Show HN: Multi-attribute decision frameworks for tech purchases
Copy-paste LLM prompts that turn ChatGPT or Claude into a structured decision analyst for laptops, monitors, tablets, phones, and SaaS subscriptions. You define constraints, weight what matters to your workflow, and get scored recommendations with sensitivity analysis. Why I built this:
With the rise of LLMs (AI), I wanted to find a way to harness the computing power and ease of use the chat interface provides. The major problem: LLMs don’t always provide repeatable, traceable results if you ask the same question twice or even against 2 competing products. That is the dilemma this product aims to solve. Is this a PDF, yes, but it harnesses my systems analysis experience to help hard-code a framework for a person off the street to turn their AI chat box into an objective decision-helper in just 15 to 20 minutes of use.
I spent 10+ years applying decision science in defense and systems analysis—graduate work at Naval Postgraduate School, leading teams through decisions where the cost of choosing wrong wasn't just money; it was mission failure or lives at risk.
The method used uses multi-attribute utility theory: define hard constraints (binary gates), eliminate non-viable options, score remaining candidates on mission-critical attributes with explicit weights, then run sensitivity analysis to see what changes the outcome.
I use this myself all the time. The most recent was trying to upgrade my own laptop (Surface Pro stuck at Windows 10).
BLUF benefits:
• Helps prevent over-obsessing over specs (32GB RAM! RTX 4080!) while ignoring mission fit (do I really game that often?)
• Fleshes out hard constraints that sometimes come up until after purchase (bought Windows laptop, needs a way to support a MacOS app)
• Future-proofing: ensuring I won’t pay feature I'll statistically never use
• Aims to parse through the noise (SEO type posts) and get you a great first-pass research report of what you should value and why.
Consumer purchases don't need full enterprise rigor, but they deserve better than "Top 10 Laptops 2026" affiliate listicles or chatbots hallucinating specs.
How the framework works: 1. Mission definition: What must work reliably? (Video editing vs office work vs travel)
2. Hard constraints: Binary gates (budget ceiling, OS requirements, battery minimums)
3. Candidate generation: AI searches current market without SEO or affiliate bias
4. Weighted scoring: Performance, battery, reliability, portability—you control the weights
5. Efficient frontier: Which options dominate? Which are just expensive?
6. Sensitivity analysis: "If battery life matters 25% instead of 15%, MacBook Air wins. If reliability matters more, ThinkPad wins."
The PDFs include example case studies I’ve developed: policy analyst choosing ThinkPad X1 Carbon over MacBook Air (why reliability and docking beat battery life for enterprise work), freelance designer choosing Figma over Affinity Designer (why collaboration features justified 6x higher cost), consultant choosing Obsidian over Notion (why offline-first beat ease-of-use).
No barriers: No sign-up. No account. You get a PDF with prompts and case studies. Open ChatGPT or Claude (free version works), paste the prompt, answer questions. That's it.
I built this because I was tired of seeing people (and myself) wasting money on impressive-sounding specs that don't match their actual workflow. If you've ever regretted a tech purchase 3 weeks later, this might help.
Try it (I'd offer it free but then I loose my IP): • Tech & Electronics: https://decisioncontrolworks.gumroad.com/l/auzhsa • Software & Subscriptions: https://decisioncontrolworks.gumroad.com/l/zaucxt
Curious what HN thinks—especially if anyone's tried applying formal decision methods to everyday purchases.