
traxy.ai
LeadX- Linkedin Leads
Customers engage on LinkedIn every day: on your posts, on your comments, and under competitors' public content. Embers scores each person against your ICP and tells you who to DM first. No LinkedIn login needed. 7-day free trial.

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

Which is more popular?
Based on our record, mlsql seems to be more popular. It has been mentioned 2 times since March 2021.
Website, pricing, platforms and company facts side by side.
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| Website | useembers.com | github.com |
| Pricing | — | |
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What each product offers, as listed by its team.


Possible disadvantages
No features have been listed yet.
An editorial look at what each product does well and who it suits.


No analysis of Use Embers yet.
Overall verdict
Why this product is good
Recommended for
How often each product is chosen within a category, 0–100% relative to the other.


As answered by people managing Use Embers and mlsql.
Use Embers's answer
Does not require the user to login with their LinkedIn Account.
Use Embers's answer
LinkedIn Ghostwriters and Founders
Use Embers's answer
React, Django, LLMs, Tanstack Start, TailwindCSS
Use Embers's answer
Getting leads from LinkedIn via inbound is really hard. There is too much of noise from replies an engagements. Embers helps you catch signals from this noise. It helps match the engagements with your ICP.
Use Embers's answer
Founders, SDRs and Ghostwriters.
Use Embers's answer
LinkedIn account safety, price and evolving feature set.
Share your experience with using Use Embers and mlsql. For example, how are they different and which one is better?
Recommendations tracked on public social media and blogs since March 2021.


Tracking Use Embers since May 2026.
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
I found this great resource here: https://github.com/paulfitz/mlsql. Source: almost 5 years ago
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