Rui C.

OLTP databases and analytical systems both try to minimize I/O, but they do it through entirely different abstractions. B-trees and secondary indexes serve transactional workloads where selectivity and random access dominate. Open table formats like Iceberg and Delta rely on partitioning, sorting, and metadata-driven pruning to achieve the same goal at analytical scale. In this post I explore what “index” really means in this world, and why we can’t just bolt secondary indexes onto analytical systems. https://lnkd.in/d6wrgD9t

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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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📝 Blogged: "Let's Take a Look at... Lower Java Tail Latencies With ZGC" #Java 25, released earlier this week, is the first Java release with long-term support (LTS) which ships with Generational ZGC. I took this garbage collector for a spin, exploring how it lives up to its promise of sub-millisecond GC pause times. 👉 https://lnkd.in/dYW_9KfU

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Gemini CLI updates for week of 7/21/2025: - Custom Slash Commands: Create your own /<command>s (Abhi Patel): https://lnkd.in/g7pFZP88 - MCP:    - 🧩Prompts: list/prompt s now converted into slash commands (Harold M. and Christine Betts)    - 🔐OAuth: Authenticate via /mcp auth (emeryray2002 and Greg Shikhman)    - Manual Refresh: /mcp refresh (Ramón Medrano Llamas) - Vim Mode: Activate with /vim and use vim keybindings (sijieamoy) - Better Diffs: Files are now colorized when diffed (Miguel Solorio) - Better Workspace Account Login: Resolved a number of issues around workspace account logins - Copy Last Model Output: /copy (devpool007) - Headless Auth:    - CI/CD Token Auth: You can now use cloud access tokens (Gal Z.)    - Login with Google + Vertex Auth: Now supported (Jerop Kipruto) - GeMiNi.mD: Files are now case insensitive (olk) - Gemini CLI Aware Shells: Shell commands now invoked with GEMINI_CLI=1 environment variable (taeold) - Telemetry Output: Redirect output using the --telemetry-outfile (smhendrickson) - Exclude Files from Context: Better support for .geminiignore (psinha40898) - System Settings Override: GEMINI_CONFIG_PATH to specify a custom path for system settings (Christine Betts) - A massive amount of small improvements across the board. Upgrade to latest: v0.1.15 https://lnkd.in/gPhQmGUE #GeminiCLI #Gemini #AI

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Aspire + Coding Agents: 27 authors → 109 commits → 41 PRs in a week. What we learned: - Code reviews are critical. AI slop slips in. - Tests aren’t enough - humans still judge experience. - Everyone can contribute - even PMs are using agents for fixes. - Automate everything you can. - Make every PR shippable - make it as easy as possible to experiment. 💡 Humans are UI threads. Agents are background threads. Protect your UI threads (props to Mitch Denny) The bottleneck isn’t writing code anymore - it’s scaling review, validation, and quality at AI speed. #github #codingagent #aspire

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Can every Kafka topic become an Iceberg table: instantly, zero‑copy, and 100 % open‑source? At Aiven we think so. A native “Iceberg Topic” could erase connector tax, end lock‑in, and make *topic = table* the new default. We’ve just blogged about the roadblocks we see today and the path forward to build together. 🚀 #ApacheKafka #ApacheIceberg #OpenSource #DataStreaming https://lnkd.in/eptGbBTf

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“Using the ScaleOps Platform is like having a whole department of full-time engineers whose only job is to make sure every workload has the exact amount of resources it needs, 24/7.” That’s how Jeff B., Lead Platform Engineer at Maxar Technologies, described ScaleOps. Hearing that from an infrastructure leader at a leading company like Maxar Technologies says everything about the platform we’re building and the strong partnerships we’ve formed with our customers. Maxar Technologies, a trusted NASA partner, powers over 90% of the U.S. government’s geospatial data and supports mission-critical intelligence and defense agencies. In production environments like theirs, performance and reliability matter, and they come first. ScaleOps has helped Maxar Technologies overcome one of the toughest challenges DevOps and Platform teams face today - ensuring every workload performs at its peak, without disruption, without compromise, and at a significantly lower cost. This is what it looks like when one of the most advanced engineering teams in the world chooses the most powerful resource management platform in the ecosystem. We’re proud to partner with Maxar Technologies and even prouder to deliver huge value to our customers at this level. Read more here - https://lnkd.in/dSQvfBVf

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Every computing paradigm has an open source reference application. There was the JPetStore for Java EE. There was the Sock Shop for cloud native computing. And now we have coffeeAGNTCY for multi-agent computing. coffeeAGNTCY is the fastest way to experiment with the agentic open source ecosystem. It's a complete reference app that shows how to build multi-agent software using #AGNTCY, Agent2Agent, MCP components. Think of it as the "Hello World" for the Internet of Agents. Most agent demos are smoke and mirrors. This one is a reference application - not a demo! You get to see the guts of the code. You get to extend it in interesting ways. You get to attach any software component to it to see how it would behave, benchmark it. It actually handles the unglamorous stuff - discovery, cross-framework communication, scaling past two agents, scaling across organizational and vendor boundaries. Start with Corto to see A2A and SLIM in action, then graduate to Lungo for the full multi-agent multi-org orchestration, including MCP endpoints. Read the blog by Luke Tucker and try it yourself: https://lnkd.in/grcVA96j

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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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Cache eviction strategies are crucial in optimizing memory usage and maximizing system performance by efficiently managing cached data. 𝗕𝗲𝗹𝗼𝘄 𝗮𝗿𝗲 𝘀𝗼𝗺𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗖𝗮𝗰𝗵𝗲 𝗘𝘃𝗶𝗰𝘁𝗶𝗼𝗻 𝗣𝗼𝗹𝗶𝗰𝗶𝗲𝘀 : ✔️ First In First Out (FIFO): The cache removes the first accessed block without considering its frequency or previous access count. ✔️ Last In First Out (LIFO): The cache removes the most recently accessed block without considering its frequency or previous access count. ✔️ Least Recently Used (LRU): Removes the least recently used ✔️ Most Recently Used (MRU): Removes the most recently used items. ✔️ Least Frequently Used (LFU): Removes least frequently used items by counting. ✔️ Window TinyLFU (W-TinyLFU): Prioritizes cache entries based on recentness and frequency of access to retain the most relevant data. ✔️ Time To Live (TTL): Cache entries expire based on set dates, ignoring access frequency or recency. #cache #distributedsystems #systemdesign #redis

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This is quality work. So glad to see compiler team digging in and getting a richer picture than “build fast” or “build slow”. I definitely think we need to focus on the perception of rust as having slow builds (and the reality it’s based on), but to do that, we need to be clear on what workflows and use patterns we are targeting. The fix for fast CI turnaround is not the same as the fix for fast iteration on a broken test, and iterating on a test is not the same as debugging a type error. https://lnkd.in/eSKkNgXb

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