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How to Scale Your Impact at the Staff-Plus Level
This article demystifies what "Staff-Plus" expectations actually look like, drawing on real promotion and performance reviews experiences. It maps out career ladders, digs into promotion patterns and the key behaviors that consistently help high-performing engineers to reinvent themselves, and introduces the concept of "staff projects" which top performers use to drive their careers forward.
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Eclipse LMOS: Launching AI Agents across Europe at Breakneck Speed
In this talk, the authors share some of our company’s key learnings in developing customer-facing LLM-powered applications deployed across Europe. They used multi-agent architecture and systems design to create an open-source set of tools, a framework, and a full-fledged platform to accelerate the development of AI agents. This is a summary of a presentation from InfoQ Dev Summit Boston 2024.
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Prompt Injection for Large Language Models
This article will cover two common attack vectors against large language models and tools based on them, prompt injection and prompt stealing. We will additionally introduce three approaches to make your LLM-based systems and tools less vulnerable to this kind of attacks and review their benefits and limitations, including fine-tuning, adversarial detectors, and system prompt hardening.
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Elevate Developer Experience with Generative AI Capabilities on AWS
This is a summary of a talk I gave at InfoQ Dev Summit Munich 2024. I discussed the transformative potential of generative AI in enhancing developer experiences, particularly through AWS. I’ll introduce key tools like Amazon Bedrock, Code Review Assistant, Agentic Code Generation, and Code Summarization in this article.
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Being a Responsible Developer in the Age of AI Hype
Justin Sheehy emphasizes that AI is code, not magic, and warns against inflated claims about AI capabilities. He urges developers to approach AI with healthy skepticism, seeking verifiable evidence and focusing on ethical practices, including addressing bias, privacy, and data integrity. Clear communication about AI’s limitations and accountable use are essential to prevent hype and misuse.