Self-hosting provides a hands-on way to learn modern infrastructure, covering essential skills such as deployment, networking, storage, monitoring, and system reliability.
1. **Awesome Selfhosted**: A curated list of open-source applications across various service categories.
2. **Coolify**: An open-source PaaS for deploying apps, databases, and services on your own servers.
3. **n8n**: A visual workflow automation platform for connecting APIs and services.
4. **Uptime Kuma**: A monitoring system for tracking service uptime, status dashboards, and alerts.
5. **Nextcloud Server**: A private cloud platform for file synchronization, storage, and collaboration.
6. **Immich**: A self-hosted photo and video management and backup platform.
7. **Memos**: A lightweight Markdown note-taking tool with a timeline interface.
8. **Proxmox VE Helper Scripts**: Community scripts for managing LXC containers and VMs on Proxmox VE.
9. **Awesome Tunneling**: A curated list of tools for secure remote access to local services via tunneling.
10. **Self-Hosting Guide**: A comprehensive reference guide covering hardware, software, and infrastructure concepts.
1. **Awesome Selfhosted**: A curated list of open-source applications across various service categories.
2. **Coolify**: An open-source PaaS for deploying apps, databases, and services on your own servers.
3. **n8n**: A visual workflow automation platform for connecting APIs and services.
4. **Uptime Kuma**: A monitoring system for tracking service uptime, status dashboards, and alerts.
5. **Nextcloud Server**: A private cloud platform for file synchronization, storage, and collaboration.
6. **Immich**: A self-hosted photo and video management and backup platform.
7. **Memos**: A lightweight Markdown note-taking tool with a timeline interface.
8. **Proxmox VE Helper Scripts**: Community scripts for managing LXC containers and VMs on Proxmox VE.
9. **Awesome Tunneling**: A curated list of tools for secure remote access to local services via tunneling.
10. **Self-Hosting Guide**: A comprehensive reference guide covering hardware, software, and infrastructure concepts.
Explores how to use the llm CLI tool within a script's shebang line by utilizing the env -S pattern. This approach allows plain text files to become executable via large language models, treating file contents as prompts. The article covers using flags like -f for fragments and -x for code extraction, integrating tools such as llm_time, and leveraging YAML templates with parameters or embedded Python tool functions to create highly functional scripts.
Main topics:
- Using env -S with the llm CLI
- Making text files executable via LLM
- Integrating tools within shebang lines
- Utilizing YAML templates for prompts
- Embedding Python functions in templates
Main topics:
- Using env -S with the llm CLI
- Making text files executable via LLM
- Integrating tools within shebang lines
- Utilizing YAML templates for prompts
- Embedding Python functions in templates
gogcli is a script-friendly Google Command Line Interface designed for terminals, shell scripts, CI/CD pipelines, and coding agents. It provides programmatic access to a wide range of Google services including Gmail, Calendar, Drive, Docs, Sheets, YouTube, Analytics, and Workspace admin flows. The tool features predictable JSON or plain output formats, supports multiple OAuth configurations (including service accounts), and includes robust safety mechanisms like command allowlists/denylists and read-only audit modes for sensitive data surfaces.
gitcrawl is a local-first GitHub triage tool and a drop-in caching shim for the gh CLI. It mirrors repository issues and pull requests into a local SQLite database, enabling semantic clustering and full-text search while preventing API rate limit exhaustion. This setup allows maintainers and AI agents to perform heavy read operations against a local cache rather than live GitHub servers.
Main features:
Local SQLite storage for all issue, PR, and commit metadata.
A gh-compatible shim that handles most read-only calls locally.
Semantic clustering using OpenAI embeddings to group related reports.
An interactive terminal UI for cluster browsing.
JSON support for easy automation with AI agents.
Main features:
Local SQLite storage for all issue, PR, and commit metadata.
A gh-compatible shim that handles most read-only calls locally.
Semantic clustering using OpenAI embeddings to group related reports.
An interactive terminal UI for cluster browsing.
JSON support for easy automation with AI agents.
An exploration of an experiment involving connecting a local Large Language Model to Home Assistant to control a smart light bulb. By assigning the AI a specific persona through custom system prompts, the author attempted to make the lighting respond emotionally to environmental data. While successful in creating reactive lighting, the experience ultimately became unsettling as the model made autonomous decisions without direct input.
- Connecting local LLMs via LM Studio and Home Assistant
- Using system prompts to define device personalities
- Automating smart bulb color and brightness through AI reasoning
- The psychological impact of unsupervised AI autonomy in a smart home environment
- Connecting local LLMs via LM Studio and Home Assistant
- Using system prompts to define device personalities
- Automating smart bulb color and brightness through AI reasoning
- The psychological impact of unsupervised AI autonomy in a smart home environment
PiShrink is a bash script designed to automatically shrink Raspberry Pi images, making them easier to store and faster to flash onto SD cards. Once the shrunk image is booted, it will automatically resize itself to match the maximum capacity of the target SD card. The tool also supports compression using gzip and xz, with an option for parallel compression across multiple CPU cores to improve speed.
Key points:
- Automatic shrinking of Raspberry Pi images
- Boot-time filesystem expansion to full SD card size
- Support for parallel multi-core compression
- Compatibility with Linux, Windows (via WSL 2), and macOS (via Docker)
Key points:
- Automatic shrinking of Raspberry Pi images
- Boot-time filesystem expansion to full SD card size
- Support for parallel multi-core compression
- Compatibility with Linux, Windows (via WSL 2), and macOS (via Docker)
Lightpanda is a high-performance, lightweight browser engine built from scratch using the Zig programming language. Designed specifically for automation, web crawling, and AI agents, it eliminates the overhead of graphical rendering to provide massive improvements in speed and resource efficiency compared to traditional browsers like Chrome.
Key features and benefits:
- Built with Zig for low-level performance and memory efficiency.
- Optimized for headless operation without unnecessary rendering code.
- Significantly faster execution (up to 9x) and much lower memory usage (up to 16x less).
- Compatible with existing automation tools like Puppeteer and Playwright via CDP support.
- Provides isolated environments to improve security for automated tasks.
Key features and benefits:
- Built with Zig for low-level performance and memory efficiency.
- Optimized for headless operation without unnecessary rendering code.
- Significantly faster execution (up to 9x) and much lower memory usage (up to 16x less).
- Compatible with existing automation tools like Puppeteer and Playwright via CDP support.
- Provides isolated environments to improve security for automated tasks.
Obscura is an open-source, lightweight headless browser engine written in Rust, specifically designed for web scraping and AI agent automation. It serves as a high-performance replacement for headless Chrome, offering significantly lower memory usage and faster page load times. The engine runs real JavaScript via V8 and supports the Chrome DevTools Protocol, making it compatible with Puppeteer and Playwright.
Key features include:
- Built-in stealth mode with anti-fingerprinting and tracker blocking capabilities.
- High efficiency with minimal memory footprint (approx 30 MB) and instant startup.
- Support for parallel scraping via CLI and CDP server integration.
- Seamless compatibility with existing Puppeteer and Playwright workflows.
Key features include:
- Built-in stealth mode with anti-fingerprinting and tracker blocking capabilities.
- High efficiency with minimal memory footprint (approx 30 MB) and instant startup.
- Support for parallel scraping via CLI and CDP server integration.
- Seamless compatibility with existing Puppeteer and Playwright workflows.
The author explores how Gemini Scheduled Actions represents a significant shift in Android automation by moving from rigid, trigger-based logic like Tasker to an intent-first architecture powered by Large Language Models. Unlike traditional tools that require programming knowledge and are prone to breaking when UI changes occur, Gemini understands natural language requests and manages complex workflows across devices via the cloud.
Key points:
* Comparison between brittle IFTTT engines and flexible LLM-based automation.
* The benefit of cross-device synchronization through Google accounts.
* Using the desktop web interface for easier setup and access to an Inspiration Gallery.
* Practical use cases including automated SEO idea generation, sports updates, grocery list creation in Google Keep, and email summaries.
* Current limitation of up to 10 active scheduled actions at a time.
Key points:
* Comparison between brittle IFTTT engines and flexible LLM-based automation.
* The benefit of cross-device synchronization through Google accounts.
* Using the desktop web interface for easier setup and access to an Inspiration Gallery.
* Practical use cases including automated SEO idea generation, sports updates, grocery list creation in Google Keep, and email summaries.
* Current limitation of up to 10 active scheduled actions at a time.
The author explains how using GPT-4 for a nightly data extraction pipeline caused constant failures due to its non-deterministic nature. Even with strict prompting and temperature settings, the model would occasionally change key names or formatting, breaking the automated workflow. To solve this, the team switched to running smaller local models like Qwen2.5 via Ollama. By using seeded inference on their own hardware, they achieved the consistency needed for a reliable pipeline, finding that while small models lack GPT-4's reasoning depth, they are much better at performing repetitive, structured tasks identically every time.
