#llm
Llm: 26 AI articles covering llm news, analysis, and research
Articles
Token Costs Become the New EDA Budget BattlegroundNEWβ8
Token-based pricing for AI features in EDA tools is becoming a budget concern, pushing chip designers to optimize usage as costs rival traditional software lice...
OpenAI's Astra Model: A Powerful New Tool for Cybersecurityβand a Potential Threatβ7
OpenAI's Astra model can autonomously hack system vulnerabilities, raising security concerns. Learn about its capabilities, safety measures, and industry reacti...
5 AI Skills That Will Keep Data Scientists Relevant in 2027β10
5 AI skills that will keep data scientists relevant in 2027: retrieval, routing, guardrails, evals, and agent loops, with runnable Python code.
FAQ as RAG: When You Get to Design the Corpusβ10
FAQ as RAG rethinks retrieval when the corpus is Q&A pairs: parsing becomes trivial, retrieval works as a cache, and few-shot prompting turns into a lookup prob...
How to Get Reliable Structured Data Out of an LLMβ10
Reliable structured data from LLMs requires enforced schemas, validation, and smart retry loops to prevent JSON parsing failures.
Is Agentic AI Just Automation?β10
Determining whether agentic AI is truly intelligent or just advanced automation, with a real-world test to separate hype from substance.
10 Positions for Enterprise RAG That Mainstream Tutorials Get Wrongβ8
Enterprise RAG systems fail when tutorials oversimplify. This guide reveals 10 expert positions on structure-first retrieval, rerankers, and enterprise-scale ch...
AI Agents Donβt Need More Context β They Need Typed Contextβ10
Why AI agents fail from type confusion in promptsβlearn how typed context prevents instruction contamination and improves observability.
Kimi K3's 1M Token Context Window vs. RAG: Cost, Latency and Answer Qualityβ9
Comparing Kimi K3βs 1M token context to RAG across cost, latency, and answer quality in a blind test.
Create a Reasoning-Focused LLM: A Practical Guide to Streaming,β9
Learn to build a reasoning-focused LLM with SupraLabs corpus: stream, filter, fine-tune SmolLM2 with LoRA, and export structured outputs.