OtaskuAI
An exploratory anime recommendation project that treats architecture decisions (cost, deployment strategy, and safety constraints) as first-class requirements — not afterthoughts.
Most recommender demos skip the hard parts: cost drift, deployment choices, and the temptation to oversell capabilities. OtaskuAI is built to explore those tradeoffs explicitly.
- How to structure recommendations without magic-thinking
- How to keep infrastructure light while experimenting
- How to communicate uncertainty and limitations clearly
- Similarity search foundations (vectors, cosine distance)
- Deployment benchmarking (serverless vs persistent)
- RAG-style augmentation planning for explainable results
- Guardrails that prevent confident nonsense
Early experimentation emphasizes a minimal, cost-conscious footprint. Rather than committing to heavyweight infrastructure early, the project compares options and matches the stack to the stage of the problem.
Near-zero idle cost. Acceptable cold-start tradeoffs for low-traffic exploration and rapid iteration.
Useful when latency and sustained traffic justify it — but can be cost-heavy and premature for early-stage recommendation logic.
Responsible AI posture
OtaskuAI is intentionally designed to avoid authoritative voice when evidence is weak. Recommendations should be given based on the signals and information given, not guessing/assumptions.