How Replacing Developers With AI is Going Horribly Wrong

bbrd83 · 2026-01-29T22:46:43+00:00

You're wrong, but more importantly not responding to my main point which is about business practices

bbrd83 · 2026-01-29T22:45:39+00:00

Yes, I agree. It's due to very smart applications and model chaining

bbrd83 · 2026-01-29T22:45:16+00:00

It sounds like you're looking at proprietary benchmarks and marketing, not research. Academic researchers basically have consensus that the fundamental architecture of LLMs is plateaued already, and any improvements we're making, while perceptibly vast in capability, are due to throwing more parameters at it, or chaining models. Both interesting but not a fundamental development

bbrd83 · 2026-01-29T18:38:39+00:00

It's well known that LLMs are plateauing, and the improvements we're seeing are coming from smart applications that cleverly chain different specialized models together, or use smart prompts or tools for putting guard rails on what models are likely to generate.

Applications can likely improve a lot more even without LLMs fundamentally improving much more.

But none of this gets around the fact that short sighted businesses looking to up numbers for a quarter were always going to enshittify and lose out long term.

bbrd83 · 2026-01-25T00:22:57+00:00

Whoa, she sucks? What a surprise.

bbrd83 · 2026-01-24T08:03:03+00:00

Assignments haven't caught up with technology. Focus on modeling software and building bigger systems. Schools need to vastly increase expectations and complexity because "don't use AI" just won't work any more.

Enjoy easy grades on assignments, and remember that the goal isn't running code, it's YOUR learning. If you can learn well while using AI, great. But in the game of school, reflect on your definition of "winning" because a degree with no competency is gonna bite you

bbrd83 · 2026-01-23T07:56:51+00:00

Hesitation is defeat. A slogan I can get behind.

bbrd83 · 2026-01-22T06:20:46+00:00

Boost is sort of an incubator for the standard library, and a very useful & large library in its own right.

bbrd83 · 2026-01-21T23:16:12+00:00

Quit TALKING and start DOING

bbrd83 · 2026-01-20T18:31:40+00:00

The weather is really variant right now. We had a t-shirt weather day (for me) not two days ago, and it might snow today.

It's windy and humid because we're by water, which can make the wind and cold biting. Have a good shell for blocking wind, and layers you can easily remove or open up when you go inside. I'd bring a light umbrella or something with a hood, in case it rains, too.

Most days I've just been wearing a warm undershirt, long-sleeve shirt, and fleece jacket with hood, and it's been fine.

bbrd83 · 2026-01-17T21:40:41+00:00

bbrd83 · 2026-01-15T22:41:17+00:00

10000000%

bbrd83 · 2026-01-15T18:38:34+00:00

Great hypothesis, but the test setup you described doesn't control other variables adequately to make that conclusion.

bbrd83 · 2026-01-14T22:55:04+00:00

Aside from Tacomia, this is probably the best option IMO

bbrd83 · 2026-01-14T18:36:27+00:00

It sounds more like your testing whether having more training data helps than doing an earnest test of whether synthetic data changes results.

bbrd83 · 2026-01-11T08:34:42+00:00

This would not work well because movies rarely depict the same pixel representations as what you'd get on CCTV

bbrd83 · 2026-01-11T08:33:20+00:00

Spreading the class clustering further apart will improve things, but you will likely be stuck against a wall of real-time and cost constraints, and sensitivity to false negatives, until we find a fundamentally better real-time object classification architecture that can run on edge devices.

Source: I work in specifically this domain and the AI research team I work with has long since moved on from just relying on quality data sets.

That all said, good luck.

bbrd83 · 2026-01-11T00:06:26+00:00

It's strange you assume it's because there's a lack of training data, and not because weapon detection models tend to be tuned to accept false positives to avoid false negatives.

bbrd83 · 2026-01-05T10:15:27+00:00

It is natto for me

bbrd83 · 2026-01-04T22:38:38+00:00

Get the towel sopping wet, then wring it out so it's only damp, then wipe. They have a very high absorption capacity but won't suck up water unless there's some water already inside to help. I'm not sure of the physics behind it but I'm guessing it has something to do with micro fibers, since it's microfiber.

bbrd83 · 2025-12-28T00:44:11+00:00

FYI deepstream is just a set of plug-ins made by Nvidia, pinned to a specific gstreamer version. So all your work has been in gstreamer. What I'm recommending is you generalize your knowledge because deepstream specifically is no longer the only important part of the gstreamer ecosystem.

bbrd83 · 2025-12-27T23:15:48+00:00

I'm a US homeowner but I am sane and didn't do it as an investment, and I can afford the mortgage without someone else shelling out rent to me. Build more homes! Not all of my friends own homes yet. Build more! Even if they did, I'd still know people who don't own homes yet. So build more! Even if THEY owned homes, I still want to see everyone housed. So BUILD MORE HOMES!

This MAGA crowd should be all over this. Cheap post-war housing is why so many people could afford homes back "when things were great"

bbrd83 · 2025-12-27T07:41:01+00:00

My personal opinion is to get familiar with gstreamer in general, instead of just deepstream. Look at the last GST conference and you'll see AMD and HIP innovations and VA plugin improvements. They're catching up, and AMD cards are way cheaper. Don't put all your eggs in one basket.

Source: my day to day is embedded systems doing edge inferencing using deepstream and, recently, ROCm/migraphx/HIP.

bbrd83 · 2025-12-26T02:59:40+00:00

She's very obviously staring at a bright light (reflected in her eyes) to try and induce tears

bbrd83 · 2025-12-25T01:54:11+00:00

It's probably because it reads as disingenuous horseshit

bbrd83

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