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mlsql VS FIND@

Compare mlsql VS FIND@ and see what are their differences

mlsql

Infer SQL queries from plain-text questions and table headers.

Rating
0 reviews
Alternatives Is it good?
FIND@

Short links, QR codes, Bio pages, booking and websites — Find@ is your all-in-one link platform used by creators and businesses worldwide. You own your audience data. Track clicks in real time. Free to start. — FIND@.

Rating
0 reviews
Pricing
Freemium Free trial
Alternatives Is it good?
warmup.rocks
warmup.rocks keeps your CDN cache warm in every edge location worldwide: Cloudflare, Fastly, Akamai, CloudFront and more. Warming from 42 countries, hit-ratio analytics. 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.

Which is more popular?

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

social mentions
2 vs 0
LMS popularity
100% vs 0%
alternatives listed
1 vs 19

Base details

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

mlsql
FIND@
Website github.com find.at
Pricing —
Freemium Free trial Official pricing
Company — 2026
Listed in
Education LMS Online Learning Web App +1
Link Management URL Shortener Link Tracking QR Codes QR Code Generator Link In Bio Online Bookings Booking Software Appointment Booking Web Hosting Email Service Provider Analytics +9

About mlsql and FIND@

In their own words, as submitted to SaaSHub.

mlsql
FIND@

No description of mlsql yet.

Find@ is a digital identity platform combining Short links, QR codes, Bio pages, booking, websites and analytics — built for creators, athletes, and businesses.

Read more about FIND@

Analysis

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

mlsql
FIND@

Overall verdict

  • MLSQL is a solid open-source unified platform that blends SQL with machine learning, making data engineering and ML workflows accessible through a single, declarative language. It's a good choice for teams looking to streamline big data and ML pipelines without switching between multiple tools.

Why this product is good

  • It unifies data processing and machine learning under a single SQL-like syntax, lowering the learning curve for data teams.
  • Built on top of Apache Spark, it leverages a proven distributed computing engine for handling large-scale data.
  • Open-source and actively developed, allowing customization and community-driven improvements.
  • Supports the full ML lifecycle including data preprocessing, training, and deployment within one workflow.
  • Reduces the need to context-switch between separate ETL, analytics, and ML tools.

Recommended for

  • Data engineers and data scientists who prefer SQL-centric workflows
  • Teams working with big data on Apache Spark
  • Organizations wanting to unify ETL and machine learning pipelines
  • Companies seeking an open-source alternative to fragmented ML tooling
  • Analysts looking to build ML models without deep programming expertise

Overall verdict

  • FIND@ (find.at) appears to be a niche or lesser-known online tool, and there is limited independent, verifiable information available about its features, reliability, or user reception. Without substantial user reviews, security audits, or documented track record, it's difficult to fully endorse it as a proven or widely trusted service.

Why this product is good

  • May offer a simple, lightweight solution for basic needs like link management or similar functionality
  • Could be cost-effective compared to larger, more established competitors
  • Might work well for very specific or narrow use cases if it matches your exact requirements

Recommended for

  • Users seeking a low-commitment trial for very specific, narrow use cases
  • Individuals comfortable testing newer or smaller platforms with limited public track record
  • Those who have already vetted the specific features and confirmed they meet their needs directly with the provider
  • Not recommended for users requiring extensive support, proven security, or enterprise-level reliability

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
mlsql
FIND@
100% 100%
LMS
0% 0%
0% 0%
Web Hosting
100% 100%
100% 100%
Education
0% 0%
0% 0%
Appointment Booking
100% 100%

Questions & Answers

As answered by people managing mlsql and FIND@.

What makes your product unique?

FIND@'s answer:

Find@ combines bio pages, branded short links, dynamic QR codes, and analytics into a single digital identity platform. Unlike basic link-in-bio tools, Find@ is built as identity infrastructure — with a developer API, verification system, and canonical routing logic that gives users permanent ownership over their name, traffic, and audience.

Why should a person choose your product over its competitors?

FIND@'s answer:

Find@ is not a landing page builder — it is a routing and identity ownership layer. It offers per-link analytics, geo and device targeting, custom domains, a public API, and a verification system in one platform. Users who need more than a styled list of links choose Find@ because it treats digital identity as infrastructure, not decoration.

How would you describe the primary audience of your product?

FIND@'s answer:

Creators, athletes, agencies, and businesses who want permanent control over their online identity and audience routing — independent of any single social platform. Find@ is used by individuals managing personal brands and by teams managing multiple client presences.

What's the story behind your product?

FIND@'s answer:

Find@ was built as part of XZ Global, a vertically integrated digital ecosystem. The core idea: social platforms own the audience relationship, and creators needed a neutral layer they actually control. Find@ launched as that layer — combining identity, routing, and analytics into one owned infrastructure point.

Which are the primary technologies used for building your product?

FIND@'s answer:

Find@ runs on a custom SaaS stack hosted on XZG Cloud infrastructure.

Who are some of the biggest customers of your product?

FIND@'s answer:

  • Combat sports athletes and fighters managing their personal brand
  • Fitness influencers and content creators
  • Small businesses and local service providers
  • Agencies managing multiple client presences
  • Authors and public figures
  • Gyms and sports clubs​​​​​​​​​​​​​​​​

User comments

Share your experience with using mlsql and FIND@. 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.

mlsql 2 mentions
FIND@ 0 mentions
  • Download the files from Docker Container and locally edit them
    I am working on a project that requires me to take user input (as English) and return SQL queries. We will eventually be working with multiple datasets, which is why the valuenet over at https://github.com/paulfitz/mlsql ended up being... Source: over 4 years ago
  • Clone and edit Docker Projects hosted on Github
    I found this great resource here: https://github.com/paulfitz/mlsql. Source: almost 5 years ago

Tracking FIND@ since Apr 2026.

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