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Equibles VS cppq

Compare Equibles VS cppq and see what are their differences

Equibles

Research US stocks — search SEC filings, hear earnings calls, and track 13F, insider & congress trades. Plus ALVIS, an AI analyst that cites every number.

Rating
0 reviews
Pricing
Open source Freemium $24.99 / Monthly (Pro — Founding Member price; $49.99 after promo)
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cppq

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

Rating
0 reviews
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VatForge
EU VAT and OSS preparation for creators and small SaaS. Brings your sales from each platform into one view, applies the right VAT rate per country, and prepares quarterly OSS figures for you or your accountant to review and file. sponsored

Which is more popular?

Based on our record, Equibles seems to be more popular. It has been mentioned 2 times since March 2021.

social mentions
2 vs 0
Stock Market popularity
100% vs 0%

Base details

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

Equibles
cppq
Website equibles.com github.com
Pricing
Open source Freemium $24.99 / Monthly (Pro — Founding Member price; $49.99 after promo) Official pricing
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Platforms
Web SaaS
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Company Startup from Portugal · 1 - 9 employees · 2025 —
Listed in
Financial Data APIs Stock Market Finance Developer Tools Fintech Investing Trading Stocks Finance Data API +6
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About Equibles and cppq

In their own words, as submitted to SaaSHub.

Equibles
cppq

Equibles is a US stock research platform that turns SEC filings, earnings calls, and regulatory disclosures into structured, verifiable data. Every US stock page includes: Financial statements and company-specific KPIs extracted from XBRL filings, with non-GAAP bridges Earnings calls with audio...

Read more about Equibles

No description of cppq yet.

Features and specs

What each product offers, as listed by its team.

Equibles 10 features
cppq 5 features
  • AI Stock Analyst
    ALVIS answers plain-English questions with interactive charts and cites every figure to the source filing
  • MCP Server
    Connect Claude, ChatGPT, or any MCP client to 90+ US finance data tools
  • REST API
    Public JSON API with API-key authentication covering the full dataset
  • SEC Filings Search
    Full-text search and AI Q&A across 10-K, 10-Q, 8-K, proxies, and more
  • Earnings Calls
    Audio playback, speaker-attributed transcripts, and AI-generated briefs
  • Financials & KPIs
    XBRL-extracted statements, segment revenue breakdowns, non-GAAP bridges, and guidance
  • Ownership Data
    13F institutional holdings, insider (Form 4) trades, and congressional trades
  • Short Data
    Short interest, daily short volume, fails-to-deliver, and squeeze scores
  • Stock Screener
    Screen US stocks across fundamentals, ownership, and price data
  • Macro Data
    FRED economic indicators, CFTC positioning, put/call ratios, and VIX history
  • 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.

Equibles
cppq

No analysis of Equibles 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
Equibles
cppq
100% 100%
Stock Market
0% 0%
100% 100%
Financial Data APIs
0% 0%

Questions & Answers

As answered by people managing Equibles and cppq.

What makes your product unique?

Equibles's answer

Equibles extracts the answers raw data feeds skip — company-specific KPIs (subscribers, deliveries, ARPU…), forward guidance, and non-GAAP bridges — from SEC filings and earnings calls, and every figure is cited back to the source document.

The same dataset is available three ways: the website, ALVIS (an AI analyst that answers with charts and citations), and an MCP server + REST API so Claude, ChatGPT, or your own code can work with primary-source data directly.

Why should a person choose your product over its competitors?

Equibles's answer

Most market-data products stop at standardized statements and prices. Equibles adds the layer investors actually open filings for:

  • Company-specific KPIs, guidance history, and non-GAAP reconciliations extracted from the source documents
  • Earnings calls with audio and speaker-attributed transcripts
  • 13F institutional, insider (Form 4), and congressional ownership data, cross-linked per stock
  • A free MCP server so your AI assistant works with the same primary-source data

There is a usable free tier, and every number links back to the filing it came from.

What's the story behind your product?

Equibles's answer

Equibles started in 2025 from a simple frustration: AI assistants talk about stocks confidently while being disconnected from the primary sources. So we built the data layer first — pipelines that continuously ingest and structure SEC filings, earnings calls, and ownership disclosures — and then put ALVIS (an AI analyst) and an MCP server on top, so both people and their AIs get answers that cite the underlying document.

How would you describe the primary audience of your product?

Equibles's answer

  • Individual investors and analysts researching US equities who want numbers they can trace to a filing
  • AI-forward users who want Claude, ChatGPT, or another assistant grounded in primary-source financial data via MCP
  • Developers building finance tools on a JSON REST API

Which are the primary technologies used for building your product?

Equibles's answer

  • Backend: .NET / ASP.NET Core with EF Core and MassTransit background pipelines
  • Database: PostgreSQL with pgvector (embeddings) and BM25 full-text search
  • AI: LLM extraction lanes with independent validation passes, plus self-hosted embedding and transcription (Whisper) models
  • Frontend: server-rendered MVC with Tailwind CSS; React for the ALVIS chat
  • Infra: Docker Compose; the modular data foundation is open source on GitHub

User comments

Share your experience with using Equibles and cppq. For example, how are they different and which one is better?

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Social recommendations and mentions

Recommendations tracked on public social media and blogs since March 2021.

Equibles 2 mentions
cppq 0 mentions
  • Broadcom and Ford dropped NetIncomeLoss from their 2025 XBRL: a quick check for SEC tag changes
    I wanted to know how common that is, so I checked every SEC filer. I build Equibles, which serves these SEC facts to ChatGPT and Claude over MCP, but everything below uses only SEC's free public API, and the script at the end runs... - Source: dev.to / 20 days ago
  • Diff two 13F filings by CUSIP (and check an AI's "what did Berkshire buy?" answer)
    A common question to put to an AI assistant is "what did this fund buy last quarter?", and the answer comes from Form 13F-HR. I build Equibles, which has an MCP server that answers it. In this post I check one of its answers, Berkshire... - Source: dev.to / 21 days ago

Tracking cppq since Nov 2022.

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