AITOPAI热报
Industry

PyTorch 基金会公布 2026 年度大使名单，覆盖亚美尼亚、菲律宾、埃及、罗马尼亚

Official accountPyTorch@PyTorch
Post
View the post
Why we picked it

PyTorch 基金会新一届大使名单公布了，4 位来自亚美尼亚、菲律宾、埃及和罗马尼亚。菲律宾那位要在马尼拉每月办线下活动，东南亚的开发者可以关注一下能不能蹭到这些本地社区资源。

PyTorch 基金会宣布 2026 年 PyTorch Foundation Ambassadors 名单，共 4 位入选：来自亚美尼亚的 Ali Tavallaie、菲律宾的 Alpha Romer Coma、埃及的 Amr Osama Abdellatif 和罗马尼亚的 Costin-Alexandru Deonise。

Body

大使计划的核心是本地社区建设。Alpha Romer Coma 明确表示将在马尼拉每月举办线下 PyTorch 活动，推动菲律宾机器学习社区围绕架构和工程实践展开讨论；Amr Osama Abdellatif 的重点是在所在区域培养能在 PyTorch 上构建系统的工程师；Ali Tavallaie 和 Costin-Alexandru Deonise 则强调开源理念与知识分享，帮助更多人把想法落地为实际 AI 方案。

这份名单显示 PyTorch 基金会正把大使资源投向东南亚、中东和东欧等非美国核心市场，本地化社区运营将成为其开源生态扩张的重要抓手，对当地开发者而言意味着更多线下交流和技术资源。

Original · PyTorch

As we welcome our 2026 PyTorch Foundation Ambassadors, get to know:

Ali Tavallaie (@AliTavallaie) · Armenia Alpha Romer Coma · Philippines Amr Osama Abdellatif · Egypt Costin-Alexandru Deonise · Romania

“I believe using AI is like being given a fish; I want to teach people how to fish. PyTorch embodies the beauty of learning how to build and understand AI, and I’m excited to share that beauty with others.” - Ali Tavallaie

“I’m excited to build a Philippine ML community where architectures and ideas come first, and to bring the frontier of PyTorch to Manila through monthly in-person events.” - Alpha Romer Coma

“I am most looking forward to fostering a vibrant, collaborative AI community in the region and empowering the next wave of engineers to build cutting-edge systems on PyTorch.” - Amr Osama Abdellatif

“I believe the future of AI should be built in the open. I’m excited to contribute to the PyTorch community, share what I’ve learned, and help more people turn their ideas into real-world AI solutions.” - Costin-Alexandru Deonise

Industry Coordinates & Source Verification

This report is categorized under AITOP's "Industry" desk. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include PyTorch, PyTorch Foundation, open-source models, developer community.

Source attribution: curated from PyTorch (@PyTorch).

#PyTorch#PyTorch Foundation#open-source models#developer community

Keep reading

Recent stories with the same tag or section
PapersBuilders14:08
Chinese contentFLEX-LBP：用反向传播学习局部二值模式参数，纹理分类更准

手工调 LBP 参数这事儿终于可以交给梯度了。FLEX-LBP 只学 90 个参数，推理开销不变，精度却超过 ResNet-50 特征和一堆经典 LBP 变体，做工业检测或医学图像分析的朋友可以直接拿 PyTorch 层来用。

#FLEX-LBP#LBP#PyTorch#computer vision
arXiv cs.LG@Mohamed Alimoussa, Amal El KaidSource
ProductsBuildersMulti-sourceSelected01:07
Helion compile-time heuristics beat handwritten Triton kernels

Compile-time kernel selection removes one of the biggest practical barriers to adopting Triton beyond a handful of expert engineers: the tuning sweep that precedes every deployment. Inference and serving teams on H100 hardware can swap handwritten reduction and attention kernels for Helion-generated ones and see gains, not just parity, against vLLM's baseline. The 27% margin over production CUDA code is the concrete comparison that should push platform teams to benchmark Helion on their own workloads.

#Helion#Triton#vLLM#PyTorch
Event
Official accountPyTorch@PyTorchSource
ProductsBuildersSelected00:51
AI Agents Help Integrate SGLang with IBM Spyre in a Week

The notable point is the integration velocity: a 1,500-line plugin plus AI coding agents replaced what is typically a multi-month porting effort. Teams evaluating non-GPU accelerators for LLM inference can now consider IBM Spyre as a drop-in option with vLLM-parity performance. Engineering leaders building serving infrastructure should watch how plugin-based hardware abstraction in SGLang lowers the cost of supporting new silicon.

#SGLang#vLLM#IBM Spyre#PyTorch
Official accountPyTorch@PyTorchSource
ModelsBuildersSelected00:50
Domyn trains 1T-parameter MoE on EuroHPC Jupiter using PyTorch

The non-obvious takeaway is that per-GPU throughput gains of this magnitude came from software stack optimisation, not hardware changes, and were upstreamed to PyTorch and TorchTitan for anyone to reuse. Teams training large MoE models on GH200 clusters should review the upstream contributions for immediate throughput wins. European companies and labs with EuroHPC access now have a demonstrated open-source path to trillion-parameter training.

#PyTorch#TorchTitan#Domyn#MoE
Official accountPyTorch@PyTorchSource