Go Summarize

Go Developer Survey 2024

programming#software engineer#software engineering#developer#web design#web development#programmer humor
62K views|2 months ago
💫 Short Summary

The video discusses the challenges and benefits of using the Go programming language, highlighting developer satisfaction, documentation issues, and the importance of community engagement. It covers the comparison between Python and Go for AI-related tasks, the deployment preferences of Go developers, and security concerns in coding practices. The importance of learning goals and hands-on learning in Go programming is emphasized, along with the integration of AI models in organizations. The video also touches on the use of generative AI models, the value of real-world experience in learning, and the need for clear guidelines in project structure and code organization.

✨ Highlights
📊 Transcript
Melky Dev discusses his professional experience with Go programming and the importance of continuous learning.
He emphasizes the significance of deep knowledge in Go and how it has motivated him.
Melky Dev recommends reading Go programming textbooks in different locations to absorb information effectively.
Reading out loud has helped him overcome stuttering and improve information retention.
The conversation shifts to the Go developer survey results for 2024 H1, with uncertainty expressed about the survey abbreviation.
Developers are using Go for AI-related use cases and are satisfied with the language and trust the Go team.
The latest major releases of Go have been well-received for advancing the language with modern features.
Survey respondents building AI-powered applications see Go as a strong platform for production use, with challenges in library and documentation ecosystems.
Organizations are beginning to adopt Go for AI workloads, but the starting paths are still centered around Python.
Comparison between Python and other languages for ML engineering and MLOps.
Python is dominant in data science and ML, excelling in building models and ETL processes.
Go is better suited for MLOps tasks like infrastructure management and deployment.
Use cases of AI services include internal-facing applications like chatbots for employee queries.
Reservations expressed about newer AI tools like LLMs due to uncertainties in the rapidly evolving AI domain.
Importance of clear and accessible documentation in large companies like Netflix.
Lack of updated resources like READMEs and wikis causing difficulties in finding essential information.
Clear documentation is crucial when dealing with niche and granular services.
Limited time and opportunities hindering learning and goal achievement in tech companies.
Well-documented tools and resources are needed to streamline processes within tech companies.
High Developer Satisfaction with Go Programming Language.
93% of respondents expressed satisfaction with Go.
Comparable rates of satisfaction among VS Code and Go users.
No significant statistical differences in satisfaction percentages from previous years.
Majority of users are content with Go as a programming language.
Importance of Developer Trust in the Go Team.
80% of respondents indicated trust in the Go team, which tends to increase with more experience in using Go.
The impact of mistakes on trust is exemplified using the Rust Foundation.
More experienced developers are more likely to participate in community events and report higher satisfaction levels in the Go community.
Benefits of participating in the Go community.
Increased satisfaction through social interaction or technical support.
Less experienced individuals are less likely to attend events, but they serve as networking opportunities and potential job recruitment platforms.
Attending conferences is recommended for networking purposes, especially for newcomers in the job market.
Overall satisfaction with Go developer communities is high, with most participants being very to somewhat satisfied.
Impact of Closed Form Questions on Developer Feedback
Participants were 2.5x more likely to respond to closed form questions compared to open-ended questions.
8% of participants selected 'other', highlighting common challenges faced by developers.
Top challenges included learning to write Go effectively and error handling.
11% of open text responses mentioned learning Go and documentation challenges, with one participant facing hidden knowledge issues leading to bugs in TCP server handling.
Challenges in Writing Go Programming
Importance of hidden knowledge and experience in effectively writing in Go.
Struggle in expressing oneself and setting up code to avoid errors.
Emphasize on continuous learning and improvement for proficient Go programming.
Recommendation to read 'Effective Go' for leveling up in Go programming and comparison with Rust's learning curve.
Challenges faced when using Go today.
Learning curve, best practices, documentation, error handling, and differences from familiar languages are key obstacles.
Missing or mature ecosystems, libraries or frameworks, tooling, and type systems add to the challenges.
Balancing expressive type systems like Rust with simplicity in Go, especially in handling heterogeneous lists, is discussed.
Perspectives on type systems like Go and TypeScript are shared, emphasizing the balance between software development time and writing type constraints.
Challenges related to refactoring code and types in TypeScript.
Mention of requests for Rust-like features and improvements in handling deeply nested structures.
Discussion on error checking verbosity, nil checking with pointers, and the desire for more efficient ways to handle nested data structures.
Highlighting the complexities and frustrations developers face when working with intricate codebases.
Emphasizing the importance of streamlined solutions in addressing these challenges.
Challenges with type systems, error handling, and stack traces in programming languages.
Importance of adapting to the language being used rather than imposing habits from other languages.
Drawbacks of using 'throw' for error handling and advocating for handling errors as values.
Utilizing result objects instead of traditional error handling methods.
Acknowledging the complexities of navigating different language paradigms and the need for a mindset shift when learning new languages.
Developer preferences in operating systems vary among different groups.
Neovim users are considered part of the core group, reflecting historical priorities of the development team.
Personal experiences of using Linux at Netflix are discussed, along with the process of switching from Windows to Linux.
Challenges with a laptop provider and frustrations faced during that experience are mentioned in the segment.
Preference for Linux and Mac OS over Windows for development purposes.
Use of Linux for streaming and Mac for work, with no expertise in Windows.
Mention of higher Windows usage in India due to pricing differences.
Discussion on Silicon Valley experience and a documentary about Apple's impact.
Focus on development preferences for WSL and version 2 among VS Code and GoLand users.
Survey results show Go developers primarily deploy to Linux environments and focus on cloud-based development.
Majority of respondents use AWS for deployment, followed by Google Cloud.
Microsoft Azure is more popular in large organizations than smaller ones.
Disappointment with AWS dropping support for Go runtimes and lambdas led to redeployment and restructuring efforts.
Speaker reminisces about past experiences with Heroku and expresses surprise at its continued presence in the market.
Dissatisfaction with Azu Ar and potential alternatives.
Disproportionate dissatisfaction across different categories highlighted.
Resource cost and security concerns for teams using Go discussed.
Engineering cost and maintaining Go services emphasized.
Exploring migrating to different languages like Rust or TypeScript as potential alternatives.
Security concerns in coding are a top priority, with insecure coding practices being a major issue across all programming languages.
Respondents are looking for tools to identify and address security issues during the coding process.
The segment also highlights supply chain risks, especially when using external hosting services like npm.
Potential risks of deploying various versions on platforms like npm and GitHub are emphasized.
Secure coding practices and careful management of software versions are crucial to mitigate these risks.
Differences between git tags and npm in mutability and version control.
npm tarballs versions may differ from GitHub repository, while git provides exact code.
Challenge of verifying consistency between npm downloads and git content.
Speaker expresses frustration with npm's approach and advocates for better practices.
Emphasis on ensuring accurate version control and code integrity.
Validating information using npm view and comparing JSON objects on the npm server.
Measuring the ease of diagnosing performance issues and utilizing tools for code performance improvement.
No notable differences in performance tool usage between VS Code and Goan users.
Emphasis on the significance of unit testing, integration testing, and vulnerability scanning in software development.
Importance of identifying and addressing performance issues in software development.
No significant differences between Go and VS Code users in diagnosing performance issues.
VS Code users were not more likely to report challenges in diagnosing performance issues.
Less experienced Go developers found it more difficult to diagnose performance issues.
Misperception that VS Code users are not as skilled in diagnosing performance issues.
Discussion on diagnosing performance issues with cloud-based tools and command line editors.
Cloud-based tools are noted for their ease of diagnosing problems compared to traditional methods.
Challenges of calling services sequentially are highlighted.
Benefits of using Google Trace event for quick issue resolution are mentioned.
Developers find it difficult to diagnose performance issues, with cloud-based tools offering potential solutions.
Developers with less than two years of experience in Go encounter challenges, regardless of their choice of editor or tooling.
Respondents highlight latency, total memory, and total CPU as crucial performance issues to grasp, with the potential to impact business costs.
Latency is emphasized as the most user-visible metric, directly influenced by GC performance and memory allocation.
Survey respondents are actively incorporating AI into their organizations' services.
AI is becoming increasingly prevalent across various industries.
Nvidia stock is becoming more attractive in light of the growing importance of AI.
The discussion shifts to personal stories and interruptions, creating a casual and humorous atmosphere.
Integration of generative AI models in organizations using Hugging Face and AWS SageMaker.
Hugging Face serves as a core library for deploying and using models, akin to Docker Hub.
AWS SageMaker is a popular choice for ML deployments and processing tasks.
Both tools provide a range of functionalities for deploying, inferring, managing, and monitoring ML models.
These tools offer comprehensive toolsets for deployment and management of ML models, distinguishing them from traditional wrappers like OpenAI.
Highlights of Go Developers' Learning Goals
Only 3% of respondents are learning the basics of Go, while 40% have mastered the basics but want to learn advanced topics.
Another 40% focus on helping others learn Go.
15% have no learning goals related to Go.
The discussion also touches on the quick learning curve for mastering the basics of Go, especially for full-time learners.
Benefits of learning Go as a first programming language.
Exposure to computer science concepts, strict type system, and introduction to concurrency are emphasized.
Learning Go can provide a solid foundation for transitioning to other languages like C, Rust, or Zig.
Importance of investing time in understanding these concepts for long-term payoff is stressed.
Survey results show a preference for written content over video among respondents, especially for less experienced developers.
Importance of Practical Knowledge in Learning and Decision-Making.
Generative AI is compared to learning from experienced engineers, emphasizing the value of learning from mistakes and experiences.
The speaker questions the effectiveness of relying solely on generative AI for learning.
Real-world experience is emphasized as crucial in gaining expertise.
The speaker references an article by a machine learning director on building HTTP services in Go to illustrate how experience shapes decision-making.
Challenges faced by new Go developers.
Limited resources, missing features, and lack of professional opportunities are common challenges.
Small sample size of respondents affects data validity.
Onboarding issues include project structure confusion and lack of real-world examples.
Importance of code organization and project structure emphasized, with a need for clearer guidelines and resources.
Importance of hands-on learning and textbooks in understanding real-world problems.
Emphasizes the value of physical textbooks for active participation and guidance in the building process.
Textbooks with tangible end goals are powerful tools for learning complex subjects like compilers.
Gratitude expressed to the audience and promotion of social media platforms for further engagement.
Support for Mand on Netflix
Viewers are encouraged to leave comments on Netflix in support of a person called Mand, described as a myth and legend.
The speaker expresses admiration for Mand and urges others to do the same.
The audience is invited to show love and support for Mand by participating in leaving comments on Netflix.