What are AI agents, really: a plain-English explainer
A Singapore-based operator's plain-English explainer on what AI agents actually are, how they work, and why most of the marketing overstates it.
The best AI image generators in 2026
I tested eight AI image generators for prompt accuracy, licensing, and cost, from Midjourney to FLUX, to find which ones are actually worth paying for in 2026.
The best AI agent frameworks in 2026
Seven AI agent frameworks I've actually built with, compared on real setup time, pricing, and where each one breaks. No hype, just what shipped.
Reasoning models explained: when thinking tokens are worth it
What reasoning models are, how thinking tokens change cost and latency, and when paying for extra reasoning actually improves your results, explained.
How to write evals for an LLM feature
A practical, step-by-step guide to writing evals for an LLM feature: golden datasets, scoring methods, LLM-as-judge, and wiring results into CI.
How to stop prompt injection in an LLM app
A practical guide to stopping prompt injection in LLM apps: attack surface mapping, input/output filtering, least-privilege tool access, and testing.
How to ship an LLM feature that survives real users
A practical, no-hype guide to shipping LLM features that survive real users: eval sets, cost ceilings, fallbacks, and a gradual rollout process.
How to run an LLM locally in 2026: a complete guide
A step-by-step guide to running open-weight LLMs like Llama 3 and Qwen 2.5 on your own hardware in 2026, from picking a GPU to serving an API locally.
How to fine-tune a small model on your own data
A practical, step-by-step guide to fine-tuning a small open model like Llama 3.2 1B on your own data using LoRA, with real commands and cost estimates.
How to cut your LLM API bill in half
A practical, step-by-step guide to halving your Claude API spend using prompt caching, batching, model selection, and token counting.
How to choose an embedding model in 2026
A practical 2026 framework for picking an embedding model: MTEB benchmarks, dimension tradeoffs, cost at scale, and how to test on your own data.
How to build an AI agent that uses tools
A practical, step-by-step guide to building a tool-calling AI agent with Claude or OpenAI, including code, guardrails, and how to scale it safely.
Bright Data vs Oxylabs: which proxy provider do you actually need
Bright Data and Oxylabs both sell residential, mobile, and datacenter proxies. I compare pool size, pricing per GB, rotation, and geo coverage here.
Context windows explained: how big is big enough
What an AI context window actually is, how token limits work across GPT, Claude, and Gemini, and why bigger isn't always better for your use case.
The best AI coding assistants in 2026, ranked by real use
A hands-on look at the AI coding assistants worth paying for in 2026 — how the terminal agents, IDE copilots, and review bots actually hold up on real codebases.
Claude vs ChatGPT vs Gemini: which to actually pay for in 2026
A practical, task-by-task comparison of the three frontier AI assistants in 2026 — coding, long-context work, research, and writing — so you can pick by the job, not the hype.
How to build a RAG pipeline that doesn't hallucinate
The retrieval, chunking, grounding, and evaluation choices that decide whether a RAG system is trustworthy or a demo that falls apart on real questions.
AI API pricing compared: what you'll really pay in 2026
How AI API pricing actually works in 2026 — input vs output tokens, caching, batch discounts, and the hidden costs that make the sticker price misleading.