March 2026 monthly summary for Hao AI Lab development. Key focus areas were delivering high-value features, improving model performance, and maintaining clear, scalable code. Highlights include substantial model and training refactors, a live demo release, and targeted bug fixes that align external communications with product capabilities.

February 2026 (2026-02) – FastVideo (hao-ai-lab/FastVideo) delivered a cohesive set of feature-rich enhancements across documentation, training pipelines, and the video-generation workflow, along with critical stability and observability improvements. The work enhances usability, scalability, and maintainability, enabling faster experimentation and more reliable multi-GPU deployments.

November 2025 (hao-ai-lab/FastVideo): Delivered a documentation enhancement to showcase real-world usage of FastVideo by adding links to research projects in the README. This strengthens onboarding, demonstrates practical value to users, and invites community contributions. The work provides clear examples of FastVideo applicability across research domains and improves ecosystem credibility for potential adopters.

2025-10 monthly summary for hao-ai-lab/FastVideo: Focused on documentation quality. Delivered a documentation-only update to fix the WeChat link in README, strengthening external communications and stakeholder trust with accurate contact information. No new features or bug fixes beyond documentation occurred this month. The work improves onboarding, reduces support friction, and preserves the project's communications integrity. Demonstrated solid version-control hygiene and commitment to up-to-date docs.

September 2025: Delivered a targeted reliability improvement for FastVideo by fixing the WeChat Link URL in the repository's Readme, ensuring users access the correct WeChat group information and assets after the migration of image hosting. The fix reduces user confusion and support overhead while preserving documentation accuracy.

August 2025 monthly summary: Across hao-ai-lab/hao-ai-labhub.io.git and hao-ai-lab/FastVideo, delivered documentation enhancements, reproducibility improvements, and hardware-aware stability fixes that strengthen product reliability and onboarding. Key outcomes include clearer metrics and corrected performance figures, improved documentation structure for FastWan, and streamlined onboarding through README updates. Hardware-aware builds and synchronization fixes in VSA, plus deterministic seeding for reproducibility in DMD denoising, reduce runtime surprises and improve testability. Technologies demonstrated include documentation engineering, reproducibility practices, GPU-aware conditional builds, synchronization debugging, and deterministic RNG seeding.

July 2025 monthly summary for hao-ai-lab/FastVideo: Delivered major Video Sparse Attention (VSA) enhancements and resolved a critical integration bug, driving performance, flexibility, and robustness in video generation workflows. Key work included Triton-based block sparse kernels and support for arbitrary input resolutions, along with comprehensive documentation, benchmarks, and tests. The bug fix and accompanying updates broaden GPU compatibility and strengthen training pipelines.

June 2025 monthly summary for hao-ai-lab/FastVideo: Key features delivered, critical stability improvements, and foundational data-loading enhancements that collectively boost model training throughput, robustness, and debugging efficiency. Focused on enabling robust validation with negative prompts, hardening distributed training flows, and upgrading parquet-based data loading for faster, more reliable data access.

April 2025 monthly summary for hao-ai-lab/FastVideo focusing on attention system enhancements and backend integration. Delivered a major attention subsystem upgrade with the STA backend, improved code readability and maintainability through API refactors, and stabilized SDPA-related work.

March 2025 — FastVideo: Attention and Distributed Computing Refactor. Delivered an architecture-wide refactor to enhance attention mechanisms and enable distributed execution. Key deliverables include attention backends (FlashAttention, SDPA), abstract base classes for attention implementations, sliding tile attention with configuration, and foundational distributed components (device communicators, parallel state management). The work improves scalability and throughput for multi-GPU and distributed deployments and sets the stage for production-ready distributed training/inference. Commit: 1fee098f10e965054da407e70fd8662b89068fd4 ([do not merge] Rebased refactor (#270)). No explicit bug fixes were recorded this month; primary value came from architecture, modularity, and performance potential. Technologies/skills demonstrated: Python, software architecture, attention mechanisms, and distributed systems concepts.

February 2025 (2025-02) monthly summary for hao-ai-lab repositories. Focused on delivering performance improvements, stabilizing distillation workflows, and improving developer/docs readiness to accelerate experimentation and onboarding.

January 2025 (2025-01) focused on reliability and developer experience for hao-ai-lab/FastVideo. Implemented essential fixes in the distillation workflow, improved dataset handling, and clarified LoRA inference usage through documentation updates. These efforts enhance reproducibility, reduce misconfigurations, and accelerate iteration for future experiments.

Monthly performance summary for December 2024 covering FastVideo and HunyuanVideo development, packaging, and documentation improvements. Focused on delivering business value through end-to-end model development enhancements, robust validation, scalable training pipelines, and improved repository hygiene to accelerate adoption and collaboration.

November 2024 monthly summary for hao-ai-lab/FastVideo: Delivered a set of scalable video generation capabilities, reinforced by a robust training and validation ecosystem. The month focused on expanding model capacity, improving training scalability, and stabilizing core pipelines, with concrete progress across feature delivery, bug fixes, and engineering efficiency.

October 2024 performance highlights for hao-ai-lab/FastVideo: Delivered end-to-end Mochi-based video generation pipeline with synthetic dataset tooling; introduced VAE-based video reconstruction and cleanup in the causal video VAE module; upgraded text encoding to T5-Base; cleaned the T2V training code by removing unused arguments, obsolete adapters and dataset paths; improved installation and packaging for smoother setup.