Joseph Dolivo and Igor Fil write about Pizza Bot, an open source application that runs background AI agents and provides an email-like inbox for their outputs. The design pattern moves agent interaction away from live chat sessions toward asynchronous threads, allowing users to delegate tasks that require multiple steps or human approval without having to monitor a terminal in real time. Because it is fully self-hosted and supports local models, it allows users to maintain strict data boundaries while utilizing tools they already own through MCP servers and agent skills.
- The architecture is built on DeepAgents and LangGraph, using SQLite for local persistence and checkpointing.
- It features an "Action" queue specifically for tasks paused on human approval or decision-making.
- Cron schedules run once after a machine wakes from sleep rather than replaying every missed occurrence.
- The project name references Amazon's two-pizza team methodology.
- It currently ships with a browser-automation skill and a documentation guide skill.
- The architecture is built on DeepAgents and LangGraph, using SQLite for local persistence and checkpointing.
- It features an "Action" queue specifically for tasks paused on human approval or decision-making.
- Cron schedules run once after a machine wakes from sleep rather than replaying every missed occurrence.
- The project name references Amazon's two-pizza team methodology.
- It currently ships with a browser-automation skill and a documentation guide skill.
Mohamed Bassem writes about Karakeep, a self-hostable bookmarking application designed for "data hoarders." The app allows users to save links, notes, images, and PDFs with features like automatic metadata fetching, semantic search, LLM-based tagging/summarization (including support for local models via Ollama), and full page archiving. It is built primarily with TypeScript and NextJS, offering cross-platform access through browser extensions, mobile apps, and a web interface.
- Supports local model integration using Ollama for private AI processing
- Includes OCR capabilities to extract text from saved images
- Provides automated video archiving via yt-dlp
- Features full page archival using monolith to prevent link rot
- Supports local model integration using Ollama for private AI processing
- Includes OCR capabilities to extract text from saved images
- Provides automated video archiving via yt-dlp
- Features full page archival using monolith to prevent link rot
jeff is a self-hosted drop-in replacement for TypeSafe's jev System One API, powered by the 400M-parameter GLiFormer-large-v1 model. It serves `choice`, `score`, and `noul` classification questions over a compatible wire format, so existing applications using the official `typesafe-sdk` can point at it by changing a single base URL. Deployment targets include GPU (L4, A10G) and CPU (ONNX Runtime with int8 quantization) via Modal, with ~50 req/s per container throughput on L4. Benchmarks on 1,600 labeled items show it costs roughly a quarter of jev per million requests but trails significantly on reasoning-heavy tasks like irony and reading comprehension.
- `JEFF_ISOLATE=nouls` (default) gives separate encoder passes per noul question to reduce cross-question interference; choice and score questions share a pass unless set to `all`
- Default temperature of 3.2 calibrates noul probabilities; setting it to 1 makes `score` output match the weighted average of displayed probabilities
- A smaller `gliformer-base-v1` variant with `JEFF_NOUL_MODE=single` is recommended for faster local iteration
- `JEFF_ISOLATE=nouls` (default) gives separate encoder passes per noul question to reduce cross-question interference; choice and score questions share a pass unless set to `all`
- Default temperature of 3.2 calibrates noul probabilities; setting it to 1 makes `score` output match the weighted average of displayed probabilities
- A smaller `gliformer-base-v1` variant with `JEFF_NOUL_MODE=single` is recommended for faster local iteration
bex is an open-source, self-hostable PaaS that positions itself as an AI-native alternative to Render, letting developers push Git and receive a deployed URL on their own Kubernetes infrastructure. Coding agents operate as first-class users via MCP alongside the dashboard, CLI, REST, and GraphQL interfaces, all backed by a shared Go core. The platform uses a Kubernetes operator with Cluster API for machine provisioning, supports Render-style `render.yaml` Blueprints for declarative service definitions, and ships managed Postgres, Key Value, logs, metrics, autoscaling, custom domains with TLS, and SSH access.
- 471 stars, 50 forks, 9 contributors — including Claude, Cursor, and Copilot listed as named GitHub contributors
- Apache-2.0 licensed; explicitly marked "not ready for production workloads" (public alpha)
- Language split: Go 58.5%, TypeScript 32.9%, Shell 6.7%
- Internal "lego" Go workspace enforces a strict `operator → types ← backend` one-way dependency DAG
- Tracks Render compatibility via an evidence-backed "parity ledger" (ADR018) rather than marketing claims
- Local quickstart provisions a kind cluster + Cluster API with Docker-container machines as tenant nodes
- Includes an Expo mobile app for safe supervision workflows (App Store listing present)
- Commit history references agent-driven QA rounds (w4/w5/w6 workstreams) and live dashboard re-probes
- 471 stars, 50 forks, 9 contributors — including Claude, Cursor, and Copilot listed as named GitHub contributors
- Apache-2.0 licensed; explicitly marked "not ready for production workloads" (public alpha)
- Language split: Go 58.5%, TypeScript 32.9%, Shell 6.7%
- Internal "lego" Go workspace enforces a strict `operator → types ← backend` one-way dependency DAG
- Tracks Render compatibility via an evidence-backed "parity ledger" (ADR018) rather than marketing claims
- Local quickstart provisions a kind cluster + Cluster API with Docker-container machines as tenant nodes
- Includes an Expo mobile app for safe supervision workflows (App Store listing present)
- Commit history references agent-driven QA rounds (w4/w5/w6 workstreams) and live dashboard re-probes
Jo Hinchliffe writes a tutorial for building a self-hosted personal knowledge management system inspired by Tiago Forte's *Building a Second Brain*. The setup pairs Logseq, a privacy-focused open-source note-taking app that stores everything as local Markdown files with a powerful tagging system, with Syncthing, a peer-to-peer file synchronisation tool that keeps the note database in sync across multiple Raspberry Pi devices without passing data through any cloud server.
- Syncthing is not a backup: deletions propagate to all linked devices, so a separate cron-driven rsync script is recommended for periodic backups
- Each tag in Logseq automatically becomes a page, creating a navigable graph of linked information
- Logseq currently installs only via Snap on Raspberry Pi OS
- The system was demonstrated on a Raspberry Pi 500, a Pi Zero, and a Uconsole (CM4)
- Logseq supports embedding links, videos, and even PDF annotation with tagging of specific pages
- Syncthing is not a backup: deletions propagate to all linked devices, so a separate cron-driven rsync script is recommended for periodic backups
- Each tag in Logseq automatically becomes a page, creating a navigable graph of linked information
- Logseq currently installs only via Snap on Raspberry Pi OS
- The system was demonstrated on a Raspberry Pi 500, a Pi Zero, and a Uconsole (CM4)
- Logseq supports embedding links, videos, and even PDF annotation with tagging of specific pages
mayukh4 writes a pair of bash scripts that transform a stock Android phone into either a GPU-accelerated Linux desktop (XFCE4, KDE Plasma, LXQt, or MATE) or an always-on Home Assistant smart home hub, all running inside Termux via proot-distro and Termux-X11. No root, no PC, and no cloud dependency are required—just an arm64 device with 3+ GB of RAM.
- GPU acceleration uses the open-source Turnip driver on Qualcomm Adreno chips, falling back to Zink + SwRast for Mali and other GPUs
- A custom `safe_install_pkg` function resolves Termux package conflicts by evaluating version constraints and `Provides`/`Replaces` declarations, preventing mid-install failures
- Home Assistant Core is deprecated upstream (2025) but remains the only realistic HA install method on a phone since HA OS and Container both require Docker or bare metal
- Wine is available via Hangover + Box64 for running Windows x86 apps on ARM64
- The repo has a companion YouTube walkthrough and a Chinese translation of the README
- GPU acceleration uses the open-source Turnip driver on Qualcomm Adreno chips, falling back to Zink + SwRast for Mali and other GPUs
- A custom `safe_install_pkg` function resolves Termux package conflicts by evaluating version constraints and `Provides`/`Replaces` declarations, preventing mid-install failures
- Home Assistant Core is deprecated upstream (2025) but remains the only realistic HA install method on a phone since HA OS and Container both require Docker or bare metal
- Wine is available via Hangover + Box64 for running Windows x86 apps on ARM64
- The repo has a companion YouTube walkthrough and a Chinese translation of the README
This repository provides an open-source face recognition software development kit (SDK) for Windows and Linux systems, developed by Faceplugin. It uaes deep learning models to offer on-premise processing of facial data, ensuring privacy as no information leaves the user's device. The toolkit supports various functions including face detection, landmark detection, feature embedding generation, and similarity comparison via Python APIs.
- Supports JPG, PNG, BMP, and TIFF image formats
- Compatible with both CPU and GPU acceleration
- Requires Python 3.9 or higher and Anaconda is recommended for setup
- Includes capabilities for bounding box extraction and facial landmark detection
- Supports JPG, PNG, BMP, and TIFF image formats
- Compatible with both CPU and GPU acceleration
- Requires Python 3.9 or higher and Anaconda is recommended for setup
- Includes capabilities for bounding box extraction and facial landmark detection
Smaug writes that Alexandrie is a powerful, open-source Markdown workspace designed for developers to organize notes with speed and clarity. The platform offers features such as syntax highlighting, live preview, smart organization through nested folders and tags, real-time collaboration, and one-click publishing for beautiful read-only documentation pages. It follows offline-first principles and allows users to self-host via Docker to maintain full data ownership.
- Released under the MIT license
- Supports LaTeX math equations and syntax highlighting for over 100 languages
- Offers a built-in CDN for uploading images and files
- Provides one-click publishing with custom domain support, SEO optimization, and password protection
- Released under the MIT license
- Supports LaTeX math equations and syntax highlighting for over 100 languages
- Offers a built-in CDN for uploading images and files
- Provides one-click publishing with custom domain support, SEO optimization, and password protection
Traceway is an OpenTelemetry-native observability platform that combines logs, traces, metrics, session replay, exceptions, AI tracing and on-call paging in one MIT-licensed tool that can be self-hosted with Docker in about 90 seconds or run embedded inside a Go app.
- Provides DuckDB telemetry backend for self-hosted mode and ClickHouse + PostgreSQL for standalone deployments
- Ships agent-first CLI and SKILL.md skills for Claude Code, Cursor and Codex to set up and query telemetry
- Symbolicator resolves minified production errors via memory-mapped .tw files with sub-millisecond lookups
- Provides DuckDB telemetry backend for self-hosted mode and ClickHouse + PostgreSQL for standalone deployments
- Ships agent-first CLI and SKILL.md skills for Claude Code, Cursor and Codex to set up and query telemetry
- Symbolicator resolves minified production errors via memory-mapped .tw files with sub-millisecond lookups
OpenTag is an open-source, self-hosted alternative to Claude for Slack. It enables users to run AI agents directly within Slack threads that can read conversations, answer queries, execute tools, and render rich, generative UI elements like tables and bar charts. Built on the CopilotKit SDK, it allows for complete ownership of the runtime and model without per-seat pricing or vendor lock-in.
- Open-source Slack AI agent
- Generative UI for rich message rendering
- Human-in-the-loop approval gates
- Multi-platform support via adapters (Discord, Telegram, WhatsApp)
- Self-hosted architecture for privacy and control
- Open-source Slack AI agent
- Generative UI for rich message rendering
- Human-in-the-loop approval gates
- Multi-platform support via adapters (Discord, Telegram, WhatsApp)
- Self-hosted architecture for privacy and control
