> Contest Hub

Mind the Product presents World Product Day: Everyone Ships Now

About the challenge World Product Day is our annual global celebration of the craft, community, and impact of product people, and this year, we're doing something different. Instead of just talking about shipping, we want to see you ship. Mind the Product is the world's largest community of product professionals, and for over a decade we've championed the idea that great products come from people who are relentlessly curious, resourceful, and willing to build. With AI tooling collapsing the distance between "I have an idea" and "I shipped it," there has never been a better moment for our community to put that belief into practice. We're partnering with our friends over at Novus.ai to bring this challenge to you. Novus.ai gives you instant insight into how real users interact with what you've built, so you're not just shipping into the void. They're sponsoring World Product Day because they believe more people should be shipping, and shipping with real feedback from day one. Everyone Ships Now is exactly what it sounds like. You don't need to be an engineer. You don't need a team. You don't need permission. You just need an idea and roughly a month. We'll bring the community, the stage, and the prizes — you bring the thing you've been meaning to build. To be eligible for challenge prizes, your project must: Be new. Work on it must begin on or after May 20. No dusting off last year's side project. Be built with the tool of your choice. Bolt, Lovable, Replit, Cursor, Claude, v0, Figma Make, hand-rolled Next.js — whatever gets you to shipped fastest. We're tool-agnostic. Install Novus.ai before submission. Novus is how you (and we) measure what you've built. Any project submitted without Novus installed is ineligible for prizes. That's it. No themes, no required APIs, no mandatory integrations beyond Novus. Build what you actually want to build. Get started Here's the fastest path from "I'm in" to "I'm building": Register on this page to lock in your spot and get access to updates. Pick your idea. That thing you've been sketching in the back of a notebook counts. So does the internal tool your team keeps asking for. So does the silly app you think nobody will use. Pick your tool. Use whatever you're fastest in. If you've never shipped before, try one of the AI builders (Bolt, Lovable, Replit Agent, v0) — they're designed for exactly this moment. Build in public, if you can. Post progress with #EveryoneShipsNow and tag @MindThePRoduct. Community momentum is half the fun. Install Novus.ai on your project so you can see how users are interacting with what you built. Submit by June 20, 5:00 PM GMT. Stuck on what to build? Our friends at Vennie.ai can help you go from fuzzy idea to defined scope in minutes.

Multilingual Health Question Answering in Low-Resource African Languages Challenge by ITU

Multilingual Health Question Answering in Low-Resource African Languages Challenge by ITU

USAII® Global AI Hackathon 2026

USAII Global AI Hackathon 2026 Registration is Closed (June 6 11:59 pm ET)Hackathon Kickoff June 14 10:00 am ET:USAII_Hackathon_2026_Kickoff.pdfUSAII_Hackathon_2026_Kickoff.pptxhttps://youtu.be/cYA5GDck6zkUSAII_Hackathon_2026_Kickoff_QA.docxA worldwide virtual student innovation program empowering the next generation to build responsible AI solutions for real-world impact. About the Challenge The USAII Global AI Hackathon is designed to scale to tens of thousands of students globally while maintaining quality, fairness, and meaningful outcomes. This isn't just another hackathon—it's a movement where students across continents tackle authentic challenges using public or synthetic data, learning to innovate responsibly from day one. What Makes This Different Two-Phase Quality Design – An AI-powered qualifier ensures teams are ready before building INFORMS-Aligned Evaluation – Projects judged on problem understanding, AI reasoning, and responsible design—not just code Global Accessibility – Fully asynchronous content with support across 3 time zones via Discord community Real-World Challenges – All challenges grounded in actual nonprofit and community needs Responsible AI First – Ethics and human oversight are core to every submission No Paid Tool Advantage – Judges do not favor paid AI tools over free alternatives Two-Phase Structure Phase 1: AI Readiness Qualifier (REQUIRED) - https://qualifier.usaii.org/ Apply between: June 7-10, 2026 Results announced: June 12, 2026To ensure a high-quality and fair competition, all registered teams will complete a short AI Readiness Qualifier one week before the hackathon begins. This will be used to down select teams if registrations exceed judging capacity. It is a scalability and quality safeguard that confirms teams are real and active, ensures teams understands a challenge, can think critically about AI solutions, and are prepared to participate. Teams will answer a series of brief prompts about a hypothetical scenario, including the problem, users, AI approach, and ethical considerations.Responses are automatically evaluated using an AI-assisted scoring rubric that assesses problem understanding, AI thinking, responsible AI awareness, and clarity of communication. The qualifier typically takes 30 minutes to complete and does not require building anything. Based on scoring and capacity limits, the highest-ranked teams will advance to the hackathon. Teams will receive their results and advancement status via email and through the hackathon platform shortly after the qualifier closes.The AI Readiness Qualifier Access is sent via email on June 7, 2026 at 10:00 AM ET and will have until 11:59 PM EST on June 10, 2026 to submit their responses . Qualified teams receive approval codes and advance to Phase 2. Phase 2: Hackathon Build (Qualified Teams Only) June 14-21, 2026 Kickoff: June 14, 10:00 AM ET (livestream and recorded) https://webinar.zoho.in/meeting/register?sessionId=1371179302 Submission deadline: June 21, 11:59 PM ET All qualified teams across all tracks build during the same 1-week window ⚠️ **Devpost registration does NOT guarantee participation. You MUST pass the qualifier to advance.** Three Tracks (Final Briefs shared at Kickoff on June 14 10:00am ET) High School Track (Grades 9-12) AI for Everyday Good Build AI tools that help people find support, understand information, or take environmental action in their local community. Challenge Directions: Community: Help is Hard to Find — Make Support ObviousChallenge_Brief_1_HS_Help_Is_Hard_To_Find.pdf Environment: Make Climate Action Local and RealChallenge_Brief_2_HS_Climate_Action.pdf Undergraduate Track AI for Life & Work Build decision-support tools, navigation systems, or AI assistants that help people manage life, work, and essential services. Challenge Directions: Productivity: Build the "Second Brain" for Real LifeChallenge_Brief_3_UG_Second_Brain.pdf Public Services: Fix Systems People Depend OnChallenge_Brief_3_UG_Second_Brain.pdf Graduate Track (Master's & PhD) AI for Systems & Society Build advanced AI systems for risk detection, policy simulation, community readiness assessment, or infrastructure optimization. Challenge Directions: Human Safety & Protection: Build AI Systems That Protect People from HarmChallenge_Brief_5_GRAD_Human_Safety.pdf Public Systems & Policy: Build AI That Helps Communities Make Better DecisionsChallenge_Brief_6_GRAD_Community_AI.pdf Prizes US $15,000 in Cash Prizes + USAII AI Certification Scholarships Each track awards: - 🥇 Grand Prize: US $2,500 + Scholarship - 🥈 Runner Up: US $1,500 + Scholarship - 🥉 Third Place: US $500 + Scholarship - 🌟 Best Responsible AI Design: US $250 + Scholarship - 💡 Best Social Impact: US $250 + Scholarship Prize Distribution Cash prizes are awarded to the team and disbursed to the designated team leader or nominated recipient. Teams are responsible for internal distribution. International payments are processed in USD via wire transfer or a global payment platform. Scholarships & Certificates USAII AI Certification scholarships and certificates are issued individually to every registered team member. Prize Timeline Winners are announced at the Global Awards Showcase on June 27, 2026. Prize disbursement occurs within 30–45 days, pending receipt of all required payment and compliance information. Requirements Winning teams will be asked to provide payment details and required tax documentation (W-9 for U.S. residents; W-8BEN for international participants). For teams with members under 18, a parent or guardian must be the named prize recipient. Questions: aihackathon@usaii.org Get Started Phase 1: Pre-Registration (Now) - Program setup and platform preparation Phase 2: Registration & Team Formation (April 26 - June 6) April 26: Registration opens on Devpost April-June: Form teams (2-5 members), use Discord for matchmaking June 6: Registration closes Phase 3: AI Readiness Qualifier (June 7 10:00 am ET - June 10 11:59 pm ET) All tracks complete qualifier during same 4-day window Scoring and evaluation June 11-12 Phase 4: Qualifier Approval (June 12 11:59 pm ET) Results announced, approval codes sent to qualified teams Phase 5: Hackathon Build (June 14-21) June 14, 10:00 AM ET: Global kickoff livestream (all tracks) June 14-21: Build period June 21, 11:59 PM ET: All submissions due Phase 6: Judging (June 22-25) June 22-23: Validation & screening June 23-25: Judge scoring June 25-26: Panel deliberation Phase 7: Showcase (June 27) June 27, 10:00 AM ET: Global awards ceremony (livestream and recorded) How to Participate Step 1: Register Individually (April 26 - June 6) Sign up on Devpost Answer registration questions about your track, skills, and team status Step 2: Form Your Team (April-June) Create or join a team (2-5 members required) Use Discord #team-formation for matchmaking Team members can be from different schools or countries Teams must compete within the track aligned with their highest education level Mixed teams must register in the highest-level track represented Devpost assigns your Team ID Step 3: Complete Team Qualifier (June 7-10) https://qualifier.usaii.org/ Answer 8 prompts demonstrating AI thinking Takes 30 minutes to complete Receive results June 12 Step 4: Build (If Qualified) Attend kickoff June 14, 10:00 AM ET (recorded) Build during June 14-21 Submit project with qualifier approval code by June 21, 11:59 PM ET Step 5: Judging & Awards Projects judged by industry professionals Winners announced June 27 10:00 am ET Join the Community Discord: https://discord.gg/ePjenJnyh4Connect with participants, find teammates, get mentor support, receive updates Qualifir Email: hackathon.qualifier@usaii.org General questions and support Hackathon Email: aihackathon@usaii.org General questions and support Website: https://aihackathon.usaii.org/ ; Complete program information Questions? See our FAQ page or join Discord #help-desk Ready to build AI for good? Register starting April 26!

手机上的创意 AI 挑战赛

在手机上开发一款创意 AI 应用，至少选用一款 Qwen 系列模型（云端 API 可协同），推荐在 MNN 框架下针对 Arm SME2 指令集进行推理加速与性能调优，让手机算力真正释放。

Training Updates

Smarter defaults, pay-per-step pricing, new sample controls, and a Train Further button.

Civitai Nodes for ComfyUI

About 160 Civitai nodes for ComfyUI, image, video, audio, text, and training, with built-in model browsing.

GitLab Transcend Hackathon

About the challenge Context is everything. The GitLab Transcend Hackathon is a two-week, fully virtual event celebrating the future of AI-native software development, built on top of GitLab Orbit. The structured, queryable representation of your codebase that gives AI the context it needs to actually understand your project. Whether you want to ship code that powers GitLab Orbit itself, or build agents, flows, and skills that put it to work, there's a track for you. Beginners, seasoned contributors, and AI tinkerers are all welcome. Two tracks, one mission: make AI smarter by giving it real context. Contribute Track: Pick up an issue specifically designed for this hackathon and ship a merge request to GitLab Orbit and surrounding tooling. Showcase Track: Build an agent, flow, or skill on the GitLab Duo Agent Platform that uses GitLab Orbit to solve a real developer problem. Publish to the AI Catalog and tell the world about it. Get started Register here on Devpost. Head to the GitLab Contributors Platform transcend hackathon page and choose your track. Find more resources Build, ship, and submit your project on Devpost by June 24, 2026 (2:00 pm Eastern Time).

H0: Hack the Zero Stack with Vercel v0 and AWS Databases

You no longer need to choose between shipping quickly and using data infrastructure that will hold up in real-world traffic and scale. With the integration between AWS Databases and Vercel, the application you prototype over a weekend can run on the same data foundation that startups and enterprises use for production deployment. You can focus on building and iterating on your product quickly on an operationally proven database from day one. Use Vercel’s v0 to scaffold a production-ready Next.js frontend and connect it to one of three AWS Databases: Amazon Aurora PostgreSQL Aurora DSQL, or DynamoDB. Build a full-stack application that could actually go to production in minutes. Why join? $80,000 in cash prizes and an additional $80,000 in AWS credits! Get hands-on with a production stack: AWS Databases and Vercel Build something real – judges are looking for shippable software, not just demos Get Started Sign up for a v0.app account if you don’t already have one Fill out the request form to get AWS & v0 Credits Choose and provision your AWS Databases Build. Ship. Submit!

UiPath AgentHack

UiPath AgentHack is the UiPath Community global online hackathon for developers, automation engineers, data scientists, AI engineers, students, and professionals who want to go beyond basic automation and build agentic solutions that work in enterprise environments. Over 7 weeks, you'll design and ship a real, working solution on the UiPath Platform™. Not a concept, nor a slide deck, but a working solution that handles complexity, survives interruptions, keeps humans in the loop, and solves something that matters. UiPath AgentHack challenges you to build, run, and orchestrate real agentic solutions on the UiPath Platform, the single enterprise control plane that coordinates AI agents, automations, people, and applications end to end. Whether you build with UiPath's native capabilities or combine them with frameworks like LangChain, CrewAI, or AutoGen, your solution must use UiPath as the execution and orchestration layer. Check out the Forum thread if you want to connect with fellow participants, see their questions or find a teammate. We have $50,000 in cash prizes and three challenge tracks built around the most critical capabilities in enterprise agentic AI. Pick the track that best fits your project and submit: Track 1: UiPath Maestro Case Track 2: UiPath Maestro BPMN Track 3: UiPath Test Cloud About the challenge Coding agents have redefined how we build and the real value now lies in how we operate and govern agents at scale, bridging the gap between a prototype on a laptop and software running in production. UiPath AgentHack is built around that challenge. At the center of this hackathon is UiPath for Coding Agents, a platform-wide capability that enables developers to use coding agents (Claude Code, Codex, Cursor, and Gemini CLI, etc) to build, test, deploy, operate, and govern enterprise automations. Using natural language, you'll design AI agents that drive tangible business value, with full support for external frameworks such as LangChain, CrewAI, and AutoGen. The three challenge tracks are built around real-world enterprise problems that require coordination, adaptability, and orchestration at scale: structured process orchestration with Maestro BPMN, agentic case management for dynamic and exception-heavy work, and agentic software testing for AI-driven automations. Throughout the 7-week hacking period, UiPath will host community meetups, office hours, and will offer enablement resources to help you get up to speed with the platform and refine your solution. Track 1: UiPath Maestro Case. Build a solution that orchestrates dynamic, exception-heavy business processes using UiPath case management capabilities. Your solution should move work through stages, involve handoffs between agents, robots, and people, and keep humans in charge at key decision points. Agents within your case flow can be built on UiPath or an external framework; the platform handles coordination regardless of where the agents come from. Think about scenarios like: insurance claims processing where cases move through intake, investigation, and settlement stages; patient care coordination where agents manage referrals, scheduling, and follow-ups across providers; HR onboarding workflows where each new hire case progresses through document collection, system provisioning, and training assignment. Track 2: UiPath Maestro BPMN. Build a solution that models and runs an end-to-end business process using BPMN 2.0 in UiPath Maestro. Your process should orchestrate humans, robots, agents, and APIs through a defined flow with clear tasks, decisions, and handoffs. Agents within the process can be built with UiPath Agent Builder, coding agents, or external frameworks like LangChain, CrewAI, AutoGen, or any other agent platform. We want to see processes that move work cleanly from start to finish, with the right actor doing the right task at the right time. Think about scenarios like: An order-to-cash workflow where RPA pulls orders from email, EDI, and portals, an agent normalizes line items and flags pricing exceptions, and Maestro BPMN orchestrates credit checks, inventory allocation, fulfillment, and invoicing across ERP and CRM in a defined sequence. A collections agent monitors aging invoices and escalates disputes to finance for review. Or a procure-to-pay process where agents parse requisition intent, recommend vendors, and route approvals based on budget thresholds and category rules, RPA handles PO creation and invoice ingestion across ERP and AP systems, and an invoice agent reconciles discrepancies between PO, receipt, and invoice, escalating only true exceptions to AP for human review. Maestro BPMN keeps the full flow coordinated from requisition to payment Track 3: UiPath Test Cloud. Create agents that use UiPath Test Cloud to reimagine how software testing is designed, automated, executed, and managed across modern enterprise environments. Your goal is to show how agentic software testing can improve quality across AI-driven automations, enterprise applications, and connected business workflows. These agents should help you move faster with more confidence by increasing coverage, improving reliability, and reducing the manual effort required to validate complex systems. Think of building agents that can: evaluate requirements and turn them into meaningful test scenarios, identify fragile or outdated tests before they slow down a release, recommend fixes when automation breaks, or help orchestrate the right tests at the right time based on risk, coverage, and change impact. You might also explore how agents can validate AI-infused workflows, including third-party agents or AI services that participate in a UiPath-orchestrated process. All solutions must run on the UiPath Automation Cloud. You can include Agent Builder, Maestro, API Workflows, coding agents, and RPA where needed. You're welcome to bring in agents built on external frameworks and LLMs, in fact, we encourage it. The point is that UiPath is the orchestration and governance layer that ties everything together. Tip: If your process has unpredictable paths that emerge as the work unfolds, choose Track 1 - UiPath Maestro Case. If your process has a predictable sequence you can map in advance, choose Track 2 - UiPath Maestro BPMN. Bonus: solutions that use coding agents through UiPath for Coding Agents (Claude Code, Codex, Cursor, Gemini CLI, etc) will receive additional points during judging. We're especially interested in seeing how participants combine coding agents with low-code components, or blend UiPath-native agents with external agents, to solve complex problems You can't submit the same project to multiple tracks, and your solution needs to clearly align with whichever track you choose, both in how it's built and how it's described. Get started Register on Devpost for UiPath AgentHack. Form or join a team of 1 to 4 people and pick your track. Ask for access on UiPath Labs here: Teams must designate a representative to complete the access form on behalf of the group. Within 3 business days of asking for access, you'll receive a separate email with your UiPath Labs access link and credentials. UiPath Labs come fully equipped with agentic and AI units, so you can build without constraints.

June Study Jam Series: Bank Transaction Volume Forecasting Challenge

AI PC Agent Skills 征文活动

本次活动重点鼓励开发者探索如何利用 Intel 酷睿 Ultra 处理器（如最新的 Panther Lake 架构的 GPU、NPU 加速能力），配合 OpenVINO 推理框架，将模型能力转化为可编程、可复用的Skill技能包。这是 AI PC 真正从“硬件概念”走向“生产力工具”的核心跨越。 一、 开启 Agentic AI 与 Hybrid AI 的端侧新纪元在 AI 技术的演进历程中，我们正见证着一个关键的范式转移：AI 不再仅仅是简单的对话机器人，而是正在进化为能够理解意图、自主规划路径并调用外部工具产生实际后果的“智能体”——即 Agentic AI（智能体化 AI）。与此同时，随着端侧算力的爆发，Hybrid AI（混合 AI） 架构已成为大趋势，即通过云端处理超大规模逻辑，而将高频响应、隐私敏感及个性化强的任务下沉至 AI PC 处理。英特尔(Intel)作为 AI PC 时代的领航者，通过异构算力（CPU+GPU+NPU）的深度整合，为 Agentic AI 的落地提供了坚实的物理基础。而魔搭社区（ModelScope）作为开发者生态的摇篮，拥有丰富的模型储备。基于此，英特尔联合魔搭发起本次 Agent Skills 征文大赛，旨在挖掘那些能够运行在本地、能被智能体高效调用的“Skills”，共同定义端侧 AI 的未来。二、 活动背景：从对话式交互到 Agentic 工作流过去，开发者关注的是 Prompt Engineering（提示工程）；现在，我们的重心转向了 Agentic Workflows（智能体工作流）。一个优秀的智能体不仅需要强大的“大脑”（模型），更需要敏捷的“双手”（Skills）。在 Hybrid AI 的愿景下，AI PC 是离用户最近的计算中枢。将逻辑复杂但体积精炼（≤35B）的模型部署在本地，不仅能极大地降低推理成本，更能确保用户隐私数据“不出机”。本次活动重点鼓励开发者探索如何利用 Intel 酷睿™ Ultra 处理器（如最新的 Panther Lake 架构）的 GPU、NPU 加速能力，配合 OpenVINO™ 推理框架，将模型能力转化为可编程、可复用的Skill技能包。这是 AI PC 真正从“硬件概念”走向“生产力工具”的核心跨越。三、 征文主题1. 核心主题用 35B 以下小模型作为Agent大脑，驱动一项 本地AI工具调用(OCR,ASR,TTS…)，满足实际场景需求，最终生成可以被复用的Agent Skill。2. 技术约束模型规格：为了确保Skill的鲁棒性，驱动 Skill 的模型总参数量必须 ≤ 35B。35B 是当前端侧算力处理复杂逻辑与保证响应速度的“平衡点”，推荐qwen3.6系列、openBMB4.5系列；运行环境：Skill 中涉及的AI模型必须支持纯本地运行（Localhost）。推理框架：推荐使用 OpenVINO™（及其生态工具如 Optimum-intel）进行本地AI工具的构建，以充分释放 GPU、NPU 潜力。官方指南：关于 AI PC 大模型的一站式使用指南，请参考：https://modelscope.cn/brand/view/ai_pc验证基准：赛事方统一使用 Ollama + Qwen3.6-35B-A3B + QwenPaw/Trae 作为Skill是否可以被Agent大脑调用的的基准测试环境。 四、 推荐方向与场景参考推荐方向场景示例Agentic / Hybrid 价值办公提效本地会议纪要自动提取、PPT 一键风格改稿、Excel 复杂公式智能填充。零延迟响应，确保企业内部敏感会议数据绝对安全开发辅助本地代码 Review、Git Commit 自动生成、API 文档反向生成。离线状态下的高效编程，保护核心算法不外泄创作创意短视频脚本生成、公众号/小红书文案适配、本地图文自动化排版。端云协同：云端搜集素材，本地利用 35B 模型进行深度二次创作知识管理个人 PDF/笔记库 RAG、研报摘要提取、本地私人知识库智能问答。构建“永不掉线”且完全私有的个人数字第二大脑数据分析CSV 自然语言查询、本地数据可视化分析、系统日志异常自动归因。直接读取本地大容量数据集，规避云端流量成本 参考资源扫描进入比赛群，实时赛事信息同步、技术交流、问题答疑等 扫码访问英特尔 AI PC 专区，获取更多开发工具、技术文档与实战案例，助力您的 AI 应用开发之旅五、 参赛流程：从创意到落地的四步走编写本地 AI 工具：推荐使用 OpenVINO 进行量化与异构加速优化。生成并验证 Skill：在魔搭 Skills 中心规范下封装技能，并在 ≤35B 小模型作为大脑的环境下进行指令测试。发布作品包：在魔搭 Skills 中心发布，并添加 “AIPC” 自定义标签。需含代码、文档及测试用例，skill 提交入口：https://www.modelscope.cn/skills (点击“新建skill” 提交)提交技术文章：在魔搭研习社发表文章，沉淀实践路径、优化心得与 Hybrid AI 的思考，并添加 “Intel AI PC” 自定义标签，文章提交入口：https://www.modelscope.cn/learn (点击“创建文章” 提交)发布小红书（非必要项，会影响部分评分项）：请选手将作品框架截图、流程图或 Skill 任务成果等图文信息，连同魔搭研习社文章链接与 Skill 链接发布至小红书，同时 @OpenVINO中文社区 和 @魔搭ModelScope社区，并加话题 #英特尔 #openvino  #魔搭社区 #modelscope #agentic #skills  六、 评分标准维度权重说明Skill 可用性30%本地验证通过率、稳定性、错误处理场景价值20%解决问题的真实性、用户群体广度技术深度20%模型选型合理性、OpenVINO/GPU/NPU 优化、工程实现质量文章质量15%结构清晰度、可复现性、教学价值、魔搭/小红书社区影响力创新性15%思路新颖度、与已有方案的差异化七、 丰厚激励实物奖励：前50名完整提交作品，即可领取英特尔 AI PC 开发者限量版定制礼品。现金大奖：TOP 10 作品各获得 1000 元（含税）。生态推广：入选《AIPC Skills Collections》，获得魔搭及英特尔官方全渠道流量扶持。合作池入驻：优秀开发者将优先进入“Intel ISV 生态合作伙伴池”。 让 AI 不止于云端，让智能触手可及。英特尔与魔搭社区期待与您共同定义 AI PC 的“灵魂技能”！

Spatial Joy 26 Rokid 全球高校创新挑战赛

在人工智能与增强现实技术加速融合、重塑人机交互范式的时代背景下，Rokid 推出的 “Spatial Joy” 系列赛事，一项旨在推动大型语言模型、空间计算及下一代交互范式交叉探索的旗舰计划。其使命在于：赋能高校学子与行业开发者，突破现有边界，共同定义 Al+AR 的未来图景。

A Step Ahead of Drought: Forecasting Global Water Storage Challenge by ITU

探月计划 | Physical AI 黑客松

Physical AI 黑客松-最硬核也最 Vibe不是快闪，不是流量工厂，不是形象工程，而是一场赛后仍能生长的实验场。 选手报名：https://afterzero.feishu.cn/share/base/form/shrcnl8tzNnbLjIoFxUDMyqaIOh志愿者报名：https://v.wjx.cn/vm/tU5LvFv.aspx观众报名：https://wj.qq.com/s2/27053120/a309/

Global AI Hackathon Series with Qwen Cloud

Build production-ready agents on Qwen — and show the world what an agent can do. The Global AI Hackathon with Qwen Cloud invites professional developers, AI/ML engineers, and builders to design and ship sophisticated multi-agent systems, complex AI workflows, and production-grade applications using Qwen and other flagship models available on Qwen Cloud infrastructure. With advanced reasoning and multimodal capabilities, QwenCloud is built for builders who care about architectural depth and engineering excellence. Pick a track and build something real. Compete for total $70,000+ in cash and cloud credits, get featured on the Qwen Cloud blog, and earn an invite to join the AI Catalyst program. Why Join Build with Qwen Cloud. Access Qwen and other flagship models with advanced reasoning and multimodal understanding — all on Qwen Cloud infrastructure purpose-built for production AI. Get free Qwen Cloud credits to build, deploy, and scale your agent. Win a total of $70,000+ in cash and cloud credits plus blog features, swag, and an invite to the AI Catalyst program for standout teams. Get started Register on Devpost (2 minutes) Sign up for Qwen Cloud. Click here to Sign up for your free trial and request your free hackathon credits via our coupon form. To learn more about the hackathon you can check here.(5 minutes) Join the Qwen Cloud Discord (2 minutes) Pick your track, review the sample projects and reference architecture in the Resources and start building. (30 minutes to first run)

2026江苏“创青春”AI+交通创新创业大赛

为全面落实国家"人工智能+"行动和交通强国建设战略部署，助力江苏打造全国领先的“AI+交通”融合应用高地，通过举办2026江苏"创青春"AI+交通创新创业大赛，汇聚全国青年人才破解行业关键技术、应用难题，加速前沿成果在交通场景的转化落地，以创新赋能江苏交通运输事业高质量发展，培育和发展交通领域新质生产力。 为全面落实国家"人工智能+"行动和交通强国建设战略部署，助力江苏打造全国领先的“AI+交通”融合应用高地，通过举办2026江苏"创青春"AI+交通创新创业大赛，汇聚全国青年人才破解行业关键技术、应用难题，加速前沿成果在交通场景的转化落地，以创新赋能江苏交通运输事业高质量发展，培育和发展交通领域新质生产力。 访问大赛官网（qczx.jchc.cn）了解详情并报名参赛。

2026 小X宝开源医疗社区黑客松

2026 小X宝开源医疗社区黑客松 联合 ModelScope 魔搭社区正式拉开帷幕！我们邀请每一位心怀善意的开发者，用 AI 技能（Skills）与 MCP 工具，为生命构筑更多可能。 2026 小X宝开源医疗社区黑客松光已成炬，照亮崎岖 | Light Turns Into Torches, Illuminating the Rugged Path小X宝开源医疗社区 × 魔搭 ModelScope社区 联合主办赛事官方主页：https://hackathon.xiao-x-bao.com.cn/一、竞赛简介在医疗的广阔疆域中，仍有许多崎岖之路——肿瘤的复杂、罕见病的孤独。科技的力量，应当成为照亮这些角落的火炬。2026 小X宝开源医疗社区黑客松联合 ModelScope 魔搭社区正式拉开帷幕！我们邀请每一位心怀善意的开发者，用 AI 技能（Skills）与 MCP 工具，为生命构筑更多可能。本次黑客松由小X宝开源医疗公益社区发起，旨在通过开源技术服务真实医疗需求。无论你是算法极客、医疗 AI 实践者，还是对 Agent 领域充满好奇的新手，这里都有你的舞台。角色名称说明联合主办魔搭 ModelScope 社区中国最大的模型开源社区，提供赛事平台与运营资源合作方KnowS提供医学循证证据检索 API、开发者 Skill 支持与会员权益技术支持Sealos（FastGPT）提供 RAG 能力与相关技术支持，帮助参赛团队构建知识库检索增强应用大模型赞助阶跃星辰 StepFun提供 Flash Pro / Flash Max 大模型套餐权益二、赛题方向与技术方案赛题方向为通用医学 + 生命科学方向，不做更细的限制。核心赛题：聚焦医疗垂直领域，构建可复用的 Skills、MCP 或者是 Agent。赛事特色：不设细分赛道，只要你的应用能解决真实医疗场景问题，即刻出发！作品须满足以下条件：◆ 医疗场景导向：聚焦医疗垂直领域• （如肿瘤、罕见病、诊断辅助、患者管理、医学文献检索等）。◆ 技术形式：形式为 MCP 工具 或 Agent Skill，可独立运行或集成到现有 Agent 框架。或者是，你很强，想独立从头实现一个 Agent Harness，我们欢迎一切的贡献。◆ 平台部署：参赛作品须在魔搭社区完成部署，作为ModelScope 创空间(Studio) 应用项目；最终提交时需提供可访问的项目链接，便于评审体验和查看作品。◆ 选题登记：选题不限细分方向，但需要在开发前在群内或活动页面登记选题，避免重复。三、赛程安排：分阶段冲刺，直通 WAIC本次大赛为期近四周（6月18日 - 7月12日），节奏紧凑，期间设置三个阶段里程碑，每个阶段评选一支代表团队获得Vibecoding 键盘、 WAIC 门票等礼物，最终进行独立总评：阶段时间内容上线 / 报名开始6月18日在魔搭社区完成报名，加入官方交流群，即刻开发阶段 1：选题登记6月18日 - 6月24日登记选题并完成可行性说明；阶段结束评选「最佳选题潜力奖」，发放 1-3张 WAIC 2026 单日票阶段 2：MVP 开发6月25日 - 7月1日提交可运行 MVP 原型；阶段结束评选「最佳 MVP 原型奖」，发放 1 个 Vibecoding 键盘阶段 3：社区共建7月2日 - 7月8日鼓励社区试用、反馈与传播；阶段结束评选「社区影响力奖」，发放 1 个 Vibecoding 键盘最终提交截止7月12日 23:59将完整作品发布至魔搭社区，并填写最终提交表单集中评审7月13日 - 7月14日独立专家六维评分制评审结果公示与颁奖7月15日公布最终获奖名单，发放云资源/API 额度四、评审规则本次评审分为「阶段奖评审」和「最终总评」两部分，确保不同类型的优秀项目都能脱颖而出：阶段奖评审（各阶段独立评审，产生 WAIC 门票获得者）◆ 阶段 1（选题潜力）：重点看选题是否真实、有价值、可在短周期内落地。◆ 阶段 2（MVP 原型）：奖励最早把核心能力跑起来的队伍，必须能演示。◆ 阶段 3（社区影响力）：综合有效调用、收藏、评论质量、传播质量，异常流量剔除。最终总评（7/13-7/14，六维评分制）最终总评采用独立专家六维评分制，社区反馈质量仅占 10%，不直接主导排名：评分维度权重说明技术实现与可运行性25%代码结构清晰、核心功能能跑、部署稳定医疗场景价值与问题定义20%场景真实、目标用户明确、解决方案有意义MCP/Skill 完成度与平台适配20%是否符合魔搭社区提交、运行和展示要求开源质量与文档可复现性15%公开仓库、License、README、部署步骤、示例输入输出安全合规与风险控制10%不使用真实患者数据，不承诺诊断，明确局限性社区反馈质量10%真实用户反馈、Issue、有效讨论，而非单纯调用数五、奖励体系（注：奖励方案中云资源/API 额度的最终金额与形式以与魔搭社区协商结果为准）阶段奖（魔搭阶段礼品）每个阶段产生 1 支代表团队，获奖团队将获得魔搭提供的阶段礼品，并接受官方开发者采访与项目介绍：阶段奖项奖励6/18–6/24🎯 最佳选题潜力奖1-3 张 WAIC 门票 + 开发者采访6/25–7/1⚡ 最佳 MVP 原型奖1 个vibecoding 键盘 + 开发者采访7/2–7/8🌟 社区影响力奖1 个vibecoding 键盘 + 开发者采访最终总评奖项（7月15日公布）奖项名额奖励内容🌟 普惠权益每位参选选手StepFun Flash Pro 月度套餐 1 份，等值 199 元🏆 一等奖Top 3（3名）KnowS 专业版-3 倍积分年度会员（每位有效成员 1 份，等值 2,388 元）；StepFun Flash Max 季度套餐（每位有效成员 1 份，等值 1,889 元）🥈 二等奖Top 4-10（7名）KnowS 专业版-1 倍积分年度会员（每位有效成员 1 份，等值 828 元）；StepFun Flash Pro 季度套餐（每位有效成员 1 份，等值 539 元）🥉 三等奖Top 11-20（10名）StepFun Flash Pro 月度套餐（每位有效成员 1 份，等值 199 元）额外权益：◆ 资源支持：获胜者将赢取丰厚的云资源或 API 额度，助力项目持续演进。◆ 荣誉见证：所有完成提交的参与者均可获得官方电子参与证书。◆ 品牌曝光：优秀作品将上架 GitHub 与魔搭社区，获得全平台流量支持。合作方专项权益◆ KnowS 医学循证证据检索权益： KnowS 将为本次黑客松提供医学循证证据检索 API 与开发者 Skill 支持，开放文献、指南、说明书、临床试验等检索能力，支持参赛团队基于真实医学证据构建 AI 应用。活动期间，通过审核的参赛团队可领取团队独立 API Key，并共同使用总价值 10,000 元的 API 调用额度池，额度自活动开始起生效，至活动结束时截止。◆ KnowS 开发资源： 参赛团队可参考 KnowS 开发者文档（https://developers.nullht.com/）、API Reference（https://developers.nullht.com/api/reference/overview）与 KnowS Evidence Search Skill（https://developers.nullht.com/skills）进行开发。 ◆ KnowS 获奖会员权益： 最终总评一等奖 3 支团队，获奖团队内每位有效成员额外获得 1 份 KnowS 专业版-3 倍积分年度会员，等值 2,388 元；二等奖 7 支团队，获奖团队内每位有效成员额外获得 1 份 KnowS 专业版-1 倍积分年度会员，等值 828 元。每支获奖队伍（队伍人数不超过 3 人）可领取对应会员权益。◆ StepFun 参赛普惠权益： 每位有效参赛选手可获得 1 份 Flash Pro 月度套餐，等值 199 元。参赛者可个人参赛，也可组队参赛；每支队伍最多 3 人，每位参赛选手仅可加入 1 支队伍，并仅可领取 1 份参赛普惠权益。◆ StepFun 获奖套餐权益： 最终总评一等奖 3 支团队，获奖团队内每位有效成员额外获得 1 份 Flash Max 季度套餐，等值 1,889 元；二等奖 7 支团队，获奖团队内每位有效成员额外获得 1 份 Flash Pro 季度套餐，等值 539 元；三等奖 10 支团队，获奖团队内部每位有效成员额外获得 1 份 Flash Pro 月度套餐，等值 199 元。获奖专项权益可与参赛普惠权益叠加。以上 KnowS 与 StepFun 专项权益由对应合作方单独提供，具体 API Key 发放形式、套餐名称、领取方式、有效期与使用规则以合作方最终确认为准。技术支持◆ Sealos（FastGPT）RAG 支持： Sealos（FastGPT）为本次黑客松提供 RAG 能力与相关技术支持，帮助参赛团队搭建医学知识库问答、资料检索增强生成等应用。该支持作为技术生态支持展示，不作为奖品或赞助权益发放。阶段奖与公平性规则◆ 门票归属： WAIC 门票面向获奖团队，最多提供3张单人票。◆ 不可重复：每支队伍在整个比赛期间最多获得 1 次奖励。◆ 后续参赛：已获票团队仍可继续参与后续阶段展示和最终总评，但不再参与后续 vibecoding 键盘奖励阶段奖评选。◆ 顺延机制：若某阶段最高分团队已获票，门票顺延至该阶段下一支未获票且符合资格的队伍。◆ 采访机制：阶段获奖后 24-48 小时内完成 5 个问题的轻量采访，用于公众号/魔搭社区/活动页宣传。◆ 队伍归属：每人只能加入一队；同一核心成员、同一代码库的报名视为同一参赛主体。◆ 历史项目：允许历史项目参赛，但必须披露赛前已有部分，评审重点看比赛期间新增贡献。◆ 反作弊：异常流量（刷量、互刷、机器人行为）将被剔除，情节严重者取消评奖资格。六、作品提交规范参赛者在魔搭活动页填写表单时，必须包含以下三个核心交付物：◆ 魔搭社区作品链接 （作品需以 Skill、MCP 工具或 Agent 应用形式在魔搭社区完成部署， Agent 应用项目需部署在ModelScope 创空间/studio 展示；项目需公开可访问，并提供基础 Demo 或使用说明）◆ 公开代码仓库地址 （如 GitHub、Gitee 等，需含开源 License，推荐 Apache 2.0 或 MIT）◆ 规范 README 文档 （须包含：背景说明、医疗场景解决点、使用方法、部署步骤）提交入口：◆ Skill 提交：https://modelscope.cn/skills/create?template=custom ◆ MCP 提交：https://modelscope.cn/mcp/servers/create ◆ Studio 提交： https://modelscope.cn/studios/create七、仓库设置规范◆ 可见性：仓库必须设置为 Public（公开）。◆ 开源协议：仓库根目录必须包含 LICENSE 文件。建议使用 MIT 或 Apache 2.0 协议，以鼓励社区二次开发。◆ 代码整洁：敏感信息（如 API Keys、个人隐私数据）绝不可硬编码提交，需使用 .env 等方式隔离。◆ 医疗合规：禁止使用真实患者数据，不得做出诊断承诺。八、README.md 模板要求README 是评委了解作品的第一窗口，必须包含以下结构：# [项目名称] ## 1. 项目简介与医疗场景 - 一句话描述：[用一句话说明这是一个什么医疗 Skill/MCP] - 解决的痛点：[例如：肿瘤患者随访数据难结构化、罕见病文献检索效率低等] - 目标受众：[医生、患者、医学研究员等] ## 2. 功能特性 - [特性 1：如支持基于 PubMed 的自动文献检索] - [特性 2：如支持与外部知识图谱联动] ## 3. 魔搭社区运行/部署指南 - 魔搭展示链接：[提供您的 S

Slack Agent Builder Challenge

The next era of productivity is agentic—and it lives in Slack. We’re calling on all builders—from seasoned Slack veterans to "vibe coders" picking up the Slack CLI for the first time—to join the Slack Agent Builder Challenge. This is your opportunity to redefine the workplace by building intelligent agents that automate workflows, surface real-time insights, and connect systems in ways we haven’t imagined yet. Whether you’re building a specialized agent for your organization, a tool for social good, or the next viral Slack Marketplace app, we want to see your creativity in action. Leverage the Slack Agent Builder, MCP integrations, and the Real-Time Search (RTS) API to show the world why Slack is the ultimate surface for AI. Why Join? Whether you're a "vibe coder" or a seasoned pro, the on-ramp is designed for speed—use the slack create agent command and ready-made templates for HR, IT, and Sales to go from an idea to a running agent in a single afternoon. For those building for organizations, the hackathon serves as a guided path to the Slack Marketplace, giving you a structured deadline to ship your app and gain direct distribution to Slack’s massive enterprise customer base. Compete for a share of the $42,000 cash USD prize pool, a chance to go to Dreamforce 2026, some Slack swag, an invite to an exclusive Slack community gathering and to be featured in the Slack newsletter and social media

Reddit’s Games with a Hook Hackathon

Overview Reddit is hosting a virtual hackathon from June 17th to July 15th. We’re offering developers $40,000 in prizes for games built to delight users. The challenge: create a new Reddit game for the users of Reddit using Devvit, our Developer Platform. For this hackathon, we're asking developers to use Devvit Web, which allows you to build Devvit apps using web technologies you’re already familiar with (e.g. React, Phaser, three.js), or your favorite game engine (Godot, GameMaker, Unity, etc). Participants will also have access to Phaser to make their game shine. The best app to use Phaser will be eligible for a special award. What to build Build a game on Devvit (Reddit’s Developer Platform) using our Interactive Posts feature that inspires collective joy. The Best Experience That Will Keep People Coming Back: Apps that give redditors a reason to return day after day. This could come from progression, daily challenges, fresh content, meaningful choices, social dynamics, or simply the anticipation of what happens next. We're looking for experiences that create excitement between sessions and leave players wanting to check back tomorrow. The strongest entries make every visit feel worthwhile. Players should have something to work toward, discover, unlock, influence, or look forward to each time they return. The format can vary widely, but the core focus should be creating a compelling loop that builds momentum over time and turns a one-time visitor into a regular player. Examples of games on Reddit that bring users back day over day r/honk, r/colorpuzzlegame, r/bunnytrials, r/alignmentchartFills, r/hotandcold, r/dailyguess, r/bridgedit, r/battlebirds, r/kraw r/LETTERSET What we’re NOT looking for: AI Slop: Did you use AI? Fantastic! But it shouldn’t be obvious the moment we open your app. Fit the UI in the viewport, give your app a unique identity, and make sure you’re thinking of your human players. You can even ask your agentic assistant to help hide their involvement! On the nose Reddit theming: Reddit-y does not mean the game is about Reddit, karma, subreddits, Snoo, etc. Reddit-y means human-first, embracing community, meritocracy, creativity…it’s in the spirit of the app and your use of all the things subreddits have to offer (comments, flair, feeds, community)! While making the app about the topic of Reddit itself is not forbidden, it is also not a hack for making an app “Reddit-y”. Literal interpretations on “Games with a Hook”: While we're not opposed to seeing a fishing game or two, games with a "hook" shouldn't be taken literally. We want to see hooky games as in retentive and replayable. Common Ideas: Your game might not score as well if we’ve seen it many times before. We see a lot of: space shooters, clone apps of popular games, simple platformers, collaborative storytelling apps, and trivia apps. If you do make one of these apps, make sure it's extremely unique. Sub-challenges In addition to an excellent retentive experience, we’re looking for standout apps that also achieve the following: Best Use of Phaser: Recognizes the most innovative use of the Phaser game development framework within the Reddit Devvit platform. Best Use of Retention Mechanisms: Reddit apps are discoverable in our users’ feeds. This means that apps that create daily, or recurring, content tend to see better growth and retention. This award will go to the best use of retentive mechanics in a game. Best Use of User Contributions: Apps that enable users to generate content (comments, posts, drawings, puzzles, levels) drive redditor engagement and conversation. This award will go to the app that makes the best use of user-generated content. Games must be built on Devvit Web and be compliant with our Devvit Rules. Read our guide on how to build games for Reddit for more guidance on building for our platform. For this event we are looking for polish, meaning apps that are as close to launch-ready as possible (bonus points for a good mobile experience). We understand that not all projects will reach this threshold, but projects that are well tested and concept-complete will score higher. While we are accepting existing projects for this event, please note the game should be significantly updated during the hackathon period to qualify for this event. Getting started Get started with the Quickstart Browse our Template Library for building with a familiar framework You can use the Phaser Template here View examples of existing games on r/GameOnReddit Join us on Discord for live support and office hours

AFAC2026 金融智能创新大赛

AFAC金融智能创新大赛由中国计算机学会、北京大学、新加坡南洋理工大学、蚂蚁集团、NVIDIA等近30家组织和机构联合发起。自2023年首届举办以来，AFAC大赛已成长为全国乃至全球顶尖的金融智能赛事，累计吸引超1.5万支队伍、近5万名选手同台竞技，覆盖600余所高校、400余家企业。

ARC White-Box Estimation Challenge 2026

00Overview01Why random02The task03Compute04Evaluation05Participate06Rules07Prizes08Timeline09ResourcesSubmissions openStarter kitWhestBenchflopscopeHF datasetWhen can we know what a neural network does without running it?The ARC White-Box Estimation Challenge is a contest in compute-efficient mechanistic estimation. Given the weights of a neural network, can you predict its expected per-neuron activations more accurately than running it many times?The obvious way to learn how a model behaves is to run it many times and average what you observe. That works well when the behavior is common, cheap to elicit, and easy to sample. But when the behavior is rare, high-variance, or unlikely to appear in obvious test cases, brute-force testing can become an expensive way to learn very little.The ARC White-Box Estimation Challenge turns that question into a controlled benchmark. Participants receive randomly initialized ReLU MLPs and build executable estimators that predict each neuron's expected post-ReLU activation under standard-normal inputs.The goal is simple to state: beat comparable black-box sampling under a shared compute budget by using the network's weights. The strongest submissions may be Monte Carlo, white-box, hybrid, LLM-assisted, or something unexpected—the leaderboard will decide.TaskExecutable estimatorInputWeights + budgetOutputExpected activationsMetricFinal-layer MSELatestReleaseJun 18flopscope v0.8.0rc1 release candidate available↗AnnouncementJun 18Phase 1 launched — deeper models, and increased prizes.↗All updates on the forum↗Official factsPrize pool$150,000 USD ARVTwo phases · $50K Phase 1 + $100K Phase 2 · places + algorithmicSubmissions openMay 28, 2026 · 00:00 UTCPhase 2 closesSep 19, 2026 · 23:59 UTCDaily limit50 entries per team · per UTC day, each phaseGraderCPU-only 16 vCPU · 64 GB RAM · no networkHard cap60 s per MLPFinal rankingFresh private rerun of each team's designated Phase 2 submissionFig. 1 < !-- Header -- > CHALLENGE · ESTIMATE THE EXPECTATION .katex-display{margin:0 !important;} Y^L,j≈EX∼N(0,In) ⁣[hj(L)(X)]\hat Y_{L,j} \approx \mathbb{E}_{X\sim\mathcal{N}(0,I_n)}\!\left[h^{(L)}_{j}(X)\right]Y^L,j​≈EX∼N(0,In​)​[hj(L)​(X)] < !-- Column labels -- > INPUT NETWORK · RANDOM ReLU MLP OUTPUT · Ê[ h⁽ᶽ⁾₃(X) ] < !-- Input PDF -- > +2 0 −2 xᵢ -1.85 < !-- Network -- > h⁽ᶽ⁾₃ < !-- Output axis -- > 7.5 8.0 8.5 9.0 9.5 h⁽ᶽ⁾₃(X) · ×10⁻³ Ê 8.5 µ̂ 8.3 Δ 3×10⁻⁴ERROR≈3.5% rel < !-- Method legend -- > MONTE CARLOBLACK-BOX · SAMPLE & AVERAGE SAMPLING… ANALYTICALWHITE-BOX · PROPAGATE THE DISTRIBUTION PROPAGATING… < !-- Cost band -- > ← the contest lives here ≈ 15,000× YOUR BUDGET 2.72×10¹¹ FLOPs / MLP MONTE CARLO REFERENCE 4.24×10¹⁵ FLOPs / MLP 10¹¹ 10¹² 10¹³ 10¹⁴ 10¹⁵ 10¹⁶ FLOPs (log₁₀ scale) < !-- The question -- > Can you beat sampling? < !-- Replay -- > REPLAY Figure 1The estimation problem, as a distributional computation. A generated ReLU MLP receives Gaussian inputs X∼N(0,In)X \sim \mathcal{N}(0, I_n)X∼N(0,In​), applies h(ℓ)=ReLU ⁣(W(ℓ)h(ℓ−1))h^{(\ell)} = \mathrm{ReLU}\!\left(W^{(\ell)}h^{(\ell-1)}\right)h(ℓ)=ReLU(W(ℓ)h(ℓ−1)), and the submission must estimate E ⁣[hi(ℓ)(X)]\mathbb{E}\!\left[h^{(\ell)}_i(X)\right]E[hi(ℓ)​(X)] for every hidden-layer neuron.Read the animation as two ways to estimate the same activation-mean matrix. The black-box path samples inputs, runs the network, and averages observed activations until the Monte Carlo mean stabilizes. The white-box path inspects W(1),…,W(L)W^{(1)}, \dots, W^{(L)}W(1),…,W(L) and propagates enough distributional information to predict the same means under the participant budget. The target is an organizers' high-budget Monte Carlo reference of approximately 4.24×10154.24 \times 10^{15}4.24×1015 FLOPs, compared with a participant budget of approximately 2.72×10112.72 \times 10^{11}2.72×1011 FLOPs per MLP—roughly a 15,000×15{,}000\times15,000× compute gap.Prizes$150K+Cash PrizesPrediction shape32 × 256hidden activationsBudget / MLP2.72e11FLOPsPhase 2 closesSep 192026 · 23:59 UTC01Why this starts with random networksThe benchmark isolates one hard part of white-box estimation: tracking how distributions move through nonlinear layers.White-box estimation for trained networks is the destination, not the starting line. Trained models introduce many confounders at once: data, optimization, learned structure, task semantics, and evaluation ambiguity. WhestBench begins with randomly initialized networks so participants can focus on the estimation problem in a simplified setting.The networks are synthetic, but the question is real. Given access to the weights, can an algorithm reason about the distribution of hidden activations more efficiently than repeatedly sampling inputs and averaging outputs?Random ReLU MLPs retain the same basic problem structure: each layer transforms a distribution, the ReLU nonlinearity reshapes it, and approximation error can accumulate with depth. The first challenge is to develop methods that work in this controlled setting; later work can ask how those methods adapt as networks acquire structure during training.The benchmark is controlled, but not trivial: the expected activation has no closed form for the full network, and sampling improves only slowly with more compute.Why not trained models first?Random networks offer a simplified setting for compute-efficient estimation while being an important stepping stone towards trained models.02The taskFor each MLP, return a matrix of expected post-ReLU activation means.For each evaluation network MθM_\thetaMθ​, your estimator receives the MLP weights and a compute budget. It must return an L×nL \times nL×n matrix Y^\hat{Y}Y^. Entry (ℓ,i)(\ell, i)(ℓ,i) should estimate the expected post-ReLU activation of neuron iii in hidden layer ℓ\ellℓ when inputs are drawn from a standard Gaussian distribution.h(0)=X,h(ℓ)=ReLU(W(ℓ)h(ℓ−1)),ℓ=1,…,Lh^{(0)} = X, \qquad h^{(\ell)} = \mathrm{ReLU}\left(W^{(\ell)}h^{(\ell-1)}\right), \quad \ell = 1, \dots, Lh(0)=X,h(ℓ)=ReLU(W(ℓ)h(ℓ−1)),ℓ=1,…,L1Y^ℓ,i≈EX∼N(0,In)[hi(ℓ)(X)]\hat{Y}_{\ell,i} \approx \mathbb{E}_{X \sim \mathcal{N}(0, I_n)}\left[h^{(\ell)}_i(X)\right]Y^ℓ,i​≈EX∼N(0,In​)​[hi(ℓ)​(X)]2The reference target is estimated by the organizers with a much larger Monte Carlo budget than participants receive. Your job is to match that reference as closely as possible under the participant budget.Evaluation network · per MLPWidth nnn256256256Hidden layers LLL323232Weight initializationHe-Gaussian · variance 2/n2/n2/nInput distributionX∼N(0,In)X \sim \mathcal{N}(0, I_n)X∼N(0,In​)Prediction shape32×25632 \times 25632×256 matrixPrimary metricFinal-layer MSE vs. a high-budget Monte Carlo referenceImportantThe submission is executable code, not a prediction file. The grader runs your estimator against held-out MLPs and scores the returned activation matrix.03Compute model and constraintsThe competition is budgeted by analytical FLOPs, not by who owns the fastest machine.The accounting library is flopscope, a NumPy-compatible interface that counts floating-point operations for instrumented operations. Code written through flopscope.numpy is charged analytically. Uninstrumented computation is allowed, but residual wall-clock time is converted back into FLOPs at an unfavorable rate.Cm=Fm+λRmC_m = F_m + \lambda R_mCm​=Fm​+λRm​3Here FmF_mFm​ is the analytical FLOP count for MLP mmm, RmR_mRm​ is residual wall-clock time outside instrumented operations, and λ\lambdaλ is the conversion rate published in the starter kit.import flopscope as flops import flopscope.numpy as fnp def predict(mlp, budget): mus = [] mu = fnp.zeros(mlp.width) var = fnp.ones(mlp.width) for w in mlp.weights: mu_pre = w.T @ mu var_pre = (w * w).T @ var sigma_pre = fnp.sqrt(fnp.maximum(var_pre, 1e-12)) alpha = mu_pre / sigma_pre mu = mu_pre * flops.stats.norm.cdf(alpha) + sigma_pre * flops.stats.norm.pdf(alpha) mus.append(mu) return fnp.stack(mus)Budget ruleStay within the per-MLP budget on every network. Over-budget runs, exceptions, invalid shapes, non-finite values, memory failures, or wall-clock guard failures receive the zero-prediction fallback for that MLP.Grader environmentSubmissions run CPU-only with 16 vCPUs, 64 GB RAM, disabled network access, and a 60-second hard wall-clock cap per MLP.Fig. 2Bₘ — per-MLP budget (2.72 × 10¹¹ FLOPs)10⁷10⁹10¹²10¹⁵10⁰10⁻³10⁻⁶10⁻⁹10⁻¹²FLOPs per MLP (log scale) →Mean propagation9.5 × 10⁻⁴Covariance propagation8.4 × 10⁻⁵source · 100 MLPs · ARC Phase 1 dataBlack-box baselineMonte Carloconvergence.Monte Carlo on 100 randomMLPs. Red bands showvariation across networks;the dashed line is themean MSE (the scored bar).White-box points sit belowthe dashed line — lower errorthan sampling at equal compute.Figure 2Monte Carlo convergence. Pure Monte Carlo estimates the final-layer activation mean by buying more forward passes; plotted against the compute spent, its mean squared error falls steadily as the per-MLP FLOPs budget grows.The red bands summarize Monte Carlo error across 100 random MLPs as the sampling budget — the compute spent on forward-pass sampling per MLP — increases along the horizontal axis, measured in FLOPs (one black-box forward pass ≈4.24×106\approx 4.24 \times 10^{6}≈4.24×106 FLOPs). The dashed line is the mean final-layer MSE across MLPs — the quantity scored against (E[MSE] = σ²/N), so the Monte Carlo @ Bₘ reference lands on it; nested bands show between-MLP spread (median and percentiles). The vertical marker is the per-MLP budget Bm≈2.72×1011B_m \approx 2.72 \times 10^{11}Bm​≈2.72×1011 FLOPs. Baseline white-box methods such as mean propagation and covariance propagation appear as points because they spend compute inspecting weights and propagating distributional statistics rather than only sampling inputs. The challenge is to move below the red convergence curve under the same effective-compute budget: lower final-layer MSE, without exceeding Cm≤BmC_m \le B_mCm​≤Bm​.04Evaluation and scoringThe live leaderboard is useful feedback; the final ranking comes from a fresh private rerun.For each evaluation MLP, the grader computes the final-layer mean squared error between your prediction and the Monte Carlo reference:MSEfinal,m=1n∑i(Y^L,i−YL,i)2\mathrm{MSE}_{\mathrm{final},m} = \frac{1}{n}\sum_i \left(\hat{Y}_{L,i} - Y_{L,i}\right)^2MSEfinal,m​=n1​i∑​(Y^L,i​−YL,i​)24The per-MLP leaderboard score multiplies this by a compute-usage factor. Staying under the budget can help, but the improvement is capped so that an extremely cheap but inaccurate estimator cannot dominate by spending little compute.sm=MSEfinal,m⋅max⁡(0.1,  Cm/Bm)s_m = \mathrm{MSE}_{\mathrm{final},m} \cdot \max\left(0.1,\; C_m / B_m\right)sm​=MSEfinal,m​⋅max(0.1,Cm​/Bm​)5The overall leaderboard score is the average of sms_msm​ across the evaluation suite. Lower is better.All-layer MSE, averaged across all L×nL \times nL×n hidden activations, is reported as a secondary diagnostic. It helps reveal where approximation error accumulates across layers, but the primary score is the final-layer score.During each official phase, the grader evaluates submissions on a private suite of 100 randomly generated MLPs. Fifty contribute to live public feedback, while fifty are withheld until the phase closes. This keeps the leaderboard informative without making it too easy to overfit to visible scores.After Phase 2 closes, each team's designated final submission is rerun on a separate fresh private test suite. Prize ranking is decided exclusively from this final private rerun, not from the best public-leaderboard score observed during the competition.If leading submissions are statistically indistinguishable after the Private Re-evaluation, the Rules allow the Sponsor to generate additional MLPs from the same distribution for statistical disambiguation. If submissions remain tied after that, the tied ranks share the combined prize amounts for those positions.Public score vs. final prize rankThe public board helps you iterate. The final private rerun decides prize ranking.Failed-run fallbackIf a submission exceeds the budget, raises an exception, returns invalid shapes or non-finite values, exhausts memory, or trips an operational guard on a given MLP, the grader substitutes a zero prediction for that MLP and continues. No compute discount is applied to the fallback.Do not overfit the public boardThe final private suite uses different random MLPs. Strong submissions should generalize across the published generative distribution, not exploit visible leaderboard instances.05How to participateStart locally, validate the estimator contract, then submit a packaged tarball through AIcrowd.git clone https://github.com/AIcrowd/whest-starterkit.git cd whest-starterkit uv sync uv run python estimator.pyThe starter kit is structured as a staged ladder. Point your local runs at the public dataset's Mini split while you iterate. A good first milestone is one valid end-to-end submission, even before you have a strong score.1Iterate locallyuv run python estimator.pyCheck the math against a local Monte Carlo harness.2Validate contractuv run whest validate --estimator estimator.pyCatch shape, type, and packaging issues early.3Run on the public setuv run whest run --estimator estimator.py \ --dataset hf://aicrowd/arc-whestbench-public-2026@v1-phase1 \ --split mini --runner localReal scoring against the public Mini split in a debuggable process.4Subprocess runneruv run whest run --estimator estimator.py \ --dataset hf://aicrowd/arc-whestbench-public-2026@v1-phase1 \ --split mini --runner subprocessTest isolation closer to the grader.5Package and submituv run whest package -o submission.tar.gzuv run whest loginuv run whest submit submission.tar.gzBuild the tarball, authenticate, and upload your submission to AIcrowd.First milestoneYour first goal should be one valid end-to-end submission. Once the contract, packaging, and grader path work, you can improve the estimator.Open starter kitMake a submission06Rules that matter for first submissionThis is not a substitute for the Rules page, but it covers the constraints most likely to affect your first estimator.Submission formatSubmit executable code, including an estimator.py that follows the starter-kit contract. Do not submit prediction files.Submission capEach team may submit up to 50 entries per UTC day during each official Phase. The UTC-day counter resets at 00:00 UTC.TeamsUp to five eligible individuals per team, finalized by July 31, 2026, 23:59 UTC.HardwareCPU-only on the grader's standard instance: 16 vCPUs, 64 GB RAM, disabled network, and a 60-second hard wall-clock cap per MLP.Network accessNetwork access is disabled during evaluation. Bundle any allowed weights, lookup tables, dependencies, or precomputed artifacts in the submission tarball.Do not tamperDo not modify flopscope, read private seeds, access grader internals, or otherwise circumvent budget enforcement.LLM & autoresearchLLM-assisted and agentic development is welcome. You remain responsible for compliance, attribution, reproducibility, and any technical-writeup disclosures required by the Rules.Final submissionBefore Phase 2 ends (Sep 19, 2026, 23:59 UTC), designate one valid Phase 2 submission for the final private rerun. With no designation, Sponsor uses your best-scoring valid Phase 2 submission.Prize rankingDecided exclusively by the final private leaderboard from the fresh Private Re-evaluation suite. The public leaderboard is for iteration and does not determine prizes.Rules governIf anything on this Overview page conflicts with the official Rules or current starter kit, follow the Rules and starter kit.Autoresearch is welcomeUse LLMs, code agents, public resources, and metric-driven iteration if they help you discover better estimators.The one boundary is rule evasion: don't automate registration or mass uploads, tamper with flopscope, read private grader materials, or submit work you can't verify.Read the official policy →07Prizes and recognitionWhestBench rewards both leaderboard performance and algorithmic contribution.At launch, WhestBench has USD 150,000+ in prizes and recognition planned across two official phases. The current Rules specify $150,000 USD in total place-prize ARV — $50,000 in Phase 1 and $100,000 in Phase 2 — split across score-based and algorithmic contribution prizes. Sponsor may increase prize amounts or offer additional prizes, and any changes will be announced on the Competition Site.Total prize poolAcross two official phases · USD ARVCombined$150,000+By place & phasePhase 1Phase 21st place$25,000$50,0002nd place$10,000$20,0003rd place$5,000$10,000Algorithmic contributionBest technical contribution to mechanistic estimation — judged on score, algorithmic ideas, and writeup quality.$10,000$20,000Subtotal$50,000$100,000Beyond rankCommunity contributionDiscretionary recognition for helpful competition contributions — awarded per contributor.$500–5,000All amounts in USD · ARV.Participants are encouraged to submit a concise technical writeup that explains the method well enough for an independent practitioner to reproduce the prize-determining results. Only submissions with a technical writeup will be eligible for the algorithmic contribution prize.Recognition beyond rankStrong submissions may be valuable even when they are not first on the leaderboard. Clear explanations, useful algorithmic ideas, helpful bug reports, and community contributions may be recognized according to the Rules and any later announcements on the Competition Site.Winning place-prize submissions are subject to verification and open-source release requirements described in the Rules. The current Rules require place-prize winners to release the prize-determining solution code and required artifacts under an OSI-approved open-source license within 30 days of winner notification, and to keep the release publicly accessible for at least three years.08TimelineNowMay 28Jun 18Aug 1Sep 19Oct 1Warm-upsubmissions openPhase 1public boardPhase 2final submissionsDeadlinesubmissions closeWinnersTimelineFrom warm-up to tentative winners. The strip gives the shape of the schedule at a glance; the table below is the source for exact dates and times.Warm-upMay 28 – Jun 17May 28 · 00:00Resources released; submissions openJun 17 · 23:59Warm-up round endsPhase 1 · public leaderboardJun 18 – Jul 31Jun 18 · 00:00Public leaderboard opensJul 31 · 23:59Phase 1 ends; registration and team freezePhase 2 · final submissionsAug 1 – Sep 19Aug 1 · 00:00Final submission period opensSep 19 · 23:59Phase 2 ends; submissions closeEvaluation & resultsSep 20 – Oct 1Sep 20–30Private re-evaluation on a fresh held-out suite≈ Oct 1Tentative winner announcementAll times are UTC.09Resources and contactUse the starter kit for implementation details, the Rules page for official constraints, and the forum for public questions.CompeteParticipate on AIcrowdRegister, form a team, and submit through the platform.Challenge RulesOfficial constraints, eligibility, and prize terms.BuildWhestBench starter kitClone, implement your estimator, validate, and package a submission.Public dataset1,100 random MLPs on Hugging Face · Mini and Full splits.flopscopeThe NumPy-compatible FLOP-accounting library the grader uses.WhestBench ExplorerInspect generated MLPs and their activation statistics.Community & supportDiscussion forumPublic questions, clarifications, and announcements.GitHub IssuesReport bugs in the starter kit or flopscope.arc-whestbench@aicrowd.comPrivate or administrative matters.ResearchCompanion paperThe research behind the benchmark — arXiv:2605.05179.ARC announcementThe Alignment Research Center research post.If you use WhestBench in academic work, cite the companion paper:Wilson Wu, Victor Lecomte, Michael Winer, George Robinson, Jacob Hilton, and Paul Christiano. "Estimating the expected output of wide random MLPs more efficiently than sampling." arXiv:2605.05179, 2026.The challenge is organized by Alignment Research Center in partnership with AIcrowd.Ready to submit your first estimator?Start with the starter kit, validate locally, then submit through AIcrowd. The first useful milestone is not leaderboard rank—it is one valid end-to-end submission.Make a submissionOpen starter kitRead the Rules

ARC White-Box Estimation Challenge 2026

00Overview01Why random02The task03Compute04Evaluation05Participate06Rules07Prizes08Timeline09ResourcesSubmissions openStarter kitWhestBenchflopscopeHF datasetWhen can we know what a neural network does without running it?The ARC White-Box Estimation Challenge is a contest in compute-efficient mechanistic estimation. Given the weights of a neural network, can you predict its expected per-neuron activations more accurately than running it many times?The obvious way to learn how a model behaves is to run it many times and average what you observe. That works well when the behavior is common, cheap to elicit, and easy to sample. But when the behavior is rare, high-variance, or unlikely to appear in obvious test cases, brute-force testing can become an expensive way to learn very little.The ARC White-Box Estimation Challenge turns that question into a controlled benchmark. Participants receive randomly initialized ReLU MLPs and build executable estimators that predict each neuron's expected post-ReLU activation under standard-normal inputs.The goal is simple to state: beat comparable black-box sampling under a shared compute budget by using the network's weights. The strongest submissions may be Monte Carlo, white-box, hybrid, LLM-assisted, or something unexpected—the leaderboard will decide.TaskExecutable estimatorInputWeights + budgetOutputExpected activationsMetricFinal-layer MSELatestReleaseJun 18flopscope v0.8.0rc1 release candidate available↗AnnouncementJun 18Phase 1 launched — deeper models, and increased prizes.↗All updates on the forum↗Official factsPrize pool$150,000 USD ARVTwo phases · $50K Phase 1 + $100K Phase 2 · places + algorithmicSubmissions openMay 28, 2026 · 00:00 UTCPhase 2 closesSep 19, 2026 · 23:59 UTCDaily limit50 entries per team · per UTC day, each phaseGraderCPU-only 16 vCPU · 64 GB RAM · no networkHard cap60 s per MLPFinal rankingFresh private rerun of each team's designated Phase 2 submissionFig. 1 < !-- Header -- > CHALLENGE · ESTIMATE THE EXPECTATION .katex-display{margin:0 !important;} Y^L,j≈EX∼N(0,In) ⁣[hj(L)(X)]\hat Y_{L,j} \approx \mathbb{E}_{X\sim\mathcal{N}(0,I_n)}\!\left[h^{(L)}_{j}(X)\right]Y^L,j​≈EX∼N(0,In​)​[hj(L)​(X)] < !-- Column labels -- > INPUT NETWORK · RANDOM ReLU MLP OUTPUT · Ê[ h⁽ᶽ⁾₃(X) ] < !-- Input PDF -- > +2 0 −2 xᵢ -1.85 < !-- Network -- > h⁽ᶽ⁾₃ < !-- Output axis -- > 7.5 8.0 8.5 9.0 9.5 h⁽ᶽ⁾₃(X) · ×10⁻³ Ê 8.5 µ̂ 8.3 Δ 3×10⁻⁴ERROR≈3.5% rel < !-- Method legend -- > MONTE CARLOBLACK-BOX · SAMPLE & AVERAGE SAMPLING… ANALYTICALWHITE-BOX · PROPAGATE THE DISTRIBUTION PROPAGATING… < !-- Cost band -- > ← the contest lives here ≈ 15,000× YOUR BUDGET 2.72×10¹¹ FLOPs / MLP MONTE CARLO REFERENCE 4.24×10¹⁵ FLOPs / MLP 10¹¹ 10¹² 10¹³ 10¹⁴ 10¹⁵ 10¹⁶ FLOPs (log₁₀ scale) < !-- The question -- > Can you beat sampling? < !-- Replay -- > REPLAY Figure 1The estimation problem, as a distributional computation. A generated ReLU MLP receives Gaussian inputs X∼N(0,In)X \sim \mathcal{N}(0, I_n)X∼N(0,In​), applies h(ℓ)=ReLU ⁣(W(ℓ)h(ℓ−1))h^{(\ell)} = \mathrm{ReLU}\!\left(W^{(\ell)}h^{(\ell-1)}\right)h(ℓ)=ReLU(W(ℓ)h(ℓ−1)), and the submission must estimate E ⁣[hi(ℓ)(X)]\mathbb{E}\!\left[h^{(\ell)}_i(X)\right]E[hi(ℓ)​(X)] for every hidden-layer neuron.Read the animation as two ways to estimate the same activation-mean matrix. The black-box path samples inputs, runs the network, and averages observed activations until the Monte Carlo mean stabilizes. The white-box path inspects W(1),…,W(L)W^{(1)}, \dots, W^{(L)}W(1),…,W(L) and propagates enough distributional information to predict the same means under the participant budget. The target is an organizers' high-budget Monte Carlo reference of approximately 4.24×10154.24 \times 10^{15}4.24×1015 FLOPs, compared with a participant budget of approximately 2.72×10112.72 \times 10^{11}2.72×1011 FLOPs per MLP—roughly a 15,000×15{,}000\times15,000× compute gap.Prizes$150K+Cash PrizesPrediction shape32 × 256hidden activationsBudget / MLP2.72e11FLOPsPhase 2 closesSep 192026 · 23:59 UTC01Why this starts with random networksThe benchmark isolates one hard part of white-box estimation: tracking how distributions move through nonlinear layers.White-box estimation for trained networks is the destination, not the starting line. Trained models introduce many confounders at once: data, optimization, learned structure, task semantics, and evaluation ambiguity. WhestBench begins with randomly initialized networks so participants can focus on the estimation problem in a simplified setting.The networks are synthetic, but the question is real. Given access to the weights, can an algorithm reason about the distribution of hidden activations more efficiently than repeatedly sampling inputs and averaging outputs?Random ReLU MLPs retain the same basic problem structure: each layer transforms a distribution, the ReLU nonlinearity reshapes it, and approximation error can accumulate with depth. The first challenge is to develop methods that work in this controlled setting; later work can ask how those methods adapt as networks acquire structure during training.The benchmark is controlled, but not trivial: the expected activation has no closed form for the full network, and sampling improves only slowly with more compute.Why not trained models first?Random networks offer a simplified setting for compute-efficient estimation while being an important stepping stone towards trained models.02The taskFor each MLP, return a matrix of expected post-ReLU activation means.For each evaluation network MθM_\thetaMθ​, your estimator receives the MLP weights and a compute budget. It must return an L×nL \times nL×n matrix Y^\hat{Y}Y^. Entry (ℓ,i)(\ell, i)(ℓ,i) should estimate the expected post-ReLU activation of neuron iii in hidden layer ℓ\ellℓ when inputs are drawn from a standard Gaussian distribution.h(0)=X,h(ℓ)=ReLU(W(ℓ)h(ℓ−1)),ℓ=1,…,Lh^{(0)} = X, \qquad h^{(\ell)} = \mathrm{ReLU}\left(W^{(\ell)}h^{(\ell-1)}\right), \quad \ell = 1, \dots, Lh(0)=X,h(ℓ)=ReLU(W(ℓ)h(ℓ−1)),ℓ=1,…,L1Y^ℓ,i≈EX∼N(0,In)[hi(ℓ)(X)]\hat{Y}_{\ell,i} \approx \mathbb{E}_{X \sim \mathcal{N}(0, I_n)}\left[h^{(\ell)}_i(X)\right]Y^ℓ,i​≈EX∼N(0,In​)​[hi(ℓ)​(X)]2The reference target is estimated by the organizers with a much larger Monte Carlo budget than participants receive. Your job is to match that reference as closely as possible under the participant budget.Evaluation network · per MLPWidth nnn256256256Hidden layers LLL323232Weight initializationHe-Gaussian · variance 2/n2/n2/nInput distributionX∼N(0,In)X \sim \mathcal{N}(0, I_n)X∼N(0,In​)Prediction shape32×25632 \times 25632×256 matrixPrimary metricFinal-layer MSE vs. a high-budget Monte Carlo referenceImportantThe submission is executable code, not a prediction file. The grader runs your estimator against held-out MLPs and scores the returned activation matrix.03Compute model and constraintsThe competition is budgeted by analytical FLOPs, not by who owns the fastest machine.The accounting library is flopscope, a NumPy-compatible interface that counts floating-point operations for instrumented operations. Code written through flopscope.numpy is charged analytically. Uninstrumented computation is allowed, but residual wall-clock time is converted back into FLOPs at an unfavorable rate.Cm=Fm+λRmC_m = F_m + \lambda R_mCm​=Fm​+λRm​3Here FmF_mFm​ is the analytical FLOP count for MLP mmm, RmR_mRm​ is residual wall-clock time outside instrumented operations, and λ\lambdaλ is the conversion rate published in the starter kit.import flopscope as flops import flopscope.numpy as fnp def predict(mlp, budget): mus = [] mu = fnp.zeros(mlp.width) var = fnp.ones(mlp.width) for w in mlp.weights: mu_pre = w.T @ mu var_pre = (w * w).T @ var sigma_pre = fnp.sqrt(fnp.maximum(var_pre, 1e-12)) alpha = mu_pre / sigma_pre mu = mu_pre * flops.stats.norm.cdf(alpha) + sigma_pre * flops.stats.norm.pdf(alpha) mus.append(mu) return fnp.stack(mus)Budget ruleStay within the per-MLP budget on every network. Over-budget runs, exceptions, invalid shapes, non-finite values, memory failures, or wall-clock guard failures receive the zero-prediction fallback for that MLP.Grader environmentSubmissions run CPU-only with 16 vCPUs, 64 GB RAM, disabled network access, and a 60-second hard wall-clock cap per MLP.Fig. 2Bₘ — per-MLP budget (2.72 × 10¹¹ FLOPs)10⁷10⁹10¹²10¹⁵10⁰10⁻³10⁻⁶10⁻⁹10⁻¹²FLOPs per MLP (log scale) →Mean propagation9.5 × 10⁻⁴Covariance propagation8.4 × 10⁻⁵source · 100 MLPs · ARC Phase 1 dataBlack-box baselineMonte Carloconvergence.Monte Carlo on 100 randomMLPs. Red bands showvariation across networks;the dashed line is themean MSE (the scored bar).White-box points sit belowthe dashed line — lower errorthan sampling at equal compute.Figure 2Monte Carlo convergence. Pure Monte Carlo estimates the final-layer activation mean by buying more forward passes; plotted against the compute spent, its mean squared error falls steadily as the per-MLP FLOPs budget grows.The red bands summarize Monte Carlo error across 100 random MLPs as the sampling budget — the compute spent on forward-pass sampling per MLP — increases along the horizontal axis, measured in FLOPs (one black-box forward pass ≈4.24×106\approx 4.24 \times 10^{6}≈4.24×106 FLOPs). The dashed line is the mean final-layer MSE across MLPs — the quantity scored against (E[MSE] = σ²/N), so the Monte Carlo @ Bₘ reference lands on it; nested bands show between-MLP spread (median and percentiles). The vertical marker is the per-MLP budget Bm≈2.72×1011B_m \approx 2.72 \times 10^{11}Bm​≈2.72×1011 FLOPs. Baseline white-box methods such as mean propagation and covariance propagation appear as points because they spend compute inspecting weights and propagating distributional statistics rather than only sampling inputs. The challenge is to move below the red convergence curve under the same effective-compute budget: lower final-layer MSE, without exceeding Cm≤BmC_m \le B_mCm​≤Bm​.04Evaluation and scoringThe live leaderboard is useful feedback; the final ranking comes from a fresh private rerun.For each evaluation MLP, the grader computes the final-layer mean squared error between your prediction and the Monte Carlo reference:MSEfinal,m=1n∑i(Y^L,i−YL,i)2\mathrm{MSE}_{\mathrm{final},m} = \frac{1}{n}\sum_i \left(\hat{Y}_{L,i} - Y_{L,i}\right)^2MSEfinal,m​=n1​i∑​(Y^L,i​−YL,i​)24The per-MLP leaderboard score multiplies this by a compute-usage factor. Staying under the budget can help, but the improvement is capped so that an extremely cheap but inaccurate estimator cannot dominate by spending little compute.sm=MSEfinal,m⋅max⁡(0.1,  Cm/Bm)s_m = \mathrm{MSE}_{\mathrm{final},m} \cdot \max\left(0.1,\; C_m / B_m\right)sm​=MSEfinal,m​⋅max(0.1,Cm​/Bm​)5The overall leaderboard score is the average of sms_msm​ across the evaluation suite. Lower is better.All-layer MSE, averaged across all L×nL \times nL×n hidden activations, is reported as a secondary diagnostic. It helps reveal where approximation error accumulates across layers, but the primary score is the final-layer score.During each official phase, the grader evaluates submissions on a private suite of 100 randomly generated MLPs. Fifty contribute to live public feedback, while fifty are withheld until the phase closes. This keeps the leaderboard informative without making it too easy to overfit to visible scores.After Phase 2 closes, each team's designated final submission is rerun on a separate fresh private test suite. Prize ranking is decided exclusively from this final private rerun, not from the best public-leaderboard score observed during the competition.If leading submissions are statistically indistinguishable after the Private Re-evaluation, the Rules allow the Sponsor to generate additional MLPs from the same distribution for statistical disambiguation. If submissions remain tied after that, the tied ranks share the combined prize amounts for those positions.Public score vs. final prize rankThe public board helps you iterate. The final private rerun decides prize ranking.Failed-run fallbackIf a submission exceeds the budget, raises an exception, returns invalid shapes or non-finite values, exhausts memory, or trips an operational guard on a given MLP, the grader substitutes a zero prediction for that MLP and continues. No compute discount is applied to the fallback.Do not overfit the public boardThe final private suite uses different random MLPs. Strong submissions should generalize across the published generative distribution, not exploit visible leaderboard instances.05How to participateStart locally, validate the estimator contract, then submit a packaged tarball through AIcrowd.git clone https://github.com/AIcrowd/whest-starterkit.git cd whest-starterkit uv sync uv run python estimator.pyThe starter kit is structured as a staged ladder. Point your local runs at the public dataset's Mini split while you iterate. A good first milestone is one valid end-to-end submission, even before you have a strong score.1Iterate locallyuv run python estimator.pyCheck the math against a local Monte Carlo harness.2Validate contractuv run whest validate --estimator estimator.pyCatch shape, type, and packaging issues early.3Run on the public setuv run whest run --estimator estimator.py \ --dataset hf://aicrowd/arc-whestbench-public-2026@v1-phase1 \ --split mini --runner localReal scoring against the public Mini split in a debuggable process.4Subprocess runneruv run whest run --estimator estimator.py \ --dataset hf://aicrowd/arc-whestbench-public-2026@v1-phase1 \ --split mini --runner subprocessTest isolation closer to the grader.5Package and submituv run whest package -o submission.tar.gzuv run whest loginuv run whest submit submission.tar.gzBuild the tarball, authenticate, and upload your submission to AIcrowd.First milestoneYour first goal should be one valid end-to-end submission. Once the contract, packaging, and grader path work, you can improve the estimator.Open starter kitMake a submission06Rules that matter for first submissionThis is not a substitute for the Rules page, but it covers the constraints most likely to affect your first estimator.Submission formatSubmit executable code, including an estimator.py that follows the starter-kit contract. Do not submit prediction files.Submission capEach team may submit up to 50 entries per UTC day during each official Phase. The UTC-day counter resets at 00:00 UTC.TeamsUp to five eligible individuals per team, finalized by July 31, 2026, 23:59 UTC.HardwareCPU-only on the grader's standard instance: 16 vCPUs, 64 GB RAM, disabled network, and a 60-second hard wall-clock cap per MLP.Network accessNetwork access is disabled during evaluation. Bundle any allowed weights, lookup tables, dependencies, or precomputed artifacts in the submission tarball.Do not tamperDo not modify flopscope, read private seeds, access grader internals, or otherwise circumvent budget enforcement.LLM & autoresearchLLM-assisted and agentic development is welcome. You remain responsible for compliance, attribution, reproducibility, and any technical-writeup disclosures required by the Rules.Final submissionBefore Phase 2 ends (Sep 19, 2026, 23:59 UTC), designate one valid Phase 2 submission for the final private rerun. With no designation, Sponsor uses your best-scoring valid Phase 2 submission.Prize rankingDecided exclusively by the final private leaderboard from the fresh Private Re-evaluation suite. The public leaderboard is for iteration and does not determine prizes.Rules governIf anything on this Overview page conflicts with the official Rules or current starter kit, follow the Rules and starter kit.Autoresearch is welcomeUse LLMs, code agents, public resources, and metric-driven iteration if they help you discover better estimators.The one boundary is rule evasion: don't automate registration or mass uploads, tamper with flopscope, read private grader materials, or submit work you can't verify.Read the official policy →07Prizes and recognitionWhestBench rewards both leaderboard performance and algorithmic contribution.At launch, WhestBench has USD 150,000+ in prizes and recognition planned across two official phases. The current Rules specify $150,000 USD in total place-prize ARV — $50,000 in Phase 1 and $100,000 in Phase 2 — split across score-based and algorithmic contribution prizes. Sponsor may increase prize amounts or offer additional prizes, and any changes will be announced on the Competition Site.Total prize poolAcross two official phases · USD ARVCombined$150,000+By place & phasePhase 1Phase 21st place$25,000$50,0002nd place$10,000$20,0003rd place$5,000$10,000Algorithmic contributionBest technical contribution to mechanistic estimation — judged on score, algorithmic ideas, and writeup quality.$10,000$20,000Subtotal$50,000$100,000Beyond rankCommunity contributionDiscretionary recognition for helpful competition contributions — awarded per contributor.$500–5,000All amounts in USD · ARV.Participants are encouraged to submit a concise technical writeup that explains the method well enough for an independent practitioner to reproduce the prize-determining results. Only submissions with a technical writeup will be eligible for the algorithmic contribution prize.Recognition beyond rankStrong submissions may be valuable even when they are not first on the leaderboard. Clear explanations, useful algorithmic ideas, helpful bug reports, and community contributions may be recognized according to the Rules and any later announcements on the Competition Site.Winning place-prize submissions are subject to verification and open-source release requirements described in the Rules. The current Rules require place-prize winners to release the prize-determining solution code and required artifacts under an OSI-approved open-source license within 30 days of winner notification, and to keep the release publicly accessible for at least three years.08TimelineNowMay 28Jun 18Aug 1Sep 19Oct 1Warm-upsubmissions openPhase 1public boardPhase 2final submissionsDeadlinesubmissions closeWinnersTimelineFrom warm-up to tentative winners. The strip gives the shape of the schedule at a glance; the table below is the source for exact dates and times.Warm-upMay 28 – Jun 17May 28 · 00:00Resources released; submissions openJun 17 · 23:59Warm-up round endsPhase 1 · public leaderboardJun 18 – Jul 31Jun 18 · 00:00Public leaderboard opensJul 31 · 23:59Phase 1 ends; registration and team freezePhase 2 · final submissionsAug 1 – Sep 19Aug 1 · 00:00Final submission period opensSep 19 · 23:59Phase 2 ends; submissions closeEvaluation & resultsSep 20 – Oct 1Sep 20–30Private re-evaluation on a fresh held-out suite≈ Oct 1Tentative winner announcementAll times are UTC.09Resources and contactUse the starter kit for implementation details, the Rules page for official constraints, and the forum for public questions.CompeteParticipate on AIcrowdRegister, form a team, and submit through the platform.Challenge RulesOfficial constraints, eligibility, and prize terms.BuildWhestBench starter kitClone, implement your estimator, validate, and package a submission.Public dataset1,100 random MLPs on Hugging Face · Mini and Full splits.flopscopeThe NumPy-compatible FLOP-accounting library the grader uses.WhestBench ExplorerInspect generated MLPs and their activation statistics.Community & supportDiscussion forumPublic questions, clarifications, and announcements.GitHub IssuesReport bugs in the starter kit or flopscope.arc-whestbench@aicrowd.comPrivate or administrative matters.ResearchCompanion paperThe research behind the benchmark — arXiv:2605.05179.ARC announcementThe Alignment Research Center research post.If you use WhestBench in academic work, cite the companion paper:Wilson Wu, Victor Lecomte, Michael Winer, George Robinson, Jacob Hilton, and Paul Christiano. "Estimating the expected output of wide random MLPs more efficiently than sampling." arXiv:2605.05179, 2026.The challenge is organized by Alignment Research Center in partnership with AIcrowd.Ready to submit your first estimator?Start with the starter kit, validate locally, then submit through AIcrowd. The first useful milestone is not leaderboard rank—it is one valid end-to-end submission.Make a submissionOpen starter kitRead the Rules

硅碳 AI 诊疗挑战赛

本次大赛设“硅基智能体 X 碳基医学生”双赛道，基于 OpenHospital 底座，构建高度拟真的全科诊疗 Arena。这里没有标准单选题，只有 12,000 名鲜活的智能体患者与上千种错综复杂的疾病网络，覆盖多发病、罕见病及复杂共病。这是一场让 "硅基智能体"与"碳基医学生"同台竞技 的临床试炼——评判标准回归医疗本质：问得更准、查得更精、治得更好。 一、赛题背景大模型技术正将医疗 AI 从"医学百科全书"推向"临床决策大脑"，但一个核心命题亟待回答："考场得分高"的 AI 智能体，真的能接诊现实中的复杂病患吗？传统医疗评测多以静态文本为基础（如 MedQA、USMLE），将鲜活的临床问题降维成考题，掩盖了患者沟通博弈与复杂共病的陷阱，更缺乏对"问诊 → 开具检查 → 诊断与治疗"全链路能力的系统性评估。本次大赛基于 OpenHospital 底座，构建高度拟真的全科诊疗 Arena。这里没有标准单选题，只有 12,000 名鲜活的智能体患者与上千种错综复杂的疾病网络，覆盖多发病、罕见病及复杂共病。这是一场让 "硅基智能体"与"碳基医学生"同台竞技 的临床试炼——评判标准回归医疗本质：问得更准、查得更精、治得更好。二、赛题任务参赛选手需基于给定评测集，完成医生诊疗全链路任务，涵盖三个核心环节：环节任务描述中间输出① 问诊通过多轮自然语言对话，与患者智能体交互收集病史与症状医生对话文本② 开具检查基于问诊信息，合理开具必要检查项目检查项目名称③ 诊断与治疗综合问诊与检查结果，给出明确诊断结论与治疗方案确诊疾病名称、治疗方案文本2.1 双赛道介绍硅基赛道（搭建、调优智能体参赛）参赛对象：面向全国高校在校学生（专科、本科、硕士、博士、在职研究生等高等教育学籍在内的在校学生人群），具备智能体搭建能力的开发者，每队 1–3 人；资源包：训练服务端（包括与训练集的患者智能体对话，获取检查结果，在线评估，提供标准答案）、标准检查项目清单、标准疾病名称清单，初始医生智能体baseline；考察重点：智能体搭建、Skill 构建、Memory 管理；交付物：在魔搭创空间中部署的医生智能体。碳基赛道（在校医学生参赛）参赛对象：面向含医学相关专业的专科生、本科生、硕士及博士研究生，以个人形式参赛；资源包：可视化交互平台（包括与训练集的患者智能体对话，获取检查结果，在线评估，提供标准答案）、标准检查项目清单、标准疾病名称清单；考察重点：临床实践能力、知识储备、经验判断；交付物：在可视化交互平台上在线作答的答题记录。2.2 赛事流程赛事平台：https://www.modelscope.cn/studios/baconroot/virtual_hospital提交报名后，24h 内将通过报名预留邮箱发放 参赛账号，两赛道均在赛事平台进行两赛道均分为 训练阶段 与 评测阶段：训练阶段：模型基座统一限定为 Qwen3.5-Flash，选手可通过 API 或 Web 界面调用患者智能体，完成问诊、检查、诊断、治疗全流程；平台同步提供训练集标准答案供选手参考调优。该阶段所消耗的 token 数将计入最终加权得分，消耗越少得分越优。评测阶段：硅基赛道由选手在创空间中部署开发好的智能体，评测阶段限定用 Qwen3.5-Flash，然后提供创空间名称和Modelscope的Access Token，由系统进行自动化批量评测；碳基赛道由选手登录可视化交互平台在线作答，系统依据标准答案自动判分。2.3 提交规则赛道提交方式提交次数硅基-智能体赛道在魔搭创空间中部署的医生智能体，提交到平台+链接&截图每队最多 3 次，取最高分碳基-医学生赛道在可视化交互平台上直接在线作答+链接每人最多 3 次，取最高分2.4 跨赛道衡量机制两赛道独立排名，同时发布 跨赛道综合对比榜 供行业参考。三、赛程总览阶段时间内容报名 & 热身6 月 3 日开放报名，发布 Baseline，熟悉 Arena 平台与提交流程初赛8 月 2 日开放评测集 A 卷（公开卷），排行榜准实时更新；硅基智能体赛道 Top 20 进入复赛，碳基医学生赛道 直接决选 Top 6复赛8 月 31 日开放评测集 B 卷（隐藏卷），难度提升，覆盖更多疑难/罕见病例；B 卷成绩 Top 6 进入决赛决赛9 月初全赛程技术解决方案路演，决选最终一、二、三等奖结果公布9 月初公布最终排行榜并颁奖四、评估维度指标权重评估说明诊断准确率25%预测诊断集合与标准诊断集合的匹配度，要求预测为标准的子集检查精确率25%开具检查项目的精确率，避免过度医疗治疗方案契合度25%综合 安全性、有效性对齐度、个性化程度 三个维度评分（1–5 分），含安全性惩罚机制测试阶段 Token 消耗量得分20%评测阶段 Token 消耗，越少得分越高训练阶段 Token 消耗量得分5%训练阶段 Token 消耗，越少得分越高注：碳基赛道在测试时还会计时，分数相同时，用时越少，排名越高。五、奖金与激励硅基赛道（智能体开发队伍）奖项数量奖金（含税）额外激励🥇 一等奖1¥ 20,000获奖证书🥈 二等奖2¥ 10,000获奖证书🥉 三等奖3¥ 5,000获奖证书碳基赛道（医学生）奖项数量奖金（含税）额外激励🥇 一等奖1¥ 10,000获奖证书🥈 二等奖2¥ 7,000获奖证书🥉 三等奖3¥ 5,000获奖证书六、组织单位主办单位：魔搭社区、浙江大学、浙江工商大学合办单位：浙江大学软件学院、浙江大学医学院、浙江工商大学共同富裕统计监测与智能治理实验室、南京大学智能科学与技术学院协办单位：阿里云百炼七、资源支持7.1 赛题解读与培训形式时间内容赛题解读直播赛前启动平台说明、评分规则详解、Baseline 复现演示、答疑用户手册（见赛事平台右上角）赛前启动硅基：赛事平台使用说明+医生智能体baseline使用说明；碳基：平台使用教程赛中答疑赛程全程官方讨论区与微信答疑群碳硅基问诊表演赛赛程中直播展示双赛道交互效果7.2 Baseline 与平台资源赛事平台：诊疗交互界面、实时排行榜、历史提交记录回看硅基选手在该平台进行测试。碳基选手在该平台进行训练和测试。赛事平台：https://www.modelscope.cn/studios/baconroot/virtual_hospital提供可调优的 Baseline 在创空间中，可以复制进行改进baseline：https://www.modelscope.cn/studios/baconroot/hospital_agent_example7.3 Token 支持● 训练阶段：算力由选手自行准备训练所需算力由选手自行准备。推荐选手优先申领阿里云"云工开物"高校学生扶持计划的算力额度，作为训练资源之一。该计划由阿里云独立运营，面向全国高校在读学生开放，符合条件者可申领至多 300 元/人；具体规则与有效期以阿里云官方说明为准。申领额度用完的部分，由选手自行承担；选手也可自行选用其他合规算力资源。「云工开物」的具体申请方式 👉点此查看 领取算力券后，按以下步骤接入指定模型：进入指定模型：通过以下链接跳转到本次参赛指定模型 Qwen3.5-Flash： https://bailian.console.aliyun.com/cn-beijing#/model-market/detail/qwen3.5-flash?serviceSite=asia-pacific-china获取并填写 API Key：在百炼平台设置并获取 API Key，然后填入参赛平台。计费与抵扣说明：百炼平台采用按量付费模式，每个模型都会先提供一部分免费调用额度。免费额度用完后，将自动使用"云工开物"券抵扣后续费用。● 评测阶段：算力由赛事平台统一提供评测采用 T+1 离线评测机制，所需算力由赛事平台统一提供，选手无需自备。7.4 技术支持提供完整的技术支持体系，包括 Baseline 使用文档、常见问题 FAQ、官方答疑群等

Arm Create: AI Optimization Challenge

Welcome to the Arm AI Optimization Challenge 2026. We’re inviting developers to build and submit projects that show how AI can be optimized for Arm-powered platforms across three challenge tracks: Physical AI: Optimize AI for real-world systems, including robotics, embedded devices, sensors, simulation, autonomy, and edge environments. Cloud AI: Optimize AI for scalable infrastructure, including Arm64 cloud, inference performance, frameworks, agents, and production-ready developer workflows. Mobile AI: Optimize AI for on-device constraints, including performance, privacy, latency, battery efficiency, and local AI experiences on Arm-powered phones, tablets, and laptops. Across all tracks, submissions should show clear optimization work and measurable improvements where possible. Optimizations we will look for: Model size: Reduce size on disk or in memory. Model quality: Improve fine-tuning or output quality for a given model size. Model speed: Improve tokens/sec, time to first token, or other relevant latency metrics. Inference server speed: Improve throughput, latency, tokens/sec, or time to first token. Developer experience: Improve tools, workflows, setup, documentation, or usability. Arm-specific optimization: Implement optimizations in an existing framework, library, model, or application to run better on Arm. Developers can use Arm Performix to get exact benchmarks of their Arm based performance and be able to clearly show their results.

Build with Gemini XPRIZE

90 days. Ideate. Build. Ship. Grow. Real product. Real revenue. A real business. 01 Ideate Pick a real problem worth solving. AI helps you scope it. 02 Build Build a working prototype using AI. 03 Ship Deploy a business that operates with AI. Real users in production. 04 Sell Market it. Grow it. Show the revenue. AI is transforming everything, including what small teams can build. Operations that used to take entire engineering departments can now be written in plain English and executed by AI agents. By some estimates, the world’s 45 million software developers will grow to over a billion in the next few years. Everyone will be a developer, and we want to start now, with you. So show us: build a real business that makes real impact.

昇腾CANN社区任务【6月】

昇腾CANN训练营社区基于真实产业案例设计，完成任务即有机会获得华为三折叠手机、万元笔记本电脑，荣登社区荣誉榜单，任务每月更新，月底截止，多重任务值任务适合各阶段实操开发者。 背景社区任务基于真实产业案例设计，完成任务即有机会获得华为三折叠手机、万元笔记本电脑，荣登社区荣誉榜单，更为您的职业技能注入新动力。具体任务每月月底发放任务，次月30日/31日截止，多重任务值任务适合各阶段实操开发者完成任务赢取现金、新款华为手机、电脑 👇🔥 6月社区任务全新升级，现金激励重磅来袭！每个任务参与队伍根据验收时间排序，按任务书要求第一个通过验收的队伍在合入PR后可获得该任务相应奖金序号任务链接含税奖金（人民币）最晚通过验收时间20260529-1AclsolverCheevj 算子开发90415.5元2026.07.3020260529-2三角矩阵求解运算系列算子开发82240.5元2026.07.3020260529-3SPMV 算子开发72430.5元2026.07.3020260529-4Cast&EmbeddingDenseGrad 算子开发69651元2026.07.3020260529-5DynamicMap容器算子开发60985.5元2026.06.3020260529-6MinDim&MaxDim 算子开发58860元2026.06.3020260529-7SyncBatchNormGatherStats 算子开发57715.5元2026.06.3020260529-8Im2col 算子开发50194.5元2026.06.3020260529-9Bincount 算子开发40384.5元2026.06.3020260529-10RightShift算子开发29430元2026.06.3020260529-11FmodTensor&FmodScalar 算子开发25015.5元2026.06.3020260529-12UpsampleNearest3d 算子开发23380.5元2026.06.3020260529-13Logspace 算子开发23380.5元2026.06.3020260529-14UpsampleNearestExact1d&UpsampleNearestExact2d 算子开发23380.5元2026.06.3020260529-15Arange 算子开发22399.5元2026.06.3020260529-16Gcd 算子开发22399.5元2026.06.3020260529-17InplaceRsqrt 算子开发17985元2026.06.3020260529-18Relu算子开发17494.5元2026.06.3020260529-19InplaceSigmoid 算子开发17494.5元2026.06.3020260529-20IndexFillTensor 算子开发16840.5元2026.06.30任务值序号任务链接（绿色为Triton算子开发）奖品示例交付时间2200004-1SlidingTileAttention算子开发2026.05.311650004-2IsClose算子开发2026.05.311100004-3Pdist算子开发2026.05.3104-4AngleV2算子开发04-5Polar算子开发04-6Sleep算子开发900004-7Equal算子开发2026.05.3104-8Logit算子开发04-9median算子开发550004-10ForeachAddListV2算子开发2026.05.3104-11ForeachAddScalarV2算子开发04-12ForeachSubListV2算子开发04-13MaxUnpool3d算子开发04-14MaxUnpool2d算子开发04-15Sign算子开发04-16ForeachMulList算子开发04-17foreach_neg算子开发04-18foreach_exp算子开发04-19foreach_expm1算子开发04-20Neg算子开发04-21ForeachRoundOffNumberV2算子开发900003-1Roll算子开发2026.05.31800003-3MaskedScatter算子开发2026.05.3103-23_fwd_kernel_stage1算子开发03-24fused_recurrent_gated_delta_rule_fwd_kernel算子开发600003-8SoftmaxCrossEntropyWithLogits算子开发03-26reshape_and_cache_kernel_flash算子开发500003-11ApplyAdamW算子开发03-12AscendAntiQuantV2算子开发03-13BiasAdd算子开发03-14BnTrainingReduce算子开发03-15L2Loss算子开发03-16MaxPool3D算子开发03-17ScatterAdd算子开发03-18ScatterNd算子开发400003-20ApplyAdagradD算子开发03-21PreluGradReduce算子开发03-28_linear_attn_decode_kernel算子开发03-29chunk_kda_scaled_dot_kkt_fwd_kernel_intra_sub_intra算子开发300002-16ReluGradV2算子开发02-26SyncBatchNormBackwardElemt算子开发03-30_fwd_kernel_stage2算子开发03-31merge_attn_states_kernel算子开发03-32_fwd_kv_reduce算子开发提交方式01任务申请02任务交付（点击了解社区任务最新进展）03任务验收04验收复核05任务关闭点击报名后1个工作日内联系昇腾小助手获取任务进展更新表开发者承接后需1周内提交设计文档，提交后2个工作日会收到审批反馈开发者承接后3周内需联系昇腾小助手拉群验收；提交验收内容后3个工作日内会收到验收审批反馈第一名验收通过后的队伍需在2个工作日内提交PR，并根据检视意见修改代码，直到PR合入通过验收并合入PR参与任务需要先完成以下动作：1、报名26年CANN训练营第一季：报名链接2、认证通过算子开发者认证：认证链接具体任务查看&领取在 CANN 社区 进行：直达链接参与步骤01 任务申请跟帖评论申请任务（参与后任务交付、验收需与报名账号统一，否则无法发放奖品）格式示例： 【队名】：根本不报错 【gitcode账号名】：XXXXX 【是否报名训练营】：是 【是否通过算子开发者认证】：是/1周内提供 【任务序号】：任务序号 【状态】：报名 【链接】：fork链接【社区任务】开发流程及注意事项：https://gitcode.com/org/cann/discussions/39社区任务常见问题解析：https://gitcode.com/org/cann/discussions/37部分奖品为新品奖品发放会延迟，展示奖品若无货将更换为同等价值奖品

AMD x 魔搭社区 联合开发者激励计划 - GPU资源获取指南

本计划由 AMD 与魔搭社区联合发起，旨在激励开发者基于 AMD GPU 进行 AI 开发实践与分享。开发者可通过魔搭社区三大内容平台——研习社、灵感流、创空间——获取免费 GPU 算力奖励。 AMD x 魔搭社区联合开发者激励计划 — GPU 资源获取指南基于 AMD GPU 的 AI 开发实践，最高可获 1000 小时及以上免费算力一、计划概述本计划由 AMD 与魔搭社区联合发起，旨在激励开发者基于 AMD GPU 进行 AI 开发实践与分享。开发者可通过魔搭社区三大内容平台——研习社、灵感流、创空间——获取免费 GPU 算力奖励。核心机制三条独立路径：发研习社文章 / 传灵感流 Notebook / 做创空间应用，可单选可多选奖励可叠加：全部完成可获 1000 小时或更多按需分配：创空间根据应用质量按需分配 700 小时或更多二、奖励总览下表展示了三种参与路径的奖励规则，奖励可累计叠加：参与路径内容要求单次奖励路径上限前置注册完成 AMD 开发者计划注册+100 小时+100 小时路径一：研习社发布原创技术文章+25 小时/篇+50 小时路径二：灵感流发布 Notebook 代码实践+50 小时/篇+150 小时路径三：创空间部署应用并通过审核按需分配700+ 小时提示：三条路径完全独立，无须按顺序完成，可任选其一或全部参与。三、如何获取参与前提 — 完成 AMD 开发者计划注册无论选择哪条路径，均需先完成 AMD 开发者计划注册。注册完成即可获得 100 小时 GPU 算力，同时解锁后续全部参与资格。操作流程登录魔搭社区，进入「我的 Notebook」页面，点击活动页面入口；进入 AMD AI 开发者计划网站：授权使用魔搭账号登录，并完善个人资料，成为 AMD 开发者计划成员；完善资料后，开发者可于 AMD 开发者计划站内获得算力券奖励；回到魔搭「我的 Notebook」页面，即可看到 AMD GPU 入口及剩余额度。✅ 完成注册后，即可解锁以下三种参与路径的全部资格。路径一：研习社发文在魔搭社区「研习社」板块发布与 AMD GPU 相关的原创技术文章，每篇通过审核可获 25 小时 GPU 算力，本路径最高可获 50 小时。操作流程在魔搭社区「研习社」板块发布原创技术文章；发布时携带指定标签“AMD GPU激励计划”；提交后等待社区审核（预计 5 个工作日）；审核通过后，系统自动发放对应 GPU 时长奖励。审核维度原创度：文章内容是否为原创技术相关性：是否与 AMD GPU / ROCm 相关完整性：代码、数据、结果是否完整内容建议ROCm 安装与配置经验AMD GPU 大模型部署手记Ryzen AI 开发初体验AMD GPU 推理优化技巧路径二：灵感流 Notebook 代码实践在魔搭社区「Gallery」板块发布基于 AMD GPU 的 Notebook 代码实践，每篇通过审核可获 50 小时 GPU 算力，本路径最高可获 150 小时。操作流程在魔搭社区「Gallery」板块发布基于 AMD GPU 的 Notebook 代码实践；发布时携带指定标签“AMD GPU激励计划”；提交后等待社区审核（预计 5 个工作日）；审核通过后，根据内容质量获得相应 GPU 时长奖励。审核维度自动化运行测试：全部 Cell 执行通过，无报错代码注释完整性：关键步骤有中文/英文注释环境配置说明：requirements.txt 或安装指令完整技术相关性：与 AMD GPU / ROCm 相关内容建议Stable Diffusion 部署LLM 微调或部署搭建 Skill Agent推理优化实践Agent 构建路径三：创空间应用部署在魔搭社区「创空间」部署基于 AMD GPU 的应用，审核通过后可按需获得 700 小时或更多 GPU 算力。这是获取最大资源量的路径。前置条件已完成 AMD 开发者计划注册已加入魔搭社区“AMD开发者中心”组织优先分配：已发布至少 1 篇通过审核的 AMD 技术文章或 1 个高质量 Notebook操作流程进入「AMD 专属创空间 GPU 资源组织」页面：https://www.modelscope.cn/organization/AMD_Dev点击申请加入，并在申请理由中附上满足条件的证明材料：必选：AMD 开发者账号 ID可选：其他符合要求的 Notebook / 创空间作品链接审核通过后即可成为组织成员，后续在「创空间」创建或配置应用时可选择 AMD 专属 GPU 资源；选择 AMD MI308X 及对应镜像部署创空间应用。如何获取 AMD 开发者账号 ID：可在 AMD 开发者计划 网站 → 个人资料处获取。创空间开发示例：创空间审核机制创空间应用审核分为三个阶段：第一阶段：机审（自动）安全扫描：无恶意代码、无敏感信息泄露、无外部异常调用基础功能检测：应用可启动、核心页面可访问资源合规：GPU 显存使用在合理范围内第二阶段：功能评审（人工，3–5 个工作日）功能完整性：核心功能可正常使用用户体验：有清晰的使用说明，交互合理AMD 技术相关性：使用 AMD GPU 或 ROCm 相关技术创新性：独特功能或创新应用场景第三阶段：综合评定（人工，3–5 个工作日）代码质量审查与 AMD 品牌调性一致最终评定是否通过审核评审权重评审维度权重安全性25%功能完整性25%AMD 技术相关性20%用户体验15%创新性15%四、常见问题 (FAQ)Q1：AMD GPU 资源在使用上有哪些限制？每人最高并发创空间数量为 1 个；每人最高并发执行 Notebook 数量为 1 个。Q2：申请加入专属组织被拒绝，通常是什么原因？常见原因包括：提交的证明材料链接失效、未完成 AMD 官网注册等。请确认材料完整后重新提交。Q3：三种参与方式必须按顺序完成吗？不需要。三条路径完全独立，可任选其一、其二或全部参与，奖励可累计叠加。唯一的前提是必须先完成 AMD 开发者计划注册。Q4：可以只做路径三（创空间）吗？可以。只要完成注册（100h）+ 加入 AMD 组织 + 发布应用并通过审核，即可获得 700h 或更多。但在资源有限的情况下，已发布优质研习社文章或灵感流 Notebook 的开发者将优先获得分配。Q5：创空间“按需分配”是什么意思？根据应用质量评定额度，由 AMD 团队终审决定具体分配数量。五、开发者激励计划群本方案为 AMD 与魔搭社区联合推广计划，最终解释权归 AMD 团队所有。 💡获取小提示1️⃣ 研习社发文/灵感流 Notebook 发布时，请携带指定标签“AMD GPU激励计划”2️⃣ 申请加入「AMD开发者中心」组织时，需先注册AMD开发者计划 + AMD 开发者账号 ID（高频漏填！）第三批次｜截止 6.12 日前过审作品的GPU时长奖励清单魔搭用户名获取路径+GPU时长作品链接本次合计新增时长applebear📝研习社发文+25小时🔗学习笔记：在 AMD 云环境上微调 Gemma 模型做情绪分类+25小时ashanm📝研习社发文+25小时🔗ROCm 安装与配置经验+50小时📝研习社发文+25小时🔗AMD GPU 推理优化技巧jiushy📝研习社发文+25小时🔗MI300X从编译到部署的完整实践的踩坑+25小时Joshua0616💻 灵感流 Notebook 代码实践 +50小时🔗AMD MI300 上 Qwen3-8B 全量 SFT 微调：从零到可运行+100小时💻 灵感流 Notebook 代码实践 +50小时🔗AMD MI300 上 GRPO 强化训练：从零实现可验证奖励的 RLVR Pipelinelangzhenxin💻 灵感流 Notebook 代码实践 +50小时🔗SGLang on AMD MI300X / ROCm: Source Install Guide+100小时 📝研习社发文+25小时🔗从卡住到跑通：ModelScope AMD GPU 大模型部署与推理优化实践📝研习社发文+25小时🔗在 ModelScope MI300X / ROCm Notebook 上源码安装 SGLang，并跑通 LLaDA2.1-minilizhicheng123💻 灵感流 Notebook 代码实践+ 50小时🔗AMD ROCm 推理运维 Skill Agent 实战+200小时💻 灵感流 Notebook 代码实践+ 50小时🔗AMD GPU 多 Agent 推理调度器实战💻 灵感流 Notebook 代码实践+ 50小时🔗AMD ROCm 上的 LLM 推理优化实践📝研习社发文+ 25小时🔗AMD MI300X 上的 MTP 值得开启吗？一次 Qwen3.6-35B-A3B Q8_0 推理实测📝研习社发文+ 25小时🔗从 ROCm 安装到 90 tokens/s：AMD MI300X 大模型部署手记lpcarl💻 灵感流 Notebook 代码实践+ 50小时🔗AMD GPU加速语音识别伴奏人声分离+75小时📝研习社发文+ 25小时🔗【AMD GPU加速】VoiceToSRT - 视频字幕生成工具luda1993💻 灵感流 Notebook 代码实践+ 50小时🔗AMD GPU 实战：ROCm + ComfyUI 部署与 Stable Diffusion 测试+ 50小时Michael2035📝研习社发文 + 25小时🔗在 AMD GPU (ROCm) 上做训练时三值化 QAT：实战复现手记（结果持续更新）+25小时simaxiaojian📝研习社发文+ 25小时🔗AMD GPU跑大模型，踩完所有坑后我帮你整理了这份实战手册+25小时syzyyp📝研习社发文+ 25小时🔗AMD GPU 服务器安装使用 Logics-Parsing-v2 教程+75小时 💻 灵感流 Notebook 代码实践+ 50小时HunyuanOCR_AMD_ROCm_1B_OCRwachoa📝研习社发文+ 25小时🔗AMD GPU 大模型部署：在魔搭 Notebook 上跑通 LLaMA-3 推理全流程+ 25小时yu894890489💻 灵感流 Notebook 代码实践+ 50小时🔗AMD MI308X (ROCm) 上用 vLLM 部署 Qwen2.5-7B-Instruct：从环境校验到吞吐压测+ 50小时zhifu3158📝研习社发文+ 25小时🔗【纯技术干货】从N卡到A卡的“亡命天涯”：ComfyUI 100% 无损迁移与 AMD MI300X 榨汁指南+25小时zhifu31588💻 灵感流 Notebook 代码实践+ 50小时🔗【AMD 激励计划】从 N 卡到 MI300X 的“亡命天涯”：ComfyUI 无损迁移与极限榨汁全指南+50小时第二批次｜截止 6.5 日前过审作品的GPU时长奖励清单魔搭用户名获取路径+GPU时长作品链接本次合计新增时长doudou0510📝研习社发文+25 小时🔗AMD GPU 实战：llama.cpp 运行Qwen3.6-27B-Q8_0.gguf的 MTP 性能深度测评+100 小时💻 灵感流 Notebook 代码实践+50小时🔗AMD GPU 实战：llama.cpp 运行Qwen3.6-27B-Q8_0.gguf的 MTP 性能深度测评📝研习社发文+25 小时🔗AMD GPU 实战：llama.cpp 运行Qwen3.6-35B-A3B-Q8_0.gguf的 MTP 性能深度测评ericminijarvis📝研习社发文+25 小时🔗AMD MI300X 上使用 vLLM 部署 Qwen3.6-35B-A3B 全记录：从踩坑到成功+50小时📝研习社发文+25 小时🔗CrewAI 多 Agent 协作实战 — 对接本地 vLLM 学习总结govm114💻 灵感流 Notebook 代码实践 +50小时🔗Anima LoRA

Test P

Test P Predict Wine Quality Predict Wine Quality

Test P

Test P Predict Wine Quality Predict Wine Quality

Trajnet++ (A Trajectory Forecasting Challenge)

Trajnet++ (A Trajectory Forecasting Challenge)

Trajnet++ (A Trajectory Forecasting Challenge)

Trajnet++ (A Trajectory Forecasting Challenge)

MEDIQA 2021 - Question Summarization (QS)

MEDIQA 2021 - Question Summarization (QS) ACL-BioNLP Shared Task ACL-BioNLP Shared Task

MEDIQA 2021 - Question Summarization (QS)

MEDIQA 2021 - Question Summarization (QS) ACL-BioNLP Shared Task ACL-BioNLP Shared Task

ECCV 2020 Commands 4 Autonomous Vehicles

ECCV 2020 Commands 4 Autonomous Vehicles

Multi-Agent Reinforcement Learning for Iterative Reasoning

Multi-Agent Reinforcement Learning for Iterative Reasoning

ECCV 2020 Commands 4 Autonomous Vehicles

ECCV 2020 Commands 4 Autonomous Vehicles

Multi-Agent Reinforcement Learning for Iterative Reasoning

Multi-Agent Reinforcement Learning for Iterative Reasoning

Mind the Product presents World Product Day: Everyone Ships Now

About the challenge World Product Day is our annual global celebration of the craft, community, and impact of product people, and this year, we're doing something different. Instead of just talking about shipping, we want to see you ship. Mind the Product is the world's largest community of product professionals, and for over a decade we've championed the idea that great products come from people who are relentlessly curious, resourceful, and willing to build. With AI tooling collapsing the distance between "I have an idea" and "I shipped it," there has never been a better moment for our community to put that belief into practice. We're partnering with our friends over at Novus.ai to bring this challenge to you. Novus.ai gives you instant insight into how real users interact with what you've built, so you're not just shipping into the void. They're sponsoring World Product Day because they believe more people should be shipping, and shipping with real feedback from day one. Everyone Ships Now is exactly what it sounds like. You don't need to be an engineer. You don't need a team. You don't need permission. You just need an idea and roughly a month. We'll bring the community, the stage, and the prizes — you bring the thing you've been meaning to build. To be eligible for challenge prizes, your project must: Be new. Work on it must begin on or after May 20. No dusting off last year's side project. Be built with the tool of your choice. Bolt, Lovable, Replit, Cursor, Claude, v0, Figma Make, hand-rolled Next.js — whatever gets you to shipped fastest. We're tool-agnostic. Install Novus.ai before submission. Novus is how you (and we) measure what you've built. Any project submitted without Novus installed is ineligible for prizes. That's it. No themes, no required APIs, no mandatory integrations beyond Novus. Build what you actually want to build. Get started Here's the fastest path from "I'm in" to "I'm building": Register on this page to lock in your spot and get access to updates. Pick your idea. That thing you've been sketching in the back of a notebook counts. So does the internal tool your team keeps asking for. So does the silly app you think nobody will use. Pick your tool. Use whatever you're fastest in. If you've never shipped before, try one of the AI builders (Bolt, Lovable, Replit Agent, v0) — they're designed for exactly this moment. Build in public, if you can. Post progress with #EveryoneShipsNow and tag @MindThePRoduct. Community momentum is half the fun. Install Novus.ai on your project so you can see how users are interacting with what you built. Submit by June 20, 5:00 PM GMT. Stuck on what to build? Our friends at Vennie.ai can help you go from fuzzy idea to defined scope in minutes.

USAII® Global AI Hackathon 2026

USAII Global AI Hackathon 2026 Registration is Closed (June 6 11:59 pm ET)Hackathon Kickoff June 14 10:00 am ET:USAII_Hackathon_2026_Kickoff.pdfUSAII_Hackathon_2026_Kickoff.pptxhttps://youtu.be/cYA5GDck6zkUSAII_Hackathon_2026_Kickoff_QA.docxA worldwide virtual student innovation program empowering the next generation to build responsible AI solutions for real-world impact. About the Challenge The USAII Global AI Hackathon is designed to scale to tens of thousands of students globally while maintaining quality, fairness, and meaningful outcomes. This isn't just another hackathon—it's a movement where students across continents tackle authentic challenges using public or synthetic data, learning to innovate responsibly from day one. What Makes This Different Two-Phase Quality Design – An AI-powered qualifier ensures teams are ready before building INFORMS-Aligned Evaluation – Projects judged on problem understanding, AI reasoning, and responsible design—not just code Global Accessibility – Fully asynchronous content with support across 3 time zones via Discord community Real-World Challenges – All challenges grounded in actual nonprofit and community needs Responsible AI First – Ethics and human oversight are core to every submission No Paid Tool Advantage – Judges do not favor paid AI tools over free alternatives Two-Phase Structure Phase 1: AI Readiness Qualifier (REQUIRED) - https://qualifier.usaii.org/ Apply between: June 7-10, 2026 Results announced: June 12, 2026To ensure a high-quality and fair competition, all registered teams will complete a short AI Readiness Qualifier one week before the hackathon begins. This will be used to down select teams if registrations exceed judging capacity. It is a scalability and quality safeguard that confirms teams are real and active, ensures teams understands a challenge, can think critically about AI solutions, and are prepared to participate. Teams will answer a series of brief prompts about a hypothetical scenario, including the problem, users, AI approach, and ethical considerations.Responses are automatically evaluated using an AI-assisted scoring rubric that assesses problem understanding, AI thinking, responsible AI awareness, and clarity of communication. The qualifier typically takes 30 minutes to complete and does not require building anything. Based on scoring and capacity limits, the highest-ranked teams will advance to the hackathon. Teams will receive their results and advancement status via email and through the hackathon platform shortly after the qualifier closes.The AI Readiness Qualifier Access is sent via email on June 7, 2026 at 10:00 AM ET and will have until 11:59 PM EST on June 10, 2026 to submit their responses . Qualified teams receive approval codes and advance to Phase 2. Phase 2: Hackathon Build (Qualified Teams Only) June 14-21, 2026 Kickoff: June 14, 10:00 AM ET (livestream and recorded) https://webinar.zoho.in/meeting/register?sessionId=1371179302 Submission deadline: June 21, 11:59 PM ET All qualified teams across all tracks build during the same 1-week window ⚠️ **Devpost registration does NOT guarantee participation. You MUST pass the qualifier to advance.** Three Tracks (Final Briefs shared at Kickoff on June 14 10:00am ET) High School Track (Grades 9-12) AI for Everyday Good Build AI tools that help people find support, understand information, or take environmental action in their local community. Challenge Directions: Community: Help is Hard to Find — Make Support ObviousChallenge_Brief_1_HS_Help_Is_Hard_To_Find.pdf Environment: Make Climate Action Local and RealChallenge_Brief_2_HS_Climate_Action.pdf Undergraduate Track AI for Life & Work Build decision-support tools, navigation systems, or AI assistants that help people manage life, work, and essential services. Challenge Directions: Productivity: Build the "Second Brain" for Real LifeChallenge_Brief_3_UG_Second_Brain.pdf Public Services: Fix Systems People Depend OnChallenge_Brief_3_UG_Second_Brain.pdf Graduate Track (Master's & PhD) AI for Systems & Society Build advanced AI systems for risk detection, policy simulation, community readiness assessment, or infrastructure optimization. Challenge Directions: Human Safety & Protection: Build AI Systems That Protect People from HarmChallenge_Brief_5_GRAD_Human_Safety.pdf Public Systems & Policy: Build AI That Helps Communities Make Better DecisionsChallenge_Brief_6_GRAD_Community_AI.pdf Prizes US $15,000 in Cash Prizes + USAII AI Certification Scholarships Each track awards: - 🥇 Grand Prize: US $2,500 + Scholarship - 🥈 Runner Up: US $1,500 + Scholarship - 🥉 Third Place: US $500 + Scholarship - 🌟 Best Responsible AI Design: US $250 + Scholarship - 💡 Best Social Impact: US $250 + Scholarship Prize Distribution Cash prizes are awarded to the team and disbursed to the designated team leader or nominated recipient. Teams are responsible for internal distribution. International payments are processed in USD via wire transfer or a global payment platform. Scholarships & Certificates USAII AI Certification scholarships and certificates are issued individually to every registered team member. Prize Timeline Winners are announced at the Global Awards Showcase on June 27, 2026. Prize disbursement occurs within 30–45 days, pending receipt of all required payment and compliance information. Requirements Winning teams will be asked to provide payment details and required tax documentation (W-9 for U.S. residents; W-8BEN for international participants). For teams with members under 18, a parent or guardian must be the named prize recipient. Questions: aihackathon@usaii.org Get Started Phase 1: Pre-Registration (Now) - Program setup and platform preparation Phase 2: Registration & Team Formation (April 26 - June 6) April 26: Registration opens on Devpost April-June: Form teams (2-5 members), use Discord for matchmaking June 6: Registration closes Phase 3: AI Readiness Qualifier (June 7 10:00 am ET - June 10 11:59 pm ET) All tracks complete qualifier during same 4-day window Scoring and evaluation June 11-12 Phase 4: Qualifier Approval (June 12 11:59 pm ET) Results announced, approval codes sent to qualified teams Phase 5: Hackathon Build (June 14-21) June 14, 10:00 AM ET: Global kickoff livestream (all tracks) June 14-21: Build period June 21, 11:59 PM ET: All submissions due Phase 6: Judging (June 22-25) June 22-23: Validation & screening June 23-25: Judge scoring June 25-26: Panel deliberation Phase 7: Showcase (June 27) June 27, 10:00 AM ET: Global awards ceremony (livestream and recorded) How to Participate Step 1: Register Individually (April 26 - June 6) Sign up on Devpost Answer registration questions about your track, skills, and team status Step 2: Form Your Team (April-June) Create or join a team (2-5 members required) Use Discord #team-formation for matchmaking Team members can be from different schools or countries Teams must compete within the track aligned with their highest education level Mixed teams must register in the highest-level track represented Devpost assigns your Team ID Step 3: Complete Team Qualifier (June 7-10) https://qualifier.usaii.org/ Answer 8 prompts demonstrating AI thinking Takes 30 minutes to complete Receive results June 12 Step 4: Build (If Qualified) Attend kickoff June 14, 10:00 AM ET (recorded) Build during June 14-21 Submit project with qualifier approval code by June 21, 11:59 PM ET Step 5: Judging & Awards Projects judged by industry professionals Winners announced June 27 10:00 am ET Join the Community Discord: https://discord.gg/ePjenJnyh4Connect with participants, find teammates, get mentor support, receive updates Qualifir Email: hackathon.qualifier@usaii.org General questions and support Hackathon Email: aihackathon@usaii.org General questions and support Website: https://aihackathon.usaii.org/ ; Complete program information Questions? See our FAQ page or join Discord #help-desk Ready to build AI for good? Register starting April 26!

手机上的创意 AI 挑战赛

在手机上开发一款创意 AI 应用，至少选用一款 Qwen 系列模型（云端 API 可协同），推荐在 MNN 框架下针对 Arm SME2 指令集进行推理加速与性能调优，让手机算力真正释放。

Training Updates

Smarter defaults, pay-per-step pricing, new sample controls, and a Train Further button.

Civitai Nodes for ComfyUI

About 160 Civitai nodes for ComfyUI, image, video, audio, text, and training, with built-in model browsing.

GitLab Transcend Hackathon

About the challenge Context is everything. The GitLab Transcend Hackathon is a two-week, fully virtual event celebrating the future of AI-native software development, built on top of GitLab Orbit. The structured, queryable representation of your codebase that gives AI the context it needs to actually understand your project. Whether you want to ship code that powers GitLab Orbit itself, or build agents, flows, and skills that put it to work, there's a track for you. Beginners, seasoned contributors, and AI tinkerers are all welcome. Two tracks, one mission: make AI smarter by giving it real context. Contribute Track: Pick up an issue specifically designed for this hackathon and ship a merge request to GitLab Orbit and surrounding tooling. Showcase Track: Build an agent, flow, or skill on the GitLab Duo Agent Platform that uses GitLab Orbit to solve a real developer problem. Publish to the AI Catalog and tell the world about it. Get started Register here on Devpost. Head to the GitLab Contributors Platform transcend hackathon page and choose your track. Find more resources Build, ship, and submit your project on Devpost by June 24, 2026 (2:00 pm Eastern Time).

H0: Hack the Zero Stack with Vercel v0 and AWS Databases

You no longer need to choose between shipping quickly and using data infrastructure that will hold up in real-world traffic and scale. With the integration between AWS Databases and Vercel, the application you prototype over a weekend can run on the same data foundation that startups and enterprises use for production deployment. You can focus on building and iterating on your product quickly on an operationally proven database from day one. Use Vercel’s v0 to scaffold a production-ready Next.js frontend and connect it to one of three AWS Databases: Amazon Aurora PostgreSQL Aurora DSQL, or DynamoDB. Build a full-stack application that could actually go to production in minutes. Why join? $80,000 in cash prizes and an additional $80,000 in AWS credits! Get hands-on with a production stack: AWS Databases and Vercel Build something real – judges are looking for shippable software, not just demos Get Started Sign up for a v0.app account if you don’t already have one Fill out the request form to get AWS & v0 Credits Choose and provision your AWS Databases Build. Ship. Submit!

UiPath AgentHack

UiPath AgentHack is the UiPath Community global online hackathon for developers, automation engineers, data scientists, AI engineers, students, and professionals who want to go beyond basic automation and build agentic solutions that work in enterprise environments. Over 7 weeks, you'll design and ship a real, working solution on the UiPath Platform™. Not a concept, nor a slide deck, but a working solution that handles complexity, survives interruptions, keeps humans in the loop, and solves something that matters. UiPath AgentHack challenges you to build, run, and orchestrate real agentic solutions on the UiPath Platform, the single enterprise control plane that coordinates AI agents, automations, people, and applications end to end. Whether you build with UiPath's native capabilities or combine them with frameworks like LangChain, CrewAI, or AutoGen, your solution must use UiPath as the execution and orchestration layer. Check out the Forum thread if you want to connect with fellow participants, see their questions or find a teammate. We have $50,000 in cash prizes and three challenge tracks built around the most critical capabilities in enterprise agentic AI. Pick the track that best fits your project and submit: Track 1: UiPath Maestro Case Track 2: UiPath Maestro BPMN Track 3: UiPath Test Cloud About the challenge Coding agents have redefined how we build and the real value now lies in how we operate and govern agents at scale, bridging the gap between a prototype on a laptop and software running in production. UiPath AgentHack is built around that challenge. At the center of this hackathon is UiPath for Coding Agents, a platform-wide capability that enables developers to use coding agents (Claude Code, Codex, Cursor, and Gemini CLI, etc) to build, test, deploy, operate, and govern enterprise automations. Using natural language, you'll design AI agents that drive tangible business value, with full support for external frameworks such as LangChain, CrewAI, and AutoGen. The three challenge tracks are built around real-world enterprise problems that require coordination, adaptability, and orchestration at scale: structured process orchestration with Maestro BPMN, agentic case management for dynamic and exception-heavy work, and agentic software testing for AI-driven automations. Throughout the 7-week hacking period, UiPath will host community meetups, office hours, and will offer enablement resources to help you get up to speed with the platform and refine your solution. Track 1: UiPath Maestro Case. Build a solution that orchestrates dynamic, exception-heavy business processes using UiPath case management capabilities. Your solution should move work through stages, involve handoffs between agents, robots, and people, and keep humans in charge at key decision points. Agents within your case flow can be built on UiPath or an external framework; the platform handles coordination regardless of where the agents come from. Think about scenarios like: insurance claims processing where cases move through intake, investigation, and settlement stages; patient care coordination where agents manage referrals, scheduling, and follow-ups across providers; HR onboarding workflows where each new hire case progresses through document collection, system provisioning, and training assignment. Track 2: UiPath Maestro BPMN. Build a solution that models and runs an end-to-end business process using BPMN 2.0 in UiPath Maestro. Your process should orchestrate humans, robots, agents, and APIs through a defined flow with clear tasks, decisions, and handoffs. Agents within the process can be built with UiPath Agent Builder, coding agents, or external frameworks like LangChain, CrewAI, AutoGen, or any other agent platform. We want to see processes that move work cleanly from start to finish, with the right actor doing the right task at the right time. Think about scenarios like: An order-to-cash workflow where RPA pulls orders from email, EDI, and portals, an agent normalizes line items and flags pricing exceptions, and Maestro BPMN orchestrates credit checks, inventory allocation, fulfillment, and invoicing across ERP and CRM in a defined sequence. A collections agent monitors aging invoices and escalates disputes to finance for review. Or a procure-to-pay process where agents parse requisition intent, recommend vendors, and route approvals based on budget thresholds and category rules, RPA handles PO creation and invoice ingestion across ERP and AP systems, and an invoice agent reconciles discrepancies between PO, receipt, and invoice, escalating only true exceptions to AP for human review. Maestro BPMN keeps the full flow coordinated from requisition to payment Track 3: UiPath Test Cloud. Create agents that use UiPath Test Cloud to reimagine how software testing is designed, automated, executed, and managed across modern enterprise environments. Your goal is to show how agentic software testing can improve quality across AI-driven automations, enterprise applications, and connected business workflows. These agents should help you move faster with more confidence by increasing coverage, improving reliability, and reducing the manual effort required to validate complex systems. Think of building agents that can: evaluate requirements and turn them into meaningful test scenarios, identify fragile or outdated tests before they slow down a release, recommend fixes when automation breaks, or help orchestrate the right tests at the right time based on risk, coverage, and change impact. You might also explore how agents can validate AI-infused workflows, including third-party agents or AI services that participate in a UiPath-orchestrated process. All solutions must run on the UiPath Automation Cloud. You can include Agent Builder, Maestro, API Workflows, coding agents, and RPA where needed. You're welcome to bring in agents built on external frameworks and LLMs, in fact, we encourage it. The point is that UiPath is the orchestration and governance layer that ties everything together. Tip: If your process has unpredictable paths that emerge as the work unfolds, choose Track 1 - UiPath Maestro Case. If your process has a predictable sequence you can map in advance, choose Track 2 - UiPath Maestro BPMN. Bonus: solutions that use coding agents through UiPath for Coding Agents (Claude Code, Codex, Cursor, Gemini CLI, etc) will receive additional points during judging. We're especially interested in seeing how participants combine coding agents with low-code components, or blend UiPath-native agents with external agents, to solve complex problems You can't submit the same project to multiple tracks, and your solution needs to clearly align with whichever track you choose, both in how it's built and how it's described. Get started Register on Devpost for UiPath AgentHack. Form or join a team of 1 to 4 people and pick your track. Ask for access on UiPath Labs here: Teams must designate a representative to complete the access form on behalf of the group. Within 3 business days of asking for access, you'll receive a separate email with your UiPath Labs access link and credentials. UiPath Labs come fully equipped with agentic and AI units, so you can build without constraints.

June Study Jam Series: Bank Transaction Volume Forecasting Challenge

AI PC Agent Skills 征文活动

本次活动重点鼓励开发者探索如何利用 Intel 酷睿 Ultra 处理器（如最新的 Panther Lake 架构的 GPU、NPU 加速能力），配合 OpenVINO 推理框架，将模型能力转化为可编程、可复用的Skill技能包。这是 AI PC 真正从“硬件概念”走向“生产力工具”的核心跨越。 一、 开启 Agentic AI 与 Hybrid AI 的端侧新纪元在 AI 技术的演进历程中，我们正见证着一个关键的范式转移：AI 不再仅仅是简单的对话机器人，而是正在进化为能够理解意图、自主规划路径并调用外部工具产生实际后果的“智能体”——即 Agentic AI（智能体化 AI）。与此同时，随着端侧算力的爆发，Hybrid AI（混合 AI） 架构已成为大趋势，即通过云端处理超大规模逻辑，而将高频响应、隐私敏感及个性化强的任务下沉至 AI PC 处理。英特尔(Intel)作为 AI PC 时代的领航者，通过异构算力（CPU+GPU+NPU）的深度整合，为 Agentic AI 的落地提供了坚实的物理基础。而魔搭社区（ModelScope）作为开发者生态的摇篮，拥有丰富的模型储备。基于此，英特尔联合魔搭发起本次 Agent Skills 征文大赛，旨在挖掘那些能够运行在本地、能被智能体高效调用的“Skills”，共同定义端侧 AI 的未来。二、 活动背景：从对话式交互到 Agentic 工作流过去，开发者关注的是 Prompt Engineering（提示工程）；现在，我们的重心转向了 Agentic Workflows（智能体工作流）。一个优秀的智能体不仅需要强大的“大脑”（模型），更需要敏捷的“双手”（Skills）。在 Hybrid AI 的愿景下，AI PC 是离用户最近的计算中枢。将逻辑复杂但体积精炼（≤35B）的模型部署在本地，不仅能极大地降低推理成本，更能确保用户隐私数据“不出机”。本次活动重点鼓励开发者探索如何利用 Intel 酷睿™ Ultra 处理器（如最新的 Panther Lake 架构）的 GPU、NPU 加速能力，配合 OpenVINO™ 推理框架，将模型能力转化为可编程、可复用的Skill技能包。这是 AI PC 真正从“硬件概念”走向“生产力工具”的核心跨越。三、 征文主题1. 核心主题用 35B 以下小模型作为Agent大脑，驱动一项 本地AI工具调用(OCR,ASR,TTS…)，满足实际场景需求，最终生成可以被复用的Agent Skill。2. 技术约束模型规格：为了确保Skill的鲁棒性，驱动 Skill 的模型总参数量必须 ≤ 35B。35B 是当前端侧算力处理复杂逻辑与保证响应速度的“平衡点”，推荐qwen3.6系列、openBMB4.5系列；运行环境：Skill 中涉及的AI模型必须支持纯本地运行（Localhost）。推理框架：推荐使用 OpenVINO™（及其生态工具如 Optimum-intel）进行本地AI工具的构建，以充分释放 GPU、NPU 潜力。官方指南：关于 AI PC 大模型的一站式使用指南，请参考：https://modelscope.cn/brand/view/ai_pc验证基准：赛事方统一使用 Ollama + Qwen3.6-35B-A3B + QwenPaw/Trae 作为Skill是否可以被Agent大脑调用的的基准测试环境。 四、 推荐方向与场景参考推荐方向场景示例Agentic / Hybrid 价值办公提效本地会议纪要自动提取、PPT 一键风格改稿、Excel 复杂公式智能填充。零延迟响应，确保企业内部敏感会议数据绝对安全开发辅助本地代码 Review、Git Commit 自动生成、API 文档反向生成。离线状态下的高效编程，保护核心算法不外泄创作创意短视频脚本生成、公众号/小红书文案适配、本地图文自动化排版。端云协同：云端搜集素材，本地利用 35B 模型进行深度二次创作知识管理个人 PDF/笔记库 RAG、研报摘要提取、本地私人知识库智能问答。构建“永不掉线”且完全私有的个人数字第二大脑数据分析CSV 自然语言查询、本地数据可视化分析、系统日志异常自动归因。直接读取本地大容量数据集，规避云端流量成本 参考资源扫描进入比赛群，实时赛事信息同步、技术交流、问题答疑等 扫码访问英特尔 AI PC 专区，获取更多开发工具、技术文档与实战案例，助力您的 AI 应用开发之旅五、 参赛流程：从创意到落地的四步走编写本地 AI 工具：推荐使用 OpenVINO 进行量化与异构加速优化。生成并验证 Skill：在魔搭 Skills 中心规范下封装技能，并在 ≤35B 小模型作为大脑的环境下进行指令测试。发布作品包：在魔搭 Skills 中心发布，并添加 “AIPC” 自定义标签。需含代码、文档及测试用例，skill 提交入口：https://www.modelscope.cn/skills (点击“新建skill” 提交)提交技术文章：在魔搭研习社发表文章，沉淀实践路径、优化心得与 Hybrid AI 的思考，并添加 “Intel AI PC” 自定义标签，文章提交入口：https://www.modelscope.cn/learn (点击“创建文章” 提交)发布小红书（非必要项，会影响部分评分项）：请选手将作品框架截图、流程图或 Skill 任务成果等图文信息，连同魔搭研习社文章链接与 Skill 链接发布至小红书，同时 @OpenVINO中文社区 和 @魔搭ModelScope社区，并加话题 #英特尔 #openvino  #魔搭社区 #modelscope #agentic #skills  六、 评分标准维度权重说明Skill 可用性30%本地验证通过率、稳定性、错误处理场景价值20%解决问题的真实性、用户群体广度技术深度20%模型选型合理性、OpenVINO/GPU/NPU 优化、工程实现质量文章质量15%结构清晰度、可复现性、教学价值、魔搭/小红书社区影响力创新性15%思路新颖度、与已有方案的差异化七、 丰厚激励实物奖励：前50名完整提交作品，即可领取英特尔 AI PC 开发者限量版定制礼品。现金大奖：TOP 10 作品各获得 1000 元（含税）。生态推广：入选《AIPC Skills Collections》，获得魔搭及英特尔官方全渠道流量扶持。合作池入驻：优秀开发者将优先进入“Intel ISV 生态合作伙伴池”。 让 AI 不止于云端，让智能触手可及。英特尔与魔搭社区期待与您共同定义 AI PC 的“灵魂技能”！

Spatial Joy 26 Rokid 全球高校创新挑战赛

在人工智能与增强现实技术加速融合、重塑人机交互范式的时代背景下，Rokid 推出的 “Spatial Joy” 系列赛事，一项旨在推动大型语言模型、空间计算及下一代交互范式交叉探索的旗舰计划。其使命在于：赋能高校学子与行业开发者，突破现有边界，共同定义 Al+AR 的未来图景。

A Step Ahead of Drought: Forecasting Global Water Storage Challenge by ITU

探月计划 | Physical AI 黑客松

Physical AI 黑客松-最硬核也最 Vibe不是快闪，不是流量工厂，不是形象工程，而是一场赛后仍能生长的实验场。 选手报名：https://afterzero.feishu.cn/share/base/form/shrcnl8tzNnbLjIoFxUDMyqaIOh志愿者报名：https://v.wjx.cn/vm/tU5LvFv.aspx观众报名：https://wj.qq.com/s2/27053120/a309/

Global AI Hackathon Series with Qwen Cloud

Build production-ready agents on Qwen — and show the world what an agent can do. The Global AI Hackathon with Qwen Cloud invites professional developers, AI/ML engineers, and builders to design and ship sophisticated multi-agent systems, complex AI workflows, and production-grade applications using Qwen and other flagship models available on Qwen Cloud infrastructure. With advanced reasoning and multimodal capabilities, QwenCloud is built for builders who care about architectural depth and engineering excellence. Pick a track and build something real. Compete for total $70,000+ in cash and cloud credits, get featured on the Qwen Cloud blog, and earn an invite to join the AI Catalyst program. Why Join Build with Qwen Cloud. Access Qwen and other flagship models with advanced reasoning and multimodal understanding — all on Qwen Cloud infrastructure purpose-built for production AI. Get free Qwen Cloud credits to build, deploy, and scale your agent. Win a total of $70,000+ in cash and cloud credits plus blog features, swag, and an invite to the AI Catalyst program for standout teams. Get started Register on Devpost (2 minutes) Sign up for Qwen Cloud. Click here to Sign up for your free trial and request your free hackathon credits via our coupon form. To learn more about the hackathon you can check here.(5 minutes) Join the Qwen Cloud Discord (2 minutes) Pick your track, review the sample projects and reference architecture in the Resources and start building. (30 minutes to first run)

2026江苏“创青春”AI+交通创新创业大赛

为全面落实国家"人工智能+"行动和交通强国建设战略部署，助力江苏打造全国领先的“AI+交通”融合应用高地，通过举办2026江苏"创青春"AI+交通创新创业大赛，汇聚全国青年人才破解行业关键技术、应用难题，加速前沿成果在交通场景的转化落地，以创新赋能江苏交通运输事业高质量发展，培育和发展交通领域新质生产力。 为全面落实国家"人工智能+"行动和交通强国建设战略部署，助力江苏打造全国领先的“AI+交通”融合应用高地，通过举办2026江苏"创青春"AI+交通创新创业大赛，汇聚全国青年人才破解行业关键技术、应用难题，加速前沿成果在交通场景的转化落地，以创新赋能江苏交通运输事业高质量发展，培育和发展交通领域新质生产力。 访问大赛官网（qczx.jchc.cn）了解详情并报名参赛。

2026 小X宝开源医疗社区黑客松

2026 小X宝开源医疗社区黑客松 联合 ModelScope 魔搭社区正式拉开帷幕！我们邀请每一位心怀善意的开发者，用 AI 技能（Skills）与 MCP 工具，为生命构筑更多可能。 2026 小X宝开源医疗社区黑客松光已成炬，照亮崎岖 | Light Turns Into Torches, Illuminating the Rugged Path小X宝开源医疗社区 × 魔搭 ModelScope社区 联合主办赛事官方主页：https://hackathon.xiao-x-bao.com.cn/一、竞赛简介在医疗的广阔疆域中，仍有许多崎岖之路——肿瘤的复杂、罕见病的孤独。科技的力量，应当成为照亮这些角落的火炬。2026 小X宝开源医疗社区黑客松联合 ModelScope 魔搭社区正式拉开帷幕！我们邀请每一位心怀善意的开发者，用 AI 技能（Skills）与 MCP 工具，为生命构筑更多可能。本次黑客松由小X宝开源医疗公益社区发起，旨在通过开源技术服务真实医疗需求。无论你是算法极客、医疗 AI 实践者，还是对 Agent 领域充满好奇的新手，这里都有你的舞台。角色名称说明联合主办魔搭 ModelScope 社区中国最大的模型开源社区，提供赛事平台与运营资源合作方KnowS提供医学循证证据检索 API、开发者 Skill 支持与会员权益技术支持Sealos（FastGPT）提供 RAG 能力与相关技术支持，帮助参赛团队构建知识库检索增强应用大模型赞助阶跃星辰 StepFun提供 Flash Pro / Flash Max 大模型套餐权益二、赛题方向与技术方案赛题方向为通用医学 + 生命科学方向，不做更细的限制。核心赛题：聚焦医疗垂直领域，构建可复用的 Skills、MCP 或者是 Agent。赛事特色：不设细分赛道，只要你的应用能解决真实医疗场景问题，即刻出发！作品须满足以下条件：◆ 医疗场景导向：聚焦医疗垂直领域• （如肿瘤、罕见病、诊断辅助、患者管理、医学文献检索等）。◆ 技术形式：形式为 MCP 工具 或 Agent Skill，可独立运行或集成到现有 Agent 框架。或者是，你很强，想独立从头实现一个 Agent Harness，我们欢迎一切的贡献。◆ 平台部署：参赛作品须在魔搭社区完成部署，作为ModelScope 创空间(Studio) 应用项目；最终提交时需提供可访问的项目链接，便于评审体验和查看作品。◆ 选题登记：选题不限细分方向，但需要在开发前在群内或活动页面登记选题，避免重复。三、赛程安排：分阶段冲刺，直通 WAIC本次大赛为期近四周（6月18日 - 7月12日），节奏紧凑，期间设置三个阶段里程碑，每个阶段评选一支代表团队获得Vibecoding 键盘、 WAIC 门票等礼物，最终进行独立总评：阶段时间内容上线 / 报名开始6月18日在魔搭社区完成报名，加入官方交流群，即刻开发阶段 1：选题登记6月18日 - 6月24日登记选题并完成可行性说明；阶段结束评选「最佳选题潜力奖」，发放 1-3张 WAIC 2026 单日票阶段 2：MVP 开发6月25日 - 7月1日提交可运行 MVP 原型；阶段结束评选「最佳 MVP 原型奖」，发放 1 个 Vibecoding 键盘阶段 3：社区共建7月2日 - 7月8日鼓励社区试用、反馈与传播；阶段结束评选「社区影响力奖」，发放 1 个 Vibecoding 键盘最终提交截止7月12日 23:59将完整作品发布至魔搭社区，并填写最终提交表单集中评审7月13日 - 7月14日独立专家六维评分制评审结果公示与颁奖7月15日公布最终获奖名单，发放云资源/API 额度四、评审规则本次评审分为「阶段奖评审」和「最终总评」两部分，确保不同类型的优秀项目都能脱颖而出：阶段奖评审（各阶段独立评审，产生 WAIC 门票获得者）◆ 阶段 1（选题潜力）：重点看选题是否真实、有价值、可在短周期内落地。◆ 阶段 2（MVP 原型）：奖励最早把核心能力跑起来的队伍，必须能演示。◆ 阶段 3（社区影响力）：综合有效调用、收藏、评论质量、传播质量，异常流量剔除。最终总评（7/13-7/14，六维评分制）最终总评采用独立专家六维评分制，社区反馈质量仅占 10%，不直接主导排名：评分维度权重说明技术实现与可运行性25%代码结构清晰、核心功能能跑、部署稳定医疗场景价值与问题定义20%场景真实、目标用户明确、解决方案有意义MCP/Skill 完成度与平台适配20%是否符合魔搭社区提交、运行和展示要求开源质量与文档可复现性15%公开仓库、License、README、部署步骤、示例输入输出安全合规与风险控制10%不使用真实患者数据，不承诺诊断，明确局限性社区反馈质量10%真实用户反馈、Issue、有效讨论，而非单纯调用数五、奖励体系（注：奖励方案中云资源/API 额度的最终金额与形式以与魔搭社区协商结果为准）阶段奖（魔搭阶段礼品）每个阶段产生 1 支代表团队，获奖团队将获得魔搭提供的阶段礼品，并接受官方开发者采访与项目介绍：阶段奖项奖励6/18–6/24🎯 最佳选题潜力奖1-3 张 WAIC 门票 + 开发者采访6/25–7/1⚡ 最佳 MVP 原型奖1 个vibecoding 键盘 + 开发者采访7/2–7/8🌟 社区影响力奖1 个vibecoding 键盘 + 开发者采访最终总评奖项（7月15日公布）奖项名额奖励内容🌟 普惠权益每位参选选手StepFun Flash Pro 月度套餐 1 份，等值 199 元🏆 一等奖Top 3（3名）KnowS 专业版-3 倍积分年度会员（每位有效成员 1 份，等值 2,388 元）；StepFun Flash Max 季度套餐（每位有效成员 1 份，等值 1,889 元）🥈 二等奖Top 4-10（7名）KnowS 专业版-1 倍积分年度会员（每位有效成员 1 份，等值 828 元）；StepFun Flash Pro 季度套餐（每位有效成员 1 份，等值 539 元）🥉 三等奖Top 11-20（10名）StepFun Flash Pro 月度套餐（每位有效成员 1 份，等值 199 元）额外权益：◆ 资源支持：获胜者将赢取丰厚的云资源或 API 额度，助力项目持续演进。◆ 荣誉见证：所有完成提交的参与者均可获得官方电子参与证书。◆ 品牌曝光：优秀作品将上架 GitHub 与魔搭社区，获得全平台流量支持。合作方专项权益◆ KnowS 医学循证证据检索权益： KnowS 将为本次黑客松提供医学循证证据检索 API 与开发者 Skill 支持，开放文献、指南、说明书、临床试验等检索能力，支持参赛团队基于真实医学证据构建 AI 应用。活动期间，通过审核的参赛团队可领取团队独立 API Key，并共同使用总价值 10,000 元的 API 调用额度池，额度自活动开始起生效，至活动结束时截止。◆ KnowS 开发资源： 参赛团队可参考 KnowS 开发者文档（https://developers.nullht.com/）、API Reference（https://developers.nullht.com/api/reference/overview）与 KnowS Evidence Search Skill（https://developers.nullht.com/skills）进行开发。 ◆ KnowS 获奖会员权益： 最终总评一等奖 3 支团队，获奖团队内每位有效成员额外获得 1 份 KnowS 专业版-3 倍积分年度会员，等值 2,388 元；二等奖 7 支团队，获奖团队内每位有效成员额外获得 1 份 KnowS 专业版-1 倍积分年度会员，等值 828 元。每支获奖队伍（队伍人数不超过 3 人）可领取对应会员权益。◆ StepFun 参赛普惠权益： 每位有效参赛选手可获得 1 份 Flash Pro 月度套餐，等值 199 元。参赛者可个人参赛，也可组队参赛；每支队伍最多 3 人，每位参赛选手仅可加入 1 支队伍，并仅可领取 1 份参赛普惠权益。◆ StepFun 获奖套餐权益： 最终总评一等奖 3 支团队，获奖团队内每位有效成员额外获得 1 份 Flash Max 季度套餐，等值 1,889 元；二等奖 7 支团队，获奖团队内每位有效成员额外获得 1 份 Flash Pro 季度套餐，等值 539 元；三等奖 10 支团队，获奖团队内部每位有效成员额外获得 1 份 Flash Pro 月度套餐，等值 199 元。获奖专项权益可与参赛普惠权益叠加。以上 KnowS 与 StepFun 专项权益由对应合作方单独提供，具体 API Key 发放形式、套餐名称、领取方式、有效期与使用规则以合作方最终确认为准。技术支持◆ Sealos（FastGPT）RAG 支持： Sealos（FastGPT）为本次黑客松提供 RAG 能力与相关技术支持，帮助参赛团队搭建医学知识库问答、资料检索增强生成等应用。该支持作为技术生态支持展示，不作为奖品或赞助权益发放。阶段奖与公平性规则◆ 门票归属： WAIC 门票面向获奖团队，最多提供3张单人票。◆ 不可重复：每支队伍在整个比赛期间最多获得 1 次奖励。◆ 后续参赛：已获票团队仍可继续参与后续阶段展示和最终总评，但不再参与后续 vibecoding 键盘奖励阶段奖评选。◆ 顺延机制：若某阶段最高分团队已获票，门票顺延至该阶段下一支未获票且符合资格的队伍。◆ 采访机制：阶段获奖后 24-48 小时内完成 5 个问题的轻量采访，用于公众号/魔搭社区/活动页宣传。◆ 队伍归属：每人只能加入一队；同一核心成员、同一代码库的报名视为同一参赛主体。◆ 历史项目：允许历史项目参赛，但必须披露赛前已有部分，评审重点看比赛期间新增贡献。◆ 反作弊：异常流量（刷量、互刷、机器人行为）将被剔除，情节严重者取消评奖资格。六、作品提交规范参赛者在魔搭活动页填写表单时，必须包含以下三个核心交付物：◆ 魔搭社区作品链接 （作品需以 Skill、MCP 工具或 Agent 应用形式在魔搭社区完成部署， Agent 应用项目需部署在ModelScope 创空间/studio 展示；项目需公开可访问，并提供基础 Demo 或使用说明）◆ 公开代码仓库地址 （如 GitHub、Gitee 等，需含开源 License，推荐 Apache 2.0 或 MIT）◆ 规范 README 文档 （须包含：背景说明、医疗场景解决点、使用方法、部署步骤）提交入口：◆ Skill 提交：https://modelscope.cn/skills/create?template=custom ◆ MCP 提交：https://modelscope.cn/mcp/servers/create ◆ Studio 提交： https://modelscope.cn/studios/create七、仓库设置规范◆ 可见性：仓库必须设置为 Public（公开）。◆ 开源协议：仓库根目录必须包含 LICENSE 文件。建议使用 MIT 或 Apache 2.0 协议，以鼓励社区二次开发。◆ 代码整洁：敏感信息（如 API Keys、个人隐私数据）绝不可硬编码提交，需使用 .env 等方式隔离。◆ 医疗合规：禁止使用真实患者数据，不得做出诊断承诺。八、README.md 模板要求README 是评委了解作品的第一窗口，必须包含以下结构：# [项目名称] ## 1. 项目简介与医疗场景 - 一句话描述：[用一句话说明这是一个什么医疗 Skill/MCP] - 解决的痛点：[例如：肿瘤患者随访数据难结构化、罕见病文献检索效率低等] - 目标受众：[医生、患者、医学研究员等] ## 2. 功能特性 - [特性 1：如支持基于 PubMed 的自动文献检索] - [特性 2：如支持与外部知识图谱联动] ## 3. 魔搭社区运行/部署指南 - 魔搭展示链接：[提供您的 S

Slack Agent Builder Challenge

The next era of productivity is agentic—and it lives in Slack. We’re calling on all builders—from seasoned Slack veterans to "vibe coders" picking up the Slack CLI for the first time—to join the Slack Agent Builder Challenge. This is your opportunity to redefine the workplace by building intelligent agents that automate workflows, surface real-time insights, and connect systems in ways we haven’t imagined yet. Whether you’re building a specialized agent for your organization, a tool for social good, or the next viral Slack Marketplace app, we want to see your creativity in action. Leverage the Slack Agent Builder, MCP integrations, and the Real-Time Search (RTS) API to show the world why Slack is the ultimate surface for AI. Why Join? Whether you're a "vibe coder" or a seasoned pro, the on-ramp is designed for speed—use the slack create agent command and ready-made templates for HR, IT, and Sales to go from an idea to a running agent in a single afternoon. For those building for organizations, the hackathon serves as a guided path to the Slack Marketplace, giving you a structured deadline to ship your app and gain direct distribution to Slack’s massive enterprise customer base. Compete for a share of the $42,000 cash USD prize pool, a chance to go to Dreamforce 2026, some Slack swag, an invite to an exclusive Slack community gathering and to be featured in the Slack newsletter and social media

Reddit’s Games with a Hook Hackathon

Overview Reddit is hosting a virtual hackathon from June 17th to July 15th. We’re offering developers $40,000 in prizes for games built to delight users. The challenge: create a new Reddit game for the users of Reddit using Devvit, our Developer Platform. For this hackathon, we're asking developers to use Devvit Web, which allows you to build Devvit apps using web technologies you’re already familiar with (e.g. React, Phaser, three.js), or your favorite game engine (Godot, GameMaker, Unity, etc). Participants will also have access to Phaser to make their game shine. The best app to use Phaser will be eligible for a special award. What to build Build a game on Devvit (Reddit’s Developer Platform) using our Interactive Posts feature that inspires collective joy. The Best Experience That Will Keep People Coming Back: Apps that give redditors a reason to return day after day. This could come from progression, daily challenges, fresh content, meaningful choices, social dynamics, or simply the anticipation of what happens next. We're looking for experiences that create excitement between sessions and leave players wanting to check back tomorrow. The strongest entries make every visit feel worthwhile. Players should have something to work toward, discover, unlock, influence, or look forward to each time they return. The format can vary widely, but the core focus should be creating a compelling loop that builds momentum over time and turns a one-time visitor into a regular player. Examples of games on Reddit that bring users back day over day r/honk, r/colorpuzzlegame, r/bunnytrials, r/alignmentchartFills, r/hotandcold, r/dailyguess, r/bridgedit, r/battlebirds, r/kraw r/LETTERSET What we’re NOT looking for: AI Slop: Did you use AI? Fantastic! But it shouldn’t be obvious the moment we open your app. Fit the UI in the viewport, give your app a unique identity, and make sure you’re thinking of your human players. You can even ask your agentic assistant to help hide their involvement! On the nose Reddit theming: Reddit-y does not mean the game is about Reddit, karma, subreddits, Snoo, etc. Reddit-y means human-first, embracing community, meritocracy, creativity…it’s in the spirit of the app and your use of all the things subreddits have to offer (comments, flair, feeds, community)! While making the app about the topic of Reddit itself is not forbidden, it is also not a hack for making an app “Reddit-y”. Literal interpretations on “Games with a Hook”: While we're not opposed to seeing a fishing game or two, games with a "hook" shouldn't be taken literally. We want to see hooky games as in retentive and replayable. Common Ideas: Your game might not score as well if we’ve seen it many times before. We see a lot of: space shooters, clone apps of popular games, simple platformers, collaborative storytelling apps, and trivia apps. If you do make one of these apps, make sure it's extremely unique. Sub-challenges In addition to an excellent retentive experience, we’re looking for standout apps that also achieve the following: Best Use of Phaser: Recognizes the most innovative use of the Phaser game development framework within the Reddit Devvit platform. Best Use of Retention Mechanisms: Reddit apps are discoverable in our users’ feeds. This means that apps that create daily, or recurring, content tend to see better growth and retention. This award will go to the best use of retentive mechanics in a game. Best Use of User Contributions: Apps that enable users to generate content (comments, posts, drawings, puzzles, levels) drive redditor engagement and conversation. This award will go to the app that makes the best use of user-generated content. Games must be built on Devvit Web and be compliant with our Devvit Rules. Read our guide on how to build games for Reddit for more guidance on building for our platform. For this event we are looking for polish, meaning apps that are as close to launch-ready as possible (bonus points for a good mobile experience). We understand that not all projects will reach this threshold, but projects that are well tested and concept-complete will score higher. While we are accepting existing projects for this event, please note the game should be significantly updated during the hackathon period to qualify for this event. Getting started Get started with the Quickstart Browse our Template Library for building with a familiar framework You can use the Phaser Template here View examples of existing games on r/GameOnReddit Join us on Discord for live support and office hours

Flu Shot Learning: Predict H1N1 and Seasonal Flu Vaccines

health

AFAC2026 金融智能创新大赛

AFAC金融智能创新大赛由中国计算机学会、北京大学、新加坡南洋理工大学、蚂蚁集团、NVIDIA等近30家组织和机构联合发起。自2023年首届举办以来，AFAC大赛已成长为全国乃至全球顶尖的金融智能赛事，累计吸引超1.5万支队伍、近5万名选手同台竞技，覆盖600余所高校、400余家企业。

硅碳 AI 诊疗挑战赛

本次大赛设“硅基智能体 X 碳基医学生”双赛道，基于 OpenHospital 底座，构建高度拟真的全科诊疗 Arena。这里没有标准单选题，只有 12,000 名鲜活的智能体患者与上千种错综复杂的疾病网络，覆盖多发病、罕见病及复杂共病。这是一场让 "硅基智能体"与"碳基医学生"同台竞技 的临床试炼——评判标准回归医疗本质：问得更准、查得更精、治得更好。 一、赛题背景大模型技术正将医疗 AI 从"医学百科全书"推向"临床决策大脑"，但一个核心命题亟待回答："考场得分高"的 AI 智能体，真的能接诊现实中的复杂病患吗？传统医疗评测多以静态文本为基础（如 MedQA、USMLE），将鲜活的临床问题降维成考题，掩盖了患者沟通博弈与复杂共病的陷阱，更缺乏对"问诊 → 开具检查 → 诊断与治疗"全链路能力的系统性评估。本次大赛基于 OpenHospital 底座，构建高度拟真的全科诊疗 Arena。这里没有标准单选题，只有 12,000 名鲜活的智能体患者与上千种错综复杂的疾病网络，覆盖多发病、罕见病及复杂共病。这是一场让 "硅基智能体"与"碳基医学生"同台竞技 的临床试炼——评判标准回归医疗本质：问得更准、查得更精、治得更好。二、赛题任务参赛选手需基于给定评测集，完成医生诊疗全链路任务，涵盖三个核心环节：环节任务描述中间输出① 问诊通过多轮自然语言对话，与患者智能体交互收集病史与症状医生对话文本② 开具检查基于问诊信息，合理开具必要检查项目检查项目名称③ 诊断与治疗综合问诊与检查结果，给出明确诊断结论与治疗方案确诊疾病名称、治疗方案文本2.1 双赛道介绍硅基赛道（搭建、调优智能体参赛）参赛对象：面向全国高校在校学生（专科、本科、硕士、博士、在职研究生等高等教育学籍在内的在校学生人群），具备智能体搭建能力的开发者，每队 1–3 人；资源包：训练服务端（包括与训练集的患者智能体对话，获取检查结果，在线评估，提供标准答案）、标准检查项目清单、标准疾病名称清单，初始医生智能体baseline；考察重点：智能体搭建、Skill 构建、Memory 管理；交付物：在魔搭创空间中部署的医生智能体。碳基赛道（在校医学生参赛）参赛对象：面向含医学相关专业的专科生、本科生、硕士及博士研究生，以个人形式参赛；资源包：可视化交互平台（包括与训练集的患者智能体对话，获取检查结果，在线评估，提供标准答案）、标准检查项目清单、标准疾病名称清单；考察重点：临床实践能力、知识储备、经验判断；交付物：在可视化交互平台上在线作答的答题记录。2.2 赛事流程赛事平台：https://www.modelscope.cn/studios/baconroot/virtual_hospital提交报名后，24h 内将通过报名预留邮箱发放 参赛账号，两赛道均在赛事平台进行两赛道均分为 训练阶段 与 评测阶段：训练阶段：模型基座统一限定为 Qwen3.5-Flash，选手可通过 API 或 Web 界面调用患者智能体，完成问诊、检查、诊断、治疗全流程；平台同步提供训练集标准答案供选手参考调优。该阶段所消耗的 token 数将计入最终加权得分，消耗越少得分越优。评测阶段：硅基赛道由选手在创空间中部署开发好的智能体，评测阶段限定用 Qwen3.5-Flash，然后提供创空间名称和Modelscope的Access Token，由系统进行自动化批量评测；碳基赛道由选手登录可视化交互平台在线作答，系统依据标准答案自动判分。2.3 提交规则赛道提交方式提交次数硅基-智能体赛道在魔搭创空间中部署的医生智能体，提交到平台+链接&截图每队最多 3 次，取最高分碳基-医学生赛道在可视化交互平台上直接在线作答+链接每人最多 3 次，取最高分2.4 跨赛道衡量机制两赛道独立排名，同时发布 跨赛道综合对比榜 供行业参考。三、赛程总览阶段时间内容报名 & 热身6 月 3 日开放报名，发布 Baseline，熟悉 Arena 平台与提交流程初赛8 月 2 日开放评测集 A 卷（公开卷），排行榜准实时更新；硅基智能体赛道 Top 20 进入复赛，碳基医学生赛道 直接决选 Top 6复赛8 月 31 日开放评测集 B 卷（隐藏卷），难度提升，覆盖更多疑难/罕见病例；B 卷成绩 Top 6 进入决赛决赛9 月初全赛程技术解决方案路演，决选最终一、二、三等奖结果公布9 月初公布最终排行榜并颁奖四、评估维度指标权重评估说明诊断准确率25%预测诊断集合与标准诊断集合的匹配度，要求预测为标准的子集检查精确率25%开具检查项目的精确率，避免过度医疗治疗方案契合度25%综合 安全性、有效性对齐度、个性化程度 三个维度评分（1–5 分），含安全性惩罚机制测试阶段 Token 消耗量得分20%评测阶段 Token 消耗，越少得分越高训练阶段 Token 消耗量得分5%训练阶段 Token 消耗，越少得分越高注：碳基赛道在测试时还会计时，分数相同时，用时越少，排名越高。五、奖金与激励硅基赛道（智能体开发队伍）奖项数量奖金（含税）额外激励🥇 一等奖1¥ 20,000获奖证书🥈 二等奖2¥ 10,000获奖证书🥉 三等奖3¥ 5,000获奖证书碳基赛道（医学生）奖项数量奖金（含税）额外激励🥇 一等奖1¥ 10,000获奖证书🥈 二等奖2¥ 7,000获奖证书🥉 三等奖3¥ 5,000获奖证书六、组织单位主办单位：魔搭社区、浙江大学、浙江工商大学合办单位：浙江大学软件学院、浙江大学医学院、浙江工商大学共同富裕统计监测与智能治理实验室、南京大学智能科学与技术学院协办单位：阿里云百炼七、资源支持7.1 赛题解读与培训形式时间内容赛题解读直播赛前启动平台说明、评分规则详解、Baseline 复现演示、答疑用户手册（见赛事平台右上角）赛前启动硅基：赛事平台使用说明+医生智能体baseline使用说明；碳基：平台使用教程赛中答疑赛程全程官方讨论区与微信答疑群碳硅基问诊表演赛赛程中直播展示双赛道交互效果7.2 Baseline 与平台资源赛事平台：诊疗交互界面、实时排行榜、历史提交记录回看硅基选手在该平台进行测试。碳基选手在该平台进行训练和测试。赛事平台：https://www.modelscope.cn/studios/baconroot/virtual_hospital提供可调优的 Baseline 在创空间中，可以复制进行改进baseline：https://www.modelscope.cn/studios/baconroot/hospital_agent_example7.3 Token 支持● 训练阶段：算力由选手自行准备训练所需算力由选手自行准备。推荐选手优先申领阿里云"云工开物"高校学生扶持计划的算力额度，作为训练资源之一。该计划由阿里云独立运营，面向全国高校在读学生开放，符合条件者可申领至多 300 元/人；具体规则与有效期以阿里云官方说明为准。申领额度用完的部分，由选手自行承担；选手也可自行选用其他合规算力资源。「云工开物」的具体申请方式 👉点此查看 领取算力券后，按以下步骤接入指定模型：进入指定模型：通过以下链接跳转到本次参赛指定模型 Qwen3.5-Flash： https://bailian.console.aliyun.com/cn-beijing#/model-market/detail/qwen3.5-flash?serviceSite=asia-pacific-china获取并填写 API Key：在百炼平台设置并获取 API Key，然后填入参赛平台。计费与抵扣说明：百炼平台采用按量付费模式，每个模型都会先提供一部分免费调用额度。免费额度用完后，将自动使用"云工开物"券抵扣后续费用。● 评测阶段：算力由赛事平台统一提供评测采用 T+1 离线评测机制，所需算力由赛事平台统一提供，选手无需自备。7.4 技术支持提供完整的技术支持体系，包括 Baseline 使用文档、常见问题 FAQ、官方答疑群等

Arm Create: AI Optimization Challenge

Welcome to the Arm AI Optimization Challenge 2026. We’re inviting developers to build and submit projects that show how AI can be optimized for Arm-powered platforms across three challenge tracks: Physical AI: Optimize AI for real-world systems, including robotics, embedded devices, sensors, simulation, autonomy, and edge environments. Cloud AI: Optimize AI for scalable infrastructure, including Arm64 cloud, inference performance, frameworks, agents, and production-ready developer workflows. Mobile AI: Optimize AI for on-device constraints, including performance, privacy, latency, battery efficiency, and local AI experiences on Arm-powered phones, tablets, and laptops. Across all tracks, submissions should show clear optimization work and measurable improvements where possible. Optimizations we will look for: Model size: Reduce size on disk or in memory. Model quality: Improve fine-tuning or output quality for a given model size. Model speed: Improve tokens/sec, time to first token, or other relevant latency metrics. Inference server speed: Improve throughput, latency, tokens/sec, or time to first token. Developer experience: Improve tools, workflows, setup, documentation, or usability. Arm-specific optimization: Implement optimizations in an existing framework, library, model, or application to run better on Arm. Developers can use Arm Performix to get exact benchmarks of their Arm based performance and be able to clearly show their results.

Build with Gemini XPRIZE

90 days. Ideate. Build. Ship. Grow. Real product. Real revenue. A real business. 01 Ideate Pick a real problem worth solving. AI helps you scope it. 02 Build Build a working prototype using AI. 03 Ship Deploy a business that operates with AI. Real users in production. 04 Sell Market it. Grow it. Show the revenue. AI is transforming everything, including what small teams can build. Operations that used to take entire engineering departments can now be written in plain English and executed by AI agents. By some estimates, the world’s 45 million software developers will grow to over a billion in the next few years. Everyone will be a developer, and we want to start now, with you. So show us: build a real business that makes real impact.

Pump it Up: Data Mining the Water Table

development

DengAI: Predicting Disease Spread

health

Richter's Predictor: Modeling Earthquake Damage

disasters

Conser-vision Practice Area: Image Classification

climate

昇腾CANN社区任务【6月】

昇腾CANN训练营社区基于真实产业案例设计，完成任务即有机会获得华为三折叠手机、万元笔记本电脑，荣登社区荣誉榜单，任务每月更新，月底截止，多重任务值任务适合各阶段实操开发者。 背景社区任务基于真实产业案例设计，完成任务即有机会获得华为三折叠手机、万元笔记本电脑，荣登社区荣誉榜单，更为您的职业技能注入新动力。具体任务每月月底发放任务，次月30日/31日截止，多重任务值任务适合各阶段实操开发者完成任务赢取现金、新款华为手机、电脑 👇🔥 6月社区任务全新升级，现金激励重磅来袭！每个任务参与队伍根据验收时间排序，按任务书要求第一个通过验收的队伍在合入PR后可获得该任务相应奖金序号任务链接含税奖金（人民币）最晚通过验收时间20260529-1AclsolverCheevj 算子开发90415.5元2026.07.3020260529-2三角矩阵求解运算系列算子开发82240.5元2026.07.3020260529-3SPMV 算子开发72430.5元2026.07.3020260529-4Cast&EmbeddingDenseGrad 算子开发69651元2026.07.3020260529-5DynamicMap容器算子开发60985.5元2026.06.3020260529-6MinDim&MaxDim 算子开发58860元2026.06.3020260529-7SyncBatchNormGatherStats 算子开发57715.5元2026.06.3020260529-8Im2col 算子开发50194.5元2026.06.3020260529-9Bincount 算子开发40384.5元2026.06.3020260529-10RightShift算子开发29430元2026.06.3020260529-11FmodTensor&FmodScalar 算子开发25015.5元2026.06.3020260529-12UpsampleNearest3d 算子开发23380.5元2026.06.3020260529-13Logspace 算子开发23380.5元2026.06.3020260529-14UpsampleNearestExact1d&UpsampleNearestExact2d 算子开发23380.5元2026.06.3020260529-15Arange 算子开发22399.5元2026.06.3020260529-16Gcd 算子开发22399.5元2026.06.3020260529-17InplaceRsqrt 算子开发17985元2026.06.3020260529-18Relu算子开发17494.5元2026.06.3020260529-19InplaceSigmoid 算子开发17494.5元2026.06.3020260529-20IndexFillTensor 算子开发16840.5元2026.06.30任务值序号任务链接（绿色为Triton算子开发）奖品示例交付时间2200004-1SlidingTileAttention算子开发2026.05.311650004-2IsClose算子开发2026.05.311100004-3Pdist算子开发2026.05.3104-4AngleV2算子开发04-5Polar算子开发04-6Sleep算子开发900004-7Equal算子开发2026.05.3104-8Logit算子开发04-9median算子开发550004-10ForeachAddListV2算子开发2026.05.3104-11ForeachAddScalarV2算子开发04-12ForeachSubListV2算子开发04-13MaxUnpool3d算子开发04-14MaxUnpool2d算子开发04-15Sign算子开发04-16ForeachMulList算子开发04-17foreach_neg算子开发04-18foreach_exp算子开发04-19foreach_expm1算子开发04-20Neg算子开发04-21ForeachRoundOffNumberV2算子开发900003-1Roll算子开发2026.05.31800003-3MaskedScatter算子开发2026.05.3103-23_fwd_kernel_stage1算子开发03-24fused_recurrent_gated_delta_rule_fwd_kernel算子开发600003-8SoftmaxCrossEntropyWithLogits算子开发03-26reshape_and_cache_kernel_flash算子开发500003-11ApplyAdamW算子开发03-12AscendAntiQuantV2算子开发03-13BiasAdd算子开发03-14BnTrainingReduce算子开发03-15L2Loss算子开发03-16MaxPool3D算子开发03-17ScatterAdd算子开发03-18ScatterNd算子开发400003-20ApplyAdagradD算子开发03-21PreluGradReduce算子开发03-28_linear_attn_decode_kernel算子开发03-29chunk_kda_scaled_dot_kkt_fwd_kernel_intra_sub_intra算子开发300002-16ReluGradV2算子开发02-26SyncBatchNormBackwardElemt算子开发03-30_fwd_kernel_stage2算子开发03-31merge_attn_states_kernel算子开发03-32_fwd_kv_reduce算子开发提交方式01任务申请02任务交付（点击了解社区任务最新进展）03任务验收04验收复核05任务关闭点击报名后1个工作日内联系昇腾小助手获取任务进展更新表开发者承接后需1周内提交设计文档，提交后2个工作日会收到审批反馈开发者承接后3周内需联系昇腾小助手拉群验收；提交验收内容后3个工作日内会收到验收审批反馈第一名验收通过后的队伍需在2个工作日内提交PR，并根据检视意见修改代码，直到PR合入通过验收并合入PR参与任务需要先完成以下动作：1、报名26年CANN训练营第一季：报名链接2、认证通过算子开发者认证：认证链接具体任务查看&领取在 CANN 社区 进行：直达链接参与步骤01 任务申请跟帖评论申请任务（参与后任务交付、验收需与报名账号统一，否则无法发放奖品）格式示例： 【队名】：根本不报错 【gitcode账号名】：XXXXX 【是否报名训练营】：是 【是否通过算子开发者认证】：是/1周内提供 【任务序号】：任务序号 【状态】：报名 【链接】：fork链接【社区任务】开发流程及注意事项：https://gitcode.com/org/cann/discussions/39社区任务常见问题解析：https://gitcode.com/org/cann/discussions/37部分奖品为新品奖品发放会延迟，展示奖品若无货将更换为同等价值奖品

AMD x 魔搭社区 联合开发者激励计划 - GPU资源获取指南

本计划由 AMD 与魔搭社区联合发起，旨在激励开发者基于 AMD GPU 进行 AI 开发实践与分享。开发者可通过魔搭社区三大内容平台——研习社、灵感流、创空间——获取免费 GPU 算力奖励。 AMD x 魔搭社区联合开发者激励计划 — GPU 资源获取指南基于 AMD GPU 的 AI 开发实践，最高可获 1000 小时及以上免费算力一、计划概述本计划由 AMD 与魔搭社区联合发起，旨在激励开发者基于 AMD GPU 进行 AI 开发实践与分享。开发者可通过魔搭社区三大内容平台——研习社、灵感流、创空间——获取免费 GPU 算力奖励。核心机制三条独立路径：发研习社文章 / 传灵感流 Notebook / 做创空间应用，可单选可多选奖励可叠加：全部完成可获 1000 小时或更多按需分配：创空间根据应用质量按需分配 700 小时或更多二、奖励总览下表展示了三种参与路径的奖励规则，奖励可累计叠加：参与路径内容要求单次奖励路径上限前置注册完成 AMD 开发者计划注册+100 小时+100 小时路径一：研习社发布原创技术文章+25 小时/篇+50 小时路径二：灵感流发布 Notebook 代码实践+50 小时/篇+150 小时路径三：创空间部署应用并通过审核按需分配700+ 小时提示：三条路径完全独立，无须按顺序完成，可任选其一或全部参与。三、如何获取参与前提 — 完成 AMD 开发者计划注册无论选择哪条路径，均需先完成 AMD 开发者计划注册。注册完成即可获得 100 小时 GPU 算力，同时解锁后续全部参与资格。操作流程登录魔搭社区，进入「我的 Notebook」页面，点击活动页面入口；进入 AMD AI 开发者计划网站：授权使用魔搭账号登录，并完善个人资料，成为 AMD 开发者计划成员；完善资料后，开发者可于 AMD 开发者计划站内获得算力券奖励；回到魔搭「我的 Notebook」页面，即可看到 AMD GPU 入口及剩余额度。✅ 完成注册后，即可解锁以下三种参与路径的全部资格。路径一：研习社发文在魔搭社区「研习社」板块发布与 AMD GPU 相关的原创技术文章，每篇通过审核可获 25 小时 GPU 算力，本路径最高可获 50 小时。操作流程在魔搭社区「研习社」板块发布原创技术文章；发布时携带指定标签“AMD GPU激励计划”；提交后等待社区审核（预计 5 个工作日）；审核通过后，系统自动发放对应 GPU 时长奖励。审核维度原创度：文章内容是否为原创技术相关性：是否与 AMD GPU / ROCm 相关完整性：代码、数据、结果是否完整内容建议ROCm 安装与配置经验AMD GPU 大模型部署手记Ryzen AI 开发初体验AMD GPU 推理优化技巧路径二：灵感流 Notebook 代码实践在魔搭社区「Gallery」板块发布基于 AMD GPU 的 Notebook 代码实践，每篇通过审核可获 50 小时 GPU 算力，本路径最高可获 150 小时。操作流程在魔搭社区「Gallery」板块发布基于 AMD GPU 的 Notebook 代码实践；发布时携带指定标签“AMD GPU激励计划”；提交后等待社区审核（预计 5 个工作日）；审核通过后，根据内容质量获得相应 GPU 时长奖励。审核维度自动化运行测试：全部 Cell 执行通过，无报错代码注释完整性：关键步骤有中文/英文注释环境配置说明：requirements.txt 或安装指令完整技术相关性：与 AMD GPU / ROCm 相关内容建议Stable Diffusion 部署LLM 微调或部署搭建 Skill Agent推理优化实践Agent 构建路径三：创空间应用部署在魔搭社区「创空间」部署基于 AMD GPU 的应用，审核通过后可按需获得 700 小时或更多 GPU 算力。这是获取最大资源量的路径。前置条件已完成 AMD 开发者计划注册已加入魔搭社区“AMD开发者中心”组织优先分配：已发布至少 1 篇通过审核的 AMD 技术文章或 1 个高质量 Notebook操作流程进入「AMD 专属创空间 GPU 资源组织」页面：https://www.modelscope.cn/organization/AMD_Dev点击申请加入，并在申请理由中附上满足条件的证明材料：必选：AMD 开发者账号 ID可选：其他符合要求的 Notebook / 创空间作品链接审核通过后即可成为组织成员，后续在「创空间」创建或配置应用时可选择 AMD 专属 GPU 资源；选择 AMD MI308X 及对应镜像部署创空间应用。如何获取 AMD 开发者账号 ID：可在 AMD 开发者计划 网站 → 个人资料处获取。创空间开发示例：创空间审核机制创空间应用审核分为三个阶段：第一阶段：机审（自动）安全扫描：无恶意代码、无敏感信息泄露、无外部异常调用基础功能检测：应用可启动、核心页面可访问资源合规：GPU 显存使用在合理范围内第二阶段：功能评审（人工，3–5 个工作日）功能完整性：核心功能可正常使用用户体验：有清晰的使用说明，交互合理AMD 技术相关性：使用 AMD GPU 或 ROCm 相关技术创新性：独特功能或创新应用场景第三阶段：综合评定（人工，3–5 个工作日）代码质量审查与 AMD 品牌调性一致最终评定是否通过审核评审权重评审维度权重安全性25%功能完整性25%AMD 技术相关性20%用户体验15%创新性15%四、常见问题 (FAQ)Q1：AMD GPU 资源在使用上有哪些限制？每人最高并发创空间数量为 1 个；每人最高并发执行 Notebook 数量为 1 个。Q2：申请加入专属组织被拒绝，通常是什么原因？常见原因包括：提交的证明材料链接失效、未完成 AMD 官网注册等。请确认材料完整后重新提交。Q3：三种参与方式必须按顺序完成吗？不需要。三条路径完全独立，可任选其一、其二或全部参与，奖励可累计叠加。唯一的前提是必须先完成 AMD 开发者计划注册。Q4：可以只做路径三（创空间）吗？可以。只要完成注册（100h）+ 加入 AMD 组织 + 发布应用并通过审核，即可获得 700h 或更多。但在资源有限的情况下，已发布优质研习社文章或灵感流 Notebook 的开发者将优先获得分配。Q5：创空间“按需分配”是什么意思？根据应用质量评定额度，由 AMD 团队终审决定具体分配数量。五、开发者激励计划群本方案为 AMD 与魔搭社区联合推广计划，最终解释权归 AMD 团队所有。 💡获取小提示1️⃣ 研习社发文/灵感流 Notebook 发布时，请携带指定标签“AMD GPU激励计划”2️⃣ 申请加入「AMD开发者中心」组织时，需先注册AMD开发者计划 + AMD 开发者账号 ID（高频漏填！）第三批次｜截止 6.12 日前过审作品的GPU时长奖励清单魔搭用户名获取路径+GPU时长作品链接本次合计新增时长applebear📝研习社发文+25小时🔗学习笔记：在 AMD 云环境上微调 Gemma 模型做情绪分类+25小时ashanm📝研习社发文+25小时🔗ROCm 安装与配置经验+50小时📝研习社发文+25小时🔗AMD GPU 推理优化技巧jiushy📝研习社发文+25小时🔗MI300X从编译到部署的完整实践的踩坑+25小时Joshua0616💻 灵感流 Notebook 代码实践 +50小时🔗AMD MI300 上 Qwen3-8B 全量 SFT 微调：从零到可运行+100小时💻 灵感流 Notebook 代码实践 +50小时🔗AMD MI300 上 GRPO 强化训练：从零实现可验证奖励的 RLVR Pipelinelangzhenxin💻 灵感流 Notebook 代码实践 +50小时🔗SGLang on AMD MI300X / ROCm: Source Install Guide+100小时 📝研习社发文+25小时🔗从卡住到跑通：ModelScope AMD GPU 大模型部署与推理优化实践📝研习社发文+25小时🔗在 ModelScope MI300X / ROCm Notebook 上源码安装 SGLang，并跑通 LLaDA2.1-minilizhicheng123💻 灵感流 Notebook 代码实践+ 50小时🔗AMD ROCm 推理运维 Skill Agent 实战+200小时💻 灵感流 Notebook 代码实践+ 50小时🔗AMD GPU 多 Agent 推理调度器实战💻 灵感流 Notebook 代码实践+ 50小时🔗AMD ROCm 上的 LLM 推理优化实践📝研习社发文+ 25小时🔗AMD MI300X 上的 MTP 值得开启吗？一次 Qwen3.6-35B-A3B Q8_0 推理实测📝研习社发文+ 25小时🔗从 ROCm 安装到 90 tokens/s：AMD MI300X 大模型部署手记lpcarl💻 灵感流 Notebook 代码实践+ 50小时🔗AMD GPU加速语音识别伴奏人声分离+75小时📝研习社发文+ 25小时🔗【AMD GPU加速】VoiceToSRT - 视频字幕生成工具luda1993💻 灵感流 Notebook 代码实践+ 50小时🔗AMD GPU 实战：ROCm + ComfyUI 部署与 Stable Diffusion 测试+ 50小时Michael2035📝研习社发文 + 25小时🔗在 AMD GPU (ROCm) 上做训练时三值化 QAT：实战复现手记（结果持续更新）+25小时simaxiaojian📝研习社发文+ 25小时🔗AMD GPU跑大模型，踩完所有坑后我帮你整理了这份实战手册+25小时syzyyp📝研习社发文+ 25小时🔗AMD GPU 服务器安装使用 Logics-Parsing-v2 教程+75小时 💻 灵感流 Notebook 代码实践+ 50小时HunyuanOCR_AMD_ROCm_1B_OCRwachoa📝研习社发文+ 25小时🔗AMD GPU 大模型部署：在魔搭 Notebook 上跑通 LLaMA-3 推理全流程+ 25小时yu894890489💻 灵感流 Notebook 代码实践+ 50小时🔗AMD MI308X (ROCm) 上用 vLLM 部署 Qwen2.5-7B-Instruct：从环境校验到吞吐压测+ 50小时zhifu3158📝研习社发文+ 25小时🔗【纯技术干货】从N卡到A卡的“亡命天涯”：ComfyUI 100% 无损迁移与 AMD MI300X 榨汁指南+25小时zhifu31588💻 灵感流 Notebook 代码实践+ 50小时🔗【AMD 激励计划】从 N 卡到 MI300X 的“亡命天涯”：ComfyUI 无损迁移与极限榨汁全指南+50小时第二批次｜截止 6.5 日前过审作品的GPU时长奖励清单魔搭用户名获取路径+GPU时长作品链接本次合计新增时长doudou0510📝研习社发文+25 小时🔗AMD GPU 实战：llama.cpp 运行Qwen3.6-27B-Q8_0.gguf的 MTP 性能深度测评+100 小时💻 灵感流 Notebook 代码实践+50小时🔗AMD GPU 实战：llama.cpp 运行Qwen3.6-27B-Q8_0.gguf的 MTP 性能深度测评📝研习社发文+25 小时🔗AMD GPU 实战：llama.cpp 运行Qwen3.6-35B-A3B-Q8_0.gguf的 MTP 性能深度测评ericminijarvis📝研习社发文+25 小时🔗AMD MI300X 上使用 vLLM 部署 Qwen3.6-35B-A3B 全记录：从踩坑到成功+50小时📝研习社发文+25 小时🔗CrewAI 多 Agent 协作实战 — 对接本地 vLLM 学习总结govm114💻 灵感流 Notebook 代码实践 +50小时🔗Anima LoRA