Reminds me of the old construction photos for nuclear reactors in the US. Astoundingly complex machines at a massive scale getting out together at what now feels like impossible speed. I can't help but feel like a Roman 100 years after the fall staring up at aqueducts wondering how anyone every built such a thing.
I'm positive someone could show me an impressive thing we built recently. I don't feel like that is my point. Im just astounded those people in that time could build what they built with the tools they had as fast as they did.
Give me self host code any day. This feels like the bait and switch AWS likes to pull. Would rather rely on the server in my dresser drawer than AWS for as much as I possibly can.
That is my favorite part of GA. Gradient free optimization but it turns out making a good fitness function is hard and like 70% of the time it just exploits some assumptions or gap you have in your theories. Really reveals the problem in different ways that traditional ML.
Idk, since letting my son stay up as late as he wants reading trips to the library have been weekly, even as he transitions to actual no image chapter books. As others have said we try to time box tech best we can. I may have to physically lift him from the bed some days but it is worth it to find him passed out with a book in his hand at 10pm.
Technologies: Gen AI, Healthcare, VLM, Document Parsing, Integrations FHIR, SMART, RPA, OCR, Computer Vision, Audio Agents, Data Processing, AI Model Training
As a product manager I have found people's understanding of what they want to be tenuous at best. They know they want it to be easier, less frustrating, and do the darn thing they want. But collecting the input of all the people with the power to make choices, boiling that all down, and actually figuring out what will work is a total bottle neck. Technology and programming have hardly ever actually been a blocker. Legacy systems, conflicting requirements, and ever shifting choices represent a much harder problem.
Like high gas prices leading to sudden releases of fuel efficient vehicles in the 70s during the embargo. I love that most indie games I can find will run on a toaster.
I'd say the primary benefit of LAN parties now is how capable "old" hardware is. I have several laptops I configure with Linux and several games and environments loaded up. Smoke test the whole thing before anybody gets here. This year we played UT2004 with custom maps and characters. That game ran flawlessly on a 10 year old laptop with no dGPU. Emulation as well, but I actually still have most of those systems still.
Long story short it is a compromise between the Mdewakanton Sioux, the state, and Canterbury to keep gambling exclusive to Mystic Lake Casino and Caterbury. The State flirted with the idea of expanding gambling and started moving in that direction, but Mystic basically now props up Canterbury, lets them have some amount of gambling on site, and the State did not expand gambling and generally is protective of Mystic. It's like a pressure release valve on gambling with some small kickbacks to the nearby legacy horse racing track. It's a strange local arrangement. Grew up in Prior Lake and had a few tribe members in school so this is just my bias on the arrangement.
Ex Vaddio PM here. Like 5 years ago all our firmware defaulted to requiring non-default passwords on setup. We also created a free windows application that can mass upgrade firmware and change auth if defaults were used. We tried!
Saw the Vaddio logo and had to chime in. Gotta stick up for my Minnesota devs.
Technologies: Gen AI, Healthcare, VLM, Document Parsing, Integrations FHIR, SMART, RPA, OCR, Computer Vision, Audio Agents, Data Processing, AI Model Training
I too have found the models do well with Go. I will say despite the backwards compatibility guarantee library API changes, what counts as "good" patterns, and new language additions do add some friction to the experience. Almost always works but it can be a bit inconsistent in how the code shows up.
I love genetic algorithms and find using LLMs as part of them super compelling. I always find the fitness functions to be the most difficult part. The algorithm naturally tries to exploit any little gap you leave it in cheating. Best part is not needing back propagation in solving a problem. However that is also the worst part in all the solutions just being one level above a random walk. The LLM augmentation really helps to give it a gradient and intelligence beyond random chaos. Love the idea of it being applied to hardware.
It was literally a guy's job on the floor to just replace modules and other electronics. The equipment itself would not become contaminated so it was safe to handle afterwards. It just got bombarded by radiation and became useless as a camera. Being hit by ionizing radiation does not mean you become radioactive. The main issue is if bits of contamination would stick to our fancy duct tape we had around the cameras.
As for why we needed them it's for a bunch of reasons. This is 30 meters down. You gotta inspect welds, replace jet pumps, pick crap up that people drop in, pull plugs, help guide CRD maintenance. Tons of stuff. You gotta see it all. Camera handlers are magical and learn to swim the cameras around using puppet like movements. You manipulate these duct taped to rope cameras using either the cable or the rope. Sometimes we would attach them to stupendously long poles we assemble which were also duct taped (this changed eventually). The issue is such a long pole is basically a pool noodle in terms of handling. Keeping stuff from getting stuck and having confidence in where you were was an art. I wish I could tell you nuclear inspection used fancy drones and super high tech robotics but a ton of the visual side is duct taped cameras and talented handlers. Ultrasonic inspection is where the robotics took over and where they earn their keep. Encoding the position is worth the effort. But for visual you can't really get a sub to do much better than a guy with a long pole. Haha
For comparison the lifespan of a camera module was about 24-48 hours for work inside the water of the reactor near the "hot" fuel of the reactor. Fields around there were I believe on the order of 1000-5000 Rad/hr. Looked like the biggest confetti party you ever saw on the image. It was difficult for the encoder modules to keep up as well because they compressed so poorly and the reactor floors were usually hot and humid with the reactors open. I tried to make de-noising algorithms back in the day to help smooth out the noise in the reactor. Really hard to make electronics work in those places. Turns out constant bit flips and ionizing radiation is bad for hardware.
I find Claude code to be a token hog. No matter how confidently the papers say context rot is not an issue I find curating context to be highly important to output quality. Manually managing this in the Claude Webui has helped with my use cases more than freely tossing Claude code at it. Likely I am using both "wrong" but the way I use it is easier for me to reason about and minimize context rot.
I'm positive someone could show me an impressive thing we built recently. I don't feel like that is my point. Im just astounded those people in that time could build what they built with the tools they had as fast as they did.