My Company Trying To Make Us Use ML

STOP EVERYTHING! Someone just redefined every tech acronym and I'm absolutely DYING! 💀 AI isn't artificial intelligence anymore—it's "An Indian." API? Forget application programming interface, it's clearly "A Person in India." And don't get me started on LLM being "Low-cost Labor in Mumbai" instead of large language model! The tech outsourcing stereotype has reached catastrophic new heights with "GPT - Gujarati Professional Typist." My entire career is a lie and my resume needs a geography section now. I can't even!

When your AI coding assistant starts generating code so mediocre that you have to rebrand it in your head. "Microslop" is the perfect portmanteau for when Microsoft's tools produce output that's less "intelligent assistance" and more "copy-paste from the first StackOverflow result." The dev community has been roasting various AI coding tools for their... let's say "variable quality" outputs, and giving them degrading nicknames has become a coping mechanism. Whether it's hallucinating APIs that don't exist, suggesting deprecated methods from 2015, or just straight-up generating spaghetti code, sometimes these tools earn their new monikers. The crossed-out version number adds extra spice—like the tool is so bad you can't even acknowledge which iteration of disappointment you're using.

When she says she loves LoRA and you're thinking about the wireless communication protocol for IoT devices, but she's actually talking about Low-Rank Adaptation for fine-tuning large language models. Classic miscommunication between hardware and AI engineers. For the uninitiated: LoRA (Low-Rank Adaptation) is a technique that lets you fine-tune massive AI models without needing to retrain the entire thing—basically adding a lightweight adapter layer instead of modifying all the weights. It's like modding your game with a 50MB patch instead of redownloading the entire 100GB game. Genius, really. Meanwhile, the other LoRA is a long-range, low-power wireless protocol perfect for sending tiny packets of data across kilometers. Two completely different worlds, same acronym. The tech industry's favorite pastime: reusing abbreviations until nobody knows what anyone's talking about anymore.

OMFG, the absolute AUDACITY of this code! 😱 Someone's gone and created the most SCANDALOUS piece of programming known to mankind - a file that's half "secret" and half "public GitHub" with nothing but pure, unadulterated NONSENSE! This masterpiece of chaos defines fruits as data types, throws in random words like "Spoon" and "Frozen", and then proceeds to create the most grammatically offensive function in history that looks like someone had a seizure on their keyboard while thinking about fruit salad. The punchline? Training AI on THIS would be like teaching a toddler English by reading them the ingredients list on a shampoo bottle... BACKWARDS... WHILE SCREAMING! 💀

Putting on glasses to transform from "LLM Engineer" to "Knows about openai, anthropic and google-genai package" is peak tech industry smoke and mirrors. It's like calling yourself a "Cloud Architect" because you once deployed a WordPress site to AWS. The glasses don't add intelligence—they just help you see through the BS of your own job title. Next time someone introduces themselves as an "LLM Engineer," ask them if they can actually explain a transformer architecture or if they just know how to copy-paste API keys.

The eternal struggle of math majors who chose programming instead of the "traditional" math paths. On the left, we see the bright, colorful world of physics, machine learning, electrical engineering, statistics, and numerical analysis – all respectable career choices that utilize advanced mathematics. On the right, the noir film-style programmer, stripped of color and joy, questioning their life choices while debugging someone else's spaghetti code at 3 AM. That moment when you realize you could be solving differential equations but instead you're arguing with the compiler about why a semicolon is missing. The math degree prepared you to understand complex algorithms but forgot to mention you'd spend 90% of your time fixing indentation errors.