[LLM으로 할 수 있는 비전형적인 일 7가지

KDnuggets의 Iván Palomares Carrascosa가 기고한 글에서, LLM을 단순한 챗봇이나 검색 도구 이상의 **비전형적 활용법 7가지**를 소개한다. 각 항목은 프롬프트 예시와 함께 제공되며, 기술적/법적/교육적/커뮤니케이션적 문제 해결에 활용 가능한 독창적인 방법론을 담고 있다. 특히 **악마의 변호인 역할, 기술 오류 로그 해독, 계약서 검토, 역사적 페르소나 시뮬레이션, 러버 덕킹 자동화, 맞춤형 학습 로드맵, 문화적 맥락 브릿징** 등 AI 개발자 및 전문가들이 실무에서 활용할 수 있는 **실용적 응용 사례**를 중점적으로 다룬다. 글은 LLM의 잠재력을 넘어 **문제 해결 및 창의적 협업 도구**로서의 가능성을 강조한다.

https://news.hada.io/topic?id=28846

#llmapplications #promptengineering #aiproductivity

Flowise RCE vulnerability exploited in attacks

Hackers are actively exploiting a critical vulnerability in Flowise, a popular open-source AI tool, that allows them to take control of systems designed to run code - a fundamental flaw that raises serious questions about securing AI-powered applications. This maximum-severity flaw, tracked as CVE-2025-59528, has left developers, organizations, and…

https://osintsights.com/flowise-rce-vulnerability-exploited-in-attacks

#Flowise #RceVulnerability #Cve202559528 #AiSecurity #LlmApplications

Local LLMs as constrained data transformers: Duolingo vocab transformed to Anki using Qwen 32B on MacBook M2 Max (45min run). Key insights: larger models sometimes over-help, Qwen 2.5 32B balances quality/instruction adherence. Practical iteration on consumer hardware. #LocalLLaMA #DataTransformation #LLMApplications #AnkiIntegration #Qwen32B

https://www.reddit.com/r/LocalLLaMA/comments/1ptfibf/using_local_llms_as_constrained_data_transformers/

12-factor Agents: Patterns of reliable LLM applications

https://github.com/humanlayer/12-factor-agents

#HackerNews #12factorAgents #LLMapplications #ReliablePatterns #AIdevelopment #SoftwareEngineering

What happens when a language model solves maths problems?

"If I’m 4 years old and my partner is 3x my age – how old is my partner when I’m 20?"
Do you know the answer?

🤥 An older Llama model (by Meta) said 23.
🤓 A newer Llama model said 28 – correct.

So what made the difference?

Today I kicked off the 5-day Kaggle Generative AI Challenge.
Day 1: Fundamentals of LLMs, prompt engineering & more.

Three highlights from the session:
☕ Chain-of-Thought Prompting
→ Models that "think" step by step tend to produce more accurate answers. Sounds simple – but just look at the screenshots...

☕ Parameters like temperature and top_p
→ Try this on together.ai: Prompt a model with “Suggest 5 colors” – once with temperature 0 and once with 2.
Notice the difference?

☕ Zero-shot, One-shot, Few-shot prompting
→ The more examples you provide, the better the model understands what you want.

#PromptEngineering #GenerativeAI #LLM #Kaggle #LLMApplications #AI #DataScience #Google #Python #Tech