When People Talk About Their Favorite Model

Senior dev asks if you checked the PR before merging. Junior dev proceeds to confidently slam that table with zero hesitation, declaring "AI did it" like it's a valid code review methodology. The absolute audacity of trusting AI-generated code without review is both terrifying and relatable. We've all been there—Copilot autocompletes 50 lines, tests pass (maybe), and suddenly you're shipping to prod with the confidence of someone who definitely did NOT read the diff. The junior's unwavering certainty in the face of reasonable questions is *chef's kiss* peak developer energy. Pro tip: "AI did it" is not an acceptable answer during incident postmortems, no matter how confidently you slam the table.

Back in the day, programmers had to understand the intricate details of LSTMs (Long Short-Term Memory networks), BERT embeddings, and optimize for browser latency like absolute beasts. You needed a PhD-level understanding of neural network architectures just to classify some sentences. Now? Just slap import openai at the top of your Python file and you're suddenly an AI expert. The entire machine learning ecosystem has been abstracted into a single API call. We went from manually implementing backpropagation to literally just asking ChatGPT to write our code for us. The buffed doge represents those ML engineers who could recite transformer architecture in their sleep, while the crying doge is us modern devs who just copy-paste OpenAI API keys and call it innovation. The barrier to entry dropped from "understand advanced calculus and linear algebra" to "have a credit card."

Ah, the good ol' days of coding... Two guys safely installing an AC unit while standing on a narrow ledge, labeled as the coding equivalent of copy-pasting from Stack Overflow vs. writing your own code. The self-written code guy is literally hanging off the edge of certain death just to feel the satisfaction of originality. Meanwhile, the Stack Overflow copiers look slightly more stable but equally questionable in their life choices. Let's be honest - before AI coding assistants, we were all just one misplaced semicolon away from plummeting into the abyss of debugging. The only difference was whether we fell with borrowed code or with our own janky implementation that somehow worked despite violating every principle in the textbook.

We've reached peak workplace efficiency: using AI to inflate your two-sentence thought into a five-paragraph essay nobody wants to read, then using AI again to compress someone else's novel back into the bullet point they should've sent in the first place. It's like we've automated the entire cycle of corporate communication theater. The beautiful irony? Both sides know exactly what's happening. You're not fooling anyone—we're all just participating in this elaborate dance where AI helps us cosplay as people who have time to write thoughtful emails. Meanwhile, actual work gets done in Slack messages that say "lgtm ship it." Honestly though, if AI's killer app is helping us maintain professional politeness while everyone's just trying to get to the point, maybe we've already achieved artificial general intelligence. Just not the kind we were hoping for.

Content Also, they have an extremely aged 3Com switch that was End of Life in 2009 and has not received a firmware update since the Bush administration.

The semiconductor industry's ouroboros eating its own tail. GPU manufacturers dominate the graphics card market, but their success creates a RAM shortage because everyone's buying GPUs for AI and crypto mining. This RAM shortage then makes it impossible to manufacture more graphics cards because, you know, motherboards need RAM too. It's like a snake that got so good at eating its tail that it can't find its tail anymore. The supply chain just speedrunning its own destruction. Next up: we'll need GPUs to mine the cryptocurrency to buy the RAM to build the motherboards to hold the GPUs. Circle of life, tech edition.