The Computomatix Times

Step 1 - Choosing a Niche
The real secret is the niche. It determines everything:
The model's appearance and style
Content format and triggers
Audience engagement and account growth
The AI influencer market appeared literally a few months ago. The real potential of niches is still untapped copying successful girls makes no sense, you need to find your own direction.
Three working directions:

1. SubculturesAnime, cosplay, female streamers, football club fangirls. Timeless trends with maximally engaged audiences.
Examples: you can take a clip of a female CS2 streamer and replace her with your AI model. Or run an account of a cosplayer from the Marvel universe. A single post touching on a niche debate within a community can generate massive engagement just from one small detail.
2. Travel and eventsYour model can be on the Cannes red carpet, in Tokyo, or at a concert right now, without leaving home. This type of content feels expensive to produce because the viewer intuitively senses the effort behind it.
3. Physical featuresBirthmarks, scars, unique facial features as an additional trigger, but not as the foundation of the account. Unrealistic appearances caused a sensation a few months ago now it no longer surprises anyone.

Hypothesis: create a girl who is a fan of a football club and regularly mention one player as the best on the team. A wave of hate + a wave of support = bonus engagement and an algorithm boost.
Step 2 - Creating the Face
AI without references produces averaged-out looks technically attractive, but without character. You scroll through new AI accounts and every face looks copy-pasted. Here's why.
Tools:
Pinterest - searching for references
Nano Banana Pro (Higgsfield) - face merging

Process:
1. Find two photos of different girls with clearly visible faces
2. Upload both + the prompt to Nano Banana Pro:
Integrate a face into an existing scene. Substitute the face in the reference image with the face from the donor image. The objective is a seamless merge: the new face must inherit the exact expression, pose, and lighting interaction from the reference, while its color attributes (hair and eyes) are adapted from the donor for a perfectly harmonious and natural result.

3. Get the result. If needed add a distinguishing feature (birthmark, unusual eye color)
How to choose faces correctly
The main mistake is picking similar-looking faces. The neural network smooths out the differences and you end up with an averaged result again.
Pick contrasting faces and assign roles in advance:
First face base: sets the vibe of the niche (sharp cheekbones, "cold" look)
Second face donor: softens, adds attractiveness (baby face, full lips)

Face types by niche:
Cosplay, anime, game characters
Strong face goth, alt girl

Soft/romantic face fashion, luxury lifestyle

Hypothesis: find photos of girls of different ethnicities in the anime girl niche, merge them and add a birthmark as a distinguishing feature this instantly combines subculture and appearance triggers.
Step 3 - Creating the Video
The core of the method: AI replicates the movements of a person from the reference video and fully replaces the model in the footage.
3.1 Finding a Reference
Search on TikTok and Instagram. Use your niche as the search query goth girl, anime girl, cosplay.
Signs of a good reference:
High view count
Charismatic, expressive facial movements
Romantic undertone
Triggers from your niche
Key rule: the closer the person in the reference looks to your model — the more realistic the result. Kling struggles to transfer the movements of a long-haired girl onto a short-haired model.
3.2 Creating the First Frame
You can't just insert a photo of your model and hit "generate." Kling uses the background and pose from the uploaded photo, not from the reference. So first, you need to create a starting frame.
Upload to Nano Banana Pro:
1. A photo of your AI model

2. A screenshot of the first frame of the reference video

3. Prompt:
Take the girl's face and body from the first image, and the pose, emotion, and background from the second. Use the girl's face from the first image as the character's face and replace it in the second image, it is necessary to accurately convey emotion and playfulness, it is necessary to accurately convey the appearance of the girl from the first image without changing her appearance, the photo must be alive, the girl is not a doll, sincere real, photo taken on an iPhone phone camera.

3.3 Generating in Kling
Upload to Kling Motion Control: the first frame + the reference video. In the advanced settings, paste the prompt:

Use the attached reference video as the sole motion blueprint and transfer its movement onto the character from the attached photo(s), preserving the character's exact identity, body proportions, face and hair features, skin texture, clothing fit, and overall silhouette with zero morphing, zero style drift, and no added accessories; match the reference motion precisely frame-by-fr… continue on X ↗

We built an API that turns every SEC filing into structured, real-time data.
Dilution ratings across 2,000+ tickers. Active shelf registrations. Cash runway calculations. Bank agreements with ROFR and tail financing clauses. Pump-and-dump risk scores.
The kind of data you'd normally pay $50K+ a year to access, and for some of these fields, data that literally doesn't exist on Bloomberg or any other institutional provider.
Our API is already being used by $1B+ funds, prop desks and investment banks.
And as of today, it's open to retail for the first time.
Here's how we got here, and what you can build with it.
I — Why This Data Doesn't Exist Anywhere Else
Most traders assume that if something matters, Bloomberg has it.
For large-cap equities, that's mostly true.
For small-caps, the space where 90% of retail trading pain comes from dilution, offerings, and pump-and-dumps, the institutional data providers fall apart.
Here's what they're missing:
Float that actually reflects reality. When a company converts debt to shares, the float changes overnight. Most providers don't update for months. We tack it on within 24 hours of the filing. That one field alone influences shelf capacity, offering ability, and downstream dilution risk, and no one else is doing it right.
Right of first refusal and tail financing. When you see H.C. Wainwright underwrite a small-cap offering, there's usually a contract locking the company into them for the next 12–24 months, with tail fees that keep the relationship sticky even if the company switches banks. This data sits inside exhibit agreements buried in filings. Structured. Queryable. Nowhere else.
Accurate cash runway. Most "months of cash remaining" calculations are a quarterly cash divided by a quarterly burn. Ours accounts for recent raises, warrant exercises, and actual operating burn pulled from the most recent 10-Q, updated filing by filing.
Shelf capacity relative to float. A 10M share shelf on a 2M share float is a completely different situation than the same shelf on a 500M share float. We calculate this ratio in real time. Most providers don't even store shelf data in a queryable format.
Pump-and-dump pattern scoring. Per-ticker scores for country, underwriter, float, and scam risk — each derived from structured filing data and paired with social-media evidence of an orchestrated pump-and-dump.
II — What It Took to Build Out This Data
Three years. Sleepless nights. A lot of things that didn't work.
The core problem: SEC filings are text. Thousands of pages of unstructured legal language, filed across dozens of form types, updated constantly. If you want structured data out of them, you either hire a team of analysts to read every filing by hand, or you build a system that can do it reliably at scale.
We chose the second path. Here's the rough shape of what it took:
Monitor every filing that can change capital structure. Not just the obvious ones (10-K, 10-Q, etc). The quiet ones too, with buried warrant exercises, debt conversions in exhibits, prospectus supplements that change shelf capacity mid-flight.
Build parsers for every form type. Each filing type has its own structure, its own language, its own edge cases. What a PIPE looks like in one 8-K exhibit is not what it looks like in another. The parsers have to handle all of it.
Layer AI on top of parsing. AI finds the keywords and phrases that suggest a dilution event, a new agreement, a compliance issue. Then manual verification commits the data. AI gets us 80% of the way; human review catches the edge cases that would otherwise corrupt the dataset.
Iterate constantly. Filing templates change. New deal structures emerge. Companies find new ways to raise capital that didn't exist five years ago. If the system isn't updated in real time, the data decays.
III — What You Can Build With It
The API has a host of endpoints covering dilution ratings, offerings, registrations, Nasdaq compliance, float, ownership, and bank agreements. Here are three things you can build today that would have taken a team of analysts to assemble manually.
1. A Dilution Risk Monitor

A watchlist dashboard that surfaces dilution warning signs across your portfolio in real time.
For each ticker, you get the overall dilution risk rating, active shelf registrations with remaining capacity, Nasdaq compliance deficiencies, and a cash runway calculation that tells you when the company will need to raise. Alert on things like cash dropping below 6 months, a new shelf going effective, or ATM capacity getting large relative to float.
2. Backtest Low-Float Gappers
Use the historical float endpoint to check performance on historical gappers under 1m float. Use the news endpoint 'tags' to see how gappers performed under certain news.
3. A One-Click Due Diligence Report
Take any ticker and generate a full due diligence report in seconds, ownership concentration, float history, reverse split count, ROFR agreements with active banks, upcoming lockup expiration… continue on X ↗

I've been building iOS and macOS apps for a while, and there's one thing I kept getting wrong after shipping: the screenshots.
I treated them like a design task. Pick five good screens, add overlay text describing the features, and export at the right sizes. Done. Meanwhile, downloads were flat (I'm looking at you: Super Easy Slides).

Here's what I learned from Theodora: 70% of screenshot effectiveness comes from the text overlay — not the UI. One app saw an 80% conversion lift just from rewriting the copy on existing screenshots.

The design didn't change.
The words did.
This is the thing most developers never fix.
Want the Full Playbook?
Download the App Store Screenshot Optimization Playbook — 100 best practices drawn from @DesignerAnts, the expert behind 1,000+ App Store screenshots. Drop it into Claude or Codex and fix your listing this afternoon.

Want her to do it for you? Hire Theodora →
The mistake is easy to make
When you're deep in building, you know your app inside out. So when it's time to write screenshot text, you naturally describe what the app does:
"Dark mode support."
"iCloud sync."
"Customizable widgets."
Those statements are true. They're also completely useless to someone who doesn't know why they'd want your app.
Feature descriptions answer the wrong question. A user scanning your App Store page isn't asking "what does this app do?" They're asking "is this for me?" Feature lists don't answer that.
Your screenshots read like patch notes to someone who hasn't bought in yet.

What actually works
Change your mindset: stop describing features. Show what changes for the user.
Not: "customizable dashboard"
Rewrite: "see everything that matters, at a glance."
Another example:
Not: "workout tracking"
Rewrite: "you'll never forget what you lifted again."
Same app. Same screen. Different download rate.
The reason this works: specificity makes the promise real. "Productivity app" is invisible. "Never lose a meeting note again" is a reason to download. The more precisely you describe the user's life after your app, the more they can see themselves using it.
Cover your UI with your hand and read only the text. Does it tell a story? Or does it list features?
Most apps fail this test immediately. Mine included.
The sequence that converts
Your screenshots should only make sense in order. If they work in any sequence, you have a catalog, not a story.
Here's the sequence @designerants recommends:
Screenshot 1 — Name the pain. Their frustration, before they found you. ("Buried in notes you'll never find again?")
Screenshot 2 — State the shift. What changes when they use your app. ("Everything you capture, organized automatically.")
Screenshot 3 — Show proof. Numbers, users, and a concrete result. ("Used by 10,000 developers every day.")
Screenshots 4–5 — Feature delivery. The one or two capabilities that actually deliver the promise from Screenshot 2.
Each screenshot does one job. One message. If it needs two sentences to explain, split it into two screens.

One more rule: text is the product
Your UI is evidence. Your text is the argument.
Write the headline for each screenshot before designing the screen. If you can't say the change in 8 words, you don't understand it well enough yet. Then let the UI behind it serve as the visual proof.
Treat the words as the product. Everything else is supporting material.

What I Shipped vs. What I’d Ship Now
I never planned to publish this app. I built it for myself.
But a friend asked about it—so I quickly put together my App Store sales page before I discovered these screenshot tactics.

This is the copy Super Easy Slides launched with:
Notes -> Slides. Instantly.
Full Screen Always Readable.
Slides Over Any App.
Auto-Numbering that Works.
At the time, this felt right.
I was trying to:
Clearly explain what the app does
Highlight key features
Keep everything simple and direct
And on paper, it checks out.
What’s wrong with this
Looking at it now, the problem is obvious:
This describes the product—but it doesn’t sell it.
These read like feature labels, not headlines
There’s no clear user pain or motivation
Nothing really grabs attention or stops the scroll
It assumes the user already understands why this matters
This is the mistake I made:
I focused on what the app does instead of why someone would want it.
The rule I missed
From Theodora’s approach:
Each screenshot should work like an ad.
That means:
Lead with a pain or desire
Show the outcome
Support it with the feature
Not the other way around.
What I’d change
Here’s how I’d rewrite the same ideas:
Before
Notes → Slides. Instantly.

After
Stop Designing Slides
Write notes. Start presenting.

Before
Full Screen Always Readable
After
Stay Focused While You Present
Clean slides. No distractions.
Before
Slides Over Any App
After
Stay In Your Flow
Present without breaking your momentum.
Before
Auto-Numbering that Works
After
Never Fix Slides Manually Again
Your structure stays clean automatically.
Why… continue on X ↗