by arapkuliev ·
I keep hearing that prototyping is “solved” now — just use Cursor, Claude, Lovable, etc.
But when I talk to people inside real organizations (healthcare, regulated industries, even large non-tech companies), I keep seeing the opposite:
There’s no shortage of ideas. There’s a constant backlog of things people want to test — new workflows, internal tools, patient-facing flows, decision support UIs.
The bottleneck isn’t creativity. It’s: – internal IT teams focused on maintenance – engineers already overloaded – AI tools that still require time, context, and ownership – agencies/freelancers that are too slow or heavyweight for “just a prototype”
My hot take: AI didn’t eliminate the prototyping problem — it shifted it to the people who have the least time to deal with it.
Curious how this matches your experience: – Do you actually prototype continuously, or is it mostly one-off? – Have AI tools fully replaced the need for external help for you? – If you could get realistic prototypes in days (not months), how often would you use that?
Genuinely trying to understand whether I’m seeing a real pattern — or just a biased slice of the world.
Hi HN
Memovee is an agentic movie database that lets you explore movies using natural language instead of filters or rigid queries.
You can ask things like:
- “Movies that take place in someone’s mind”
- “Top mystery films on Netflix released in the last 5 years”
- “Slow-burn sci-fi movies with strong world-building”
Under the hood, this isn’t just an LLM wrapper. Memovee uses a structured movie database and an agent layer that translates natural-language intent into deterministic queries and aggregations, then reflects on the results before responding.
The agent implementation used by Memovee is open source: https://github.com/upmaru/memovee-tama
This repository shows how intent parsing, query planning, execution, and result refinement are handled step-by-step, rather than relying on opaque prompt chains.
The core engine itself is not open-sourced yet. Memovee is one concrete application built on top of a more general engine whose goal is to make it possible to build systems like this for any enterprise domain — not just movies.
Engine project: https://kritama.com
This is still early and focused purely on movies (not TV yet). Coverage and regional availability vary, and there’s a lot left to improve — especially around reasoning depth, evals, and edge cases.
Happy to answer questions about:
- The agent architecture
- How natural-language intent is mapped to structured data
- Agentic vs deterministic tradeoffs
- The path toward a reusable enterprise engine
Looking forward to feedback — especially critical ones.