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  • Data Quality: The Overlooked Bottleneck in Enterprise AI

    By

    @svenphilipsen

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    Everyone's asking which AI model to deploy. Almost nobody's asking whether their data is good enough for any model to actually work with. Data quality is the unsexy bottleneck for AI in the enterprise. The hype skips it. The results don't. #digitalization #AI

    → View original post on X — @svenphilipsen, 2026-03-05 06:00 UTC

  • AI Agents Observability: Monitoring Decisions in Production

    By

    @dominodatalab

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    AI agents make thousands of decisions. Observability means knowing what happened in every single one. Domino captures every trace automatically so teams can compare runs, diagnose failures fast, and keep agents performing in production. See it in action: https://
    domino.buzz/46vq6Db

    → View original post on X — @dominodatalab

  • Perplexity’s Final Pass for document analysis
    Perplexity’s Final Pass for document analysis

    By

    @testingcatalog

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    Perplexity is working on a new feature called "Final Pass," designed to perform comprehensive document analysis and fact-checking.

    → View original post on X — @testingcatalog

  • SAS Hackathon Boot Camp: Data Analysis and Real-World Scenarios

    By

    @sassoftware

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    Headed to Texas for #SASInnovate? Roll up your sleeves, fire up your laptop and dig into the data at the #SASHackathon Boot Camp. Happening on Day 1, get a taste of SAS' annual hackathon with this opportunity to analyze data that mirrors real-world scenarios and collab with other

    → View original post on X — @sassoftware

  • Modern Time Series Analysis with R: ARIMA, Tidyverse, and Causal Inference

    By

    @avikumart_

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    I’ve recently been diving into Modern Time Series Analysis with R, and it has been a game-changer for how I approach sequential data. As someone deeply invested in the intersection of Machine Learning and Data Science, seeing a structured bridge between traditional statistical theory and modern computational techniques is incredibly refreshing. Why this book stands out for me: 1. The "Arima" of it all: Understanding the Logic What I found most fascinating was the deep dive into ARIMA (AutoRegressive Integrated Moving Average) models. While we often jump straight to LSTMs or Transformers in deep learning, this book reinforces why ARIMA remains a powerhouse for business applications. The way it breaks down: Autoregression (p): Using past values to predict the future. Integration (d): Differencing data to achieve stationarity—a crucial step I’ve applied in my own research. Moving Average (q): Modeling the error term as a linear combination of past errors. Understanding these components isn't just about math; it’s about understanding the "memory" of the data. 2. Structured Learning with R & Tidyverse The book doesn’t just throw code at you; it teaches a structured workflow. Using the tidyverse and specific time-series wrappers makes data wrangling—which is usually 80% of the work—feel intuitive. From handling hierarchical models to automating reproducible reports in RStudio, the focus is on building a pipeline that is production-ready. 3. Beyond Simple Forecasting It was eye-opening to see time series applied to Causal Inference and Change Point Analysis. In complex domains like healthcare or finance, knowing when a structural change occurred is often more valuable than just predicting the next data point. If you are someone who's looking to deep dive into time series modelling using R, then this book is just right for you! Link: lnkd.in/gfRxu7zp

    → View original post on X — @avikumart_, 2026-03-03 21:19 UTC

  • Alteryx Inspire Conference Preview with ACE Cory Hubbard

    By

    @alteryx

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    The countdown to Inspire is ON! Get a preview with Cory Hubbard, Alteryx ACE, as he shares why Inspire is a can’t-miss experience for our data community. https://
    ow.ly/GQ1v50XSAYq #AlteryxInspire

    → View original post on X — @alteryx

  • Pocket Recording Device Privacy and Security Audit Reveals Major Concerns
    Pocket Recording Device Privacy and Security Audit Reveals Major Concerns

    By

    @abhi1thakur

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    Pocket (@Heypocket) sold 30,000 units in 5 months recording doctor visits, legal calls, and board meetings. So I did my due diligence like I always do. Their Google Play listing says "No data collected." Their own privacy policy lists audio recordings, transcripts, device IDs, ad identifiers, cookies, IP location, and behavioral inferences. One of those is wrong. They brand themselves "open source". Their GitHub org (github.com/heypocketai) contains exactly 4 repositories: – Raspberry Pi Zero prototype called icecream-project [from the CEO's prior Omi work], – FFmpeg Flutter fork, – docs repo, – org profile page. The actual Pocket device firmware, mobile app, backend infrastructure, and audio processing pipeline are NOT here. There is NO Pocket source code to audit. Recordings go to unnamed "cloud/AI vendors" with no disclosure of which LLMs process your audio, what jurisdiction they operate in, or how long they retain it. Users can't choose their provider or bring their own keys. If you cancel your $19.99/month subscription, your recordings get processed by whichever default model Pocket selects. The contact mic is marketed for recording phone calls without speakerphone. In 11+ US states including California (where Pocket is based) — that requires all party consent. The device has NO consent mechanism. Their terms cap liability at the amount you've paid or $100 (whichever is greater) – for data breaches involving your medical appointments, legal consultations, and proprietary meetings. A third‑party LobeHub skill has already reverse‑engineered Pocket’s web API and can pull recordings, transcripts, summaries, and action items using short‑lived Firebase bearer tokens extracted from the user’s browser session. Their privacy policy confirms that if Pocket is acquired — every recording, transcript, and summary transfers to the buyer. Y Combinator (@ycombinator) Pocket (@heypocket) is your notetaker for real world meetings. In the last 5 months, the team has delivered over 30k units with a $27M annualized run rate, growing 50% month over month. Congrats on the launch, @AkshayNarisetti and @gabrieldymowski! ycombinator.com/launches/PaX… — https://nitter.net/ycombinator/status/2028878066630709455#m

    → View original post on X — @abhi1thakur, 2026-03-03 20:26 UTC

  • US AI surveillance grows after Anthropic-Pentagon split
    US AI surveillance grows after Anthropic-Pentagon split

    By

    @scobleizer

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    Given the Anthropic vs. Pentagon divorce last week, we dig into AI surveillance in the United States. 

    @IrenaCronin and I write this newsletter every week.
     
    AI surveillance in the United States is rapidly scaling because AI makes it easy to search video, track locations

    → View original post on X — @scobleizer

  • Modern Time Series Forecasting with Python and Machine Learning
    Modern Time Series Forecasting with Python and Machine Learning

    By

    @kirkdborne

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    Modern #TimeSeries #Forecasting with #Python — Industry-ready #MachineLearning and #DeepLearning time series analysis with PyTorch and PANDAS: http://
    amzn.to/4eP5yYt v/ @PacktDataML 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼:
    Apply ML and global models to improve forecasting accuracy through

    → View original post on X — @kirkdborne

  • SASInnovate 2026: Data and AI Conference with Special Guests
    SASInnovate 2026: Data and AI Conference with Special Guests

    By

    @sassoftware

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    Now through March 11, enjoy a special rate on #SASInnovate 2026 registration! 3 packed days of leading discussions on data and AI, training and demos, networking and inspiring talks — including from special guest Mel Robbins who was just named to Time Magazine's 2026

    → View original post on X — @sassoftware