ai-ml-landscapelisted

Comprehensive practitioner reference for the 2025–2026 AI/ML landscape covering frontier model selection and routing, open-weight vs hosted API trade-offs, reasoning models and test-time compute, RAG and agent production patterns, classical ML for tabular data, deep learning foundations (transformers, attention, MoE, SSMs), LLM training and post-training (DPO/GRPO/LoRA/QLoRA), inference serving (vLLM/SGLang), evaluation benchmark skepticism, safety/alignment, EU AI Act governance, and hardware selection. Use when advising on model selection, AI architecture decisions, LLM deployment, fine-tuning strategy, benchmark interpretation, or AI governance compliance.
adammatthewsteinberger/vibey-skills · ★ 1 · AI & Automation · score 74

Install: claude install-skill adammatthewsteinberger/vibey-skills

# AI & Machine Learning Landscape — Practitioner Reference (2026) ## The Three-Layer Structure of AI in 2026 1. **Frontier model race** — fast-moving, narrow quality gaps between top models (~2.7% lead at the top as of March 2026) 2. **Production engineering stack** — maturing: vLLM/SGLang serving, MCP-standardized agents, contextual-retrieval RAG 3. **Classical ML core** — stable: gradient boosting still wins most tabular problems **Inference cost collapse**: >280-fold reduction from $20.00 to $0.07 per million tokens between Nov 2022 and Oct 2024 (GPT-3.5-equivalent quality, per Stanford HAI). This makes capability cheap and **engineering discipline** the binding constraint. --- ## Frontier Model Landscape (Mid-2026) No single model dominates. The top model leads by only ~2.7% (Stanford HAI AI Index, March 2026). The leading closed-weight model's edge over the top open-weight model on Chatbot Arena narrowed from 8.04% (Jan 2024) to 1.70% (Feb 2025). **Key practitioner principle:** Build behind an abstraction layer (LiteLLM/OpenRouter) to avoid lock-in. Model names and rankings shift weekly. --- ## Hosted APIs vs Open Weights — Decision Rule | Factor | Hosted API | Open Weights | |---|---|---| | Peak capability, zero ops | ✓ | | | Privacy, air-gap, data residency | | ✓ | | Fine-tuning control | | ✓ | | Predictable cost at sustained high QPS | | ✓ | | Fastest to start | ✓ | | **Open-weight options**: DeepSeek-V4 (MIT-licensed, 1M context, 1.6T/49B active MoE), Qwen