19X faster response time
Lincoln Loop optimized a large publishing platform's database performance. Overall, the database performance increased 19 times.
https://lincolnloop.com/insights/optimizing-response-time-19x-faster/
  
  Lincoln Loop optimized a large publishing platform's database performance. Overall, the database performance increased 19 times.
https://lincolnloop.com/insights/optimizing-response-time-19x-faster/
Lincoln Loop
  
  19X faster response time
  Lincoln Loop optimized a large publishing platform's database performance. Overall, the database performance increased 19 times.
  facebookresearch / codellama
Inference code for CodeLlama models
https://github.com/facebookresearch/codellama
  
  Inference code for CodeLlama models
https://github.com/facebookresearch/codellama
GitHub
  
  GitHub - meta-llama/codellama: Inference code for CodeLlama models
  Inference code for CodeLlama models. Contribute to meta-llama/codellama development by creating an account on GitHub.
  vpselector
Visual Pandas Selector: Visualize and interactively select time-series data.
https://github.com/manumerous/vpselector
  
  Visual Pandas Selector: Visualize and interactively select time-series data.
https://github.com/manumerous/vpselector
GitHub
  
  GitHub - manumerous/vpselector: Visual Pandas Selector: Visualize and interactively select time-series data
  Visual Pandas Selector: Visualize and interactively select time-series data - manumerous/vpselector
  Speeding up Floyd-Steinberg dithering: an optimization exercise
A worked out example: optimizing low-level code to get significant performance and memory improvements.
https://pythonspeed.com/articles/optimizing-dithering/
  
  A worked out example: optimizing low-level code to get significant performance and memory improvements.
https://pythonspeed.com/articles/optimizing-dithering/
Python⇒Speed
  
  Speeding up your code when multiple cores aren’t an option
  Parallelism isn’t the only answer: often you can optimize low-level code to get significant performance improvements.
  Why Are There So Many Python Dataframes?
This post explores the proliferation of Python dataframes, dissecting the reasons behind their prevalence in data science and analysis, shedding light on the various libraries and frameworks that contribute to their abundance.
https://ponder.io/why-are-there-so-many-python-dataframes/
  
  This post explores the proliferation of Python dataframes, dissecting the reasons behind their prevalence in data science and analysis, shedding light on the various libraries and frameworks that contribute to their abundance.
https://ponder.io/why-are-there-so-many-python-dataframes/
Ponder
  
  Why Are There So Many Python Dataframes?
  Introduction As I floated in the slow, crystalline current of the Rhine in Basel in the middle of the 2023 Python Dataframe Summit, I found myself asking:
  Building RAG-based LLM Applications for Production (Part 1)
In this guide, we will learn how to develop and productionize a retrieval augmented generation (RAG) based LLM application, with a focus on scale, evaluation and routing.
https://www.anyscale.com/blog/a-comprehensive-guide-for-building-rag-based-llm-applications-part-1
  
  In this guide, we will learn how to develop and productionize a retrieval augmented generation (RAG) based LLM application, with a focus on scale, evaluation and routing.
https://www.anyscale.com/blog/a-comprehensive-guide-for-building-rag-based-llm-applications-part-1
Anyscale
  
  Building RAG-based LLM Applications for Production
  In this guide, we will learn how to develop and productionize a retrieval augmented generation (RAG) based LLM application, with a focus on scale and evaluation.
  Logparser
Logparser provides a machine learning toolkit and benchmarks for automated log parsing, which is a crucial step for structured log analytics
https://github.com/logpai/logparser
  
  Logparser provides a machine learning toolkit and benchmarks for automated log parsing, which is a crucial step for structured log analytics
https://github.com/logpai/logparser
GitHub
  
  GitHub - logpai/logparser: A machine learning toolkit for log parsing [ICSE'19, DSN'16]
  A machine learning toolkit for log parsing [ICSE'19, DSN'16] - logpai/logparser
  QuasiQueue
QuasiQueue is a MultiProcessing library for Python that makes it super easy to have long running MultiProcess jobs. QuasiQueue handles process creation and cleanup, signal management, cross process communication, and all the other garbage that makes people hate dealing with multiprocessing.
https://github.com/tedivm/quasiqueue
  
  QuasiQueue is a MultiProcessing library for Python that makes it super easy to have long running MultiProcess jobs. QuasiQueue handles process creation and cleanup, signal management, cross process communication, and all the other garbage that makes people hate dealing with multiprocessing.
https://github.com/tedivm/quasiqueue
GitHub
  
  GitHub - tedivm/quasiqueue: Multiprocessing Queues Made Easy
  Multiprocessing Queues Made Easy. Contribute to tedivm/quasiqueue development by creating an account on GitHub.
  EvoDiff
Generation of protein sequences and evolutionary alignments via discrete diffusion models.
https://github.com/microsoft/evodiff
  
  Generation of protein sequences and evolutionary alignments via discrete diffusion models.
https://github.com/microsoft/evodiff
GitHub
  
  GitHub - microsoft/evodiff: Generation of protein sequences and evolutionary alignments via discrete diffusion models
  Generation of protein sequences and evolutionary alignments via discrete diffusion models - microsoft/evodiff
  Compiling ML models to C for fun
ML models can be compiled to graphs, which can be traversed to perform forward and backward passes. This approach can improve performance and make it easier to debug ML models.
https://bernsteinbear.com/blog/compiling-ml-models/
  
  ML models can be compiled to graphs, which can be traversed to perform forward and backward passes. This approach can improve performance and make it easier to debug ML models.
https://bernsteinbear.com/blog/compiling-ml-models/
Max Bernstein
  
  Compiling ML models to C for fun
  We make micrograd fly with a little compiler magic. In this post, we’ll write a ML compiler from scratch.
  Temporian
Temporian is a Python library for feature engineering and data augmentation of temporal data (e.g. time-series, transactions) in machine learning applications.
https://github.com/google/temporian
  
  Temporian is a Python library for feature engineering and data augmentation of temporal data (e.g. time-series, transactions) in machine learning applications.
https://github.com/google/temporian
GitHub
  
  GitHub - google/temporian: Temporian is an open-source Python library for preprocessing ⚡ and feature engineering 🛠 temporal data…
  Temporian is an open-source Python library for preprocessing ⚡ and feature engineering 🛠 temporal data 📈 for machine learning applications 🤖 - google/temporian
  Simulate the Monty Hall problem in Python
Use Python to solve this classic probability puzzle that has stumped mathematicians and Nobel Prize winners!
https://www.dataschool.io/python-probability-simulation/
  
  Use Python to solve this classic probability puzzle that has stumped mathematicians and Nobel Prize winners!
https://www.dataschool.io/python-probability-simulation/
Data School
  
  Simulate the Monty Hall problem in Python 🐐🚘🐐
  Use Python to solve this classic probability puzzle that has stumped mathematicians and Nobel Prize winners!
  PyLLMCore
A pythonic library providing light-weighted interface with LLMs
https://github.com/paschembri/py-llm-core
  
  A pythonic library providing light-weighted interface with LLMs
https://github.com/paschembri/py-llm-core
GitHub
  
  GitHub - advanced-stack/py-llm-core: A pythonic library providing light-weighted interface with LLMs
  A pythonic library providing light-weighted interface with LLMs - advanced-stack/py-llm-core
  KillianLucas / open-interpreter
OpenAI's Code Interpreter in your terminal, running locally
https://github.com/KillianLucas/open-interpreter
  
  OpenAI's Code Interpreter in your terminal, running locally
https://github.com/KillianLucas/open-interpreter
GitHub
  
  GitHub - openinterpreter/open-interpreter: A natural language interface for computers
  A natural language interface for computers. Contribute to openinterpreter/open-interpreter development by creating an account on GitHub.
  Medical_Intake
Automated pipeline for medical intake, diagnosis, tests, etc.
https://github.com/daveshap/Medical_Intake
  
  Automated pipeline for medical intake, diagnosis, tests, etc.
https://github.com/daveshap/Medical_Intake
GitHub
  
  GitHub - daveshap/Medical_Intake: Automated pipeline for medical intake, diagnosis, tests, etc.
  Automated pipeline for medical intake, diagnosis, tests, etc. - daveshap/Medical_Intake
  Building A RisingWave Connector for Django ORM
Ever wanted to connect a streaming database to Django ORM. Learn how with Django and RisingWave.
https://bas.codes/posts/django-risingwave
  
  Ever wanted to connect a streaming database to Django ORM. Learn how with Django and RisingWave.
https://bas.codes/posts/django-risingwave
Building A RisingWave Connector for Django ORM - Bas codes
  
  
  Ever wanted to connect a streaming database to Django ORM. Learn how with Django and RisingWave
  