@bossiobren:

Brenda Bossio
Brenda Bossio
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Region: AR
Wednesday 02 September 2026 13:48:45 GMT
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noentiendoquepasa
GB01 :
si brenda me dice q me quede yo me quedo
2026-09-02 17:13:52
1
juliiimadera
ᴊᴜʟɪᴇᴛᴀ ᴍᴀᴅᴇʀᴀ🪬 :
Me dieron unas re ganas
2026-09-03 23:35:48
0
mocamvera
moisescampoverde :
Hola, cuántos minutos de cocción dejas los huevos para que queden así?
2026-09-02 15:02:29
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ger41891
ger :
no te antoja algo dulce a la mañana?????????
2026-09-06 00:57:55
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siloe.ag
Mauro🦇🥷🏻🕷️_^.^_ :
😌🤝👑🌹💋💎✨
2026-09-02 19:56:20
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Context engineering, prompt engineering and harness engineering are not competing techniques for AI agents. A new paper stacks everything people call agent engineering into seven layers, from model intelligence up to ontology engineering, and shows which layers you can actually ship on today. ---- 🚀 DYNAMOUS AI COMMUNITY Want to learn agentic coding with live daily events and workshops? Check out Dynamous AI: https://dynamous.ai/?code=646a60 Get 10% off here 👉 https://shorturl.smartcode.diy/dynamous_ai_10_percent_discount ⚡ HOSTINGER — RELIABLE HOSTING FOR YOUR PROJECTS (10% OFF) Whether you're shipping a portfolio, a side project, n8n flows, or AI agents — I use Hostinger for fast, affordable VPS + web hosting. Get 10% off here 👉 https://hostinger.com/DIYSMARTCODE (Affiliate link — costs you nothing, supports the channel.) ---- What you will see in this 2-minute breakdown: → Layer 1, Model Intelligence: the closed-book exam. Weights only, no documents, no tools. → Layer 2, Prompt Engineering: same closed book, better question. Role, example, format. Caps out fast. → Layer 3, Context Engineering: the open book. You decide what lands on the table, because attention is the budget. → Layer 4, Harness Engineering: the open environment. Search, code execution, a database, memory. → Layer 5, Loop Engineering: submit, read the feedback, fix the wrong step, run it again. → Layer 6, Graph Engineering: the paper's own argument. Task organization, agent coordination, runtime state as a graph. → Layer 7, Ontology Engineering: a shared rulebook, so every agent means the same thing by
Context engineering, prompt engineering and harness engineering are not competing techniques for AI agents. A new paper stacks everything people call agent engineering into seven layers, from model intelligence up to ontology engineering, and shows which layers you can actually ship on today. ---- 🚀 DYNAMOUS AI COMMUNITY Want to learn agentic coding with live daily events and workshops? Check out Dynamous AI: https://dynamous.ai/?code=646a60 Get 10% off here 👉 https://shorturl.smartcode.diy/dynamous_ai_10_percent_discount ⚡ HOSTINGER — RELIABLE HOSTING FOR YOUR PROJECTS (10% OFF) Whether you're shipping a portfolio, a side project, n8n flows, or AI agents — I use Hostinger for fast, affordable VPS + web hosting. Get 10% off here 👉 https://hostinger.com/DIYSMARTCODE (Affiliate link — costs you nothing, supports the channel.) ---- What you will see in this 2-minute breakdown: → Layer 1, Model Intelligence: the closed-book exam. Weights only, no documents, no tools. → Layer 2, Prompt Engineering: same closed book, better question. Role, example, format. Caps out fast. → Layer 3, Context Engineering: the open book. You decide what lands on the table, because attention is the budget. → Layer 4, Harness Engineering: the open environment. Search, code execution, a database, memory. → Layer 5, Loop Engineering: submit, read the feedback, fix the wrong step, run it again. → Layer 6, Graph Engineering: the paper's own argument. Task organization, agent coordination, runtime state as a graph. → Layer 7, Ontology Engineering: a shared rulebook, so every agent means the same thing by "done". → Prompt engineering vs context engineering: why they are floors of the same ladder, not rivals. → The honest part: six and seven are the paper's proposal, not something you install this week. Source paper: Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence (alphaXiv 2608.21156, 21 Aug 2026) — https://www.alphaxiv.org/abs/2608.21156 Most setups already run a loop, so the deliberate work sits on four and five, harness and loop. Disagree? Then which layer is your setup actually on? Drop the number, nothing else. #contextengineering #promptengineering #contextengineeringexplained #promptengineeringvscontextengineering #aiagents #agenticai #agenticengineering #aiengineering #llm #harnessengineering #graphengineering #ontologyengineering #loopengineering #aiagentsexplained #multiagent #modelcontextprotocol #aidevelopment #artificialintelligence #airesearch #agentarchitecture #llmagents #aicoding #devtools #shorts

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