All Articles
I Was Handing Gemini Obfuscated Stack Traces — Until retrace Went In Front, the Diagnoses Were Confident and Wrong
Release stack traces come out of R8 with the names flattened. Feed one to Gemini as-is and the diagnosis arrives calm, well-written, and wrong. Put retrace in front, match the mapping by versionCode, and forbid confident answers when you cannot restore. Numbers from 42 reports.
The Model Didn't Ship on Its Rumored Date — Read Your Context Limit From the API, Not the Headlines
July 17 came and went with no official word on Gemini 3.5 Pro. Instead of baking rumored numbers into constants, here's a context budget layer that reads the real limit from models.get and degrades quietly when input overflows.
A Japanese query won't surface its English twin — when embeddings notice language before meaning
Embed a translation pair with gemini-embedding-2 and the two halves won't be nearest neighbours, because language itself inflates similarity. Here is how I measured cross-lingual recall using translation pairs as ground truth, and what happened when I subtracted the language centroid.
A Gemini stream drops halfway — restart it, or have the model continue?
Most apps silently restart a dropped stream. Here is the arithmetic behind continuing from the partial output instead, and where to put the threshold.
What language should your system instruction be in? Measuring three approaches when most prompts arrive in the user's language
Keep the system instruction in English, or translate it into the user's language? I measured input tokens per language with countTokens, then lined up output-language match and schema compliance to find where nine tokens is enough.
I stopped storing every generation log — three retention tiers and a prompt fingerprint that keeps traceability
I was storing every Gemini API request and response body for debugging. Here is how I moved to three retention tiers plus a prompt fingerprint, and kept the ability to diagnose issues without keeping the text.
I Asked Gemini to Grade My App Store Screenshots. Everything Scored 78–85.
Ask Gemini Vision to grade App Store screenshots out of 100 and good candidates and deliberately broken ones both land at 78–85. Here is how I measured the judge's discrimination power, dropped absolute scoring, and rebuilt it as debiased pairwise comparison.
I Put =GEMINI() in 420 Cells. The Calls Kept Coming on Days I Never Opened the Sheet
A custom function calling Gemini from Sheets looks best the moment it starts working, and it does not grow from there. Here is what the 30-second ceiling, uncontrolled recalculation, and read-only cells actually cost, measured, plus the code to move to a write-back design.
The Table Was There, but the Rows and Columns Weren't — Preserving Docs Structure Before It Reaches Gemini
getBody().getText() flattens Google Docs tables into a column of loose values. Here is what that cost me on a 42-row ledger, the Apps Script extraction layer that keeps the structure, and the acceptance test that keeps it honest.
Sample what you already accepted — an audit budget that catches silent quality drift
A confidence gate only ever looks at output the model hesitated on. Silent drift sinks into the batch that sailed through. Working from a fixed 30-minutes-a-day review budget, this walks through deriving detection time from the binomial, reallocating the same budget across risk strata, and catching slow decay with a cumulative monitor.
A near-miss label won't fix itself on retry — a normalization layer for closed-vocabulary classification
When responseSchema enum returns an out-of-set label, retrying tends to return the same near-miss. From a wallpaper app's 30-category batch, here is the distribution of how labels miss, plus a normalization layer built on an alias table and gemini-embedding-2 nearest-neighbor, with measured results.
Before One Runaway Experiment Drains the Shared Budget: Using AI Studio Spend Caps as Isolation Walls
When you run several Gemini experiments under one billing account, a single runaway loop takes everything else down with it. Here is how I use AI Studio's per-project spend caps as isolation walls, plus a client-side soft ceiling and monthly reconciliation, with working code.