Pinterest-Native Graphics: 2:3 and What Fills It
Pinterest's image pin specs explained: the 2:3 canvas, the truncation cliff past it, what actually fills the frame, and file format choices.
Versely Blog
Page 86 of 119.
Pinterest's image pin specs explained: the 2:3 canvas, the truncation cliff past it, what actually fills the frame, and file format choices.
A step-by-step outbound playbook for creators: research a brand's live ads, build a spec ad, package a portfolio, and pitch without waiting to be found.
A structured teardown reads hook, structure, on-screen text and pacing separately, then feeds a plan that reuses the pattern and none of the content.
The same caption preset reads differently over a bright kitchen than a dim night shot. Preview two styles on five seconds of your footage before committing.
A 1024px hero image is not an A4 flyer. The pixel math, the missing bleed margin, and the upscale-then-place order that print production runs on.
Shopping feeds reject on rules most sellers learn by disapproval notice, plus a scheduled minimum-resolution change worth batch-fixing ahead of the deadline.
A post tag discloses one piece of content. A profile badge recontextualizes everything you'll ever post. They are not interchangeable decisions.
One video worked. Here's what to extract into a reusable workflow — style, structure, model routing, captions — and what to deliberately leave variable.
The EU's Code of Practice on AI transparency covers two very different groups under one document. Knowing which one you are decides what you can skip.
RGB 255,255,255 is the easy part. The mask edge and the missing contact shadow are what make a white background read as pasted-on, not clean.
Refusals aren't random. The categories that reliably trigger them share one test, and knowing it lets you restructure a brief before you lose a generation.
An Elo number tells you which image strangers preferred in blind votes, not which model will nail your specific job. Here's the gap, and how to close it.
A class of image model that plans before it paints. Where the extra deliberation measurably helps, where it's wasted, and which models actually do it.
Moodboarding didn't disappear when generation got fast — it got more important. How to gather references, study them, and never copy one directly.
Grok Imagine, MiniMax H3 and Seedance 2.5 now take stacks of reference files instead of one. How to build a brand kit and match it to the right endpoint.
Most failed AI image edits aren't quality failures — they're scope failures. Masking, bounding boxes, and prompt language bound an edit differently.
Relighting holds the product still and moves the light — a distinct operation from a regenerate, and the cheapest way to turn one shot into a campaign.
The sticker price of an AI video model isn't the real cost per finished shot. How to measure your own reroll rate and budget credits against it.
YouTube is testing Top Fans, a video visible only to your top 1% of viewers. Not a paywall — a new distribution primitive gated by earned attention.
Portrait retouching now sits inside a real disclosure regime in some markets. What triggers a label, what doesn't, and how to build a compliant preset.
Most recurring content is the same edit with new footage dropped in. Save the timeline once, swap the clips each week, and learn the create-vs-update rule.
Anatomy of the soft-sell ritual ad: ritual open, product-in-context, payoff, why it pairs with Spark Ads, and when it beats a problem-solution structure.
Open weights get pitched as a strict upgrade over hosted video models. The honest version of that decision has a no branch — here's how to actually work it.
Runway launched a model router instead of a new frontier model. What that says about release cadence in AI video, and how creators should think about it.