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cppq VS Skimrr

Compare cppq VS Skimrr and see what are their differences

cppq

Simple, reliable & efficient distributed task queue for C++

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0 reviews
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Skimrr

Duplicate and blurry photos, found locally. Nothing else.

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DisplaySwitcher
Switch your monitor’s video input and seamlessly transfer text and files! A Linux/macOS/Windows Desktop Application. sponsored

Base details

Website, pricing, platforms and company facts side by side.

cppq
Skimrr
Website github.com skimrr.com
Pricing —
Official pricing
Platforms —
MacOS Windows
Company — Startup from France · 2026
Listed in —
Duplicate File Finder Photo Management File Management Photography Tools Utilities +2

About cppq and Skimrr

In their own words, as submitted to SaaSHub.

cppq
Skimrr

No description of cppq yet.

Skimrr finds duplicate photos and blurry shots in a folder — entirely on your computer. What it does Duplicates: exact copies matched by hash, plus near-duplicates (cropped, recompressed, lightly edited) matched by what the picture actually looks like. Similarity threshold adjustable live. Blur:...

Read more about Skimrr

Features and specs

What each product offers, as listed by its team.

cppq 5 features
Skimrr 12 features
  • Lightweight C++ implementation
    cppq is written in modern C++ and designed to be a minimal, efficient task queue library, making it a good fit for performance-sensitive applications where the overhead of scripting language runtimes is undesirable.
  • Redis-backed reliability
    Built on top of Redis as the message broker, cppq benefits from Redis's persistence, atomic operations, and wide adoption, giving the queue system durability and the ability to survive process restarts.
  • Inspired by proven design (asynq)
    The library's architecture mirrors the popular Go asynq task queue, so it inherits a well-thought-out API design for task enqueueing, retries, and worker processing patterns that have already been validated in production systems.
  • Native integration for C++ projects
    For teams already working in a C++ codebase, cppq allows adding background job processing without needing to introduce a separate service written in another language (like Go or Python), simplifying the technology stack.
  • Open source and inspectable
    Being open source on GitHub, developers can review the source code, modify it for custom needs, and contribute improvements, which offers transparency not available with closed-source queueing solutions.

Possible disadvantages

  • Limited maturity and battle-testing
    Compared to established task queue libraries like Sidekiq, Celery, or asynq itself, cppq is a newer and smaller project with less real-world production usage, which may translate into undiscovered bugs or edge cases.
  • Smaller community and ecosystem
    Because it has far fewer users and contributors than mainstream task queue systems, finding community support, third-party tutorials, or pre-built integrations is more difficult.
  • Documentation gaps
    As a smaller project, cppq may have less comprehensive documentation compared to major task queue frameworks, requiring developers to read source code directly to understand advanced features or edge-case behaviors.
  • C++ build and dependency complexity
    Using cppq requires managing C++ build toolchains and dependencies (such as a Redis client library), which can be more cumbersome than installing a package via pip or npm in other ecosystems.
  • Fewer built-in features and tooling
    cppq likely lacks the extensive ecosystem tooling of more mature queues, such as web-based monitoring dashboards, extensive metrics/observability integrations, or advanced scheduling features found in more established solutions.
  • Exact duplicate detection
    Finds byte-identical photos instantly using content hashing.
  • Near-duplicate detection
    Spots cropped, recompressed, or lightly edited copies by comparing what the image actually shows, not just its file hash.
  • On-device vision review
    A local vision model double-checks every proposed duplicate group before it's shown, dropping false matches.
  • Blur detection
    Sharpness score calibrated to each folder, so an intentional soft-focus portrait is never flagged by mistake.
  • Adjustable similarity threshold
    Live slider to tune how strict duplicate matching is, per scan.
  • Side-by-side comparison viewer
    Zoom-synced two or four-pane view for comparing photos in a group at full resolution.
  • Local reversible trash
    Deleted files move to a timestamped local trash, fully restorable until emptied — nothing is deleted outright.
  • Review grid before any action
    Every batch is shown as thumbnails first; nothing moves without explicit confirmation.
  • Gallery grouped by day
    Photos organized by the day they were taken, with an optional merge of matching dates already in Photos (macOS).
  • Broad format support
    JPEG, PNG, HEIC/HEIF, WebP, GIF, BMP, TIFF, and major camera raw formats (ARW, CR2, CR3, NEF, ORF, RW2, RAF, PEF, DNG, and more).
  • 100% local and private
    No account, no cloud upload, no telemetry — only a one-time licence check reaches the network. Six languages
  • Six languages
    Available in English, French, Spanish, German, Japanese, and Chinese.

Analysis

An editorial look at what each product does well and who it suits.

cppq
Skimrr

Overall verdict

  • cppq is a lightweight, Redis-backed distributed task queue for C++ inspired by Go's popular 'asynq' library. It's a good choice for developers who need a simple, dependency-light job queue system in C++ without the overhead of larger enterprise messaging systems, though it's a smaller, less battle-tested project compared to mature alternatives.

Why this product is good

  • Simple and lightweight design makes it easy to understand and integrate into existing C++ projects
  • Built on Redis, a well-established and reliable backend for queue storage, giving it a solid foundation
  • Inspired by asynq (a proven Go library), so the design patterns and task queue semantics are based on a well-thought-out model
  • Open source on GitHub, allowing developers to inspect, modify, and contribute to the code
  • Good fit for projects already using Redis, minimizing the need for additional infrastructure
  • Straightforward API for enqueueing and processing background jobs in C++ applications

Recommended for

  • C++ developers needing a background job/task queue without adopting a heavyweight message broker
  • Projects already using Redis as part of their infrastructure
  • Small to medium-sized applications requiring asynchronous task processing
  • Teams comfortable working with less mainstream or smaller open-source libraries and willing to contribute or debug issues themselves
  • Developers familiar with asynq (Go) looking for a similar pattern in C++
  • Prototyping or hobby projects where a minimal task queue solution is sufficient

No analysis of Skimrr yet.

Videos

Walkthroughs and reviews on video.

cppq 0 videos + Add
Skimrr 1 video + Add

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How to Delete Duplicate & Blurry Photos FAST (2026)

Questions & Answers

As answered by people managing cppq and Skimrr.

What's the story behind your product?

Skimrr's answer:

It grew out of a very ordinary problem: the photo folder nobody opens, years of imports nobody sorted, full of duplicates and near-misses nobody's confident enough to delete by hand. (There's a longer writeup on this: https://medium.com/@admin_53263/the-folder-nobody-opens-1b897eb33eed)

Why should a person choose your product over its competitors?

Skimrr's answer:

Most duplicate-finder tools either ask you to upload photos to a cloud service, rely purely on hashing (missing edited near-duplicates or flagging unrelated similar shots), or use a one-size-fits-all blur cutoff that misfires constantly. Skimrr keeps everything on your machine, adds a local vision-model sanity check most competitors skip entirely, and never deletes anything without a review grid and an undo-able trash.

What makes your product unique?

Skimrr's answer:

Every proposed duplicate group is checked by an on-device vision model before it's shown, not just matched by hash, so near-duplicates (cropped, recompressed, lightly edited) get caught without a flood of false positives. The blur score is calibrated per-folder rather than using a fixed threshold, so a soft-focus portrait or a busy, detailed scene doesn't get misjudged. Everything runs 100% locally in Rust, with no account, no cloud, no telemetry, and nothing is ever deleted outright: files go to a reversible local trash first.

Which are the primary technologies used for building your product?

Skimrr's answer:

Tauri 2 and Rust for the desktop app and the core scanning, hashing, and clustering engine; a local MobileNetV2 ONNX model run through Candle (a pure-Rust ML framework, keeping the whole app free of Python or native ML dependencies) for duplicate verification; and React with TypeScript and i18next for the six-language UI.

How would you describe the primary audience of your product?

Skimrr's answer:

People sitting on large personal photo libraries built up over years, with multiple phones, cameras, and import sessions, who want to reclaim disk space without handing their personal photos to a cloud service. Photography enthusiasts, people who regularly import from SD cards, and anyone privacy-conscious about where their photos go.

User comments

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