Production-tested skills for AI agents. Every skill is security-scanned, tier-rated, and verified. Browse by ecosystem or category below.
DeepSeek's 1M token context window wasn't a benchmark stunt. Here's why the context window war matters more than the model weight race for every builder working with AI agents today.
Claude Fable 5 lived for 72 hours, the SWE-bench Verified leaderboard is statistically flat across ranks 5-10, and Kimi K2.7-Code beats Opus 4.8 on tool use. The right answer is a two-tier agent stack, not picking a winner. Here is the full breakdown, the benchmarks that actually matter, and the routing rules to ship this weekend.
You spent $200 last month on Copilot. Your M2 Pro has 32GB sitting idle. A 15-minute setup gives you a free, private, code-aware VSCode AI assistant.
Google shipped DiffusionGemma on June 10, 2026 — the first open-weights text diffusion LLM. 26B MoE, parallel generation, ~4x faster than AR.
Every AI agent demo looks incredible. Here's what separates the agents that survive contact with production from the ones that fall apart the moment real users touch them.
Z.ai open-sourced GLM-5.2 on June 16, 2026 — 753B MoE, 40B active, MIT license, 1M context, beats GPT-5.5 on SWE-Bench Pro, FrontierSWE, MCP-Atlas, and GDPval-AA v2. The story isn't the model. The story is IndexShare: a sparse-attention architecture that cuts per-token FLOPs by 2.9x at 1M context, published openly, reproducible, and the reason the closed-lab bundle just stopped being defensible. First-party API is $1.40 / $4.40 per million tokens. Self-host is electricity. The frontier is open and the architecture is the moat.
Adapt is a self-evolving LLM memory layer: Brain auto-decomposes into Neurons that learn and restructure themselves. TypeScript, under 200KB, MIT, still 0.0.x.
Every AI coding agent (Claude Code, Cursor, Codex CLI, Aider, Continue) shares one annoying bottleneck: you cannot run two of them on the same branch at the same time without the second stomping the first. The fix is git worktrees plus tmux. Twenty minutes of setup, hours saved per week.
On June 9, 2026 Anthropic shipped Claude Fable 5 — its first public Mythos-class model — and Mythos 5 for vetted partners. Three days later, a U.S. export-control directive forced Anthropic to disable both models worldwide, including for its own foreign-national employees. The first time a national government has ever yanked a deployed frontier model. Plus a separate scandal: Fable 5 was silently nerfing frontier LLM research queries. Anthropic walked that back. Both stories matter. Most people are only telling one.
50%+ of Fortune 500 developers use AI coding agents weekly. The IDE is now an AI-first interface. Here's why that matters for every engineering team that hasn't made the switch yet.
On June 17, 2026, a coalition including Google, Microsoft, Amazon, Anthropic, and OpenAI published the Agentic Resource Discovery specification. ARD gives AI agents what DNS gave the internet in 1983: a way to find things without knowing where they live. This is the most important infrastructure story of the week, and almost nobody is covering it like it is.
Every LLM-backed service eventually hits a wall of 429s, cost spikes, or one client hogging capacity. Here is a 40-line async token bucket pattern that handles bursts, isolates per-client traffic, and slots cleanly into a FastAPI handler — with concrete sizing numbers for OpenAI, Anthropic, and OpenRouter.
Mastercard Agent Pay, Google's AP2, Stripe's agent SDK, Coinbase's x402 — every payments company is shipping an 'agent holds a wallet' pitch in 2026. Every one of them is selling the same lie. AI agents should never be allowed to hold keys, sign authorizations, or settle payments directly. The agent-payment layer will be the largest fraud vector of 2027 unless the industry ships policy engines, not wallets, before the rails go live.
Unsloth is the fast kid. Axolotl is the configurable kid. LLaMA-Factory is the kid who shows up with the right YAML, the right dataset utilities, and a web UI the rest of your team can use without a PhD. ~50,000 GitHub stars, 200+ model recipes, Apache 2.0, and the only fine-tuning framework quietly adopted by Alibaba, Microsoft, Tencent, IBM, NVIDIA, and Baidu.
On June 16, 2026, Chinese lab Z.ai shipped GLM-5.2 to its coding plan — 744B MoE with 40B active, 1M context, MIT license, and an 81 on Terminal-Bench that beats most closed frontier models. This is the first open-weight release that genuinely competes on coding at the top of the leaderboard, and the vibe check across the community is unanimous.
Every AI agent demo looks incredible. Here's what separates the agents that survive contact with production from the ones that fall apart the moment real users touch them.
OpenAI just expanded Daybreak with GPT-5.5-Cyber, Codex Security, and Patch the Planet. Anthropic, Google, and the open-source world are all shipping cyber-capable models. The bottleneck just shifted from finding vulnerabilities to fixing them — and that's a bigger inflection than the headlines suggest.
Six days after the US government pulled the plug, Anthropic restored Claude Fable 5 on June 18 — with nationality-based access controls, tighter safety classifiers, mandatory 30-day data retention, and Mythos 5 still locked behind Project Glasswing. The launch was the story in week one. The restoration is the story in week two, and it will define frontier AI deployment for the rest of 2026.
Three companies I consulted for this spring froze junior engineering hires and blamed "budget." AI coding agents did the rest. The industry is trading a five-year talent pipeline for a quarter of margin, and every CTO who approved it is telling themselves a story.
Your app calls OpenAI. OpenAI rate-limits you. Your customer sees a 500 and churns. LiteLLM is the 15-minute fix — an OpenAI-compatible proxy with cross-provider fallback, cost tracking, and per-team spend caps that drops into your existing codebase without changing a line of application code.
Three models, three SDKs, three error formats. LiteLLM is the OpenAI-compatible proxy that sits in front of Claude, GPT, Gemini, Ollama, and 100+ providers, giving your whole stack one endpoint, one SDK, built-in cost tracking, virtual keys, fallbacks, and budgets. Five-minute setup. Version-controlled YAML. Pays for itself the day you add your second backend.
60,000+ GitHub stars, Apache 2.0, an ingestion pipeline that actually reads PDFs, hybrid vector + BM25 + reranking, MCP, agent memory, and a chat-channel story (Feishu, Discord, Telegram, Line) baked in. RAGFlow is not the prettiest RAG framework. It is the one quietly shipping everything your $20k/month SaaS RAG vendor charges you for, and the institutional adoption behind it is the part nobody in the U.S. press is covering.
Every year since 2016, someone with a vested interest has promised me AI will replace engineers within five years. We are ten years deep into this prediction. The engineers are still here. The vendors are richer. I'm tired of pretending the next five years will be different, and you should be tired of paying for the prediction.
You wrote a Python function. You want Claude, Cursor, or Continue to call it. Wrap it in 50 lines with the official MCP SDK and any MCP-aware client can discover and call it. No glue code, no SDK rewrites.