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AI-native software engineering may be closer than developers think

CIO

Developers unimpressed by the early returns of generative AI for coding take note: Software development is headed toward a new era, when most code will be written by AI agents and reviewed by experienced developers, Gartner predicts. That’s what we call an AI software engineering agent.

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10 ways to kill your IT culture

CIO

Without a strong IT culture, inspiring IT teams to extend beyond their “run the business” responsibilities into areas requiring collaboration between business colleagues, data scientists, and partners is challenging. Additionally, 84% of leaders believed their organizations had strong teamwork compared to 60% of team members.

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Code analysis tool AppMap wants to become Google Maps for developers

TechCrunch

The 10/10-rated Log4Shell flaw in Log4j, an open source logging software that’s found practically everywhere, from online games to enterprise software and cloud data centers, claimed numerous victims from Adobe and Cloudflare to Twitter and Minecraft due to its ubiquitous presence. Image Credits: AppMap.

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How to Build a High-Performance Team

Hacker Earth Developers Blog

A high-performance team thrives by fostering trust, encouraging open communication, and setting clear goals for all members to work towards. Effective team performance is further enhanced when you align team members’ roles with their strengths and foster a prosocial purpose.

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Static vs Dynamic Code Analysis: How to Choose Between Them

OverOps

What’s the difference between static and dynamic code analysis, and how do you know which to use? Static code analysis is analogous to practicing your baseball swing with a practice net and a pitching machine. Static Code Analysis. This is usually done by analyzing the code against a given set of rules or coding standards.

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AI & the enterprise: protect your data, protect your enterprise value

CIO

I wrote, “ It may be even more important for the security team to protect and maintain the integrity of proprietary data to generate true, long-term enterprise value. This has made data even more of a target for bad actors and increased the damage resulting from malicious or accidental exposures. Years later, here we are.

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Unlocking the full potential of enterprise AI

CIO

Research from Gartner, for example, shows that approximately 30% of generative AI (GenAI) will not make it past the proof-of-concept phase by the end of 2025, due to factors including poor data quality, inadequate risk controls, and escalating costs. [1] Reliability and security is paramount.