CiteTrue
The go-to citation checker and verification tool for academia.
AI & Machine Learning·Productivity·Education
About
CiteTrue is an AI-powered citation verification tool designed to help researchers, students, editors, and professors ensure their references are authentic and accurate. By searching through vast authoritative academic databases, the platform verifies citations in seconds and flags any references that appear to be fake, mismatched, or AI-generated, helping users stay clear of retraction risks and misconduct suspicions.
Built for undergraduate, master's, and PhD students, as well as academic reviewers and journal editors, CiteTrue offers features such as a citation checker for batch reference lists, a citation finder to discover real peer-reviewed sources for text claims, a paper draft generator, and an AI humanizer. It allows users to cross-reference academic materials instantly to uphold academic rigor and streamline the review process.
What sets CiteTrue apart is its comprehensive verification system that cross-references user inputs with leading academic databases to catch bogus or fabricated references that are difficult to spot by hand. Trusted by over 30,000 scholars and used across hundreds of universities, it provides an essential safeguard against unreliable sources and AI hallucinations in modern academic writing.
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FounderPlaybooks.
What other founders did to grow.
2722 dispatches from hundreds of founders, pulled from the week's best podcasts.
as just you get more and more content being published it gets harder to stand out I mean that's just like a pretty fundamental premise I mean I don't think anybody would disagree with that right.
Distribution has always been the bottleneck — easier building makes it harder to stand out, not easier
Seufert's core argument: every historical wave of easier publishing (blogging platforms, Unity for games, the App Store itself) has made discovery harder proportionally. AI-assisted vibe coding is inflationary for the app ecosystem — more supply competing for the same user attention, meaning distribution becomes even more valuable, not less. The winners are the ones who solve distribution, regardless of how the code gets written.
In the US on Android, anywhere from $200 to $300 a day. On iOS it's got to be more significant — at a minimum about $500 a day to see if you can get enough traffic and then actually have something to do with that traffic to be able to make adjustments.
Minimum viable UAC budgets: $300/day Android, $500/day iOS — anything less gives unusable data
iOS campaigns cost significantly more than Android because CPMs and install costs are higher. These figures from someone who saw thousands of accounts at Google are the true floor for getting the algorithm enough signal to optimize — not arbitrary recommendations. Below these thresholds the algorithm runs in permanent learning mode.
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