April 17, 2024

Are You Delivering on Developer Experience?

A critical concept in modern developer experience is the “inner loop” of feedback on code changes. When a developer has a quick and familiar system to get feedback on their code, it encourages multiple cycles of testing and experimentation before code is deployed to a final test environment or production. The “outer loop” of feedback involves a more formal process of proposing tests, merging changes, running integration and then end-to-end tests. When problems are found on the outer loop, the result is larger, slower deployments with developers receiving feedback hours or days after they write code. Outer loop testing can still be testing that is automated and kicked off by the original developer, but another common issue with feedback that comes later in the release cycle is that it comes from human testers or others in the release process. This often results in feedback that is symptomatic rather than identifying root causes. When feedback isn’t clear, it’s as bad or worse than unclear requirements: Developers can’t work quickly on problems they haven’t diagnosed, and they’ve often moved on to other projects in the time between deployment and finding an issue. 

The digital tapestry: Safeguarding our future in a hyper-connected world

Data centers, acting as the computational hearts, power grids as the electrical circulatory system, and communication networks as the interconnected neural pathways – these elements form the infrastructure that facilitates the flow of information, the very essence of modern life. But like any complex biological system, they have vulnerabilities. A sophisticated cyberattack can infiltrate a data center, disrupting critical services. A natural disaster can sever communication links, isolating entire regions. These vulnerabilities highlight the paramount importance of resilience. We must design and maintain infrastructure that can withstand these disruptions, adapt to changing demands, and recover swiftly from setbacks. This intricate dance becomes even more critical as we attempt to seamlessly integrate revolutionary technologies like artificial intelligence (AI) into the fabric of our critical infrastructure. As we know, AI offers incredible potential, functioning like a highly sophisticated adaptive learning algorithm within the data center and critical infrastructure network. 

5 Strategies To Get People To Listen To You At Work

Credibility is currency at work. It is built over time, not by title or position but through displays of integrity, expertise, and knowledge. To be considered credible we need to have something valuable to say, and we can hone that by investing in continuous learning, staying abreast of industry trends, and demonstrating an ability to contribute to the success of the team through our actions and contributions. ... Tailor your message to resonate with the concerns, interests, and communication preferences of those you’re addressing. Speaking to executives, for instance, demands clarity, brevity, and alignment with strategic goals. Anticipate their probing questions about risks and opportunities and emphasize the impact on the bottom line. ... When people come to speak with you, silence your phone and computer and give them your full attention. Ask them follow-up questions, take notes, and adopt a mindset of learning. By demonstrating genuine interest and appreciation for your team members’ viewpoints, you will foster a culture of collaboration and mutual respect that encourages others to listen to you in turn.

Thinking outside the code: How the hacker mindset drives innovation

The hacker mindset has a healthy disrespect for limitations. It enjoys challenging the status quo and looking at problems with a “what if” mentality: “what if a malicious actor did this?” or “what if we look at data security from a different angle? This pushes tech teams to think outside the code, and explore more unconventional solutions. In its essence, hacking is about creating new technologies or using existing technologies in unexpected ways. It’s about curiosity, the pursuit for knowledge, wondering “what else can this do?” I can relate this to movies like The Matrix; it’s about not accepting reality as a “read-only” situation. It’s about changing your technical reality, learning which software elements can be manipulated, changed or re-written completely. ... Curiosity is one of the most important elements to fuel growth. Organizations with a “question everything” attitude will be the first to adapt to new threats; first to seize opportunities; and last to become obsolete. For me, ideal organizations are tech-driven playgrounds that encourage experimentation and celebrate failure as progress.

SAS Viya and the pursuit of trustworthy AI

Ensuring ethical use of AI starts before a model is deployed—in fact, even before a line of code is written. A focus on ethics must be present from the time an idea is conceived and persist through the research and development process, testing, and deployment, and must include comprehensive monitoring once models are deployed. Ethics should be as essential to AI as high-quality data. It can start with educating organizations and their technology leaders about responsible AI practices. So many of the negative outcomes outlined here arise simply from a lack of awareness of the risks involved. If IT professionals regularly employed the techniques of ethical inquiry, the unintended harm that some models cause could be dramatically reduced. ... Because building a trustworthy AI model requires a robust set of training data, SAS Viya is equipped with strong data processing, preparation, integration, governance, visualization, and reporting capabilities. Product development is guided by the SAS Data Ethics Practice (DEP), a cross-functional team that coordinates efforts to promote the ideals of ethical development—including human centricity and equity—in data-driven systems. 

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