Lightning talk: Envisioning a new inherently shared programming language
•
0 likes•524 views
This presentation was originally given as a lightning talk on the final day of PolyConf15 in Poznan, PL. The talk came to be as the result of a interesting lunch conversation the day before, leading to a brainstorm about a conceptual new language. The conceptual idea is to invent a new language where sharing, and thus open source, was somehow baked into the language itself. The slides contain some of the thoughts that triggered the brainstorm, and some of the properties I imagine would be useful in such a language, like a strong type-system inspired by Haskell, build in function contracts (Eiffel among others), and type providers like F#. Don't expect any answers, this was just a presentation of an interesting brainstorm, partly to present the idea itself, and partly just to celebrate the amazing brain-soup that congregates at such an event as PolyConf.
Report
Share
Report
Share
Download to read offline
![Language
brainstorm
[PolyConf] -> [Lunch] -> [Crazy idea?]
Scala of Lancarrow@jankrag](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
Jedi Mind Tricks for Git
Slides for a pre-conference workshop I delivered together with Johan Abildskov (@randomsort) at Git Merge 2017 in Brussels.
In the workshop we covered fun things to do with Git hooks, Git attributes and custom drivers.
In the first half, we demonstrate how you can implement a fully local continuous integration workflow using git hooks.
In the second half, we cover cool and creative ways to diff binary files and custom filters for modifying file content while commit'ing.
Making the big data ecosystem work together with Python & Apache Arrow, Apach...
Slides from PyData London exploring how the big data ecosystem (currently) works together as well as how different parts of the ecosystem work with Python. Proof-of-concept examples are provided using nltk & spacy with Spark. Then we look to the future and how we can improve.
Making the big data ecosystem work together with python apache arrow, spark,...
The document discusses the current state of the big data ecosystem and how tools interact across different programming languages and environments. It focuses on Python tools like Dask, Spark, Beam, and Kafka and how they can communicate with systems built on the Java Virtual Machine like Spark. It notes that there is often unnecessary data copying between systems due to differences in languages and environments. Emerging technologies like Apache Arrow aim to allow more direct data sharing to improve performance. The document advocates for continued work to better integrate Python tools into the larger big data ecosystem.
Powering tensor flow with big data using apache beam, flink, and spark cern...
This document summarizes Holden Karau's presentation on powering TensorFlow with big data using Apache Beam, Apache Spark, and Apache Flink. The presentation covers why deep learning requires large datasets for training, how to prepare features from big data for TensorFlow using TensorFlow Transform, and how TensorFlow Transform can run on Apache Beam and integrate feature preparation into model serving. It also discusses challenges in integrating Python and big data systems beyond the Java Virtual Machine and efforts to improve cross-language interoperability.
Powering tensorflow with big data (apache spark, flink, and beam) dataworks...
This document discusses powering TensorFlow with big data tools like Apache Spark, Apache Flink, and Apache Beam. It provides an overview of TensorFlowOnSpark and how Apache Arrow can improve data interchange performance. It also covers the current state of non-JVM support in Beam and what this means for TensorFlow. The document discusses how data processing frameworks like TFX fit with TensorFlow and pre-processing tools like TF.Transform. It concludes by discussing options for using big data tools from outside the JVM like Kafka and Dask.
Big data beyond the JVM - DDTX 2018
Many of the recent big data systems, like Hadoop, Spark, and Kafka, are written primarily in JVM languages. At the same time, there is a wealth of tools for data science and data analytics that exist outside of the JVM. Holden Karau and Rachel Warren explore the state of the current big data ecosystem and explain how to best work with it in non-JVM languages. While much of the focus will be on Python + Spark, the talk will also include interesting anecdotes about how these lessons apply to other systems (including Kafka).
Holden and Rachel detail how to bridge the gap using PySpark and discuss other solutions like Kafka Streams as well. They also outline the challenges of pure Python solutions like dask. Holden and Rachel start with the current architecture of PySpark and its evolution. They then turn to the future, covering Arrow-accelerated interchange for Python functions, how to expose Python machine learning models into Spark, and how to use systems like Spark to accelerate training of traditional Python models. They also dive into what other similar systems are doing as well as what the options are for (almost) completely ignoring the JVM in the big data space.
Python users will learn how to more effectively use systems like Spark and understand how the design is changing. JVM developers will gain an understanding of how to Python code from data scientist and Python developers while avoiding the traditional trap of needing to rewrite everything.
Golab.io
Zaratan is a project management tool written in Go that allows users to easily manage and orchestrate Docker projects without extensive technical skills. It acts as a wrapper for the docker-compose library and lets users create and use custom templates to set up new projects quickly in a local environment. The tool was created by a student as a learning project and continues to be improved with features like a GUI, template registry, and integrated DNS management.
Accelerating Big Data beyond the JVM - Fosdem 2018
Many popular big data technologies (such as Apache Spark, BEAM, Flink, and Kafka) are built in the JVM, and many interesting tools are built in other languages (ranging from Python to CUDA). For simple operations the cost of copying the data can quickly dominate, and in complex cases can limit our ability to take advantage of specialty hardware. This talk explores how improved formats are being integrated to reduce these hurdles to co-operation.
Many popular big data technologies (such as Apache Spark, BEAM, and Flink) are built in the JVM, and many interesting AI tools are built in other languages, and some requiring copying to the GPU. As many folks have experienced, while we may wish that we spend all of our time playing with cool algorithms -- we often need to spend more of our time working on data prep. Having to copy our data slowly between the JVM and the target language of computation can remove much of the benefit of being able to access our specialized tooling. Thankfully, as illustrated in the soon to be released Spark 2.3, Apache Arrow and related tools offer the ability to reduce this overhead. This talk will explore how Arrow is being integrated into Spark, and how it can be integrated into other systems, but also limitations and places where Apache Arrow will not magically save us.
Link: https://fosdem.org/2018/schedule/event/big_data_outside_jvm/
Recommended
Jedi Mind Tricks for Git
Slides for a pre-conference workshop I delivered together with Johan Abildskov (@randomsort) at Git Merge 2017 in Brussels.
In the workshop we covered fun things to do with Git hooks, Git attributes and custom drivers.
In the first half, we demonstrate how you can implement a fully local continuous integration workflow using git hooks.
In the second half, we cover cool and creative ways to diff binary files and custom filters for modifying file content while commit'ing.
Making the big data ecosystem work together with Python & Apache Arrow, Apach...
Slides from PyData London exploring how the big data ecosystem (currently) works together as well as how different parts of the ecosystem work with Python. Proof-of-concept examples are provided using nltk & spacy with Spark. Then we look to the future and how we can improve.
Making the big data ecosystem work together with python apache arrow, spark,...
The document discusses the current state of the big data ecosystem and how tools interact across different programming languages and environments. It focuses on Python tools like Dask, Spark, Beam, and Kafka and how they can communicate with systems built on the Java Virtual Machine like Spark. It notes that there is often unnecessary data copying between systems due to differences in languages and environments. Emerging technologies like Apache Arrow aim to allow more direct data sharing to improve performance. The document advocates for continued work to better integrate Python tools into the larger big data ecosystem.
Powering tensor flow with big data using apache beam, flink, and spark cern...
This document summarizes Holden Karau's presentation on powering TensorFlow with big data using Apache Beam, Apache Spark, and Apache Flink. The presentation covers why deep learning requires large datasets for training, how to prepare features from big data for TensorFlow using TensorFlow Transform, and how TensorFlow Transform can run on Apache Beam and integrate feature preparation into model serving. It also discusses challenges in integrating Python and big data systems beyond the Java Virtual Machine and efforts to improve cross-language interoperability.
Powering tensorflow with big data (apache spark, flink, and beam) dataworks...
This document discusses powering TensorFlow with big data tools like Apache Spark, Apache Flink, and Apache Beam. It provides an overview of TensorFlowOnSpark and how Apache Arrow can improve data interchange performance. It also covers the current state of non-JVM support in Beam and what this means for TensorFlow. The document discusses how data processing frameworks like TFX fit with TensorFlow and pre-processing tools like TF.Transform. It concludes by discussing options for using big data tools from outside the JVM like Kafka and Dask.
Big data beyond the JVM - DDTX 2018
Many of the recent big data systems, like Hadoop, Spark, and Kafka, are written primarily in JVM languages. At the same time, there is a wealth of tools for data science and data analytics that exist outside of the JVM. Holden Karau and Rachel Warren explore the state of the current big data ecosystem and explain how to best work with it in non-JVM languages. While much of the focus will be on Python + Spark, the talk will also include interesting anecdotes about how these lessons apply to other systems (including Kafka).
Holden and Rachel detail how to bridge the gap using PySpark and discuss other solutions like Kafka Streams as well. They also outline the challenges of pure Python solutions like dask. Holden and Rachel start with the current architecture of PySpark and its evolution. They then turn to the future, covering Arrow-accelerated interchange for Python functions, how to expose Python machine learning models into Spark, and how to use systems like Spark to accelerate training of traditional Python models. They also dive into what other similar systems are doing as well as what the options are for (almost) completely ignoring the JVM in the big data space.
Python users will learn how to more effectively use systems like Spark and understand how the design is changing. JVM developers will gain an understanding of how to Python code from data scientist and Python developers while avoiding the traditional trap of needing to rewrite everything.
Golab.io
Zaratan is a project management tool written in Go that allows users to easily manage and orchestrate Docker projects without extensive technical skills. It acts as a wrapper for the docker-compose library and lets users create and use custom templates to set up new projects quickly in a local environment. The tool was created by a student as a learning project and continues to be improved with features like a GUI, template registry, and integrated DNS management.
Accelerating Big Data beyond the JVM - Fosdem 2018
Many popular big data technologies (such as Apache Spark, BEAM, Flink, and Kafka) are built in the JVM, and many interesting tools are built in other languages (ranging from Python to CUDA). For simple operations the cost of copying the data can quickly dominate, and in complex cases can limit our ability to take advantage of specialty hardware. This talk explores how improved formats are being integrated to reduce these hurdles to co-operation.
Many popular big data technologies (such as Apache Spark, BEAM, and Flink) are built in the JVM, and many interesting AI tools are built in other languages, and some requiring copying to the GPU. As many folks have experienced, while we may wish that we spend all of our time playing with cool algorithms -- we often need to spend more of our time working on data prep. Having to copy our data slowly between the JVM and the target language of computation can remove much of the benefit of being able to access our specialized tooling. Thankfully, as illustrated in the soon to be released Spark 2.3, Apache Arrow and related tools offer the ability to reduce this overhead. This talk will explore how Arrow is being integrated into Spark, and how it can be integrated into other systems, but also limitations and places where Apache Arrow will not magically save us.
Link: https://fosdem.org/2018/schedule/event/big_data_outside_jvm/
Kafka Summit SF 2017 - Streaming Processing in Python – 10 ways to avoid summ...
The document discusses stream processing with Python and options to avoid summoning Cuthulu when doing so. It summarizes Apache Spark's capabilities for stream processing with Python, current limitations, and potential future improvements. It also discusses alternative approaches like using pure Python or Spark Structured Streaming. The document recommends Spark Streaming for Python stream processing needs today while noting potential performance improvements in the future.
The Final Frontier
The document discusses the future of development tools across various categories such as programming languages, IDEs, source control, testing, and collaboration tools. It envisions tools that better understand code semantics, support modern paradigms, provide recommendations, visualize data and threads, integrate testing into documentation, and focus on semantic differences in code reviews. Overall, the document presents an optimistic vision of tools that make development more productive by understanding user intent and automating more tasks.
TIFF Nexus: Compile With Style
Slides presented at TIFF Nexus: Women in Film, Games and New Media (Dec 9th, 2011) on the importance of code literacy.
Last 3 slides are bonus slides with resources to start learning programming.
Lightning Fast Dataframes with Polars
Polars is a lightning fast dataframe library written in Rust that provides an alternative to Pandas. It is often an order of magnitude faster than Pandas and supports lazy evaluation and larger-than-memory datasets. Polars uses an intuitive Pythonic syntax that is easy to read. While still missing stability and ecosystem support, Polars is emerging as a promising alternative for those frustrated by Pandas' performance or syntax.
Berlin Buzzwords 2019 - Taming the language border in data analytics and scie...
In the space of building products with data, either by dealing with huge amounts of data or by applying machine learning, many different ecosystems meet. Larger volumes of data have to be passed between these systems. The handling of the data is not only down to divide between systems written in Java that need to pass it on to the machine learning model in Python. When you take into account that you want to integrate with the existing business infrastructure, you also need to cater for legacy systems as well do you need to bring the large volumes of data to the user via UIs.
Natural Language Processing Tools for the Digital Humanities
Natural Language Processing Tools for the Digital Humanities, published by Christopher Manning at Stanford University
Big Data Beyond the JVM - Strata San Jose 2018
The document discusses accelerating big data processing beyond just the Java Virtual Machine (JVM). It introduces Rachel Warren and Holden Karau, the presenters. It then covers the current state of PySpark and its performance limitations due to serialization between Python and the JVM. Future improvements discussed include using Apache Arrow to accelerate UDFs, Dask for pure Python processing, and Apache Beam for additional languages. The presenters promote their new book on high performance Spark and take questions at the end.
Contributing to Apache Spark 3
This document discusses contributing to Apache Spark. It provides an overview of finding issues to work on, the different components of Spark one could contribute to, and the process for contributing code changes through pull requests and code reviews. Key steps include searching Spark's JIRA issue tracker for starter issues, choosing a component to work in, making code and test changes, submitting a pull request for review, addressing review feedback, and getting the change merged once approved.
PyTorch for Delphi - Python Data Sciences Libraries.pdf
Replay and more: https://blogs.embarcadero.com/pytorch-for-delphi-with-the-python-data-sciences-libraries/
The next installment of the Embarcadero Open Source Live Stream takes a look at the Delphi side of the Python Ecosystem with the new Python Data Sciences Libraries and related projects that make it super easy write Delphi code against Python libraries and easily deploy on Windows, Linux, MacOS, and Android. Specific examples with the Python Natural Language Toolkit and PyTorch, the library that powers projects like Tesla Autopilot, Uber's Pyro, Hugging Face's Transformers.
This is part of a series of regular live streams discussing the latest in Embarcadero open source projects. Hosted by Jim McKeeth and joined by members of the community and developers involved in these open source projects, as well as members of Embarcadero and Idera’s Product Management. A great opportunity to see behind the scenes and help shape the future of Embarcadero’s Open Source projects.
Jaoo irony
IronPython combines the best of Python and .NET by allowing Python code to run on the .NET framework. Microsoft developed IronRuby and IronPython which implement Ruby and Python respectively on .NET. These dynamic languages are supported by the Dynamic Language Runtime (DLR) which provides a common environment for dynamic languages to run on .NET.
Bringing WordPress to the front-end. o2 is the new P2
See more at http://geto2.com/
o2 is the next generation of the P2 theme from Automattic, a Twitter/Yammer-style communication platform, based on WordPress. This version is all written in Backbone and brings distributed collaboration up to the cutting edge of web technology.
Towards a Commons RDF Library - ApacheCon Europe 2014
The document introduces Commons RDF, an open source project to develop common interfaces for working with RDF data across different Java libraries. It is led by researchers from Salzburg Research, Epimorphics, and CSIRO who are committers to Apache Jena, OpenRDF Sesame, and Apache Marmotta. The goals are to avoid forcing implementations of one library over another and provide simple interfaces to solve most use cases. Current features include interfaces for core RDF concepts, and the project seeks feedback to evolve the design and implementations.
cadec-2017-golang
This document provides an overview of developing microservices using the Go programming language. It discusses how Go can help reduce the footprint of microservices compared to JVM-based solutions. It then provides background on the Go language, its design goals and pros and cons for development. The rest of the document discusses using Go for microservices, including integrating with services for configuration, logging, distributed tracing, circuit breaking and other concerns. It also compares developing microservices in Go versus Spring Boot and provides code samples.
PyData Frankfurt - (Efficient) Data Exchange with "Foreign" Ecosystems
As a Data Scientist/Engineer in Python, we focus in our work to solve problems with large amounts of data but still stay in Python. This is where we are the most effective and feel comfortable. Libraries like Pandas and NumPy provide us with efficient interfaces to deal with this data while still getting optimal performance. The main problem appears when we have to deal with systems outside of our comfort ecosystem. We need to write cumbersome and mostly slow conversion code that ingests data from there into our pipeline until we can work efficiently. Using Apache Arrow and Parquet as base technologies, we get a set of tools that eases this interaction and also brings us a huge performance improvement. As part of the talk we will show a basic problem where we take data coming from a Java application through Python into using these tools.
Are general purpose big data systems eating the world?
Every-time there is a new piece of big data technology we often see many different specific implementations of the concepts, which often eventually consolidate down to a few viable options, and then frequently end up getting rolled into part of another larger project. This talk will examine this trend in big data ecosystem, look at the exceptions to the "rule", and look at how better interchange formats like Apache Arrow have the potential to change this going forward. In addition to general vague happy feelings (or sad depending on your ideas about how software should be made), this talk will look at some specific examples with deep learning, so if anyone is looking for a little bit of pixie dust to sprinkle on a failing business plan to take to silicon valley to raise a series A, you'll get something out this as well.
Video - https://www.youtube.com/watch?v=P_YKrLFZQJo
Snakes on the Web; Developing web applications in python
From kickstart 2014 tech-talks.
The presentation gives an overview of web application development in python.
Defcon 21-caceres-massive-attacks-with-distributed-computing by pseudor00t
This document discusses using distributed computing for massive attacks. It begins by describing the speaker's side project (PunkSPIDER) that inspired the talk. The document then discusses how distributed computing is commonly used today, mainly for data analytics, and the speaker's vision of using it for large-scale attacks. Examples provided include distributed reconnaissance, distributed SQL injection attacks, and distributing post-exploitation activities like password cracking across many targets. The document emphasizes that tools like Apache Hadoop can make distributed computing and attacks quite simple to implement.
Towards a Commons RDF Java library
The document introduces Commons RDF, an open source Java library that aims to provide common interfaces for working with RDF across different RDF frameworks. It was created due to historical incompatibilities between RDF Java libraries and provides type-safe APIs focused initially on core RDF 1.1 concepts like IRIs, blank nodes, literals, and triples. The library is currently hosted on GitHub but may eventually move to Apache Commons. It seeks feedback from the community on additional desired interfaces as the design evolves.
Introduction to Go
Zack Hubert gave a presentation on the Go programming language. He discussed how Go was created by Rob Pike, Ken Thompson, and Robert Griesemer to be a fast, modern, and simplified language. Go aims for speed through compilation while feeling like an interpreted language. It also supports concurrency through lightweight processes and channels. The language is designed to be simple with features like garbage collection and a small standard library to support modern web development.
Using AWS, Terraform, and Ansible to Automate Splunk at Scale
The DreamPort Splunk Project; How We Use AWS, Terraform, and Ansible to Automate Everything About a Splunk Cluster
At DreamPort, we use cloud platforms, infrastructure-as-code tooling, configuration tools, automation software, and container technologies to very quickly design, develop, and prototype projects. This particular talk focuses on the tools used to deploy and configure a Splunk cluster for a particular project we recently ran. We will cover the deployment, configuration, and orchestration of a large 16 node Splunk cluster using tools that are a core set to DreamPort's cloud infrastructure toolbox; AWS, Terraform, Ansible, and Docker.
It is recommended that attendees have a general understanding of AWS, Linux, Splunk, and Docker, and know about automation tools such as Terraform and Ansible.
Attendees will learn how to use AWS, Terraform, Ansible, and Docker to deploy a large Splunk cluster, how to use Ansible to orchestrate and manage the Splunk cluster, and how to use Ansible to orchestrate and manage the Splunk cluster.
-------------------------------------------------
Bill Cawthra is a Principal Cloud Infrastructure Architect for CyberPoint, managing project-related cloud systems and platforms. He works primarily on the AWS platform, using various automation tools to rapidly deploy and manage infrastructure. Bill has over 18 years of experience in computers and technology, working in a range of fields, including construction, DoD, health care, and social media.
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays, June 2024 with Maria Copot 20
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
More Related Content
Similar to Lightning talk: Envisioning a new inherently shared programming language
Kafka Summit SF 2017 - Streaming Processing in Python – 10 ways to avoid summ...
The document discusses stream processing with Python and options to avoid summoning Cuthulu when doing so. It summarizes Apache Spark's capabilities for stream processing with Python, current limitations, and potential future improvements. It also discusses alternative approaches like using pure Python or Spark Structured Streaming. The document recommends Spark Streaming for Python stream processing needs today while noting potential performance improvements in the future.
The Final Frontier
The document discusses the future of development tools across various categories such as programming languages, IDEs, source control, testing, and collaboration tools. It envisions tools that better understand code semantics, support modern paradigms, provide recommendations, visualize data and threads, integrate testing into documentation, and focus on semantic differences in code reviews. Overall, the document presents an optimistic vision of tools that make development more productive by understanding user intent and automating more tasks.
TIFF Nexus: Compile With Style
Slides presented at TIFF Nexus: Women in Film, Games and New Media (Dec 9th, 2011) on the importance of code literacy.
Last 3 slides are bonus slides with resources to start learning programming.
Lightning Fast Dataframes with Polars
Polars is a lightning fast dataframe library written in Rust that provides an alternative to Pandas. It is often an order of magnitude faster than Pandas and supports lazy evaluation and larger-than-memory datasets. Polars uses an intuitive Pythonic syntax that is easy to read. While still missing stability and ecosystem support, Polars is emerging as a promising alternative for those frustrated by Pandas' performance or syntax.
Berlin Buzzwords 2019 - Taming the language border in data analytics and scie...
In the space of building products with data, either by dealing with huge amounts of data or by applying machine learning, many different ecosystems meet. Larger volumes of data have to be passed between these systems. The handling of the data is not only down to divide between systems written in Java that need to pass it on to the machine learning model in Python. When you take into account that you want to integrate with the existing business infrastructure, you also need to cater for legacy systems as well do you need to bring the large volumes of data to the user via UIs.
Natural Language Processing Tools for the Digital Humanities
Natural Language Processing Tools for the Digital Humanities, published by Christopher Manning at Stanford University
Big Data Beyond the JVM - Strata San Jose 2018
The document discusses accelerating big data processing beyond just the Java Virtual Machine (JVM). It introduces Rachel Warren and Holden Karau, the presenters. It then covers the current state of PySpark and its performance limitations due to serialization between Python and the JVM. Future improvements discussed include using Apache Arrow to accelerate UDFs, Dask for pure Python processing, and Apache Beam for additional languages. The presenters promote their new book on high performance Spark and take questions at the end.
Contributing to Apache Spark 3
This document discusses contributing to Apache Spark. It provides an overview of finding issues to work on, the different components of Spark one could contribute to, and the process for contributing code changes through pull requests and code reviews. Key steps include searching Spark's JIRA issue tracker for starter issues, choosing a component to work in, making code and test changes, submitting a pull request for review, addressing review feedback, and getting the change merged once approved.
PyTorch for Delphi - Python Data Sciences Libraries.pdf
Replay and more: https://blogs.embarcadero.com/pytorch-for-delphi-with-the-python-data-sciences-libraries/
The next installment of the Embarcadero Open Source Live Stream takes a look at the Delphi side of the Python Ecosystem with the new Python Data Sciences Libraries and related projects that make it super easy write Delphi code against Python libraries and easily deploy on Windows, Linux, MacOS, and Android. Specific examples with the Python Natural Language Toolkit and PyTorch, the library that powers projects like Tesla Autopilot, Uber's Pyro, Hugging Face's Transformers.
This is part of a series of regular live streams discussing the latest in Embarcadero open source projects. Hosted by Jim McKeeth and joined by members of the community and developers involved in these open source projects, as well as members of Embarcadero and Idera’s Product Management. A great opportunity to see behind the scenes and help shape the future of Embarcadero’s Open Source projects.
Jaoo irony
IronPython combines the best of Python and .NET by allowing Python code to run on the .NET framework. Microsoft developed IronRuby and IronPython which implement Ruby and Python respectively on .NET. These dynamic languages are supported by the Dynamic Language Runtime (DLR) which provides a common environment for dynamic languages to run on .NET.
Bringing WordPress to the front-end. o2 is the new P2
See more at http://geto2.com/
o2 is the next generation of the P2 theme from Automattic, a Twitter/Yammer-style communication platform, based on WordPress. This version is all written in Backbone and brings distributed collaboration up to the cutting edge of web technology.
Towards a Commons RDF Library - ApacheCon Europe 2014
The document introduces Commons RDF, an open source project to develop common interfaces for working with RDF data across different Java libraries. It is led by researchers from Salzburg Research, Epimorphics, and CSIRO who are committers to Apache Jena, OpenRDF Sesame, and Apache Marmotta. The goals are to avoid forcing implementations of one library over another and provide simple interfaces to solve most use cases. Current features include interfaces for core RDF concepts, and the project seeks feedback to evolve the design and implementations.
cadec-2017-golang
This document provides an overview of developing microservices using the Go programming language. It discusses how Go can help reduce the footprint of microservices compared to JVM-based solutions. It then provides background on the Go language, its design goals and pros and cons for development. The rest of the document discusses using Go for microservices, including integrating with services for configuration, logging, distributed tracing, circuit breaking and other concerns. It also compares developing microservices in Go versus Spring Boot and provides code samples.
PyData Frankfurt - (Efficient) Data Exchange with "Foreign" Ecosystems
As a Data Scientist/Engineer in Python, we focus in our work to solve problems with large amounts of data but still stay in Python. This is where we are the most effective and feel comfortable. Libraries like Pandas and NumPy provide us with efficient interfaces to deal with this data while still getting optimal performance. The main problem appears when we have to deal with systems outside of our comfort ecosystem. We need to write cumbersome and mostly slow conversion code that ingests data from there into our pipeline until we can work efficiently. Using Apache Arrow and Parquet as base technologies, we get a set of tools that eases this interaction and also brings us a huge performance improvement. As part of the talk we will show a basic problem where we take data coming from a Java application through Python into using these tools.
Are general purpose big data systems eating the world?
Every-time there is a new piece of big data technology we often see many different specific implementations of the concepts, which often eventually consolidate down to a few viable options, and then frequently end up getting rolled into part of another larger project. This talk will examine this trend in big data ecosystem, look at the exceptions to the "rule", and look at how better interchange formats like Apache Arrow have the potential to change this going forward. In addition to general vague happy feelings (or sad depending on your ideas about how software should be made), this talk will look at some specific examples with deep learning, so if anyone is looking for a little bit of pixie dust to sprinkle on a failing business plan to take to silicon valley to raise a series A, you'll get something out this as well.
Video - https://www.youtube.com/watch?v=P_YKrLFZQJo
Snakes on the Web; Developing web applications in python
From kickstart 2014 tech-talks.
The presentation gives an overview of web application development in python.
Defcon 21-caceres-massive-attacks-with-distributed-computing by pseudor00t
This document discusses using distributed computing for massive attacks. It begins by describing the speaker's side project (PunkSPIDER) that inspired the talk. The document then discusses how distributed computing is commonly used today, mainly for data analytics, and the speaker's vision of using it for large-scale attacks. Examples provided include distributed reconnaissance, distributed SQL injection attacks, and distributing post-exploitation activities like password cracking across many targets. The document emphasizes that tools like Apache Hadoop can make distributed computing and attacks quite simple to implement.
Towards a Commons RDF Java library
The document introduces Commons RDF, an open source Java library that aims to provide common interfaces for working with RDF across different RDF frameworks. It was created due to historical incompatibilities between RDF Java libraries and provides type-safe APIs focused initially on core RDF 1.1 concepts like IRIs, blank nodes, literals, and triples. The library is currently hosted on GitHub but may eventually move to Apache Commons. It seeks feedback from the community on additional desired interfaces as the design evolves.
Introduction to Go
Zack Hubert gave a presentation on the Go programming language. He discussed how Go was created by Rob Pike, Ken Thompson, and Robert Griesemer to be a fast, modern, and simplified language. Go aims for speed through compilation while feeling like an interpreted language. It also supports concurrency through lightweight processes and channels. The language is designed to be simple with features like garbage collection and a small standard library to support modern web development.
Using AWS, Terraform, and Ansible to Automate Splunk at Scale
The DreamPort Splunk Project; How We Use AWS, Terraform, and Ansible to Automate Everything About a Splunk Cluster
At DreamPort, we use cloud platforms, infrastructure-as-code tooling, configuration tools, automation software, and container technologies to very quickly design, develop, and prototype projects. This particular talk focuses on the tools used to deploy and configure a Splunk cluster for a particular project we recently ran. We will cover the deployment, configuration, and orchestration of a large 16 node Splunk cluster using tools that are a core set to DreamPort's cloud infrastructure toolbox; AWS, Terraform, Ansible, and Docker.
It is recommended that attendees have a general understanding of AWS, Linux, Splunk, and Docker, and know about automation tools such as Terraform and Ansible.
Attendees will learn how to use AWS, Terraform, Ansible, and Docker to deploy a large Splunk cluster, how to use Ansible to orchestrate and manage the Splunk cluster, and how to use Ansible to orchestrate and manage the Splunk cluster.
-------------------------------------------------
Bill Cawthra is a Principal Cloud Infrastructure Architect for CyberPoint, managing project-related cloud systems and platforms. He works primarily on the AWS platform, using various automation tools to rapidly deploy and manage infrastructure. Bill has over 18 years of experience in computers and technology, working in a range of fields, including construction, DoD, health care, and social media.
Similar to Lightning talk: Envisioning a new inherently shared programming language (20)
Kafka Summit SF 2017 - Streaming Processing in Python – 10 ways to avoid summ...
Kafka Summit SF 2017 - Streaming Processing in Python – 10 ways to avoid summ...
Berlin Buzzwords 2019 - Taming the language border in data analytics and scie...
Berlin Buzzwords 2019 - Taming the language border in data analytics and scie...
Natural Language Processing Tools for the Digital Humanities
Natural Language Processing Tools for the Digital Humanities
PyTorch for Delphi - Python Data Sciences Libraries.pdf
PyTorch for Delphi - Python Data Sciences Libraries.pdf
Bringing WordPress to the front-end. o2 is the new P2
Bringing WordPress to the front-end. o2 is the new P2
Towards a Commons RDF Library - ApacheCon Europe 2014
Towards a Commons RDF Library - ApacheCon Europe 2014
PyData Frankfurt - (Efficient) Data Exchange with "Foreign" Ecosystems
PyData Frankfurt - (Efficient) Data Exchange with "Foreign" Ecosystems
Are general purpose big data systems eating the world?
Are general purpose big data systems eating the world?
Snakes on the Web; Developing web applications in python
Snakes on the Web; Developing web applications in python
Defcon 21-caceres-massive-attacks-with-distributed-computing by pseudor00t
Defcon 21-caceres-massive-attacks-with-distributed-computing by pseudor00t
Using AWS, Terraform, and Ansible to Automate Splunk at Scale
Using AWS, Terraform, and Ansible to Automate Splunk at Scale
Recently uploaded
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays, June 2024 with Maria Copot 20
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Best 20 SEO Techniques To Improve Website Visibility In SERP
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Building Production Ready Search Pipelines with Spark and Milvus
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Presentation of the OECD Artificial Intelligence Review of Germany
Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
Generating privacy-protected synthetic data using Secludy and Milvus
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
June Patch Tuesday
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Trusted Execution Environment for Decentralized Process Mining
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
Skybuffer SAM4U tool for SAP license adoption
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Programming Foundation Models with DSPy - Meetup Slides
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Astute Business Solutions | Oracle Cloud Partner |
Your goto partner for Oracle Cloud, PeopleSoft, E-Business Suite, and Ellucian Banner. We are a firm specialized in managed services and consulting.
TrustArc Webinar - 2024 Global Privacy Survey
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Recently uploaded (20)
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
Building Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and Milvus
Nordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptx
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Trusted Execution Environment for Decentralized Process Mining
Trusted Execution Environment for Decentralized Process Mining
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
Astute Business Solutions | Oracle Cloud Partner |
Astute Business Solutions | Oracle Cloud Partner |
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
Lightning talk: Envisioning a new inherently shared programming language
- 1. –Ford Prefect “Time is an illusion, lunchtime doubly so”
- 2. Language brainstorm [PolyConf] -> [Lunch] -> [Crazy idea?] Scala of Lancarrow@jankrag
- 3. @jankrag What happens at PolyConf stays at… A lunch talk extrapolated to a brainstorm with Korneliusz Caputa
- 4. @jankrag Initial thoughts • Much code has already been done - somewhere • What a waste…
- 5. @jankrag Initial thoughts • At least one company has already ingested ALL public data on GitHub for analysis • and use it (only?) for recruitment • … what a waste
- 7. @jankrag Language where sharing is built in
- 8. @jankrag thus open source is built in
- 9. @jankrag …the compiler …the runtime …or something? To
- 10. @jankrag Discovery built into your vIDEmacs
- 11. @jankrag Premise: We already (mostly) trust std. libs And have them at our fingertips Why not more?
- 13. @jankrag
- 14. @jankrag Pure functions might make it easier?
- 15. @jankrag Advanced type system (like Haskell but more readable)
- 17. @jankrag Type providers like F# (for IDE magic)
- 20. @jankrag JIT function “caching” or redistribution • Like BitTorrent magic? • Copy frequent functions to local runtime or at least closer? • If 10 people in this room are calling map, it would be copied to one of us? • If I use it a lot, I might get local version?
- 22. @jankrag Performance especially if distributed runtime
- 24. @jankrag Naah, that shouldn’t stop a good idea :-)
- 25. @jankragmake it happen? Shoot Think Suggest Deny Improve Tweet…