NextTech Insights
Security-first playbooks for Web3 + AI + modern engineering.
Short, practical, copy-pasteable.
Start here
GSC setup (Next.js + Vercel)
Verify, submit a sitemap, and clear the most common indexing blockers.
Read →
Safe airdrop claim checklist
Avoid signing scams and approvals that can be abused later.
Read →
Permit2 (2026)
What changed about approvals, and how to use it safely.
Read →
Next.js security update playbook
Patch fast, reduce blast radius, and keep evidence for incident follow-ups.
Read →
Featured
How to fix Google-selected canonical mismatches
A practical checklist for when URL Inspection shows a mismatch between the user-declared canonical and the Google-selected canonical. Normalize URLs, use self-referencing canonicals, keep sitemaps aligned, and avoid changing canonicals in JavaScript.
Latest Articles
Hybrid search and reranking for RAG: a practical checklist (2026)
A practical checklist to improve RAG retrieval with hybrid search (BM25 + vectors), rerankers, and query rewriting. Reduce “wrong chunk wins” by tuning filters, top-k, and evaluation.
RAG chunking and indexing: a practical checklist for better retrieval (2026)
A practical checklist for RAG chunking and indexing. Improve retrieval quality by choosing chunk sizes, overlaps, metadata, filters, and re-index policies that reduce noise and prevent “wrong chunk wins.”
RAG citations and grounding: a practical checklist for trustworthy answers (2026)
A practical checklist to make RAG answers trustworthy. Enforce citations, track doc_ids, handle conflicts, and prevent the model from inventing sources. Includes logging and UX patterns that scale.
LLM evals and regression tests: a shipping checklist for AI apps (2026)
A practical checklist to prevent silent quality regressions when you change prompts, models, retrieval, or tools. Learn what to evaluate, how to version datasets, and how to gate releases.
LLM cost guardrails: budgets, caps, and alerts for AI apps (2026)
A practical checklist for preventing runaway LLM spend. Set per-request caps, per-user budgets, alerting, and safe fallbacks so costs stay predictable even under retries and abuse.
LLM rate limits and retries: a reliability checklist for AI apps (2026)
A practical checklist to make AI apps reliable under rate limits and transient failures. Learn how to set timeouts, retries, backoff, idempotency, and fallbacks without creating hidden loops or runaway costs.