aabajian

I like it a lot, but I have to ask: Is this better that speaking very quietly? For example, a lot of people "read out loud" to themselves in a very quite voice. I think it would almost seem more natural to make a very quiet sound than to have no sound at all.

I'm going to offer a use case for touch screens: Augmented reality (AR) development.

I think there's a real possibility that AR takes off in the next 1-5 years. The combination of LLMs + smaller AR glasses makes AR a viable computing platform.

For example, simple things like, "Where are my keys?" is a lot easier to solve if (a) you're wearing your AR glasses already and (b) they have access to a frontier LLM.

However, it's very difficult to simulate "touching something" in AR using a mouse/trackpad and keyboard. What if you want to "twist open" a peanut butter jar? I could imagine touching the screen and twisting my fingers in a rotational pattern along the top of the can.

Can anyone answer me: What is the hardware cost to run a single instance of a frontier model? I've heard everything from $25K to $3M.

I think it comes down to: Will the hardware cost for frontier models come down?

Also, here's a business idea: Web crawler for open weight models. You just pay a monthly fee so that your home model can continuously update on a large web crawl each hour/day.

I don't think this is avoidable even at the present day. Anyone with sufficient capital could download a leading open-source model and run it on ~$10-25K hardware. They could align it to their own interest, including making a biological weapon. This law is akin to blocking Torrent downloads. Sure, you can slow them, but there will always be back channels.

If I'm understanding this correctly, it uses the electric motor to compress air into large tanks? How much thrust could that generate (and for how long)? It'd be amazing to jump over East River or the Hudson.

More importantly, why is photorealism not implied: https://www.reddit.com/r/GeminiAI/comments/1x54q78/argons_sv...

Why does an SVG pelican imply that it should look polygonal?

I ran into this issue recently when asking Claude to design a logo for my van. It made a polygonal SVG. I asked it to make it more photorealistic and it looked way better, still an SVG though.

I recently had a delivery to my Kaiser office in Portland. It was printer ink! My printer had run out of ink. Except, I don't own a printer, and I've never owned one that self-reorders ink. Someone at some time had registered their printer to my office address and enabled self-reordering of printer ink.

It's a tool to help with catheter choice, C-arm angle, and contrast injection rate. These are things I think about in every case anyhow, might as well add some data to drive my decisions. If the results don't match reality, I can always fall-back on my personal defaults.

I think the takeaway is that we have to think bigger, much bigger, now that AI coding is essentially "solved". Anyone can "write" an ARM simulator now, so that can't be the pinnacle of engineering. I'm thinking about buildings that couldn't be built (reasonably) by one person.

I'll give an example of what I'm working on. There's a model for segmenting organs from CT scans. There's another model for calculating radial diameters of blood vessels from CT scans. You can combine the two and approximate how much blood flow each organ gets based on its segmented volume and the sum total radial diameter of vessels feeding it. You can then render all of that in an app that shows perfusion to that organ.

The idea is to combine these models to extract every ounce of usable information from a CT scan that might be useful for procedural planning (I'm an interventional radiologist). While each algorithm would take weeks/months to implement, Opus 5.5 can do them in minutes to hours and then stitch them together. The results thus far have been better than anything I've seen.

Opus 5.5 is on another level, especially when it comes to mathematics implementations. You can drop it a PhD-level physical simulation (for example, a contrast-injection simulation for angiography in my case), and it just...implements it. With full-on WebGL rendering in the browser, from scratch (or using an existing library, if you prefer).

I don't mean to be mean, but the problem is that AI has exposed how much programming is designed around human usability. C/Rust/Go wrap assembly. TypeScript wraps JS. Java/Python wrap C.

Do you need all these layers of abstraction when the human is no longer looking at the code?

Best analogy is forgetting how to use a slide rule following the advent of calculators. The former was made to make hand-calculation of logarithms easy. The latter does these calculations directly (obviating the need for a slide rule at all).

I think what humans still need to learn are the theory and domain fundamentals for their industry. If that industry is computer science, that means algorithms, calculus, linear algebra, etc. I think the future of CS is then (a) theoretical human-drive design and (b) prompt engineering to implement and verify that design.

It would also be helpful to have domain knowledge outside of CS as having the skills to build something is nearly commoditized (outside of the above fundamentals).

This article...does not explain how to run frontier AI on your own hardware. If that were possible, it would drop the floor out from under OpenAI and Anthropic.

The answer to this is: countries with the most natural resources will build robots to farm all their food, mine all their minerals, and build all their products. It will be up to governments to enforce that outputs are equitably distributed to the populace. Countries without natural resources, or ones with corrupt governments, will continue to have serious, and likely worsening, problems.

Thought experiment: If no thought workers are needed to design or engineer a Ferrari, what is needed? My answer is time and natural resources (include energy).

Law is so ripe for replacement by AI. It is entirely based on established written arguments with gray areas, edge cases, linguistic subtleties, etc., being the crux upon which many controversial decisions are drawn. That is, law arguments are based on a mastery of language coupled with knowledge of legal precedent. LLMs are extremely good at linguistic reasoning and can be trained on ever legal case every published.

I shipped my iOS app without opening Xcode (well, I had to open it to get some provisioning stuff to work). I still use Visual Studio Code to kick off the simulator, but other than that, I use no IDE at all. Claude writes all the code in Flutter.

IDEs are dead, prove me wrong.

I don't think anyone has mentioned the obvious middle ground: PO Boxes at USPS post offices. Nearly every town and city in the USA has a post office. Instead of driving packages to individual's homes or having businesses deliver to their specific warehouses, the middle ground is to deliver everything to the USPS offices.

The last take-home test I did was for EE364a: Convex Optimization. It was a 24 hour test, and I had a cold. I booked a hotel room as my apartment didn't have air conditioning. It was brutal. I got most of the programming questions correct, but only a few of the proofs. The average of the class on this test (and most every other assignment) was 80%+. I got an A- overall in that class.

Perhaps this class isn't too hard for Stanford students, but I have to wonder about cheating when the averages on nearly every single assignment is that high. One clue was office-hours: Whenever I dropped by office hours, there was always a line (didn't matter the class). I quickly learned that TAs would often drop a hint that would make particularly hard problems easy to complete. It provided an unfair advantage to students who could attend office hours.

It also reminds me of the huge scam of cheating on the USMLE amongst Nepali medical students: https://www.medpagetoday.com/special-reports/features/113627

I've met many wonderful international medical graduates. Many have shockingly high USMLE scores. It is true that there's no time limit on their preparation (U.S. students have under 2 years to prepare during medical school, international students may wait years after graduation before taking the exam). Before that scandal, I never would've thought someone could cheat on the USMLE. Prometric test centers are crazy locked-down. But that's not how they did it. They did the long-game. Prior test takers would remember a handful of questions and just add them to a secret database. After many years, that database contained nearly every question on the exam. Test takers would work extremely hard - memorizing every single question. The reward of a U.S. residency is life-changing, I get why cheating was(is?) so rampant.

Hard is relative. The Apple IIe could do roughly a half-million instructions per second. Yet, application develop was long and tedious. Layers upon layers of abstraction requires more powerful CPUs, but translated into highly complex applications and user interfaces. What if we could use the LLM tools we have now to go back and see what is possible with older machines?

For example: The Python print('A') statement translates into thousands of lines of assembly (using mostly compiled libraries). The actual minimum assembly to generate a print statement is less than a hundred. Claude code can do this:

>>Generate the minimum amount of assembly code to obtain the same result as print('A') in Python.

The result is 43-bytes of machine code (it works, unsurprisingly). Claude estimates that Python requires 5 megabytes of compiled libraries to get print('A') to work.

How can someone so successful be so naïve? The statement, "impossible to earn a billion dollars," is not meant to be taken literally.

It is used to underscore the fact that founders (and early investors) are paid ridiculously disproportionately to their employees. There is no labor that a single person can do (without the help of their employees, family, friends, network) that justifies earning $1B.

Devil's advocate: My wife thankfully just got her green card three months ago. She first came on a J-1 and then an F-1. She knows many, many people who come with the intent of staying either without status or via questionable marriage licenses. The vast majority of her acquaintances in the J-1 visa program were young (<25 year old) au pairs with no intention of learning English (they had 'mandatory' English language cultural lessons per J-1). My wife is an extreme outlier in that she learned English in 5 years and just got accepted to veterinary school in USA.

The bottom-line is that she thinks the J-1 / au pair program should be discontinued.

I'm going to offer a counterpoint suggestion. You need to watch Claude try to implement small features many times without planning to see where it is likely to fail. It will often do the same mistakes over and over (e.g. trying to SSH without opening a bastion, mangling special characters in bash shell, trying to communicate with a server that self-shuts down after 10 minutes). Once you have a sense for all the repeated failure points of your workflow, then you can add them to future plan files.

Claude set up my whole backend on AWS. That includes a load balancer, web server, email server, three application servers, and a bastion server to connect to their VPN.

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).

The combination of Flutter + Claude Code makes cross-platform app development really, really fast. I've been impressed with how well Clause handles prompts like, "This list should expand on the web, but not on iOS." I then ask it (Claude) to run both a web instance and an iOS simulator instance. Can usability test in-tandem.

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.

It seems AI is putting senior developers into two camps. Both groups relate to the statement, "I started programming when I was seven because a machine did exactly what I told it to, felt like something I could explore and ultimately know, and that felt like magic. I’m fifty now, and the magic is different, and I’m learning to sit with that."

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.

That is the problem with software developers with expertise in software, but no deep domain knowledge outside the CS world.

I don't know enough about lighting, but if I bought five of these, would I reach 50,000 lumens? Is it just additive? This would cost $250.

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.

There are many stories on r/ClaudeCode of developers realizing the power of that particular model.

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."

When your most potent competitor companies (FB, MSFT, Apple) and investments (OpenAI) were all founded by college drop-outs, it does make you wonder whether college itself was holding these individuals back. I'm sure they are exceptions rather than the rule.

AI is making us face the reality of non-physical goods and services. If a product exists solely as data (writing, music, code, drawings, movies, etc.) then the value is in the idea and its distribution, and less on its production.

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.

As an interventional radiologist, I want it to be easier to see images from outside hospitals. Epic has nearly solved the problem of seeing outside medical records. Yet, I still can't see the images for the CT scan you had from the hospital across the street unless I call the file room and get the images transferred.

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