Thriving 
TS 
RS Desktop annotation tool for medical images: segmentation, keypoints, registration, gigapixel slides, volumes and multi-annotator review. Runs locally, one file per project.
https://didascalie.readthedocs.io/en/latest/ ↗// readme
A desktop tool for annotating medical images, running entirely on your own machine.
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Didascalie annotates medical images with segmentation masks, vector shapes, classification labels and keypoints. Drawing, image processing and model training all run on your computer. Nothing is uploaded.
It started as a personal project, out of frustration with the tools I knew. Many researchers and clinicians still annotate images in Paint or PowerPoint, and the tools built for the task often assume skills they don’t have: asking a clinician to pip install a package and run a script is less trivial than it sounds. Didascalie is an installer and a project file.
The core annotation workflow is usable day to day and has been used in published research. The project is under active development, and the features marked experimental are less reliable.
A didascalie (di-da-ska-LEE) is the French word for a stage direction, one of the notes in a play script. Annotations are notes added to data.
How much a project earns versus how much attention it actually gets. Above 50 means the work is outrunning its audience. Recomputed nightly from commit velocity, contributor effort, issue resolution, fork utility, release cadence, and project maturity — divided by a logarithmic reach factor.
score = signal / reach
signal = 0.25·commit_velocity // commits in last 90 days (cap 30)
+ 0.20·contributor_work // unique authors × velocity (cap 100)
+ 0.20·issue_resolution // closed ÷ total issues
+ 0.20·fork_ratio // forks ÷ stars (proxy for real usage)
+ 0.10·release_cadence // releases in 90 days (cap 3)
+ age_bonus // +0 to +0.30 after 6 months
+ homepage_bonus // +0.05 if homepage is set
reach = log₁₀(stars + watchers + 10)█ Is the project alive and maintained right now? A 0–100 pulse recomputed nightly from commit recency, rhythm, how fast issues close, and how quickly PRs get merged.
health = 0.35·recency // days since last commit (90d decay)
+ 0.25·cadence // commit rhythm consistency
+ 0.20·issue_health // closed ÷ total issues
+ 0.20·pr_health // merged ÷ total PRs█ The colour and label on every card come straight from the health score.
Healthy 80 – 100 active, responsive, regular releases
Stable 60 – 79 maintained, steady, no alarms
Quiet 40 – 59 slowing down — watch this one
At Risk 0 – 39 going dark · candidate for rescue█ Tags are independent behavioral signals computed nightly. A project can hold multiple at once. They drive the home page sections.
solo_builder one person holds > 80% of commits (last 180d)
needs_contributors has open "help wanted" or "good first issue" labels
hidden_gem < 100 stars · active in last 3 months · documented
legacy_hero repo > 5 years old · committed this year
fork_magnet forks/stars > 0.5 · used as template or dependency
release_machine 5+ releases in the last 90 days
under_pressure > 10 open issues · ≤ 2 contributors · health ≥ 60
community_watch watchers > stars · devs tracking before the public
community_hub GitHub Discussions enabled · > 20 discussions
funded maintainer has active funding channel█ Stars are an outcome, not effort. A project with 8 stars and daily commits is doing more interesting work than one coasting on 8k. We measure the building, then divide by the attention already received — so the genuinely undervalued rise to the top.
// stars = lagging indicator
// commits = leading indicator
// we rank by the leading one█