Lambda – The first-ever open-source privacy based social media app

Hacker News

Hi HN community! NOTE: This is a repost due to missing of links I’m excited to introduce Lambda, a project I've been working on. Lambda is the world's first open-source social media app designed to prioritize your privacy and well-being. It's still in its early stages, but I'm eager to share it with you and get your feedback. What is Lambda? Lambda is an open-source social media app that addresses privacy concerns and reduces social media addiction. Here's what makes Lambda unique: - Privacy First: Lambda does not collect personal information beyond your email address for account creation. Your data is yours alone. - Organic Feed: No AI-generated feeds or targeted advertisements. You only see posts from users you follow. - Open Source: Built-in public, with community feedback and contributions highly encouraged. What's Next? Currently, Lambda is a basic version with text-only posts. Here are some things I’m working on: - Adding support for images and videos - Reducing latency - Enhancing the overall user experience You can check out Lambda and create an account here[ https://lambda-official.vercel.app ]. Feel free to explore, and I’d love to hear your thoughts and suggestions here[ https://github.com/ezpie1/lambda-official/discussions/catego... ] GitHub Repository If you're interested in contributing or just curious about the code, you can find the repository here[ https://github.com/ezpie1/lambda-official ]. A star on GitHub would be... private... I am not a comedian. Feedback I’m around to discuss and answer any questions you might have, you can always jump over to GitHub discussion[ https://github.com/ezpie1/lambda-official/discussions/catego... ] and drop feedback there. Your feedback is extremely valuable, and I'm looking forward to improving Lambda with the help of the HN community. Thanks for taking the time to check out Lambda! Best regards, EzpieCo(not my real name)

Visit product View on Hacker NewsJun 27, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, email, code · Missing: mac, agents, macos

80%80% 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 · Missing: supports, reddit linkedin, podcasting

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, lua, open source · Missing: https docs, just released, exist

56%56% 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: personal, video, users · Missing: mobile apps, ios, entrepreneurs

40%40% 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

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: introduce · 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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