GENERATIVE AI, THE FUTURE OF PRODUCTIVITYAndre Muscat
Discuss the impact and opportunity of using Generative AI to support your development and creative teams
* Explore business challenges in content creation
* Cost-per-unit of different types of content
* Use AI to reduce cost-per-unit
* New partnerships being formed that will have a material impact on the way we search and engage with content
Part 4 of a 9 Part Research Series named "What matters in AI" published on www.andremuscat.com
Digital Transformation: What it is and how to get thereEconsultancy
Digital Transformation: What it is and how to get there.
Authored by Econsultancy CEO Ashley Friedlein, this presentation on the topic of 'Digital Transformation', is broken down into six sections covering:
1. Digital Transformation - what it is and recent data and research on the topic
2. Strategy - what a digital strategy should include
3. Technology - the challenges of technology and the skills gap
4. People - looking at organisational structure, culture, roles & responsibilities, environment recquired
5. Process - how to address the speed, innovation and agility required
6. Business Transformation - how digital transformation is actually business transformation
As NFT projects continue to pop up and censorship woes become a reality, decentralized storage has become a beacon of hope for many. Let’s check out how much the decentralized storage sector has grown!
The essential elements of a digital transformation strategyMarcel Santilli
Learn more: https://insights.hpe.com
Enterprises can survive digital disruption as well as grow revenue, improve profitability and increase market valuation — if they start rethinking what they do.
Digital transformation. It’s the use of technology to create a better customer experience, improve products and services, and increase the effectiveness of business operations. But it really means what your enterprise must do to adapt and thrive.
Today’s smaller, emerging companies are born digital. They can — and do — change quickly to answer consumer demand or a competitive offering. Larger, mature enterprises must start with a shift in strategy, because all industries will be changed or already have been changed by digital transformation. Many, like news media and publishers, music, video and retail have been or significantly disrupted. Up next: financial services, healthcare manufacturing, insurance, legal, education, utilities and energy. The good news? No industry has been or will be completely upended.
The first step is to recognize that disruption does not have to be a mass-extinction event. Enterprises can survive as well as grow revenue, improve profitability and increase market valuation. Here’s how to start rethinking what you do.
[Note: This is a partial preview. To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This presentation is a collection of PowerPoint diagrams and templates used to convey 20 different digital transformation frameworks and models.
INCLUDED FRAMEWORKS/MODELS:
1. Ten Guiding Principles of Digital Transformation
2. The BCG Strategy Palette
3. Digital Value Chain Model
4. Four Levels of Digital Maturity
5. Customer Experience Matrix
6. Design Thinking Framework
7. Business Model Canvas
8. Customer Journey Map
9. OECD Digital Government Transformation Framework
10. Accenture's Nonstop Customer Experience Model
11. MIT's Digital Transformation Framework
12. McKinsey's Digital Transformation Framework
13. Capgemini's Digital Transformation Framework
14. DXC Technology's Digital Transformation Framework
15. Gartner's Digital Transformation Framework
16. Cognizant's Digital Transformation Framework
17. PwC's Digital Transformation Framework
18. Ionolgy's Digital Transformation Framework
19. Accenture's Digital Business Strategy Framework
20. Deloitte's Digital Industrial Transformation Framework
Business today is starting to understand the value of data, and some organisations are outperforming their competition by putting data at the heart of their thinking. Leveraging data to change business models, understand their customers and employees better and deliver new revenue streams is the driving force in this new data centric era.
Jon Woodward - MSFT
Dave Coplin - MSFT
Mike Bugembe - JustGiving
Gary Richardson - KPMG
GENERATIVE AI, THE FUTURE OF PRODUCTIVITYAndre Muscat
Discuss the impact and opportunity of using Generative AI to support your development and creative teams
* Explore business challenges in content creation
* Cost-per-unit of different types of content
* Use AI to reduce cost-per-unit
* New partnerships being formed that will have a material impact on the way we search and engage with content
Part 4 of a 9 Part Research Series named "What matters in AI" published on www.andremuscat.com
Digital Transformation: What it is and how to get thereEconsultancy
Digital Transformation: What it is and how to get there.
Authored by Econsultancy CEO Ashley Friedlein, this presentation on the topic of 'Digital Transformation', is broken down into six sections covering:
1. Digital Transformation - what it is and recent data and research on the topic
2. Strategy - what a digital strategy should include
3. Technology - the challenges of technology and the skills gap
4. People - looking at organisational structure, culture, roles & responsibilities, environment recquired
5. Process - how to address the speed, innovation and agility required
6. Business Transformation - how digital transformation is actually business transformation
As NFT projects continue to pop up and censorship woes become a reality, decentralized storage has become a beacon of hope for many. Let’s check out how much the decentralized storage sector has grown!
The essential elements of a digital transformation strategyMarcel Santilli
Learn more: https://insights.hpe.com
Enterprises can survive digital disruption as well as grow revenue, improve profitability and increase market valuation — if they start rethinking what they do.
Digital transformation. It’s the use of technology to create a better customer experience, improve products and services, and increase the effectiveness of business operations. But it really means what your enterprise must do to adapt and thrive.
Today’s smaller, emerging companies are born digital. They can — and do — change quickly to answer consumer demand or a competitive offering. Larger, mature enterprises must start with a shift in strategy, because all industries will be changed or already have been changed by digital transformation. Many, like news media and publishers, music, video and retail have been or significantly disrupted. Up next: financial services, healthcare manufacturing, insurance, legal, education, utilities and energy. The good news? No industry has been or will be completely upended.
The first step is to recognize that disruption does not have to be a mass-extinction event. Enterprises can survive as well as grow revenue, improve profitability and increase market valuation. Here’s how to start rethinking what you do.
[Note: This is a partial preview. To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This presentation is a collection of PowerPoint diagrams and templates used to convey 20 different digital transformation frameworks and models.
INCLUDED FRAMEWORKS/MODELS:
1. Ten Guiding Principles of Digital Transformation
2. The BCG Strategy Palette
3. Digital Value Chain Model
4. Four Levels of Digital Maturity
5. Customer Experience Matrix
6. Design Thinking Framework
7. Business Model Canvas
8. Customer Journey Map
9. OECD Digital Government Transformation Framework
10. Accenture's Nonstop Customer Experience Model
11. MIT's Digital Transformation Framework
12. McKinsey's Digital Transformation Framework
13. Capgemini's Digital Transformation Framework
14. DXC Technology's Digital Transformation Framework
15. Gartner's Digital Transformation Framework
16. Cognizant's Digital Transformation Framework
17. PwC's Digital Transformation Framework
18. Ionolgy's Digital Transformation Framework
19. Accenture's Digital Business Strategy Framework
20. Deloitte's Digital Industrial Transformation Framework
Business today is starting to understand the value of data, and some organisations are outperforming their competition by putting data at the heart of their thinking. Leveraging data to change business models, understand their customers and employees better and deliver new revenue streams is the driving force in this new data centric era.
Jon Woodward - MSFT
Dave Coplin - MSFT
Mike Bugembe - JustGiving
Gary Richardson - KPMG
Explore how different industries are embracing the utility of AI to create and deliver new value for their customers and organisation
* Discuss the state of maturity of AI across industries
* Get an appreciation of business posture to AI projects
We also review the utility of AI across several industries including:
* Healthcare
* Newsroom & Journalism
* Travel
* Finance
* Supply Chain / eCommerce / Retail
* Streaming & Gaming
* Transportation
* Logistics
* Manufacturing
* Agriculture
* Defense & Cybersecurity
Part of the What Matters in AI series as published on www.andremuscat.com
Innnovations in online teaching and learning: CHatGPT and other artificial as...Rebecca Ferguson
Talk given by Agnes Kukulska-Hulme and Rebecca Ferguson to SciLab (a centre for pedagogical research and innovation in business and law) at The Open University, Milton Keynes, UK on Wednesday 3 May 2023.
Digital transformation sweet spot: Business operationsMarcel Santilli
Learn more: https://insights.hpe.com
Your enterprise can digitally transform by gaining insights from your data to improve the experience for your customers.
Enterprises need to make over all aspects of their business, because today’s customers expect frictionless experiences — and because new competitors launched with the latest technologies can change and respond to customers faster than mature companies.
Start with the fact that your enterprise has valuable assets that start-ups don’t — your customers. Fostering loyalty among these customers requires improving their interaction with not only your products and services, but also sales, billing, support and shipping operations. Successful companies count on digital technologies to transform the total customer experience. As consumers, we’ve come to expect digitally enabled products as the new normal. But what’s the next step for your enterprise? Find ways to translate into their business lives what people love and expect as consumers.
Enterprises can learn from the digital leaders who look for ways that apps and data can be added to products to create new value over time. Digital leaders use what they learn from the data to reshape core operations to drive the enterprise forward. What’s considered a core operation varies from industry to industry, but the common characteristic is that core operations make up a sizable portion of the enterprise budget. Gaining even a modest amount of efficiency through digital transformation can significantly impact the bottom line. Data also can be used to predict mechanical failure and to schedule preventive maintenance to avoid business disruptions.
Digital transformation begins with data. So how can your enterprise gain insights from your data to improve the experience for your customers?
Digital Transformation From Strategy To ImplementationScopernia
Creating a digital transformation strategy is one thing but how do you put the insights and plans into practice. This presentation deals with vision, strategy, roadmap, governance, leadership, channel hacking, start-up-thinking and many more issues.
What industries have been digitally disrupted? What are being disrupted? What types of digital disruption are there? Where should you focus your digital disruption/transformation efforts?
Leveraging Generative AI & Best practicesDianaGray10
In this event we will cover:
- What is Generative AI and how it is being for future of work.
- Best practices for developing and deploying generative AI based models in productions.
- Future of Generative AI, how generative AI is expected to evolve in the coming years.
Accelerate AI w/ Synthetic Data using GANsRenee Yao
Strata Data Conference in Sep 2018 Presentation
Description:
Synthetic data will drive the next wave of deployment and application of deep learning in the real world across a variety of problems involving speech recognition, image classification, object recognition and language. All industries and companies will benefit, as synthetic data can create conditions through simulation, instead of authentic situations (virtual worlds enable you to avoid the cost of damages, spare human injuries, and other factors that come into play); unparalleled ability to test products, and interactions with them in any environment.
Join us for this introductory session to learn more about how Generative Adversarial Networks (GAN) are successfully used to improve data generation. We will cover specific real-world examples where customers have deployed GAN to solve challenges in healthcare, space, transportation, and retail industries.
Renee Yao explains how generative adversarial networks (GAN) are successfully used to improve data generation and explores specific real-world examples where customers have deployed GANs to solve challenges in healthcare, space, transportation, and retail industries.
A comprehensive review of AI use within the public relations profession.
At time of writing (February 2023), there’s been a burst of new AI-driven tools, services and use cases with the potential to impact virtually every aspect of the public relations profession.
This report is an attempt to assess the likely rapid progress of AI technology over the next year and the longer-term strategic considerations for all public relations practitioners as a result.
Co-authored by Andrew Bruce Smith and Stephen Waddington, with contributions from Professor Anne Gregory, Jean Valin and Scott Brinker.
Superweek 2022 - Solid & Digital Analytics TrackingJente De Ridder
Our industry is changing rapidly: tracking standards are under pressure, consumers are more privacy conscious and legislators are trying to regain control of big tech.
Solid offers an answer to all these challenges. It is a framework initiated by Tim Berners Lee, the inventor of the world wide web. And it is clear that this will be a game changer for how personal data will be handled in the future.
The question is how we as an industry will prepare ourselves for this and how we all can contribute to the adoption of this new standard?
Sales Decks for Founders - Founding Sales - December 2015 Peter Kazanjy
Presentation on "sales decks for founders" covering the best way to present your new technology product to a business-to-business buyer.
Presentation is an adaption of a chapter from Founding Sales (book on technology sales for founders and other first-time sellers): https://twitter.com/FoundingSales
Chapter excerpt here: http://firstround.com/review/building-your-best-sales-deck-starts-here/
Workshop digital transformation strategy digital road-map trainingMiodrag Kostic, CMC
Presentation "Digital transformation strategy workshop" Interactive training course on how to create digital strategy and digital road map for digital transformation?
Miodrag Kostic, CMC, CDC
Certified digital transformation expert - consultant
http://www.businessknowledge.biz/
http://www.miodragkostic.com/
The best digital transformation frameworks in 2020run_frictionless
We review digital transformation frameworks from the world’s top digital transformation consulting companies. PwC, McKinsey, Accenture, EY, Gartner, CapGemini, MIT, Cognizant, Altimeter, Ionology.
> Read the full review here: http://bit.ly/31e9dtP
Battery Ventures State of the OpenCloud Report 2022Battery Ventures
Battery Ventures' 2022 State of the OpenCloud report, compiled by General Partner Dharmesh Thakker and his team Danel Dayan, Jason Mendel and Patrick Hsu. The report analyzes the macro technology and economic trends impacting the cloud market, and provides advice for cloud-native entrepreneurs who are navigating these trends to build large, enduring businesses.
Data analytics presentation- Management career institute PoojaPatidar11
1. The basic definition of Data, Analytics, and Data Analytics
2. Definition: Data: Data is a set of values of qualitative or quantitative variables. It is information in the raw or unorganized form. It may be a fact, figure, characters, symbols etc
Analytics: Analytics is the discovery, interpretation, and communication of meaningful patterns in data and applying those patterns towards effective decision making.
Data Analytics: Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain.
3.Types of analytics: Predictive Analytics (What could happen?)
Prescriptive Analytics (What should we do)
Descriptive Analytics (What has happened?)
4.Why Data analytics? Data Analytics is needed in Business to Consumer applications (B2C)
5.The process of Data analytics: Data requirements,
Data collection, Data processing, Data cleaning, Exploratory data analysis,
Modeling and algorithms, Data product, Communication
6.The scope of Data Analytics: Bright future of data analytics, many professionals and students are interested in a career in data analytics.
7.Importance of data analytics:1. Predict customer trends and behaviors
Analyze,
2 interpret and deliver data in meaningful ways
3.Increase business productivity
4.Drive effective decision-making
8.why become a data analyst? talented gaps of skill candidates, good salaries for freshers, great future growth path
9. What recruiters look for in applicants: Problem-Solving Skills, Analytical Mind, Maths and Statistic Skills, Communication (both oral and written), Teamwork Abilities
10. Skill is required for Data analytics?
1.) Analytical Skills
2.) Numeracy Skills
3.) Technical and Computer Skills
4.) Attention to Details
5.) Business Skills
6.) Communication Skills
11. Data analytics tools
1.SAS: SAS (Statistical Analysis System) is a software suite developed by SAS Institute. sas language can be defined as a programming language in the computing field. This language is generally used for the purpose of statistical analysis. The language has the ability to read data from databases and common spreadsheets.
2. R: R is a programming language and software environment for statistical analysis, graphics representation and reporting.R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows, and Mac.
3.PYTHON: Python is a popular programming language Python is a powerful, flexible, open-sources language that is easy to use,
and has a powerful library for data manipulation and analysis.
4.TABLEAU: Tableau Software is a software company that produces interactive data visualization products focused on business intelligence.
A great idea can be built with almost any technology. The success or failure of your project has more to do with vision, leadership, execution, and market than technological choices.
Besides the vision, a lot of startups focus on culture. what isn’t often mentioned is that the technical decisions will have a direct effect on the company culture. Great things have been built with each of the technologies. But they do come with a culture.
The purpose of this presentation is to help developers, managers, founders, etc. to make an insightful decision about the framework they want to use to create their product.
Explore how different industries are embracing the utility of AI to create and deliver new value for their customers and organisation
* Discuss the state of maturity of AI across industries
* Get an appreciation of business posture to AI projects
We also review the utility of AI across several industries including:
* Healthcare
* Newsroom & Journalism
* Travel
* Finance
* Supply Chain / eCommerce / Retail
* Streaming & Gaming
* Transportation
* Logistics
* Manufacturing
* Agriculture
* Defense & Cybersecurity
Part of the What Matters in AI series as published on www.andremuscat.com
Innnovations in online teaching and learning: CHatGPT and other artificial as...Rebecca Ferguson
Talk given by Agnes Kukulska-Hulme and Rebecca Ferguson to SciLab (a centre for pedagogical research and innovation in business and law) at The Open University, Milton Keynes, UK on Wednesday 3 May 2023.
Digital transformation sweet spot: Business operationsMarcel Santilli
Learn more: https://insights.hpe.com
Your enterprise can digitally transform by gaining insights from your data to improve the experience for your customers.
Enterprises need to make over all aspects of their business, because today’s customers expect frictionless experiences — and because new competitors launched with the latest technologies can change and respond to customers faster than mature companies.
Start with the fact that your enterprise has valuable assets that start-ups don’t — your customers. Fostering loyalty among these customers requires improving their interaction with not only your products and services, but also sales, billing, support and shipping operations. Successful companies count on digital technologies to transform the total customer experience. As consumers, we’ve come to expect digitally enabled products as the new normal. But what’s the next step for your enterprise? Find ways to translate into their business lives what people love and expect as consumers.
Enterprises can learn from the digital leaders who look for ways that apps and data can be added to products to create new value over time. Digital leaders use what they learn from the data to reshape core operations to drive the enterprise forward. What’s considered a core operation varies from industry to industry, but the common characteristic is that core operations make up a sizable portion of the enterprise budget. Gaining even a modest amount of efficiency through digital transformation can significantly impact the bottom line. Data also can be used to predict mechanical failure and to schedule preventive maintenance to avoid business disruptions.
Digital transformation begins with data. So how can your enterprise gain insights from your data to improve the experience for your customers?
Digital Transformation From Strategy To ImplementationScopernia
Creating a digital transformation strategy is one thing but how do you put the insights and plans into practice. This presentation deals with vision, strategy, roadmap, governance, leadership, channel hacking, start-up-thinking and many more issues.
What industries have been digitally disrupted? What are being disrupted? What types of digital disruption are there? Where should you focus your digital disruption/transformation efforts?
Leveraging Generative AI & Best practicesDianaGray10
In this event we will cover:
- What is Generative AI and how it is being for future of work.
- Best practices for developing and deploying generative AI based models in productions.
- Future of Generative AI, how generative AI is expected to evolve in the coming years.
Accelerate AI w/ Synthetic Data using GANsRenee Yao
Strata Data Conference in Sep 2018 Presentation
Description:
Synthetic data will drive the next wave of deployment and application of deep learning in the real world across a variety of problems involving speech recognition, image classification, object recognition and language. All industries and companies will benefit, as synthetic data can create conditions through simulation, instead of authentic situations (virtual worlds enable you to avoid the cost of damages, spare human injuries, and other factors that come into play); unparalleled ability to test products, and interactions with them in any environment.
Join us for this introductory session to learn more about how Generative Adversarial Networks (GAN) are successfully used to improve data generation. We will cover specific real-world examples where customers have deployed GAN to solve challenges in healthcare, space, transportation, and retail industries.
Renee Yao explains how generative adversarial networks (GAN) are successfully used to improve data generation and explores specific real-world examples where customers have deployed GANs to solve challenges in healthcare, space, transportation, and retail industries.
A comprehensive review of AI use within the public relations profession.
At time of writing (February 2023), there’s been a burst of new AI-driven tools, services and use cases with the potential to impact virtually every aspect of the public relations profession.
This report is an attempt to assess the likely rapid progress of AI technology over the next year and the longer-term strategic considerations for all public relations practitioners as a result.
Co-authored by Andrew Bruce Smith and Stephen Waddington, with contributions from Professor Anne Gregory, Jean Valin and Scott Brinker.
Superweek 2022 - Solid & Digital Analytics TrackingJente De Ridder
Our industry is changing rapidly: tracking standards are under pressure, consumers are more privacy conscious and legislators are trying to regain control of big tech.
Solid offers an answer to all these challenges. It is a framework initiated by Tim Berners Lee, the inventor of the world wide web. And it is clear that this will be a game changer for how personal data will be handled in the future.
The question is how we as an industry will prepare ourselves for this and how we all can contribute to the adoption of this new standard?
Sales Decks for Founders - Founding Sales - December 2015 Peter Kazanjy
Presentation on "sales decks for founders" covering the best way to present your new technology product to a business-to-business buyer.
Presentation is an adaption of a chapter from Founding Sales (book on technology sales for founders and other first-time sellers): https://twitter.com/FoundingSales
Chapter excerpt here: http://firstround.com/review/building-your-best-sales-deck-starts-here/
Workshop digital transformation strategy digital road-map trainingMiodrag Kostic, CMC
Presentation "Digital transformation strategy workshop" Interactive training course on how to create digital strategy and digital road map for digital transformation?
Miodrag Kostic, CMC, CDC
Certified digital transformation expert - consultant
http://www.businessknowledge.biz/
http://www.miodragkostic.com/
The best digital transformation frameworks in 2020run_frictionless
We review digital transformation frameworks from the world’s top digital transformation consulting companies. PwC, McKinsey, Accenture, EY, Gartner, CapGemini, MIT, Cognizant, Altimeter, Ionology.
> Read the full review here: http://bit.ly/31e9dtP
Battery Ventures State of the OpenCloud Report 2022Battery Ventures
Battery Ventures' 2022 State of the OpenCloud report, compiled by General Partner Dharmesh Thakker and his team Danel Dayan, Jason Mendel and Patrick Hsu. The report analyzes the macro technology and economic trends impacting the cloud market, and provides advice for cloud-native entrepreneurs who are navigating these trends to build large, enduring businesses.
Data analytics presentation- Management career institute PoojaPatidar11
1. The basic definition of Data, Analytics, and Data Analytics
2. Definition: Data: Data is a set of values of qualitative or quantitative variables. It is information in the raw or unorganized form. It may be a fact, figure, characters, symbols etc
Analytics: Analytics is the discovery, interpretation, and communication of meaningful patterns in data and applying those patterns towards effective decision making.
Data Analytics: Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain.
3.Types of analytics: Predictive Analytics (What could happen?)
Prescriptive Analytics (What should we do)
Descriptive Analytics (What has happened?)
4.Why Data analytics? Data Analytics is needed in Business to Consumer applications (B2C)
5.The process of Data analytics: Data requirements,
Data collection, Data processing, Data cleaning, Exploratory data analysis,
Modeling and algorithms, Data product, Communication
6.The scope of Data Analytics: Bright future of data analytics, many professionals and students are interested in a career in data analytics.
7.Importance of data analytics:1. Predict customer trends and behaviors
Analyze,
2 interpret and deliver data in meaningful ways
3.Increase business productivity
4.Drive effective decision-making
8.why become a data analyst? talented gaps of skill candidates, good salaries for freshers, great future growth path
9. What recruiters look for in applicants: Problem-Solving Skills, Analytical Mind, Maths and Statistic Skills, Communication (both oral and written), Teamwork Abilities
10. Skill is required for Data analytics?
1.) Analytical Skills
2.) Numeracy Skills
3.) Technical and Computer Skills
4.) Attention to Details
5.) Business Skills
6.) Communication Skills
11. Data analytics tools
1.SAS: SAS (Statistical Analysis System) is a software suite developed by SAS Institute. sas language can be defined as a programming language in the computing field. This language is generally used for the purpose of statistical analysis. The language has the ability to read data from databases and common spreadsheets.
2. R: R is a programming language and software environment for statistical analysis, graphics representation and reporting.R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows, and Mac.
3.PYTHON: Python is a popular programming language Python is a powerful, flexible, open-sources language that is easy to use,
and has a powerful library for data manipulation and analysis.
4.TABLEAU: Tableau Software is a software company that produces interactive data visualization products focused on business intelligence.
A great idea can be built with almost any technology. The success or failure of your project has more to do with vision, leadership, execution, and market than technological choices.
Besides the vision, a lot of startups focus on culture. what isn’t often mentioned is that the technical decisions will have a direct effect on the company culture. Great things have been built with each of the technologies. But they do come with a culture.
The purpose of this presentation is to help developers, managers, founders, etc. to make an insightful decision about the framework they want to use to create their product.
Architecting a Large Software Project - Lessons LearnedJoão Pedro Martins
In large projects, the Software Architect role includes both the client communication and requirements management, and the solution design itself, making sure technical quality is garanteed. This presentation describes the lessons learned with a highly successful project that took 3 years of development until its production phase, in technical, functional, and architeture aspects.
Presented at the 50th meeting of the Netponto Community in Lisboa, Portugal.
Wikki Verma Suggest Before opening an IT consultancy, I did my homework. I interviewed lifelong consultants. I read books. I even took personality tests to confirm that my psychological constitution matched the challenges I did face as an entrepreneur owning and operating my own business.
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
Utilocate offers a comprehensive solution for locate ticket management by automating and streamlining the entire process. By integrating with Geospatial Information Systems (GIS), it provides accurate mapping and visualization of utility locations, enhancing decision-making and reducing the risk of errors. The system's advanced data analytics tools help identify trends, predict potential issues, and optimize resource allocation, making the locate ticket management process smarter and more efficient. Additionally, automated ticket management ensures consistency and reduces human error, while real-time notifications keep all relevant personnel informed and ready to respond promptly.
The system's ability to streamline workflows and automate ticket routing significantly reduces the time taken to process each ticket, making the process faster and more efficient. Mobile access allows field technicians to update ticket information on the go, ensuring that the latest information is always available and accelerating the locate process. Overall, Utilocate not only enhances the efficiency and accuracy of locate ticket management but also improves safety by minimizing the risk of utility damage through precise and timely locates.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Zoom is a comprehensive platform designed to connect individuals and teams efficiently. With its user-friendly interface and powerful features, Zoom has become a go-to solution for virtual communication and collaboration. It offers a range of tools, including virtual meetings, team chat, VoIP phone systems, online whiteboards, and AI companions, to streamline workflows and enhance productivity.
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Crescat
Crescat is industry-trusted event management software, built by event professionals for event professionals. Founded in 2017, we have three key products tailored for the live event industry.
Crescat Event for concert promoters and event agencies. Crescat Venue for music venues, conference centers, wedding venues, concert halls and more. And Crescat Festival for festivals, conferences and complex events.
With a wide range of popular features such as event scheduling, shift management, volunteer and crew coordination, artist booking and much more, Crescat is designed for customisation and ease-of-use.
Over 125,000 events have been planned in Crescat and with hundreds of customers of all shapes and sizes, from boutique event agencies through to international concert promoters, Crescat is rigged for success. What's more, we highly value feedback from our users and we are constantly improving our software with updates, new features and improvements.
If you plan events, run a venue or produce festivals and you're looking for ways to make your life easier, then we have a solution for you. Try our software for free or schedule a no-obligation demo with one of our product specialists today at crescat.io
Launch Your Streaming Platforms in MinutesRoshan Dwivedi
The claim of launching a streaming platform in minutes might be a bit of an exaggeration, but there are services that can significantly streamline the process. Here's a breakdown:
Pros of Speedy Streaming Platform Launch Services:
No coding required: These services often use drag-and-drop interfaces or pre-built templates, eliminating the need for programming knowledge.
Faster setup: Compared to building from scratch, these platforms can get you up and running much quicker.
All-in-one solutions: Many services offer features like content management systems (CMS), video players, and monetization tools, reducing the need for multiple integrations.
Things to Consider:
Limited customization: These platforms may offer less flexibility in design and functionality compared to custom-built solutions.
Scalability: As your audience grows, you might need to upgrade to a more robust platform or encounter limitations with the "quick launch" option.
Features: Carefully evaluate which features are included and if they meet your specific needs (e.g., live streaming, subscription options).
Examples of Services for Launching Streaming Platforms:
Muvi [muvi com]
Uscreen [usencreen tv]
Alternatives to Consider:
Existing Streaming platforms: Platforms like YouTube or Twitch might be suitable for basic streaming needs, though monetization options might be limited.
Custom Development: While more time-consuming, custom development offers the most control and flexibility for your platform.
Overall, launching a streaming platform in minutes might not be entirely realistic, but these services can significantly speed up the process compared to building from scratch. Carefully consider your needs and budget when choosing the best option for you.
Artificia Intellicence and XPath Extension FunctionsOctavian Nadolu
The purpose of this presentation is to provide an overview of how you can use AI from XSLT, XQuery, Schematron, or XML Refactoring operations, the potential benefits of using AI, and some of the challenges we face.
A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeAftab Hussain
Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
Need for Speed: Removing speed bumps from your Symfony projects ⚡️Łukasz Chruściel
No one wants their application to drag like a car stuck in the slow lane! Yet it’s all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.
AI Genie Review: World’s First Open AI WordPress Website CreatorGoogle
AI Genie Review: World’s First Open AI WordPress Website Creator
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https://sumonreview.com/ai-genie-review
AI Genie Review: Key Features
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✅Just Enter the title, and your Content for Pages and Posts will be ready on your website
✅Automatically insert visually appealing images into posts based on keywords and titles.
✅Choose the temperature of the content and control its randomness.
✅Control the length of the content to be generated.
✅Never Worry About Paying Huge Money Monthly To Top Content Creation Platforms
✅100% Easy-to-Use, Newbie-Friendly Technology
✅30-Days Money-Back Guarantee
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
#AIGenieApp #AIGenieBonus #AIGenieBonuses #AIGenieDemo #AIGenieDownload #AIGenieLegit #AIGenieLiveDemo #AIGenieOTO #AIGeniePreview #AIGenieReview #AIGenieReviewandBonus #AIGenieScamorLegit #AIGenieSoftware #AIGenieUpgrades #AIGenieUpsells #HowDoesAlGenie #HowtoBuyAIGenie #HowtoMakeMoneywithAIGenie #MakeMoneyOnline #MakeMoneywithAIGenie
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
3. What is startup
A startup is a human institution designed to create a new product or
service under conditions of extreme uncertainty.
— ERIC RIES, THE LEAN STARTUP [RIES 2011A, 27]
A startup is a company designed to grow fast. Being newly founded
does not in itself make a company a startup. Nor is it necessary for a
startup to work on technology, or take venture funding, or have some
sort of “exit.” The only essential thing is growth. Everything else we
associate with start- ups follows from growth.
— PAUL GRAHAM, CO-FOUNDER OF Y COMBINATOR
A tech startup is an organization with the following characteristics:
1. Product: technology.
2. Environment: extremely uncertain.
3. Goal: massive growth.
4. Mode of operation: search.
5. Work at a startup
Why you should?
- More opportunities
- More ownership
- More fun
Why you should not?
- Not glamorous: a startup is 99.9%
hard, unglamorous work
- Sacrifice
- Probably won’t get rich: 75% fail
- Joining vs Founding: 10x
Life can be much broader once you discover one simple fact: Everything around you that you
call life was made up by people that were no smarter than you and you can change it, you can
influence it, you can build your own things that other people can use.
Once you learn that, you’ll never be the same again.
— STEVE JOBS
10. Tech stack
Golden rule
A good tech stack is one that scales faster than the people required to maintain it.
11. Evolving the tech stack
- Go with what you and your team are familiar
with
- Implement incrementalism for evolvement
12. In-house vs. buy vs. open-source
Build in-house Buy commercial Open-source
- Pros:
- Full control
- Cons:
- Heavy cost: developer time
for maintenance and evolve
the apps
- No community
- Hard to find company to use
knowledge in developers'
career.
- Pros:
- Save developer time (one
company dedicated for a
product)
- Community of customers
- Cons:
- Lose control of your biz
- Depend on your provider →
take more cost
- Vendor lock-in (migrate to a
different tech can be very
difficult).
- Pros:
- Benefits of commercial
products
- Community of developers
- Developers can reuse
knowledge later
- Cons:
- Mostly done on a volunteer
basis → hard to get a specific
bug fixed …
- Don’t entirely control, but be
able to mitigate some of that
risk.
13. Technologies should never be built
1. Security: cryptography, password storage, credit card storage.
2. Web technologies: HTTP servers, server-side and client-side frameworks.
3. Data systems: databases, NoSQL stores, caches, message queues.
4. Software delivery: version control, build systems, deployment automation.
5. CS 101: basic data structures (map, list, set), sorting algorithms.
6. Libraries for common data formats: XML, HTML, CSV, JSON, URLs.
7. Utility libraries: date/time manipulation, string manipulation, logging.
8. Operating systems.
9. Programming languages.
14. Recap of tech solutions
For a startup, using open source is usually the best choice,
closely followed by using a commercial product.
Building infrastructure in-house should be treated as a last
resort that you use only when you have no other options.
15. Picking a programming language (1)
Programming paradigms
1. Object oriented programming
- Model the world as objects—that is, data structures that package together both
data and behavior
- Map nicely onto the real world of nouns and verbs
- Two main problems: no consensus on what “object oriented” really means or how
to do it correctly; most OOP languages encourage the use of mutable state and
side effects → harder to reason about, maintain, and test code, especially in
concurrent environments
- Java, C#, C++ ...
1. Functional programming
- Model the world as the evaluation of functions and significantly limit the use of
mutable data and side effects.
- Why not popular? steep learning curve, functional programming is removed from
the real world/ moves away from the underlying hardware architecture (it uses recursion
instead of loops, immutable data instead of mutable data, garbage collection instead of
manual memory management, lazy evaluation instead of eager evaluation → there are
ways to fix it but it makes more burdens for programmer)
- Haskell, Clojure, F# ...
16. Picking a programming language (2)
Problem fit
In theory, all modern programming languages are Turing complete, so they are all equivalent.
In practice, certain types of problems are much easier to solve in some programming languages than others
The community around the language has a significant impact on problem fit.
Performance
The programming language is usually not the bottleneck for most companies.
The two most common performance bottlenecks in programming languages are garbage collection and
concurrency.
Garbage collection, as we discussed in the programming paradigms section, consumes CPU and memory and can
pause program execution. Some garbage collection algorithms are more mature and tunable than others.
With concurrency, the most important factors are what concurrency constructs are supported by the language
and how it handles I/O.
Productivity
Programmer performance is a bigger bottleneck for most startups
Productivity consists of two main aspects: how much existing code you can reuse and how fast you can
create new code.
The amount of existing code is determined by the popularity of the language and the size of its
community.
How fast you can create new code depends on three factors: experience, feedback loop ( how long it
takes to see the effect of a code change, expressiveness of the language (how many lines of code it takes to implement any
given idea)
When you choose a language, you’re choosing more than a set of technical trade-offs—you’re
choosing a community. Over time the community builds up around the language—not only the people,
17. Picking a programming language (3)
Here are the key trade-offs to consider when evaluating a programming language:
1. Programming paradigms: Is it an object-oriented or functional programming language? Does it support static typing or
automatic memory management?
2. Problem fit: For example, C is particularly good for embedded systems, Erlang for fault-tolerant distributed systems, and
R for statistics.
3. Performance: How does the language handle concurrency? Does the language use garbage collection and how tunable is
the collector?
4. Productivity: How popular is the language? How many frameworks and libraries are available for it? How concise is it?
18. Picking a server side framework (1)
The biggest driving factor for picking a framework is the size of the community around it, as it affects your ability to hire, find learning resources, and leverage
open source libraries and plugins. Here are some of the most popular and mature frameworks, broken down by programming language and ordered
alphabetically, based on HotFrameworks:
1. C#: .NET
2. Clojure: Ring, Compojure, Hoplon
3. Go: Revel, Gorilla
4. Groovy: Grails
5. Haskell: Snap, Happstack, Scotty
6. Java: Spring, Play Framework, DropWizard, JSF, Struts
7. JavaScript: express.js, sails.js, derby.js, geddy.js, koa, kraken.js, meteor 8. Perl: Mojolicious, Catalyst, Dancer
9. PHP: Laravel, Phalcon, Symfony, CakePHP, Yii, Zend
10. Python: Django, Flask
11. Ruby: Ruby on Rails, Sinatra
12. Scala: Play Framework, Spray
You can quickly cut this list down by applying three filters: programming language, problem fit, and scalability.
19. Picking a server side framework (2)
Here are the key trade-offs to consider when evaluating a server-side framework:
1. Problem fit: For example, Rails is particularly good for CRUD applications, DropWizard for RESTful API servers, and Node.js for real-time webapps.
2. Data layer: Does the framework help you manipulate URLs, JSON, and XML?
3. View layer: Are there many built-in templating helpers? Does it use server- side or client-side rendering? Does it allow logic or is it logic-less?
4. Testing: Is it easy to write unit tests for apps built on top of the framework? Is the framework itself well-tested?
5. Scalability: Does the framework use blocking or non-blocking I/O? Have you benchmarked the framework with your use cases?
6. Deployment: Do you know how to integrate the framework into your build? Do you know how to configure, deploy, and monitor it in production?
7. Security: Is there an built-in, well-tested way with the framework to handle authentication, CSRF, code injection, and security advisories?
20. Picking a database (1)
- When picking a data store, the most important consideration is maturity.
- Relational databases should be the default option for any data storage decision. Start by modeling your problem with a relational database and see how
far you can get before you run into walls.
- If you do hit a wall with a relational database, it will most likely be because your data volume and availability requirements have exceeded the capacity
of a single server. At this point, your priority is to identify the simplest solution that will scale. In order of increasing complexity, here are the most
common options:
1. Optimize the data storage format and queries on your existing database.
2. Set up a cache in front of the database (e.g. memcached).
3. Setup master-slave replication.
4. Vertically partition unrelated tables.
5. Horizontally partition a single table. 6. Setup multi-master replication.
- There are two trends to watch out for in the future.
The first trend is the NoSQL ecosystem becoming more mature.
The second trend is the emergence of NewSQL databases, which are data stores designed to run on a cluster while still supporting the relational model
21. Picking a database (2)
Here are the key trade-offs to consider when evaluating a database:
1. Database type: Is it a relational database or a NoSQL store (key-value store, document store, column-oriented database, or graph database)?
2. Reading data: Do you need to look up data by primary key or secondary key? Do you need JOINs? How do you map the data into an in-memory representation?
3. Writing data: Do your writes update just a single aggregate or many? Do you need atomic updates or transactions?
4. Schemas: Is the schema stored explicitly in the database or implicitly in your application code? Is your data uniform or unstructured?
5. Scalability: Can you get by with just vertically scaling your database? If not, does the database support replication, partitioning, or both?
6. Failure modes: How many different ways can the system fail? How easy are the failures to debug?
7. Maturity: For how long has this database been in development? How many companies are using it? How rich is the ecosystem of supporting tools?
22. Software engineering at startup
- Test-driven development
- Document continuously
- Active stakeholder
participation
- Iteration modeling
- Prioritization
23. References
1. Business model canvas generation
2. Balance scorecard
3. Hello, startup
4. Technology as a patterns
5. Disciplined Agile delivery
6. The art of scalability
7. Scalability patterns
Editor's Notes
Speed of iteration
There’s a big difference between building a business and solving interesting engineering problems. There are interesting engineering problems in startups, but very frequently, your business will not succeed or fail based on how well you solve those engineering problems. The exception to this is startups focused on hard scientific problems. For example, I have a friend who has a battery company and his business will succeed or fail based on the scientific breakthroughs they make, as well as how well they run the business. This hard science component is not applicable to 99% of web startups; most web startups succeed or fail based almost entirely on execution, meaning marketing, sales, product, and engineering. We think as engineers that if we can write great code and build something that can scale to a million people that we will be successful and lauded in the community and people will say, “oh, you’re so amazing”, and they’ll want to acqui-hire us for millions of dollars. That’s what we read in TechCrunch and that’s what we hear about at meetups, but it is very very far from reality.
Many developers have trouble transitioning from a coding role to a leadership role, such as CEO, CTO, or VP. If you’ve never had such a role, you might expect that being part of the executive team will make you feel important, respec- ted, and powerful. You imagine yourself spending all your time drawing up strategies, giving orders, and moving chess pieces around a board, like a five star general. In reality, you’ll be more of a salesman crossed with a psychiatrist. You’ll spend a lot of time trying to get someone, anyone, anywhere, to care about your company. You’ll also spend lots of time listening to your employees, trying to figure out their needs, dealing with their complaints, and figuring out how to motivate them. You will get to make decisions, but many of them will be painful, risky, and unpopular. And no matter how much you try, you’ll get some of these decisions wrong. Some people thrive in this sort of environment, but if you’re not one of them, a leadership role might not be for you.
Some people manage it better than others, but working at a young startup often means you won’t get to see your friends and family as much as you might like and your health may suffer. Startups have ruined marriages, caused mental and physical health problems, and worst of all, even driven some founders to suicide
As a founder, you will have to make 10x the sacrifice in exchange for a chance at 10x the reward
Build the product market fit:
1.Determine your target customer
2.Identify underserved needs of that customer
3.Define your value proposition
4.Specify your minimum viable product (MVP) feature set
5.Develop your MVP
6.Test your MVP with customers
Boyd’s Law: speed of iteration beats quality of iteration. It’s a surprising law, but it makes sense when you’re dealing with complicated, unpredictable, and often chaotic systems.
Putting it all together, if you get the right idea, design, data, and distribution, you’ll be able to make something people want.
• An idea is not a design
• A design is not a prototype
• A prototype is not a program
• A program is not a product
• A product is not a business
• A business is not profits
• Profits are not an exit
• And an exit is not happiness.
A tech stack is a tool. It’s a means to build a product, not an end in and of itself. Do not pick a technology because it sounds cool or looks fun
Your goal is to be able to scale your tech stack—scale it to more users, more traffic, more data, and more code—by throwing money or hardware at the problem, and not people. If every time your user base doubles, you can just pay for a few more servers and everything keeps working, you’re in good shape. A great example of leverage is the Whatsapp team, which built a tech stack around Erlang that could support 70 million Erlang messages per second, 450 million users, 50 billion messages per day, and 7.2 trillion messages per year.1 And they did this all with a team of just 32 engineers.
In fact, there is no “best” tech stack. Picking a technology without considering the type of product, the team, and the company culture is like deciding what furniture to buy before you’ve bought a house, set a budget, or figured out who you are going to live with. Context matters.
How do you pick the initial tech stack for a startup? Go with what you know. LinkedIn was built with Java because the founding team knew Java; GitHub’s founders were all Ruby developers, so they built the site with Ruby; Twitter used Rails primarily because they had a lot of early employees who were familiar with it; Foursquare started out in PHP because that’s what co-founder Dennis Crowley knew; Pinterest used Python because the founding team was familiar with it
It can be fun to learn a new technology that promises all sorts of theoretical benefits, but your goal in the early days of a startup is to learn what your users want, and anything that takes time away from that is a waste. During those early days, your product has few users and little code, so scalability will not be much of a challenge, and all that matters is that you can iterate as quickly as possible.
LinkedIn was going through a period of hyper- growth, both in terms of site traffic and the number of employees, and the infrastructure was buckling under the load. I was part of the service infrastructure team and we knew they had to make some significant changes to be able to scale the tech stack to the rapidly growing demands. Presented slide shows the incremental steps LinkedIn followed to arrive at this decision and how they performed the actual migration, including what would happen at each stage of the project if it succeeded or why it would be worth doing even if it was canceled.
There are certain technologies that are so complicated, error-prone, and time consuming to build yourself, and solved so well in the open source or commercial world that, as a startup, you should never build them yourself.
The only reason you would ever build one of these systems from scratch is either
a) you’re using it as a personal side project for learning or
b) your startup has extremely unique requirements for one of these technologies. The latter case is uncommon.
For a startup, using open source is usually the best choice, closely followed by using a commercial product. Building infrastructure in-house should be treated as a last resort that you use only when you have no other options. This can be hard to remember, as many developers get excited at the chance to build a complicated piece of infrastructure, and will be quick to claim that no existing technology fits their needs. But given that there are more than 10 million open source repositories to choose from, and developer time is the most scarce and expensive resource at a startup, you cannot afford to spend time reinventing the wheel when there is an off-the-shelf solution readily available.
The programming language is often the first technical decision you’ll make at a company. It’s also the one that has the most influence on all the other decisions. The “go with what you know” principle means that most startups initially use whatever language the founders know best. However, as a startup grows and evolves, it’s not uncommon to introduce other languages.
Languages with strong metaprogramming capabilities, such as Clojure and Ruby, make it easy to define custom DSLs. Erlang is particularly effective at building fault tolerant, distributed systems. Assembly and C are usually your only options for low level, real time, or embedded systems.
C++ and Python have a huge number of Computer Vision libraries. Matlab, Mathematica, and R come with comprehensive libraries for math, graphing, and statistics. PHP, Ruby, Python, JavaScript, and Java have vast eco- systems of libraries and frameworks for building web applications. For certain problem domains, picking the right language can give you a huge productivity boost because a lot of the code is already written for you.
For example, the JVM is well known for having one of the better garbage collectors in the world, while the Ruby VM’s garbage collector is known for numerous performance problems.7 However, neither language can compare performance-wise to languages where there is no garbage collection. If your application cannot tolerate any GC pauses or any CPU or memory overhead, then you may want to use a language with manual memory management, such as C or C++.
With concurrency, the most important factors are what concurrency constructs are supported by the language and how it handles I/O. For example, Ruby supports threads, but it has a Global Interpreter Lock (GIL), which means that only one thread at a time can execute. Moreover, most popular Ruby libraries perform synchronous I/O, blocking the thread while waiting for a disk read or a network call to return. The result is that Ruby is not an efficient language for dealing with lots of concurrency. There are workarounds, such as running multiple Ruby processes (e.g. one per CPU core), using non-blocking libraries (e.g. EventMachine), or using a different VM (e.g. JRuby), but they all involve trade-offs and overheads.
This is one of the reasons Twitter moved off of Ruby and onto the JVM. The JVM has full support for multithreading with no global interpreter lock. It also has full support for non-blocking I/O and a variety of concurrency constructs, including threads & locks, Futures, Actors, and Software Transactional Memory. The move from Ruby to Scala helped Twitter reduce search latencies by 3x and CPU usage by half
Popular languages will have more learning resources, more people you can hire who already know the language, and more open source libraries you can use. Mature languages also come with an ecosystem of productivity tools, such as IDEs, profilers, static analysis tools, and build systems. The more code you reuse, the less you have to write and maintain yourself.
Although there are hundreds of programming languages to choose from, there are only a handful of languages that are mature enough and have a large enough community to make them viable choices for a startup. Here is the list as of 2015, ordered alphabetically, based on programming language popularity indexes (TIOBE, LangPop, and RedMonk), the StackOverflow Developer Survey:
1. C family (C, C++, C#) 2. Go
3. Groovy
4. Haskell
5. Java
6. JavaScript
7. Lisp family (e.g. Clojure or Scheme) 8. Perl
9. PHP
10. Python
11. Ruby
12. Scala
You can quickly shorten this list by applying three filters: problem fit, programming paradigm, and performance requirements. For example, a startup building computer vision and machine learning systems would limit this list to just three languages based on problem fit: C++, Java, and Python. If that startup preferred static typing, they would knock Python off the list. Finally, if they were building a high performance, real-time system, they would not be able to use garbage collection, so they would be left with C++.
If after applying the first three filters, you still have multiple languages to choose from, you should pick the language that makes you the most productive. For example, a startup that is building web applications will most likely find that Java, JavaScript, PHP, Python, Ruby, and Scala are the best fit for this problem. If the team prefers dynamic typing, they might remove Java and Scala from the list. And if a few of them already know Python and found a few Django plugins that will save them lots of time, then Python will be their best choice.
If your team prefers writing everything in Java, your options are Spring, Play Framework, DropWizard, JSF, Struts. If your goal is to build a RESTful API server, Spring, Play Framework, and DropWizard will be the best fit. And if you know this app will be I/O bound, the non-blocking I/O model of Play Framework makes it the best choice. If after the first three filters, you still have several options, then it’s a ques- tion of picking the framework that best fits your deployment, security require- ments, data, templating, and testing needs.
Active Stakeholder Participation. Stakeholders should provide information in a timely manner, make decisions in a timely manner, and be as actively involved in the development process through the use of inclusive tools and techniques.
Good recipe for product excellence overall:
1) Craft missions that everyone in the organization believes in and tie to company goals .
2) Don’t let everyone solve the same mission but ensure anyone who owns a mission can act in any way to make an impact – don’t silo them to a specific code base, platform, or feature/view set. Otherwise, they will try to solve the goal but only with resources at disposal, or only by exerting pixels they can control.
3) Organize agile teams around missions – they inherit goals but can focus on unique tactics. However, you don’t really want a bunch of teams coming up with a cacophony of goals. Companies need to have priorities. Similarly, you don’t want a lot of top-down “here’s what you need to do” – that stifles innovation.
4) Finally, if you don’t have clear goals and each team invents their own goals you have a disaster so remember to spend time ensuring people understand the goals and if need be pin those goals up and over-emphasize them at all hands and in written or video communication.