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2 yr. ago

  • The government only released a modeled video and a book to prove their story why should anyone bother with something that's clearly fake.

  • Is it indie if the creators can be laid off? That's a head scratcher.

  • This seems like overcompensating for the child limit. Are they going to be like a yoyo, swinging from one extreme to the other until they find a balance, like all things should be?

  • Or maybe somewhere where you don't have to spend half of your salary paying rent? Like China.

  • Imperialist shill.

  • Asklemmy @lemmy.ml

    What's the worst you've heard from the Cristian church?

  • ShareGPT @reddthat.com

    Automatically Sync Most-Used Small Files to a USB on Plug-In (Manjaro Linux)

  • Maybe don't stick your nose where it isn't asked, you're not a mod here.

  • SherpaTTS — Text-to-Speech using Piper and Coqui models.

  • RadarWeather — Watch the weather without location tracking.

  • AnkiDroid — Android client for the popular Anki spaced repetition system.

  • Syncthing — Continuous file synchronization, self-hosted alternative to cloud.

  • Element X — Matrix-based secure group and 1:1 messaging with E2EE support.

  • FlorisBoard — Privacy-friendly, highly customizable open-source keyboard.

  • NewPipe — Lightweight YouTube front-end: background playback and downloads without Google Play services.

  • Dumb argument. That's just your opinion. You can't know what people would or wouldn't buy.

  • It would overwhelm the market but more choice would mean more purchases, but I guess not enough to bother.

  • Here are several open-source GitHub projects that implement time-series or outlier / anomaly detection — you can adapt them to detect “posts with likes >> expected trend” on a feed. I grouped them by suitability for your use (simple time-series, streaming, advanced / ML).


    ✅ Good GitHub projects for outlier detection in time series / counts (e.g. likes)

    Project / RepoDescription / Strength
    ADTK — Anomaly Detection ToolkitA Python toolkit for unsupervised / rule-based time-series anomaly detection (seasonal, trend, threshold, rolling-/moving-average, etc.). (GitHub)
    TODS — Time-series Outlier Detection SystemA full-stack automated ML system for outlier detection on multivariate (or univariate) time-series: includes preprocessing, feature extraction, detection algorithms, and pipeline automation. (GitHub)
    dtaianomaly — Python library for time-series anomaly detectionA newer library (2025) offering a broad range of built-in anomaly detectors, preprocessing and visualization tools — useful if you want a flexible, modern API. (arXiv)
    chic‑ts‑outlierdetect — Time Series Forecasting for Outlier DetectionA smaller repo that helps implement & compare candidate forecasting / anomaly-detection models for univariate time series — useful if you prefer forecasting + residual-based detection rather than simple thresholding. (GitHub)
    Outlier‑Detection (AdysTech) — Outlier detection in time seriesA more classical (R-inspired) approach doing time-series outlier detection; can be simpler to integrate if your use case is basic (e.g. count spikes). (GitHub)

    In addition — for a broader survey / catalogue rather than a single tool — awesome‑TS‑anomaly‑detection provides a curated list of many libraries, datasets, and resources; comes in handy if you want to explore multiple methods to find the one that works best. (GitHub)


    🔎 Which to pick for “post-likes outlier” detection and why

    • If you want quick, simple detection (e.g. flag posts with likes greatly above rolling/trend average), start with ADTK — its rolling/threshold/seasonal detectors match well to a time-series of “likes per post over time.”
    • If you anticipate more complex patterns (daily cycles, seasonal variation, bursts) or want an automated pipeline, TODS or dtaianomaly give more flexibility and power.
    • If you prefer forecast-based residual analysis (compute expected likes via forecasting, then detect residual spikes), chic-ts-outlierdetect is a good fit.
    • If you want tried-and-true classical statistical methods (less dependency, simpler code), Outlier-Detection (AdysTech) is a minimalist alternative.

  • The pyramids where built by slaves.

  • Right after 9/11, before they got the memo, how TV channels kept saying first responders had heard a bomb in the base of the building and later they all started saying it collapsed due to structural damage from the plane impact, and it just got accepted with no one bothering to question it. This was prior to the investigations so they couldn't possibly have known.

  • Cooked ham is healthy. I think people have truly believed the ads in that case.