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Reference file

Sources

sources.md

Source tiers and query patterns

Launch monitoring needs speed and precision. Run tiers in this order: highest-reach first, then community, then niche — a wrong claim in a high-reach outlet spreads to secondary coverage within hours, so that is where the first minutes of any run go. Within a tier, run all queries in parallel.

All patterns below use site: operators, which work in any web search tool. Substitute the product's confirmed name variants (from alternate-name resolution) into every "[product name]" slot — run the high-signal queries once per variant when variants differ materially.

Tier 1 — High-reach press (run first)

Widest reach, highest chance of a mischaracterization spreading to secondary coverage. Check within the first pass of any run.

  • "[product name]" site:techcrunch.com
  • "[product name]" site:theverge.com
  • "[product name]" site:wired.com
  • "[product name]" site:arstechnica.com
  • "[product name]" site:venturebeat.com
  • "[product name]" site:siliconangle.com
  • "[product name]" site:theregister.com
  • "[product name]" site:zdnet.com
  • "[product name]" site:bloomberg.com
  • "[product name]" site:reuters.com
  • "[product name]" "[company name]" announcement OR launch OR release

Category-specific press — add based on the context profile:

  • Developer tools / APIs: site:infoq.com, site:sdtimes.com, site:thenewstack.io
  • AI/ML: site:theinformation.com, plus the major AI newsletters
  • Enterprise SaaS: site:cio.com, site:computerworld.com
  • Consumer: site:mashable.com, site:engadget.com

Tier 2 — Community & developer forums (parallel with Tier 1)

Community threads move faster than press and contain the most honest reactions — including the mischaracterizations that later spread to press.

Hacker News

  • "[product name]" site:news.ycombinator.com
  • Also search HN's own search index (hn.algolia.com) with a last-24h date filter
  • Signal: threads with 50+ comments are high-priority; read the top comments specifically for wrong claims — an upvoted wrong claim is the seed of tomorrow's press error

Reddit — broad + subreddit-specific (run all, not just one)

  • "[product name]" site:reddit.com — broad sweep
  • "[product name]" site:reddit.com/r/[category subreddit] — from the context profile
  • "[product name]" site:reddit.com/r/[company name] — brand subreddit if it exists
  • "[product name]" site:reddit.com/r/technology, plus the profile-relevant subs (r/programming, r/MachineLearning, r/SaaS, r/webdev, r/devops, r/artificial, …)
  • Signal: sort by "new" for real-time reaction, "top" for highest-reach threads; "I'm disappointed" and "this is actually X not Y" threads are the priority reads

Dev communities (if the audience is technical)

  • "[product name]" site:dev.to
  • "[product name]" site:hashnode.com
  • "[product name]" site:medium.com
  • "[product name]" site:stackoverflow.com
  • "[product name]" site:github.com — issues, discussions, reactions on the repo if public
  • "[product name]" discord — find the official or community server; early feedback lands there before it surfaces publicly

Tier 3 — Social (real-time volume and influencer signal)

Sweep every pass, not just the first run — social moves faster than everything else.

X / Twitter

  • "[product name]" site:x.com
  • "[product name]" launch OR announced OR "just released" site:x.com
  • "[product name]" wrong OR broken OR disappointed site:x.com — complaint hunt
  • "[product name]" "actually" OR "turns out" OR correcting site:x.com — correction chains
  • Check quote-posts of the official launch post — reactions and mischaracterizations concentrate there
  • Signal: threads with 100+ likes within 24h; journalist corrections; influencer takes

LinkedIn

  • "[product name]" site:linkedin.com
  • "[product name]" launched OR "my take" site:linkedin.com
  • Signal: founder/exec posts, practitioner commentary, buyer takes — for B2B products LinkedIn often carries higher-quality signal than X

YouTube

  • "[product name]" review OR "hands on" OR reaction site:youtube.com
  • "[product name]" "first impressions" OR unboxing site:youtube.com
  • "[product name]" problems OR issues OR "doesn't work" site:youtube.com
  • Signal: view count within 48h and comment themes; large tech channels shape mainstream perception faster than most press

Instagram / TikTok / Facebook / Threads

These platforms block most direct search — if your web-research tool has a social-focused search mode, use it here; otherwise pair site: queries with platform-name keyword queries:

  • "[product name]" site:tiktok.com, or social-mode query "[product name]" review OR reaction
  • "[product name]" site:instagram.com, or social-mode query "[product name]" instagram
  • "[product name]" site:facebook.com / site:threads.net
  • Signal: viral reaction videos in the first 48h can reach audiences larger than any press outlet; comment sections surface raw consumer sentiment fast

Tier 4 — Competitor monitoring (if enabled)

  • [competitor] "[product name]" OR "[category keyword]" — are they writing about the launch?
  • [competitor] "compared to" OR "vs" OR "alternative" — positioning content
  • [competitor] site:x.com — real-time reactions from their accounts
  • Check each competitor's blog and changelog for counter-announcements within 48h of launch
  • "[competitor] vs [product name]" — comparison content published since launch date
  • Silence is also a data point — record "no visible response" per competitor rather than omitting them

Tier 5 — Mischaracterization-specific queries (every pass)

Build these from the context profile's "what would a mischaracterization look like":

  • Pricing: "[product name]" pricing OR price OR "costs $" — compare against actual pricing
  • Category confusion: "[product name]" "[wrong category]" — e.g. an API being called a "no-code tool"
  • Capability inflation: "[product name]" "[feature it doesn't have]"
  • Capability deflation: "[product name]" "only" OR "just" OR "limited to"
  • Wrong comparisons: "[product name]" vs "[wrong competitor]"
  • Attribution errors: "[product name]" "[wrong company name]"
  • Spread check: re-run any confirmed wrong claim as its own query — "[product name]" "[wrong claim]" and "[product name]" site:reddit.com "[wrong claim]" — to see who else is citing it

Tier 6 — Consumer & app-specific sources

Add for consumer launches (apps, hardware, consumer software); skip for pure B2B/developer tools.

  • App stores: "[product name]" site:apps.apple.com / site:play.google.com — check the recent-reviews tab directly; 1-star reviews after an update flag regressions before press notices, and platform-specific issues surface here first
  • Podcasts: "[product name]" podcast OR episode site:open.spotify.com OR site:podcasts.apple.com — major tech podcasts shape consumer narrative
  • Product Hunt: "[product name]" site:producthunt.com — launch-day comments and "alternatives to" pages
  • Ecosystem forums: Apple → site:forums.macrumors.com, site:9to5mac.com, site:appleinsider.com; Android → site:androidpolice.com, site:9to5google.com; gaming → site:resetera.com, r/gaming

Alternate name resolution — query patterns

Run before asking the user anything; fold every confirmed variant into the query lists above.

  • "[product name]" codename OR "internal name" OR "project name"
  • "[product name]" "formerly known as" OR "previously called" OR rebranded
  • "[company name]" "[product category]" names OR versions
  • Hardware: search both the marketing name and the internal model identifier
  • AI products: check API name, model name, and consumer-facing name separately — they are often different
  • Software: check the GitHub repo name, package name, and SDK name