Shared memory ended my need to explain to AI
In the rapidly evolving landscape of artificial intelligence, managing context is the new battlefield. For many of us, the process of interacting with powerful models like Claude and Gemini often involves a repetitive, slightly frustrating ritual: painstakingly feeding each assistant the same background information, the project scope, and the desired output structure.
It turns out, the common approach—keeping separate mental copies of the context for each AI—is deeply inefficient. We often end up splitting our work across different AI assistants, which necessitates keeping two separate copies of our own context locked away in our heads.
But what happens when you decide to stop hoarding knowledge and start sharing it? Instead of managing multiple silos of context, the solution might lie in a centralized data hub. One savvy approach is to give both AI models a single source of truth: a shared database.
By implementing a shared Postgres database, both Claude and Gemini can read from and write to the exact same set of information. This immediately eliminates the headache of maintaining redundant context and ensures consistency across all interactions.
While this setup streamlines the workflow dramatically, the reality of AI interaction remains wonderfully human. Even with a shared database, one assistant might use the context perfectly, while the other still needs a friendly, consistent reminder. It’s a perfect illustration of how technology, however advanced, still requires a bit of human oversight to make the magic happen.
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