Paul Kinlan

Core Integration Patterns for Modern Systems 🔹 Peer-to-Peer Enables direct communication between two components without requiring a central coordinator. 🔹 API Gateway Serves as a single entry point for all client requests to backend services. 🔹 Pub-Sub Decouples message producers from consumers through a message broker for flexible event-driven architectures. 🔹 Request-Response The foundational integration pattern where a client sends a request to a server and waits for a response. 🔹 Event Sourcing Stores application state changes as an immutable sequence of events. Provides a complete audit trail. 🔹 ETL Extracts data from multiple sources, transforms it into a structured format, and loads it into a destination database for analysis and reporting. 🔹 Batching Accumulates data over time or until reaching a specified threshold before processing it as a single group. 🔹 Stream Processing Enables continuous ingestion, processing, and analysis of data streams in real-time for immediate insights and actions. 🔹 Orchestration Uses a central coordinator to manage interactions between distributed components or services in a workflow. -- Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): https://bit.ly/496keA7 #systemdesign #coding #interviewtips

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Accidentally discovered a new way to work with GitHub Copilot today: write new tests based off test coverage reports. Copilot (or I) run this command to generate a report of all the uncovered lines in the current diff: pytest --cov --cov-report=xml && \ diff-cover coverage.xml --html-report coverage_report.html && \ open coverage_report.html Then Copilot reviews the report, summarizes the uncovered situations, and proposes new test cases to add, or if the code is truly unreachable, suggests deleting it. Just another reason why you should add diff-cover (or similar tool) to your projects!

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I’ve been tinkering on a side project and finally pushed the first version live: https://lnkd.in/gnqJegP8 It’s an open-source product agent that tries to take raw product ideas and help shape them into something more structured. Still early (bugs and rough edges), but here’s what’s in v1: ✅ PRD agent: Converts raw product ideas into structured PRDs. There is a prompt preprocessor that converts natural language prompt or structured documents into a system prompt that feeds into the PRD agent. ✅ Document Processor Agent: Uses your own PRDs as context to refine the output. It is in-memory doc processing of PRDs. ✅ Market research agent: Conducts automated market research and competitive analysis. ✅ Prototype agent: Generates prototypes or prompt ready to be fed into Vercel / Bolt etc. Suggest using it as a prompt generator to feed into better 3rd party prototyping tools. ✅ Evals agent: Provides evaluation and scoring of the PRD and critiques the LLM generated content. There is provision in the code to support Claude and GPT models. One of the aspects that evals does is to feed into both and evaluate the response or chain one after another and evaluate. ✅ There is basis orchestration with cost metrics to understand token spend. All feedback is welcome. I am focused next on improving the MarketResearchAgent - How can we make market sizing and TAM/SAM analysis more accurate?  EvaluationAgent - What dimensions should we score products on? Currently using market opportunity, technical feasibility, competitive advantage... anything else? Just add your OpenAI/Claude API key and run "npm run demo" (fair warning - this is v1, bugs and road bumps will exist) Looking for feedback from PMs, Engineers, founders, and anyone who's building products. Thanks in advance for any thoughts, critiques, or even “this is useless” feedback—it all helps 🙏 #ProductManagement #AI #OpenSource #Feedback

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The myth of the “AI SRE” is everywhere. Most of it is noise. Engineers are under pressure to move fast and prevent downtime. But when it comes to “AI SREs,” not all approaches are equal. Language models can help, making it easier to summarize observational data and suggest possible explanations based on history, but they will only get you so far without the structured causal knowledge Causely provides. In my latest piece for CIO Dive, I break down how to separate hype from reality in #AI for reliability #engineering: https://lnkd.in/euPpscGp For a deeper, research-backed perspective, our Senior Applied AI Scientist explains in InfoQ why causal reasoning is the missing piece for those chasing the promise of an “AI SRE”: https://lnkd.in/eHZECD8a

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Deepseek continues to prove that we can make LLM inference cheaper and faster. Their latest Sparse Attention method reduces inference API costs by half. Smart technique of identifying and focusing on most relevant parts of the input. https://lnkd.in/gnwKtgqk

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While the core architecture remains, today’s top LLMs (like GPT, Claude, Gemini, and Qwen) have introduced subtle but important changes — and interestingly, many of these choices are converging: ✅ Multi-query attention ✅ SwiGLU activations ✅ Revised LayerNorm placement ✅ New positional encoding strategies ...and more. To stay on top of these trends, I highly recommend two resources that have helped me deeply understand both what's changing and why it matters: 📘 This blog post by Sebastian Raschka is a fantastic summary: The Big LLM Architecture Comparison https://lnkd.in/dJBZFWsG 🎓 Stanford’s recent LLM lectures, which provide great context behind these architectural shifts and trade-offs. These changes aren’t just tweaks — they reflect deeper insights into scaling, efficiency, and alignment.

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