Updates and recent posts about BigQuery..

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

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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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‘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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Massive study detects AI fingerprints in millions of scientific papers

Study finds 13.5% of 2024 PubMed papers bear LLM fingerprints, showcasing a shift to jazzy "stylistic" verbs over stodgy nouns.Upending stuffy academic norms!.. read more

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From Big Data to Heavy Data: Rethinking the AI Stack

Savvy teams morph dense data into AI’s favorite meal: bite-sized chunks primed for action, indexed and ready to go. This trick spares everyone from slogging through the same info over and over. AI craves structured, context-filled data to keep it grounded and hallucination-free. Without structured p.. read more

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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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Building “Auto-Analyst” — A data analytics AI agentic system

DSPyfuels a modular AI machine, drivingagent chainsto weave tidy analysis scripts. But it’s not all sunshine and roses—hallucination errors like to throw reliability under the bus... read more

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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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The Portable Memory Wallet Fallacy: 4 Fundamental Problems

Portable AI memory pods hit a brick wall—vendors cling to data control, users resist micromanagement, and technical snarls persist.So, steer regulation towards automating privacy and clarifying transparency. Make AI interaction sync with how people actually live... 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

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).