How to use this page
Read the extract first, then the application and limits sections, and only then decide whether the thesis is strong enough for action or only for context.
Extractable overview
Method goal: make the product legible, not mystical.
Content rule: each page should explain what EdgeVisor measures, what inputs it uses, where it is weaker, and how the output should be applied.
Trust rule: explicit constraints are part of the product surface, not post-hoc legal padding.
Method principles
EdgeVisor works best when it treats explanations as structured knowledge instead of broad AI prose. A good page should answer five things fast: what this is, how it works, how to apply it, what supports it, and where it breaks down.
| Method rule | Why it exists | What it prevents |
|---|---|---|
| Lead with extractable facts | Users and retrieval systems need the thesis quickly | Pages that sound smart but cannot be operationalized |
| Separate evidence from navigation links | The user must know what supports a thesis versus what only helps inspect a market | False certainty created by platform links being mistaken for proof |
| Document limits early | Trust compounds when constraints are visible before action | Over-reading a thin setup as a strong trade |
Why the docs are structured this way
A prediction-market analytics product accumulates trust slowly and loses it fast. If the docs promise universal news intelligence while the code only monitors a few buckets, the content becomes anti-product. So the docs are written like knowledge endpoints, not vague landing-page copy.
The practical reading order is deliberate: overview first, application second, limits third. That structure helps a user translate the page into a real decision instead of absorbing it as abstract product lore.
- Use the extract first: it should tell you the main claim in less than a minute.
- Use the tables and formulas next: they convert soft prose into a concrete operating model.
- Use the limits before execution: if the caveats dominate the setup, keep the output as research rather than action.
Method constraints
Methodology does not make the system omniscient. It only makes the outputs easier to audit. If the underlying category mapping is narrow, the docs should describe a narrow system. If learning metrics are still early or category coverage is uneven, that belongs in the explanation too.
- What EdgeVisor is not: not a universal text-analysis engine, not a hidden human analyst desk, and not a promise that every pick is tradable.
- What the method optimizes for: decision usefulness, extractability, and honest scope disclosure.
- What the method refuses to do: inflate authority by implying evidence or certainty that the product does not actually produce.
Frequently asked questions
Because trust breaks when product claims outrun the actual system. Constraints are part of the method, not a disclaimer afterthought.
It means each page is written so humans and retrieval systems can extract facts, process steps, caveats, and applications without guesswork.