1) The document discusses the psychology behind technology decision making and identifies common archetypes that can influence choices.
2) It describes archetypes like the Over-Optimizer, Fanboy, and Trend Follower who base decisions on metrics, popularity, and what others use rather than problem-solving ability.
3) The author advocates selecting a few options that fit needs, evaluating risks, making compromises, and prototyping to avoid biases and make the best technological choice.
Shrini Kulkarni - Software Metrics - So Simple, Yet So Dangerous TEST Huddle
EuroSTAR Software Testing Conference 2009 presentation on Software Metrics - So Simple, Yet So Dangerous by Shrini Kulkarni. See more at conferences.eurostarsoftwaretesting.com/past-presentations/
How Many Dimensions of Compatibility?: Discovering What's Right for Your Users Marliese Thomas
How Many Dimensions of Compatibility: Discovering What's Right for Your Users
This was the keynote address at University of Houston Library's Discovery Day Camp on June 10, 2011. Some extra screenshots of admin interfaces have been added after the actual presentation.
As famous investor Marc Andreessen said, “Software is eating the world.” In other words, software is and will remain relevant, making software startups popular and attractive. For aspiring and current entrepreneurs, this talk will focus on the practical aspects of creating and running a software startup. Topics will include: managing a software project, hiring tech people, hosting and operations, security, intellectual property, which programming language to use, and social media, with a dual emphasis on what to do and why to do it that way.
Explain why it is important to use KT Problem Analysis to quickly get to a precise problem description and to avoid building biases into problem-solving processes.
Shrini Kulkarni - Software Metrics - So Simple, Yet So Dangerous TEST Huddle
EuroSTAR Software Testing Conference 2009 presentation on Software Metrics - So Simple, Yet So Dangerous by Shrini Kulkarni. See more at conferences.eurostarsoftwaretesting.com/past-presentations/
How Many Dimensions of Compatibility?: Discovering What's Right for Your Users Marliese Thomas
How Many Dimensions of Compatibility: Discovering What's Right for Your Users
This was the keynote address at University of Houston Library's Discovery Day Camp on June 10, 2011. Some extra screenshots of admin interfaces have been added after the actual presentation.
As famous investor Marc Andreessen said, “Software is eating the world.” In other words, software is and will remain relevant, making software startups popular and attractive. For aspiring and current entrepreneurs, this talk will focus on the practical aspects of creating and running a software startup. Topics will include: managing a software project, hiring tech people, hosting and operations, security, intellectual property, which programming language to use, and social media, with a dual emphasis on what to do and why to do it that way.
Explain why it is important to use KT Problem Analysis to quickly get to a precise problem description and to avoid building biases into problem-solving processes.
Developing Innovative Marketing Plan to Augment the Visitation of Egyptian Wo...Mohamed Badry
This title is my dissertation topic which was specified in heritage marketing planning and was submitted to obtain the master degree in Heritage Conservation and Site Management jointly held from Helwan University, Egypt and BTU Cottbus, Germany. You can review this topic as well as other assignments via scientific channel "Research Gate" (https://www.researchgate.net/profile/Mohamed_Amer32).
Lightning Talk #11: Designer spaces by Alastair Simpsonux singapore
You can’t take creative people, stick them in sterile, lowest cost per square foot spaces, and expect them to achieve the best work of their lives. Atlassian has been focussing heavily on the design of their work spaces, to create flexible, engaging, delightful, and yes productive places for their teams to work in.
Hear Alastair Simpson from Atlassian talk about the creative spaces they’ve designed that have scaled with the changing needs of their teams and what they’ve learned about the benefits of creating better environments through thoughtful design.
Taboola's experience with Apache Spark (presentation @ Reversim 2014)tsliwowicz
At taboola we are getting a constant feed of data (many billions of user events a day) and are using Apache Spark together with Cassandra for both real time data stream processing as well as offline data processing. We'd like to share our experience with these cutting edge technologies.
Apache Spark is an open source project - Hadoop-compatible computing engine that makes big data analysis drastically faster, through in-memory computing, and simpler to write, through easy APIs in Java, Scala and Python. This project was born as part of a PHD work in UC Berkley's AMPLab (part of the BDAS - pronounced "Bad Ass") and turned into an incubating Apache project with more active contributors than Hadoop. Surprisingly, Yahoo! are one of the biggest contributors to the project and already have large production clusters of Spark on YARN.
Spark can run either standalone cluster, or using either Apache mesos and ZooKeeper or YARN and can run side by side with Hadoop/Hive on the same data.
One of the biggest benefits of Spark is that the API is very simple and the same analytics code can be used for both streaming data and offline data processing.
Lightning Talk #7: Outwards and Inwards Experiential Transformation: A KASKUS...ux singapore
Kaskus was founded in 1999, and ever since, has been the largest online community in Indonesia. Many of the old-time users have reluctance to change, and any changes done can shake the ground of the hard-core fans. On the other hand, with the shift in the user behavior and the new wave of competitions, change is inevitable. KASKUS needs to adapt to stay relevant and to continuously deliver great experiences for its users.
In this presentation, the presenters will share two sides of the stories: first is the transformation of the Kaskus products, and second is the transformation of the organisation to support this new direction.
Recsys 2015: Making Meaningful Restaurant Recommendations at OpenTableSudeep Das, Ph.D.
At OpenTable, recommendations play a key role in connecting diners with restaurants. The act of recommending a restaurant to a diner relies heavily on aligning everything we know about the restaurant with everything we can infer about the diner. Our methods go beyond using the diner-restaurant interaction history as the sole input — we use click and search data, the metadata of restaurants, as well as insights gleaned from reviews, together with any contextual information to make meaningful recommendations. In this talk, I will highlight the main aspects of our recommendation stack built with Scala using Apache Spark.
Ethics in EngineeringLecture #2 Ethical Dilemmas, ChoBetseyCalderon89
Ethics in EngineeringLecture #2: Ethical Dilemmas, Choices, and Codes of Ethics
Resolving Moral Dilemmas
1. Moral clarity
· Need to know something is wrong! Do not ignore problems!
· Loyalty to employer, responsibilities to public and environment
(and complex relations between these)
2. Know the facts
· Get hard, documented facts, discuss with others
· Competence matters in gathering technical facts
3. Consider options
· Diversity of actions to take? Evaluate/discuss.
· Long-term, short-term perspectives, repercussions?
· “Creative middle solution”?
4. Make a reasonable decision
· Weigh all factors, recognize “gray areas”/compromises
· An engineering design problem?
NSPE, BER Case 96-4
· Engineer A is employed by a software company and is involved in the design of specialized software in connection with the operations of facilities affecting the public health and safety (i.e., nuclear, air quality control, water quality control). As the part of the design of a particular software system, Engineer A conducts extensive testing and although the tests demonstrate…
· that the software is safe to use under existing standards, Engineer A is aware of new draft standards that are about to be released by a standard setting organization-standards which the newly designed software may not meet. Testing is extremely costly and the company’s clients are eager to begin to move forward. The software company is eager to satisfy its clients, protect the software company’s finances, and protect…
· …existing jobs; but at the same time, the management of the software company wants to be sure that the software is safe to use. A series of tests proposed by Engineer A will likely result in a decision whether to move forward with the use of the software. The tests are costly and will delay the use of the software by at least six months, which will put the company at a competitive…
·
· …disadvantage and cost the company a significant amount of money. Also, delaying implementation will mean the state public service commission utility rates will rise significantly during this time. The company requests Engineer A’s recommendation concerning the need for additional software testing.
Question: Should Engineer A design the software to meet the new standards?
Analyzing the case…
· Moral clarity:
· What is wrong? What is the core issue/ question?
· Will the software meet the new standards? – Why are there new standards?
· Experience shows new failure modes
· New tests designed to test new failure modes – Engineer’s role in new standards?
· Development of new standards
· Following new standards
Analyzing the case, continued…
• Know the facts
· It is critical software (health/safety of public)
· New standards to test new failure modes (that you need to understand)
· Testing is costly, company finances at stake
· Need to protect existing jobs
· Testing will delay ...
Operationalizing Machine Learning in the Enterprisemark madsen
TDWI Munich 2019
What does it take to operationalize machine learning and AI in an enterprise setting?
Machine learning in an enterprise setting is difficult, but it seems easy. All you need is some smart people, some tools, and some data. It’s a long way from the environment needed to build ML applications to the environment to run them in an enterprise.
Most of what we know about production ML and AI come from the world of web and digital startups and consumer services, where ML is a core part of the services they provide. These companies have fewer constraints than most enterprises do.
This session describes the nature of ML and AI applications and the overall environment they operate in, explains some important concepts about production operations, and offers some observations and advice for anyone trying to build and deploy such systems.
Developing Innovative Marketing Plan to Augment the Visitation of Egyptian Wo...Mohamed Badry
This title is my dissertation topic which was specified in heritage marketing planning and was submitted to obtain the master degree in Heritage Conservation and Site Management jointly held from Helwan University, Egypt and BTU Cottbus, Germany. You can review this topic as well as other assignments via scientific channel "Research Gate" (https://www.researchgate.net/profile/Mohamed_Amer32).
Lightning Talk #11: Designer spaces by Alastair Simpsonux singapore
You can’t take creative people, stick them in sterile, lowest cost per square foot spaces, and expect them to achieve the best work of their lives. Atlassian has been focussing heavily on the design of their work spaces, to create flexible, engaging, delightful, and yes productive places for their teams to work in.
Hear Alastair Simpson from Atlassian talk about the creative spaces they’ve designed that have scaled with the changing needs of their teams and what they’ve learned about the benefits of creating better environments through thoughtful design.
Taboola's experience with Apache Spark (presentation @ Reversim 2014)tsliwowicz
At taboola we are getting a constant feed of data (many billions of user events a day) and are using Apache Spark together with Cassandra for both real time data stream processing as well as offline data processing. We'd like to share our experience with these cutting edge technologies.
Apache Spark is an open source project - Hadoop-compatible computing engine that makes big data analysis drastically faster, through in-memory computing, and simpler to write, through easy APIs in Java, Scala and Python. This project was born as part of a PHD work in UC Berkley's AMPLab (part of the BDAS - pronounced "Bad Ass") and turned into an incubating Apache project with more active contributors than Hadoop. Surprisingly, Yahoo! are one of the biggest contributors to the project and already have large production clusters of Spark on YARN.
Spark can run either standalone cluster, or using either Apache mesos and ZooKeeper or YARN and can run side by side with Hadoop/Hive on the same data.
One of the biggest benefits of Spark is that the API is very simple and the same analytics code can be used for both streaming data and offline data processing.
Lightning Talk #7: Outwards and Inwards Experiential Transformation: A KASKUS...ux singapore
Kaskus was founded in 1999, and ever since, has been the largest online community in Indonesia. Many of the old-time users have reluctance to change, and any changes done can shake the ground of the hard-core fans. On the other hand, with the shift in the user behavior and the new wave of competitions, change is inevitable. KASKUS needs to adapt to stay relevant and to continuously deliver great experiences for its users.
In this presentation, the presenters will share two sides of the stories: first is the transformation of the Kaskus products, and second is the transformation of the organisation to support this new direction.
Recsys 2015: Making Meaningful Restaurant Recommendations at OpenTableSudeep Das, Ph.D.
At OpenTable, recommendations play a key role in connecting diners with restaurants. The act of recommending a restaurant to a diner relies heavily on aligning everything we know about the restaurant with everything we can infer about the diner. Our methods go beyond using the diner-restaurant interaction history as the sole input — we use click and search data, the metadata of restaurants, as well as insights gleaned from reviews, together with any contextual information to make meaningful recommendations. In this talk, I will highlight the main aspects of our recommendation stack built with Scala using Apache Spark.
Ethics in EngineeringLecture #2 Ethical Dilemmas, ChoBetseyCalderon89
Ethics in EngineeringLecture #2: Ethical Dilemmas, Choices, and Codes of Ethics
Resolving Moral Dilemmas
1. Moral clarity
· Need to know something is wrong! Do not ignore problems!
· Loyalty to employer, responsibilities to public and environment
(and complex relations between these)
2. Know the facts
· Get hard, documented facts, discuss with others
· Competence matters in gathering technical facts
3. Consider options
· Diversity of actions to take? Evaluate/discuss.
· Long-term, short-term perspectives, repercussions?
· “Creative middle solution”?
4. Make a reasonable decision
· Weigh all factors, recognize “gray areas”/compromises
· An engineering design problem?
NSPE, BER Case 96-4
· Engineer A is employed by a software company and is involved in the design of specialized software in connection with the operations of facilities affecting the public health and safety (i.e., nuclear, air quality control, water quality control). As the part of the design of a particular software system, Engineer A conducts extensive testing and although the tests demonstrate…
· that the software is safe to use under existing standards, Engineer A is aware of new draft standards that are about to be released by a standard setting organization-standards which the newly designed software may not meet. Testing is extremely costly and the company’s clients are eager to begin to move forward. The software company is eager to satisfy its clients, protect the software company’s finances, and protect…
· …existing jobs; but at the same time, the management of the software company wants to be sure that the software is safe to use. A series of tests proposed by Engineer A will likely result in a decision whether to move forward with the use of the software. The tests are costly and will delay the use of the software by at least six months, which will put the company at a competitive…
·
· …disadvantage and cost the company a significant amount of money. Also, delaying implementation will mean the state public service commission utility rates will rise significantly during this time. The company requests Engineer A’s recommendation concerning the need for additional software testing.
Question: Should Engineer A design the software to meet the new standards?
Analyzing the case…
· Moral clarity:
· What is wrong? What is the core issue/ question?
· Will the software meet the new standards? – Why are there new standards?
· Experience shows new failure modes
· New tests designed to test new failure modes – Engineer’s role in new standards?
· Development of new standards
· Following new standards
Analyzing the case, continued…
• Know the facts
· It is critical software (health/safety of public)
· New standards to test new failure modes (that you need to understand)
· Testing is costly, company finances at stake
· Need to protect existing jobs
· Testing will delay ...
Operationalizing Machine Learning in the Enterprisemark madsen
TDWI Munich 2019
What does it take to operationalize machine learning and AI in an enterprise setting?
Machine learning in an enterprise setting is difficult, but it seems easy. All you need is some smart people, some tools, and some data. It’s a long way from the environment needed to build ML applications to the environment to run them in an enterprise.
Most of what we know about production ML and AI come from the world of web and digital startups and consumer services, where ML is a core part of the services they provide. These companies have fewer constraints than most enterprises do.
This session describes the nature of ML and AI applications and the overall environment they operate in, explains some important concepts about production operations, and offers some observations and advice for anyone trying to build and deploy such systems.
This is a keynote given on Oct 21, 2011 to kick off the 2011 Trend Micro Engineering Summit at the Tech Museum in San Jose. The focus is on how to build a culture of innovation, inspiration, and distributed decision making at technology companies.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
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Call us at : 08263069601
(Prefer mailing. Call in emergency )
The right tool / technology for the right job : by Yakup Kalin (ACA IT-Soluti...ACA IT-Solutions
How to choose the right technology or tooling in IT? A presentation by Yakup Kalin (ACA IT-Solutions).
It's not easy to know which technology you should keep an eye on or which tool you can use best in a specific situation. Customers, however, expect that consultants are in line with everything going on in the market. Yakup Kalin will present various techniques to show how you can deal with this in the best possible way.
Some examples of methods that will be discussed:
IT Maturity: Capability Maturity Model
Magic Quadrant
Forrester Wave
Cocomo Model
Gartner Cricital Capabilities Methodology
Michael Bolton - Two Futures of Software TestingTEST Huddle
EuroSTAR Software Testing Conference 2008 presentation on Two Futures of Software Testing by Michael Bolton. See more at conferences.eurostarsoftwaretesting.com/past-presentations/
The Black Box: Interpretability, Reproducibility, and Data Managementmark madsen
The growing complexity of data science leads to black box solutions that few people in an organization understand. You often hear about the difficulty of interpretability—explaining how an analytic model works—and that you need it to deploy models. But people use many black boxes without understanding them…if they’re reliable. It’s when the black box becomes unreliable that people lose trust.
Mistrust is more likely to be created by the lack of reliability, and the lack of reliability is often the result of misunderstanding essential elements of analytics infrastructure and practice. The concept of reproducibility—the ability to get the same results given the same information—extends your view to include the environment and the data used to build and execute models.
Mark Madsen examines reproducibility and the areas that underlie production analytics and explores the most frequently ignored and yet most essential capability, data management. The industry needs to consider its practices so that systems are more transparent and reliable, improving trust and increasing the likelihood that your analytic solutions will succeed.
This talk will treat the black boxed of ML the way management perceives them, as black boxes.
There is much work on explainable models, interpretability, etc. that are important to the task of reproducibility. Much of that is relevant to the practitioner, but the practitioner can become too focused on the part they are most familiar with and focused on. Reproducing the results needs more.
Using big data and implementing hadoop is a trend that people jump all to quickly to. Instead understanding the run time complexity of one's algorithms, reducing said complexity and managing the process from start to finish in a lean and agile way can yield massive cost savings - or save your organization.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
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 the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 3
The psychology of technology / Zohar arad
1. Zohar Arad. November 2016
The Psychology
of Technology
Why we make the wrong
decisions for the all right
reasons
1
2. ❖ Developer since 2004
❖ Currently CTO & Lead Architect @ Quicklizard Ltd.
❖ Consulting on Big-Data, Architecture and tech as a side-
gig
❖ New tech. freak - In particular Web frameworks and
DBs.
a little about me
2
3. Today we're going to talk about how people
approach technology selection, and why they make
the choices they make.
4. Choosing tech. can be tricky to get “right” and is
often a very emotional process.
ego, stress, responsibility and fear can get in the
way of seeing things clearly.
6. If we know how to identify these emotions, we can
stir clear of them
and choose the tech. that solves our problem
best, rather than the one
that soothes our anxiety more quickly.
7. … and how to avoid them
The N archetypes of technology
decision makers
8. The Over-Optimizer
❖ Characteristics - accepts or rejects technology based
on semi-realistic performance metrics.
❖ Main worry - will it work on production with such and
such load?
❖ Natural habitat - JVM (usually)
❖ Wrong because - Performance is not the only criteria
for appropriate tech.
9. The Fanboy
❖ Characteristics - accepts or rejects technology based
on popularity and newness (newer is better).
❖ Main worry - if no one is using it / it’s not modern, then
it’s not cool, and by extension I’m not cool.
❖ Natural habitat - Github trends, Hacker News, macOS
❖ Wrong because - tech. should be evaluated based on
usefulness not age, adoption rate or coolness.
10. The Trend Follower
❖ Characteristics - accepts or rejects technology based
on what other people are using.
❖ Main worry - doesn’t want to be the sucker who made
the wrong choice.
❖ Natural habitat - StackOverflow & Github trends.
❖ Wrong because - popularity is not a measurement of
how suitable tech. is to solve a particular problem.
11. Additional Archetypes
❖ The Risk Averter - accepts or rejects technology based
on how long it’s been used and by whom.
❖ The Bureaucrat - accepts or rejects technology based
on organization protocols (mainly security / support).
❖ The Relic - accepts or rejects technology based on
whether they know it or not.
12. Moving Forward
Introducing new tech involves taking risks and
introducing change.
Each archetype employs their own mechanism to
minimize risk and cope with change.
13. Moving Forward
Making the right choices is about
finding a compromise between
the advantages of a new piece of tech,
vs.
the risk it involves and the cost of change.
14. A better way of doing it
❖ Select up to 3 options that fit technologically (usually
there’s no single “best” fit).
❖ Look for risky money pits - maintainability, inter-op.,
cost-of-ownership over time, learning curve etc.
❖ Make a compromise between fitness and riskiness.
❖ Prototype to test your theory and repeat above.
❖ Rinse and repeat every couple of years.
15. Parting notes
❖ Usually selecting tech. is not a life/death decision.
There’s a large grey area…
❖ Think about current vs. future tech. debt - Quick and
dirty might be a good choice, as long as you have a
clear plan of how to change things later.
❖ Try to avoid passing trends. Choosing popular tech. is
good, as long as you choice it for the right reasons.
16. If we have time…
A short anecdote about fanboys…
