Kent Quirk, an experienced software engineer, and Christian Gabrielsson, who specializes in pitching Go in professional settings, discuss the contemporary relevance of Go amid evolving programming languages. They tackle the challenges of introducing new technologies in organizations and the stability and maintainability that Go offers. The conversation also touches on the impact of AI tools in software engineering, emphasizing the importance of human expertise. Additionally, they share insights on technology choices and how best to utilize Go in modern applications.
Go's evolving features, including generics, allow it to remain relevant in cloud-native applications despite pressures from newer languages.
Postgres' extensibility and capability to handle diverse data types make it an ideal choice for modern AI applications and tools.
Cultural inertia within organizations often hinders the adoption of innovative technologies like Go, despite clear technical advantages.
Deep dives
The Versatility of Postgres for AI Applications
Postgres is increasingly recognized as a prime choice for AI applications due to its extensibility and strong community support. Its capability to handle a variety of data types, including time series and vector data, positions it well for modern use cases related to IoT, sensors, and AI technologies. Companies like Timescale leverage Postgres to build powerful extensions such as PG Vector Scale, enhancing performance and scalability for large-scale applications like recommendation systems. This versatility allows developers to create AI applications without needing to manage multiple databases, streamlining the development process significantly.
The Continued Relevance of Go
The relevance of the Go programming language is questioned as modern languages evolve, and developers consider if Go still holds its own in the tech space. Despite past criticisms regarding its limitations, Go has adapted with new features, such as the introduction of generics, leaving even long-term users to reconsider its place in the developer ecosystem. Experts assert that Go remains a strong candidate for cloud-native applications and continues to be utilized in significant projects across various industries. Its performance, simplicity, and the ability to compile to static binaries maintain its appeal in the face of emerging programming alternatives.
Cultural Resistance in Technology Adoption
Cultural factors often play a significant role in technology adoption decisions within organizations, as highlighted by a developer's experience attempting to implement Go in a team accustomed to using MuleSoft. Despite presenting a proof of concept demonstrating Go's efficiency for simple API tasks, the organizational inertia surrounding established technologies prevented the transition. Often, teams experience a reluctance to deviate from familiar tools, leading to a scenario where the perceived complexity of adopting a new solution outweighs the technical advantages it offers. This phenomenon illustrates the broader challenge of innovation within established enterprises, where comfort with existing systems can stymie progress.
Understanding Tool and Language Selection
Choosing the right programming language or tool hinges on understanding the specific use case rather than solely following trends or popular opinions. Developers have pointed out that while languages like Python excel in data-heavy tasks and Java can be a powerhouse for enterprise applications, each also has limitations that must be acknowledged. The longevity and stability of languages like Go offer a counterbalance to the frequent shifts experienced in languages like JavaScript, which may require updates to maintain functionality. This perspective encourages developers to think critically about their technology stack in context, ensuring that decisions are based on the project's needs and the team's expertise.
Embracing Change and Continual Learning
As the landscape of software development evolves with the introduction of AI and other advanced tools, the imperative for developers is to continually adapt and learn new skills. While many engineers express concern about AI's impact on job security, others view these advancements as opportunities to enhance productivity and innovation within their roles. Engineers are encouraged to stay curious and engage with emerging technologies, rather than relying solely on traditional methods. This proactive approach not only prepares developers for an uncertain future but also positions them to leverage new tools to improve existing systems and processes.
With so many great programming languages having emerged in the last decade, many of them purpose-built, when and where does Go still make sense and how do you make the case for it at work?
Changelog++ members save 8 minutes on this episode because they made the ads disappear. Join today!
Sponsors:
Timescale – Purpose-built performance for AI Build RAG, search, and AI agents on the cloud and with PostgreSQL and purpose-built extensions for AI: pgvector, pgvectorscale, and pgai.
Retool – The low-code platform for developers to build internal tools — Some of the best teams out there trust Retool…Brex, Coinbase, Plaid, Doordash, LegalGenius, Amazon, Allbirds, Peloton, and so many more – the developers at these teams trust Retool as the platform to build their internal tools. Try it free at retool.com/changelog
JetBrains – Sign up for the free “Mastering Go with GoLand” course and receive a complimentary 1-year GoLand subscription at bytesizego.com/goland
