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

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Automate Facebook Page management with Rypl: AI powered auto-replies, context-aware conversations, sentiment analysis, and smart comment moderation. Engage users instantly, stop losing leads, boost sales by ~10%, and stay active 24/7.

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 | github.com | rypl.is |
| Pricing | — | |
| Company | — | 1 - 9 employees · 2025 |
| Listed in |
In their own words, as submitted to SaaSHub.


No description of mlsql yet.
RYPL is an AI-powered tool that replies to Facebook comments instantly and naturally, turning every interaction into a sales opportunity. It understands context, detects intent, and responds in your brand’s voice, helping you convert more leads without sounding like a bot. Built for brands,...
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As answered by people managing mlsql and Rypl.
Rypl's answer:
Minutes to set up, replies that actually drive intent forward, strong brand-safety controls, clear analytics, and pricing that fits small teams plus native Facebook workflows (auto-like, hide, escalate) so you engage, sell, and protect without extra tools.
Rypl's answer:
SMBs and growth teams with active Facebook Pages e-commerce brands, local services, restaurants, creators, and agencies that need consistent, on-brand replies to capture leads from comments.
Rypl's answer:
RYPL was built after seeing “lead leakage” in Facebook comments/DMs: warm buyers asked questions, no one answered in time, and sales died. The goal is a fast, on-brand AI that treats every comment like a sales touch without feeling like automation.
Rypl's answer:
Next.js/React, FastAPI (Python), Node services, PostgreSQL/Supabase, Redis, Docker, Cloudflare (Workers/Turnstile), Meta Graph API & Webhooks, and LLMs for language understanding and generation.
Rypl's answer:
Confidential Data
Rypl's answer:
RYPL focuses on sales-first, human-like replies for Facebook comments. It reads per-user context, keeps your brand tone, and blends LLMs with guardrails (spam/negativity filters, auto-like, escalation rules) to convert comment threads into real conversations at low latency and without sounding like a bot.
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Recommendations tracked on public social media and blogs since March 2021.


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
Tracking Rypl since Oct 2025.
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