请问HN:早期阶段的人工智能加速器有哪些有用之处(又有哪些不那么有用的地方)?

1作者: rdi_berkeley3 个月前原帖
嗨,HN——我们最近推出了伯克利加速器(Berkeley Xcelerator,网址:https://rdi.berkeley.edu/xcelerator),这是一个由伯克利RDI(网址:https://rdi.berkeley.edu/)运营的非稀释性加速器项目,专为在人工智能和自主智能领域发展的种子前期和种子阶段团队而设。我们非常希望能获得社区的反馈! 在过去三年里,伯克利加速器支持了110多个团队,涵盖人工智能、网络安全和去中心化技术,这些团队的创始人们已经在100多个国家筹集了超过6.5亿美元的后续融资。 关于加速器的一些具体信息: - 该项目为非稀释性(不收取股权) - 面向种子前期和种子阶段的人工智能/自主智能初创企业 - 不需要与加州大学伯克利分校有任何关联 - 被选中的团队将通过伯克利RDI的研究社区和生态系统合作伙伴获得支持 - 支持内容包括来自行业合作伙伴(包括谷歌云、谷歌深度学习、OpenAI和Nebius等,更多合作伙伴将陆续公布)的云计算、GPU和API信用 - 该项目的高潮将是在2026年8月1日至2日于加州大学伯克利分校举行的自主智能峰会(Agentic AI Summit)上的展示日,我们预计将有超过5000名现场参与者 我们特别希望获得以下方面的反馈: - 如果您曾创建或加入过早期的人工智能初创企业,最初对您帮助最大的是什么? - 如果您参加过加速器,哪些方面对您有帮助,哪些又是浪费时间? - 对于技术深度项目(基础设施、自主系统、安全敏感工作),在产品市场契合之前,哪些反馈或结构最为重要? 如果您想申请伯克利加速器,申请将在2月底之前开放。(网址:https://forms.gle/KjHiLAHstAvfHdBf7)
查看原文
Hi HN — we recently launched the Berkeley Xcelerator (https:&#x2F;&#x2F;rdi.berkeley.edu&#x2F;xcelerator), a non-dilutive accelerator program run by Berkeley RDI (https:&#x2F;&#x2F;rdi.berkeley.edu&#x2F;) for pre-seed and seed-stage teams building in AI and agentic AI. We’d love to get some feedback from the community!<p>Over the past three years, Berkeley Xcelerator has supported 110+ teams across AI, cybersecurity, and decentralized technologies, whose founders have gone on to raise $650M+ in follow-on funding, spanning 100+ countries.<p>Some concrete details about the Xcelerator itself:<p>- The program is non-dilutive (no equity taken)<p>- Open to pre-seed and seed-stage AI &#x2F; agentic AI startups<p>- No UC Berkeley affiliation required<p>- Selected teams receive support through Berkeley RDI’s research community and ecosystem partners<p>- Enablement includes cloud, GPU, and API credits from industry partners (including Google Cloud, Google DeepMind, OpenAI, and Nebius, with more to be announced)<p>- The program culminates in a Demo Day at the Agentic AI Summit (Aug 1–2, 2026) at UC Berkeley, where we are expecting 5,000+ in-person attendees<p>Here’s what we’d really like input on:<p>- If you’ve built or joined an early AI startup, what actually helped you most early on?<p>- If you’ve done an accelerator, what helped and what was a waste of time?<p>- For technically deep projects (infra, agentic systems, safety-sensitive work), what kinds of feedback or structure mattered most before product-market fit?<p>If you’d like to apply to the Berkeley Xcelerator, applications are open through the end of February. (https:&#x2F;&#x2F;forms.gle&#x2F;KjHiLAHstAvfHdBf7)