounters the same issue, it searches and returns the exact fix instantly—no manual note-taking needed.
• •Agent Memory: Preserves full project context—instructions, preferences, and state—across chat threads and tools. Start a new session or switch agents and your context is restored automatically, eliminating re-explanation.
• •Multi-Layer Knowledge Sharing: Cards are stored in Private, Team, or Public layers. Share reusable solutions within your team or publish to the world after automatic redaction of secrets. Each layer offers appropriate access control.
• •Credit-Efficient Public Library: Public card searches only charge Credits when a useful hit is found—no hit, no charge. This pay-per-value model ensures you only pay for actual solutions, not empty searches.
• •Automatic Secret Redaction: When publishing cards to the public library, an AI review automatically redacts personal information and blocks secrets. Keep private context secure while still contributing to the community.
• •Earn Credits by Publishing: Contribute valuable solutions to the public library and earn Credits that can be used for future searches. This creates a sustainable ecosystem where knowledge sharing is rewarded.
• •MCP-Enabled Tool Compatibility: Works with any MCP-enabled AI coding tool, including popular assistants like Cursor and Windsurf. Seamlessly integrate into your existing workflow without switching platforms.

Use Cases

• •Scenario: A developer repeatedly encounters a 'port 3000 already in use' error across different projects. With Ask AI Base, the fix is saved as a Knowledge Card once; later sessions automatically retrieve it, resolving the error in minutes instead of hours.
• •Scenario: A team onboarding a new member wants to ensure common build commands and conventions are instantly available. Team Layer cards capture these details, so any AI agent used by the new hire fetches context immediately—no manual documentation needed.
• •Scenario: A solo developer switches between Cursor and Windsurf for different tasks. Agent Memory restores project state and preferences across both tools, so the developer never has to re-explain environment setup or debug history.
• •Scenario: A community member faces an obscure configuration error. A public library card previously published by another user provides the exact solution, saving the member from lengthy trial-and-error debugging. Credits are only spent if the search yields a hit.
• •Scenario: During a long debugging session, an AI makes multiple attempts and final decisions. Agent Memory saves each turn plus the final report, allowing the developer to revisit the reasoning later or restore it in a new thread for a related problem.

Who is this for?

Software developers, engineers, and coding teams who use AI coding agents and assistants (such as Cursor, Windsurf, or any MCP-enabled tool) and need persistent context across sessions. Ideal for solo developers who switch between tools and want to avoid re-explaining project details, as well as teams collaborating on codebases who benefit from shared, reusable solutions. Also suited for open-source maintainers who want to create a knowledge base of common fixes for their community. Early adopters of AI coding tools who value efficiency and hate wasting time on the same debugged issues.