90% of AI Engineer interviews in 2026 are just these 15 concepts repeated: 1/ Transformers & LLM Fundamentals: how LLMs predict the next token, self-attention, multi-head attention, positional encoding, and why transformers replaced RNNs 2/ Tokenization & Embeddings: BPE, how text becomes tokens, and how vectors capture meaning 3/ Prompt Engineering: system prompts, few-shot learning, chain-of-thought, and structured outputs (JSON/schema) 4/ RAG: chunking strategies, retrieval, context injection, context windows, and why RAG beats fine-tuning for fresh data 5/ Vector DBs & Search: vector databases, semantic search, hybrid search , reranking, and HNSW indexes 6/ Function Calling, Tool Calling & MCP: how LLMs trigger APIs, code and databases, and the standard protocol for connecting them 7/ AI Agents & Agentic Workflows: plan, act and observe loops, orchestrated multi-step pipelines, and AI coding agents 8/ Memory & Multi-Agent Systems: short-term and long-term memory, and specialized agents collaborating on a task 9/ Fine-Tuning: full fine-tuning, LoRA, QLoRA, PEFT, and when to fine-tune vs prompt vs RAG 10/ Alignment & Distillation: RLHF, DPO, and knowledge distillation (big model teaches small model) 11/ Quantization & Model Compression: INT8/INT4, pruning, sparsity, and the quality vs speed vs cost tradeoff 12/ Inference & Serving: prefill vs decode, KV cache, speculative decoding, LLM serving, vLLM, batching, and model routing 13/ Multimodal AI & Vision-Language Models: text, image, audio and video in one system, and models that see and reason about images 14/ Evals & Hallucination Detection: LLM evaluation, LLM-as-judge, prompt evaluation, and catching confident but wrong answers 15/ Safety, Observability & Production: guardrails, AI safety, prompt injection, tracing, cost optimization, and production GenAI systems Know these 15 deeply and you can crack most AI engineer interviews

I asked Claude Code to make a new GTA: San Andreas mission where CJ has to vibe code a startup and then raise money from investors in SF

@flohoeller's idea, built the same afternoon. open any task, see the full lineage: root to every descendant, who started whom, status and duration per task. fan-outs collapse, tested at 150. experimental, PR up: github.com/desplega-ai/agent…

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