Great thread full of potential https://t.co/jA8h08M48z ideas! 💡

📸 Social disposable camera app

Create a digital "roll" with friends. Take pictures from the roll. You can't see them until: the roll is used up, a time limit expires, or you get to a specific location.
🎙 Asynch interview platform

Send a guest a video or audio clip asking a question. Guest replies when able with same medium. Repeat. When done, share produced video/podcast.
✅ 24 Hour Startup Checklist/Overlay

A web app to track the progress of your project: ideation, naming, shipping code, launching.

Page can also be a dynamic browser overlay for live streaming.

Have tools for polls, surveys, sharing, launching to various outlets, etc.
👩‍⚖️ Healthcare "Lawyers"

If health insurance screws you over, no one has your back but you. Instead, can pay a small monthly fee to a service to take care of BS like this for you should issues arise.
🚐 Discounts for digital nomads/van people

Find local/regional businesses, gyms, co-working, cafés, etc. that offer discounts for digital nomads or van lifers. Akin to Epic Pass.
👨‍🍳 Recipe collection/printing-as-a-service

Send URLs and/or pictures of recipes to service and receive a simple, well formatted/standard book, catalog or cue cards in box of those recipes.
💵 Our First Sale

Simple site for displaying a company's first sale (like framed dollar bills in small businesses).
📱 Screenshot to to-do list/pinterest-like board

On addition of screenshot to photo album, identify what it is of and perform appropriate action (e.g. if clothing from website, add to list of clothing with URL to item, if recipe, put in recipe list, etc.) Create to do lists, etc

More from Tech

THREAD: How is it possible to train a well-performing, advanced Computer Vision model 𝗼𝗻 𝘁𝗵𝗲 𝗖𝗣𝗨? 🤔

At the heart of this lies the most important technique in modern deep learning - transfer learning.

Let's analyze how it


2/ For starters, let's look at what a neural network (NN for short) does.

An NN is like a stack of pancakes, with computation flowing up when we make predictions.

How does it all work?


3/ We show an image to our model.

An image is a collection of pixels. Each pixel is just a bunch of numbers describing its color.

Here is what it might look like for a black and white image


4/ The picture goes into the layer at the bottom.

Each layer performs computation on the image, transforming it and passing it upwards.


5/ By the time the image reaches the uppermost layer, it has been transformed to the point that it now consists of two numbers only.

The outputs of a layer are called activations, and the outputs of the last layer have a special meaning... they are the predictions!

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