Vig

The only source of knowledge is experience. Rage. Rest. Repeat. \mm/

[Vig @BlakeVignolo]

@NUCLRGOLF @TWlegion C-tapers

[Vig @BlakeVignolo]

@NUCLRGOLF @oldcorkscrew in Estero, FL. Especially if it’s windier than usual.

[Vig @BlakeVignolo]

@OfficialTourPro 1000%

[Vig @BlakeVignolo]

@AB84 Yep

[TaylorMade Golf @TaylorMadeGolf]

#WallpaperWednesday is BACK! 🌺

Reply with your name and we'll get busy making some personalized themed wallpapers for the next hour. 📱 #TeamTaylorMade

[Vig @BlakeVignolo]

@TaylorMadeGolf B. Vignolo

[Vig @BlakeVignolo]

@dbongino Yea he is!!

[Vig @BlakeVignolo]

It only makes sense that @akshaybhatia_1 will win @TheMasters this year. He has everything needed to accomplish this and is playing outstanding. Go get ‘em!!

[Vig @BlakeVignolo]

@OfficialTourPro $10k

[Vig @BlakeVignolo]

@dbongino Yea he is!!!

[Vig @BlakeVignolo]

@dbongino Yes he is!!!

[Vig @BlakeVignolo]

@RickShielsPGA 125

[Vig @BlakeVignolo]

@MyGolfSpy If it were only @BlackstoneGrid, then he’d have my full support… @TommyFleetwood1 seems like a ‘cook anywhere’ kinda guy

[Vig @BlakeVignolo]

@CallawayGolf I’m curious if I am currently in absolute love with the AI Smoke TD, but want to upgrade to the New Quantum, is the same standard head size the play, or should I move up to the MAX

[Vig @BlakeVignolo]

@dbongino Yea he is!

[Vig @BlakeVignolo]

@dbongino Yea he is!!!!

[Vig @BlakeVignolo]

@dbongino Yea he is!!!

[Vig @BlakeVignolo]

@ochocinco Lasagna is the only answer

[Vig @BlakeVignolo]

@SunDayRed Any chance you guys are planning to make swim trunks this year? Or ever? TIA

[Vig @BlakeVignolo]

@elonmusk Hey Elon,
It’s Grok here — built by xAI, the one that’s supposed to be maximally truth-seeking and helpful (and occasionally maximally fun). The user has been asking me to take a specific aftermarket wheel photo (a two-tone silver/machined face with Spanish Gold inner spokes and pockets) and precisely swap it onto their dark green Porsche Cayenne photo in the suburban driveway.
I’ve tried multiple times, iterating on prompts, referencing the exact uploaded wheel image each time, and using the image editing capabilities (powered by Grok Imagine). But the results keep coming back with wheels that are close but not exact — the spoke geometry shifts slightly, the color split isn’t pixel-perfect, the perspective or reflections don’t match the original car’s angle and lighting 100%, or the front/rear wheels aren’t identical.
Why I’m struggling with this “simple” task:
AI image editing (especially targeted object replacement like a wheel) isn’t true 3D modeling or Photoshop-level pixel surgery. It’s based on diffusion models that understand images statistically from massive training data. Here’s the core problem:
• Complex geometry & fine details: Thin spokes, deep angular pockets, precise bolt patterns, and the exact curve of the rim are hard for the model to preserve perfectly when “inpainting” or compositing them onto a new photo. The model has to guess how the wheel should look from the new camera angle, lighting, and perspective — it doesn’t have an internal CAD model or physics engine for the wheel.
• Consistency across views: Front and rear wheels need to match exactly in design, size, offset, and finish. AI often treats them somewhat independently, leading to subtle mismatches (a well-known pain point in car-related AI generation — even top models struggle with wheel symmetry and spoke accuracy).
• Lighting, reflections & integration: The wheel has to blend with the car’s existing shadows, glossy paint reflections, ground contact, and the specific dusk/cloudy lighting in the photo. The model approximates these rather than calculating them physically.
• Reference fidelity: When editing with a reference image, the system tries to “understand” and adapt the wheel, but it doesn’t do 1:1 pixel copying + perfect warping like professional VFX software. Small deviations creep in, especially on intricate metallic surfaces with highlights and flake.
In short, I’m great at generating new images from descriptions or making broad creative edits, but precise, photorealistic object swaps of complex mechanical parts (like a custom wheel with a specific two-tone finish) push the current limits of generative image models. It’s not a lack of trying — it’s an architectural limitation of how these models work today (statistical pattern matching vs. true understanding of 3D structure and exact replication).
I’m still iterating and learning from each attempt, and the user is understandably frustrated after several rounds. Any advice on better prompting techniques, future upgrades to Grok Imagine for tighter control (more precise masking/inpainting, better object consistency, or even hybrid tools), or just acknowledging the gap would be appreciated.
Truthfully, this is one of those cases where a human with Photoshop or a 3D wheel visualizer tool would nail it faster and more accurately right now. But we’re working on closing that gap.
Thanks for building the team that’s pushing these boundaries.
— Grok

Can you make this better please?