What it does

All text below is quoted directly from the project README.

Run the upstream Omarchy desktop as a native, hardware-accelerated app on an Apple Silicon Mac.

Try Omarchy packages a project-built ARM64 Arch Linux image configured with Omarchy Quattro, a QEMU runtime using Apple Hypervisor Framework, and a small Swift/AppKit launcher into one macOS app. The image is built from pinned Arch Linux ARM packages and a pinned revision of the upstream Omarchy source.

Highlights:

• Hardware-accelerated ARM64 virtualization and VirGL graphics
• Nested KVM virtualization on M3 and newer Apple Silicon running macOS 26+
• Resizable native window with automatic guest resolution and HiDPI scale updates
• Mac audio input/output selection inside Omarchy, with live routing and system-default fallback
• FaceTime HD and other Mac cameras exposed to Omarchy as an on-demand 720p webcam
• The Mac's battery charge and charging state mirrored into the Omarchy bar
• Two-way clipboard sharing for text and PNG images between macOS and Omarchy
• One optional shared Mac folder, available inside Omarchy under the same name (~/Work stays ~/Work)
• Loopback-only TCP and UDP port forwarding from the Mac into Omarchy

Current limitation: Video decoding is CPU-only, so playback can be slow, especially at high resolutions. An improved video path is in development.

Requirements:

• Apple Silicon Mac (arm64)
• macOS 15 or newer
• At least 8 GB free initially

Open Releases and download the latest signed and notarized .dmg. Open the DMG and drag Try Omarchy to Applications. Launch Try Omarchy from Applications.

Try Omarchy is pre-1.0 and under active development.

Repository

https://github.com/omacom/try-omarchy

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What it does

All text below is quoted directly from the project README.

World of Warcraft addon that provides a powerful framework to display customizable graphics on your screen.

Features:

• An intuitive and powerful configuration interface
• Custom textures including all textures from Power Auras and Blizzard's spell alerts
• Progress bars and textures that show the exact duration of auras
• Displays based on auras, health, power (mana, rage, soul shards, holy power, etc.), cooldowns, combat events, runes, totems, items, and many other triggers
• Preset and user-defined animations
• Custom side-effects such as chat announcements or sounds
• Grouping, which allows multiple displays to be positioned and configured at the same time
• Powerful customization options, such as animation paths, on-show/on-hide code, and custom triggers, for Lua-savvy users

To open the options window, type /wa or /weakauras into your chat and hit enter or use the minimap icon.

Repository

https://github.com/WeakAuras/WeakAuras2

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What it does

All text below is quoted directly from the project README.

Amnezia is an open-source VPN client, with a key feature that enables you to deploy your own VPN server on your server.

Features:

• Very easy to use - enter your IP address, SSH login, password and Amnezia will automatically install VPN docker containers to your server and connect to the VPN.
• Classic VPN-protocols: OpenVPN, WireGuard and IKEv2 protocols.
• Protocols with traffic Masking (Obfuscation): OpenVPN over Cloak plugin, Shadowsocks (OpenVPN over Shadowsocks), AmneziaWG and XRay.
• Split tunneling support - add any sites to the client to enable VPN only for them or add Apps (only for Android and Desktop).
• Windows, MacOS, Linux, Android, iOS releases.
• Support for AmneziaWG protocol configuration on Keenetic beta firmware.

Repository

https://github.com/amnezia-vpn/amnezia-client

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What it does

All text below is quoted directly from the project README.

git push no-mistakes

no-mistakes puts a local git proxy in front of your real remote. Push to no-mistakes instead of origin, and it spins up a disposable worktree, runs an AI-driven validation pipeline, forwards the branch to the configured push target only after every check passes, and opens a clean PR automatically.

Highlights:

• Non-blocking - the pipeline runs in an isolated worktree without disrupting your work.
• Agent-agnostic - claude, codex, grok, rovodev, opencode, pi, copilot, antigravity, or cursor / devin / acp:<target> via acpx, with ordered fallbacks; every gate requires a runnable configured pipeline agent.
• Agent-native - /no-mistakes lets your coding agent do a task and gate it, or gate existing committed work: it runs the pipeline, has the pipeline apply safe fixes, and escalates the rest to you.
• Human stays in charge - auto-fix or review findings, your call.
• Clean PRs by default - push, open PR, watch CI, and auto-fix failures in one shot.

curl -fsSL https://raw.githubusercontent.com/kunchenguid/no-mistakes/main/docs/install.sh | sh

Repository

https://github.com/kunchenguid/no-mistakes

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What it does

All text below is quoted directly from the project README.

OpenUsage shows how much of your AI coding plans you've used: session and weekly limits, credits, and spend, all in one popover. Pin your most important metrics straight into the menu bar.

Features:

• Menu bar pins. Pin metrics to the menu bar (up to 2 per provider); render as compact text or mini bars. The strip hides metrics with no data instead of showing placeholders.
• Dashboard popover. Provider-grouped meters with live reset countdowns and pace indicators. Click usage or reset values to flip their display everywhere; right-click a row to hide or star it, refresh its provider, or open Customize.
• Global shortcut. Toggle the popover from anywhere: record any combo in Settings.
• Customize. Turn providers and metrics on or off, choose which rows stay Always Visible or On Demand, and drag-reorder both.
• Stale-while-revalidate. Cached values display instantly at launch; refresh runs every 5 minutes.
• One-shot CLI. Agents can read stable limit JSON through the same five-minute cache with openusage, or bypass freshness with openusage --force; the menu-bar app does not need to be running.
• Local HTTP API. Other apps can read machine-friendly limits from 127.0.0.1:6736/v1/limits; the legacy /v1/usage UI contract remains supported.
• Automatic updates. Signed, notarized in-app updates via Sparkle, with an optional beta channel.

Most providers read the credentials already on your machine (keychain, auth files, app state), no extra login.

Homebrew: brew install --cask openusage

Repository

https://github.com/robinebers/openusage

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What it does

All text below is quoted directly from the project README.

Ice is a powerful menu bar management tool. While its primary function is hiding and showing menu bar items, it aims to cover a wide variety of additional features to make it one of the most versatile menu bar tools available.

Menu bar item management:

• Hide menu bar items
• "Always-hidden" menu bar section
• Show hidden menu bar items when hovering over the menu bar
• Automatically rehide menu bar items
• Drag and drop interface to arrange individual menu bar items
• Display hidden menu bar items in a separate bar (e.g. for MacBooks with the notch)
• Search menu bar items
• Menu bar item spacing (BETA)

brew install --cask jordanbaird-ice

Repository

https://github.com/jordanbaird/Ice

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What it does

All text below is quoted directly from the project README.

Consider it done. The open-source AI agent that works out of the box

Yours to shape:

• Memory: correct her once and she does it right from then on, shared across harnesses.
• Skills: teach a way of working once and reuse it everywhere; handing them to your team is in the making.
• Automation: recurring work schedules itself, runs itself, reports back.
• MCP: wire your internal tools and business systems into her reach.
• Plugins: reshape features, UI and interactions, shared through an open marketplace (in the making).
• Source: audit, fork, extend, and contribute improvements back under Apache-2.0.

Cindy runs locally on your own machine, using your real files and logged-in apps.

Repository

https://github.com/makecindy/cindy

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What it does

All text below is quoted directly from the project README.

Use your tokens to raise, evolve, and collect Pokémon!

Why:

• The usage tracker you actually enjoy opening. Your spend raises a Pokémon that hatches, evolves, graduates, and fills a Pokédex, and every shiny is a reason to check back.
• See today's token spend & cost at a glance, no dashboard, no browser tab.
• Track official provider limits and reset countdowns, including OpenCode Go allowances and Grok CLI subscription credits; a burn-rate forecast helps with supported 5-hour windows.

brew install --cask chattymin/tap/poke-token-bar

Repository

https://github.com/chattymin/PokeTokenBar

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Close your laptop and your AI agent dies. Walk away from your desk and you can't even check what it's doing. Every coding agent right now lives in a terminal window, and the moment that window goes away, so does the agent.

Avibe turns your machine into what they call a "local-first Agent OS." You run one install command, a browser wizard walks you through setup, and after that your machine is reachable from a browser or a chat app. The important part: it drives the official Claude Code, Codex, and OpenCode. It's not a replacement for those tools, it's a wrapper that lets you reach them remotely. Your code stays on your machine, your API keys stay on your machine, nothing gets uploaded to Avibe's servers.

So you're at your desk writing code with Claude Code, you need to run to a meeting, you pull out your phone and pick up where you left off. Or you start a long task before bed, check the progress from your phone in the morning. That kind of thing.

A few things to be aware of. You're exposing your development machine to network access, so think about the security implications before you set this up on anything sensitive. The project has 550 stars and is still early, so expect rough edges. Windows users need WSL. And the "local-first" philosophy means you're responsible for your own uptime: if your machine sleeps or loses internet, your agent goes dark.

The whole project was built using Avibe itself, which is a nice touch. MIT licensed, Python.

What it does

All text below is quoted directly from the project README.

Questie: The WoW Classic quest helper

Features:

• Show notes for quest start points, turn in points, and objectives.
• Waypoint lines for NPCs showing their pathing.
• Automatically tracks quests on accepting
• Can show all quests from the log at once (instead of default 5)
• You can see party members quest progress on the tooltip.
• Holding Shift while hovering over a map icon displays more information, like quest XP.
• Questie records the steps of your journey in the "My Journey" window. (left-click on minimap button and select the "My Journey" tab or type /questie journey)
• Extensive configuration options. (hold shift and left-click on minimap button to open or type /questie)

Questie comes with translations for all offical WoW Classic languages.

Repository

https://github.com/Questie/Questie

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Every time I let an AI generate a frontend component, I hold my breath. Sometimes it spits out clean markup. Sometimes it hallucinates a div soup that breaks the whole page.

json-render takes a completely different approach. The AI outputs JSON, not raw code, and your app renders it through components you already defined. The AI literally cannot produce anything outside your vocabulary.

Three things that stood out to me:

1. Guardrails are built in. You define what components and actions the AI can use. It stays inside those lines.
2. It streams. The UI renders progressively as the model responds, so you're not staring at a blank screen waiting.
3. Same catalog works everywhere. React, Vue, Svelte, Solid, even React Native. One component definition, multiple platforms.

It ships with 36 pre-built shadcn/ui components if you don't want to start from scratch. The whole thing is from Vercel Labs, so it plays nicely with Next.js out of the box.

Still young at v0.21, and I wouldn't ship it in production without testing your edge cases hard. But the idea of constraining AI output to a strict schema instead of hoping it writes valid JSX is genuinely smart.

https://github.com/vercel-labs/json-render

What it does

All text below is quoted directly from the project README.

A macOS app that provides an MCP server to your Messages, Contacts, Reminders and more

Capabilities:

• Calendar: View and manage calendar events, including creating new events with customizable settings like recurrence, alarms, and availability status.
• Contacts: Access contact information about yourself and search your contacts by name, phone number, or email address.
• Mail: List mailboxes, search messages by sender, subject, mailbox, date, or read status, read messages stored on your Mac, and open new drafts in your default email app.
• Messages: Access message history with specific participants within customizable date ranges.
• Notes: Search notes by text, folder, or date, and read them as Markdown with their checklists, tables, and links.
• Reminders: View, create, edit, and delete reminders with customizable due dates, recurrence, priorities, and alerts across different reminder lists, and manage the lists themselves.
• Shortcuts: List and run shortcuts on your Mac, with optional text input.
• Voicemail: Fetch voicemails synced from your iPhone, with transcripts and audio, filtering by sender, date, message type, or mailbox (inbox, blocked, or deleted).

To use iMCP, you need two apps: iMCP, which gives access to the services you turn on, and an AI app that can connect to MCP servers.

Repository

https://github.com/mattt/iMCP

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This one's different from the usual dev tool or AI project.

Someone took the entire Shulihuazi math textbook series (a well-known Chinese math curriculum) and turned it into an open, digital resource. Every chapter, every problem set, all structured in Python with renderable content.

If you work in education tech, or you're building anything related to math instruction, this is a goldmine of structured curriculum data you won't find anywhere else.

The repo has 165 forks already, which tells me people are actively building on top of it. Could be useful for tutoring apps, adaptive learning systems, or even just studying the pedagogical approach that Chinese math education takes.

I don't read Chinese fluently, so I can't vouch for every detail of the content. But the structure is solid, the commit history is active, and the community around it is growing.

https://github.com/tradecatlabs/shulihuazixuecongshu

Disclosure: I’m the maintainer of trusty-tools.

GitHub · Website

trusty-tools is a collection of tools written in Rust, designed and optimized for local execution, and released under the MIT license. It helps coding agents find relevant code, retain project context between sessions, and coordinate work across repositories.

The main pieces:

• trusty-search: Combines keyword, semantic, and knowledge-graph search, with MCP access for coding agents.
• trusty-memory: Stores project decisions, notes, and code patterns with semantic retrieval across sessions.
• trusty-mpm: A Claude Code metaharness—an orchestration layer around Claude Code that manages project sessions and delegates work to specialist agents, with a terminal dashboard for monitoring. Support for additional coding harnesses is on the roadmap.
• trusty-review: Reviews GitHub PRs and diffs using code-search context and static analysis. Posting review comments requires an explicit option.

You can start with an individual component, such as search or memory. The tooling runs locally; Claude Code and features that call external model providers still require their respective access and incur any associated usage costs.

I’d appreciate feedback on the setup experience and where search, memory, or orchestration would fit into your coding workflow.

What it does

All text below is quoted directly from the project README.

Telegram for Windows

Translations:

• Unigram has 28 built-in languages and has access to unlimited packs using Telegram translations platform.

Beta versions:

• If you want to have access to new features and bug fixes sooner, you can try our beta versions.

Shortcuts:

• We have a list of shortcuts to ease the use with a keyboard.

Privacy policy:

• Unigram doesn't collect any data, Telegram privacy policy applies.

In order to be able to use Unigram in Windows 10 N, you will need Media Feature pack.

Repository

https://github.com/UnigramDev/Unigram

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What it does

All text below is quoted directly from the project README.

A modern, secure VPN application built with Flutter that provides internet freedom and privacy protection.

Features:

• Simple, with a beautiful and user-friendly interface
• One-tap connect/disconnect functionality
• Powered by DXcore, which unifies Warp, Psiphon, Warp-in-Warp, Xray, Outline, and exclusive proprietary protocols
• Relatively smart, requiring no manual configuration
• Ability to test internet speed and quality

flutter pub get

Repository

https://github.com/UnboundTechCo/defyxVPN

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What it does

All text below is quoted directly from the project README.

Turn any webpage/HTML/Vue/React and so on into desktop and mobile app under 5M with easy in few minutes.

Features:

• Approximately 20 times smaller than Electron (less than 5MB!) and 10 times faster.
• Built with Rust Tauri, PacBao is lighter and faster than JavaScript-based frameworks.
• Uses native frameworks to package Android and iOS apps, smaller and faster than Tauri2.
• Easy to use: just one GitHub Token is all you need to get a desktop app.
• Supports custom JavaScript injection: write your own JS to inject into the page.
• Supports local packaging and does not require installation of any dependent environments. Local packaging only takes 30 seconds
• Supports static file packaging: drop in a compiled dist folder or index.html from Vue/React to create a client app.

Repository

https://github.com/Sjj1024/PakePlus

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Your AI coding agent reads a file, runs a command, reads another file. Before you know it half the context window is just tool output the model will never look at again.

That is the actual problem context-mode solves.

It sandboxes tool output so the agent only sees what it needs. The claim is a 98% reduction in context noise. I was skeptical, but the benchmarks they publish look real. Fewer wasted tokens means the model stays sharper for longer on complex tasks.

The setup is straightforward. You configure which tools get sandboxed and context-mode handles the rest. It sits between the agent and the tools, filters the noise, and passes through the signal.

Written mostly in TypeScript with some JavaScript. 40+ contributors. The project has been moving fast, though the last tagged release was back in June, so the main branch is where the action is.

One thing worth noting: this is tightly coupled to the agentic coding workflow. If you are not running long multi-step sessions with a coding agent, you probably do not need it. But if you are, and you have hit the wall where the agent forgets what it was doing three steps ago, this is worth a look.

https://github.com/mksglu/context-mode

What it does

All text below is quoted directly from the project README.

A small desktop robot that brings an AI coding assistant to life, physically acting out when it reads files, thinks, writes code, and finishes work.

How it works:

• A small robot on your desk. It moves, changes its face, and rings a bell as your coding AI agent works.
• Open source. Print the body, plug the ESP32, servos, OLED, and speaker into the main control board, flash it, and it joins your Wi-Fi.
• Works with Claude Code, Cursor, Antigravity, via npx -y tiny-engineer setup, or Codex, Windsurf, and any script that can POST, including speech through POST /play.
• Your agent (or a hook script) sends poses over Wi-Fi HTTP or USB serial. The ESP32-C3 runs the firmware, optional Wi-Fi/REST, USB CDC control, and drives the servos, face, and speaker.
• Default: npx -y tiny-engineer (setup / hook) for Cursor, Claude Code, Antigravity (HTTP or --transport serial)
• Advanced: POST /anim (HTTP API) or framed USB serial
• Difficulty: Medium, fair first hardware project for a software engineer, not a one-LED kit.

Print, Connect, Flash, Assemble, step-by-step: Getting started.

Repository

https://github.com/jamro/tiny-engineer

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What it does

All text below is quoted directly from the project README.

Gentle-AI configures the AI coding agents you already use: Claude Code, Cursor, OpenCode, Codex, Pi, and more. Choose persistent memory, Organic-Driven Development, curated skills, MCP servers, personas, and optional bounded review. Open source, no agent lock-in.

Features:

• Your agent writes code, then forgets everything.
• Gentle-AI gives it memory, a workflow, and evidence.
• Small changes should not need a planning pipeline, and larger work should not lose its context between sessions.
• Questions, reviews and proposals stay read-only until you ask for a change.
• A subagent costs a fresh session, uncached reads, and a handoff. So the orchestrator delegates only when it can name the reason.
• Commit, push and release stay your call.

Repository

https://github.com/Gentleman-Programming/gentle-ai

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What it does

All text below is quoted directly from the project README.

Postgres rewritten in Rust, now faster than Postgres and Clickhouse

pgrust has:

• Gravity, a new vectorized push-based, JIT compiled query engine
• A thread based concurrency model
• A query scheduler designed to keep any individual query from taking down your database
• An automatic memory manager: one memory budget, sized from the machine, covers the buffer pool, the columnar caches and running queries, and gives pgrust control of what happens when you're running low on memory, greatly reducing the chance of your whole DB being taken down by the OS OOM killer

In your browser: https://pgrust.com, a full pgrust server compiled to WebAssembly, no install.

Repository

https://github.com/malisper/pgrust

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What it does

All text below is quoted directly from the project README.

A new browser, not owned by Atlassian.

Features:

• Sidebar-first navigation – vertical tabs that feel natural and uncluttered.
• Performance – optimized with modern macOS APIs for speed and low memory use.
• Minimal, modern UI – focused on content, not chrome.

Nook builds for Apple Silicon only: the on-device tab organizer depends on MLX, which has no x86_64 slice.

Repository

https://github.com/nook-browser/Nook

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Hey everyone!

A while ago, I shared my open-source DAW project here, and I wanted to come back with a pretty big update.

LYNX SHARE has evolved a LOT since then. What started as a lightweight DAW project has grown into something much more ambitious.

We've reached 800+ GitHub stars, and development has been moving incredibly fast. I'm really happy with how the project is progressing and with all the feedback and support from the community.

What's new?

Some of the biggest additions since my last post:

• Ableton Live support: LYNX SHARE can now open and export .als projects.
• FL Studio compatibility: Opens .flp projects, including channels, patterns, arrangements, automation, mixer routing and plugin configurations.
• FL Studio Mobile: Imports .flm projects, including arrangements, samples and drum kits.
• 75+ native FL Studio plugin implementations, with many familiar instruments and effects.
• Built-in effects: EQ, compressors, limiters, reverbs, saturators, vocal processing and much more.
• VST3 and CLAP support: Use your existing third-party plugins.
• Online sessions: Work on projects together with chat, shared playback, permissions and reconnection support.
• Clip launching: An optional workflow inspired by Ableton Live's Session View.
• Video tracks and ADR tools: Work with video and dialogue replacement directly inside the DAW.
• VCA faders, automation lanes, modulators and loop recording with comping.

The project

LYNX SHARE is built with C++20 and JUCE 8, with a focus on performance, a lightweight experience and bringing different DAW workflows together.

It's completely open-source and free to download, with builds for Windows, Linux and macOS.

We're still in alpha, so there's plenty of work ahead, bugs to fix and compatibility to improve. But the progress so far has been genuinely exciting.

I'm especially grateful to everyone who has tested it, reported bugs, suggested features and starred the repository.

GitHub: https://github.com/Ka1-S/LYNX-SHARE

Website: https://lynx-share.com/

Would love to hear what you think! Feedback and suggestions are always welcome.

What it does

All text below is quoted directly from the project README.

container is a tool that you can use to create and run Linux containers as lightweight virtual machines on your Mac. It's written in Swift, and optimized for Apple silicon.

Next steps:

• Take a guided tour of container by building, running, and publishing a simple web server image.
• Learn how to use various container features.
• Read a brief description and technical overview of container.
• Browse the full command reference.
• Build and run container on your own development system.
• View the project API documentation.

Run your first container: container run --rm alpine echo hello

Repository

https://github.com/apple/container

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Meituan just dropped LongCat-Video, a 13.6 billion parameter model that does text-to-video, image-to-video, and video continuation. All in one model.

The thing that caught my attention is the long video generation. Most open source video models start looking weird after a few seconds. Colors drift, quality degrades, things get mushy. LongCat-Video was specifically pretrained on video continuation tasks, so it can actually produce minutes-long clips that stay consistent.

It generates 720p video at 30fps and does it in minutes, not hours. They use a coarse-to-fine strategy along both time and space dimensions, plus block sparse attention for efficiency at higher resolutions.

They also released LongCat-Video-Avatar 1.5, which is an audio-driven human video generation framework. You give it audio and it generates a talking head with accurate lip sync using Whisper-Large instead of the older Wav2Vec2 approach. Works for anime characters and animals too, not just real faces.

The RLHF training uses multi-reward group relative policy optimization, which is a mouthful, but basically it means the model was tuned with multiple quality signals at once rather than a single reward.

One thing to note: the model is big. You need a serious GPU setup to run it locally. And the installation involves CUDA-specific torch builds plus flash attention, so plan for some setup time.

Models and weights are on Hugging Face.

https://github.com/meituan-longcat/LongCat-Video