PDoomVideo Animation Codebase
Animate hand-painted cartoon music videos featuring the Clawd character using p5.js, p5.brush, and multi-agent Claude pipelines.
The definitive hub for **Agent Harnesses**, **Autonomous Skills**, and **Agentic Tools**.
Featured Harnesses
Animate hand-painted cartoon music videos featuring the Clawd character using p5.js, p5.brush, and multi-agent Claude pipelines.
Render high-framerate music videos with audio-synced typography and 3D scenes using Three.js and headless browser pipelines.
Direct animated short films in 51 visual styles using Claude Code and pure programmatic rendering without video generation models.
Explore viral Claude Opus 5.5 code-rendered video demos with reusable prompt templates and side-by-side remakes.
Inspect, edit, and analyze raw binary files, firmware, and data structures with a visual hex editor.
Generate new images from text prompts and edit existing pictures using up to ten reference images.
Automate tasks and mobile app tests on real Android phones using plain-language instructions and a web dashboard.
Automate actions on a real iPhone using an AI vision model and an inexpensive microcontroller board without jailbreaking.
Strategic guidance on agent skills and autonomous orchestration.
Paste your CV and a job ad into your AI app. Answer three quick questions to get an honest, tailored CV and cover letter that sound like you.
Snap your notes, paste one prompt, and your AI quizzes you one question at a time, using only what's in your notes. Free, phone only.
By combining Google's Quantization-Aware Training models with GGUF quantization and speculative decoding, developers can run highly accurate 12B parameter LLMs at production-level speeds on consumer hardware, drastically reducing latency…
Your agent isn't failing because the model is too dumb. It's failing because tool design, state management, and error recovery are broken — and that's an engineering problem.
Naive web scrapers often fail due to IP bans and CAPTCHAs; advanced tools with anti-blocking mechanisms, JavaScript rendering, and stealth features are essential for large-scale data extraction.
Treating prompt design like creative direction—defining role, sequencing steps, and setting boundaries—helps bridge the gap between vague intent and reliable AI output.
Daily curated RSS feeds from across the agentic landscape, parsed and summarized for rapid ingestion.
nano-lmcache provides a step-by-step educational implementation of LLM KV cache reuse, crucial for optimizing inference speed and cost.
llama.cpp updated its MoE implementation to use per-expert-token scaling for down projections, enhancing efficiency and enabling tensor parallelism.
Vangrid is a new platform designed to streamline the collection of real-world spatial data by connecting data capturers with businesses building AI and robotics systems.
Vangrid introduces a marketplace for real-world 3D data collection, enabling AI agents to programmatically request and pay for up-to-date environmental information.
An indie developer details using local AI agents to process and extract value from a large personal archive of digital content, demonstrating practical local AI application beyond frontier models.
Ollama v0.40.7 enhances OpenAI API compatibility, fixes streaming issues, and adds model introspection features, improving local LLM development workflows.
Vangrid.io launched a platform allowing AI agents to request real-world 3D data from human contributors, facilitating novel data acquisition for AI and robotics.
ggerganov/llama.cpp's b11779 release refactors LFM2 components, streamlining the architecture for local LLM inference.