I build a platform for building ML models, Seeking feedback

Hacker News

Hi everyone! I’m excited to share that I am launching the MVP of a platform designed to simplify machine learning model building. If you’ve ever found yourself spending more time on setup, coding, or infrastructure than on building and optimizing models, this platform might be just what you need. Here's what I have released in MVP: 1. Build, train, and compare multiple models in parallel with just a few clicks. 2. No Code, No Infrastructure Required. 3. Designed to be intuitive, regardless of your technical background. I am looking for feedback on: 1. Workflow Simplification: Does this feel like it would streamline your work? 2. Key Features: Any must-haves or missing elements you’d suggest? 3. Overall Usability: Does it match your expectations for ease of use and functionality? Try it Out: https://quasarminds.com/ I am eager to hear about your experiences, suggestions, and any challenges you face while using it! Your input will shape our next steps in making this platform as impactful as possible. Thank you for being part of this journey with us! You can also submit your feedback on `support@quasarminds.com`

Visit product View on Hacker NewsOct 30, 2024

Share card

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting

75%75% 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 HuntOn track for Day 1 leaderboard · Strong signals: mac, model, models · Missing: agents, macos, agent

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: platform, intuitive · Missing: plus, reviews, host

56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: excited, io · Missing: https docs, just released, exist

36%36% 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: para · Missing: mobile apps, ios, personal

31%31% 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

10%10% 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

Similar products

Efemarai – Visualizing and debugging ML models71%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Efemarai – Visualizing and debugging ML models

Hacker News8

Build predictive models with no prior ML experience85%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Build predictive models with no prior ML experience

Hacker News70

Panini AI – Serve ML/DL Models in Few Minutes47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Panini AI – Serve ML/DL Models in Few Minutes

Hacker News11

Bizooy25%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Reputation building platform

Indie Hackerscommitment-full-time

Feeedbackr68%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Build from feedback, not assumptions

Product Hunt+8

Basic ML Platform based on Pulumi, DVC, MLFlow and more50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Basic ML Platform based on Pulumi, DVC, MLFlow and more

Hacker News3

TrenTorch48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Learn Frontier ML by building it yourself.

Product Hunt+2

Platform to serve ML/DL models at low latency68%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Platform to serve ML/DL models at low latency

Hacker News2

Feedback Widget for Production ML53%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Feedback Widget for Production ML

Hacker News1

UnionML – a Python framework for building ML microservices63%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

UnionML – a Python framework for building ML microservices

Hacker News12

Browse all products Analyze your launch