“an agent that automatically reads your screenshots and files them into the right project folder”
SnapSorter 9000
“Congratulations, you just reinvented Hazel from 2009 but with a GPU and existential dread.”
An agent that uses vision AI to read screenshot content and automatically routes files into the correct project folder based on what it sees.
The core loop — watch a folder, read image content, move file — is maybe 80 lines of Python using GPT-4o Vision and watchdog. The 'filing into the right project' part is the only interesting bit, and it collapses the moment someone has 40 similarly-named projects. This is a utility, not a product.
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Viability Analysis
Pros & Cons
What's going for it
What's against it
Who You're Up Against
Auto-tags and organizes screenshots by visual content — dead/abandoned
Open Source Alternatives
When Will Big AI Kill This?
Most Likely Killer
Apple
Timeline: 12-18 months
How They'll Do It
macOS 16 ships with a Finder Intelligence feature that auto-tags and suggests folders for new files using on-device Vision. It's free, private, and already on everyone's machine.
Your Survival Strategy
Go cross-platform (Windows + macOS) and focus on team shared folder structures — Apple won't touch enterprise multi-user filing workflows.
Confidence
If You're Crazy Enough to Build It
Solo Dev Time
3-5 days for a working prototype, 3 weeks to make it not embarrassing
Team Size
One bored developer on a long weekend who owns a Mac
Estimated Cost
$200-800 in API costs to validate, $3K-8K to ship a polished v1
Tech Stack
Agent-Readiness Score
Ready to scaffold today. SnapSorter 9000 could be a working prototype in a week.
- Memory ↗22/25
Stateless or single-session — minimal memory layer.
- Tools ↗11/25
Crowded market: at least 8 integrations to compete.
- Policy ↗16/25
Mid-size policy surface — define refusal categories before launch.
- Evals ↗22/25
Established eval pattern — golden datasets and public benchmarks already exist.
DETERMINISTIC SCORE — DERIVED FROM EXISTING ANALYSIS, NO SECOND LLM CALL
⚡ Ship it anyway
The version that survives
The bot says you're late. Fine. Here's the one version of this that isn't dead on arrival — if you're stubborn enough to build it.
The wedge that isn't taken
Auto-learn project folders by watching WHERE YOU manually move files for 2 weeks — zero setup, pure behavioral inference. Nobody's done the learning layer.
Test this before you write a line of code
That users have consistent enough folder structures for the AI to get it right >80% of the time. One wrong move and they'll never trust it again.
The honest cost — and who should walk away
~$5K and 3 weeks. Do NOT build this if you're on Windows, hate Swift, or expect enterprise sales — this is a $9/month indie app, not a Series A.
Think the wedge holds? ↓ Pressure-test it live before you sink a weekend into it — 20 min, free, no signup.
🔥 Second opinion
Verdict says don’t. Want a second opinion from the human who built the roaster? 20 min, free.
We'll pressure-test the wedge above together — is that differentiator really still open, does the riskiest assumption survive contact, what to build first. No signup, no slides.
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A human (the person who built the roaster) reads it and emails you back — whether it's worth building, what to skip, and the fastest V0. No signup, no list.
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How this was generated
Production-readiness odds
Worth pursuing — but expect the production gap to be the long pole, not the prototype.
ANCHORED TO OUR OWN READINESS RUBRIC — NO EXTERNAL STAT CITED
🛡 Safety considerations
What these mean →Heuristic, not exhaustive. Surfaces the 3 biggest categories an operator should think about for this idea. Hover any chip for the mitigation pointer.
⚖ Governance checklist
7 controls applyThings to have in place before you ship. Pairs with the OWASP-style risk chips above — that catalog answers “what could go wrong?”, this one answers “what should you have ready?”
Audit trail of every tool call
criticalPersist a structured per-call log of inputs, outputs, and decisions for at least the legal retention window. Without this, post-incident review is impossible.
Role-based access control on the agent surface
criticalDifferent users, different scopes. The agent should never default to "admin can do everything." Pair with per-task capability scoping.
Tenant / workspace isolation
criticalA multi-tenant agent must never leak data across tenants in either direction (inputs OR cached intermediate state).
Secrets management
highTokens and API keys live in a vault, not in env vars on a CI runner. Rotate on a documented schedule, not "when something happens."
Eval coverage on every release
highA frozen eval suite that runs on every model / prompt change. "It worked when I demoed it" is not a release gate.
Per-user / per-tenant rate limits
mediumAgent loops are pathologically expensive when wrong. Cap tokens-per-session, tool-calls-per-session, and dollars-per-day before launch.
Pin model versions; track the changelog
mediumA silent provider-side model upgrade can shift behavior overnight. Pin to a versioned model ID; subscribe to the provider changelog.
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