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 high-level architect of agentic systems, bridging the gap between theoretical AI research and production-grade engineering. His content strategy centers on the rigorous technical deconstruction of AI infrastructure, moving beyond simple prompts to focus on the "plumbing" of intelligence, such as context engineering, memory topologies, and MLOps deployment patterns. He is notable for his "from the trenches" perspective, often critiquing hyped technologies like MCP against native implementations based on real-world scalability and flexibility. His work represents a sophisticated intersection of data engineering and physical AI, where he applies classical software reliability principles like tracing and schema contracts to the non-deterministic world of autonomous agents and robotics.
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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…
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…
You must know these 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗦𝘆𝘀𝘁𝗲𝗺 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 as an 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿. If you are building Agentic Systems in an Enterprise setting you will soon discover that…
5.4 posts/week
Posts / Week
1.4 days
Days Between Posts
7
Total Posts Analyzed
HIGH
Posting Frequency
259.7%
Avg Engagement Rate
STABLE
Performance Trend
1200
Avg Length (Words)
HIGH
Depth Level
ADVANCED
Expertise Level
0.85/10
Uniqueness Score
YES
Question Usage
0.8%
Response Rate
Writing style breakdown
The author writes with the voice of a Senior AI Architect or Lead Engineer—someone who is 'in the trenches' but capable of high-level strategic oversight. The core characteristics are professional, methodical, and authoritative yet accessible. It is a 'teaching' style that aims to simplify complex technical architectures (MCP, RAG, CAG, MLOps) into digestible frameworks.
Tone and Energy: The energy is steady and instructional. It is not 'hype-driven' or frantic; instead, it feels like a whiteboard session with an expert. The pace is controlled, moving logically from definitions to implementation steps to final thoughts.
Signature Traits: The author heavily uses 'categorization' (grouping concepts into numbered lists or types). Rhetorical questions are used almost exclusively at the end of posts to drive engagement ('Any war stories?', 'What types of deployments are you mostly working on?'). There is a frequent use of the 'downward pointing backhand index' emoji (👇) to signal that a detailed breakdown follows a bold opening claim.
Addressing the Reader: The author uses a mix of first-person ('I', 'My thoughts') to establish authority and second-person ('You', 'Your tool belt') to create a mentorship dynamic. Direct commands are common in instructional sections ('Use available context wisely', 'Read until the end'), while softer suggestions appear in the 'My thoughts' or 'Considerations' sections.
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