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Platle – The Better ChatGPT

Hacker News

Platle – The Better ChatGPT

Hey there HN! I'm Ekomobong, and today I'm launching Platle, an innovative autonomous learning platform designed to empower students to reach their full potential! With features that encourage collaboration, research, and meaningful discussions. I noticed the general trend in the way LLM apps were built, with the terrible and lazy designs, some of which include ChatGPT, Claude.ai, and so on. Most of the focus was placed on improving the underlying models, and little to no attention given to user experience. Anytime I stare at the design of ChatGPT and Claude.ai, I genuinely want to puke. I also noticed how I couldn't collaborate with actual humans while researching. Oh, I know why—because the whole messaging from day one has been about surpassing humans: "Oh, this is better than a PhD-level student." Actual customers don't care about that, lol. I also believe that the goal of AI should be to enable people to reach their full potential; having a goal that speaks to the effect of surpassing people is just lazy. Tired of all this, I set out to build Platle. Platle enables you to research and chat simultaneously and in real time. Sharing research on Platle does not create a 'dumb' duplicate; rather, it's more of an invite for someone so you can collaborate with each other in real-time. You don't chat with an LLM; you research with an LLM and then chat with other humans. Spoiler alert: the LLM is not a living thing; it's inanimate. With Platle: [1] You can do a 'live search' if you are not satisfied with a response. This brings relevant and latest news on the subject, if the LLM is not aware of it yet. [2] You can get a solid visual diagram for any response you don't understand from the LLM. You can also share that diagram. [3] You can export responses from the LLM in any of the following formats: .md, .png, .tex, .docx, .pdf, .html. [4] You can research YouTube videos and websites by actually entering the links into a provided input like a normal person. ChatGPT really sucks at this for a company that is worth $150B. [5] You can also research those really long and boring lecture videos you watch all the time, whether it's from youtube or your local device. Yeah, ChatGPT also really sucks at that - $150B???????? [6] Platle also has a LaTeX editor, which of all these AI platforms seem not to ship with. You can give it a spin at https://platle.com . I'd love the feedback! :)

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, apps · Missing: mac, agents, macos
94%94% 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: latex · Missing: supports, reddit linkedin, podcasting
94%94% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews
62%62% 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, io · Missing: https docs, excited, just released
40%40% 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: apps, video, way · Missing: mobile apps, ios, personal
40%40% 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
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat, real time, collaborate · Missing: web3, crypto, cryptocurrency
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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