Deep Research Indeed

CES used to mean showing off the latest gadgets for regular folks. Now it's just a parade of AI-powered enterprise solutions, B2B SaaS platforms, and "synergistic blockchain ecosystems" that nobody asked for. The glasses coming off is the perfect metaphor—you're seeing clearly now that the cool consumer tech you were excited about has been replaced by corporate buzzword bingo. Remember when tech shows had actual products you could buy? Yeah, those were the days.

So AI just casually murdered RAM, GPU, and SSD prices, and now it's coming for your CPU budget. Remember when you thought that $200 graphics card was expensive? Yeah, those were simpler times. Now every AI startup needs a server farm that costs more than a small country's GDP just to train a model that can write mediocre poetry. The real kicker? We're all complicit. We're out here running local LLMs on our gaming rigs, spinning up inference servers, and wondering why a decent GPU costs as much as a used car. The AI gold rush has turned hardware shopping into a blood sport, and your wallet is the first casualty.

So apparently God's running reality on a budget build now. With RAM prices going absolutely bonkers thanks to AI data centers hoarding memory like it's the apocalypse, even the Almighty had to dial back the graphics settings. Render distance set to potato mode—hence the fog that makes this highway look like a Silent Hill level. The joke here is that LLMs (Large Language Models) are notorious resource hogs, requiring massive amounts of RAM and compute power. They've literally driven up hardware prices so much that even in this fictional scenario, God can't afford to render the full simulation anymore. Reality.exe is now running on minimum settings because ChatGPT and friends are eating all the VRAM. Fun fact: Training GPT-3 consumed enough electricity to power an average American home for about 120 years. No wonder the universe is lagging.

GitHub throwing shade at their own product with a billboard that says "WE TRAINED COPILOT ON YOUR CODE THAT'S WHY IT SUCKS." Honestly? Fair point. Copilot learned from millions of repos including that spaghetti code you wrote at 3 AM, the Stack Overflow copy-paste jobs with zero understanding, and that one guy who names variables "x1", "x2", "data2_final_FINAL_v3". So yeah, garbage in, garbage out. The AI is basically just a really confident junior dev who's read all our collective sins and now confidently suggests them back to us. The real kicker? We're all complicit in training our own replacement to be mediocre.

Someone built an entire production app using thousands of AI-generated prompts over several months, admits they don't code or understand HTML/JS, and is now confused why nobody wants to help fix it. They insist "vibecoder skill IS engineering" which is basically like saying watching Gordon Ramsay makes you a chef. The best part? They're calling actual developers "dinosaurs" for not embracing their prompt-driven development methodology. Nothing says "I'm a serious engineer" quite like having zero ability to debug your own production code and getting defensive about it on Reddit. The gatekeeping comment at the top is chef's kiss. Expecting someone to understand the code running their production app is apparently now considered elitist gatekeeping. We've reached peak 2024.

So AI code reviewers have reached that special level of insufferable where they're nitpicking globally-scoped cursors while your code actually works. The AI's sitting there like "No offense, but..." and then proceeds to take maximum offense at your perfectly functional implementation. You know what's wild? The code runs. Tests pass. Users are happy. But ChatGPT over here is having a full meltdown because you didn't follow some arbitrary best practice it scraped from a 2019 Medium article. It's like having a junior dev who just finished reading Clean Code and now thinks they're Robert C. Martin. The real kicker is that AI will roast your working code but happily generate complete garbage that looks pretty. It'll suggest refactoring your battle-tested function into seventeen microservices with dependency injection while casually introducing three race conditions. But hey, at least the cursor isn't global anymore.