📦 dingduff/dingduff-public

DingDuff Public: Free Legal Research for Lawyers

DingDuff Public gives lawyers instant access to millions of court opinions and statutes right inside the Claude chat they already use. Built by attorneys for attorneys, this project acts as a connector that lets you ask legal questions in plain English and receive answers backed by real, verifiable citations from federal and state sources. Instead of memorizing complex search commands, you simply describe what you need, and the tool pulls the exact texts of case law, rules, and PACER filings to help you build accurate research memos.

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📦 attemorysystem/attemory

Attemory: AI Memory That Actually Remembers

Attemory is a retrieval engine that lets AI agents search their own long-term memory using attention, not just vector similarity. It indexes documents and code into reusable key-value state, then runs a local model to reason over that data and pull up the exact evidence needed. This approach cuts coding-agent token usage by nearly half while keeping search quality high. Instead of relying on compressed embeddings, Attemory lets the model read raw text directly, making it much better at finding specific details in huge context windows. It works locally on Linux and macOS, supporting CPU and GPU backends.

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📦 hermes-brasil/hermes-brasil

Hermes Brasil: AI Agent Skills in Portuguese

Hermes Brasil is the Brazilian community hub for Hermes Agent, offering Portuguese-language skills, guides, and integrations for the open-source AI agent by Nous Research. The repository features ready-to-use skills for tasks like B2B prospecting and kanban orchestration, alongside practical installation guides for production environments. It enables developers to easily expand their AI agent's capabilities with locally relevant resources and community-driven contributions.

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📦 accio-lab/realreplicabench

RealReplicaBench: Test AI Agents on Real Business Tasks

See how your AI agents actually perform when thrown into realistic, messy business workflows. This tool builds safe, reproducible clones of popular online services like Gmail, Slack, and Alibaba, complete with authentic files and state changes. Instead of simple coding quizzes, it lets you run complex, long-horizon tasks like auditing supplier invoices, managing procurement lists, or triaging customer complaints. You get to watch an agent navigate these high-fidelity replicas and handle multi-step chores that mimic real work pressure. It is the perfect way to stress-test automation skills without risking actual company data or breaking live systems.

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📦 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.

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📦 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 computer’s 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.

📰 https://news.ycombinator.com/item?id=49125532

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📦 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.

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📦 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.

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📦 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.

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📦 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.

📰 https://news.ycombinator.com/item?id=49192771

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📦 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.

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📦 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.

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📦 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.

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📦 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 teacher’s 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.

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📦 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.

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📦 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 computer’s 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.

📰 https://news.ycombinator.com/item?id=48282551

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📦 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.

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📦 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.

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📦 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.

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📦 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.

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