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Awnsy VS cppq

Compare Awnsy VS cppq and see what are their differences

Awnsy

Menu bar translator for macOS. Select text, press ⌘C twice, and the translation appears over the window. Runs a local model: no account, no key, offline.

Rating
0 reviews
Pricing
Freemium $1.99 / Monthly (Pro)
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cppq

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

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0 reviews
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Usejarvis
Turn business tasks into reusable workflows with company context, AI judgement and human approvals. sponsored
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

Base details

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

Awnsy
cppq
Website awnsy.com github.com
Pricing
Freemium $1.99 / Monthly (Pro)
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Platforms
Mac
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Company 1 - 9 employees —
Listed in
Translation AI Translation Mac Tools Translation Service Languages AI Productivity Language Translation +5
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About Awnsy and cppq

In their own words, as submitted to SaaSHub.

Awnsy
cppq

Awnsy translates text anywhere on your Mac, even text you can't select. Select text in any app and press ⌘C twice, or drag a box over part of the screen, and the translation streams into a small popup right where you are working. The translation model is downloaded inside the app and runs on your...

Read more about Awnsy

No description of cppq yet.

Features and specs

What each product offers, as listed by its team.

Awnsy 6 features
cppq 5 features
  • Double ⌘C translation
    Select text in any app, press ⌘C twice or a hotkey; the translation streams into a popup over the window
  • Screen area translation
    Drag a box over images, scanned PDFs or video; on-device OCR reads and translates the text
  • On-device translation
    Local models run through MLX: no account, no key, offline, text stays on the Mac
  • 78 languages
    Target language and model switchable right in the popup
  • Bring Your Own Key
    Use your own OpenAI or Anthropic key, or any OpenAI-compatible server
  • Read aloud and 14 UI languages
    Translations can be read aloud; interface localized into 14 languages
  • 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.

Analysis

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

Awnsy
cppq

No analysis of Awnsy yet.

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

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
Awnsy
cppq
100% 100%
Productivity
0% 0%
100% 100%
Language Translation
0% 0%
100% 100%
Languages
0% 0%
100% 100%
Translation
0% 0%

Questions & Answers

As answered by people managing Awnsy and cppq.

How would you describe the primary audience of your product?

Awnsy's answer

Mac users who work or read in languages that aren't their native one and translate many times a day: documentation, PDFs, emails, chats, screenshots. Especially people who don't want their text to leave the machine, or who need translation offline. Requires macOS 15 or later on an Apple silicon Mac.

What makes your product unique?

Awnsy's answer

The translation model is downloaded inside the app and runs locally on the Mac. There is no account, no API key and no internet needed, and the text never leaves the machine. Three on-device models are included, each covering its own set of languages; the default, Hy-MT2 1.8B, translates a sentence in about a second on an M1 MacBook Air.

It also translates text you can't select: drag a box around an image, a scanned PDF or a video and on-device OCR reads it. Users who want cloud quality can add their own OpenAI or Anthropic key, or use any OpenAI-compatible endpoint.

What's the story behind your product?

Awnsy's answer

I'm Alexey, a software developer for about 15 years, and Awnsy is my first Mac app. I work in languages that aren't my native one and read a lot of documentation in PDFs, and I got tired of the copy, switch to a browser, paste, read, switch back loop, dozens of times a day.

The first version, in May 2025, was a hotkey that sent the clipboard to the OpenAI API and showed the answer in a popup. It worked, but it needed a key and internet, and the text left the machine. In summer 2026 the translation moved onto the Mac itself: the model downloads inside the app and runs locally. Choosing the model took a script that ran several translation models over the same texts and scored them. The one kept as default, Hy-MT2 1.8B, translates a sentence in about a second on an M1 MacBook Air; the 4B models scored about the same but were 2 to 5 times slower. Double ⌘C and screen area translation came later and are now the two most used ways in.

Which are the primary technologies used for building your product?

Awnsy's answer

  • Swift, SwiftUI and AppKit (native, no Electron, no web view)
  • MLX via mlx-swift-lm for on-device models: Hy-MT2 1.8B, TranslateGemma 4B, Qwen3.5 4B
  • Apple Vision for on-device OCR
  • StoreKit for in-app purchases
  • macOS Keychain for API keys; fully sandboxed

Why should a person choose your product over its competitors?

Awnsy's answer

  • Local by default: unlike DeepL for Mac or Google Translate, the default engine runs on the Mac, so it works offline and nothing is sent to a server.
  • Ships its own model: Easydict offers offline translation only through Apple Translate or a separately installed Ollama; Awnsy downloads its model inside the app.
  • Wider language list than the built-in macOS translation: it covers languages Apple's list lacks and needs no Apple ID, Siri or region.
  • Selected text and screen areas in one app: double ⌘C for selectable text, a drag-to-translate box for images, scanned PDFs and video.
  • No account for any tier; text translation is free and unlimited.

User comments

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