#google-ai
6 posts tagged with #google-ai
Every article below is hand-written, technically reviewed, and focused on google-ai. Posts cover real-world architecture decisions, code-level implementation patterns, and trade-offs you'll only discover after shipping production systems.
![NotebookLM Agentic AI Upgrade: What It Does [2026]](https://img.kunalganglani.com/images/vzekdneq/production/cba87920f2c5c27b79d3ca9b9e35374f0e10b72d-1200x675.webp?auto=format&fit=max&q=75&w=600)
AI and Machine Learning NotebookLM Agentic AI Upgrade: What It Does [2026]
Google's NotebookLM evolved from a document Q&A tool into a multi-modal agentic platform — here's what the coding agent actually does and whether it can replace dedicated tools like Claude Code.
AI and Machine Learning Gemma 4 12B vs GPT-4o Mini vs Claude Haiku: Is Google's Local LLM Good Enough to Replace API Calls? [2026]
I ran Gemma's 12B model locally via Ollama and compared it against GPT-4o Mini and Claude Haiku on real dev tasks — here's when the free local model actually beats paid APIs.
AI and Machine Learning GPT-4.1 vs Gemini 2.5 Pro 2026: Which Flagship LLM Wins?
GPT-4.1 wins for instruction-following, coding workflows, and API-first production deployments; Gemini 2.5 Pro wins for long-context reasoning, multimodal tasks, and deep Google ecosystem integration. Your choice hinges on workload, not hype.
AI and Machine Learning Gemini Flash vs Pro for Developers: Which Google AI Model Actually Fits Your Use Case [2026]
Google's Gemini lineup keeps growing — Gemini 3.1 Pro, Gemini 3 Flash, massive context windows, Project Astra. Here's what actually matters for shipping real applications.
AI and Machine Learning Run Gemma 3 Locally on Windows: The VRAM Guide Nobody Gave You [2026]
I break down exactly how much VRAM you need for every Gemma 3 model size, the Ollama setup on Windows with NVIDIA GPUs, and the performance tweaks that actually matter.
AI and Machine Learning Fine-Tuning Gemma 2 for Code Generation: 31 Percentage Points of Accuracy for Under $15 [2026 Guide]
I fine-tuned Google's Gemma 2 9B model for Python code generation using QLoRA on a single GPU. Accuracy jumped from 42% to 73% — here's exactly how, what it cost, and what I learned.
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