Cutting Down 'Plausible but Wrong' RAG Answers — A Retrieval Evaluation Harness for Gemma 4 and Antigravity
Replace gut feeling with recall@5, MRR and faithfulness scores — a 30-question golden dataset and a small Python harness for evaluating a local Gemma 4 RAG stack.
Which Local-LLM Path to Keep with Antigravity: Ollama, LM Studio, or Raw Gemma 4 (Indie Judgment Notes)
After running three local-LLM paths from Antigravity in parallel, here is what I kept and what I cut, written from a one-Mac-mini indie operation.
Gemma 4 on Antigravity: Picking Q4 vs Q5 — What I Found After a Week on M2 Mac
A hands-on comparison of Gemma 4 quantization variants (Q4_K_M / Q5_K_M / Q8_0 / fp16) running locally with Antigravity on a 16GB M2 Mac, measured across speed, memory, and output quality.
Gemma 4 Fine-Tuning in Practice: Preventing Data Starvation, Overfitting, and Quality Problems
A practitioner's guide to Gemma 4 fine-tuning—covering data quality validation, LoRA vs QLoRA selection, overfitting prevention with early stopping, checkpoint selection, and pre-deployment quality evaluation with complete code examples.
Antigravity x LiteLLM: Routing Multiple LLM Providers Through a Single Proxy
A practical guide to placing LiteLLM in front of Antigravity so you can route across Gemini, Claude, OpenAI, and local Gemma 4 from a single endpoint, including fallback chains and operational pitfalls.
Force Gemma 4 to Stay Inside Your Schema: A Production Guide to Constrained Decoding in Antigravity with GBNF, Outlines, and vLLM
A production-grade guide to running Gemma 4 locally in Antigravity while guaranteeing schema-compliant output. Covers llama.cpp GBNF grammars, Outlines with Pydantic, and vLLM guided_json — with concrete Python code and failure-mode fallbacks.
Memory Budget Design for Embedding Gemma 4 in Mobile Apps
When embedding Gemma 4 into a mobile app, peak memory during inference — not the model file size — becomes the real bottleneck. A memory budget design based on measured values, plus a device-tier switching strategy.
Running Multiple Gemma 4 LoRAs in Production — A Practical Guide to Merging and Dynamic Adapter Switching
You've trained three LoRAs on Gemma 4 — one for summarization, one for translation, one for code review. Now the real question: how do you serve them in production without tripling your GPU bill? This is my working notebook on merging and dynamic switching, written with Antigravity alongside.
Running Gemma 4 on a Self-Hosted vLLM Server from Antigravity — A Practical Guide to Building High-Throughput Inference
Gemma 4 runs beautifully in Ollama for a single user. The moment a handful of teammates start hitting the same endpoint, throughput collapses. This guide walks you through replacing Ollama with a self-hosted vLLM server, wiring it into Antigravity as a custom endpoint, choosing a quantization level with real numbers, and hardening the stack with rate limits and LoRA routing — end to end.
Tuning Gemma 4 for Yourself — A Realistic LoRA / QLoRA Workflow on a Solo Developer's Budget
Full fine-tuning of Gemma 4 is out of reach for most individuals, but LoRA / QLoRA makes personalization realistic on a solo budget. This guide walks through data prep, training settings, evaluation, and wiring the result into an Antigravity workflow — from hard-earned practical experience.
Antigravity AI Agent Design: Multimodal Production Implementation Patterns
A complete guide to building multimodal AI agents with Google Antigravity (Gemma 4). Covers image+text integration, Function Calling, async batch processing, state management, error handling, and cost estimation — with production-ready code.
Fine-Tuning Gemma 4 in Practice: From Google Colab to Vertex AI
A practical guide to fine-tuning Gemma 4 with QLoRA on Google Colab (free GPU) and Vertex AI. Covers dataset preparation, 4-bit quantization, LoRA adapter configuration, training, and inference — with working code throughout.