Focusing on impact in your work can accelerate your career growth and lead to more satisfying outcomes.
To have more impact in tech, run towards unsolved problems, be scrappy in finding solutions, and prioritize ruthlessly.
Impact can be achieved by reducing costs or increasing revenue, and understanding how your work contributes to these areas is essential for career advancement in engineering.
Many young software engineers make common mistakes that can hold back their careers. It’s important to recognize these traps early on.
Good communication skills are essential for solving problems and sharing ideas effectively. Learning to articulate your thoughts can make a big difference.
Experience in different domains, like academia and tech companies, can provide valuable insights. Be open to learning from various industries to grow your career.
Service Level Objectives (SLOs) are important for understanding if services are reliable, but many organizations find them hard to use effectively. It's like a tool that sounds great but often doesn't work as well in practice.
Adopting and managing SLOs usually requires a lot of effort and support from the whole team, not just the SREs. If the company culture isn't ready for it, SLOs often get ignored.
There's a big gap between the theory of SLOs and how they're applied in real companies. Many teams struggle with choosing the right metrics and getting everyone to care about reliability over new features.
VTEX successfully scaled its monitoring system to handle 150 million metrics using Amazon's Managed Service for Prometheus. This helped them keep track of their numerous services efficiently.
By adopting this system, VTEX cut its observability expenses by about 41%. This shows that smart choices in technology can save money.
The new architecture allows VTEX to respond to problems faster and reduces the chances of system failures. It increased the reliability of their metrics, making everyday operations smoother.
AI tools will enhance software developers' productivity and create new possibilities.
Historically, productivity increases in software engineering have occurred with advancements like high-level programming languages, open-source culture, and cloud computing.
Lower barriers to coding will attract more people to software engineering, leading to new opportunities, growth, and products.
Ensure all necessary steps are taken before landing a pull request to the main branch, such as passing all tests and code reviews.
Deploy new software versions gradually to production, starting with a small number of machines first.
Consider implementing CI/CD for continuous deployment to improve observability, but balance it with on-demand deployments to ensure all changes are attended to.
Combining state space models (SSMs) with attention layers can create better hybrid architectures. This fusion allows for improved learning capabilities and efficiency.
Zamba is an innovative model that enhances learning by using a mix of Mamba blocks and a shared attention layer. This approach helps it manage long-range dependencies more effectively.
The new architecture reduces the computational load during training and inference compared to traditional transformers, making it more efficient for AI tasks.
Software engineering myths include the idea that you have to learn everything in the field, but it's more practical to focus on specific areas and have a general understanding of others.
The belief that adding more programmers speeds up development isn't always true; it can lead to more delays due to increased need for communication and management.
Software development involves more than just writing code; it includes tasks like planning, testing, deploying, and maintaining software.
Scaling AI tools like ChatGPT involves overcoming many engineering challenges to handle large user demands. It's important to manage growth effectively to keep users satisfied.
There's a lot of information out there about generative AI, making it hard to keep up. A guidebook can help condense this information and provide practical insights.
Linear regression is still a valuable tool in data science. Sometimes going back to basics can yield better results than relying on complex models.
At Netflix, there was a serious concurrency bug causing CPU problems, and they needed a quick solution. They couldn't fix it right away and had to come up with a way to keep their systems running through the weekend.
Instead of manually fixing everything, they created a self-healing system. They randomly killed a few server instances every 15 minutes, replacing them with fresh ones, which allowed the team to relax during the crisis.
This situation taught them that sometimes unconventional solutions are necessary. Prioritizing the team's well-being can be just as important as fixing technical issues.
Data teams need to learn best practices from software engineering, but that's not enough. They also need engineers who understand how data works and can work well with them.
Collaboration between data teams and software engineers is really important for success. If they don't communicate well, they can struggle to implement necessary changes and solve issues together.
The idea of a 'data-conscious software engineer' is becoming essential. These engineers understand the value of data and can help improve how both teams work together, making both sides more efficient.
Simplicity in software engineering is crucial for elegant solutions. Simple code is easier to maintain, read, and collaborate on.
Prioritizing simplicity leads to streamlined debugging, improved scalability, and lower technical debt. It makes adapting and deploying software faster and more user-centric.
Applying simplicity principles involves starting simple, avoiding premature optimization, focusing on core features, implementing incrementally, and leveraging existing tools. Embracing simplicity in coding doesn't mean avoiding complexity entirely, but finding beauty and efficiency in straightforward solutions.
AI coding tools are changing how software developers work. Using these tools can make coding faster and help solve complex problems more easily.
There are different types of AI tools for coding, like IDEs that assist with writing code and AI agents that can handle bigger tasks on their own. Each type serves a unique purpose in the coding process.
There is a need for better tools to create personalized AI agents and improve project management. These improvements could help teams work more efficiently together.
There is a new Slack community for paid subscribers focused on learning new tools and techniques in data science and career growth. It's a good place for support and sharing information.
A/B testing is important for experiments and there are recommended resources to help design and run successful tests. Proper planning and communication are key to making A/B testing effective.
Large Language Models (LLMs) are becoming more useful, and several resources are available for learning how to work with them. Understanding how they operate can help create valuable applications.
To understand stateless architecture, it's important to know the background of traditional client-server patterns and why moving towards stateless is beneficial.
The concept of state in an application is crucial, and stateless architecture outsources state handling to more efficient systems like using cookies and shared instances for storing state.
Stateless architecture simplifies state management, enhances client-side performance, and makes server scaling easier, aligning well with modern computing capabilities.
Trees are powerful data structures that are great for efficient organization and retrieval of data in software engineering.
Recursion works well with trees due to their recursive substructure, making implementation of recursive functions easier.
Decision trees in AI excel at discerning complex patterns, providing interpretable results, and are versatile in various domains such as finance, healthcare, and marketing.
During a hiring process, it's important to assess candidates based on coachable vs non-coachable gaps to align with the team's needs.
For junior engineers, watch out for extreme design decisions like overly complex or overly simplistic solutions, as they may indicate a lack of awareness.
When interviewing, consider candidates' coding nature, such as the balance between writing clean code and practical functionality testing, as it reflects their approach to software development.
Google Cloud Dataflow is a service that helps process both streaming and batch data. It aims to ensure correct results quickly and cost-effectively, useful for businesses needing real-time insights.
The Dataflow model separates the logical data processing from the engine that runs it. This allows users to choose how they want to process their data while still using the same fundamental tools.
Windowing and triggers are important features in Dataflow. They help organize and manage how data is processed over time, allowing for better handling of events that come in at different times.
The Normal Distribution is a probability distribution used to model real-world data, with a bell-shaped curve and key points located at the center.
The Normal Distribution is essential as it is commonly used in various fields to model real-world phenomena, calculate probabilities, and make informed decisions in software development.
Understanding and using the Normal Distribution in software can help in making approximations for performance, making the right sacrifices, and optimizing solutions based on real-world data.
Productivity in software engineering is not just about how much code you write. It's more important to focus on code quality and how well the software works.
At VTEX, they listen to developers to improve their work experience. This helps boost productivity by addressing the challenges developers face.
Combining feedback from developers with quantitative data can help understand the impact of changes in tools and processes on productivity.