Join us

ContentUpdates and recent posts about BigQuery..
Link
@kaptain shared a link, 4 months, 1 week ago
FAUN.dev()

How to Add MCP Servers to ChatGPT

ChatGPT leveled up with fullModel Context Protocol (MCP)support. It can now run real developer tasks, scraping, writing to a database, even making GitHub commits, through secure, containerized tools in Docker. TheDocker MCP Toolkitconnects ChatGPT’s language smarts to production-safe tools like Stri.. read more  

How to Add MCP Servers to ChatGPT
Story
@jamesmiller shared a post, 4 months, 2 weeks ago

Automating Penetration Testing in CI/CD: A Practical Guide for Developers

All in One SEO Pack SEMrush SquirrelMail Yoast SEO SchemaHero

Automating pentesting in CI/CD helps developers catch vulnerabilities early, reduce MTTR, and keep releases secure without slowing the pipeline. This guide breaks down why automation matters, the tools developers rely on, common mistakes to avoid, and practical steps to build a reliable pentesting workflow inside modern CI/CD pipelines.

Automating Penetration Testing in CI/CD
Story
@elenamia shared a post, 4 months, 2 weeks ago
Technical Consultant, Damco Solutions

Google Cloud Services: A Comprehensive Overview for Modern Businesses

Read this blog to learn about Google Cloud Platform services and its key features, pricing, and use cases across industries.

6086042_22246
Link
@kala shared a link, 4 months, 2 weeks ago
FAUN.dev()

How to Create an Effective Prompt for Nano Banana Pro

The author details how to effectively prompt Google’s Nano Banana Pro, a visual reasoning model, emphasizing that success relies on structured design documents rather than vague requests. The method prioritizes four key steps: defining the Work Surface (e.g., dashboard or comic), specifying the prec.. read more  

Link
@kala shared a link, 4 months, 2 weeks ago
FAUN.dev()

So you wanna build a local RAG?

Skald spun up a full local RAG stack, withpgvector,Sentence Transformers,Docling, andllama.cpp, in under 10 minutes. The thing hums on English point queries. Benchmarks show open-source models and rerankers can go toe-to-toe with SaaS tools in most tasks. They stumble, though, on multilingual prompt.. read more  

Link
@kala shared a link, 4 months, 2 weeks ago
FAUN.dev()

Learning Collatz - The Mother of all Rabbit Holes

Researchers trained small transformer models to predict the "long Collatz step," an arithmetic rule for the infamous unsolved Collatz conjecture, achieving surprisingly high accuracy up to 99.8%. The models did not learn the universal algorithm, but instead showed quantized learning, mastering speci.. read more  

Link
@kala shared a link, 4 months, 2 weeks ago
FAUN.dev()

200k Tokens Is Plenty

Amp’s team isn’t chasing token limits. Even with ~200k available via Opus 4.5, they stick toshort, modular threads, around 80k tokens each. Why? Smaller threads are cheaper, more stable, and just work better. Instead of stuffing everything into a single mega-context, they slice big tasks into focuse.. read more  

200k Tokens Is Plenty
Link
@kala shared a link, 4 months, 2 weeks ago
FAUN.dev()

Google tests new Gemini 3 models on LM Arena

Google’s been quietly field-testing two shadow models,Fierce FalconandGhost Falcon, on LM Arena. Early signs? They're probably warm-ups for the next Gemini 3 Flash or Pro drop. Classic Google move: float a checkpoint, stir up curiosity, then go GA... read more  

Google tests new Gemini 3 models on LM Arena
Link
@kala shared a link, 4 months, 2 weeks ago
FAUN.dev()

A trillion dollars is a terrible thing to waste

OpenAI co-founder Ilya Sutskever just said the quiet part out loud: scaling laws are breaking down. Bigger models aren’t getting better at thinking, they’re getting worse at generalizing and reasoning. Now he’s eyeingneurosymbolic AIandinnate inductive constraints. Yep, the “just make it huge” era m.. read more  

A trillion dollars is a terrible thing to waste
Link
@kala shared a link, 4 months, 2 weeks ago
FAUN.dev()

Practical LLM Security Advice from the NVIDIA AI Red Team

NVIDIA’s AI Red Team nailed three security sinkholes in LLMs:reckless use ofexec/eval,RAG pipelines that grab too much data, andmarkdown that doesn't get cleaned. These cracks open doors to remote code execution, sneaky prompt injection, and link-based data leaks. The fix-it trend:App security’s lea.. 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).