CS

CS2Mulch – Physical Manipulative for Data Structures

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CS2Mulch – Physical Manipulative for Data Structures

Hey all, I teach CS at a SLAC in Arkansas. My students were regularly struggling to put the abstract concepts from data structures lectures into code, so a few years ago I created these decks of cards and chits to help them out. Now we can shuffle the decks and deal out cards to quickly create new individual examples for each structure; being able to physically manipulate the cards to carry out the algorithms starts to make everything click for them and is an easy way to catch misunderstandings early. Students can also use Tabletopia or print their own cards, everything is creative commons licenced. My favorites are cuckoo hashing and red-black trees, and the repo is open for pull requests for adding more, thanks for checking it out!

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, physical, code · Missing: mac, agents, macos
75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released
51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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