Spine Swarm: Multi-Agent AI System on Visual Canvas for Non-Coding Projects
Spine Swarm is a multi-agent AI system that operates on an infinite visual canvas designed for complex non-coding projects. The founders argue that chat interfaces are inadequate for complex AI work because they're linear, while real projects aren't linear. They built a workspace where work structure is explicit and user-controllable.
Core Architecture
The system uses blocks as abstractions on top of AI models. There are dedicated block types for:
- LLM calls
- Image generation
- Web browsing
- Apps
- Slides
- Spreadsheets
Blocks can be connected to any other block, with connections guaranteeing context passing regardless of block type. The system is model-agnostic, allowing workflows to move between different AI models within a single project.
Agent Operation
When a user submits a task, a central orchestrator decomposes it into subtasks and delegates each to specialized persona agents. These agents:
- Operate on canvas blocks
- Can override default settings (model and prompt) for each subtask
- Pick the best model for each block
- Sometimes run the same block with multiple models to compare outputs
- Work in parallel when subtasks don't have dependencies
Agents can pause execution to ask for user clarification or feedback before continuing. Once agents produce output, users can select a subset of blocks and iterate on them through chat without rerunning the entire workflow.
Technical Advantages
The canvas provides agents with a persistent, structured representation of the entire project that any agent can read and contribute to at any point. This addresses context degradation issues in typical multi-agent systems by:
- Storing intermediary results in blocks rather than holding everything in memory
- Creating explicit structured handoffs designed for consumption by other agents
- Allowing agents to run longer while keeping context windows clean
Users can dispatch multiple tasks at once, and the system will queue dependent ones or start independent ones immediately.
📖 Read the full source: HN AI Agents
👀 See Also

Logseq Brain v0.6.0: Persistent Memory Plugin for Claude Code Adds Journey Log and Section-Targeted Reads
Logseq Brain v0.6.0 adds a journey log for all operations, section-targeted reads for token savings, and progressive disclosure for skill files.

Bitcoin MCP Server with 43 Tools for AI Coding Agents
bitcoin-mcp is an MCP server with 43 Bitcoin tools including fee advisors, mempool analysis, and inscription detection. It works with Claude Desktop, Claude Code, Cursor, VS Code, and Windsurf using live data from APIs or local nodes.

Skillware adds synthetic data generator with entropy scoring for local model fine-tuning
Skillware has released a new synthetic data generator skill that uses zlib compression-ratio heuristics to score output diversity, helping prevent model collapse. The tool works out-of-the-box with Ollama, supports Gemini/Anthropic for high-reasoning batches, and outputs JSON batches for .jsonl fine-tuning pipelines.

Sx: An Open-Source Package Manager for AI Skills, MCPs, and Commands
Sx is a private npm-like package manager for AI assets—skills, MCP configs, commands, hooks, and agents—that lets teams share, version, and scope AI configurations across any AI client (Claude Code, Cursor, Copilot, Gemini).