Projects

Overview: The Gemini Prompt Engineering Toolkit is a comprehensive application designed to streamline AI prompt engineering and fine-tuning dataset workflows. It utilizes Google's latest Gemini models to generate high-quality prompts, analyze files, and create synthetic datasets for AI model fine-tuning. Key features: - **Prompt Generation:** Craft effective prompts for various tasks, including creative writing and code generation. - **File Analysis:** Gain insights from uploaded and analyzed CSV, TXT, Markdown (MD), and image files. - **Test Data Generation:** Create synthetic datasets for fine-tuning AI models, ensuring optimal performance. - **Multi-Model Support:** Choose from Gemini-1.5-flash-exp-0827, Gemini-1.5-pro-exp-0827, and Gemini-1.5-flash-8b-exp-0827 models to leverage different capabilities. - **User-Friendly Interface:** Intuitive Streamlit interface for easy navigation and access. Tech stack: Streamlit, Python, and Gemini AI. Why it matters: The Gemini Prompt Engineering Toolkit is designed to help developers become proficient in AI prompt engineering, enabling them to fine-tune AI models effectively and streamline their workflows. By leveraging the latest Gemini models, it provides powerful tools for generating high-quality prompts, analyzing data, and creating synthetic datasets, making it a valuable resource for AI enthusiasts and professionals alike. Repository: aiengineeringforgrandmas/gemini-prompt-engineer-toolkit GitHub stars: 8 GitHub description: ⚡ Powered by the latest (Aug 27, 2024)

Overview: Aethelgard is a secure Federated Retrieval-Augmented Generation (FedRAG) framework designed to query highly sensitive distributed vector databases without moving raw data or opening inbound corporate firewalls. It eliminates the need for Data Use Agreements (DUA) and creates a scalable infrastructure for global clinical consensus. Key features: - Pure-Pull Architecture: Edge nodes use outbound asynchronous polling, eliminating the need for inbound port-forwarding. - 100% Pluggable: Ships with FastAPI and Redis defaults, but core abstractions allow easy swapping to gRPC, Kafka, AWS SQS, or GCP Pub/Sub. - Semantic Firewall Ready: Designed to easily integrate local LLM verification (e.g., MedGemma) to sanitize vector search results before transmission. Tech stack: Python Why it matters: Aethelgard addresses the rare disease "Diagnostic Odyssey" by providing a decentralized nervous system for clinical intelligence, enabling global clinical consensus without compromising patient privacy or data security.

Overview: MJ-Desktop - Midjourney AI image generator for Windows, featuring offline mode and batch processing, with a version of v2026. Key features: - Text-to-image generation with V6 model - Batch processing up to 100 images - Offline mode with cached models - Custom style presets and templates - Upscale up to 16K resolution - Prompt history and favorites Tech stack: Unknown Why it matters: This application provides a powerful tool for generating AI-generated images, ideal for both personal and professional use, including offline processing and batch image generation. Repository: amusedboot/MJ-Desktop---Midjourney-AI-Desktop-App-2026 GitHub stars: 8 GitHub description: MJ-Desktop - Midjourney AI image generator for Windows with offline mode and batch processing. Downloads: 5.8m+

### Overview: A versatile text-to-chart API that converts Graph Description Language (GDL) and simple text into images using various plotting engines like Graphviz, Gnuplot, Ditaa, and more. ### Key Features: - **Text-to-Image Conversion**: Converts GDL and simple text into images. - **Multiple Plot Engines**: Supports engines such as Graphviz, Gnuplot, Ditaa, and more. - **Web Editor Demo**: Available for quick testing. - **API Documentation**: Detailed documentation for API usage, including available engines and parameters. ### Tech Stack: PHP ### Why It Matters: This API is useful for developers looking to visualize data and concepts in a simple and intuitive way, making it easier to communicate complex information visually.

Overview: This project provides a Python library for efficiently transforming PDFs and Wikipedia pages into a Questions & Answers dataset, suitable for fine-tuning machine learning models. Key features: - Extract text from PDFs or Wikipedia pages - Remove references and introduction sections - Clean and split text into meaningful chunks - Generate questions and answers from text - Customizable text processing and dataset generation settings Tech stack: Python Why it matters: This library streamlines the process of creating a dataset from text files, making it easier to fine-tune models for various applications. Repository: antoninichiq/QADatasetBuilder Primary language: Python GitHub stars: 8 GitHub description: Efficiently Transform PDFs and Wikipedia Pages into a Questions & Answers Dataset for Fine-Tuning.

### Overview: Ozan is an open-source, low-slop model fine-tuned on a 12B-character long-form creative-writing LLM. It aims to produce prose that sounds more natural and avoids overused language patterns commonly found in large language models. ### Key Features: - **Low Slop**: Ozan is specifically designed to minimize slop, a common issue in large language models where phrases like "barely above a whisper" or "couldn't help but" are used. - **Craft Quality**: The model is evaluated on a comprehensive rubric, scoring highly in voice, imagery, coherence, and other aspects of writing quality. - **Training Data**: It is trained on curated, low-slop examples from strong non-GPT models and filtered human Reddit WritingPrompts, aiming to produce clean, readable prose. ### Tech Stack: The model is built using PyTorch and fine-tuned on Mistral-Nemo-Instruct-2407, with weights and sample stories hosted on Hugging Face. ### Why It Matters: Ozan aims to produce high-quality, low-slop creative writing that sounds more natural and avoids the overused language patterns commonly found in large language models. This makes it a valuable tool for writers looking to improve the readability and authenticity of their work.

### Overview: BERT (Bidirectional Encoder Representations from Transformers) fine-tuning is used to classify SMS spam/ham messages. The project compares Naive Bayes, BERT as a frozen feature extractor, and BERT fine-tuned on the same test set. Metrics include accuracy, precision, recall, and F1 score, with BERT fine-tuned achieving the highest accuracy and recall. ### Key Features: - Fine-tuning BERT for SMS spam/ham classification. - Benchmarking against Naive Bayes baseline and BERT as a frozen feature extractor. - Evaluated on Google Colab with verified evaluation metrics and confusion matrix. - Includes a detailed dataset split and evaluation metrics. ### Tech Stack: Python (Jupyter Notebook) ### Why It Matters: This project provides a comprehensive comparison of different classification methods for SMS spam/ham detection, highlighting the effectiveness of fine-tuning BERT for improved accuracy and recall.

### Overview: Qwen-Image is a production-ready RunPod serverless endpoint that provides a powerful text-to-image generation model with exceptional English and Chinese text rendering capabilities. This model is based on Alibaba's Qwen-Image, a 20B MMDiT image foundation model optimized for GPU performance. ### Key Features: - **Official Qwen-Image Model**: 20B MMDiT image foundation model. - **GPU Optimized**: Runs on A100 80GB, H100 PCIe, H100 HBM3, H100 NVL, and high-end workstation GPUs. - **Auto-scaling**: Scales to 0 when idle to save costs. - **Network Volume Storage**: Model cached persistently across all workers. - **Fast Cold Starts**: Optimized Docker image with pre-installed dependencies. ### Tech Stack: - Shell ### Why It Matters: Qwen-Image offers exceptional text rendering capabilities, making it ideal for applications requiring high-quality image generation with accurate text descriptions. The model's performance and scalability make it a valuable tool for developers and content creators looking to enhance their text-to-image projects.

Overview: Artlite is a lightweight AI runtime library designed for language models and image generation on AMD Ryzen AI processors, specifically the XDNA 2 NPU. It leverages the AMD XRT driver to run kernels directly, optimizing performance and energy efficiency. Key features: - Runs language models and Stable Diffusion on the XDNA 2 NPU. - Provides a small C++ library for runtime. - Supports prompt and generation on the NPU. - Offers tools like `artlite-cli`, `artlite-server`, and `artlite-bench`. - Includes a local model for coding agents. - Supports embeddings, rerank, and decision models. - Prefills the NPU with a local model for reduced energy consumption. Tech stack: C++ library, AMD XRT driver, oneDNN, LLVM, and xbyak. Why it matters: Artlite is a research project focused on pushing the boundaries of AI hardware, particularly the NPU, while optimizing energy efficiency and performance.

Overview: This repository contains recipes for running models on NVIDIA DGX Spark, focusing on LLM serving, image, video, and speech tasks. Each recipe includes the pinned image, engine version, and model revision, ensuring reproducibility and minimal host changes. Key features: - Recipes for LLM serving, image, video, and speech tasks - Runs on one or two DGX Spark nodes - Measured performance on each Spark - Includes model revision and engine version - Ships with the measured numbers - Runs without host changes: read-only model cache, no added privileges, output in `./out` Tech stack: Python Why it matters: These recipes provide a standardized way to run models on DGX Spark, ensuring consistent performance and ease of reproducibility across different environments. Repository: atakann/dgx-spark-recipes GitHub stars: 7 GitHub description: Measured recipes for the NVIDIA DGX Spark, on one Spark or two: LLM serving, image, video and speech, and vision (detection and tracking on video). Each one was run and measured on Sparks. Every recipe: pins the container image, the engine version and the model revision, ships with the numbers it was measured at, runs without host changes: read-only model cache, no added privileges, output in `./out`. Not affiliated with NVIDIA or any model maker. Each model keeps its own license.

### Overview: Godzilla llama.cpp is a fork of llama.cpp optimized for Windows with CUDA support, designed for specific hardware configurations like RTX 3080 and long-context workloads. It includes features like fused GDN paths, KV placement options, and speculative draft mechanisms. ### Key features: - **KDA / FlashKDA**: CUDA `gated_delta_net_cuda<…, KDA=true>` path; `LLM_ARCH_KIMI_LINEAR`. - **Fused GDN**: Fused GDN on by default for supported architectures. - **KV Surface**: Classic quants + turbo/TCQ/KVarN where compiled; `-ctk` / `-ctv`. - **Memory**: `--kv-ram`, `--kv-vram-only`, chunked prefill (`-ub`). - **Speculative**: DFlash, DSpark, native `draft-mtp`, CopySpec. - **Windows Build**: Documented MSVC + UCRT + CUDA pin. ### Tech stack: C++ and CUDA. ### Why it matters: This fork is optimized for specific hardware configurations and workloads, providing features like fused GDN paths, KV placement options, and speculative draft mechanisms. The default settings are based on lab measurements on a single box, not marketing SLAs. The features ship when they work, and those that fail are opt-in or off. ### GitHub stars: 8 ### Repository: atomicmilkshake/godzilla-llama.cpp

Overview: NeuroMem is a memory layer designed for AI assistants, providing persistent memory, semantic retrieval, and evaluation tools to ensure assistants remember users over time. Key features: - Memory storage - Smart retrieval - Chat integration - Provider flexibility - Evaluation suite - Dataset tooling Tech stack: Python Why it matters: NeuroMem offers a practical long-term memory stack for AI assistants, ensuring they remember users over time, improving the quality of their interactions and evaluations. GitHub stars: 8

### Overview Beatra Skills is a collection of AI agent skills designed to enhance creative production. These skills integrate seamlessly with Beatra, allowing users to generate, edit, and render AI-generated images, videos, music, and voiceovers directly on the Beatra platform. ### Key Features - **Image Generation**: Create high-quality AI-generated images. - **Voiceover**: Generate talking head videos. - **Music**: Generate AI-generated music. - **Video**: Edit and render videos. - **Text to Image**: Convert text into images. - **Image to Image**: Convert one image to another. - **Image Editing**: Edit and enhance images. ### Tech Stack The skills are built using Python and leverage the Beatra platform. ### Why It Matters These skills are essential tools for anyone looking to integrate AI-generated content into their creative projects. Whether you're a content creator, designer, or marketer, Beatra Skills can help streamline your workflow and elevate your projects to new heights. ### Repository Repository: beatra-ai/beatra-skills Primary Language: Python GitHub Stars: 8 GitHub Description: Agent Skills for Claude Code, Cursor, Codex, Windsurf, Cline, OpenClaw: AI image generation, text to speech, talking head video, AI music, and video generation skills that render on Beatra.

Overview: Fine-tuning of LiquidAI LFM2-VL vision-language models on food image classification using LoRA. Key features: - 450M Base Model - 450M Fine-tuned Model - 1.6B Base Model - 1.6B Fine-tuned Model Tech stack: Jupyter Notebook Why it matters: This project provides a fine-tuning solution for LiquidAI LFM2-VL models on food image classification, showcasing improvements in model accuracy through fine-tuning.

Overview: LyricDiffusion is a versatile application that transforms song lyrics into visually stunning images and then those images can be combined into a slideshow. Key features: - Genius API Integration: Automatically fetches lyrics for the specified artist and song. - Customizable Inference: Choose the number of inference steps (1-100) to fine-tune the generated images. - Extra Prompt: Enhance the generated images with an extra prompt, enabling specific styles (e.g., dark ambience, extremely detailed). - Model Selection: Select a language model from Hugging Face's collection (e.g., dataautogpt3/OpenDalleV1.1). - Skip Irrelevant Verses: Exclude empty or non-lyrical verses fetched from the Genius API. - Output Path Configuration: Change the output path where the images and videos will be saved. - Video FPS: Choose how fast the slideshow should move. - Video Format: Choose in which format the video should be generated. Tech stack: Python Why it matters: LyricDiffusion is a powerful tool for visualizing song lyrics, allowing users to create stunning images and videos that can be customized to their preferences. The integration with the Genius API makes it easy to generate lyrics for any artist and song, while the customizable inference and extra prompt features enable users to fine-tune the generated images to their liking. The versatility of the application makes it a valuable tool for anyone interested in visualizing song lyrics in a creative way.

Overview: This project aims to enhance the interaction between a large language model and synthetic health data in the FHIR format, contributing to the LabLabAI AI Challenge Hackathon in October 2023. Key features: - User interface for displaying synthetic health data - Integration with a language model (GPT-3.5, GPT-4) for enhanced interaction - Connection between synthetic data and the language model - Definition and documentation of workflows and queries Tech stack: Jupyter Notebook Why it matters: This project demonstrates the integration of AI models with medical data, showcasing the potential of using AI in healthcare. Repository: bsenst/llm-enhanced-ehr Primary language: Jupyter Notebook GitHub stars: 8 GitHub description: Contribution to the LabLabAI AI Challenge Hackathon October 2023 [Project Team Page](https://lablab.ai/event/ai-challenge-with-gpt-3-5-codex-dall-e-and-whisper-api/fritzlabs) & [Project Submission Page](https://lablab.ai/event/ai-challenge-with-gpt-3-5-codex-dall-e-and-whisper-api/fritzlabs/llm-enhanced-medical-notes)  Run

Overview: This repository contains the training code for a beginner-friendly tutorial on fine-tuning image classification models using PyTorch and the timm library for a hand gesture recognizer. Key features: - Fine-tuning image classification models with PyTorch and the timm library - Creating a hand gesture recognizer - Step-by-step guide accessible to beginners Tech stack: Jupyter Notebook Why it matters: This tutorial provides a practical, accessible way for beginners to learn and apply deep learning concepts, specifically focusing on image classification and fine-tuning PyTorch models. Repository: cj-mills/pytorch-timm-gesture-recognition-tutorial-code Primary language: Jupyter Notebook GitHub stars: 8 GitHub description: Contains the training code for a beginner-friendly tutorial titled "Fine-Tuning Image Classifiers with PyTorch and the timm library for Beginners". README excerpt: This tutorial provides a step-by-step guide on fine-tuning image classification models using PyTorch and the timm library to create a hand gesture recognizer. The content is structured to be accessible even to beginners in deep learning.

### Overview: This repository is a comprehensive collection of AI logo generator prompts, workflows, and branding resources designed to help creators and entrepreneurs explore logo concepts, brand identity ideas, and visual style directions using artificial intelligence. ### Key Features: - AI logo generator prompts - AI logo maker workflows - AI logo creator ideas - AI logo maker examples - Brand identity prompts - Icon concepts - Style directions - Color palette workflows - Creative branding resources ### Tech Stack: Python and AI models. ### Why it Matters: This repository provides a practical and SEO-focused toolkit for founders, creators, marketers, designers, indie makers, agencies, e-commerce sellers, and small businesses looking to leverage AI for fast ideation in logo and visual identity design. It helps in exploring logo directions, symbols, icons, and style systems without replacing final brand design judgment or legal review.

### Overview: This repository provides a comprehensive guide to creating free AI image prompts, workflows, and resources for beginners and those looking to explore AI art generation without incurring high costs. ### Key Features: - **Free AI Image Generator Prompts**: Examples and patterns for creating AI images without spending money. - **Text-to-Image Workflows**: Prompts for generating images from text, suitable for beginners. - **AI Art Prompts**: Creative prompts for generating AI art. - **Realistic Image Prompts**: Prompts that are realistic and practical for various use cases. - **Product Image Prompts**: Prompts for creating product images. - **Profile Picture Prompts**: Prompts for creating profile pictures. - **YouTube Thumbnail Prompts**: Prompts for creating thumbnail images for YouTube videos. - **Logo Concept Prompts**: Prompts for creating logo concepts. - **Print-on-Demand Prompts**: Prompts for creating images that can be printed on demand. - **Image Editing and Upscaling Workflow Notes**: Tips and guidelines for editing and upscaling generated images. - **Prompt Formulas**: Formulas to reduce failed generations. - **Honest Guidance**: Honest advice about free limits and realistic expectations. - **Quality Checklists**: Checklists to ensure the quality of generated images. - **Safety, Copyright, and Commercial-Use Notes**: Important information about safety, copyright, and commercial use. - **Links to Relevant Cliprise Pages, Tools, and Guides**: Access to additional resources for further learning. ### Tech Stack: None ### Why It Matters: This repository is essential

Overview: This repository is a comprehensive resource for Seedream 5 prompts, Seedream prompts, Seedream 5 image prompts, AI image generation prompts, product photography prompts, ad creative templates, image editing ideas, and Cliprise resources. It serves creators, marketers, founders, agencies, e-commerce teams, app builders, musicians, educators, and visual storytellers looking for better prompts, stronger image outputs, and reusable creative assets. Key features: - Seedream 5 prompts - Seedream prompts - Seedream 5 image prompts - Seedream 5 product photography prompts - Seedream 5 poster prompts - Seedream 5 ad creative prompts - Seedream 5 social media image prompts - Seedream 5 editorial prompts - Seedream 5 character prompts - Seedream 5 concept art prompts - Seedream 5 text-to-image prompts - Seedream 5 image editing prompts - AI image generation prompts - AI commercial image prompts - AI product image prompts - AI poster design prompts - AI source-frame prompts Tech stack: None specified. Why it matters: This repository provides a practical, SEO-friendly hub for creators seeking better prompts, stronger image outputs, and reusable creative assets in the Seedream 5 and AI image generation workflows.