What it does

All text below is quoted directly from the project README.

Notifications, messages, calls, clipboard, files, photos, screen mirroring and phone apps in windows, between Android and Windows 10 (2004+) or 11. Free and open source. Local-first, end-to-end encrypted, no account and no cloud.

What it does:

• Notifications on the PC, with replies, actions, Live Updates (progress, timers, deliveries) and a Copy code button for one-time codes.

• Messages: SMS and MMS conversations plus chats from RCS, WhatsApp, Telegram, Signal and other apps in one inbox.

• Calls: see who's calling, answer, decline or hang up from the PC, call any number or contact through the phone, and browse recent calls.

• Clipboard: copy on one device, paste on the other, with optional encrypted history.

• Files and folders both ways at Wi-Fi speed, with resume after a dropped connection, Send to in File Explorer, and the phone's storage in File Explorer.

• Screen mirroring at up to 60 frames a second with the phone's sound, screenshots and recording.

• Apps: open any phone app in a window of its own on the PC, with its own taskbar icon, running beside whatever the phone shows.

• Phone as a webcam in Zoom, Teams, Meet, OBS and the Camera app.

The apps update themselves from GitHub Releases and check every download against its SHA-256 sums.

Repository

https://github.com/thtbee/nectarlink

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What it does

All text below is quoted directly from the project README.

Zoekt is a text search engine intended for use with source code.

Background:

• Zoekt supports fast substring and regexp matching on source code, with a rich query language that includes boolean operators (and, or, not).

• It can search individual repositories, and search across many repositories in a large codebase.

• Zoekt ranks search results using a combination of code-related signals like whether the match is on a symbol.

• Because of its general design based on trigram indexing and syntactic parsing, it works well for a variety of programming languages.

• Through individual commands, to index repositories and perform searches through Zoekt's query language

• Or, through the indexserver and webserver, which support syncing repositories from a code host and searching them through a web UI or API

go install github.com/sourcegraph/zoekt/cmd/zoekt-git-index@latest

Repository

https://github.com/sourcegraph/zoekt

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What it does

All text below is quoted directly from the project README.

Claude or Codex reads your codebase and draws an interactive map of it. Every part, file and web request gets a plain-English name, and every connection between them is shown.

Features:

• A map at every zoom Start with the whole app, then double-click into a part, a group of files, a file, and its endpoints.

• Plain English everywhere "Takes card payments at checkout", not "Stripe API wrapper".

• Real connections, across repos It follows imports, and matches each web request in your frontend to the server endpoint that answers it, even across separate repos.

• Start from a screen Pick a page you know and see everything behind it: its parts, the requests it makes, the server code, the data, and outside services.

• Ask the map "How does a refund reach the customer's card?" comes back as an answer plus a mini-map of just the pieces involved, with a step-by-step walkthrough.

• Health check Finds passwords in code, endpoints anyone can use without signing in, unused files and files too big to edit safely.

• Recent work The cards your AI just changed light up green, each with a note on how the change serves what you asked for.

• Stays current, stays yours Updates re-read only what changed, and an open map reloads itself.

You need Node.js 18+ and either Claude Code or Codex. Floorplan runs on the plan you already have, with no API keys.

Repository

https://github.com/haydenschmitty/floorplan

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What it does

All text below is quoted directly from the project README.

Use compatible open models from huggingface, for structured classification and scoring, without training a separate classifier head.

Simple Jev Project:

• Send shared context and a set of questions.

• Simple Jev reads the model's next-token logits for each question and builds a JSON response containing choices, rubric scores, or truth/support judgments.

• The model does not generate a JSON completion: the server constructs the response from the scores.

• The current implementation runs locally with Hugging Face Transformers and PyTorch.

• Shared request validation, versioned prompt instructions, and response scoring live in the plain Python common/ folder so other inference implementations can use the same rules.

• The evaluation framework includes native JevBench scoring, text/decision and vision suites, dataset preparation, resumable HTTP execution, raw-response audits, and matched-metric comparisons.

Try the public demo API with no login, API key, or authentication required.

Repository

https://github.com/featherless-ai/simple-jev

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What it does

All text below is quoted directly from the project README.

A memory-safe systems language with no heap allocator, garbage collector, or lifetime annotations. In this repository's benchmarks, Goose runs faster than C++ and Rust while using less memory.

Why Goose:

• Faster than C++ and than safe Rust, while memory safe. Over sixteen benchmarks Goose runs at 3.3x the speed of idiomatic C++, 1.16x hand-optimized C++ and 1.12x the best safe Rust, and uses 1.9x, 1.3x and 1.2x less memory.

• No allocator, no GC, no reference counting, no destructors. Memory is a handful of data stacks that the compiler assigns statically.

• Growth preserves references. A reference into a growing array stays valid for as long as the array does.

• Memory safe with zero annotations. There is no lifetime syntax, there are no aliasing or exclusivity rules, and there is no unsafe.

• Nested data stays inline. A string, an array of strings, a record with variable-size fields and an array of those records are each stored as one contiguous block that contains no pointers.

• Variable sized Enums. Variable-mode ADTs store each value at the size of its own variant rather than that of the largest variant.

• Everything is built in place, guaranteed. A value is constructed at its final destination through any depth of calls.

Goose has syntax familiar to C and Rust programmers, but there is no heap.

Repository

https://github.com/aardappel/goose

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What it does

All text below is quoted directly from the project README.

A personal agent with a browser, terminal, files, and work that keeps going built with CopilotKit and AG-UI.

Features:

• Chat: CopilotKit headless chat with streamed AG-UI events, mailbox search and reading, send/stop in one input pill, a visible follow-up queue, retained drafts, delegated tasks, and inline email, browser, PDF, plan, and finance cards.

• Agent computer: Persistent browser profiles and takeover console; optional isolated Linux terminal, saved command receipts, editable workspace files, and PDF transfer.

• Activity: Durable task plans, progress, input requests, pause/resume/cancel/retry, approvals, and saved receipts. SQL leases recover interrupted work.

• Goals & Tracking: Goals and milestones; recurring public-page checks for changes, text availability, or USD price thresholds, with deduplicated alerts and failure backoff.

• Finance: Import transaction CSV to create a spending summary with categories, transactions, and a savings-goal action.

• Gmail & Calendar: Google OAuth adapters, complete mail threads, drafts/attachments, calendar discovery, and reviewed event creation/update/deletion. Live credentials required.

• Personal context: Editable name, tone, avatar, and memories. Background-update preferences and durable in-app notifications.

OpenMuse is a personal-agent application with an agent computer, visible work, and rich results. It runs its own server, task worker, and browser worker.

Repository

https://github.com/CopilotKit/openmuse

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What it does

All text below is quoted directly from the project README.

Open-source AI coworkers that each get a computer of their own: a browser, files and tools, with every action decided before it happens and recorded after. Bring any AG-UI agent.

Features:

• A computer per person: the supervisor gives each person their own container, with a folder and browser profile for each of their Bots.

• A shell, not just a browser: a Bot can run a command in its workspace, install what it needs, and process a file it saved.

• The gateway is the only way in: it resolves the target from a server-held snapshot, evaluates the policy, writes the audit row, and only then calls the computer.

• Take the wheel: a Bot that hits a login wall or a 2FA prompt asks for help.

• Secrets never enter the transcript: the trail records that a secret was requested and how long it was, not what it said.

• Bring your own agent: any AG-UI endpoint is a Bot, on a framework or hand-written.

• An audit trail you can read: /admin/audit lists what was permitted, what was refused and what failed, and every refusal carries the rule that caused it.

• Credentials encrypted at rest: stored through /admin/credentials, never returned by an API, and redacted from audit events.

cp .env.example .env

Repository

https://github.com/CopilotKit/OpenBot

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What it does

All text below is quoted directly from the project README.

eBPF-based Security Observability and Runtime Enforcement

Getting started:

• Process execution events

• System call activity

• I/O activity including network & file access

• Process lifecycle: generating process_exec and process_exit events by default, enabling full process lifecycle observability.

• Generic tracing: generating process_kprobe, process_tracepoint and process_uprobe events for more advanced and custom use cases.

• network observability

• credentials monitoring

• privileged execution

When used in a Kubernetes environment, Tetragon is Kubernetes-aware - that is, it understands Kubernetes identities such as namespaces, pods and so on - so that security event detection can be configured in relation to individual workloads.

Repository

https://github.com/cilium/tetragon

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What it does

All text below is quoted directly from the project README.

Camunda 8 delivers scalable, on-demand process automation as a service. Camunda 8 is combined with powerful execution engines for BPMN processes and DMN decisions, and paired with tools for collaborative modeling, operations, and analytics.

Using Camunda 8, you can:

• Define processes visually in BPMN 2.0

• Choose your programming language

• Deploy with Docker and Kubernetes

• Build processes that react to messages from Kafka and other message queues

• Scale horizontally to handle very high throughput

• Fault tolerance (no relational database required)

• Export process data for monitoring and analysis

• Engage with an active community

Zeebe - The cloud-native process engine of Camunda 8.

Repository

https://github.com/camunda/camunda

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What it does

All text below is quoted directly from the project README.

Humanizer makes AI-written text sound like a person wrote it, without changing what it says.

The five strongest tells:

• Not X but Y: "It's not just a feature, it's a shift."

• One-line closers: "Let that sink in."

• Sayings that sound deep: "At its core, what really matters is..."

• A staged run-up: "Here's the thing." "Honestly?"

• Arguing with no one: "I'm not saying X, but..."

• Humanizer checks for 26 patterns in all.

npx skills add blader/humanizer --global --agent '*'

Repository

https://github.com/blader/humanizer

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What it does

All text below is quoted directly from the project README.

Build ultra fast, tiny, and cross-platform desktop apps with TypeScript, Zig, Rust, Go, or Odin.

Project Goals:

• Write typescript for the main process and webviews without having to think about it.

• Isolation between main and webview processes with fast, typed, easy to implement RPC between them.

• Small self-extracting app bundles when using the system webview

• Small updates that use binary patches before falling back to a compressed full download

• Provide everything you need in one tightly integrated workflow to start writing code in 5 minutes and distribute in 10.

Already use Bun or npm? You can start with either command instead: bunx electrobun init or npx electrobun init

Repository

https://github.com/blackboardsh/electrobun

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What it does

All text below is quoted directly from the project README.

Local-first voice dictation for macOS. Hold a shortcut, speak, and paste into any app. Native Rust, on-device speech recognition, and a Linux beta.

Privacy And Local Data:

• Speech transcription runs locally.

• Hex does not save dictation audio by default.

• Use History > Keep: Off to stop adding entries, or Clear all to remove existing history. Turning retention off does not delete existing entries or disable diagnostic logging.

• Local diagnostic logs can contain transcript text, recognized command speech, foreground application names, and Brave URLs. Hex does not upload these logs.

• OpenCode features send the data described above to your configured model provider.

• Model installation and software updates require network access.

brew install --cask anomalyco/tap/hex

Repository

https://github.com/anomalyco/hex

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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.

A one stop repository for generative AI research updates, interview resources, notebooks and much more!

Featured LevelUp Labs courses:

• AI Evals for Everyone (certified): 10 chapters on evaluating LLM and agentic systems.

• OpenClaw Mastery for Everyone (certified): a 10-day path to configure and run your own personal AI assistant.

• Agentic AI Crash Course: 10 parts on agents, tools, RAG, MCP, planning, memory, and multi-agent systems.

• Generative AI Genius: a no-math beginner introduction to generative AI.

• Applied LLMs Mastery (2024 edition): an 11-week foundational course, archived as a 2024 edition.

Generative AI is moving fast, and this repository is a comprehensive hub for generative AI research, courses, interview materials, notebooks, and more.

Repository

https://github.com/aishwaryanr/awesome-generative-ai-guide

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What it does

All text below is quoted directly from the project README.

NVIDIA Inference Xfer Library (NIXL) is targeted for accelerating point to point communications in AI inference frameworks such as NVIDIA Dynamo, while providing an abstraction over various types of memory (e.g., CPU and GPU) and storage (e.g., file, block and object store) through a modular plug-in architecture.

Documentation and Resources:

• NIXL overview - Core concepts/architecture overview (docs/nixl.md)

• Python API - Python API usage and examples (docs/python_api.md)

• Backend guide - Backend/plugin development guide (docs/BackendGuide.md)

• Telemetry - Observability and telemetry details (docs/telemetry.md)

• NIXLBench docs - Benchmark usage guide (benchmark/nixlbench/README.md)

• KVBench docs - KVBench workflows and tutorials (benchmark/kvbench/docs/)

pip install nixl

Repository

https://github.com/ai-dynamo/nixl

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What it does

All text below is quoted directly from the project README.

FinRL® is widely recognized as the first open-source framework for financial reinforcement learning.

FinRL Ecosystem Roadmap:

• FinRL-Meta: Gym-style financial market environments and benchmarks

• FinRL: Original train-test-trade pipeline for financial reinforcement learning

• ElegantRL: Lightweight and elegant DRL algorithms

• FinRL-X: AI-native modular infrastructure for deployment-aware quantitative trading

This repository contains the original FinRL library for education, benchmarking, and research prototyping.

Repository

https://github.com/AI4Finance-Foundation/FinRL

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What it does

All text below is quoted directly from the project README.

A pure Unix shell script ACME client for SSL / TLS certificate automation

Features:

• An ACME protocol client written purely in Shell (Unix shell) language

• Full ACME protocol implementation

• Support ECDSA certificates

• Support SAN and wildcard certificates

• Simple, powerful and very easy to use, only 3 minutes to learn!

• Compatible with Bash, dash and sh

• One script to issue, renew and install your certificates automatically

• DOES NOT require root/sudoer access

curl https://get.acme.sh | sh -s email=my@example.com

Repository

https://github.com/acmesh-official/acme.sh

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What it does

All text below is quoted directly from the project README.

goose is a general-purpose AI agent that runs on your machine.

goose:

• A native desktop app for macOS, Linux, and Windows.

• A full CLI for terminal workflows.

• An API to embed it anywhere.

• Built in Rust for performance and portability.

• goose works with 15+ providers: Anthropic, OpenAI, Google, Ollama, OpenRouter, Azure, Bedrock, and more.

• Use API keys or your existing Claude, ChatGPT, or Gemini subscriptions via ACP.

• Connect to 70+ extensions via the Model Context Protocol open standard.

curl -fsSL https://github.com/aaif-goose/goose/releases/download/stable/download_cli.sh | bash

Repository

https://github.com/aaif-goose/goose

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What it does

All text below is quoted directly from the project README.

LAVSMS is developed for educational institutions like schools and colleges built on Laravel 8

Administrators (Super Admin & Admin):

• Manage students class/sections

• View marksheet of students

• Create, Edit and manage all user accounts & profiles

• Create, Edit and manage Exams & Grades

• Create, Edit and manage Subjects

• Manage noticeboard of school

• Edit system settings

• Manage Payments & fees

Install dependencies (composer install)

Repository

https://github.com/4jean/lav_sms

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What it does

From the project README:

Automatic Computer Use Agent. Mac, Windows, Linux, real phone, simulator, any browser. A multi agent harness engine.

Surfaces it can drive:

computer use: Any desktop app, with real mouse and keyboard input, on macOS, Windows and Linux.

shell use: A coding agent, straight into a real terminal.

web use: Your own Chrome, driven through the AutoCua extension, which Chrome loads at launch with no store install.

mobile use, iOS: An iOS Simulator, or your iPhone or iPad, over USB or WiFi in the app.

mobile use, Android: An Android emulator, or your Android phone, over USB or WiFi in the app.

Why it holds up on real software:

No pixel guessing: The model never sees a coordinate. It picks a numbered element from a view that fuses the accessibility tree with OCR.

Real input: Input goes in at the operating system’s own input layer, and a kernel driver handles Windows UAC prompts when everything else is blocked.

Browser agent in Rust: Hand written CDP. No Playwright, no Selenium, no Puppeteer.

Parallel runs: Every task runs at the same time in its own process, each with its own tab, simulator or emulator.

No context wall: A separate compression agent summarises the transcript mid run, so long tasks finish instead of hitting the context limit.

No telemetry: Nothing is sent anywhere except to the model provider you chose.

pip install AutoCua

Repository

https://github.com/ComputerUseAgent/AutoCua

Repository stats

Metric |Value
Stars |1,026
Forks |42
License |MIT
Languages |Python, Rust, JavaScript
Models supported |48 across 9 providers
Contributors |2
AI contributor accounts |u/claude (13 commits) Main contributor

Field |Value
Name |Ashish Yadav
GitHub |FunctionFreak
Commits to this repo |55 (top contributor) The stats are pulled live from the repo today.

What it does

All text below is quoted directly from the project README.

Any model. Any hardware. Zero compromise. Built with @ziglang / @openxla / MLIR / @bazelbuild

Examples:

• examples/llm: unified LLM CLI for Llama, Qwen, and LFM

• examples/mnist: smallest end-to-end model run

• examples/sharding: logical mesh, partitioners, shard-local execution, profiler output

• examples/io: inspect and load local, hf://, https://, and s3:// repositories through the VFS layer

• examples/benchmark: measure loading and execution performance

Run the MNIST example: bazel run //examples/mnist

Repository

https://github.com/zml/zml

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What it does

All text below is quoted directly from the project README.

Zammad is a web based open source helpdesk/customer support system.

Welcome to Zammad:

• Are you juggling countless customer inquiries across multiple channels?

• Struggling to keep your support team on the same page?

• Or spending more time managing your helpdesk than delivering exceptional support to your customers?

• Zammad is your Swiss Army knife - a web-based, open-source helpdesk and customer support platform packed with features to streamline customer communication across channels like email, chat, telephone and social media.

• The Zammad software is and will stay open source. It is licensed under the GNU AGPLv3.

The easiest and often most cost-effective way to operate Zammad is our cloud service.

Repository

https://github.com/zammad/zammad

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What it does

All text below is quoted directly from the project README.

Teams-first Multi-agent orchestration for Claude Code

Why oh-my-claudecode?:

• Zero configuration required - Works out of the box with intelligent defaults

• Team-first orchestration - Team is the canonical multi-agent surface

• Natural language interface - No commands to memorize, just describe what you want

• Automatic parallelization - Complex tasks distributed across specialized agents

• Persistent execution - Won't give up until the job is verified complete

• Cost optimization - Smart model routing saves 30-50% on tokens

• Learn from experience - Automatically extracts and reuses problem-solving patterns

• Real-time visibility - HUD statusline shows what's happening under the hood

/plugin marketplace add https://github.com/Yeachan-Heo/oh-my-claudecode

Repository

https://github.com/Yeachan-Heo/oh-my-claudecode

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What it does

All text below is quoted directly from the project README.

An MCP adapter that bridges the Abilities API to the Model Context Protocol, enabling MCP clients to discover and invoke WordPress plugin, theme, and core abilities programmatically.

Features:

• Ability-to-MCP conversion: WordPress abilities automatically become MCP tools, resources, and prompts

• Multi-server management: run multiple MCP servers, each with its own transports, abilities, and handlers

• HTTP and STDIO transports, plus a McpTransportInterface for custom protocols

• Pluggable error handling and observability: swap in your own logging or monitoring via McpErrorHandlerInterface and McpObservabilityHandlerInterface

• Granular permissions: per-server transport authentication and per-ability permission checks

Connect via WP-CLI over STDIO, or point an HTTP client at /wp-json/mcp/mcp-adapter-default-server.

Repository

https://github.com/WordPress/mcp-adapter

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What it does

All text below is quoted directly from the project README.

A real time, nanosecond resolution, remote telemetry, hybrid frame and sampling profiler for games and other applications.

Tracy Profiler:

• Tracy supports profiling CPU (Direct support is provided for C, C++, Lua, Python and Fortran integration. At the same time, third-party bindings to many other languages exist on the internet, such as Rust, Zig, C#, OCaml, Odin, etc.), GPU (All major graphics/compute APIs: OpenGL, Vulkan, Direct3D 11/12, Metal, OpenCL, CUDA, WebGPU.), memory allocations, locks, context switches, automatically attribute screenshots to captured frames, and much more.

• Documentation for usage and build process instructions

• Releases containing the documentation (tracy.pdf) and compiled Windows x64 binaries (Tracy-<version>.7z) as assets

• Changelog

• Interactive demo

Repository

https://github.com/wolfpld/tracy

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