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Is Learning DSA Boring? Let's Use DSA View View ๐Ÿ‘€๐Ÿ‘€ (Two Sum, Binary Search, and Bubble Sort)

A TypeScript visualizer helps learners explore common DSA algorithms.

Steady

34

signal score

as of 5d ago

๐Ÿ”— 3

sources ยท typescript

โ†’ Steady

7-day est. ~38

Why this scored 34

every term, weighted
Velocity+0.0 / 40 max

Engagement gained per hour since the last capture, against the fastest item on its own source

Acceleration+11.3 / 25 max

Whether that velocity is itself speeding up, as a per-hour rate

Cross-source spread+16.7 / 25 max

How many independent communities are talking about the same entity

Recency+7.9 / 10 max

Decays to zero over 14 days

Saturation penaltyโˆ’2.0 / 30 max

Subtracted once something is big and old โ€” we rank what's next, not what's peaked

Composite33.8

Weights are hand-tuned, not learned โ€” we're calibrating them against realized trends as history accumulates. On a topic's first sighting there's no previous reading to compare against, so acceleration starts from a neutral prior rather than a measurement, and velocity falls back to engagement over its whole lifetime until a second reading exists. Full methodology

Outlook

low confidence ยท estimate, not a guarantee

7-day

~38

range 1โ€“50

14-day

~39

range 0โ€“53

30-day

~39

range 0โ€“58

Signal history

7-day window (free)

โ€” โ€” projected trajectory (estimate, not a guarantee)

Entities

typescript
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Drop this in a README or blog post โ€” it updates automatically as the score moves.