How to Run AI Workflows Locally on a Mac Using n8n and Ollama

·2 views

Developers can set up fully local AI workflows on a Mac by combining n8n for automation and Ollama for running large language models, with no data sent to external servers. The setup relies on Docker and n8n's official Self-Hosted AI Starter Kit, a Docker Compose template that bundles n8n, Ollama, Qdrant, and PostgreSQL. Because Docker on Apple Silicon cannot access the Mac's GPU, Ollama should be installed natively on the host machine and connected to the containers via the host.docker.internal address. Once running, users can access n8n at localhost:5678, configure an Ollama credential pointing to the local API, and wire models like Gemma4 or Llama3.1 into chat triggers and AI agent workflows. Agents can also be extended with tool nodes such as HTTP requests or vector store lookups, provided the chosen model supports tool use.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Developer builds local-first password manager to cut out cloud sync by default

A developer has released SafeVault, a cross-platform password manager built with Flutter that stores vault data locally rather than syncing it to a third-party cloud server. The app uses AES-256-GCM encryption and relies on OS-level secure storage for keys, with multi-device sync available only over local or nearby networks that the user controls. SafeVault is available on iOS, Android, macOS, and Windows across 13 locales, and includes desktop features such as a system tray, global hotkey search, and auto-lock. A built-in security dashboard highlights weak, reused, or outdated passwords and offers inline fixes to address them. The project is aimed at privacy-conscious users and developers who prefer not to trust a hosted backend with an encrypted copy of their credentials.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

SPOKE Board Turns Fruit and Copper Pads into a MIDI Instrument via RP2040

The SPOKE is a CD-sized capacitive touch controller built around the RP2040 microchip, featuring 27 copper pads that each trigger a musical note when touched. It connects to a computer via USB-C and is recognized as a standard MIDI controller, with an accompanying website offering audio visualizers, a drum kit, and a Harmonic Table. Running CircuitPython firmware, the board can be programmed like a Raspberry Pi Pico, and its default pentatonic scale can be customized by editing note values in code. Two expansion ports allow additional sensors to be added, and each pad is paired with a NeoPixel LED that changes color on touch. Notably, any conductive material — including fruit, copper tape, or pencil graphite — can extend the touch inputs, effectively turning everyday objects into playable instruments.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

AI Demand Fuels Memory Chip Boom, but Supply Risks Loom for Investors

Artificial intelligence has transformed the memory semiconductor industry by driving unprecedented demand for high-bandwidth memory (HBM), which is essential for powering AI accelerators. HBM, conventional DRAM, enterprise flash, and hard drives each play distinct roles in the AI infrastructure stack and carry different investment profiles. Micron is currently shipping HBM4 in volume, giving it a competitive edge, though analysts warn that product leadership must be continuously defended as rivals close the gap. Capital expenditure is rising sharply — Micron alone expects around $27 billion in spending — meaning today's investments could become tomorrow's oversupply if demand growth slows. Investors are advised to monitor bit shipment volumes versus pricing trends, inventory levels, and gross margins to distinguish sustainable growth from a cyclical peak.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

How to Deploy LangGraph + MCP Agents as Stable Production Services

Developers running LangGraph and Model Context Protocol (MCP) agents locally often face crashes and erratic behavior when moving to production environments. The core challenge lies in structuring the agent as a long-running service capable of handling continuous requests, recovering from failures, and adapting to distributed system realities. Using Python's signal module, developers can implement graceful shutdown handling for SIGTERM and SIGINT signals to prevent abrupt termination. A key production concern is state persistence — the langgraph.Checkpointer class enables periodic saving and restoration of agent state, ensuring recovery after unexpected crashes. The article presents a foundational service pattern as a starting point for building more scalable and resilient agent deployments.

0 comments Read more at DEV Community