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Who should be involved in the process of making decisions about software architecture? That's a question that's been puzzling Thoughtworker Andrew Harmel-Law for some time — so much so that he decided to write a book about it. The result is Facilitating Software Architecture. Published by O'Reilly in December 2024, it's both an argument for and a guide to involving more people in the architecture decision process.
To discuss the topic and the book, Andrew joined hosts Neal Ford and Prem Chandrasekaran on the Technology Podcast. They explore why including more roles in software architecture matters today, some of the common objections to and risks of such an approach, alongside techniques and practices that can make doing it in fast-paced and dynamic organizations easier.
"It's quite magical when you see this blossoming of understanding of what it is that architects do... It's not less architecture, it's more. It's just happening in a broader sphere." — Andrew Harmel-Law
You can find Andrew's book on the O'Reilly website: https://www.oreilly.com/library/view/facilitating-software-architecture/9781098151850/
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With the rise of generative AI, the concept of the uncanny valley — where human resemblance unsettles, disturbs or disgusts — is more relevant than ever. But is it a problem that technologists need to tackle? Or does it offer an opportunity for greater thoughtfulness about the ways generative AI is being built, deployed and used?
In this episode of the Technology Podcast, host Lilly Ryan is joined by Srinivasan Raguraman to discuss generative AI's uncanny valley and explore how it might offer a model for thinking through our expectations about generative AI outputs and effects. Taking in everything from the experiences of end users to the mental models engineers bring to AI development, listen for a wide-ranging dive into the implications of the uncanny valley in our experience of generative AI today.
Read Srinivasan's recent article (written with Ken Mugrage): https://www.technologyreview.com/2024/10/24/1106110/reckoning-with-generative-ais-uncanny-valley/
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Legacy modernization is an enduring challenge — and as systems become more complex, the difficulty of understanding and modelling a system so it can be modernized only becomes more difficult. However, at Thoughtworks we've seen some recent success bringing generative AI into the legacy modernization process.
To discuss what this means in practice and the benefits it can deliver, host Ken Mugrage is joined by Thoughtworks colleagues Shodhan Sheth and Tom Coggrave. Shodhan and Tom have been working together in this space in recent months and, in this episode of the Technology Podcast, offer their insights into finding success with this novel combination. They explain how it can be implemented, the challenges and experiments they did on their way to positive results and what it means for how teams and organizations think about modernization in the future.
Read Shodhan and Tom's article on legacy modernization and generative AI (written with Alessio Ferri): https://martinfowler.com/articles/legacy-modernization-gen-ai.html
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Data contracts are a bit like APIs for data — they make it possible to interface with data in a way that ensures the transfer of data from one place to another is stable and reliable. This is particularly important for building more reliable data-driven applications.
To discuss data contracts, host Lilly Ryan is joined on the Technology Podcast by Andrew Jones, the creator of the data contract concept (in 2021) and author of Driving Data Quality with Data Contracts (2023), and Thoughtworker Ryan Collingwood who is currently writing their own book on data contracts due to be published in 2025. Andrew and Ryan offer their perspectives on the topic, explaining the origins and motivation for the idea and outlining how they can be used in practice.
You can find Andrew’s book here: https://www.amazon.com/Driving-Data-Quality-Contracts-comprehensive/dp/1837635005
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What does it mean to be a technology leader today? What kind of challenges must you address? What questions do you need to answer? To explore all that — and dive into what it looks like from a Thoughtworks perspective — host Ken Mugrage spoke to Thomas Squeo, the CTO for Thoughtworks in the Americas.
They discuss everything from keeping track of emerging technologies and wider industry shifts, to product thinking, AI and career development. Listen to get to know a Thoughtworks leader and discover fresh perspectives on some of the big questions and debates all of us in tech keep finding ourselves returning to.
Find Thomas on X: @squeot
LinkedIn: https://www.linkedin.com/in/thomassqueo/
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Volume 31 of the Technology Radar will be released on October 23, 2024. As always, it will feature 100+ technologies and techniques that we've been using with clients around the world. Alongside them will be a set of key themes that emerged during the process of putting it together. We think they offer another way into the Radar and give a unique insight on some of the most interesting issues impacting the software industry.
In this episode of the Technology Podcast we discuss them: coding assistance antipatterns, Rust being anything but rusty, the rise of WebAssembly and what we describe as the "cambrian explosion of generative AI tools." To do so, Alexey Boas is joined by guests and podcast regulars Ken Mugrage and Neal Ford. Ken and Neal provide an insight into the conversations that happened during the process, and offer their perspective on the implications of these themes for the wider tech industry.
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The Thoughtworks Technology Radar is, first and foremost, a publication. It's a document that anyone in the tech industry can read twice a year to learn about our experiences and perspectives on technology. However, it's also more than that: it's built on top of a process of deliberation, discussion and curation. We think that's particularly important — it's something we encourage technology teams and organizations to do and which we support with our Build Your Own Radar tool.
On this episode of the Technology Podcast, Neal Ford and Ken Mugrage join Prem Chandrasekaran to discuss Build Your Own Radar. They outline why the Radar process is just as important as the artifact that gets created at the end, and explain how organizations can use it to facilitate conversations about how and what technology they use and want to use in the future.
Learn more about Build Your Own Radar: https://www.thoughtworks.com/radar/byor
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There are no shortage of options when it comes to relational databases. While the likes of PostgreSQL have proven enduring, even as the market has evolved, for data scientists and data engineers that need to manage and query particularly complex or large data sets, the most popular databases aren't always right for the job. Thankfully, this is where projects like DuckDB can help. Built for what's called 'vectorized query execution', it's well-suited to the demands of online analytical processing (OLAP).
To get a deeper understanding of DuckDB and how the product has developed, on this episode of the Technology Podcast, hosts Ken Mugrage and Lilly Ryan are joined by Thoughtworker Ned Letcher and Thoughtworks alumnus Simon Aubury. Ned and Simon explain the thinking behind DuckDB, the design decisions made by the project and how its being used by data practitioners in the wild.
Learn more about DuckDB: https://duckdb.org/why_duckdb.html
Explore Ned and Simon's book Getting Started with DuckDB: https://www.amazon.com/Getting-Started-DuckDB-practical-efficiently/dp/1803241004
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It's widely accepted that, in most cases at least, software systems should be modular, consisting of separate, discrete services. But what about the size of those services? How big or small should they be? This is where the question of service granularity comes in: too small and your system will become needlessly complicated; too big and you lose all the benefits of modularity you were seeking in the first place.
In this episode of the Thoughtworks Technology Podcast, host Ken Mugrage is joined by Neal Ford and Mark Richards — authors of multiple books on software architecture — to discuss service granularity. They explain why it matters and how software architects can go about getting it right, through the lens of granularity integrators and disintegrators.
Learn more about Neal and Mark's 2021 book Software Architecture: The Hard Parts (co-authored with Zhamak Dehghaniand Pramod Sadalage): https://www.thoughtworks.com/insights/books/software-architecture-hard-parts
Find out more about Neal and Mark's second edition of The Fundamentals of Software Architecture, set to be released in early 2025: https://www.oreilly.com/library/view/fundamentals-of-software/9781098175504/
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Trying to measure developer effectiveness or productivity isn't a new problem. However, with the rise of fields like platform engineering and a new wave of potential opportunities from generative AI, the issue has come into greater focus in recent years.
In this episode of the Technology Podcast, hosts Scott Shaw and Prem Chandrasekaran speak to Abi Noda, CEO of software engineering intelligence platform DX, about measuring developer experience using the DevEx Framework — which Abi developed alongside Nicole Forsgren, Margaret-Anne Storey and Michaela Greiler.
Taking in everything from the origins of the DevEx framework in SPACE metrics, to how technologists can better 'sell' the importance of developer experience to business stakeholders, listen for a fresh perspective on a topic that's likely to remain at the top of the industry's agenda for the forseeable future.
Read the DevEx Framework paper: https://queue.acm.org/detail.cfm?id=3595878
Read Abi's article (co-authored with Tim Cochran) on martinfowler.com: https://martinfowler.com/articles/measuring-developer-productivity-humans.html
Listen to Abi's Engineering Enablement podcast: https://getdx.com/podcast/
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Artificial intelligence has been presented as a technology with the potential to transform many different fields and professions. One of the most notable is design — but if we want to design in a way that's truly human-centric and inclusive, to what extent can artificial intelligence really help us do better work?
In this episode of the Technology Podcast, hosts Rebecca Parsons and Lilly Ryan speak to Thoughtworks design leaders Kate Linton and Esther Tham to get their perspective on how AI might be able to support designers. They discuss what AI tools could help the design process, how these tools could fit neatly into current practices and what the emergence of this technology could mean for design practices more broadly.
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If you work in technology, you're constantly making decisions: not just what you should do, but also how you should do it. That's why we developed the concept of "sensible defaults" — practices and technology decisions that we generally see — in most scenarios — as the right way to do things.
Although we've been talking about sensible defaults internally for a few years now, we recently decided to share them publicly on our website. We did so because we believe it can help organizations think through their own approach to technology decision-making, something which is becoming increasingly challenging in a rapidly changing and complex world.
So, to discuss sensible defaults and explain precisely why we want to share them with the world, hosts Rebecca Parsons and Ken Mugrage are joined by Brandon Cook and Kief Morris, two Thoughtworkers that played an important role in putting our sensible defaults together. They discuss the origins of the sensible default idea, some examples, as well as the challenges of putting them into practice.
Explore Thoughtworks' sensible defaults: https://www.thoughtworks.com/insights/topic/sensible-defaults
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Understanding your technology estate and how it's being leveraged is critical for organizations; it impacts everything from financial planning to capability development. But given the rapid pace of change — even inside a single company, let alone the wider industry — how can this be done effectively? One approach we've landed on at Thoughtworks is something called a Tech Dash: it's a method of internal research that surfaces information about an organization's technology use, and even software developers' experiences.
In this episode of the Technology Podcast, Camilla Crispim and Renan Martins talk to hosts Alexey Boas and Ken Mugrage about the value of a Tech Dash and explain how it can help track technology use. They also discuss where the idea came from and how they put it into practice across Thoughtworks Brazil.
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Bahmni started life as an open-source hospital information management system and electronic medical record for a single hospital in rural India. Today, it has more than 500 implementations in 50 countries across Africa and Asia, and is recognized as one of only 165 digital public goods by the Digital Public Goods Alliance.
Thoughtworks played a key part in bringing Bahmni into the world back in 2012. And although today it’s run and supported by a coalition of organizations, Thoughtworks continues to have a leading role in the project as a member of its Governing Committee.
To tell Bahmni’s unique story, Rebecca Parsons and Ken Mugrage speak with Satish Viswanathan and Angshuman Sarkar, two Thoughtworkers actively participating and contributing to the project. They discuss Bahmni’s origins and how it grew from a small, local tool to become a vital component in healthcare infrastructure in parts of the world that have long faced resource challenges.
Learn more about Bahmni: https://www.bahmni.org/
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One of the fundamentals of security is self-awareness: knowing where you may be vulnerable, the practices and processes that aren't yet quite in place and what actions you need to prioritize are essential if your organization is to excel at security. But how can that be done? In complex and distributed teams, surfacing such knowledge can be incredibly difficult. One solution, though, is something called a security maturity model.
In this episode of the Thoughtworks Technology Podcast, Thoughtworks alumnus Diana Adorno and current Thoughtworkers Lisa Junger and Robin Doherty speak to host Alexey Boas about a security maturity model they've developed that was recognized by the prestigious CSO50 Awards. They explain the purpose of developing and using one, how theirs works and why it should matter to any organization that wants to get serious about the way it does security.
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Despite occasional confusion, the difference between continuous delivery and continuous deployment is simple: should deploying to production be on demand or every good build? Answering which approach is 'best' is difficult; any attempt at dogmatism is likely to just look foolish, given it is, like many other debates in software development, context-dependent. But that doesn't mean we shouldn't try and unpick the issues at the heart of the discussion. It's all well and good saying the debate is context-dependent, but what does that actually mean in practice?
In this episode of the Technology Podcast, Ken Mugrage and Valentina Servile debate the merits of both continuous delivery and continuous deployment. Talking with hosts Prem Chandrasekaran and Birgitta Böckeler, they offer their perspectives on when and where both should be used — in making the case for their chosen approaches, they shed some much needed light on a discussion that every software engineering team should have.
Learn more about Valentina Servile's book Continuous Deployment: https://www.thoughtworks.com/insights/books/continuous-deployment
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Volume 30 of the Thoughtworks Technology Radar was published in April 2024. Alongside 105 blips, the edition also featured four themes selected by the team of technologists that puts the Radar together. They were: open-ish source licenses, AI-assisted software development teams, emerging architecture patterns for LLMs and dragging pull requests closer to continuous integration. Each one cuts across the technologies and techniques included on the Radar and highlights a key issue or challenge for software developers — and other technologists — working today.
In this episode of the Technology Podcast, Birgitta Böckeler and Erik Dörnenberg join Neal Ford and Ken Mugrage to discuss the themes for Technology Radar Vol.30. They explain what they mean, why they were picked and what their implications are for the wider industry.
Explore volume 30 of the Technology Radar: https://www.thoughtworks.com/radar
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Bringing machine learning models into production is challenging. This is why, as demand for machine learning capabilities in products and services increases, new kinds of teams and new ways of working are emerging to bridge the gap between data science and software engineering. Effective Machine Learning Teams — written by Thoughtworkers David Tan, Ada Leung and Dave Colls — was created to help practitioners get to grips with these challenges and master everything needed to deliver exceptional machine learning-backed products.
In this episode of the Technology Podcast, the authors join Scott Shaw and Ken Mugrage to discuss their book. They explain how it addresses current issues in the field, taking in everything from the technical challenges of testing and deployment to the cultural work of building teams that span different disciplines and areas of expertise.
Learn more about Effective Machine Learning Teams: https://www.thoughtworks.com/insights/books/effective-machine-learning-teams
Read a Q&A with the authors: https://www.thoughtworks.com/insights/blog/machine-learning-and-ai/author-q-and-a-effective-machine-learning-teams
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Can AI improve the quality of our code? A recent white paper published by code analysis company CodeScene — "Refactoring vs. Refuctoring: Advancing the state of AI-automated code improvements" — highlighted some significant challenges: in tests, AI solutions only delivered functionally correct refactorings 37% of the time. However, there are nevertheless opportunities. The white paper suggests it might be possible to dramatically boost the success rate of AI refactoring to 90%.
In this episode of the Technology Podcast, Adam Tornhill, CTO and Founder of CodeScene, joins Thoughtworks' Rebecca Parsons (CTO Emerita), Birgitta Böckeler (Global Lead for AI-assisted software delivery) and Martin Fowler (Chief Scientist and author of the influential Refactoring book) to discuss all things AI and code. From refactoring and code quality to the benefits and limitations of coding assistants, this is an essential conversation for anyone that wants to understand how AI is going to shape the way we build software.
Read CodeScene's Refactoring vs. Refuctoring white paper, which explores AI's role in improving code: https://codescene.com/hubfs/whitepapers/Refactoring-vs-Refuctoring-Advancing-the-state-of-AI-automated-code-improvements.pdf
Read CodeScene's Code Red white paper to learn how code quality impacts time-to-market and product experience: https://codescene.com/hubfs/web_docs/Business-impact-of-code-quality.pdf
CodeScene's new automated refactoring tool is now in beta. Learn more: https://codescene.com/campaigns/ai
Listen to our podcast discussion about AI-assisted coding from November 2023: https://www.thoughtworks.com/insights/podcasts/technology-podcasts/ai-assisted-coding-experiences-perspectives
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If you've ever wondered how to measure your cloud carbon footprint, you can — thanks to a tool that's called, somewhat unsurprisingly, Cloud Carbon Footprint. Launched in March 2021 by Thoughtworks as an open-source project, it allows users to monitor and measure carbon emissions and energy use from cloud services.
On this episode of the Technology Podcast, senior software engineers Cameron Casher and Arik Smith join Alexey Boas and Ken Mugrage to talk about Cloud Carbon Footprint in depth. They explain why CCF is different from the measurement tools offered by established cloud vendors, how it actually works and how you can get started with it yourself.
CCF on GitHub: https://github.com/cloud-carbon-footprint
Learn more: https://www.cloudcarbonfootprint.org/
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