Production-tested skills for AI agents. Every skill is security-scanned, tier-rated, and verified. Browse by ecosystem or category below.
Spin up a private ChatGPT clone on your laptop in 10 minutes with Ollama and Open WebUI. Zero data leaves your machine, zero subscription, full OpenAI-compatible API.
Browserbase owns the narrative. Steel.dev owns the deploys. Apache-2.0, one Docker command, 7K stars, batteries-included sessions. The right browser harness in 2026 is the one you can run yourself.
Pinecone, Weaviate, and Qdrant are a tax on your ignorance. pgvector with DiskANN does the same job on the Postgres instance already running your app — for one tenth the cost, with one tenth the latency. The vector database category is closing. Pay the migration tax now or keep funding someone else's Series C.
Hugging Face just shipped FineWeb-3: 15 trillion tokens. Six times the 2024 release, twice the indexed English web. The dataset is billed as progress. It is an admission — that the data play has shifted from engineering to extraction, and the labs are out of clever ideas.
Frontier APIs, prompt caching, distillation, and 1M-token context windows have made most custom LoRAs worse than calling Claude with a good system prompt. The fine-tuning cottage industry is selling 2023 infrastructure to a 2026 market.
Velocity is up. Comprehension, debugging, and architectural judgment are collapsing. We are celebrating the wrong number.
OpenAI dropped the full GPT-5.5-Cyber, a beefed-up Codex Security plugin, and a 'Patch the Planet' OSS initiative. The week's biggest LLM story.
NVIDIA Dynamo, Apache-2.0: KV-aware routing, disaggregated prefill/decode, NIXL transfers. The inference OS MoE teams have been waiting for.
Mistral released OCR 4 on June 24: 170 languages, bounding boxes, typed blocks, confidence scores, $4 per 1,000 pages, 72% human-preference win rate. The boring last-mile RAG layer just became strategically valuable.
Everyone has a multi-agent framework. Most reinvented the org chart without the part that actually made the company work. MetaGPT encoded Standardized Operating Procedures as the runtime — Code = SOP(Team) — and that single decision is more interesting than anything LangGraph, CrewAI, or AutoGen shipped in 2025.
Google shipped Gemini 3.5 Flash as GA at I/O on May 19 — default in the Gemini app and AI Mode in Search — beating Gemini 3.1 Pro on coding and agentic benchmarks at roughly 4x the speed. Then Sundar Pichai told the room to expect 3.5 Pro next month. The Flash-as-flagship pattern just got validated.
Extended thinking is the single biggest token-spend lever on Claude, and most teams leave it set to 'as much as it wants.' That is the default. Anthropic shipped a `budget_tokens` field that caps how much Claude is allowed to think before it must answer. One parameter, 40 to 60 percent cost reduction on most agent workloads. Here is the recipe.
Stop guessing where your LLM spend goes. One decorator wraps any LLM call and logs tokens, cost, and latency to a JSONL file you can tail in real time.
Vector search gets you 80% there. Cross-encoder reranking closes the gap. Here's a 30-line Python implementation that lifts retrieval accuracy from ~62% to ~84% on a typical RAG benchmark.
Every lab is racing to ship the longest context window — and every team building agents is paying 10x for 10% of the benefit. Long context is a workaround for a problem we refuse to solve: memory.
The Rust-based JavaScript toolchain is the real deal — and the AI features bolted on top are exactly the kind of hype-driven engineering decisions I wish the ecosystem would stop making.
vLLM wasn't built for MoE. Dynamo is. That changes who wins the inference layer.
OpenAI's first custom inference ASIC took nine months to design, claims 50% lower cost per token, and is targeting 1.3 gigawatts of deployment — this is the day AI stopped being a pure NVIDIA rental business.
Your streaming LLM endpoint has 200ms median first-token latency and a 9-second p99. The model is fine — your streaming plumbing is buffering tokens it should be flushing. Four knobs fix it without touching the model server.
Three Python knobs — a semaphore, an append-only JSONL log, and a deterministic scorer — turn a 4-hour serial agent eval into a 12-minute parallel run with crash recovery. No Ray, no LangSmith bill.
11.5K stars, Rust, ~1% of global LLM API traffic, $7.3M seed. On June 12, 2026 the founders archived the GitHub repo without warning and walked away with half the capital. The product was not the problem. The category got eaten while they were still building.
The 'open-source AI caught up' narrative is wrong. The Western labs quit on it, the gap is structural, and the frontier is closed. China is the only game left — and even China is 8–10 months behind.
The smart money in 2026 is not on AI wiping out junior devs — it's on coding agents dissolving the 50% of staff engineers who are competent but unremarkable. The middle of the bell curve is ground zero.
On June 30, 2026, Huawei dropped openPangu-2.0-Flash — a 92B-total/6B-active MoE with 512K context, 34T pretraining tokens, and a non-Apache license. SWE-bench Verified 63.1%, LiveCodeBench V6 85.1%, AIME 2026 98.1% w/ Python. The training stack is the real news.