thermostat
MindsDB
thermostat | MindsDB | |
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1 | 78 | |
0 | 21,312 | |
- | 1.5% | |
0.0 | 10.0 | |
over 2 years ago | 5 days ago | |
Python | Python | |
- | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
thermostat
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thermostat optimization, trying to find the right algorithm to apply to the right framing
all the past data could be used to run simulations/verify models, but really this should be a thing that learns along the way. Like you move into a house, install thermostat and temp sensors, and then as it runs it gets better and better at cooling your house. Many hard coded things need to be turned into dynamic ones, heater functionality needs to be added, and a handful of other quality of life things need to be added, but the idea is that someone could git clone it and go even as is. https://github.com/G4te-Keep3r/thermostat ***project summary**\*Custom thermostat that does more than simple on/off cycling. The 2 main goals that it started with was to not have it 80+ degrees during the day like the old thermostat would, and for the overall consistency of temperature to be better (old thermostat had almost a 10+ degree swing before it cycled back on). This started out so simple as "build a better thermostat" and just kept getting more complicated.
MindsDB
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Whatβs the Difference Between Fine-tuning, Retraining, and RAG?
Check us out on GitHub.
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How to Forecast Air Temperatures with AI + IoT Sensor Data
If your data lacks uniform time intervals between consecutive entries, QuestDB offers a solution by allowing you to sample your data. After that, MindsDB facilitates creating, training, and deploying your time-series models.
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Fine-tuning a Mistral Language Model with Anyscale
MindsDB is an open-source AI platform for developers that connects AI/ML models with real-time data. It provides tools and automation to easily build and maintain personalized AI solutions.
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- FLaNK Weekly 08 Jan 2024
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MindsDB Docker Extension: Build ML powered apps at a much faster pace
MindsDB combines both AI and SQL functions in one; users can create, train, optimize, and deploy ML models without the need for external tools. Data analysts can create and visualize forecasts without having to navigate the complexities of ML pipelines.MindsDB is open-source and works with well-known databases like MySQL, Postgres, Redit, Snowflakes, etc.
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How Modern SQL Databases Are Changing Web Development - #4 Into the AI Era
Mindsdb is a good example. It abstracts everything related to an AI workflow as "virtual tables". For example, you can import OpenAI API as a "virtual table":
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ππ 23 issues to grow yourself as an exceptional open-source Python expert π§βπ» π₯
Repo : https://github.com/mindsdb/mindsdb
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AI-Powered Selection of Asset Management Companies using MindsDB and LlamaIndex
MindsDB is an AI Automation platform for building AI/ML powered features and applications. It works by connecting any data source with any AI/ML model or framework and automating how real-time data flows between them. MindsDB is integrated with LlamaIndex, which makes use of its data framework for connecting custom data sources to large language models. LlamaIndex data ingestion allows you to connect to data sources like PDFβs, webpages, etc., provides data indexing and a query interface that takes input prompts from your data and provides knowledge-augmented responses, thus making it easy to Q&A over documents and webpages.
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Using Large Language Models inside your database with MindsDB
Now, imagine if you can deploy these highly trained models in your database to get insights, make predictions, understand your users, auto-generate content, and more. MindsDB makes this possible! MindsDB is an open-source AI database middleware that allows you to supercharge your databases by integrating various machine learning (ML) engines.
What are some alternatives?
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tensorflow - An Open Source Machine Learning Framework for Everyone
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infinitude - Open control of Carrier/Bryant thermostats
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CapRover - Scalable PaaS (automated Docker+nginx) - aka Heroku on Steroids
django-mysql - :dolphin: :horse: Extensions to Django for use with MySQL/MariaDB
scikit-learn - scikit-learn: machine learning in Python
lightwood - Lightwood is Legos for Machine Learning.
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow