LEARN
AI Learn
Learn AI from scratch — what RAG, agents, prompts, fine-tuning, alignment, and context windows actually mean; how to pick a tool; practical use cases; advanced techniques.
Write better business emails with AI without sounding like AI
AI is great at the unblocking parts of email. It's terrible at the parts that determine whether you get a reply.
Build a Discord bot powered by Claude or GPT in an evening
Discord plus an LLM is a perfect first AI side project — small surface area, real users, fast iteration.
Generate product launch copy + images + video with AI
AI compresses launch prep from weeks to days — but only if you know which artifacts AI handles vs which need humans.
Use AI to write SQL queries when you're not an engineer
AI-generated SQL is dangerous when you trust it blindly. Here's the workflow that lets non-engineers query data confidently.
Replace internal status meetings with AI summaries
Most status meetings could be a 5-minute read. Here's the structure that gets your team to actually do that.
Prep podcast questions and generate viral clips with AI
AI is great at the laborious before/after. Avoid AI for the conversation itself.
Use AI to clean up messy CSV / Excel data
AI compresses hours of data wrangling into minutes — for the right kind of data, with the right verification habits.
AI product photos that don't look fake (for ecommerce)
Most AI ecommerce photos look obviously AI. Here's the workflow that produces ones customers don't notice.
Debug production issues with an LLM in the loop
Practical patterns for using Claude / GPT-5 / Cursor when production is on fire and you have to ship a fix.
Temperature, top-p, top-k: sampling parameters explained
Three knobs that control how creative or boring your model output is. Most people leave them at default; the defaults are usually wrong.
System prompt vs user prompt: who's in charge here
Two parts of an LLM message that get treated very differently. Mixing them up is the most common API mistake.
LoRA vs fine-tuning vs RAG: which solves which problem
Three different ways to make an LLM do what you want. Picking the wrong one wastes weeks. Here's the decision framework.
How to cut your LLM API bill in half without dropping quality
Prompt caching, model routing, output capping, and four other optimizations that compound. Real production teams cut bills by 50-80%.
What AI resume screening actually does (and the bias trap)
Most 'AI screening' is keyword matching with extra steps — and it can launder bias if you let it.
Localize your product into Traditional + Simplified Chinese with AI
Practical workflow for shipping zh-TW + zh-CN versions without burning $20k on a translation agency.
RLHF vs DPO: how modern alignment techniques differ
After pre-training, how do labs make a model actually answer your question instead of finishing your sentence? Two main families.
What 'Mixture of Experts' (MoE) actually means
Why a 'huge' model can be cheaper to run than a smaller one — and why DeepSeek, Mixtral, and Llama 4 all bet on this architecture.
AI alignment: what it is, why labs argue about it
Alignment is the work of making models do what humans actually want — not what we literally said.
AGI vs ASI: definitions, timelines, and why they matter
Two acronyms get thrown around as if everyone agrees what they mean. They don't.
Build an agent loop from scratch (no framework)
Strip away the abstractions. The actual loop is 50 lines and you should write it once before you reach for LangGraph.
Defending against prompt injection: realistic guardrails for 2026
There is no perfect defense. Here's the layered playbook that reduces real-world risk by 95%.
LLM observability: logging, tracing, evals
If you can't see what your agent did and why, you can't fix it. The 2026 stack and the four signals that matter.
Fine-tune a Llama 3 70B locally with LoRA
What it actually takes — hardware, dataset, hyperparameters — to fine-tune a 70B model on your own machine in 2026.
Self-host a high-throughput inference server with vLLM
When self-hosting actually beats API calls — and how to get vLLM serving 1000 req/min on a single GPU.