This should be a user setting. Finding an instance is already hard enough with all the politicking and bans between instances. It shouldn’t be made even harder by turning these into instance-level settings instead of user-level ones.
I think PieFed should allow disabling down-votes. I genuinely dislike seeing down-votes, especially on my own content. I behave very differently on platforms like Bluesky that only have like/repost, compared to Lemmy, where saying something unpopular results in down-votes that feel like a punch in the gut. In real life, if someone disagrees, they either tell you or disengage. On PieFed, down-votes go further, they actively suppress your voice so others don’t see it. That is effectively what down-votes accomplish, and I don’t think it leads to healthier discussion.
#!/bin/sh
# Select a file with fzf from a database sorted by frecency and open it using
# xdg-open. frece can be found at https://github.com/YodaEmbedding/frece
DB_FILE=${FRECE_FILES_DB:-$HOME/.cache/frecent-files.csv}
item=$(frece print "$DB_FILE" | fzf --tiebreak=index --scheme=path)
[ -z "$item" ] && exit 1
frece increment "$DB_FILE" "$item"
xdg-open "$item"
#!/bin/sh
# Update frece database
DB_FILE=${FRECE_FILES_DB:-$HOME/.cache/frecent-files.csv}
tmp_file=$(mktemp)
fd -H . ~ > "$tmp_file" # use ~/.fdignore file to exclude certain dirs
frece update "$DB_FILE" "$tmp_file" --purge-old
rm "$tmp_file"
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?
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 / Repo
Description / Strength
ADTK — Anomaly Detection Toolkit
A Python toolkit for unsupervised / rule-based time-series anomaly detection (seasonal, trend, threshold, rolling-/moving-average, etc.). (GitHub)
TODS — Time-series Outlier Detection System
A 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 detection
A 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 Detection
A 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 series
A 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.
This should be a user setting. Finding an instance is already hard enough with all the politicking and bans between instances. It shouldn’t be made even harder by turning these into instance-level settings instead of user-level ones.
I think PieFed should allow disabling down-votes. I genuinely dislike seeing down-votes, especially on my own content. I behave very differently on platforms like Bluesky that only have like/repost, compared to Lemmy, where saying something unpopular results in down-votes that feel like a punch in the gut. In real life, if someone disagrees, they either tell you or disengage. On PieFed, down-votes go further, they actively suppress your voice so others don’t see it. That is effectively what down-votes accomplish, and I don’t think it leads to healthier discussion.
https://lemmy.ml/comment/5593416
luddite
The government only released a modeled video and a book to prove their story why should anyone bother with something that's clearly fake.
First time I hear it outside r/conspiracy.
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.
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)
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