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Zhanna Hamilton (Substack)I'm writing an article on how practitioners are moving beyond prompt engineering into context engineering and what that shift looks like in the real world. I'm looking for leaders who have direct experience with both approaches. Please share: A specific use case where you moved from prompt engineering to context engineering. What changed in your results? Where do you draw the line between the two in your actual workflow? Has context engineering ever made things more complex or backfired? What happened? Are there situations where prompt engineering is still the better choice? What does that look like?

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Legal Hero MarketingI'm writing a practical guide on AI tools for law firm marketing. I'd like to hear from lawyers and legal marketers. What AI tools are you currently using in your marketing workflow? What tasks have you found AI genuinely useful for versus overhyped? Have you had any quality control issues with AI-generated content? How are you balancing AI efficiency with maintaining an authentic voice? Has AI changed your content output volume or quality? Looking for honest, specific experiences.

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Communications of the ACMWriting a story on recursive self-improvement in AI. Looking for AI experts to answer the following questions: 1) OpenAI says that GPT-5.3-Codex was instrumental in creating itself. From an engineering perspective, how do we distinguish between genuine recursive self-improvement and sophisticated internal automation in 2026? Which parts of the "AI factory"—such as debugging training runs, managing deployment, or diagnosing evaluation failures—are truly recursion-ready, and which remain strictly human-gated? 2) Some industry experts suggest that AI is now intelligent enough to meaningfully contribute to its own progress. In your view, does this recursion mainly compress development cycles, or does it expand capability frontiers in ways that human-only engineering teams cannot? 3) Where does human verification become the hard bottleneck in a self-improving loop, and what specific failure modes emerge when agentic models can modify their own evaluation machinery? How should frontier labs disclose and audit these model-assisted development processes to maintain human accountability and safety?

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