SynCity 3000: Bootstrapping Scene-Scale 3D Diffusion
Paper β’ 2607.05392 β’ Published β’ 4
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CVPR Demo Track @ CVPR 2022
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paulengstlerΒ
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ggxxiiΒ
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Shipped v0.1.2 of vtx β a minimalist coding agent for the terminal.
Most agentic CLIs ship 10k+ token system prompts. Vtx is ~2,200. Less prompt overhead means more room for your code in the model's context window.
Vtx is a from-scratch Python implementation of the design philosophy behind pi-mono β same principles, pure Python, no transpiled runtime.
What ships out of the box:
β Textual TUI + headless CLI (vtx -p "fix the failing test")
β 49 LLM provider gateways, all declared in a single provider.yaml
β 5 core tools (read / edit / write / bash / find) plus web search and fetch
β Session tree with compaction, handoff, and resume
β AGENTS.md / CLAUDE.md auto-discovery
β Skills system β drop SKILL.md files in .agents/skills/ and they become slash commands
β Two OAuth flows (GitHub Copilot device flow, OpenAI Codex PKCE)
β Two-mode permissions: prompt (default) or auto, with a safe-command allowlist
This release adds a proper extension system. Register new LLM-callable tools, intercept tool calls, hook lifecycle events, and add slash commands from a single register(api) function in a Python file under ~/.vtx/agent/extensions/. Extensions can override built-in tools by name and chain handler logic across subscribers.
Apache 2.0. uv tool install vtx-coding-agent and you're running.
GitHub: https://github.com/OEvortex/vtx-coding-agent
PyPI: https://pypi.org/project/vtx-coding-agent
Built in the open. Feedback, extensions, and PRs welcome.
Most agentic CLIs ship 10k+ token system prompts. Vtx is ~2,200. Less prompt overhead means more room for your code in the model's context window.
Vtx is a from-scratch Python implementation of the design philosophy behind pi-mono β same principles, pure Python, no transpiled runtime.
What ships out of the box:
β Textual TUI + headless CLI (vtx -p "fix the failing test")
β 49 LLM provider gateways, all declared in a single provider.yaml
β 5 core tools (read / edit / write / bash / find) plus web search and fetch
β Session tree with compaction, handoff, and resume
β AGENTS.md / CLAUDE.md auto-discovery
β Skills system β drop SKILL.md files in .agents/skills/ and they become slash commands
β Two OAuth flows (GitHub Copilot device flow, OpenAI Codex PKCE)
β Two-mode permissions: prompt (default) or auto, with a safe-command allowlist
This release adds a proper extension system. Register new LLM-callable tools, intercept tool calls, hook lifecycle events, and add slash commands from a single register(api) function in a Python file under ~/.vtx/agent/extensions/. Extensions can override built-in tools by name and chain handler logic across subscribers.
Apache 2.0. uv tool install vtx-coding-agent and you're running.
GitHub: https://github.com/OEvortex/vtx-coding-agent
PyPI: https://pypi.org/project/vtx-coding-agent
Built in the open. Feedback, extensions, and PRs welcome.
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noamrotΒ
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yuna0x0Β
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yumingjΒ
authored 2
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π Ever dreamed of training your own Large Language Model from scratch? What if I told you it doesn't require a supercomputer or PhD in ML? π€―
Introducing LLM Trainer - the educational framework that makes LLM training accessible to EVERYONE! Whether you're on a CPU-only laptop or scaling to distributed GPUs, we've got you covered. π»β‘οΈπ₯οΈ
Why LLM Trainer? Because existing tools are either too simplistic (hiding the magic) or too complex (requiring expert knowledge). We bridge the gap with:
π Educational transparency - every component built from scratch with clear code
π» CPU-first approach - start training immediately, no GPU needed
π§ Full customization - modify anything you want
π Seamless scaling - from laptop to cluster without code changes
π€ HuggingFace integration - works with existing models & tokenizers
Key highlights:
β Built-in tokenizers (BPE, WordPiece, HF wrappers)
β Complete Transformer implementation from scratch
β Optimized for CPU training
β Advanced features: mixed precision, gradient checkpointing, multiple generation strategies
β Comprehensive monitoring & metrics
Perfect for:
- Students learning transformers
- Researchers prototyping new ideas
- Developers building domain-specific models
Ready to train your first LLM? It's easier than you think!
π Check it out: https://github.com/HelpingAI/llm-trainer
π Docs: Getting Started Guide
π¬ Join the community: GitHub Discussions
#AI #MachineLearning #LLM #DeepLearning #OpenSource #Python #HuggingFace #NLP
Special thanks to HuggingFace and PyTorch teams for the amazing ecosystem! π
Introducing LLM Trainer - the educational framework that makes LLM training accessible to EVERYONE! Whether you're on a CPU-only laptop or scaling to distributed GPUs, we've got you covered. π»β‘οΈπ₯οΈ
Why LLM Trainer? Because existing tools are either too simplistic (hiding the magic) or too complex (requiring expert knowledge). We bridge the gap with:
π Educational transparency - every component built from scratch with clear code
π» CPU-first approach - start training immediately, no GPU needed
π§ Full customization - modify anything you want
π Seamless scaling - from laptop to cluster without code changes
π€ HuggingFace integration - works with existing models & tokenizers
Key highlights:
β Built-in tokenizers (BPE, WordPiece, HF wrappers)
β Complete Transformer implementation from scratch
β Optimized for CPU training
β Advanced features: mixed precision, gradient checkpointing, multiple generation strategies
β Comprehensive monitoring & metrics
Perfect for:
- Students learning transformers
- Researchers prototyping new ideas
- Developers building domain-specific models
Ready to train your first LLM? It's easier than you think!
π Check it out: https://github.com/HelpingAI/llm-trainer
π Docs: Getting Started Guide
π¬ Join the community: GitHub Discussions
#AI #MachineLearning #LLM #DeepLearning #OpenSource #Python #HuggingFace #NLP
Special thanks to HuggingFace and PyTorch teams for the amazing ecosystem! π
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ynalcakanΒ
authored 2
papers 11 months ago
BwZhangΒ
authored 4
papers 12 months ago
StyleSwin: Transformer-based GAN for High-resolution Image Generation
Paper β’ 2112.10762 β’ Published
GaussianCube: Structuring Gaussian Splatting using Optimal Transport for 3D Generative Modeling
Paper β’ 2403.19655 β’ Published β’ 19
RodinHD: High-Fidelity 3D Avatar Generation with Diffusion Models
Paper β’ 2407.06938 β’ Published β’ 25
Gaussian Variation Field Diffusion for High-fidelity Video-to-4D Synthesis
Paper β’ 2507.23785 β’ Published β’ 18