The Date Column Was the Most Expensive Part of My Sales CSV — Measuring What Attachments Really Cost
2.4.3 lets you attach .json, .md, and .csv files directly. I rendered the same table eight ways, priced every column in tokens, and boiled it down to a 1,062-token digest. Every number here came from a run on my own machine.
Half My Tasks Went to Pro — and So Did Only 61% of the Tokens
A singular model setting became a models collection, which means routing across models is now something you define yourself. Here is how I re-measured a task-type routing rule against the actual context-size distribution of 67 tasks in my own repository.
Three Quarters of My Reference Notes Never Reached the Agent: Measuring What head Cuts Away
I fed reference notes to a scheduled agent with cat and head, and the lines that mattered were quietly cut. Here is the measurement, and how I replaced a line count with a section-level contract.
Three Ways to Hand 4,000 Lines of Logs to an Agent — Paste, .txt Attachment, or @ Reference
v2.3.0 added plain-text attachments, which means there are now three ways to hand a long log to an agent — and a new question about which one to pick. Here is how I trim, measure, and decide, with the scripts I actually run.
Where Context Should Live — Routing It Across Persistent, Per-Task, and In-the-Moment Layers
Antigravity 2.0's /btw lets you inject context mid-task. But unless you separate what belongs in a persistent rule, a task instruction, and a passing aside, your agent's accuracy actually drops. Here is a design for three context layers.
Measure Before You Trim: A Context Ledger for Antigravity CLI Token and Latency Costs
Prompted by the ~70% token reduction reported for the Android CLI agent, I built a thin wrapper and a weekly review to measure my own agent runs. Here is how I replaced whole-file context with line ranges and cut wait times.
Antigravity Advanced Prompt Engineering — Agent Instructions, AGENTS.md & Context Design
Extract maximum performance from Antigravity's AI agents through advanced prompt engineering. Covers AGENTS.md design, context window optimization, role-playing techniques, chain-of-thought, self-verification loops, and real-world agent rule definitions.