How the central question of building with AI has shifted three times in four years — from 'what should I say?' to 'what system should I build?' A deep dive into prompt engineering, context engineering, and harness engineering with architecture diagrams, decision frameworks, and production lessons for engineering leaders.
A deep dive into building a local LLM API server on an RTX 3060. Ollama + FastAPI + Cloudflare Tunnel. 6 layers of security, real benchmarks, and hard-won lessons.
A complete technical guide to building a profitable agentic AI system using only open-source tools — with retrieval, orchestration, tool use, and observability. Includes architecture diagrams and real cost analysis.
OpenAI's GPT-5.4 crosses the human baseline on OSWorld-V with native computer-use, 1M token context, and parallel tool calling. Here's what it actually means for teams building AI systems in 2026.
An 8B model that fits in 1GB RAM, runs on your iPhone, and stays competitive with full-precision models. PrismML's Bonsai changes the math on edge AI deployment. Here's the technical breakdown.
Meta dropped Llama 4 Scout, Maverick, and Behemoth. Google fired back with Gemma 4. As a Technical Lead, here's what these releases actually mean for your teams and projects.
Alibaba just released its third proprietary model in days. Google's Gemini Flash-Lite costs $0.25 per million tokens. NVIDIA's Nemotron runs 2.2x faster than GPT-OSS-120B. The LLM cost war has arrived — here's what it means for architects choosing AI infrastructure in 2026.
OpenAI just extended the Responses API with a shell tool, hosted container workspace, context compaction, and reusable agent skills. Here's what this means for teams building real agentic workflows — with hands-on examples.
GPT-5.4 crossed human performance on desktop tasks. With a hosted shell, 1M context window, and native compaction, the era of AI as an autonomous coworker is finally here — and the implications for developers are profound.
GPT-4 level AI cost $30/M tokens in 2023. Today it's under $1. Here's the technical architecture that lets you capture 90%+ of that savings without sacrificing quality.
Deep dive into Gemini 3.1 Pro's practical developer features — 77.1% ARC-AGI-2, dynamic thinking API, 1M token context, and what these benchmarks actually mean when building production AI systems.
Beyond benchmarks — a real comparison of DeepSeek V3.2 and Gemini 3 Pro for production developer workflows. Cost, code quality, context window, multimodal capabilities, and when to use which.
Mistral Large 3's MoE architecture delivers 92% of GPT-5.2's performance at 15% of the cost. As a technical lead who has run open-source LLMs in production, here's where it works and where it fails.
Claude Opus 4.6, GPT-5.4 Thinking, and Gemini 3.1 Pro all shipped within the same month. Here's a practical guide to choosing the right model for your team — based on real benchmarks and production patterns, not marketing slides.
Mistral's Devstral 2 scores 72.2% on SWE-bench, ships under MIT license, and costs up to 7x less than Claude Sonnet. Here's how it works, when to use it, and whether open-source agentic coding is finally production-ready.
How AI is reshaping software architecture from the inside — ML-powered pattern selection, LLM orchestration, AI gateways, event-driven agents, and a full team implementation roadmap with diagrams and production-ready code.
GPT-5.4, Gemini 3.1 Pro, and Claude 4.6 are now neck-and-neck on benchmarks. When the models are equal, everything else becomes the differentiator. Here's how to choose.
Machine learning, LLMs, embeddings, and RAG — explained without hype. Two tech leads break down what AI actually does, using analogies about libraries, pattern matching, and really smart parrots.
Bonus Part 8 — Testing real-time voice AI is notoriously hard. Learn strategies for mocking, latency benchmarking, and LLM-as-judge evaluation. Then build a transcript analysis system that generates a scored interview report with specific action items.
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