AI & Machine Learning
Applied AI/ML product development and integration.
agentsModel Context Protocol (MCP) for Developers
The Model Context Protocol is an open standard for connecting AI applications to your data and tools. A practitioner's guide to how MCP works, the host-client-server model, its tools, resources, and prompts, building an MCP server, and the security part most demos skip.
agentsClaude Code Skills: Teaching Your AI Coding Agent Your Stack
Claude Code skills are folders of instructions a coding agent loads only when relevant. A practitioner's guide to the SKILL.md model, progressive disclosure, how skills differ from MCP, and how to author ones that encode your stack's conventions instead of bloating context.
agentsThe AI Software Engineer: What the Role Actually Is Now
An AI software engineer is an engineer who builds software with AI in the loop, directing agents and owning the result. A grounded look at how the role changed, the skills that now matter, how it differs from ML and prompt engineering, and how to grow into it.
agentsAI Pair Programming: How Senior Engineers Work With AI
AI pair programming is working with an AI as your coding partner: it drives or navigates while you hold judgment. A grounded look at the day-to-day practice, a real session, where it shines, where it quietly fails, and the habits senior engineers use to keep it honest.
agentsAI Coding Tools in 2026: The Landscape, Honestly
There is no single best AI coding tool, and any ranking is stale within a month. This is a vendor-neutral map of the categories that hold their shape, the selection criteria that actually matter, and the trade-offs nobody puts in the demo. A dated mid-2026 snapshot.
agentsAgentic Coding: Building Software With AI Agents in the Loop
Agentic coding is directing an AI agent through real engineering work: it plans, acts, and checks itself in a loop while you set direction and review. A grounded guide to how the loop runs, where agents earn their keep, where they thrash, and the guardrails that keep it safe.
agentsVibe Coding: A Practitioner's Guide to Shipping With AI
Vibe coding is describing what you want in plain language and letting an AI write the code. A grounded guide to the workflow, where it breaks on security and maintainability, and how senior teams ship with it without shipping garbage.
llmChatGPT for Startups: How LLMs Reshape Software Delivery
ChatGPT for startups went from novelty to standard tooling. Here's where LLMs actually move the needle on software delivery, where they quietly create risk, and the guardrails a small team needs to ship fast without shipping garbage.
agentsAI Customer Support: Deflection, Agent-Assist, and Real ROI
AI customer support is past the hype cycle. Here's what actually works in production in 2026: deflection that holds up, agent-assist that saves real handle time, sentiment and routing that route, and the failure modes vendors won't show you in the demo.
ragRAG LLM Systems: A Production Architecture Guide
Most RAG LLM demos work on day one and fall apart in production. This guide walks the full pipeline a team actually ships — chunking, embeddings, retrieval and reranking, prompt assembly, hallucination control, and the evaluation loop that keeps it honest.
llmLLM Development: A Practical Guide for 2026
LLM development is less about the model and more about everything around it: retrieval, orchestration, evals, and deployment. Here is a practical map of what the work actually involves, where the hard parts are, and how to decide what to build versus buy.
agentsGenerative AI for Business: Where It Pays Off
Generative AI for business is now a question of where it pays off, not whether it can. A function-by-function look at the use cases that return real money, the ones that are mostly hype, what deployment actually takes, and how to manage the risk honestly.
llmGenerative AI Development: An End-to-End Build Guide
An engineering-led walkthrough of generative AI development: how to pick a use case worth building, choose between API and open-weight models, design the architecture, add guardrails, control cost, and evaluate quality before and after launch.
llmFine Tuning LLM: When to Fine-Tune vs RAG (2026 Guide)
Most teams reach for fine-tuning when prompting or RAG would have shipped faster and cheaper. This guide covers the real decision: when fine tuning an LLM pays off, how to prep data, LoRA vs QLoRA, evaluation, cost, and the pitfalls we see in production.
agentsBuilding an Enterprise AI Strategy in 2026
Most enterprise AI strategies are decks, not plans. This is the version that ships: what an AI strategy actually is, the five pillars under it, a roadmap from pilot to scale, the mistakes that drain budgets, and how to tie the whole thing to outcomes you can measure.
llmEnterprise AI: Use Cases, Risks, and How to Adopt It
Enterprise AI is less about models and more about everything around them — data, risk, and readiness. Here are the use cases that pay off, how large organizations actually adopt AI, and how to know if you're ready.
agentsMeasuring AI ROI: What Actually Moves the Needle
Average reported return on AI is 3.7x per dollar, yet most pilots move the bottom line by nothing. The gap is not the model. A grounded look at the real cost of an AI project, where AI ROI actually comes from, and how to measure it without fooling yourself.
agentsAI Readiness: Is Your Company Ready to Ship AI
Most companies feel ready for AI and are not. AI readiness is the honest measure of whether your data, infrastructure, skills, governance, and use cases can carry a model to production. A practitioner's self-check, the gaps teams miss, and how to ship a first project.
llmAI Integration: How to Add AI to Your Product
Adding AI to a product you already run rarely means building a model from scratch. Here's how AI integration actually works — the main approaches, what each costs and takes, and the risks worth planning for before you ship.
agentsAI Implementation: From Pilot to Production
Most AI pilots never reach production. AI implementation is the work of closing that gap: the data, integration, evaluation, governance, and change management that turn a demo into something real users depend on. A grounded playbook from a team that ships.
cost-guideAI Development Cost: What AI Features Really Cost in 2026
There's no price list for AI features, and the API-vs-build choice swings the number more than anything. Here's what actually drives AI development cost, the project shapes to expect, and how to keep spend tracking value.
architectureAI Development Company: How to Choose a Partner That Ships
A senior, no-hype guide to what an AI development company does across discovery, data, model and LLM selection, evaluation, deployment, and monitoring — and how to pick a partner that ships AI that holds up under real traffic, not just a slick demo.
agentsAI Agents Explained: How Agentic Systems Actually Work
AI agents are language models wired into a loop with tools, memory, and a goal. A grounded explainer of how agentic AI works, the architecture underneath it, where multi-agent setups help, and the jobs where agents earn their keep.
agentsAI Agent Use Cases: Where Autonomous Agents Pay Off
Most 'AI agent' demos never survive contact with production. Here are the AI agent use cases that actually pay off — in support, sales, ops, data, and engineering — what makes them work, and the ones that quietly don't.
agentsAI Agent Development: How to Build Production Agents
Most AI agent demos break the moment real users touch them. This is how senior teams actually build production agents: tool integration, guardrails, evaluation harnesses, human-in-the-loop, and the observability that keeps them honest under load.
agentsAgentic AI in Production: Beyond the Pilot
Agentic AI demos beautifully and ships rarely. Gartner expects over 40% of agentic AI projects to be cancelled by 2027. A practitioner's view of what production-grade agentic AI takes, where it pays off, and how to get one from demo to real users without it becoming an incident.
architectureAI Software Development: How It Gets Built End to End
AI software development isn't regular software with a model bolted on. This is the real pipeline we ship: discovery, data plumbing, LLM and model integration, evaluation harnesses, and the MLOps that keeps it from rotting in production.