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Hide your tweets in plain sight – Twitter steganography

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Hide your tweets in plain sight – Twitter steganography

We built a simple Twitter client that can post and read steganographic tweets at www.seecret.io using the plaintext steganography library from https://github.com/simpledynamics/seecret One big issue is that Tweets must be 140 characters but the steganography technique expands the message considerably (by a factor of 4 to 5). Our first thought was to just limit the tweets to very short messages. But that's hardly useful. (This whole thing is not really useful but whatever). We decided to chain the message across multiple tweets, and re-stitch them back together when viewing the timeline via the Seecret app. W‍‍‌‌​​​‌‍‌​‌‌​​‌‍‌‌​‌‌‌‌‍‌‌‌​‌​‌‍‌​​​​​‍‌‌​​‌‌‌‍‌‌​‌‌‌‌‍‌‌‌​‌​​‍‌​​​​​‍‌‌​‌​​‌‍‌‌‌​‌​​‍‌​​​​‌‍‍‍e then considered just making an app for chaining regular tweets this way but really the core of the Twitter appeal is the microblogging aspect. We played with it and found that Really Long Tweets just don't work. Kudos to Twitter for holding that line on that. They get a lot of criticism over that but it's really a core part of their value. (On the other hand, they recently expanded direct messages to be 10,000 characters which was a great move on their part. There's just no reason to limit the direct messages to 140 characters.) The main thing to note here is that this is NOT encrypted. We get a lot of first reactions from lay-persons where they assume that it could be used for nefarious activity. Anyone can view the hidden messages via the Seecret client app. The messages are hidden in other clients (phone and web views of your tweets) but still extractible if you know they are there. Tell us what you think!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
81%81% 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 HuntUnlikely to reach the leaderboard · Strong signals: activity, using, plain · Missing: mac, agents, macos
47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, 000, io · Missing: https docs, excited, just released
42%42% 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: way · Missing: mobile apps, ios, personal
34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% 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.

Correct prediction on native model

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