Blog Archive
Gemini 3 in LegalTech: Automating Complex Contract Audits with High-Precision Reasoning
Contracts are full of computational clauses that probabilistic models get wrong. Learn to build a production-grade contract audit system that pairs Gemini 3 extraction with a deterministic rule engine to eliminate the "reasoning cliff" and the hallucination risk.
Green AI: Optimizing Gemini 3 Reasoning for Low-Power Infrastructure
AI inference is primarily a data-movement problem, not a compute problem. Learn practical strategies to reduce the energy cost of Gemini 3 reasoning: token budgeting, model selection, quantization, speculative decoding, and measuring impact per prompt.
Gemini 3 in Cyber-Defense: Real-time Threat Hunting and Automated Mitigation
Threat hunting is a reasoning problem, not a pattern-matching problem. Learn to build a Gemini 3-powered defensive agent that hunts threats across SIEM data, maps behavior to MITRE ATT&CK, and automates mitigation with human-in-the-loop gates.
Agentic Game Design: Procedural World Building with Gemini 3 Multimodal Guidance
Games are multimodal by nature: maps, sprites, audio, and physics all have to agree. Learn to build an agent that designs and builds 3D game worlds with Gemini 3, using a constrained schema compiler and a generate-see-correct vision loop.
Agents in VR/AR: Navigating 3D Spaces with Gemini 3 Native Spatial Reasoning
Spatial reasoning is what lets an agent understand the difference between "on the table" and "next to the table." Learn to build VR/AR agents that perceive 3D space with Gemini 3, ground actions with bounding boxes and trajectories, and act through WebXR.
Batch Inference at Scale with Ray
Offline LLM work — scoring, summarization, backfills — needs throughput and reliability, not latency. Distribute thousands of calls with Ray tasks and actors, retries, and structured cost tracking.
Streaming Inference with FastAPI and Server-Sent Events
Turn a blocking LLM call into an incremental, observable stream of tokens with FastAPI StreamingResponse and Server-Sent Events — including cancellation and backpressure.
Fine-Tuning vs. RAG: When to Use Which Technique
Fine-tuning changes how the model behaves; RAG changes what it sees. Use a checklist and a small offline eval to decide between the two — and when to combine them into a hybrid.
Structured Outputs: JSON Schema Validation for LLMs
Ask an LLM for JSON and you will get broken JSON. Learn to constrain generation with Gemini response_schema and Pydantic, then verify the output as an explicit, catchable step.
MLOps for LLMs: Versioning Prompts, Data, and Weights
LLM apps drift for reasons unrelated to weights. Version prompts, datasets, and models against the metrics that decide whether a change is an improvement, using MLflow tracking plus the Model Registry.
The Importance of Explainable AI (XAI) for Debugging and Trust
Explainability is the interface for human judgment. Learn the four practical XAI levers for LLM apps — RAG attribution, logprobs, traces, and SHAP — to make systems debuggable and trustworthy.
Secure AI Development: Secrets Management and Prompt Injection Defense
Instructions and data share the same token stream. Build the defensive stack for AI apps: env-based secrets, system-instruction hygiene, provider safety filters, output validation, and least-privilege tooling.