Question 1

What is this site?

Accepted Answer

I built this because every other motorcycle specs site feels like a trip back to a 2005 forum.

Question 2

Why is it called 299hub?

Accepted Answer

299 km/h — just shy of 300. It’s the edge, the limit, the kind of number you see on a dream spec sheet or an unrestricted stretch of highway. We're not saying you should aim for it... but it's where performance whispers 'almost there.'

Question 3

Why no colors or images?

Accepted Answer

Because standing out isn’t about color — carbon fiber doesn’t need paint. No images? Call it weight reduction. (Kidding — user-submitted bike images are always welcome.)

Question 4

Can I suggest bikes or features?

Accepted Answer

Absolutely! I'm always looking to expand the database and improve the site. If you have suggestions for bikes to add or features you'd like to see, please reach out through the contact page.

Question 5

What kind of specs can I compare?

Accepted Answer

Currently, you can compare key specifications like engine displacement, power and torque, weight, dimensions, fuel capacity, and some key features like ABS. I plan to add more data points over time.

Question 6

What technology is this site built on?

Accepted Answer

The site is built using Next.js for the frontend, Supabase for the database, and hosted on Vercel. It's also a learning project for me to explore these modern web technologies.

Question 7

How does your motorcycle similarity algorithm work?

Accepted Answer

Our similarity engine employs a sophisticated multi-dimensional vector analysis system that goes far beyond simple category matching. The algorithm utilizes weighted composite scoring across four primary vectors: displacement similarity matrices (60% weight), power-to-weight correlation analysis (15%), database-driven categorical classification with semantic matching (15%), and temporal recency algorithms with logarithmic decay functions (10%). The system implements dynamic displacement range banding with fuzzy boundaries, cross-brand diversity enforcement to prevent manufacturer bias, and real-time mathematical weighting functions processing thousands of data points. It's essentially a recommendation engine that applies machine learning principles to motorcycle specification analysis, delivering contextually relevant competitor suggestions through multi-pass candidate selection algorithms. Read more about the Advanced Similarity Engine on our About page.

Question 8

How often is the data updated?

Accepted Answer

As a solo project, updates happen when I have time to add new models or refresh existing data. I try to keep the information as current as possible, especially for new models or significant spec changes.

Question 9

What if I find an error in the data?

Accepted Answer

Please let me know! Accurate data is crucial, and I appreciate any help in identifying and correcting errors. You can report them through the contact page.

Question 10

Where does the data come from?

Accepted Answer

The data is compiled from publicly available sources, including CC0 (Public Domain) datasets from platforms like Kaggle. I do my best to ensure accuracy, but it's always a good idea to double-check with official sources if you need precise details.

Question 11

Why no scooters?

Accepted Answer

because Racebike.

Frequently Asked Questions

Read about the tech behind it

Frequently Asked Questions

Read about the tech behind it