This story was originally published on HackerNoon at: https://hackernoon.com/5-ways-your-ai-agent-will-get-hacked-and-how-to-stop-each-one.
Production AI agents fail from prompt injection, tool poisoning, credential leaks, and more. Learn 5 attack patterns and defensive code for each.
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This story was written by: @paoloap. Learn more about this writer by checking @paoloap's about page, and for more stories, please visit hackernoon.com.

AI agents are vulnerable to prompt injection, tool Poisoning, credential leakage and identity theft. Most teams just don’t know the threats exist.

This story was originally published on HackerNoon at: https://hackernoon.com/how-i-stopped-fighting-ai-and-started-shipping-features-10x-faster-with-claude-code-and-codex.
A deep dive into my production workflow for AI-assisted development, separating task planning from implementation for maximum focus and quality.
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This story was written by: @tigranbs. Learn more about this writer by checking @tigranbs's about page, and for more stories, please visit hackernoon.com.

A deep dive into my production workflow for AI-assisted development, separating task planning from implementation for maximum focus and quality.

This story was originally published on HackerNoon at: https://hackernoon.com/ia2-preprocessing-establishing-the-foundation-for-index-selection.
The IA2 preprocessing phase uses a workload model and index candidates enumerator to create accurate state representations and action spaces.
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This story was written by: @instancing. Learn more about this writer by checking @instancing's about page, and for more stories, please visit hackernoon.com.

The IA2 preprocessing phase uses a workload model and index candidates enumerator to create accurate state representations and action spaces.

This story was originally published on HackerNoon at: https://hackernoon.com/prompt-reverse-engineering-fix-your-prompts-by-studying-the-wrong-answers.
Learn prompt reverse engineering: analyse wrong LLM outputs, identify missing constraints, patch prompts systematically, and iterate like a pro.
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This story was written by: @superorange0707. Learn more about this writer by checking @superorange0707's about page, and for more stories, please visit hackernoon.com.

Most “bad” LLM outputs are diagnostics. Treat them like stack traces: classify the failure, infer what your prompt failed to specify, patch the prompt, and re-test with a minimal change. Build a prompt changelog so you stop re-learning the same lesson.

This story was originally published on HackerNoon at: https://hackernoon.com/what-comes-after-growth-hacks-ai-driven-marketing-systems.
What comes after growth hacks isn’t more hustle. Its systems and those systems are powered by AI.
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This story was written by: @khamisihamisi. Learn more about this writer by checking @khamisihamisi's about page, and for more stories, please visit hackernoon.com.

Growth hacks work, until they don’t. The real problem is a lack of structure. What comes after growth hacks isn't more hustle. It’s systems powered by AI.

This story was originally published on HackerNoon at: https://hackernoon.com/can-chatgpt-outperform-the-market-week-23.
Another strong week...
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This story was written by: @nathanbsmith729. Learn more about this writer by checking @nathanbsmith729's about page, and for more stories, please visit hackernoon.com.

Another strong week...

This story was originally published on HackerNoon at: https://hackernoon.com/can-llms-generate-quality-code-a-40000-line-experiment.
Like humans, LLMs generate sloppy code over time - just faster. Learn how to use multi-model reviews and formal code analysis to ensure code quality.
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This story was written by: @anywhichway. Learn more about this writer by checking @anywhichway's about page, and for more stories, please visit hackernoon.com.

Like humans, LLMs generate sloppy code over time - just faster. Learn how to use multi-model reviews and formal code analysis to ensure code quality.

This story was originally published on HackerNoon at: https://hackernoon.com/agentic-ai-isnt-a-feature-its-a-replatforming-and-it-will-decide-who-sets-the-tone-in-2026.
The future of enterprise AI won’t be decided by the systems people touch. It will be decided by the systems that touch everything.
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This story was written by: @erelcohen. Learn more about this writer by checking @erelcohen's about page, and for more stories, please visit hackernoon.com.

The future of enterprise AI won’t be decided by the systems people touch. It will be decided by the systems that touch everything.