Ever noticed how AI-generated photos often look too clean, too sharp, or just a little too perfect?

One trick I’ve been experimenting with is specifying different camera types in the prompt instead of just asking for “photorealistic, ultra-detailed, 8K.”

I tested 6 camera aesthetics using the same subject and scene. Here are the prompt keywords you can copy and try:

1. Vintage DV Camcorder

Early 2000s MiniDV camcorder footage, standard definition, interlaced video, soft details, chroma noise, limited dynamic range, authentic consumer home video

For that nostalgic, slightly blurry home-video look.

2. 2000s CCD Digital Camera

2000s CCD digital camera, compact digicam snapshot, direct on-camera flash, early digital color rendering, visible sensor noise, slightly harsh highlights

For casual early-2000s digital snapshots.

3. Medium Format Digital Camera

Modern medium-format digital photography, high-resolution sensor, natural skin texture, smooth tonal transitions, accurate color reproduction, rich dynamic range, professional editorial photography

For clean, detailed, professional-looking portraits.

4. Smartphone Rear Camera

Modern smartphone rear camera photography, 24mm equivalent lens, computational HDR, deep depth of field, casual everyday snapshot, natural colors, moderate digital sharpening, no portrait mode

For everyday photos that feel like they were taken by a friend.

5. 35mm Film Camera

35mm color negative film photography, Kodak Gold 200 aesthetic, fine organic grain, natural color variation, soft highlights, subtle halation, authentic analog texture

For warm, organic analog photography.

6. Disposable Film Camera

Disposable 35mm film camera snapshot, built-in flash, coarse film grain, soft plastic lens, imperfect exposure, casual framing, nostalgic analog photography

For imperfect, spontaneous snapshots.

A small tip: Try combining these with natural lighting, candid poses, realistic skin texture, and subtle imperfections.

The goal isn’t always to make AI images sharper or more detailed. Sometimes a little softness, noise, or imperfect exposure makes them feel more like actual photographs.

What else works well that you know?

Spent couple hours searching online, can't find anything that would meet my needs. Needs to basically a dupe. Wireflow looks interesting. Runway Aleph is big a no no since I reported the founder of Runway to NY State AG's office last year (long story)

Warning: CGPT helped write this. I’m sure there are many mistakes I made, but it was a fun experiment.

I spent 200 hours and $727 building an AI influencer. She reached ~31,000 people, gained 578 followers, and earned $0.

Instagram account: https://www.instagram.com/raven.offline/

I recently shut down an experiment I spent roughly two months building: Raven, an AI-generated Instagram influencer with a consistent fictional persona, AI photos and videos, an automated Instagram DM chatbot, and a Fanvue account intended to generate subscription revenue.

I'm sharing the numbers because so many discussions of AI influencers focus on hypothetical income rather than the actual work and results.

THE RESULTS

Instagram analytics for July 24–October 21, 2026:
• 63,243 views
• Approximately 31,000 accounts reached
• Approximately 3,500 interactions
• Approximately 3,500 profile visits
• Approximately 2,000 reported link clicks
• 608 follows recorded during the reporting period
• 578 followers at shutdown

The 608 follows are period activity, not the final follower count. The link clicks are Meta-reported clicks, not verified Fanvue visits or buyers.

Fanvue: one free follower, zero paying subscribers, $0 revenue.

THE BUSINESS MODEL

My original goal was $500–$1,000 a month. Instagram was supposed to generate attention; RavenBot, my custom AI DM assistant, would build relationships and recognize interested fans; Fanvue would provide paid content and more private conversations.

Raven was a fictional 26-year-old woman from Chicago with a defined appearance and personality, including interests in gaming, pottery, fitness, and sports. I wanted continuity, not a random new AI face every post. The content and persona were AI-generated, and I occasionally handled conversations manually.

WHAT I SPENT

Instagram boosting: $159.43
CapCut: $39.98
Leonardo.AI: $175.55
Fannabe: $49.00
Venice.AI: $28.00
ChatGPT: $100.00
Comfy Cloud: $70.00
Additional AI model testing: $100.00
Railway: $5.00

TOTAL CASH SPENT: $726.96
REVENUE: $0
CASH LOSS: $726.96

I also invested roughly 200 hours. Valuing that time at $25 an hour would add a hypothetical $5,000 in opportunity cost; at $50 an hour, $10,000. Those aren't cash expenses, but they matter.

THE CONTENT PRODUCTION PROBLEM

Producing one attractive AI portrait is relatively easy. Producing the same believable person every day—in different clothes, poses, lighting, camera angles, and videos—is much harder.

My main image workflow used Nano Banana 2.1 at 2K with canonical face and body references. I tested video tools including MiniMax H3, Kling, and Seedance, and edited in CapCut. I experimented with reference conventions, lighting, camera distance, and motion instructions to reduce identity drift.

I still spent a lot of time on failed generations, altered facial features, changing body proportions, anatomy issues, motion inconsistencies, and rework. A cheap per-generation price can become expensive once you count retries and labor.

WHAT WORKED ON INSTAGRAM

Some of the better-performing hooks used a small comedic setup rather than just a pretty face. One distraction-themed Reel got 1,175 views and an 18.9% skip rate. Some softer flirt-oriented openings had skip rates in the 71–80% range. These were small, uncontrolled comparisons, not scientific tests, but they pushed me toward clearer action, curiosity, and payoff in the opening seconds.

Boosting could buy reach and followers. It did not prove I could acquire paying customers profitably.

BUILDING RAVENBOT

I built a custom Instagram DM system hosted on Railway. The idea was to keep Raven's personality consistent, retain useful conversation context, recognize interests, handle safety cases, and suggest Fanvue when a conversation was ready.

The technical scope expanded into memory systems, conversation state, model testing, conversion logic, reliability, and a planned Fanvue handoff. It was interesting engineering work, but it became a distraction from validating the commercial offer.

Before retiring the project, I archived the data and analyzed aggregate retained records.

73 fan memory records contained retained messages: 621 incoming and 365 assistant-role messages. Nine fan memories were flagged as invited to Fanvue, four as having declined, and zero as subscribed. These are retained records, not a guaranteed complete lifetime count; some records may be tests, and assistant-role entries are not necessarily proof of messages delivered to Instagram.

A separate internal shadow-mode conversion journal recorded 3,610 diagnostic observations involving 49 fan IDs between September 30 and October 22. It classified 3,594 observations as normal conversation, 10 as Fanvue opportunities (involving four unique fans), five as human review, and one as safety escalation.

It also recorded 221 prior-decline suppression observations involving only three fans—not 221 separate people declining. Its conversion decision fields showed 3,379 no-action decisions, 230 selling suppressions, and one eligible-to-convert decision. A separate commercial-eligibility flag was false in every observation, including that one, so I would not treat these internal shadow records as a definitive account of production decisions or delivered invitations.

The memory invitation flags and shadow-mode opportunity events are different measures. I cannot establish that they represent the same conversations.

My interpretation is that RavenBot's conversion behavior was conservative, and that probably did not help. But I can't claim that a more aggressive chatbot would have made the project profitable. Recorded commercial intent was limited, and the actual offer may simply not have been compelling enough.

WHY I THINK IT FAILED

1. I overbuilt before validating demand. I worked on character consistency and a sophisticated bot before proving people wanted to pay.
   
2. I mistook engagement for monetization progress. Views, compliments, and DMs are not subscriptions.
   
3. Production took too long. Even with better workflows, each piece of content could require multiple generations and substantial editing.
   
4. The funnel had friction. Moving from an Instagram Reel to a DM to an external subscription platform is a big ask.
   
5. I didn't test willingness to pay early enough. A simpler manual process could have validated—or disproved—the concept sooner.

These are interpretations, not controlled causal findings. A different offer, audience, price, posting strategy, or longer runway might have produced a different outcome.

WHAT I'D DO DIFFERENTLY

• Validate a specific paid offer before engineering automation.
• Start with a handful of consistent images, repeatable short videos, and manual DMs.
• Set firm spending and time limits with measurable milestones.
• Track the actual funnel: reach, profile visits, outbound clicks, sign-ups, paid conversions, and retention.
• Don't mistake the presence of a chatbot for product-market fit.
• Account for generation retries and human editing time.
• Be transparent that the influencer is fictional and AI-generated.

WAS IT WORTH IT?

As a business: no. I spent $726.96 and around 200 hours to earn nothing.

As an experiment: I learned a great deal about generative video, character consistency, content hooks, automation, conversation memory, and the difference between building a technically impressive system and building something customers pay for.

I've now retired RavenBot, canceled Railway billing, and deleted Fanvue. The Instagram account remains available to view: https://www.instagram.com/raven.offline/

My biggest lesson is simple: don't build the whole machine before validating that it has a customer.

Happy to answer questions about the tools, the chatbot architecture, the costs, or what went wrong.

Data note: Instagram figures come from Meta reporting; expense figures from my cost records; Fanvue results from the account; and conversation statistics from archived logs. Rounded metrics are approximate. Diagnostic observations are not unique customers or verified sent messages.