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An Agentic AI dataset for deepfake detection

Hacker News

An Agentic AI dataset for deepfake detection

Hi HN! Really excited for our first dataset release. We're co-founders at Mendit.ai, a small startup in the PNW, we focus on customized and explainable AI models. One thing we noticed as we were building our first product was the need for a curated dataset with diverse resolutions, lighting conditions and scenarios resembling mundane, everyday depictions of characters and objects. We tried a number of agentic ai frameworks and landed on our own lightweight implementation that first identifies the content properties that are relevant for the use case defined in the prompt and then randomly generates content based on those properties. An interesting takeaway from our experiments is that the latest LLMs that include reasoning in their response tend to generate noisier outputs than instruction tuned LLMs that generate the output without an in depth plan and explanation. The dataset can be found on huggingface at: https://huggingface.co/datasets/ninamoss/sleeetview_agentic_... Keep in mind this is only a first sample, we intend to add new content to the dataset based on community feedback on a regular basis. Looking forward to any feedback, pointers or insights!

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, agentic · Missing: mac, agents, macos
81%81% 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: ios · Missing: supports, reddit linkedin, podcasting
73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, way · Missing: mobile apps, personal, entrepreneurs
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, ide, io · Missing: https docs, just released, exist
37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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