Stop a Batch Before It Overspends — A Budget Gate Built on countTokens That Survives a Default-Model Swap
Nightly batches overspend because you only learn the cost after billing. Starting from countTokens, this guide builds a budget gate that folds in thinking tokens and keeps your estimate intact even when the default model changes underneath you.
Restarting a Long Agent Run From Where It Broke — A Step-Ledger Design for Gemini 3.5 Flash Long-Horizon Tasks
Gemini 3.5 Flash is good at long-horizon tasks, but when a 40-step run dies on step 29, you usually start over. An append-only step ledger gives you resume, idempotency, and audit in one place. Here is the design with working Python and measured results.
Keeping Nightly Batches Alive After the Gemini CLI Stops Responding: A google-genai SDK Fallback
On June 18 the Gemini CLI stops answering requests. Here is a small fallback harness that probes whether the CLI can still respond and quietly reroutes unattended batch jobs to the google-genai SDK, built from my own automation.
Keep Your Flash-to-Pro Routing Threshold Honest with Shadow Re-evaluation
A Flash-generates, Pro-on-low-confidence router starts drifting the moment you hand-pick its threshold. This is a working build of a loop that samples your kept-Flash outputs, scores them against Pro, and recalibrates the threshold from a quality budget.
Running Gemini Chat History on Redis — Field Notes on Not Losing Conversation State in Production
Keep a Gemini ChatSession in process memory and it evaporates on every redeploy or scale event. Here is how I back it with Redis in production, covering token budgets, concurrent sends, SDK coupling, and graceful degradation, with the code I actually run.
Watching the 'Voice' of Generated Text: Catching a Silent Default-Model Swap Through Style Drift
When the default model changes over your head, the output can stay factually correct while its voice quietly shifts. This walks through fingerprinting the style of generated text and detecting drift statistically, with a dependency-free implementation you can drop into your pipeline.
Before You Let a Managed Agent Ship: Designing Your Own Acceptance Gate
Let the public-preview Managed Agents generate files and broken artifacts will flow straight into production. Here is how to build a verification gate that artifacts must pass before you accept them, with runnable Python and a rejection-feedback loop.
Don't Break When the Default Model Moves: A Startup Capability-Probing Layer for Gemini
Pinning a model name breaks on deprecation; trusting the default breaks when the weights swap silently. This is the design I settled on: probe what the served model can actually do at startup, then build every request from that answer. Includes runnable Python.
When Your Firestore × Gemini Embeddings RAG Quietly Degrades — Designing for Re-Embedding
A RAG built on Firestore native vector search and Gemini Embeddings drifts when the embedding model changes generations, and retrieval quality drops with no errors. Here is how to detect the drift, re-embed without downtime, and keep retrieval cost in check.
When the Default Model Silently Upgrades: Catching Prompt Regressions in Numbers
Gemini 3.5 Flash is now the default and you can no longer turn it off. Assuming your responses can shift without you touching the prompt, here is how to bundle prompt, model, and sampling into one variant and catch regressions with canaries and an LLM judge — in working code.
Defending Against Prompt Injection When You Pass External Text to the Gemini API
User reviews, scraped articles, and other untrusted text are the entry point for indirect prompt injection when you feed them to the Gemini API. Here is a prioritized, code-backed defense you can drop into a production pipeline: trust-boundary isolation, schema constraints, a two-stage screening pass, and output sanitization.
Permission-Aware RAG — Designing Gemini Search That Only Cites What the User Is Allowed to See
The day you add RAG to internal search, drafts and finance memos nobody should see start leaking into answers. This is a production design — metadata filtering, defense in depth, and audit logging — for letting Gemini search while respecting permissions, with working code.