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@faun shared a link, 11 months ago
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Tzafon builds the next generation of agentic machine intelligence with Google Cloud infrastructure

Tzafondives headfirst intoGoogle Cloud'sAI-ready playground, juicing up multi-agent systems withNVIDIA GPUsand the nimbleness ofKubernetes... read more  

Tzafon builds the next generation of agentic machine intelligence with Google Cloud infrastructure
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Open-source skills can save your career when AI comes knocking

94%of companies expect AI to skyrocket their value. But here’s the twist:70%of AI transformations hinge more on humans than machines. Upskilling speeds things up by62%over hiring and improves retention by a whopping91%... read more  

Open-source skills can save your career when AI comes knocking
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AWS goes full speed ahead on the AI agent train

AWS Bedrock AgentCorepromises AI agent deployment at ungodly scales. But hang onto your hats: by 2027, up to 40% of these endeavors might implode without a squeak of success... read more  

AWS goes full speed ahead on the AI agent train
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LangSmith and LangGraph Platform are now available in AWS Marketplace

LangSmith and LangGraph Platform just hit AWS Marketplace, ready to turbocharge AI deployment and fine-tune your workflow right in your AWS VPC... read more  

LangSmith and LangGraph Platform are now available in AWS Marketplace
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Building Self-Evolving Knowledge Graphs Using Agentic Systems

Graph databasesturn chaos into order usingnodes, edges, and properties. They race through data withindex-free traversal, unveiling complex relationships faster than you can say "data overload." Toss in someAI agents, and watch these databases become brainy creatures that evolve on their own, explori.. read more  

Building Self-Evolving Knowledge Graphs Using Agentic Systems
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Linux Foundation Report Finds Organizations Embrace Upskilling and Open Source to Meet AI-driven Job Demands

AI is set to overhaul 94% of businesses, yet fewer than half possess the crucial AI chops. They scramble to bridge this gap withupskillingandopen-sourcecollaboration. Companies, always finding a loophole, claim upskilling outpaces hiring by 62%. Meanwhile, open source impressively bumps up retention.. read more  

Linux Foundation Report Finds Organizations Embrace Upskilling and Open Source to Meet AI-driven Job Demands
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Stop Saying RAG Is Dead

RAG isn’t dead — lazy RAG is.Compressing whole docs into single vectors fails; smarter retrieval needs diversity, reasoning, and richer representations. The future: evaluate what matters, retrieve with intent, and route across specialized, info-preserving indices... read more  

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Atlassian research: AI adoption is rising, but friction persists

AI tools now save 68% of developers over 10 hours a week.Impressive, right? Yet for 50% of them, chaos and bureaucratic nonsense eat up more than 10 precious hours. The culprit? A staggering 63% empathy gap between the developers in the trenches and leaders who overlook big pain points. The result: .. read more  

Atlassian research: AI adoption is rising, but friction persists
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Meta reveals plan for several multi-GW datacenter clusters

Zuck's gearing up to unleash "Prometheus" by 2026—an AI beast sprawling across 80% of Manhattan's width and revving up to 5GW.Meta's going all-in with hundreds of billions on superintelligence. But remember, their earlier VR/AI forays? Not exactly setting the user world or profit charts on fire... read more  

Meta reveals plan for several multi-GW datacenter clusters
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Introducing FlexOlmo: a new paradigm for language model training and data collaboration

FlexOlmoempowers data owners to train models on their own turf, syncing up later to build a powerhouse shared model. Data stays secret, yet the model still crushes it, rivaling its all-data counterpart. And with differential privacy, it keeps snoops at bay, boasting a mere0.7%data extraction rate... read more  

Introducing FlexOlmo: a new paradigm for language model training and data collaboration
Grafana Tempo is a distributed tracing backend built for massive scale and low operational overhead. Unlike traditional tracing systems that depend on complex databases, Tempo uses object storage—such as S3, GCS, or Azure Blob Storage—to store trace data, making it highly cost-effective and resilient. Tempo is part of the Grafana observability stack and integrates natively with Grafana, Prometheus, and Loki, enabling unified visualization and correlation across metrics, logs, and traces.

Technically, Tempo supports ingestion from major tracing protocols including Jaeger, Zipkin, OpenCensus, and OpenTelemetry, ensuring easy interoperability. It features TraceQL, a domain-specific query language for traces inspired by PromQL and LogQL, allowing developers to perform targeted searches and complex trace-based analytics. The newer TraceQL Metrics capability even lets users derive metrics directly from trace data, bridging the gap between tracing and performance analysis.

Tempo’s Traces Drilldown UI further enhances usability by providing intuitive, queryless analysis of latency, errors, and performance bottlenecks. Combined with the tempo-cli and tempo-vulture tools, it delivers a full suite for trace collection, verification, and debugging.

Built in Go and following OpenTelemetry standards, Grafana Tempo is ideal for organizations seeking scalable, vendor-neutral distributed tracing to power observability at cloud scale.