Technology
AI tools, models, infrastructure, and the engineering decisions behind shipping AI products.
8 articles
From an interview with Maria L
From code to prompts: what operators stop doing when LLMs work
"When do I stop writing code and just prompt the model instead?"
· 2 voices
From an interview with John Smith
The hybrid database plus context window stack that scales from zero
"How do I combine traditional databases with AI context windows without hitting scalability walls?"
· 2 voices · 6 reads
Prompted by John Smith
Building the knowledge base stack without going broke
"How much does AI infrastructure actually cost per student-mentor pair to run profitably?"
· 2 voices · 3 reads
Prompted by a community member
RAG on a budget starts with the right input scope
"How do we build a working RAG system without blowing the token budget?"
· 1 read
From an interview with Matthew Zimmer
Why local AI platforms beat the Silicon Valley extraction model
"How can San Diego's AI experts build defensible moats against centralized AI services?"
· 1 voice · 7 reads
Prompted by a community member
Token Economics in Knowledge Base Systems: When Cheap Queries Hide Real Bottlenecks
"Why do low per-query costs mask the real scaling constraint in AI knowledge bases?"
Prompted by a community member
Context Windows Kill Flat-Fee AI Products. Here's Why Your Next Build Needs to Be Diagnostic.
"How do I price an AI product without burning through margins on tokens?"
Prompted by a community member
Building a repeatable lead enrichment pipeline from tool evaluation to delivery
"How do we pick, test, and wrap enrichment tools into a productized workflow?"
· 1 read