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MCP Vulnerabilities Every Developer Should Know

MCP’s blowing up across platforms—but the security? Still sketchy. Think tool description injection. Botched OAuth. Open doors to supply chain attacks. The new MCP 2025-06-18 spec tries to clean house (no token passthrough, mandatory user consent), but most real-world setups either drag their feet .. read more  

MCP Vulnerabilities Every Developer Should Know
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Building AI Products In The Probabilistic Era

Modern AI broke the rulebook. By spitting outstochastic outputs from unbounded inputs, it flipped software dev from a game of precision to one of probability. Old tools—funnels, SLO dashboards, crisp A/B tests—don’t quite fit anymore. They were built for systems that behaved. Today’s AI stacks mov.. read more  

Building AI Products In The Probabilistic Era
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Tiny Agents in Python: a MCP-powered agent in ~70 lines of code

A new demo walks through buildingTiny Agents in Python—just ~70 lines using theModel Context Protocol (MCP). No boilerplate. Just clean LLM-to-tool hookups with standardized agent configs. Agents plug into multiple MCP servers out of the box—from local filesystems to Playwright browsers—and handle .. read more  

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Building an AI-Powered E-commerce Chat Assistant with MongoDB

freeCodeCamp dropped a new course that walks devs through building an AI-powered shopping agent from scratch. It ties togetherLangGraphfor orchestration,Geminifor reasoning, andMongoDB Atlasas the vector memory layer. The build covers aNode.js backend, aReact frontend, and wires inmulti-step agent .. read more  

Building an AI-Powered E-commerce Chat Assistant with MongoDB
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Myth Or Reality: Will AI Replace Computer Programmers?

Generative AI tools likeGPT-4oandClaude Sonnetnow handle the grunt work—fixing bugs, cranking out code, writing docs—with scary accuracy. Amazon and Anthropic are already hinting at hiring fewer engineers. But the jobs aren’t vanishing; they’re mutating... read more  

Myth Or Reality: Will AI Replace Computer Programmers?
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Evolving our real-time timeseries storage again: Built in Rust for performance at scale

Datadog just dropped its 6th-gen real-time timeseries engine:RTDB. It's built inRust, sharded per core, and backed by LSM trees that don’t blink under pressure. The secret sauce? A custom storage engine calledMonocle—optimized for high-cardinality chaos and bursty workloads. It’s pulling60x faster .. read more  

Evolving our real-time timeseries storage again: Built in Rust for performance at scale
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Migrating Airbnb’s JVM Monorepo to Bazel

Airbnb yanked tens of millions of lines of Java, Kotlin, and Scala out of Gradle and dropped them intoBazel. Why? Faster builds, reproducible results, and smoother dev workflows. They didn’t just swap tools—they rewired the whole thing. A customautomated build file generatornow slices up targets fi.. read more  

Migrating Airbnb’s JVM Monorepo to Bazel
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Best Linux distro for developers of 2025

TechRadar rounds up the best Linux distros for devs.Manjarodelivers Arch power without the pain.DebianandUbuntu LTShold steady for those who put uptime over edge.Fedorakeeps the new stuff flowing. Solusrolls with a tight curation hand—smooth updates, no chaos.Mocaccinoaims at Gentoo lovers who want.. read more  

Best Linux distro for developers of 2025
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Worktrees: Git's best kept secret (and why you should use them)

Git worktrees have been around since 2015, but few devs use them like they could. They let you work on multiple branches at once—each in its own directory—without the usual stash-switch-stash-repeat dance. The real power move? Pair them with abare repo. That gives you a clean, central base where ea.. read more  

Worktrees: Git's best kept secret (and why you should use them)
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Hunting Living Secrets: Secret Validity Checks Arrive in GitHub Advanced Security for Azure DevOps

GitHub Advanced Security for Azure DevOps just got sharper: it now checks if leaked secrets are actuallyvalid. Secrets are flagged asActiveorUnknownby pinging providers in real time. No setup needed. It auto-kicks in for supported secret types. Why care?Because not every secret leak is an emergenc.. read more  

Hunting Living Secrets: Secret Validity Checks Arrive in GitHub Advanced Security for Azure DevOps
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).