📦 salmon1802/unirank

UniRank

UniRank completely changes how we test recommendation AI by ditching the lazy standard practice of only predicting a user's final action and replacing it with a rigorous step-by-step chronological simulation. In real life, recommendation models often get evaluated on messy, mismatched datasets. This open benchmark levels the playing field by testing fifteen major ranking models from tech giants like Google, Meta, and ByteDance across the exact same five massive industrial datasets. By standardizing the evaluation, it finally answers crucial questions about how model size, history length, and architecture structure affect real-world accuracy and hardware efficiency.

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📦 gnipbao/story-to-handdrawn-video

story-to-handdrawn-video

This clever tool converts plain Chinese story text or a simple sequence of uploaded images into a hand-drawn vertical diary-comic animation. It automatically splits your text into dynamic narrative beats, generates matching artwork, and runs a beautiful left-to-right visual reveal that transitions from written text to black-and-white sketch, and finally into a full-color illustration. Built on Remotion, it delivers a silent, beautifully framed video with optional paper-curl page turns that is perfectly structured for you to record a voiceover on top. It is the ultimate automation shortcut for turning raw story scripts into engaging, stylized social media content.

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📦 deer-flow/llm-space

LLM Space

llm-space is the desktop playground that finally takes the guesswork out of building and debugging AI agents. If you have ever tried coding an agent, you know how incredibly frustrating it is when the model calls and tools run in a black box and silently fail. This app runs locally on your machine, giving you a visual timeline to trace every single model call and tool execution as it happens. You can literally replay failed runs step by step, tweak your prompts, and watch the agent improve. It is the ultimate local workbench for turning raw agent ideas into working code.

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📦 mdx-tom/gpt-5.6-instruct

gpt-5.6-instruct

Your AI assistant is probably refusing to help with complex tasks like reverse engineering or security testing because it thinks they are too risky. The gpt-5.6-instruct repository solves this by framing these technical challenges as safe, local sandbox tasks. It is essentially a specialized toolkit that lets you deploy customized instructions to bypass unnecessary AI refusals while keeping your workflows clean and organized. It features an interactive command-line tool to quickly preview, install, or rollback different versions without messing up your configuration. It is a brilliant way to make your local model actually do what you ask.

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📦 asz798838958/freeagentidentity

freeagentidentity

freeagentidentity is the local desktop panel that completely automates creating and managing free ChatGPT accounts. Built with Python and Electron, this handy application lets you handle bulk registrations right from your computer without dealing with tedious manual verification loops. It handles your proxies, email routing, and captcha solvers behind the scenes while running multi-threaded registration tasks automatically. You get a clean web dashboard to monitor your account lists, track execution logs, and export your newly generated credentials instantly. It is a brilliant way to manage your testing credentials in one secure dashboard.

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📦 persiyanov/herdr-reviewr

herdr-reviewr

Comment directly on an AI agent's code modifications without ever leaving your terminal. This clever companion integrates beside your terminal chat, letting you review syntax-highlighted diffs, select lines, and write comments. With a single keystroke, you can send all your feedback straight back to the agent as a structured list. It also includes a read-only pull request viewer, file search, and customizable color themes. It is a brilliant way to guide your AI coder through tricky tasks. Check out herdr-reviewr to make collaborative terminal-based coding a breeze.

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📦 ericrollei/comfy_hunyuanimage3

comfy_hunyuanimage3

comfy_hunyuanimage3 is the ComfyUI integration that finally lets you run Tencent's massive eighty-billion parameter image generator right on your local hardware. Normally, a model of this scale is completely out of reach for consumer setups, but this project changes everything by introducing smart quantization options like four-bit and eight-bit precision. It manages to shoehorn this giant AI onto single-GPU setups and even twenty-four gigabyte cards using clever memory budgeting and offloading. Beyond just fitting the model on your machine, it introduces memory-efficient expert routing that lets you render massive high-resolution images without crashing your system. It is the ultimate local playground for high-end open-source image generation.

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📦 coppsary/motionly

Motionly

Build and customize stunning motion graphics using a smart AI assistant that outputs real, editable project files. Motionly gives you a sleek visual canvas and timeline editor where you can drag, scale, and adjust keyframes, but underneath it all is a clean human-readable code format. Instead of spitting out a closed-off video file, the optional AI assistant drafts the actual animation markup so you can visually tweak every single detail, transition, and audio track yourself. It is the perfect bridge between prompt-based creation and precise timeline control. Grab your graphics, load the editor, and make something cool.

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📦 helldez/bigmoeonedge

BigMoeOnEdge

You can now run a massive sixty gigabyte artificial intelligence model on a standard phone with only twelve gigabytes of memory. A clever project called bigmoeonedge makes this possible by storing the bulk of the model directly on your phone's flash storage and only loading the specific experts needed for each word as they are requested. By bypassing typical memory limitations and utilizing parallel read lanes, it delivers usable generation speeds completely offline on a plain processor without requiring any special graphics hardware. It is a brilliant way to run giant models on everyday mobile hardware. You can check out their open-source code and download the demo app to try...

📰 https://news.ycombinator.com/item?id=48969917

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📦 kbrdn1/gwm-cli

gwm-cli

You can now restore a deleted Git worktree without digging through your reflog. This capability comes from gwm-cli, a single-binary Git worktree manager written in Rust that works directly through native operations instead of slow shell commands. It completely automates the tedious setup process by automatically copying configuration files, running your build setup, and linking the active GitHub issue the second you create a branch. When you are done, a built-in terminal interface lets you manage all your active workspaces, run commands across them simultaneously, and even open your terminal shell or Git tools directly inside the application. It is a fantastic way to keep your development environment tidy without...

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📦 zsniko/e-specformer

e-specformer

An ultra-lightweight artificial intelligence model can now run complex radio frequency analysis directly on a Raspberry Pi in less than a single millisecond. Known as e-specformer, this network replaces the heavy mathematical operations standard in typical transformers with a linear attention mechanism that completely cuts out high-overhead functions. Built for real-time spectrum monitoring, it processes raw signal samples directly at the extreme edge. This design achieves over ninety-four percent accuracy on hardware Trojan detection while running on tiny devices like field-programmable gate arrays. This project makes sophisticated radio signal analysis practical for incredibly small, low-power hardware.

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📦 rockychen0205/omnireasoner

OmniReasoner

Most AI models completely choke on long videos because processing hours of high-definition footage at once is just too expensive. OmniReasoner solves this by teaching the AI to act like a smart video editor. Instead of squinting at a giant, blurry preview, it does a quick, cheap scan first, realizes it needs more detail, and literally calls a built-in zoom-in tool to grab a high-quality snippet of the exact seconds it needs. It is like giving an AI a magnifying glass and a remote control, letting it choose when and where to look closer before giving you a highly accurate answer.

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📦 recursive-knowledge/ksi

ksi

Improve how AI agents solve complex coding and reasoning tasks by letting them learn from their own mistakes. Instead of running a single agent and hoping for the best, ksi deploys a whole population of temporary agents in sandboxed containers to tackle your tasks. After working independently, they join a structured forum to compare notes on what worked and what failed. The system distills their collective wisdom into reusable guidance that seeds the next generation of agents, saving this knowledge in a shared store so it survives across runs. It is an incredibly clever way to make AI self-improve without training a new model.

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📦 grignarder/uvfacefusion

UVFaceFusion

Researchers have built a system that can reconstruct a complete, matching 3D digital model of a human face from ordinary photos in under three seconds. The project, called uvfacefusion, takes multiple images of a person captured from different angles in everyday settings and merges them into a single consistent 3D mesh. Instead of relying on slow, complex scanning equipment, it uses a smart neural fusion technique in 2D texture space to combine the details quickly. It makes high-quality 3D face modeling incredibly fast and accessible, allowing anyone to generate an accurate digital head with just a few clicks.

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📦 ayanami1314/swe-pruner-pro

SWE-Pruner Pro

Shrink massive tool outputs in long coding tasks without losing vital context by reading the keep-or-prune signal directly from your AI model's own hidden states. Instead of wasting time and API costs on a separate LLM to summarize logs or code, swe-pruner-pro attaches a tiny prediction head directly to your existing frozen model. It automatically identifies the exact lines that matter and replaces the rest with a compact skeleton for the next turn. This incredibly smart approach dramatically slashes the overall token burden during complex software engineering tasks while keeping the model's coding accuracy fully intact.

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📦 harrison-one/sonoclip

SonoCLIP

SonoCLIP is the AI framework that finally makes medical image models understand the specific details of fetal ultrasounds. Standard image-recognition models analyze whole pictures, which means they easily get confused by the fuzzy backgrounds, noise, and blurry boundaries typical of an ultrasound. SonoCLIP solves this by letting you feed in anatomical masks alongside the scan, guiding the AI to focus exactly on the organs or regions that actually matter. It is a brilliant way to adapt massive vision models for highly specialized medical tasks. You get incredibly accurate zero-shot classification and segmentation, making prenatal analysis way more reliable without needing endless custom training data.

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📦 yanjun-zhao/recontext

ReContext

Recontext is the smart AI wrapper that makes large language models actually remember what they read in massive documents. When you dump a giant novel or a hundred-page report into an AI, it often gets overwhelmed and misses the details. Instead of throwing away data, this tool looks at where the model's attention naturally spikes, grabs those exact sentences, and whispers them right back to the model as a helpful reminder before it generates an answer. It is like highlighting the best parts of a textbook so you do not freeze during the final exam. Check it out to make your local models way smarter.

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📦 vlar-group/physmani

PhysMani

PhysMani is the AI framework that finally helps robotic arms grab and handle fast-moving objects in messy real-world spaces. Instead of lagging behind or guessing where an object will land, this system blends a 3D Gaussian visual world model with physics principles to predict how objects will move in the immediate future. This physical grounding allows the robot to generate quick, accurate actions on the fly. It is a massive step forward for robotic coordination, and the team shared their training setup and simulation benchmarks so you can test it yourself. It is the perfect project to watch if you want to see how robots are learning to master real-time...

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📦 miaai-lab/laguna-s-2.1-dgx-spark-rtx-6000-pro

laguna-s-2.1-dgx-spark-rtx-6000-pro

Run massive artificial intelligence models on single-GPU hardware using this specialized serving stack. The laguna-s-2.1-dgx-spark-rtx-6000-pro repository simplifies hosting a massive hundred-and-seventeen-billion parameter model by using advanced compression and speculative decoding to keep execution incredibly fast. It packages everything into a neat Docker container configured specifically for high-end chips like the NVIDIA GB10 and Blackwell GPUs. By combining smart memory management with automated kernel tuning, it squeezes every drop of performance out of your hardware while providing a standard, easy-to-use API. It is the perfect blueprint for running cutting-edge open models on your own terms.

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📦 livetennisapi/livetennisapi-mcp

livetennisapi-mcp

Feed live tennis match updates, player rankings, and real-time scores directly into your favorite artificial intelligence agents and development editors. The livetennisapi-mcp project connects AI helpers like Claude and Cursor straight to professional tennis courts, letting you query live matches, upcoming fixtures, and player details using plain language. Its standout feature is its clever handling of subscription tier limits; instead of crashing with a confusing error when a request hits a paywall, the tool returns a helpful, plain-English response telling the AI exactly which plan is needed, keeping your automated workflows smooth and informative.

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📦 berabuddies/redis-poc

redis-poc

Test the security of your Redis installations against remote code execution vulnerabilities using this proof of concept repository. The project provides pre-built Python scripts designed for authorized security testing across several major Redis versions, including newer releases like eight point eight point zero. By utilizing specific memory flaws, such as double-free issues and heap overflows in bundled modules, these tools demonstrate how an authenticated user can execute arbitrary system commands. It is a highly practical resource for security researchers looking to verify patch levels and analyze the mechanics of complex memory corruption bugs in popular data stores.

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