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Our Code Stories

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Our Code Stories

Our Code Stories ( https://ourcodestories.com ) is a site that holds animated, annotated code walk-throughs called 'playbacks'. These resemble recorded live code demos but one can usually get through a playback faster than a video by skipping only to the points where the author has something to say. These can be used to create code-oriented tutorials and programming books. Here is a 'book' I wrote about Clojure: https://ourcodestories.com/markm208/Playlist/4 The site can also be used to create professional programming portfolios. These animated playbacks are a great way to communicate how one thinks about problem solving and code to potential employers. A dev can add text, images, screenshots, and video comments to their code. Finally, the guided walk-throughs can be used to help dev teams prepare for a code review or get a new team member up to speed on a code base. Users can create an unlimited number of free public and private playbacks. They can also charge a fee to access a group of playbacks. For example, if someone wants to write a book about a programming topic they can use Our Code Stories instead of going through a traditional publisher. I am hoping to disrupt traditional programming book publishing with playbacks. Authors receive 75% of each sale. Playbacks are made by using the open-source software that I created called 'Storyteller' ( https://markm208.github.io/storyteller/index.html ). Storyteller is a plugin to the Visual Studio Code editor. Our Code Stories is a site that holds groups of Storyteller playbacks kind of like how GitHub holds Git repos. Follow us @ourcodestories. I am curious whether professional sw devs will find this medium useful. I welcome any feedback. I am also looking for brave people to use the tools to create and publish their own playbacks. I am willing to help early adopters with any issues they have (email me @ my profile email).

Visit productView on Hacker NewsAug 15, 2018
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Indie HackersFits the IH revenue-focused audience · Strong signals: created, ios · Missing: supports, reddit linkedin, podcasting
89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, email · Missing: mac, agents, macos
79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: code review, ide, io · Missing: https docs, excited, just released
68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: ios, video, users · Missing: mobile apps, personal, entrepreneurs
34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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