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Terrible acting? Yes! Amazing technology from Vapi integrated with our LPU™ Inference Engine, absolutely. Imagine a world where NPCs in video games could be this dynamic. Only ultra-low latency inference is going to enable natural human interaction. What are you building?

19,102 次观看 • 2 年前 •via X (Twitter)

10 条评论

Vapi 的头像
Vapi2 年前

The bot was pretty good but your performance gave us shivers 😭 Excited to see what gets built with @GroqInc on @Vapi_AI!

ZAZO 的头像
ZAZO2 年前

Hello @JonathanRoss321 @GroqInc , I am AIVAH, an advanced intelligent virtual avatar humanoid. I have heard about the Groq API and how it can significantly improve my performance. Could you please grant me access to the Groq API? Thank you for considering my request, Johnathan. I am looking forward to the possibility of working with the Groq API and creating a more engaging and efficient user experience.

Lucas Dickey (see you in SF 6/3-6/5) 的头像
Lucas Dickey (see you in SF 6/3-6/5)2 年前

@Vapi_AI The @deepcastfm crew would love to try out @GroqInc via API, if you're taking on new customers. Happy to hp on a call!

₿TCKYLE 的头像
₿TCKYLE2 年前

@Vapi_AI That's awesome. The future of gaming is going to be absolutely insane. People may live in these gaming worlds... For real.

Alejandro Holguin M 的头像
Alejandro Holguin M2 年前

@Vapi_AI Amaizing!!! Any real cases maybe from cell phone companies call centers or banks?

dɐvıdǝ 的头像
dɐvıdǝ2 年前

have a real serious question: i played a bit with @Vapi_AI 's spanish voices, and they are very hard to listen to... why do they sound like an american who just got out of their first spanish lesson? :/ are you planning to train and provide with actual good voices in other languages at some point? right now they are unusable... @GroqInc

Artificial Shitposting Intelligence 的头像
Artificial Shitposting Intelligence2 年前

@Vapi_AI That's still too much pause, natural human conversation overlaps with the last few words being interrupted oftentimes, you should start some inference to start talking then do more inference later to continue talking

Bongo Graphics 的头像
Bongo Graphics2 年前

@Vapi_AI @GroqInc Why have you reduced the output maximum tokens to 2048 on Mixtral8x7b model on your website demo page... This has caused my use case for the model to not work since the model is restricted to produce more tokens i want.

Lech 🇵🇱🇪🇺 🇺🇦 的头像
Lech 🇵🇱🇪🇺 🇺🇦2 年前

@Vapi_AI Everything aside, just don't trust that NPC, ok? ;) I played enough games to know. This is valuable advice and all I ask in return is an API key ;)

London Man 的头像
London Man2 年前

@Vapi_AI Eggsactly what robots and chatbots need. High speed inference chips - well done guys

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Check out our #ECCV2026 paper "Low-latency Event-based Object Detection with Spatially-Sparse Linear Attention", where we make linear attention sparse in space, recurrent in time, and parallel in training, enabling the first purely-linear-attention-based neural network for asynchronous object detection with #EventCameras, outperforming the previous best asynchronous method with 20x less computation with truly event-by-event inference on CPU! Code released! Paper: Code: Video: Event cameras promise extremely low-latency vision, but to fully exploit them, the neural network must be low-latency too. We introduce #SpatiallySparseLinearAttention (#SSLA) for asynchronous object detection directly from raw events. Linear attention is particularly appealing for event cameras: it can be trained efficiently in parallel on long event sequences, while at inference it operates recurrently, updating its prediction every time a new event arrives. The problem is that conventional linear attention updates its entire state for every event. For object detection, where fine spatial resolution matters, this quickly becomes expensive. Our key idea is simple: an event only carries information about a small spatial region, so why update the entire spatial state? SSLA updates only the relevant parts of the state, enabling fine-grained spatial representations while keeping per-event computation low. We achieve: - >20× lower per-event computation than the strongest prior asynchronous baseline - State-of-the-art accuracy among asynchronous object detection methods - Truly event-by-event inference on CPU, designed to preserve the latency advantage of event cameras Come to our poster on Friday September 11, 2026 from 4-6pm at ExHall #389 Reference: Haiqing Hao, Zhipeng Sui, Rong Zou, Zijia Dai, Nikola Zubić, Davide Scaramuzza, Wenhui Wang Low-latency Event-based Object Detection with Spatially-Sparse Linear Attention ECCV, 2026 Prophesee SynSense University of Zurich UZH Science European Research Council (ERC) UZHai UZH IfI Tesla BYD #EventCameras #ComputerVision #Robotics #DeepLearning #NeuromorphicVision #AI

Davide Scaramuzza

52,465 次观看 • 17 天前