The Sweet Dopamine Hit Of Green Checkboxes

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Content Users thinking it is safe to use the app Me fixing bugs on Production

That smug satisfaction when your 3 AM code abomination—complete with seven nested ternary operators and a random sleep(1)—somehow fixes the production bug that's been haunting your team for weeks. Sure, nobody (including you) will understand how it works tomorrow, but right now you're the office hero with your digital duct tape solution. Future you can deal with the technical debt; present you is too busy basking in undeserved glory.

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The infamous time-estimation paradox strikes again! What starts as "just a simple bug" in the morning transforms into a full-blown existential crisis by nightfall. That confident "I'll fix it in a few minutes" energy completely evaporates as the developer gets sucked into the rabbit hole of dependency issues, undocumented edge cases, and the inevitable realization that the "simple bug" is actually exposing fundamental architectural flaws that have been lurking in the codebase since 2017. The transition from daylight to darkness perfectly captures how our souls get crushed by the cruel reality of debugging. Hofstadter's Law in action: "It always takes longer than you expect, even when you take into account Hofstadter's Law."

Content README.md FauxPilot This is an attempt to build a locally hosted version of GitHub Copilot. It uses the SalesForce CodeGen models inside of NVIDIA's Triton Inference Server with the FasterTransformer backend. Prerequisites You'll need: Docker docker -compose 1.28 An NVIDIA GPU with enough VRAM to run the model you want. nvidia-docker Note that the VRAM requirements listed by setup. sh are total - if you have multiple GPUs, you can split the model across them. So, if you have two NVIDIA RTX 3080 GPUs, you should be able to run the 6B model by putting half on each GPU Support and Warranty Imao