THE BOUNDEV JOURNAL
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Earlier writing on software, hiring, and building AI. Articles retain their original URLs and publication dates; some historical offers and references are outdated.
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Fix a slow LLM feature: cut time to first token
Why your LLM feature feels slow, what time to first token really measures, and the production levers that cut it: end-to-end streaming, model choice, and caching.
pgvector vs Pinecone vs Qdrant: picking a vector DB
Real cost and latency numbers for pgvector, Pinecone, and Qdrant at 10M and 100M vectors, plus a simple rule for picking a vector database for production RAG.
Cut LLM API costs with prompt caching: the real math
Prompt caching cuts LLM input costs 80-95 percent, but only if your prompts are structured for it. The real break-even math and the three mistakes that cause cache misses
Why bigger context windows make your AI agent worse
A bigger context window will not fix an unreliable AI agent. Here is what context engineering is and the production tactics that keep accuracy high.
Acceptance criteria for AI features that actually ship
Why "given X, when Y, then Z" breaks for AI features, and how to rebuild acceptance criteria and your definition of done around evals and pass rates.
How to ship an AI feature without breaking production
Shadow mode, canary rollouts, kill switches, and a numbers-based definition of "safe to ship" for AI features inside a live SaaS product.
AI in Cybersecurity: Benefits, Risks, and What SaaS Teams Need to Know
AI strengthens defenses and enables new attacks simultaneously. For SaaS founders and CTOs building AI features, understanding both sides of this dynamic is no longer opt
Machines and Trust: How to Mitigate AI Bias
Unwanted AI bias is already a widespread problem. Machine learning models replicate and exacerbate existing biases — often in ways that aren't detected until after releas
Building an AI Product? Maximize Value With an Implementation Framework
Building an AI product involves more disciplines, more unknowns, and more failure modes than a standard SaaS feature. This four-phase framework — Discovery, Validation, S
The 4 Crucial Stages of Successful Generative AI Integration
Only 10% of companies are successfully scaling AI initiatives, according to BCG. The gap isn't ambition or budget — it's execution sequence. This framework maps the four
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