It configured everything by writing an AWS Terraform file. Stored all secrets in AWS as well.

Everything I do is on the command line with Claude running in Visual Studio Code. I have a lot of MacOS X / Ubuntu Linux command line experience. Watching Claude work is like watching myself working. It blew my mind the first time it connected through the bastion to individual AWS instances to run scripts and check their logs.

So yeah, the same Claude Code instance that configured the backend is running inside a terminal in VS Code where I’m developing the frontend. Backend is Django/Python. Frontend is Flutter/Dart. Claude set up the WebSocket in Django/Gunicorn and the WebSocket in Flutter.

It also walked me through the generation of keys to configure push notifications on iOS. You have to know something about public/private key security, but that amounts to just generating the files in the right formats (PEM vs P12).

I recently (as in, last night) added WebSockets to my backend, push notifications to my frontend iOS, and notification banner to the webapp. It all kinda just works. Biggest issues have been version-matching across with Django/Gunicorn/Amazon Linux images.

The difference is that the first camp is re-experiencing that feeling of wonder while the second camp is lamenting it. I thankfully fall in the first camp. AI is allowing me to build things I couldn't, not due to a lack of skills, but a lack of time. Do you want to spend all your time building the app user interface, or do you want to focus on that core ability that makes your program unique? Most of us want the latter, but the former takes up so much time.

https://www.harborfreight.com/10000-lumen-4-ft-linkable-diam...

Also, if you've ever been in a Walmart or Forever 21 at night, you'll know that constant LED white light is probably not the best thing for your eyes.

Think of all the solo developer webapp/mobile "hot" startups (FB, Craigslist, Instagram, SnapChat, Pinterest, etc.).

It is no joke that a seasoned developer can build similar apps with Claude in...days. Not just the app, but the entire infrastructure (e.g. scalable on AWS using Terraform). This includes setting up domain registration, Elastic IPs, provisions the instances, setting up keys/ELBs/email server/Twilio/etc.

What is astonishing is how well Claude can plan. You write a spec, and it will give you an entire plan including ASCII UML diagrams, infrastructure changes, database updates, the code itself, user stories, and test cases. It will then do all the work, including "tricky" things like SSHing over a bastion to run the scripts that it wrote on an instance behind a VPC.

The main obstacle now is the context window. If it were to increase 100x or so, Claude could probably manage an entire software company's codebase at scale.

I'm sorry to say, but there's no way you could use Claude for a month or so without feeling, "We are going to need way fewer software engineers."

I'll give some examples:

Novel algorithms: PageRank, BitTorrent, Like buttons, disappearing pictures, etc.

Artistic styles: Cubism, impressionism, Wes Anderson, etc.

The above algorithms are (relatively) straightforward to implement and could be implemented by Claude Code in a matter of hours if not minutes. But, you'd still need a means to distribute them.

Similarly, you can have AI generate an image in any of the above styles (or a combination thereof), but the image won't have intrinsic value unless you can finds a means of (profitable) distribution.

Put another way, there's limited value in being able to master physical tasks (playing piano, typing fast), but fundamental skills that lead to creative innovation will remain important...along with being able to package/market/distribute the AI-implementation of your ideas.

I imagine once data sharing is more robust, it would be easier to validate AI models (at least specifically for radiology).

Those are the four use cases featured by the Microsoft 365 Copilot App (https://m365.cloud.microsoft/).

Conversely, I bet there are a lot of people who want AI to improve things they are already doing repeatedly. For example, I click the same button in Epic every day because Epic can't remove a tab. Maybe Copilot could learn that I do this and just...do it for me? Like, Copilot could watch my daily habits and offer automation for recurring things.

Think about how you learned anatomy. You probably looked at Netter drawings or Grey's long before you ever saw a CT or MRI. You probably knew the English word "laceration" before you saw a liver lac. You probably knew what a ground glass bathroom window looked like before the term was used to describe lung findings.

LLMs/LVMs ingest a huge amount of training data, more than humans can appreciate, and learn connections between that data. I can ask these models to render an elephant in outer space with a hematoma on its snout in the style of a CT scan. Surely, there is no such image in the training set, yet the model knows what I want from the enormous number of associations in its network.

Also, the word "finite" has a very specific definition in mathematics. It's a natural human fallacy to equate very large with infinite. And the variation in images is finite. Given a 16-bit, 512 x 512 x 100 slice CT scan, you're looking at 2^16 * 26214400 possible images. Very large, but still finite.

Of course, the reality is way, way smaller. As a human, you can't even look at the entire grayscale spectrum. We just say, < -500 Hounsfield units (HU), that's air, -200 < fat < 0, bone/metal > 100, etc. A gifted radiologist can maybe distinguish 100 different tissue types based on the HU. So, instead of 2^16 pixel values, you have...100. That's 100 * 26214400 = 262,440,000 possible CT scans. That's a realistic upper-limit on how many different CT scans there could possibly be. So, let's pre-draft 260 million reports and just pick the one that fits best at inference time. The amount you'd have to change would be miniscule.

On a basic level, software exists to expedite repetitive human tasks. Diagnostic radiology is an extremely repetitive human task. When I read diagnostics, there’s a voice in the back of my head saying, “I should be writing code to automate this rather than dictating it myself.”

4 years med school

2 years computer science

6 years of residency (intern year, 4 years of DR, 1 year of IR)

16 years...

Can AI read diagnostic images better than a radiologist? Almost certainly the answer is (or will be) yes.

Will radiologists be replaced? Almost certainly the answer is no.

Why not? Medical risk. Unless the law changes, a radiologist will have to sign off on each imaging report. So say you have an AI that reads images primarily and writes pristine reports. The bottleneck will still be the time it takes for the radiologist to look at the images and validate the automated report. Today, radiologist read very quickly, with a private practice rads averaging maybe 60-100 studies per day (XRs, ultrasounds, MRIs, CTs, nuclear medicine studies, mammograms, etc). This is near the limit of what a human being can reasonably do. Yes, there will be slight gains at not having to dictate anything, but still having to validate everything takes nearly as much time.

Now, I'm sure there's a cavalier radiologist out htere who would just click "sign, sign, sign..." but you know there's a malpractice attorney just waiting for that lawsuit.

Here is the original version of their book: https://www.amazon.com/Inbound-Marketing-Found-Google-Social...

IBM reached out to Microsoft sometime in 1980 about an operating system, so SCP would've had at least 8 months to look into why Microsoft wanted their DOS before selling it entirely to them.

Did Microsoft resell 86-DOS to anybody before changing the name to MS-DOS? Did SCP make any effort find out why Microsoft wanted their DOS?