AI systems = layers • LLMs → intelligence
• RAG → knowledge
• GenAI → creation
• Agents → execution
• Agentic AI → autonomy Power comes from integration Via Giuliano Liguori (
@ingliguori
) #AI #AIAgents #Tech
LLMS
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A Conceptual Framework for AI System Integration
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LLMs: Memorization vs. Regurgitation Debate
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“Regurgitation” is a mischaracterization. It’s entirely appropriate for LLMs to memorize data, and they certainly do more with it than just regurgitate.
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AI Interfaces Evolving Beyond Chat Towards Context-Aware Agents
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“AI needs more than chat” is probably the most important line here. Chat interfaces were never the end product. They were just the temporary UI until agents got real context.
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Using LLM Prompts to Integrate Keywords into Resume Job History
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Second prompt. Paste your resume with the keyword list and ask: "Rewrite this so the keywords show up inside my actual job history, not in a separate skills section." A keyword stuck in a list scores worse than the same keyword in a sentence about something you did.
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Reinforcing Recursive Language Models for Long-Context Tasks
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Reinforcing Recursive Language Models Can a 4B model learn to recursively call itself to answer hard long-context questions? We RL fine-tuned a small model to behave as a native RLM. On evidence selection across scientific papers, our 4B RLM matches Sonnet 4.6 in quality
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Detailed Example of a Functional AI Workflow in Action
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Here is what a broken AI workflow actually looks like:
— Nico (@nicos_ai) 12 mai 2026
You wake up. OpenClaw has been running since 5am.
It read your emails, sorted your tasks, caught the production alert, drafted three replies, reordered your Linear board by what is actually blocking what.
Three hours of… https://t.co/wiVsA0ryac pic.twitter.com/MSgE7QY9NdHere is what a broken AI workflow actually looks like: You wake up. OpenClaw has been running since 5am. It read your emails, sorted your tasks, caught the production alert, drafted three replies, reordered your Linear board by what is actually blocking what. Three hours of
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Agent-based learning as the future of AI interfaces
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if an agent can really learn your taste from what you consume, that becomes a much more powerful interface than prompts https://t.co/BQm2Bl3FWq
— SONIA (@S0N_IA_) 12 mai 2026if an agent can really learn your taste from what you consume, that becomes a much more powerful interface than prompts
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Google DeepMind introduces AI Co-Mathematician agent for research
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NEW paper from Google DeepMind. (bookmark it) AI Co-Mathematician is an agentic research workbench for mathematicians, and it just hit 48% on FrontierMath Tier 4, a new high score among AI systems evaluated. The system is an asynchronous, stateful environment that supports
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AI Coder Builds OS with Claude Code: Masterclass, Guide, and Free Repo
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This guy literally built an ENTIRE operating system with Claude Code 🤯
— Charly Wargnier (@DataChaz) 12 mai 2026
now he is showing you exactly how.
Nate just dropped the ultimate AI coding starter pack:
→ 2+ hour masterclass
→ Complete framework guide
→ Free GitHub repo to start building
bookmark this 👇 https://t.co/DwToTNzjwH pic.twitter.com/Atk3Nd4croThis guy literally built an ENTIRE operating system with Claude Code now he is showing you exactly how. Nate just dropped the ultimate AI coding starter pack: → 2+ hour masterclass
→ Complete framework guide
→ Free GitHub repo to start building bookmark this -
Optimizing AI Infrastructure via Intelligent Model Routing
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Most AI apps are still burning money by sending every prompt to the most expensive model.
— God of Prompt (@godofprompt) 12 mai 2026
That won’t last.
The real unlock is routing: cheap models for simple jobs, stronger models when it actually matters, fallback when providers break.
This is the infra layer everyone… https://t.co/mpM5L6tuwwMost AI apps are still burning money by sending every prompt to the most expensive model. That won’t last. The real unlock is routing: cheap models for simple jobs, stronger models when it actually matters, fallback when providers break. This is the infra layer everyone