Software Alternatives, Accelerators & Startups

LumiGap VS s3-lambda

Compare LumiGap VS s3-lambda and see what are their differences

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.

LumiGap logo LumiGap

AI vision lab for poker tables. Train AI models to recognize poker tables.

s3-lambda logo s3-lambda

Lambda functions over S3 objects: each, map, reduce, filter
  • LumiGap LumiGap | AI Poker Vision Lab | 1
    LumiGap | AI Poker Vision Lab | 1 //
    2026-06-17
  • LumiGap LumiGap | AI Poker Vision Lab | 2
    LumiGap | AI Poker Vision Lab | 2 //
    2026-06-17
  • LumiGap LumiGap | AI Poker Vision Lab | 3
    LumiGap | AI Poker Vision Lab | 3 //
    2026-06-17
  • LumiGap LumiGap | AI Poker Vision Lab | 4
    LumiGap | AI Poker Vision Lab | 4 //
    2026-06-17
  • LumiGap LumiGap | AI Poker Vision Lab | 5
    LumiGap | AI Poker Vision Lab | 5 //
    2026-06-17
  • LumiGap LumiGap | AI Poker Vision Lab | 6
    LumiGap | AI Poker Vision Lab | 6 //
    2026-06-17
  • LumiGap LumiGap | AI Poker Vision Lab | 7
    LumiGap | AI Poker Vision Lab | 7 //
    2026-06-17
  • LumiGap LumiGap | AI Poker Vision Lab | 8
    LumiGap | AI Poker Vision Lab | 8 //
    2026-06-17
  • LumiGap LumiGap | AI Poker Vision Lab | 9
    LumiGap | AI Poker Vision Lab | 9 //
    2026-06-17
  • LumiGap LumiGap | AI Poker Vision Lab | 10
    LumiGap | AI Poker Vision Lab | 10 //
    2026-06-17
  • LumiGap LumiGap | AI Poker Vision Lab | 11
    LumiGap | AI Poker Vision Lab | 11 //
    2026-06-17

LumiGap is a macOS AI vision workspace for poker table recognition.

It reads visible online poker tables from your screen, uses OCR and Core ML to recognize names, stacks, bets, cards, board, pot, and table regions, then turns everything into structured data you can review, correct, export, and use for training custom models.

Build your own poker vision datasets, annotate cards and player tokens, tune recognition thresholds, connect external detectors, convert manifests into model-ready datasets, and test how your models perform on real table layouts.

LumiGap is built for poker researchers, ML experimenters, coaches, and advanced players who want to create their own recognition pipeline instead of relying only on generic trackers or manual screenshots.

It is designed for training, research, dataset creation, and post-session analysis. Users are responsible for following the rules of any poker platforms they use.

  • s3-lambda Landing page
    Landing page //
    2022-11-04

LumiGap

$ Details
freemium €38 / Monthly (LumiGap Pro (Monthly EUR 38.00))
Platforms
Mac MacOS
Release Date
2025 December
Startup details
Country
Spain
State
Barcelona
City
Barcelona
Employees
1 - 9

s3-lambda

Website
github.com
Pricing URL
-
$ Details
-
Platforms
-
Release Date
-

LumiGap features and specs

  • AI table recognition
    Detect cards, stacks, bets, names, board, pot, and table regions from screen data
  • Screen capture pipeline
    Read visible poker tables on macOS without connecting to poker rooms
  • OCR + Core ML
    Combine text recognition and ML models for structured table-state extraction
  • Custom datasets
    Collect screenshots, crops, labels, metadata, and manifests for model training
  • Card annotation
    Correct player cards, board cards, labels, and regions across frames
  • Player token editing
    Edit names, stacks, bets, and player-specific table tokens
  • Bring-your-own models
    Connect external detectors and tune recognition thresholds
  • Dataset converter
    Build object detection and card classifier datasets for Create ML workflows
  • Live table-state export
    Export recognized cards, stacks, bets, board, and pot through an API
  • Model performance tracking
    Compare recognition results, sessions, profit, and model performance over time

s3-lambda features and specs

  • Batch processing of S3 objects
    s3-lambda provides a straightforward way to perform batch operations on large numbers of S3 objects, enabling map, filter, and reduce-style processing over entire S3 buckets or prefixes without writing boilerplate code.
  • Familiar functional API
    The library uses a functional programming paradigm with operations like map, filter, and reduce, making it intuitive for JavaScript developers to process S3 objects using patterns they already know.
  • Built-in concurrency control
    s3-lambda handles parallel processing of S3 objects with configurable concurrency, allowing users to control how many operations run simultaneously and avoid overwhelming AWS resources or hitting rate limits.
  • Context-aware operations
    The library provides a context object within each operation that includes useful metadata about the current object being processed, simplifying access to S3 object properties during transformations.
  • Easy integration with Lambda
    Designed to work seamlessly within AWS Lambda functions, making it straightforward to set up event-driven, serverless pipelines for processing large volumes of S3 data without managing infrastructure.

Possible disadvantages of s3-lambda

  • Unmaintained project
    The repository appears to be no longer actively maintained, with limited recent commits and unresolved issues, which raises concerns about long-term reliability, security patches, and compatibility with newer AWS SDK versions.
  • Limited documentation
    The project's documentation is relatively sparse, lacking comprehensive examples, edge case handling guidance, and detailed API references, which can make it challenging for new users to adopt effectively.
  • AWS SDK version dependency
    The library depends on an older version of the AWS SDK for JavaScript, which may conflict with projects using the newer AWS SDK v3 and could miss out on performance improvements and features in updated SDKs.
  • Limited error handling flexibility
    The built-in error handling mechanisms are relatively basic, and handling partial failures or implementing sophisticated retry logic for individual object operations requires additional custom code from the developer.
  • Narrow scope of functionality
    The library is tightly focused on S3 object processing and does not integrate with other AWS services or provide utilities beyond basic map/filter/reduce operations, limiting its usefulness in more complex data pipeline scenarios.

Analysis of LumiGap

Overall verdict

  • I don't have verified information about LumiGap (lumigap.com) in my knowledge base, so I can't confirm its legitimacy, quality, or reputation. Before using or purchasing from this site, I'd recommend doing independent research.

Why this product is good

  • I don't have reliable data on this specific product or service to evaluate its features or quality
  • No verified customer reviews or reputation data available to me
  • Unable to confirm business legitimacy or track record

Recommended for

  • Anyone considering this site should first check independent reviews on platforms like Trustpilot or the BBB
  • Verify the company's contact information, return policy, and business registration
  • Check domain age and reviews on scam-detection sites like ScamAdviser before making a purchase
  • Consider using secure payment methods that offer buyer protection if you decide to proceed

Analysis of s3-lambda

Overall verdict

  • s3-lambda is a useful Node.js library for performing operations like map, reduce, and filter directly on S3 objects using Lambda, making it good for developers who need efficient, serverless-based batch processing of S3 data without managing infrastructure. It is well suited for smaller to medium projects but may not be actively maintained for enterprise-scale needs.

Why this product is good

  • Simplifies common S3 batch operations (map, filter, reduce) with a clean, functional API
  • Leverages AWS Lambda for scalable, serverless parallel processing of S3 objects
  • Reduces boilerplate code for iterating over and transforming large numbers of S3 objects
  • Open-source and free to use, allowing customization for specific workflows
  • Integrates well with existing AWS infrastructure and Node.js applications

Recommended for

  • Developers building serverless data pipelines on AWS
  • Teams needing to process or transform large sets of S3 objects without provisioning servers
  • Node.js developers looking for a functional programming approach to S3 operations
  • Projects with batch processing needs that fit within Lambda's execution limits
  • Prototyping or small-to-medium scale ETL tasks involving S3 data

Category Popularity

0-100% (relative to LumiGap and s3-lambda)
Data Analytics
100 100%
0% 0
Data Dashboard
0 0%
100% 100
Data Science And Machine Learning
Databases
0 0%
100% 100

Questions & Answers

As answered by people managing LumiGap and s3-lambda.

What makes your product unique?

LumiGap's answer

LumiGap is not just a poker tracker or a note-taking app. It is an AI vision workspace for poker table recognition: capture the table from your screen, recognize cards, stacks, bets, names, board and pot, correct the results, build datasets, train custom models, and export structured table state for research workflows.

Why should a person choose your product over its competitors?

LumiGap's answer

Most poker tools focus on hand histories, solvers, or finished analytics. LumiGap focuses on the recognition pipeline itself: screen capture, OCR, Core ML, table-region mapping, annotation, custom datasets, model testing, and export. It is for users who want to build and improve their own AI-powered poker research workflow.

How would you describe the primary audience of your product?

LumiGap's answer

LumiGap is for advanced poker players, coaches, poker researchers, ML builders, data-driven analysts, and macOS users who want to recognize poker table state visually, create custom datasets, and train models for their own layouts and research needs.

What's the story behind your product?

LumiGap's answer

LumiGap started from a simple gap: serious poker work often depends on screenshots, manual notes, hand histories, and tools that cannot easily be adapted to your own table layouts or model experiments. LumiGap was built to turn visible table states into structured data, datasets, and custom AI recognition workflows.

Which are the primary technologies used for building your product?

LumiGap's answer

Native macOS stack: Swift, SwiftUI, ScreenCaptureKit, Vision OCR, Core ML, local data storage, annotation tools, dataset converters, external detector support, and live export APIs.

Who are some of the biggest customers of your product?

LumiGap's answer

  • Advanced poker players
  • Poker coaches
  • Poker researchers
  • ML dataset builders
  • Computer vision experimenters
  • Strategy analysts
  • macOS users building custom recognition workflows

User comments

Share your experience with using LumiGap and s3-lambda. For example, how are they different and which one is better?
Log in or Post with

What are some alternatives?

When comparing LumiGap and s3-lambda, you can also consider the following products

GTO Wizard - GTO Wizard is an advanced tool designed for poker players aiming to enhance their skills and strategies.

Pokertracker - PokerTracker is the worlds largest and most popular online poker tracking and analysis software...

Holdem Manager - HoldEm Manager Poker Tracking and Analysis Software From the Leading Online Poker Tracking Company.

Hand2note - Poker HUD trading software

Label Studio - Open Source Data Labeling Platform for AI Model Tuning

Roboflow - Eliminating your boilerplate computer vision code