𝗔𝗜 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗠𝗲𝗺𝗼𝗿𝘆 is the most important piece of 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴, this is how we define it 👇 In general, the memory for an agent is something that we provide via…


LinkedIn Content Strategy & Writing Style
Founder @ SwirlAI • Ex-CPO @ neptune.ai (Acquired by OpenAI) • UpSkilling the Next Generation of AI Talent • Author of SwirlAI Newsletter • Public Speaker
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Aurimas Griciūnas positions himself as a bridge between high-level AI strategy and production-grade engineering, leveraging his background as a former CPO at an OpenAI-acquired firm to mentor the next generation of technical talent. His content strategy centers on demystifying complex architectural patterns-such as Agentic RAG, Model Context Protocol (MCP), and vector database fundamentals-by translating abstract concepts into practical, deployment-ready workflows. He stands out by moving beyond "vibecoding" to emphasize the rigorous operational metrics of AI, including latency, model compression, and observability. Aurimas uniquely intersects deep technical education with entrepreneurial transparency, using his SwirlAI platform to provide a "from the trenches" perspective that prioritizes business value and system stability over AI hype.
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𝗔𝗜 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗠𝗲𝗺𝗼𝗿𝘆 is the most important piece of 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴, this is how we define it 👇 In general, the memory for an agent is something that we provide via…

Integrating 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 Systems via 𝗠𝗖𝗣 👇 If you are building RAG systems and packing many data sources for retrieval, most likely there is some agency present at least at the data sour…

Integrating 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 Systems via 𝗠𝗖𝗣 👇 If you are building RAG systems and packing many data sources for retrieval, most likely there is some agency present at least at the data sour…

I have been building and operating Agentic AI Systems for the past few years and the same patterns keep emerging. 👇 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗗𝗿𝗶𝘃𝗲𝗻 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 is the most reliable way…

Fusion of 𝗥𝗔𝗚 (Retrieval Augmented Generation) and 𝗖𝗔𝗚 (Cache Augmented Generation). You must understand fundamentals behind this architecture to save costs and reduce your system latency effici…

Don't get fooled, building a 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗴𝗿𝗮𝗱𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) 𝗯𝗮𝘀𝗲𝗱 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺 is a challenging task. Read until the end…

3.0 posts/week
Posts / Week
12
Total Posts Analyzed
MEDIUM
Posting Frequency
275.3%
Avg Engagement Rate
STABLE
Performance Trend
1200
Avg Length (Words)
HIGH
Depth Level
ADVANCED
Expertise Level
0.85/10
Uniqueness Score
YES
Question Usage
0.9%
Response Rate
Writing style breakdown
The author writes with the voice of a seasoned Technical Lead or Architect—authoritative, educational, and highly structured. The core characteristics are professional, informative, and direct, with a distinct 'mentor-to-peer' energy. It is not overly academic; instead, it feels like a high-level briefing designed for busy engineers who need to understand complex systems quickly.
The emotional tone is high-energy and forward-leaning. The author uses a 'from the trenches' perspective to build credibility, often contrasting hype with practical reality. The writing is methodical, moving from definitions to architectural breakdowns and finally to personal insights or calls to engagement.
Heavy use of bolded Unicode characters for emphasis (e.g., 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚).
Frequent use of rhetorical questions to spark engagement (e.g., 'How are you using Vector DBs?').
Structural signposting using emojis (➡️, ✅, ❗️, ❓, ℹ️).
A 'read till the end' hook strategy.
Direct audience engagement, specifically targeting 'AI Engineers.'
The author primarily uses the first-person ('I', 'My thoughts') to share experience and the second-person ('You', 'Your') to address the reader directly. Commands are common but framed as professional advice ('You must know...', 'Before going for full fledged Agents you should...').
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