The author installs Antigravity CLI, runs a full workflow with Gemini 3.7 Flash, consumes less than 10% of a 5‑hour quota, benchmarks the model against Claude Opus and GPT‑5.5, and concludes the free Google AI Pro year offers strong performance and cost‑effectiveness.
The author shows that adding three lines to Codex’s config file enables a 1.05 million‑token context window, but experiments reveal a 1.7× token‑usage increase, a steep accuracy drop, and clear guidelines on when (and when not) to use this setting.
After upgrading to GPT‑5.6, many Codex users find that the large number of accumulated Skills no longer speeds up work but actually lengthens execution time, increases token consumption, and adds extra tool calls because the model now reads and enforces Skill rules more rigorously, turning Skills from execution helpers into contextual overhead.
Gemini‑3‑pro’s surprise debut and OpenAI’s emergency release of GPT‑5.2 highlight a shift toward faster inference, deeper reasoning, and lower hallucination rates, with detailed performance metrics, three‑tier model options, extended context windows, and mixed community test results that reveal both strengths and shortcomings.
Meta's newly released Llama 4 quickly became a controversy as internal leaks reveal training‑data cheating, benchmark over‑optimization, and disappointing code‑generation performance that fails to match even older models, prompting resignations and widespread criticism from the AI community.
