Vibecode Aftershoot
track this build5 steps, step by step0%NOT REALLY · don't bother
price $15/moyou'd save $180/yrbuild time closest consolation build: one sittingcategory 📷 photo editingreplaced by 0 people
A consolation build is possible, but the paid product's decisive value sits outside a solo rebuild. For Aftershoot, run local duplicate detection and transparent culling heuristics over a photo folder. The hard boundary is proprietary culling and editing models, photographer training data, speed, and workflow, plus image pipeline quality, models, and workflow polish.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of Aftershoot. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # Aftershoot indie build ## Goal Build the smallest trustworthy replacement for the core Aftershoot workflow for one developer or a tiny team. ## Scope Catalog local photos, run duplicate detection and transparent culling heuristics over a folder, generate previews, and export the selected originals or rendered copies. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - proprietary culling and editing models, photographer training data, speed, and workflow - proprietary raw-processing quality - cloud sync and sharing - large AI models - camera and print ecosystem If those capabilities are essential, use Immich instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build a closest honest personal substitute for Aftershoot in an empty repository. Use Tauri 2, React, TypeScript, Rust image libraries, SQLite, and local filesystem access; do not offer alternative stacks. The core loop is: catalog local photos, run duplicate detection and transparent culling heuristics over a folder, generate previews, and export the selected originals or rendered copies. Make the first run work locally with one documented command. Store all user data locally by default and make export straightforward. Put secrets in .env, ship .env.example, and never commit credentials. Index selected folders without moving or rewriting originals and store only catalog data in SQLite. Generate thumbnails and previews, read EXIF, detect duplicates by hash, and monitor file changes. Provide timeline, folders, albums, ratings, flags, tags, search, and side-by-side compare. Store edits as reversible parameters for crop, rotate, exposure, contrast, white balance, and saturation. Render exports to a new folder with explicit color space, quality, size, and metadata choices. Add catalog backup, missing-file repair, integrity scan, and a clear original-safety guarantee. Include clear empty, loading, success, and recoverable error states. Add input validation, safe filenames, and graceful handling of unavailable APIs. Write focused tests for the core transformation and one end-to-end happy path. Create a README with setup, architecture, permissions, data location, and backup steps. Do not add accounts, billing, telemetry, analytics, or a hosted control plane. Do not claim to reproduce proprietary data, network liquidity, regulated access, or frontier infrastructure. Deliberately leave out a proprietary raw-rendering engine. Deliberately leave out hosted cross-device photo sync and client galleries. Deliberately leave out frontier culling, retouching, and generative models. Finish by running the tests and listing the exact commands used. ## Required capabilities - desktop computer - local photo library - sufficient disk space - optional GPU for AI features ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a lean indie version of Aftershoot. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # Aftershoot indie build ## Goal Build the smallest trustworthy replacement for the core Aftershoot workflow for one developer or a tiny team. ## Scope Catalog local photos, run duplicate detection and transparent culling heuristics over a folder, generate previews, and export the selected originals or rendered copies. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - proprietary culling and editing models, photographer training data, speed, and workflow - proprietary raw-processing quality - cloud sync and sharing - large AI models - camera and print ecosystem If those capabilities are essential, use Immich instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build a closest honest personal substitute for Aftershoot in an empty repository. Use Tauri 2, React, TypeScript, Rust image libraries, SQLite, and local filesystem access; do not offer alternative stacks. The core loop is: catalog local photos, run duplicate detection and transparent culling heuristics over a folder, generate previews, and export the selected originals or rendered copies. Make the first run work locally with one documented command. Store all user data locally by default and make export straightforward. Put secrets in .env, ship .env.example, and never commit credentials. Index selected folders without moving or rewriting originals and store only catalog data in SQLite. Generate thumbnails and previews, read EXIF, detect duplicates by hash, and monitor file changes. Provide timeline, folders, albums, ratings, flags, tags, search, and side-by-side compare. Store edits as reversible parameters for crop, rotate, exposure, contrast, white balance, and saturation. Render exports to a new folder with explicit color space, quality, size, and metadata choices. Add catalog backup, missing-file repair, integrity scan, and a clear original-safety guarantee. Include clear empty, loading, success, and recoverable error states. Add input validation, safe filenames, and graceful handling of unavailable APIs. Write focused tests for the core transformation and one end-to-end happy path. Create a README with setup, architecture, permissions, data location, and backup steps. Do not add accounts, billing, telemetry, analytics, or a hosted control plane. Do not claim to reproduce proprietary data, network liquidity, regulated access, or frontier infrastructure. Deliberately leave out a proprietary raw-rendering engine. Deliberately leave out hosted cross-device photo sync and client galleries. Deliberately leave out frontier culling, retouching, and generative models. Finish by running the tests and listing the exact commands used. ## Required capabilities - desktop computer - local photo library - sufficient disk space - optional GPU for AI features ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a production product version of Aftershoot. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== PRODUCT.md ===== # Aftershoot product brief ## Problem A consolation build is possible, but the paid product's decisive value sits outside a solo rebuild. For Aftershoot, run local duplicate detection and transparent culling heuristics over a photo folder. The hard boundary is proprietary culling and editing models, photographer training data, speed, and workflow, plus image pipeline quality, models, and workflow polish. ## Product outcome Catalog local photos, run duplicate detection and transparent culling heuristics over a folder, generate previews, and export the selected originals or rendered copies. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - desktop computer - local photo library - sufficient disk space - optional GPU for AI features ## Explicit non-goals for v1 - proprietary culling and editing models, photographer training data, speed, and workflow - proprietary raw-processing quality - cloud sync and sharing - large AI models - camera and print ecosystem ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees. ===== ARCHITECTURE.md ===== # Architecture ## Starting brief Build a closest honest personal substitute for Aftershoot in an empty repository. Use Tauri 2, React, TypeScript, Rust image libraries, SQLite, and local filesystem access; do not offer alternative stacks. The core loop is: catalog local photos, run duplicate detection and transparent culling heuristics over a folder, generate previews, and export the selected originals or rendered copies. Make the first run work locally with one documented command. Store all user data locally by default and make export straightforward. Put secrets in .env, ship .env.example, and never commit credentials. Index selected folders without moving or rewriting originals and store only catalog data in SQLite. Generate thumbnails and previews, read EXIF, detect duplicates by hash, and monitor file changes. Provide timeline, folders, albums, ratings, flags, tags, search, and side-by-side compare. Store edits as reversible parameters for crop, rotate, exposure, contrast, white balance, and saturation. Render exports to a new folder with explicit color space, quality, size, and metadata choices. Add catalog backup, missing-file repair, integrity scan, and a clear original-safety guarantee. Include clear empty, loading, success, and recoverable error states. Add input validation, safe filenames, and graceful handling of unavailable APIs. Write focused tests for the core transformation and one end-to-end happy path. Create a README with setup, architecture, permissions, data location, and backup steps. Do not add accounts, billing, telemetry, analytics, or a hosted control plane. Do not claim to reproduce proprietary data, network liquidity, regulated access, or frontier infrastructure. Deliberately leave out a proprietary raw-rendering engine. Deliberately leave out hosted cross-device photo sync and client galleries. Deliberately leave out frontier culling, retouching, and generative models. Finish by running the tests and listing the exact commands used. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it. ===== AGENTS.md ===== # Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone. ===== MILESTONES.md ===== # Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations. ===== OPERATIONS.md ===== # Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted Aftershoot capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
README.md
# Aftershoot indie build ## Goal Build the smallest trustworthy replacement for the core Aftershoot workflow for one developer or a tiny team. ## Scope Catalog local photos, run duplicate detection and transparent culling heuristics over a folder, generate previews, and export the selected originals or rendered copies. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - proprietary culling and editing models, photographer training data, speed, and workflow - proprietary raw-processing quality - cloud sync and sharing - large AI models - camera and print ecosystem If those capabilities are essential, use Immich instead of pretending the gap is solved.
AGENTS.md
# Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs".
BUILD_PLAN.md
# Build plan ## Original build brief Build a closest honest personal substitute for Aftershoot in an empty repository. Use Tauri 2, React, TypeScript, Rust image libraries, SQLite, and local filesystem access; do not offer alternative stacks. The core loop is: catalog local photos, run duplicate detection and transparent culling heuristics over a folder, generate previews, and export the selected originals or rendered copies. Make the first run work locally with one documented command. Store all user data locally by default and make export straightforward. Put secrets in .env, ship .env.example, and never commit credentials. Index selected folders without moving or rewriting originals and store only catalog data in SQLite. Generate thumbnails and previews, read EXIF, detect duplicates by hash, and monitor file changes. Provide timeline, folders, albums, ratings, flags, tags, search, and side-by-side compare. Store edits as reversible parameters for crop, rotate, exposure, contrast, white balance, and saturation. Render exports to a new folder with explicit color space, quality, size, and metadata choices. Add catalog backup, missing-file repair, integrity scan, and a clear original-safety guarantee. Include clear empty, loading, success, and recoverable error states. Add input validation, safe filenames, and graceful handling of unavailable APIs. Write focused tests for the core transformation and one end-to-end happy path. Create a README with setup, architecture, permissions, data location, and backup steps. Do not add accounts, billing, telemetry, analytics, or a hosted control plane. Do not claim to reproduce proprietary data, network liquidity, regulated access, or frontier infrastructure. Deliberately leave out a proprietary raw-rendering engine. Deliberately leave out hosted cross-device photo sync and client galleries. Deliberately leave out frontier culling, retouching, and generative models. Finish by running the tests and listing the exact commands used. ## Required capabilities - desktop computer - local photo library - sufficient disk space - optional GPU for AI features ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden.
.env.example
# Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
PRODUCT.md
# Aftershoot product brief ## Problem A consolation build is possible, but the paid product's decisive value sits outside a solo rebuild. For Aftershoot, run local duplicate detection and transparent culling heuristics over a photo folder. The hard boundary is proprietary culling and editing models, photographer training data, speed, and workflow, plus image pipeline quality, models, and workflow polish. ## Product outcome Catalog local photos, run duplicate detection and transparent culling heuristics over a folder, generate previews, and export the selected originals or rendered copies. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - desktop computer - local photo library - sufficient disk space - optional GPU for AI features ## Explicit non-goals for v1 - proprietary culling and editing models, photographer training data, speed, and workflow - proprietary raw-processing quality - cloud sync and sharing - large AI models - camera and print ecosystem ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees.
ARCHITECTURE.md
# Architecture ## Starting brief Build a closest honest personal substitute for Aftershoot in an empty repository. Use Tauri 2, React, TypeScript, Rust image libraries, SQLite, and local filesystem access; do not offer alternative stacks. The core loop is: catalog local photos, run duplicate detection and transparent culling heuristics over a folder, generate previews, and export the selected originals or rendered copies. Make the first run work locally with one documented command. Store all user data locally by default and make export straightforward. Put secrets in .env, ship .env.example, and never commit credentials. Index selected folders without moving or rewriting originals and store only catalog data in SQLite. Generate thumbnails and previews, read EXIF, detect duplicates by hash, and monitor file changes. Provide timeline, folders, albums, ratings, flags, tags, search, and side-by-side compare. Store edits as reversible parameters for crop, rotate, exposure, contrast, white balance, and saturation. Render exports to a new folder with explicit color space, quality, size, and metadata choices. Add catalog backup, missing-file repair, integrity scan, and a clear original-safety guarantee. Include clear empty, loading, success, and recoverable error states. Add input validation, safe filenames, and graceful handling of unavailable APIs. Write focused tests for the core transformation and one end-to-end happy path. Create a README with setup, architecture, permissions, data location, and backup steps. Do not add accounts, billing, telemetry, analytics, or a hosted control plane. Do not claim to reproduce proprietary data, network liquidity, regulated access, or frontier infrastructure. Deliberately leave out a proprietary raw-rendering engine. Deliberately leave out hosted cross-device photo sync and client galleries. Deliberately leave out frontier culling, retouching, and generative models. Finish by running the tests and listing the exact commands used. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.
AGENTS.md
# Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone.
MILESTONES.md
# Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.
OPERATIONS.md
# Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted Aftershoot capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
$ choose a build depth, inspect the files, then open the complete pack in your agent · this prompt is generated from the build plan · improve it via PR
People still pay for Aftershoot because photographers pay because image quality, catalog safety, and fast handling of huge libraries matter more than cloning sliders. The recurring cost buys raw codecs, color management, metadata, previews, face models, GPU support, storage, backups, sync, and export fidelity, not just the visible interface.
xproprietary culling and editing models, photographer training data, speed, and workflow
xproprietary raw-processing quality
xcloud sync and sharing
xlarge AI models
xcamera and print ecosystem
share on X ↗"I just replaced Aftershoot ($15/mo) with one prompt"
Don't feel like building it? These folks already made it free.
RapidRAWImports folders, groups duplicates, flags blur, supports ratings, and hands the keepers into a RAW workflow without a subscription.open source↗
digiKamDuplicate search, similarity tools, aesthetic detection, pick labels, and metadata export in a mature desktop catalog; setup is less friendly.open source↗
SelektA packaged local culler with RAW previews, duplicate and burst grouping, explainable scores, ratings, and XMP export; still labelled beta.free↗all 3 free alternatives to Aftershoot →· no votes, no pay-to-list · just what's real
Aftershoot pricing
| plan | monthly | annual (per mo) | what you get |
|---|---|---|---|
| select | $15/user | $10/user | Unlimited photo culling; 1 user. |
| edit | $35/user | $30/user | Unlimited photo editing; 1 user. |
| retouch | $25/user | $20/user | Unlimited supported retouching; 1 user. |
| select + edit | $50/user | $40/user | Unlimited culling and editing; 1 user. |
| select + retouch | $40/user | $30/user | Unlimited culling and supported retouching; 1 user. |
| edit + retouch | $60/user | $50/user | Unlimited editing and supported retouching; 1 user. |
| complete | $55/user | $45/user | Unlimited culling, editing and supported retouching; 1 user. |
free tierno free tier; 30-day full-access trial with no card required
billingmonthly + annual; subscriptions auto-renew
hidden costsAn additional custom AI profile is $7/month or $70/year; keeping custom profiles after canceling Edit/Retouch costs $4/month; a subscription can be paused only once per year for up to 3 months.
verified 2026-08-14 · source ↗
Vibecode Aftershoot
Not really. Aftershoot's value is not the code: Recheck price before merge. See the honest breakdown above.
How much does Aftershoot cost?
Aftershoot costs about $15/month (Select, checked 2026-08-14), which is $180 per year.
What do I lose by replacing Aftershoot?
Honestly: proprietary culling and editing models, photographer training data, speed, and workflow; proprietary raw-processing quality; cloud sync and sharing; large AI models; camera and print ecosystem. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to Aftershoot?
Yes: RapidRAW (Imports folders, groups duplicates, flags blur, supports ratings, and hands the keepers into a RAW workflow without a subscription.) digiKam (Duplicate search, similarity tools, aesthetic detection, pick labels, and metadata export in a mature desktop catalog; setup is less friendly.) Selekt (A packaged local culler with RAW previews, duplicate and burst grouping, explainable scores, ratings, and XMP export; still labelled beta.) All 3 curated free alternatives are at vibecodeit.com/aftershoot/alternatives. The prompt is for when you want it exactly your way.