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affaan-m

Continuous-learning is a skill that automatically evaluates Claude Code sessions to extract reusable patterns and saves them as learned skills for future use.

affaan-m

nanoclaw-repl is a zero-dependency, session-aware REPL built on Claude for operating and extending the NanoClaw v2 AI agent. It features persistent markdown-backed sessions, dynamic skill loading, session branching, history compaction, and export capabilities to MD, JSON, and TXT formats.

lekman

Content-addressed image storage and distribution using Cloudflare CDN with Azure backend.

affaan-m

agent-harness-construction is a developer skill for designing and optimizing AI agent action spaces, tool definitions, and observation formatting. It focuses on core constraints like action space quality and recovery quality to achieve higher completion rates for agents like Claude.

affaan-m

ai-first-engineering is an operating model for development teams where AI agents generate a significant portion of code implementation. It focuses on process design, review methodologies, and architecture requirements specifically optimized for AI-assisted development workflows with tools like Claude Code.

furystack

home cloud and media solutions that runs even on a cutting board

pkuppens

On-Premises RAG Solution: Talk with your documents and data without the Cloud concerns

dlupiak

Claude Code Session Dashboard — local observability for ~/.claude sessions

HoangNguyen0403

Push notifications and local notifications for Flutter using Firebase Cloud Messaging and flutter_local_notifications.

sfc-gh-dflippo

This project demonstrates many of dbt's features when used with the Snowflake Data Cloud

huggingface

Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.

golemcloud

Golem Cloud is the agent-native platform for building AI agents and distributed applications that never lose state, never duplicate work, and never require you to build infrastructure.