Film Photography and AI: My experience

10

October

2025

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I started using ChatGPT for school, then realized it could help my photography too. With film, every click costs money and time. Having a quick second brain lowered the stress and helped me make better choices before I even loaded a roll.

Most of my shoots are low light or mixed neon. I ask for a quick plan: likely shutter speeds for Cinestill 800T or my MARIX 135 T800 and Amber T800, what EV to expect at blue hour, and how far I can push before motion blur ruins the look. It is not magic. It just gives me a sensible starting point so I do not waste half a roll testing the obvious.

I also use it for composition practice. I describe a scene from my contact sheet, like “subject under a shop sign, bright window behind, messy foreground.” It suggests two or three framings to try next time. Step left to kill a distraction. Drop the angle to separate the subject from the background. Add a leading line from the curb. Simple ideas, but it keeps me iterating. My contact sheets feel less random and more like a series with intent.

Metering and color are where it saves me the most. If I am debating 1 stop over for skin indoors, or how much to bias exposure for tungsten under mixed LEDs, I ask for trade-offs. It reminds me what will happen to highlights on 800T and what to expect from halation. When a scan comes back with a green cast, I run a quick checklist for likely causes and fixes. It is the same with push or pull. I still note my lab’s advice, but I go in with clearer expectations.

Trust grew with results. The more useful the output, the more I tried. I still keep guardrails. I verify technical claims, write shot lists, and never paste personal data. The goal is not to outsource taste. The goal is to give my taste more chances to show up.

If you shoot film, try this next roll. Write a one paragraph brief, ask for two lighting setups and a backup plan, and make a tiny shot list. Then compare that contact sheet to your usual one. Did you see more, or just shoot faster?

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Dell’s Project Maverick: A Top-Secret Plan to Reinvent the Systems Behind AI

17

September

2025

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When we think of disruptive business models today, we often look toward AI startups or consumer a. But some of the most radical change is happening inside legacy enterprises, far behind the scenes. Dell’s “Project Maverick” is a prime example of this silent disruptio

Unlike firms built from the ground up for AI, Dell is dealing with vast organizational resources: 4,700 applications, 70,000 servers, and over 10,000 databases. The plan, launched in late 2024, is to consolidate and modernize them into a standardized platform, initially affecting Dell’s Client Solutions Group in early 2026, followed by the Infrastructure Solutions Group (Business Insider, 2025).

Simultaneously, Dell is building out its AI infrastructure stack. With innovations in ObjectScale, the latest AI Data Platform update (developed with NVIDIA and Elastic) can handle everything from unstructured data ingestion to real-time analysis across massive datasets. New servers powered by Blackwell Ultra GPUs promise up to four times faster AI training than previous generations (Technology Magazine, 2025; Reuters, 2025).

Crucially, Dell is not doing this alone. The company has partnered with Deloitte consultants to guide Project Maverick and is advancing its AI Factory initiative. This combines hardware, software, and services so enterprise customers can deploy AI more seamlessly, whether on-prem or in the cloud (Business Insider, 2025; Technology Magazine, 2025).

However, scaling internal infrastructure is expensive and complex. Risks include delays, data migration errors, employee resistance, and the uncertainty of whether customers will immediately feel the benefit. The transformation may improve agility and capability, but only if Dell avoids another cycle of tech-debt accumulation.

Project Maverick demonstrates that true disruption often does not lie in flashy apps, but in the systems that support them. The question is: can Dell reinvent itself fast enough to compete with AI-native rivals, or will its outdated foundations prove impossible to escape?

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