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Emil Wallner
@EmilWallner
internet-educated. into machine learning and colorization. maker of palette.fm (). former resident at .
🇫🇷🇸🇪emilwallner.comJoined July 2010

Emil Wallner’s Tweets

every tech documentation should have a button to copy the entire doc so you can paste it into a chat engine and ask questions and generate templates
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feels like siri should pivot to a voice-based app store. unbundling the voice parser, LLM backends, domain logic, and APIs. apple does hardware and systems really well but seldom end-user experiences.
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if someone works in AI and sends you an email, it’s often more honest and concise to reply with a prompt: “a wholesome and personal decline to [email content]” via text-davinci-003
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yesterday was my last day at google. i’ve had a wonderful time and made so many new friends 🫶🙏 today is my first day as a full-time indie ML researcher and indie hacker
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Resume screening is pretty much a black box for most candidates. Few hiring managers have publicly discussed this. Whether you're interested in our startup, I hope my perspective can shed some light on what is happening on the other side of the table.
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student: write a 3-page review of the lord of the flies in the style of a B+ senior high school student teacher: analyze the below review and extract the key points, write a paragraph of feedback on its format and originality, and score it from Fail to A+
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I'm thinking of using this for my production ML serving. Any feedback or suggestions? - AWS Loadbalancer + HTTPS - Sagemaker Endpoint - Uvicorn - FastAPI - TritonClient Py - Triton Server (on Nvidia A10G) - Nvidia Dali (pre/post-process) - TensorRT via ONNX
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i’m still surprised that the creative AI scene went from a few thousand to millions over a few months. 2022 started out as a house party and grew to woodstock
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i just wanna see if people click on upload. mixpanel: …so you'll see at least an extra $7,237 on your next bill. 👀
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has anyone had any luck with TensorRT and PyTorch? seems like most tend to compile via ONNX but it leads to numerical instability and it has marginal improvement compared to TorchScript. plus, it adds a lot of dev and infra complexity
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some interesting parts from the stratechery midjourney pod: ⁃ MJ uses more than 10k GPUs - 90% for inference - david: the GPU cloud need to scale with 1000x ⁃ cloud providers regularly reach max GPU capacity ⁃ v4 took 9 months ⁃ 60 sec is a sweet spot for lat/quality
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After 2 years, Practical Deep Learning for Coders v5 is finally ready! 🎊 This is a from-scratch rewrite of our most popular course. It has a focus on interactive explorations, & covers , , DeBERTa, ConvNeXt, & other goodies 🧵
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