Briefs · engineering

Tech-Stack Selection

Choose the right stack without falling for hype or sunk cost.

You walk away with

A recommended stack with the trade-offs and the decision criteria made explicit.

Decidi convenes

🏛️ The Software Architect 🧠 The ML Engineer 🔧 The Pragmatist ⚡ The Performance Engineer 🧱 The First-Principles Thinker 🔀 The Contrarian

Recommended level: Standard — Proven pro models — the everyday default.

What the council debates

Help us choose a technology stack (or a specific tool) and debate it on the merits, not the hype.

THE DECISION:
[what you are choosing — language, framework, database, platform, etc.]
THE OPTIONS: [the candidates on the table]
CONTEXT: [team skills, scale, timeline, existing systems, budget]
PRIORITIES: [what matters most — speed to ship, scale, hiring, cost, maintainability]

Debate:
1. Fit to the actual problem and scale — not the problem you wish you had.
2. Team capability and the real cost of learning curve.
3. Maintainability, hiring pool and long-term support.
4. Performance and scale headroom versus over-engineering for it.
5. Lock-in, ecosystem maturity and the boring-tech argument.
6. The cost of being wrong, and how reversible the choice is.

FINAL SYNTHESIS:
- A clear recommendation with the top three reasons.
- The strongest case for the runner-up, so the trade-off is honest.
- The conditions under which we would choose differently.

Run this brief as a council

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