Miguel Armenta

insightful and true.... so far "The schema *is* the domain. The logic *is* the code. " thus making it difficult to face the AI challenge, or a part of it. thanks Timothy Cook for sharing

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Context engineering shifts LLMs from oracle to analyst—frame the task, curate tokens, and apply reusable patterns (RAG, tool calls, memory) to keep complex systems robust. A must-read for product and Agile teams building trustworthy AI-enabled workflows. Explore Chris Loy's take: https://buff.ly/Ca2GrrO #Product

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Will AlphaEvolve take off in 2026? When Google DeepMind first announced #AlphaEvolve, I was surprised it didn’t capture the same "mainstream" momentum as #AlphaFold or #AlphaGo. While those milestones solved major bottlenecks or complexities that are more relatable, AlphaEvolve is tackling something fundamental: the autonomous evolution of the code and algorithms that runs our world and industries. I’ve been bullish on its potential for a while now [https://lnkd.in/gmCmw_MA], it became much more accessible recently: AlphaEvolve is officially in public preview on Google Cloud. We are witnessing a shift from AI that simply "assists" with code or design of algorithms to agentic systems that “autonomously evolve it”. If you’re looking at this technology for your own roadmap, consider these results: • Infrastructure: Recovered 0.7% of global compute resources. • Speed: Accelerated AI training kernels by 23%. • Discovery: Solved math problems stagnant since 1969. By bringing this to the Cloud, Google Cloud is finally closing the gap between "research breakthrough" and "enterprise reality." I suspect 2026 will be the year this "silent" revolution becomes the new standard for efficiency. Or, perhaps it’s just me enamored by the influence of the patterns of #life and the #humanbrain—the very systems that drove our greatest #AI breakthroughs—are now being mirrored in this new development. Either way, the question remains: Are enterprise systems actually ready to harness this evolution, or will it take another year or two to bridge that gap? Let’s see! #AlphaEvolve #DeepMind #GoogleCloud #AlgorithmOptimization #AI2026 #TechLeadership #BioInspiration Alexander Novikov Matej Balog Pushmeet Kohli Oliver Parker Vladimir Vuskovic Anant Nawalgaria Thomas Kurian

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🚀 Built for Your Most Demanding AI Use Cases If you're scaling Retrieval-Augmented Generation (RAG), recommendation engines, or enterprise-grade semantic search, - performance isolation and predictable low-latency are no longer “nice to have” rather they’re foundational. That’s why Pinecone’s new Dedicated Read Nodes caught my eye. They are purpose-built for environments where every millisecond matters and workloads can’t afford noisy neighbors. 🔧 Why Dedicated Read Nodes? Because modern AI systems demand: • Predictable latency under heavy load • Linear scaling as vectors, traffic, and QPS multiply • Full isolation so one workload never impacts another • Cost predictability at enterprise scale 🔥 Perfect for high-demand scenarios such as: • Billion-vector semantic search with strict latency targets • High-QPS recommender systems (feeds, ads, marketplaces) • Mission-critical AI services with hard SLOs • Large enterprise / multitenant platforms that need strong workload isolation As vector databases become the backbone of enterprise AI, innovations like these are essential for reliability, safety, and scale. If you're architecting for high throughput and real-time intelligence, this is worth a look. 🔗 https://lnkd.in/dWTY7CXx

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How do you observe generative AI (GenAI) systems in production when traditional metrics fall short? As part of the I/built with Elastic series at Elastic{ON} Singapore, Adrian Cole, Principal Engineer at Tetrate, takes the stage to demonstrate how Envoy AI Gateway, powered by Elastic, turns opaque large language model (LLM) traffic into observable systems. Explore AI-focused observability patterns, OpenTelemetry integration, and a live demo connecting GenAI and traditional observability. Join the Observability track and register today. https://gag.gl/wTgP3X

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An internal platform should describe the capability it provides, not only the technology it operates. “We run Kafka” and “we run Postgres” tell consumers about the engine. They do not define what a team can accomplish, which guarantees it may rely on, where responsibility ends, or how the implementation can evolve. A useful capability contract has four parts: 1. Outcome: what can the consumer reliably accomplish? 2. Guarantees: which behavior can the consumer depend on? 3. Boundary: what does the consumer own, and what does the platform own? 4. Evolution: what can change behind the contract without coordinated consumer rewrites? Hiding the engine is not the goal. Consumers still need to see health, limits, failure behavior, and responsibilities. A vague abstraction can be as costly as exposed machinery. The migration test is straightforward: If the engine changes while the promised capability stays the same, which consumer work is unavoidable, and which work exists only because implementation details crossed the boundary? Technology choices matter. Platform leverage comes from a contract that absorbs machinery changes without hiding responsibility or tradeoffs.

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Your GenAI stack doesn’t fail in testing. It fails quietly after launch. When latency creeps up, retrieval breaks, and hallucinations slip through, That’s not an accident. It’s bad architecture. We’ve audited more than 40 enterprise AI systems this year. The same seven fail points keep showing up again and again. From weak retrieval logic to missing audit trails, Every failure maps back to one missing layer: observability. If you’re not watching these signals, You’re not in production. You’re still in demo mode. Swipe through the breakdown. Then ask yourself: Which layer in your GenAI stack is most fragile?

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🚀 Anthropic Claude 4.6 Sonnet is now live on Amazon Bedrock! The Vending-Bench Arena simulation (pictured below) highlights a significant advancement in strategic reasoning. Unlike previous iterations, Sonnet 4.6 demonstrates the ability to prioritize long-term capacity investment over immediate gains, resulting in a dramatic pivot toward profitability in the final stages. For our customers, this means access to a model that doesn’t just execute tasks, but strategically optimizes for complex, long-horizon business outcomes. You can begin building and scaling with Claude 4.6 Sonnet on Amazon Bedrock today: https://lnkd.in/gmE68-9N

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