We always joked he was going to get something from the chemicals, not so funny now that it hit.
A close family member was diagnosed 5 years ago and went through a stem cell transplant at Dana farber and the cancer still hasn’t returned…although statistically by now I believe it should have. But when it does return there is now a massive menu of next treatments for her that will likely hold it at bay.
Things are changing so fast now that I’m not sure the stem cell treatment is the first step.
Good luck to your dad.
- when a user changes the score slider, encode that in the URL with a hash tag, so they can bookmark the page with their preferred settings
- a left button allowing me to step back to yesterday's news
- to simplify newsletter signups, just accept an e-mail address right on that page
- your **advanced** options:
- have GPT score each news story across common labels: science, politics, entertainment, news, etc. Then allow these as a filter. If I want to see the top science stories of the day, that should be easy.
- have GPT write a 2 sentence summary of each story as a lead-in after the headline title
- a user/saved whitelist/blacklist of news sites
- any advanced setting should be shareable. For example, if someone puts the effort in to make a page with just Australian news sources, focused on sports, with a minimum score of 5.0, they could save that with a title that can be shared for anyone.
Congrats on a well-executed project.One of the most well-known examples of a foundational model is the GPT (Generative Pre-trained Transformer) series developed by OpenAI. GPT models, like GPT-3 or GPT-4, are trained on large datasets containing diverse text from the internet, which enables them to generate human-like text, answer questions, translate languages, and perform various other tasks.
Foundational models are significant in the AI field because they allow researchers and developers to create a wide range of applications and solutions without having to train a new model from scratch for each specific task. This approach saves time, resources, and computational power while still providing a high level of performance across different tasks.
Let's suppose you don't have a particularly strong network to draw from for recommendations.
Googling or using Yelp don't seem like particularly good options. They mostly turn up personal injury and divorce lawyers.
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