So Anthropic announced they built a new model called Claude Mythos and then decided NOT to release it to the public because of safety concerns. First time a major AI lab has done this. And honestly, the ecom community should pay attention to why this matters. Here's the thing. This tells us these models are getting seriously powerful, fast. If a lab is building something they're scared to ship, you can bet the stuff they ARE releasing is already more capable than most sellers are using day to day. Most people in this community are still using AI to write a product bullet or two. The gap between what's possible and what people actually do is massive. For our businesses, the practical takeaway is this: don't wait for the "perfect" model before you build real AI workflows. The tools we have right now, Claude, GPT-4o, Gemini, are already good enough to cut your customer service ticket time in half, generate 30 ad creative variations in an hour, and rewrite your entire listing catalog in a weekend. The ceiling keeps moving up but the floor is already high enough to build on. The other thing worth thinking about is trust. Anthropic choosing not to ship something because it wasn't safe yet is actually a signal that they take the reliability of their tools seriously. For sellers running automated review responses, AI-generated ad copy, or supplier negotiation scripts, that matters. You want the company behind your tools to have some standards. Bottom line, the AI race is moving faster than any of us expected. The sellers who win aren't going to be the ones who waited for the best model. They're going to be the ones who built repeatable systems with what's available right now and iterated from there. What's one workflow in your store, could be ads, listings, support, anything, where you know AI could help but you just haven't set it up yet?

Welcome! You just joined a community of people who are done using AI like a search engine. Here's how to get the most out of this group: Step 1: Introduce yourself Drop a comment below with your name, what you do, how you're currently using AI, and what you want to automate first. Step 2: Grab the free guides We have two PDF guides pinned in this category: - OpenClaw Setup Guide: Go from zero to a running AI assistant - Advanced Playbook: Multi-agent teams, cost optimization, battle-tested prompts Step 3: Pick ONE thing to automate this week Don't try to do everything. Pick one: - Set up an AI assistant on your phone (WhatsApp) - Automate your inbox - Build your first scheduled automation - Run a competitor analysis in 30 seconds Step 4: Share your win in the Wins category Nothing motivates like results. When you save time or solve a problem with AI, post it. Community rules: 1. Be helpful. Share what you know. 2. No spam, no self-promo without value. 3. Ask questions — there are no dumb ones here. 4. Share real results, not hype. See you inside.

If you're running AI agents with MCP tools, you've felt the pain: every tool's full schema gets injected into the system prompt on every single turn. A 50-endpoint API burns 3,500+ tokens of context before your conversation even starts — and that bill repeats on every message. mcp2cli fixes this. It's a Python CLI that turns any MCP server or OpenAPI spec into a fully functional command-line tool — at runtime, with zero code generation. Instead of stuffing thousands of tokens of tool schemas into your AI's context window, your agent just shells out to mcp2cli and gets the exact data it needs. The token savings are insane: 96–99% reduction in wasted schema tokens. HOW IT WORKS → Point it at any MCP server (HTTP/SSE or stdio) or OpenAPI spec → It dynamically generates subcommands from the tool definitions → Your AI agent calls it like any other CLI tool — clean, fast, token-efficient KEY FEATURES → Works with MCP servers AND OpenAPI specs (JSON or YAML) → Built-in OAuth support (PKCE flow, client credentials, token caching) → Secure secret handling (env: and file: prefixes — no secrets in process listings) → TOON output format — token-efficient encoding that cuts another 40-60% for LLM consumption → Smart caching with configurable TTL → Ships with an installable AI skill so Claude Code, Cursor, and Codex can use it natively QUICK START pip install mcp2cli List tools from an MCP server: mcp2cli --mcp https://mcp.example.com/sse --list Call a tool: mcp2cli --mcp https://mcp.example.com/sse search --query "test" Use with OpenAPI specs: mcp2cli --spec https://petstore3.swagger.io/api/v3/openapi.json --list If you're building with MCP or connecting AI agents to APIs, this is a no-brainer addition to your toolkit. Stop paying the token tax. GitHub: https://github.com/knowsuchagency/mcp2cli

Most people are still using Claude Code like a fancy autocomplete. Meanwhile, Anthropic just shipped what I think is the biggest upgrade since launch — and almost nobody is talking about it. Skills aren't instructions anymore. They're programs. Here's what dropped: 🔧 SKILLS SYSTEM (merged with Commands) - Custom slash commands and skills are now the same thing - Every skill gets a SKILL.md file with YAML frontmatter — description, tool restrictions, model routing, execution isolation - Claude auto-loads the right skill based on what you're doing. No manual switching. - Skills are directories now — templates, examples, validation scripts, all bundled together 4 built-in skills ship out of the box: - /simplify — spawns 3 parallel review agents to clean your code - /batch — decomposes large changes into 5-30 independent units, each gets its own agent + PR - /debug — reads debug logs and troubleshoots your session - /loop — runs prompts on a cron schedule ("check if deploy finished every 5 min") 🤖 SUBAGENTS (Specialized Workers) - Dedicated AI workers with their own context window, system prompt, and tool restrictions - Route simple tasks to Haiku (cheap), complex ones to Opus (smart). Per-skill, per-agent. - 5 built-in: Explore (read-only search), Plan (research), General-purpose, Bash, and Claude Code Guide - Custom subagents defined in markdown — store at project or personal level 👥 AGENT TEAMS (Experimental) - Multiple Claude Code instances working together with shared task lists - Teammates message each other directly — not just reporting back to a main agent - Best for: cross-layer changes (frontend + backend + tests), competing debugging hypotheses, research from multiple angles ⚡ HOOKS (17 Event Types) - User-defined handlers that fire at lifecycle points: SessionStart, PreToolUse, PostToolUse, SubagentStart, PreCompact, etc. - Three types: shell commands, HTTP endpoints, or LLM prompts - Can block dangerous operations before they execute