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mindsdb
mindshub
Makefile2019–2026
mindsdb / mindshub

mindshub

The unified workspace where open-source models get things done for you.

9Distinct people
12Mentions
0Explicit recommenders
39.9KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

The unified workspace where open-source models get things done for you.

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

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 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 year12 dated mentions

Observed mentions: 2019-02-20 14:00:00 UTC to 2026-07-13 19:51:04 UTC.

01232019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments20192020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 3 mentions; 1 submissions, 2 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 2 mentions; 2 submissions, 0 comments20222023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 2 mentions; 2 submissions, 0 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

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
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC110
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC000
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC312
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC220
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC110
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC211
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC110
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC220

What Hacker News says

12 sources
torrmal·Jul 13, 2026Submission

Claudio? Why we need and open source Claude

Imagine if operating systems, browsers, or databases had evolved with only closed systems. We wouldn’t have the internet we know today. AI needs world-class open alternatives to great tech. In response to this, we’ve been busy building Mindshub. A high quality, open-source AI coworker you can truly make your own. Where we know. Good enough wasn’t an option. You can check it out in https://github.com/mindsdb/mindshub Inspect it. Modify it. Extend it. Fork it. Make it uniquely yours. Call it Claudio if you want ;) Choose any model, open or proprietary. Pick and customize the open-source agent that fits your workflow. Connect your own tools. Keep your data where you want it. We’d love your feedback.
4 points · Open discussion
torrmal·Sep 25, 2025Submission

Hello to Unstructured Analytics

Traditional analytics “loved” neat rows and columns (oh, how they adored orderly databases!), so anything messy—emails, PDFs, customer feedback, doctor notes, ... your grumpy aunt's product complaints— account for about 80–90% of enterprise data, yes it lives in that messy land of unstructured chaos [ask MIT, IBM]. Sitting pretty, ignored, shunned like the weird cousin at Thanksgiving. Which is totally nuts. Anywho, Thanks to MindsDB Knowledge Bases, though, we no longer need to treat unstructured data like forbidden fruit: we can chew, digest, and excrete real insights as if it were perfectly tabular all along. Would love to hear your thoughts on this: repo: [https://github.com/mindsdb/mindsdb](https://github.com/mindsdb/mindsdb) blog about knowledge bases: [https://mindsdb.com/blog/mindsdb-knowledge-bases-revolutionizing-ai-powered-dat
Read the rest
6 points · Open discussion
I would love to support Scylla, I ** love that database, those guys are magicians. And I assume in supporting that we'd also offer de-facto support for Cassandra. I don't think either Scylla or dynamo are on the roadmap now, but if you want them feel free to create an issue asking for them: https://github.com/mindsdb/mindsdb It should be noted that there's two level of support: 1. As a source of data (easy to implement) 2. Being able to publish models into the database (a bit harder) If you work with those and are interested in doing ML from the database please get in touch, ideally via github, but you can also use the contact form (https://mindsdb.com/contact-us/) or email one of us directly. The best case scenario for us is that when we do one of these integrations we have an actual user in mind, and we're open to "first users" for any
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database where we can find a reasonable way of integrating.
In Launch HN: MindsDB (YC W20) – Machine Learning Inside Your Database

Launch HN: MindsDB (YC W20) – Machine Learning Inside Your Database

Hi HN, Adam and Jorge here, and today we’re very excited to share MindsDB with you (http://github.com/mindsdb/mindsdb). MindsDB AutoML Server is an open-source platform designed to accelerate machine learning workflows for people with data inside databases by introducing virtual AI tables. We allow you to create and consume machine learning models as regular database tables. Jorge and I have been friends for many years, having first met at college. We have previously founded and failed at another startup, but we stuck together as a team to start MindsDB. Initially a passion project, MindsDB began as an idea to help those who could not afford to hire a team of data scientists, which at the time was (and still is) very expensive. It has since grown into a thriving open-source community with contributors and users all over the globe. With t
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he plethora of data available in databases today, predictive modeling can often be a pain, especially if you need to write complex applications for ingesting data, training encoders and embedders, writing sampling algorithms, training models, optimizing, scheduling, versioning, moving models into production environments, maintaining them and then having to explain the predictions and the degree of confidence… we knew there had to be a better way! We aim to steer you away from constantly reinventing the wheel by abstracting most of the unnecessary complexities around building, training, and deploying machine learning models. MindsDB provides you with two techniques for this: build and train models as simply as you would write an SQL query, and seamlessly “publish” and manage machine learning models as virtual tables inside your databases (we support Clickhouse, MariaDB, MySQL, PostgreSQL, and MSSQL. MongoDB is coming soon.) We also support getting data from other sources, such as Snowflake, s3, SQLite, and any excel, JSON, or CSV file. When we talk to our community, we find that they are using MindsDB for anything ranging from reducing financial risk in the payments sector to predicting in-app usage statistics - one user is even trying to predict the price of Bitcoin using sentiment analysis (we wish them luck). No matter what the use-case, what we hear most often is that the two most painful parts of the whole process are model generation (R&D) and/or moving the model into production. For those who already have models (i.e. who have already done the R&D part), we are launching the ability to bring your own models from frameworks like Pytorch, Tensorflow, scikit-learn, Keras, XGBoost, CatBoost, LightGBM, etc. directly into your database. If you’d like to try this experimental feature, you can sign-up here: (https://mindsdb.com/bring-your-own-ml-models) We currently have a handful of customers who pay us for support. However, we will soon be launching a cloud version of MindsDB for those who do not want to worry about DevOps, scalability, and managing GPU clusters. Nevertheless, MindsDB will always remain free and open-source, because democratizing machine learning is at the core of every decision we make. We’re making good progress thanks to our open-source community and are also grateful to have the backing of the founders of MySQL & MariaDB. We would love your feedback and invite you to try it out. We’d also love to hear about your experience, so please share your feedback, thoughts, comments, and ideas below. https://docs.mindsdb.com/ or https://mindsdb.com/ Thanks in advance, Adam & Jorge
176 points · Open discussion

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