That's a good call. While there's no general public feed, individual profiles are public. For example, here's mine: https://voxconvo.com/siim

True. However making voice input has higher friction than typing chatgpt write me a reply.

I'm working on https://X11.Social, a voice-first content creation tool for X.

The initial idea was "call to tweet", the ability to compose posts on the go by having a natural conversation with an AI assistant over a simple phone call. This is useful for turning thoughts from a walk or drive into a polished "brain dump" post, or for engaging with user lists without being at a computer.

It has since evolved into a broader system:

Chrome Extension: A context-aware assistant that lives in the browser. It has a Quake-style console (activated by opt+space) for quick chat and can analyze the content of any page you're on (e.g., YouTube transcripts, articles, other tweets) to help you create relevant content.

Engagement Predictor: A feature that scores tweet drafts in real-time to predict their potential for engagement. It's built on a model trained on my own dataset pulled from the X API and other public dataset from Kaggle[0].

Scheduled AI Calls: The system can call you on a predefined schedule to proactively brainstorm content ideas.

Here is the tech stack:

- Frontend: React, Tailwind, shadcn/ui

- Auth: X OAuth

- Payments: Stripe Subscriptions

- Voice AI: ElevenLabs Conversational AI, Twilio

- Engagement Predictor ML: Python, scikit-learn, XGBoost on a data pipeline from X API v2 and a base dataset from Kaggle.

- Chrome Extension: Same as Frontend and Chrome Extensions API

- Blog: Jekyll

- Infrastructure: Deployed on AWS Fargate using AWS Copilot for orchestration (ECS).

I'm building solo and just got the first trial user after 87 days of building in public. It's a long road but the feedback so far is encouraging.

[0] https://www.kaggle.com/code/shpatrickguo/tweet-virality-pred...

Fair. X11 is ElevenLabs-inspired voice tech, X for the platform + 11 for AI voice.

I kept the name for the call-to-tweet vision. Thoughts on the demo?

After looking into to the code I found out that this app is made by using Voronoi diagrams. [1]

The actual positions are saved in a json file. [2]

[1] http://en.wikipedia.org/wiki/Voronoi_diagram

[2]http://www.pointerpointer.com/gridPositions.json

Yes, but how did the author create that .json file? Manually or using some ML algorithm?

What would be interesting is if new images were periodically added and the grid adjusted in response.

Edicy is a nice CMS solution with in-line editing. http://www.edicy.com/

I found a quick 15 page introduction to Scala and took me about an hour to digest it (of course I didn't dive in very deeply). It gave me a sufficient knowledge to understand the article about monads.

So here it is: http://www.scala-lang.org/docu/files/ScalaTutorial.pdf

Thanks, that was helpful.

This latching and piping seems intuitive if you have done some monadic programming in Haskell.

I recently discovered, that I can hide Opera's tab bar and use panels 'Windows' panel instead (easy to toggle with F4).