2857 Model Context Protocol servers — searchable, rated, ready to install.
Open weights briefly beat every proprietary model in early 2025 (deepseek-r1); today the best.
The best open model trails the best closed model by 42 Arena points. In a blind vote it would.
qwen3.8-max trails claude-fable-5.1-max by 27 points. Over the past year the gap has stayed.
claude-opus-4-6-high leads Arena coding at 1535. Anthropic has held #1 for 280 days, but Z.ai.
Mockup to native, editable Elementor page from claude.ai or ChatGPT: design tokens, SEO, Schema.org.
Mac app that runs Claude or Codex on a schedule against your mail, calendar, and files.
Free AI visibility check: can AI crawlers read a site, and do ChatGPT, Gemini, Perplexity cite it?
Independent test scores for open-weight LLMs: best model for your GPU or Mac, comparisons.
HVAC MCP server — fault codes, model lookup and answers cited to the OEM manual page.
Read how often ChatGPT, Perplexity, Gemini and Google AI name your brand, its rivals and sources.
Lets Claude, ChatGPT, and Muse read and update your beckett tasks, notes, calendar, and lists.
Ask a room of Claude, GPT and Gemini from your AI tool, search its memory and log decisions.
Search, read and grow a Karpathy-style LLM wiki of linked Markdown pages, with cited answers.
HealthDCAT-AP catalogs of published Semantic Data Charter models; Turtle or JSON-LD; no account.
Plain-English directory of which apps connect to ChatGPT, Claude, Gemini and Grok, with steps.
Audit any URL for ChatGPT, Claude & Perplexity GEO citation readiness, llms.txt & Schema.org.
Create and manage dynamic QR codes and read their scan analytics from Claude and other MCP clients.
AI meta harness for Codex, Claude Code, Cursor and VS Code: direction and completed-work review.
Count and cut tokens in prompts, chats and agents for any model. Runs locally, collects no data.
Find web scrapers that work; compare scraping APIs, proxy prices and RAG extractors.
Between 31 Aug–6 Sep and 25 Sep–1 Oct, anthropic's PyPI downloads grew 25% and openai's 14%,.
What changed in your stack since the model's cutoff, error fixes, and security-scanned skills/MCPs
Probes vector-database endpoints for unauthenticated exposure of embeddings/RAG data
Which parts of an ML need Hugging Face already covers. Every model and dataset checked live.
POST /mcp