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π¦ wildminder/comfyui-dype
ComfyUI DyPE: Generate 4K Images Without Artifacts
ComfyUI DyPE lets your diffusion models generate crisp four thousand by four thousand images by dynamically adjusting positional settings during the creation process. Standard tools struggle with mega pixels, usually turning details into blurry messes or repeating patterns. This project fixes that by shifting the model focus from broad shapes early on to fine details later. It works as a simple plugin for popular AI tools and supports several major model types. You get high resolution without needing complex training. It is a practical way to push image quality further with minimal effort.
π @hackernewsgithubprojects
ComfyUI DyPE: Generate 4K Images Without Artifacts
ComfyUI DyPE lets your diffusion models generate crisp four thousand by four thousand images by dynamically adjusting positional settings during the creation process. Standard tools struggle with mega pixels, usually turning details into blurry messes or repeating patterns. This project fixes that by shifting the model focus from broad shapes early on to fine details later. It works as a simple plugin for popular AI tools and supports several major model types. You get high resolution without needing complex training. It is a practical way to push image quality further with minimal effort.
π @hackernewsgithubprojects
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π¦ morishuz/delaunay32
Delaunay32: Blazing Fast 2D Meshes
Delaunay32 lets you instantly connect thousands of scattered points into a perfect, triangle-filled mesh without any messy glitches. While most tools struggle with floating-point errors when dealing with massive datasets, this clever C++ library secretly converts your coordinates into exact integers to guarantee perfect results every single time. You can throw in simple whole numbers or direct decimal floats, and it handles the heavy lifting in parallel using all your computerβs cores. The result is a triangulation that is not only incredibly robust against weird edge cases but also over ten times faster than its closest competitors for large point sets.
π° https://news.ycombinator.com/item?id=49125532
π @hackernewsgithubprojects
Delaunay32: Blazing Fast 2D Meshes
Delaunay32 lets you instantly connect thousands of scattered points into a perfect, triangle-filled mesh without any messy glitches. While most tools struggle with floating-point errors when dealing with massive datasets, this clever C++ library secretly converts your coordinates into exact integers to guarantee perfect results every single time. You can throw in simple whole numbers or direct decimal floats, and it handles the heavy lifting in parallel using all your computerβs cores. The result is a triangulation that is not only incredibly robust against weird edge cases but also over ten times faster than its closest competitors for large point sets.
π° https://news.ycombinator.com/item?id=49125532
π @hackernewsgithubprojects
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π¦ sorryhyun/anima_lora
Anima LoRA: Fast Model Training
Anima LoRA lets you train custom AI image models on your own graphics card in minutes, not days. This open-source toolkit is built specifically for the new Anima diffusion model, which uses a modern architecture that is both faster and sharper than older tools. The real magic here is how it handles speed. It uses smart compiler tricks to shrink the memory footprint so you can train high-resolution models even on consumer-grade hardware that usually chokes on this work. Instead of just throwing more power at the problem, it carefully optimizes every step of the training loop.
π @hackernewsgithubprojects
Anima LoRA: Fast Model Training
Anima LoRA lets you train custom AI image models on your own graphics card in minutes, not days. This open-source toolkit is built specifically for the new Anima diffusion model, which uses a modern architecture that is both faster and sharper than older tools. The real magic here is how it handles speed. It uses smart compiler tricks to shrink the memory footprint so you can train high-resolution models even on consumer-grade hardware that usually chokes on this work. Instead of just throwing more power at the problem, it carefully optimizes every step of the training loop.
π @hackernewsgithubprojects
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π¦ pku-yuangroup/uniworld-view
UniWorld-View
UniWorld-View turns a simple video or single photo into realistic new camera angles without needing expensive equipment. Researchers from Peking University built this tool to solve a tricky problem: making a video look like it was filmed from a completely different spot. Instead of complex 3D modeling, the system uses advanced video generation to predict what the scene looks like from new viewpoints. You can even control exactly where the camera moves, allowing for precise adjustments. It ranks highly on industry leaderboards because it handles everyday objects and clear motion surprisingly well. This open-source code lets developers experiment with view synthesis directly on their own machines.
π @hackernewsgithubprojects
UniWorld-View
UniWorld-View turns a simple video or single photo into realistic new camera angles without needing expensive equipment. Researchers from Peking University built this tool to solve a tricky problem: making a video look like it was filmed from a completely different spot. Instead of complex 3D modeling, the system uses advanced video generation to predict what the scene looks like from new viewpoints. You can even control exactly where the camera moves, allowing for precise adjustments. It ranks highly on industry leaderboards because it handles everyday objects and clear motion surprisingly well. This open-source code lets developers experiment with view synthesis directly on their own machines.
π @hackernewsgithubprojects
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π¦ sespoir/reground
ReGround: AI That Double-Checks Its Eyes
Teaches vision models to catch their own mistakes. When a computer program tries to answer a hard question about a picture, it sometimes guesses wrong after too many steps. ReGround fixes this by letting the model pause and look again. If it gets unsure, it emits a special signal to re-examine the original image before giving a final answer. It is like having a second pair of eyes. The system trains itself to recognize when it needs a closer look and then re-runs the visual check automatically. You get better answers without changing the core model architecture. It just adds a smart self-diagnosis loop that actually works.
π @hackernewsgithubprojects
ReGround: AI That Double-Checks Its Eyes
Teaches vision models to catch their own mistakes. When a computer program tries to answer a hard question about a picture, it sometimes guesses wrong after too many steps. ReGround fixes this by letting the model pause and look again. If it gets unsure, it emits a special signal to re-examine the original image before giving a final answer. It is like having a second pair of eyes. The system trains itself to recognize when it needs a closer look and then re-runs the visual check automatically. You get better answers without changing the core model architecture. It just adds a smart self-diagnosis loop that actually works.
π @hackernewsgithubprojects
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π¦ 0x23/micromanipulatorstepper
Submicron 3D Motion Control Platform
This open-source device achieves submicron precision by combining three stepper motors with a unique magnetic gearing trick that boosts cheap encoder resolution thirtyfold. You build it from printed parts, magnets, and a Raspberry Pi Pico, then control it simply by sending standard G-Code commands over a serial connection. The clever design uses ball joints for smooth movement and includes everything you need from circuit board files to a Python interface for programming. It is genuinely fascinating because it offers lab-grade accuracy for tasks like microscopy or electronics probing without costing a fortune. Grab the files, build your own micro-manipulator, and move things with terrifying precision.
π° https://news.ycombinator.com/item?id=49192771
π @hackernewsgithubprojects
Submicron 3D Motion Control Platform
This open-source device achieves submicron precision by combining three stepper motors with a unique magnetic gearing trick that boosts cheap encoder resolution thirtyfold. You build it from printed parts, magnets, and a Raspberry Pi Pico, then control it simply by sending standard G-Code commands over a serial connection. The clever design uses ball joints for smooth movement and includes everything you need from circuit board files to a Python interface for programming. It is genuinely fascinating because it offers lab-grade accuracy for tasks like microscopy or electronics probing without costing a fortune. Grab the files, build your own micro-manipulator, and move things with terrifying precision.
π° https://news.ycombinator.com/item?id=49192771
π @hackernewsgithubprojects
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π¦ magicrew/doc7
doc7
You can now transform any complex PDF or messy screenshot into clean, AI-ready Markdown using your own local vision model, with absolutely zero document parsing fees. A clever tool called doc7 bypasses traditional, rigid OCR engines entirely. Instead, it takes your document, renders the pages, and feeds them directly to a local model running in Ollama or LM Studio to reconstruct everything. It can perfectly recover complex mathematical formulas, diagram relationships, and even visual chart data from flat images. It is incredibly easy to run right from your terminal, keeping all your sensitive data completely private. Download doc7 today and unlock the power of local document understanding.
π @hackernewsgithubprojects
doc7
You can now transform any complex PDF or messy screenshot into clean, AI-ready Markdown using your own local vision model, with absolutely zero document parsing fees. A clever tool called doc7 bypasses traditional, rigid OCR engines entirely. Instead, it takes your document, renders the pages, and feeds them directly to a local model running in Ollama or LM Studio to reconstruct everything. It can perfectly recover complex mathematical formulas, diagram relationships, and even visual chart data from flat images. It is incredibly easy to run right from your terminal, keeping all your sensitive data completely private. Download doc7 today and unlock the power of local document understanding.
π @hackernewsgithubprojects
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π¦ worldbench/awesome-agentic-world-model
Awesome Agentic World Model: A Map for Smarter AI
Explore how artificial intelligence learns to predict the future by checking out this curated collection of research on agentic world modeling. It maps out the shift from passive simulations to interactive environments where AI agents can test plans, learn from mistakes, and improve continuously without risking real-world damage. Think of it as a cheat code for training smarter robots and software assistants by letting them practice in safe, virtual simulations. It organizes dozens of papers into a clear guide, showing how machines can imagine consequences before acting.
π @hackernewsgithubprojects
Awesome Agentic World Model: A Map for Smarter AI
Explore how artificial intelligence learns to predict the future by checking out this curated collection of research on agentic world modeling. It maps out the shift from passive simulations to interactive environments where AI agents can test plans, learn from mistakes, and improve continuously without risking real-world damage. Think of it as a cheat code for training smarter robots and software assistants by letting them practice in safe, virtual simulations. It organizes dozens of papers into a clear guide, showing how machines can imagine consequences before acting.
π @hackernewsgithubprojects
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π¦ kenton-gmi/sakura-crossing
Sakura Crossing: Anime World From Code
Walk through a fully 3D Japanese neighborhood that looks exactly like a hand-painted anime background, all created without a single image file. This project uses clever rendering tricks to turn depth data into crisp ink lines and flat, colorful shading, making a real-time world feel like a studio production. You can explore streets, shrines, and a railway loop on a tiny planet, even hopping onto an electric bike to ride around. It is a stunning example of how code can mimic art.
π @hackernewsgithubprojects
Sakura Crossing: Anime World From Code
Walk through a fully 3D Japanese neighborhood that looks exactly like a hand-painted anime background, all created without a single image file. This project uses clever rendering tricks to turn depth data into crisp ink lines and flat, colorful shading, making a real-time world feel like a studio production. You can explore streets, shrines, and a railway loop on a tiny planet, even hopping onto an electric bike to ride around. It is a stunning example of how code can mimic art.
π @hackernewsgithubprojects
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π¦ chenchishui/futurebridge-opd
FutureBridge-OPD: Teaching AI to Look Before It Leaps
FutureBridge-OPD solves a nagging problem in AI learning where small mistakes snowball into big failures. Instead of just copying a teacherβs final answer, this tool makes the student look ahead. It spots a confusing moment in a task, tries a different path suggested by the teacher, and then checks if that new path actually works better. If it does, the student keeps it. This simple validation step keeps the learner on track and prevents it from drifting into wrong territory. It is a clever way to make self-learning models much more reliable without needing endless trial and error.
π @hackernewsgithubprojects
FutureBridge-OPD: Teaching AI to Look Before It Leaps
FutureBridge-OPD solves a nagging problem in AI learning where small mistakes snowball into big failures. Instead of just copying a teacherβs final answer, this tool makes the student look ahead. It spots a confusing moment in a task, tries a different path suggested by the teacher, and then checks if that new path actually works better. If it does, the student keeps it. This simple validation step keeps the learner on track and prevents it from drifting into wrong territory. It is a clever way to make self-learning models much more reliable without needing endless trial and error.
π @hackernewsgithubprojects
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π¦ oxpig/nispo
NISPO: Turn Molecules into Names
Nispo turns molecular blueprints into perfectly formatted chemical names that always reverse back to the original structure. Instead of guessing how to name complex compounds, you just feed it a simple string representation and get back a valid IUPAC name every time. This tool solves the headache of chemical nomenclature by using an AI-generated codebase that obsessively tests every output against a verification engine to ensure a perfect round trip. It is genuinely cool because it handles massive datasets with nearly perfect accuracy, turning a tedious manual task into a reliable automated process.
π @hackernewsgithubprojects
NISPO: Turn Molecules into Names
Nispo turns molecular blueprints into perfectly formatted chemical names that always reverse back to the original structure. Instead of guessing how to name complex compounds, you just feed it a simple string representation and get back a valid IUPAC name every time. This tool solves the headache of chemical nomenclature by using an AI-generated codebase that obsessively tests every output against a verification engine to ensure a perfect round trip. It is genuinely cool because it handles massive datasets with nearly perfect accuracy, turning a tedious manual task into a reliable automated process.
π @hackernewsgithubprojects
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π¦ avencera/speakrs
Speakrs: Rust Speaker Diarization
On a Mac laptop, this project called speakrs listens to a recording and tells you exactly who is speaking, running nearly five hundred times faster than standard tools without losing accuracy. It is a complete Rust library that breaks down audio into individual voices, identifying when each person talks and who they are. Instead of relying on slow Python scripts, it uses your computerβs native hardware to process sound instantly. This means developers and creators can add professional speech-to-text features to their apps without the usual speed bumps or heavy setup. It turns complex audio analysis into a simple, fast task anyone can use.
π° https://news.ycombinator.com/item?id=48282551
π @hackernewsgithubprojects
Speakrs: Rust Speaker Diarization
On a Mac laptop, this project called speakrs listens to a recording and tells you exactly who is speaking, running nearly five hundred times faster than standard tools without losing accuracy. It is a complete Rust library that breaks down audio into individual voices, identifying when each person talks and who they are. Instead of relying on slow Python scripts, it uses your computerβs native hardware to process sound instantly. This means developers and creators can add professional speech-to-text features to their apps without the usual speed bumps or heavy setup. It turns complex audio analysis into a simple, fast task anyone can use.
π° https://news.ycombinator.com/item?id=48282551
π @hackernewsgithubprojects
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π¦ cohesity/scalablerag
ScalableRAG: AI That Answers Questions Without Pre-Processing
ScalableRAG is the question answering system that finally lets you query massive libraries of documents without spending days pre-processing them. Traditional tools usually require you to build complex databases or vector indexes before you can ask anything, which is expensive and slow. This project flips that script by offering a zero-ingestion mode that skips all that heavy lifting entirely. Instead of searching static indexes, it gives the AI a workspace of dynamic sets to explore, filter, and analyze right as you ask. It mimics how a human would logically group and count information across files, matching or beating far more complex systems.
π @hackernewsgithubprojects
ScalableRAG: AI That Answers Questions Without Pre-Processing
ScalableRAG is the question answering system that finally lets you query massive libraries of documents without spending days pre-processing them. Traditional tools usually require you to build complex databases or vector indexes before you can ask anything, which is expensive and slow. This project flips that script by offering a zero-ingestion mode that skips all that heavy lifting entirely. Instead of searching static indexes, it gives the AI a workspace of dynamic sets to explore, filter, and analyze right as you ask. It mimics how a human would logically group and count information across files, matching or beating far more complex systems.
π @hackernewsgithubprojects
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π¦ ielab/skim-search-agent
SkimSearchAgent: Deep Research for Your Data
SkimSearchAgent lets you build intelligent research assistants that dig through your own document collections to answer complex questions without you writing code. Instead of guessing answers from a static database, this tool gives an AI model a loop where it can actively search, inspect results, and fetch specific sections of your documents to piece together accurate answers. It is fascinating because it separates every part of the process, meaning you can swap out different search methods, document structures, or even the AI brain driving the logic without breaking the whole system.
π @hackernewsgithubprojects
SkimSearchAgent: Deep Research for Your Data
SkimSearchAgent lets you build intelligent research assistants that dig through your own document collections to answer complex questions without you writing code. Instead of guessing answers from a static database, this tool gives an AI model a loop where it can actively search, inspect results, and fetch specific sections of your documents to piece together accurate answers. It is fascinating because it separates every part of the process, meaning you can swap out different search methods, document structures, or even the AI brain driving the logic without breaking the whole system.
π @hackernewsgithubprojects
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π¦ petroni-lab/librarian
AI Librarian: Natural Questions to Scientific Evidence
Ask a messy biology question like whether metformin extends lifespan and get back the exact sentences that answer it, not just a list of papers. This project acts as a smart librarian for artificial intelligence, taking your natural language query and turning it into a series of precise search commands across the Europe PMC database. It doesn't just dump results; it reads through the actual full text of open-access studies, filters out the noise, and extracts only the specific evidence snippets that matter. Think of it as a research assistant that skips the fluff and hands you the hard proof.
π @hackernewsgithubprojects
AI Librarian: Natural Questions to Scientific Evidence
Ask a messy biology question like whether metformin extends lifespan and get back the exact sentences that answer it, not just a list of papers. This project acts as a smart librarian for artificial intelligence, taking your natural language query and turning it into a series of precise search commands across the Europe PMC database. It doesn't just dump results; it reads through the actual full text of open-access studies, filters out the noise, and extracts only the specific evidence snippets that matter. Think of it as a research assistant that skips the fluff and hands you the hard proof.
π @hackernewsgithubprojects
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π¦ 198808xc/dramasr-lrm
DramaSR-LRM: AI That Guesses Who Is Talking in TV Dramas
DramaSR-LRM turns large language models into sharp listeners that figure out who is speaking in long, messy TV dramas. Traditional speech recognition fails when voices change or characters disappear off-screen. This project solves that by training a model to think before it speaks. Instead of guessing instantly, the AI calls tools like audio comparison and character relationship maps to build a solid argument. It learns to weave together visual clues and voice patterns, catching subtle shifts that standard software misses. The result is a system that actually understands context, not just sound. It is a clever way to make AI pay attention to the story, not just the audio.
π @hackernewsgithubprojects
DramaSR-LRM: AI That Guesses Who Is Talking in TV Dramas
DramaSR-LRM turns large language models into sharp listeners that figure out who is speaking in long, messy TV dramas. Traditional speech recognition fails when voices change or characters disappear off-screen. This project solves that by training a model to think before it speaks. Instead of guessing instantly, the AI calls tools like audio comparison and character relationship maps to build a solid argument. It learns to weave together visual clues and voice patterns, catching subtle shifts that standard software misses. The result is a system that actually understands context, not just sound. It is a clever way to make AI pay attention to the story, not just the audio.
π @hackernewsgithubprojects
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π¦ apphane-dev/nehir
Nehir: Scrolling Window Manager for macOS
Nehir lets you manage your macOS desktop using a horizontal scrolling layout that feels like a river of windows. Instead of stacking apps on top of each other or jumping between static workspaces, Nehir arranges your open windows into columns that slide left and right across your screen. It brings the popular Niri workflow to Mac, meaning you get smooth, animated transitions between apps without losing context. You can customize hotkeys, app rules, and monitor setups with simple text files that update instantly. It handles multiple displays well, keeps your dock out of the way, and even lets you trace exactly what caused a glitch if something goes wrong.
π° https://news.ycombinator.com/item?id=49194123
π @hackernewsgithubprojects
Nehir: Scrolling Window Manager for macOS
Nehir lets you manage your macOS desktop using a horizontal scrolling layout that feels like a river of windows. Instead of stacking apps on top of each other or jumping between static workspaces, Nehir arranges your open windows into columns that slide left and right across your screen. It brings the popular Niri workflow to Mac, meaning you get smooth, animated transitions between apps without losing context. You can customize hotkeys, app rules, and monitor setups with simple text files that update instantly. It handles multiple displays well, keeps your dock out of the way, and even lets you trace exactly what caused a glitch if something goes wrong.
π° https://news.ycombinator.com/item?id=49194123
π @hackernewsgithubprojects
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π¦ raven-july/cfpo
CFPO: Smarter Multimodal Reasoning
CFPO is a counterfactual reinforcement learning framework that finally forces large vision-language models to actually look at images instead of guessing from text. Standard AI methods often let models cheat by relying on linguistic shortcuts rather than visual evidence, and CFPO solves this by testing whether the prediction changes when critical visual cues are suppressed. It builds a counterfactual path that blocks high-saliency visual signals, then rewards the model only if its reasoning genuinely depends on the image rather than blind language patterns. This approach consistently beats standard training baselines on math and logic benchmarks without needing external reward models.
π @hackernewsgithubprojects
CFPO: Smarter Multimodal Reasoning
CFPO is a counterfactual reinforcement learning framework that finally forces large vision-language models to actually look at images instead of guessing from text. Standard AI methods often let models cheat by relying on linguistic shortcuts rather than visual evidence, and CFPO solves this by testing whether the prediction changes when critical visual cues are suppressed. It builds a counterfactual path that blocks high-saliency visual signals, then rewards the model only if its reasoning genuinely depends on the image rather than blind language patterns. This approach consistently beats standard training baselines on math and logic benchmarks without needing external reward models.
π @hackernewsgithubprojects
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π¦ larryvrh/comfyui-minimax-h3-turbo
ComfyUI-MiniMax-H3-Turbo
Create synced video and audio in just four generation steps using the ComfyUI-MiniMax-H3-Turbo project, which transforms how you handle multimedia generation. The repository provides a custom sampler and LoRA loader that bypass the usual twenty-step process, cutting generation time to a fraction of what it normally takes. It solves the tricky problem of audio distortion that usually wreals havoc when you rush video models, ensuring your sound stays crisp even at ultra-low step counts. By dropping two simple nodes into your existing workflow, you get fast results without complex tweaks. This is a neat trick for anyone who wants to experiment with fast-paced multimedia creation without waiting around.
π @hackernewsgithubprojects
ComfyUI-MiniMax-H3-Turbo
Create synced video and audio in just four generation steps using the ComfyUI-MiniMax-H3-Turbo project, which transforms how you handle multimedia generation. The repository provides a custom sampler and LoRA loader that bypass the usual twenty-step process, cutting generation time to a fraction of what it normally takes. It solves the tricky problem of audio distortion that usually wreals havoc when you rush video models, ensuring your sound stays crisp even at ultra-low step counts. By dropping two simple nodes into your existing workflow, you get fast results without complex tweaks. This is a neat trick for anyone who wants to experiment with fast-paced multimedia creation without waiting around.
π @hackernewsgithubprojects
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π¦ aaddrick/attention-control
Attention Control: Air Traffic Discipline for AI
Attention Control forces coding agents to talk like air traffic controllers, stripping away the usual fluff so you get immediate, actionable instructions. It treats your attention like a pilotβs focus, demanding that every response lead with the next executable command instead of a polite preamble. This style was built for readers with ADHD but helps anyone drowning in context switches, by replacing vague suggestions with concrete steps and hard deadlines. You stop guessing and start running code. If your AI assistant is currently writing essays, this style forces it to get out of the way and let you work. It is a simple shift that makes complex help actually usable.
π @hackernewsgithubprojects
Attention Control: Air Traffic Discipline for AI
Attention Control forces coding agents to talk like air traffic controllers, stripping away the usual fluff so you get immediate, actionable instructions. It treats your attention like a pilotβs focus, demanding that every response lead with the next executable command instead of a polite preamble. This style was built for readers with ADHD but helps anyone drowning in context switches, by replacing vague suggestions with concrete steps and hard deadlines. You stop guessing and start running code. If your AI assistant is currently writing essays, this style forces it to get out of the way and let you work. It is a simple shift that makes complex help actually usable.
π @hackernewsgithubprojects