Updates and recent posts about BigQuery..

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@faun shared a link, 11 months, 2 weeks ago

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MCP — The Missing Link Between AI Models and Your Applications

Model Context Protocol (MCP)tackles the "MxN problem" in AI by creating a universal handshake for tool interactions. It simplifies howLLMstap into external resources. MCP leans onJSON-RPC 2.0for streamlined dialogues, building modular, maintainable, and secure ecosystems that boast reusable and inte.. read more

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@faun shared a link, 11 months, 2 weeks ago

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LLM Evaluation Metrics: The Ultimate LLM Evaluation Guide - Confident AI

Dump BLEU and ROUGE. Let LLM-as-a-judge tools like G-Eval propel you to pinpoint accuracy.The old scorers? They whiff on meaning, like a cat batting at a laser dot.DeepEval? It wrangles bleeding-edge metrics with five lines of neat code.Want a personal touch? G-Eval's got your back. DAG keeps benchm.. read more

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@faun shared a link, 11 months, 2 weeks ago

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‘Shit in, shit out’: AI is coming for agriculture, but farmers aren’t convinced

Aussie farmers want "more automation, fewer bells and whistles"—technology should work like a tractor, not act like an app:straightforward, adaptable, and rock-solid... read more

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Google Cloud donates A2A to Linux Foundation- Google Developers Blog

IntroducingAgent2Agentand brace yourself for the heavyweights—AWS, Cisco, Google, and a few more, are in on it. Their mission? Crafting the universal lingo for AI agents. It's called theA2A protocol. Finally, they're smashing the silos holding AI back... read more

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Meta Hires OpenAI Researchers to Boost AI Capabilities

Metacranks up its AI antics. They've snagged former OpenAI whiz kids, snatched 49% ofScale AI, and roped in enough nuclear energy to keep their data hubs humming all night long... read more

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@faun shared a link, 11 months, 2 weeks ago

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A non-anthropomorphized view of LLMs

CallingLLMssentient or ethical? That's a stretch. Behind the curtain, they're just fancy algorithms dressed up as text wizards. Humans? They're a whole mess of complexity... read more

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@faun shared a link, 11 months, 2 weeks ago

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Building tiny AI tools for developer productivity

Tiny AI scripts won't make you the next tech billionaire, but they're unbeatable for rescuing hours from the drudgery of repetitive tasks. Whether it's wrangling those dreadedGitHub rollupsor automating the minutiae, these little miracles grant engineers the luxury to actually think... read more

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@faun shared a link, 11 months, 2 weeks ago

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Document Search with NLP: What Actually Works (and Why)

NLP document search trounces old-school keyword hunting. It taps into scalable*vector databasesandsemantic vectorsto grasp meaning, not just parrot words.* Pictureword vector arithmetic: "King - Man + Woman = Queen." It's magic. Searches become lightning-fast and drenched in context... read more

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@faun shared a link, 11 months, 2 weeks ago

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Automatically Evaluating AI Coding Assistants with Each Git Commit · TensorZero

TensorZerotransforms developer lives by nabbing feedback fromCursor'sLLM inferences. It dives into the details withtree edit distance (TED)to dissect code. Over in a different corner,Claude 3.7 SonnetschoolsGPT-4.1when it comes to personalized coding. Who knew? Not all AI flexes equally... read more

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@faun shared a link, 11 months, 2 weeks ago

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Linux 6.16 Performance Regression Tracked Down In New Futex Code

Linux 6.16takes a36% performance nosediveon AMD EPYC 9005 all thanks toFUTEXPRIVATEHASH. The quick fix? Yank it. Engineers scramble for a smarter solution... read more

BigQuery is a cloud-native, serverless analytics platform designed to store, query, and analyze massive volumes of structured and semi-structured data using standard SQL. It separates storage from compute, automatically scales resources, and eliminates the need for infrastructure management, indexing, or capacity planning.

BigQuery is optimized for analytical workloads such as business intelligence, log analysis, data science, and machine learning. It supports real-time data ingestion via streaming, batch loading from cloud storage, and federated queries across external data sources like Cloud Storage, Bigtable, and Google Drive.

Query execution is distributed and highly parallel, enabling interactive performance even on petabyte-scale datasets. The platform integrates deeply with the Google Cloud ecosystem, including Looker for BI, Vertex AI for ML workflows, Dataflow for streaming pipelines, and BigQuery ML, which allows users to train and run machine learning models directly using SQL.

Built-in security features include fine-grained IAM controls, column- and row-level security, encryption by default, and audit logging. BigQuery follows a consumption-based pricing model, charging for storage and queries (on-demand or reserved capacity).