² Actually change the result pool #
-
Force "expert" signals
Search engines strongly favor pages that look educational or primary-source when explicitly requested.
. benchmark
. design
. documentation
. implementation
. internals
. pitfalls
. reference
. spec
. tradeoffs -
Exclude SEO-farm patterns (this helps a LOT)
Most ad-heavy beginner sites reuse the same vocabulary.
Start excluding these words aggressively:
. -howto -tutorial -guide -step-by-step -explained -beginners -example -
Domain-level negative filtering (rotating blacklist)
. -kaggle -medium -towardsdatascience -geeksforgeeks -tutorialspoint -
Search where experts actually write
Instead of fighting Google’s ranking, redirect it.
. site:github.com issue OR discussion
. site:stackoverflow.com question
. site:readthedocs.io
. site:docs.python.org
. site:rust-lang.github.io
. site:llvm.org -
Use “filetype” to escape SEO entirely
memory model reordering filetype:pdf ppt txt -
Add constraints, not keywords
Constraints change ranking more than synonyms: avoids “intro” pages and pulls in deeper discussions:
. compared
. failure
. limitations
. vs
. why not
. without
why not use mutex instead of spinlock -
Use time filters strategically
Old but high-quality engineering posts resurface:
Use custom ranges like 2012–2016 or 2018–2020 -
If you really want randomness: query mutation
Instead of random words, randomize query structure:
. Comparison-focused
. Failure-focused
. Noun phrase
. Question form
Rotate between: Same topic, different ranking paths. -
Brutal but effective mindset shift
When searching tech topics:
. Assume the best answer is NOT optimized for search
. Discussions
. Docs
. Issues
. Niche domains
. Old posts
. PDFs
Not blogs.
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If you want, I can:
Build a short blacklist starter pack
Give you a copy-paste “anti-SEO” query template
Suggest better engines per topic (systems, ML, web, languages)
Just tell me which direction you want.
