Experience & Education
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Procella: Unifying serving and analytical data at YouTube
Proceedings of the VLDB Endowment
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Tenzing: A SQL Implementation On The MapReduce Framework
Proceedings of the VLDB Endowment
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Japanese
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Patrick B.
If you're at Anyscale's Ray Summit this week, three members of the Samsara team, Hao Yuan, Freeman Mak, and Eagle Lo, are sharing how we use Ray to run inference for physical AI. Their work is a major cornerstone of the amazing safety impact Samsara's technology brings to the world to the tune of 380,000 accidents prevented leading to about 4 lives saved per day over 100B miles last year. Check out their great work!
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Behind the CMO
Evan Spiegel is pulling resources out of Snap's engineering org and moving them to distribution. More than two-thirds of Snap's new code is written by AI now. A service that used to need a team takes "half a person's time." So when the CEO looks at where the advantage went, he doesn't point at the product. He points at distribution, and he's moving the budget to match. Read that as a CMO. A public-company CEO just made the case for marketing better than most marketing teams make it for themselves. The part that should keep you up: distribution is the new moat, and it's also getting eaten. Rented reach isn't a moat. It's a bill that climbs with demand. The defensible asset is the distribution a platform can't tax away from you. This week in Behind the CMO: - Why AI retires the product advantage, not the marketing one - The one question that separates an owned moat from a rented cost - How to take this into the 2026 budget conversation Read it free, more in the comments section.
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Julia Proskurnia
Weekly arXiv Scan: 3 Papers that caught my eye (and my Agent’s) My custom Gemini x OpenClaw agent scanned recent papers on arXiv this week in Audio, NLP, and GenAI. It filtered for novelty and engineering rigor. I filtered for practical application and "human" impact. Here are the winners that made the cut during the baby nap time :) 1. Scaling Open Discrete Audio Foundation Models (SODA) https://lnkd.in/eXYnjfpd The Agent's TL;DR: Introduces SODA, a 4B parameter native audio model trained on interleaved semantic, acoustic, and text tokens, establishing new scaling laws for non-cascaded speech tasks. My Take: Human conversation relies on tone, pauses, and emotion. Those are the things that get stripped away when we convert Speech-to-Text for an LLM. By processing audio tokens natively, we aren't just processing data; we are preserving the humanity of the interaction. Native audio-in architecture is what we need for empathetic AI. 2. Reverso: Efficient Time Series Foundation Models https://lnkd.in/eyAgsN_h The Agent's TL;DR: Proposes a hybrid architecture interleaving linear RNNs and long convolutions that matches Transformer zero-shot forecasting performance while being 100x smaller in parameter count. My Take: Time-series analysis and prediction might be the problem where the sledgehammer solution is an overkill. A model that is 100x smaller is a model one can actually use for the personal gains (like some extra signals for investments? maybe?). It’s faster to train, cheaper to serve, and easier to debug. 3. A Generative-First Neural Audio Autoencoder https://lnkd.in/eeFtDpkB The Agent's TL;DR: Proposes an architecture with a massive 3360x temporal downsampling factor, allowing 60 seconds of audio to be represented by fewer than 800 tokens for extremely fast generation. My Take: High fidelity is great, but latency is the user experience killer. Compressing a minute of audio into <800 tokens is a massive engineering win. This reduces the context burden on the LLM significantly, meaning faster, cheaper, and more responsive voice agents. This is a great step towards the feeling of a snappy real-time conversations. The trend this week is “Efficiency over Excess”, finding architectures that do the same work with a fraction of the compute. #NLP #AudioAI #MachineLearning #HumanInTheLoop #arXivScan
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