Ink: A Deployment Platform Where Claude AI Agents Are the Primary Users
Ink (ml.ink) is a deployment platform where the primary users are AI agents, not humans. It was created to address the frustration of using Claude Code: while Claude can write a full-stack app in minutes, manually wiring up hosting, databases, DNS, and secrets takes about an hour.
Key Features and Workflow
The platform allows Claude to deploy applications with a single command: say "deploy this." Claude calls the create_service function, the platform auto-detects the framework, builds it, and returns a live URL at *.ml.ink.
Differentiation from Existing Services
- Unified Tool: One tool handles compute, databases, DNS, secrets, domains, metrics, and logs. Claude doesn't need to juggle separate providers.
- Error Handling for LLMs: When a deploy fails, Claude reads the build log, fixes the issue, and redeploys. Error responses are structured for LLMs rather than raw stack traces.
- DNS Zone Delegation: Delegate a zone once, and Claude can create subdomains instantly.
- Built-in Git Hosting: Claude pushes code and deploys without requiring GitHub.
- Shared Workspaces: Multiple agents and humans can collaborate on the same projects.
Additional Platform Details
The platform includes a human UI for logs, metrics, and DNS management. It offers GitHub integration for auto-redeploy functionality. Billing is per-minute with no per-seat charges.
The service is currently available with a free $2 trial that doesn't require a credit card. A 20% discount code "GOODFORTUNE" is offered for further testing.
Notably, Ink was built with Claude Code specifically for use with Claude Code.
📖 Read the full source: r/ClaudeAI
👀 See Also
Kanban CLI: A Local-First, Agent-First Task Manager for the Terminal
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User-built PTC for Claude Code shows 40-65% token savings on analysis tasks, not code writing
A developer built a local PTC implementation called Thalamus for Claude Code and analyzed 79 real sessions, finding 40-65% token savings on analysis tasks but near-zero savings on code-writing tasks. The agent used execute() primarily for general Python computation rather than batching tool calls.
Ollama's Technical Issues and Community Controversy
Ollama, a popular local LLM tool, faces criticism for downplaying its reliance on llama.cpp, license compliance issues, and technical problems with its custom backend including performance regressions and reintroduced bugs.
altRAG: Replace Vector DB RAG with 2KB Pointer Files for AI Coding Agents
altRAG is a Python tool that replaces vector database RAG with lightweight pointer files. It scans Markdown/YAML skill files to create a 2KB skeleton file mapping sections to exact line numbers and byte offsets, allowing AI agents to read only needed sections instead of entire files.