Reflection
This week was about turning an “AI demo” into something that behaves like a real product.
I extended the analysis feature so results are persisted and cached instead of being recomputed every time. This meant adding a new database table, thinking about derived data, and introducing concepts like hashing, upserts, and cache validation.
What stood out most was that the hard part wasn’t the analysis logic itself — it was designing the data flow and contracts so frontend, backend, and storage stay in sync.
The app now survives restarts with both entries and their analyses intact, which makes the system feel stable and intentional. It’s a small step, but an important shift from “toy AI feature” to “engineering an AI capability.”
What I learned
- How to persist and cache AI-derived data instead of recomputing it
- Why hashing is useful to detect meaningful changes in input text
- How to design “analyze once, reuse many times” workflows
- How frontend types and backend contracts must evolve together
- That AI engineering is as much about data and state as it is about models
What was hard / surprising / confusing
- Keeping frontend and backend response models in sync
- Understanding when to recompute analysis vs return cached results
- Dealing with TypeScript errors caused by small schema mismatches
- Debugging issues that were about process and state, not code syntax
What I’ll do next week
Next week, I want to build on this foundation by introducing a more realistic AI component — either by integrating a real model/API or by adding evaluation and comparison between different analysis approaches.
The focus will stay on architecture and learning, not on building something “impressive.”
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