Craig Lee

For understanding the virtual thread state transitions... Specifically, thread pinning on Java 21 vs. Java 24 and when the thread waits on IO how the platform thread views the state compared against the virtual thread enabling interruption

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rqlite -- and the way I develop it -- is fundamentally changing. LLMs are in the loop -- from ideation to Pull Request. https://lnkd.in/ejwXGddR

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Rolled out Jan 1. California residents can now continuously opt-out of 500+ data brokers for free. https://lnkd.in/ephsgD-K Happy new year from the California Delete Act

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With AI, according to all of the marketing materials from all of the companies, including Meta, SWE not required anymore and you can just scale your engineering up by using LLM models alone... Yet: - Meta cannot keep Desktop app for their own Facebook Messenger alive - https://lnkd.in/ekKTbEfG keeps growing and Google cannot continue to develop already released products - Windsurf cannot keep up and to have their IJ plugin for Remote Development in sync with their non-RD plugin https://lnkd.in/eeMjrZ_6 - Claude IJ plugin is also far from perfect What has happened to "You need to practice what you preach"?

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I wrote an article on how we translate user-generated content at scale at Mercari. It summarizes two years of work with various models, LLMs but not only, and how cost evolved, reducing it 100x over the period. I also go over non-AI features and user experience. https://lnkd.in/gbpQAQmX

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Developing a large ecosystem with both open- and closed-source components comes with some interesting challenges. Back when we were bootstrapping CKEditor, my purist mindset pushed us to develop each of our 30+ packages in separate repositories. The goal was to sort of "force" higher modularity and more responsible architecture for every component. As you can probably guess 😅, it did not scale. Later, we consolidated most of the packages into two monorepos – one open source, one closed source – while keeping a few granular packages separate where it still made sense. This helped a lot, but it didn’t fully solve the problem. We were still working across multiple repositories and kept running into issues like dependency coordination and lockfiles. Recently, we took another step forward. We migrated to pnpm and redesigned our overall approach. In this post, Filip Sobol explains the new setup implemented by the CKEditor 5 Platform team and what we learned along the way: https://lnkd.in/eHS28qxe

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Google Changed RAG with NEW Gemini File Search Tool (RAG Made Simple) 5 Days Ago, Google Took a Big Step Forward in the RAG Ecosystem Google has launched the File Search Tool, a fully managed Retrieval-Augmented Generation (RAG) system built directly into the Gemini API. This new capability abstracts away the complexity of retrieval pipelines, offering a simple, scalable, and developer-friendly way to ground Gemini models with your own data. Key Highlights: 1. Fully managed RAG system: No need to build or maintain custom retrievers or vector stores. 2. Integrated & scalable: Works seamlessly within the Gemini API. 3. Improved grounding: Delivers responses that are more accurate, relevant, and verifiable. 4. Cost-efficient billing: Embedding generation at query time is free; you only pay for initial file indexing at $0.15 per 1M tokens. This launch marks a major leap toward making enterprise-grade RAG systems accessible to all developers, empowering teams to build intelligent, data-grounded applications faster with precision and transparency. #GenAI #GoogleAI #Gemini #RAG #AIInnovation #LLMs #AIEngineering #GeminiFileSearchTool

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