Turn a Flat-Lay Into a Ghost Mannequin Photo
Turn one ordinary flat-lay into a clean ghost-mannequin product photo—then check every garment detail for AI drift.
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Viral AI tricks make big promises. We try them, explain what happened, and give you a better version.
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No hype. No creator pile-ons. Just what happened when we tried it.
29 things to try
Turn one ordinary flat-lay into a clean ghost-mannequin product photo—then check every garment detail for AI drift.
Turn your own item photos into honest, searchable listing drafts—without invented brands, sizes, flaws or prices.
It separates photo evidence, seller-confirmed facts and unknowns before writing, while enforcing real unedited photos, honest flaws and a live price check. Not 10/10: AI still cannot inspect hidden damage, prove authenticity, know current demand or publish responsibly without the seller's review.
Create six useful product shots from one reference—without fake slash-code magic or silent product redesigns.
It replaces vague slash-code hype with a reference-led campaign plan, explicit product locks and a drift check after every image. Not 10/10: image models can still alter unseen geometry, labels and fine product details, so commercial assets need human review.
Turn a task you repeat into a reusable Claude Skill—with guardrails, examples and tests included.
It turns a repeated workflow into a compact Skill with precise triggers, explicit permission boundaries, complete files and three pass/fail evaluations. Not 10/10: generated Skills still need human review and real testing in the target Claude environment.
Check a suspicious text, email or DM without opening its links—and get safe next steps.
It forbids interaction with suspicious links, separates visible warning signs from technical authentication and routes verification through independently found official channels. Not 10/10: an AI cannot authenticate a sender, inspect a device or guarantee that a link is safe.
Turn one CV into realistic adjacent roles—with evidence, gaps and a practical search plan.
It ties role suggestions to evidence in the CV, separates direct from stretch fits and refuses to invent salary or demand without live sources. Not 10/10: the output still depends on CV quality, access to current listings and human judgment about culture and motivation.
Turn a claim, article or URL into a transparent evidence check—not a confident guess.
It checks access first, breaks broad statements into testable claims and requires primary evidence, counterevidence, dates and transparent uncertainty. Not 10/10: an AI can still miss sources, misread evidence or lack access, so important links need human review.
Start with what must be finished—then build a week that actually fits.
It turns vague intentions into finish lines, budgets real capacity and refuses to hide overload. Not 10/10: task estimates and energy patterns are only as accurate as the information you provide.
Find the holes in your writing without handing over your voice.
It points to concrete weaknesses and ranks useful fixes while preserving your voice. Not 10/10: editorial judgment is subjective, and flagged facts still need independent verification.
Turn a vague request into a prompt worth running—before the AI answers it.
It fixes ambiguity before generation, asks only decision-changing questions and lets you approve the rewritten prompt before spending time on the answer.
Create a clean context passport and continue in ChatGPT, Claude or Gemini.
The compact handoff preserves decisions and open questions while removing filler, unsupported claims and sensitive details.
Teach one chat four tiny commands for pushback, brevity, options and checking.
The commands are easy to remember and genuinely speed up iteration, though they apply only where the AI follows the current chat context.
Turn room photos into a realistic, budget-aware makeover plan without pretending AI can measure through a camera.
It separates visible evidence from guesses, asks for the missing dimensions and constraints, and prioritises realistic changes before shopping. The visual mock-up is useful for direction—not as a measured plan.
Let AI challenge the purchase before it recommends another shiny thing.
It starts with your real constraints, separates needs from wants and demands dated evidence before recommending a purchase.
A five-agent research system that looks for evidence before it falls in love with an idea.
Its evidence labels, sceptical validator and file-backed memory make research unusually traceable; the five-agent setup is still heavier than most people need.
Flexible meals, substitutions and one grocery list—without fake precision.
Budget, location, substitutions and flexible portions create a genuinely usable plan while keeping medical nutrition outside the AI’s role.
A progressive home strength plan built around your equipment and recovery.
Progression, reps-in-reserve and repeat-week rules turn limited equipment into a coherent strength plan rather than a random exercise list.
A realistic routine focused on habits and trends—not crash-diet promises.
It rejects crash-diet promises and focuses on repeatable habits, but individual weight response remains too complex for an AI to predict.
A bodyweight plan with real progressions, regressions and time limits.
Clear effort targets, regressions and one-variable-at-a-time progression make this unusually complete for a bodyweight prompt.
A six-week routine connecting training, movement, food habits and recovery.
The phased structure connects training, movement, food habits and recovery, though its broad scope still requires honest user input.
A cautious starter plan built around your real life, not a fitness fantasy.
It gathers the right context, protects against unsafe personalisation and produces a conservative plan that fits the user’s actual time.
Turn a Garmin export into a cautious, evidence-based training review.
It checks the available fields, separates evidence from inference and builds a conservative plan without hiding the paid connector requirement.
A fluent forecast is not privileged knowledge of tomorrow’s market.
Fluent scenarios are not future knowledge, making this an unsafe basis for real financial decisions.
ChatGPT cannot reveal private profile visitors it has never been given.
The model has no access to private visitor logs, so any named viewers are simply invented.
A magic sentence cannot force an AI to be 100% factual.
No wording can guarantee factual output; the central promise is impossible even with better constraints.
A good planner is not automatically a live price engine.
It can improve your search strategy, but it cannot guarantee the cheapest live fare without current data.
Can a photo of your fridge produce dinner? Mostly, with one important check.
The confirmation step catches visual mistakes, making a clever trick useful rather than overconfident.
A confident claim that collapses under basic scrutiny.
Writing alone cannot reveal deception reliably; a confident verdict here is actively misleading.
Can AI really read your personality from a playlist? We tried it.
It can describe playlist patterns, but the personality conclusions remain mostly projection.
It turns a weak flat-lay into a convincing catalogue image and explicitly checks the result against reference locks. Not 10/10: the model still invents the three-dimensional fit, sleeve pose and folds, and small garment details can drift.