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nolabs-ai
deepfabric
Python2024–2025
nolabs-ai / deepfabric

deepfabric

Generate High-Quality Synthetics, Train, Measure, and Evaluate in a Single Pipeline

Science & EngineeringAI & Machine LearningLearning & Reference
Explore on GitHubRead Hacker News discussions
2Distinct people
3Mentions
0Explicit recommenders
896GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Generate High-Quality Synthetics, Train, Measure, and Evaluate in a Single Pipeline

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month3 dated mentions

Observed mentions: 2024-10-28 18:33:50 UTC to 2025-09-26 20:29:09 UTC.

0122024-10-01 00:00:00 UTC to 2024-11-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 commentsOct 20242024-11-01 00:00:00 UTC to 2024-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-12-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-01 00:00:00 UTC to 2025-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-02-01 00:00:00 UTC to 2025-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-03-01 00:00:00 UTC to 2025-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsMar 20252025-04-01 00:00:00 UTC to 2025-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-05-01 00:00:00 UTC to 2025-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-06-01 00:00:00 UTC to 2025-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-07-01 00:00:00 UTC to 2025-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-08-01 00:00:00 UTC to 2025-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-01 00:00:00 UTC to 2025-10-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsSep 2025

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Oct 20242024-10-01 00:00:00 UTC2024-11-01 00:00:00 UTC211
Nov 20242024-11-01 00:00:00 UTC2024-12-01 00:00:00 UTC000
Dec 20242024-12-01 00:00:00 UTC2025-01-01 00:00:00 UTC000
Jan 20252025-01-01 00:00:00 UTC2025-02-01 00:00:00 UTC000
Feb 20252025-02-01 00:00:00 UTC2025-03-01 00:00:00 UTC000
Mar 20252025-03-01 00:00:00 UTC2025-04-01 00:00:00 UTC000
Apr 20252025-04-01 00:00:00 UTC2025-05-01 00:00:00 UTC000
May 20252025-05-01 00:00:00 UTC2025-06-01 00:00:00 UTC000
Jun 20252025-06-01 00:00:00 UTC2025-07-01 00:00:00 UTC000
Jul 20252025-07-01 00:00:00 UTC2025-08-01 00:00:00 UTC000
Aug 20252025-08-01 00:00:00 UTC2025-09-01 00:00:00 UTC000
Sep 20252025-09-01 00:00:00 UTC2025-10-01 00:00:00 UTC101

What Hacker News says

3 sources
trickleup·Oct 28, 2024
Hey Folks! Us folks over at Stacklok, needed a means to generate large synthetic datasets using a local LLM, over say OpenAI or a cloud service. So we built Promptwright, a Python library that lets you generate synthetic datasets using local models via Ollama Why we built it: * We were using OpenAI's API for dataset generation, but the costs were getting expensive for large-scale experiments. * We looked at existing solutions like pluto, but they were only capable of running on OpenAI. This project started as a fork of [pluto](https://github.com/redotvideo/pluto), but we soon started to extend and change it so much, it was practically new - still kudos to the redotvideo folks for the idea. * We wanted something that could run entirely locally and would means no concerns about leaking private information. * We wanted the flexibility of us
Read the rest
ing any model we needed to. What it does: * Runs entirely on your local machine using Ollama (works great with llama2, mistral, etc.) * Super simple Python interface for dataset generation * Configurable instructions and system prompts * Outputs clean JSONL format that's ready for training * Direct integration with Hugging Face Hub for sharing datasets We've been using it internally for a few projects, and it's been working well. You can process thousands of samples without the worry of API costs or rate limits. Plus, since everything runs locally, you don't have to worry about sensitive data leaving your environment. Checkout the examples/* folder , for examples for generating code, scientific or creative writing We'd love to get feedback from the community, if you're doing any kind of synthetic data generation for ML, give it a try and let us know what you think! GitHub: https://github.com/StacklokLabs/promptwright
In Promptwright: Generate large synthetic datasets using a local LLM

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