# analysis Published articles for analysis. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Learn How to Deploy, Secure, and Automate Full-Stack Web Apps DevFeed: [Learn How to Deploy, Secure, and Automate Full-Stack Web Apps]() Original publisher: [Read original article]() Author: Beau Carnes Published: 2026-09-16T15:28:18Z Content type: tutorial Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [web applications](), [Deployment](), [CI/CD](), [FastAPI](), [GitHub Actions](), [Vue.js](), [nginx](), [firewalls](), [Python](), [Cloudflare](), [SSL](), [Ubuntu]() Tags: [analysis](), [authentication](), [ci-cd](), [deploy](), [fastapi](), [full-stack](), [github-actions](), [how-to](), [web](), [web-apps](), [youtube]() ### AI overview This article introduces an 11-hour freeCodeCamp course on deploying, securing, and automating a Vue.js and FastAPI web application. It covers Ubuntu server setup, SSH hardening, firewalls, application runtimes, Meilisearch, DNS, SSL, Nginx, Cloudflare, GitHub Actions, security testing, and observability. ### Source excerpt Taking a web application from local development to a live, secure production environment can be daunting if you've never looked under the hood. We just published a comprehensive course on the freeCode ## Using static analysis in Python, JavaScript and more to make your system safer DevFeed: [Using static analysis in Python, JavaScript and more to make your system safer]() Original publisher: [Read original article]() Author: Khan Academy Published: 2018-07-26T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [JavaScript](), [Code quality](), [Python](), [Code review](), [Web Development](), [React]() Tags: [analysis](), [best-practices](), [code](), [code-quality](), [engineering](), [eslint](), [formatting](), [frontend](), [infrastructure](), [javascript](), [lint](), [news](), [python]() ### AI overview This article describes how Khan Academy uses custom linters and static analysis across JavaScript, Python, and Kotlin to prevent known bad patterns, keep duplicated logic or shared interfaces synchronized, and enforce frontend migration practices. ### Source excerpt By Kevin Dangoor "Linting" source code to look for errors is nothing new (the original "lint" tool turned ... Read more ## Improving Test Coverage in the Interview Coach Application with GitHub Copilot DevFeed: [Improving Test Coverage in the Interview Coach Application with GitHub Copilot]() Original publisher: [Read original article]() Author: Aaron Powell Published: 2026-09-15T16:00:21Z Content type: tutorial Language: en Sources: [Visual Studio Blog]() Topics: [test-coverage](), [Test coverage](), [GitHub Copilot](), [App](), [Visual Studio](), [Code]() Tags: [agent](), [analysis](), [code-coverage](), [copilot](), [github-copilot](), [productivity](), [projects](), [run](), [test](), [test-coverage](), [testing](), [tests](), [unit-tests](), [visual-studio]() ### AI overview This tutorial explains how to improve test coverage in the Interview Coach application by establishing a baseline, using GitHub Copilot's Test Agent to generate unit tests, and analyzing coverage across the solution. The article reports coverage increasing from 37% to 81%. ### Source excerpt Today I want to look at how we can improve on the test coverage that we have in the Interview Coach application. This application has some unit tests in it already, but I'm sure there is room for improvement. Defining our baseline Before we start writing tests, it's a good idea to have a baseline [...] The post Today I will... improve test coverage appeared first on Visual Studio Blog. ## Testing Trust in Prediction Markets | Part 2 DevFeed: [Testing Trust in Prediction Markets | Part 2]() Original publisher: [Read original article]() Author: David Phillips Published: 2026-09-10T17:44:43Z Content type: opinion Language: en Sources: [Sift Science]() Topics: [integrity](), [data]() Tags: [analysis](), [comparison](), [data-insights](), [digital-trust](), [fraud](), [integrity](), [prediction-markets](), [trust]() ### AI overview Part 2 examines whether prediction markets can reliably produce trustworthy information. It compares Kalshi's centralized contract resolution with Polymarket's decentralized, oracle-based model and discusses risks involving contract interpretation, voting power, concentrated profits, and market integrity. ### Source excerpt Missed Part 1? Read it here. Prediction markets make a bold claim that they are engines of truth discovery. Proponents argue that by aggregating collective judgment, prediction markets generate forecasts that are far more valuable than traditional gambling or sports betting. While that claim has merit, a prediction market's output is not merely a well [...] The post Testing Trust in Prediction Markets | Part 2 appeared first on Sift. ## Manage Cursor costs with Datadog Cloud Cost Management DevFeed: [Manage Cursor costs with Datadog Cloud Cost Management]() Original publisher: [Read original article]() Author: Dom Nguyen; Doug Gunter Published: 2026-09-15T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [cloud cost management](), [cursor](), [finops](), [ai-coding]() Tags: [ai](), [ai-coding](), [analysis](), [cloud-cost-management](), [configuration](), [datadog](), [features](), [filter](), [finops](), [monitor](), [monitors](), [product](), [software]() ### AI overview This tutorial explains how Datadog Cloud Cost Management helps teams analyze Cursor spending by user, model, usage mode, and billing group. It covers identifying cost drivers, detecting unexpected changes, correlating spend with usage, and using monitors, budgets, and dashboards to manage AI coding costs alongside cloud and SaaS spending. ### Source excerpt Analyze Cursor spend by user and model, catch unexpected cost changes, and manage AI coding costs alongside your cloud and SaaS spend. ## Abnormal AI: Amazon Bedrock AgentCore for agentic email security at scale DevFeed: [Abnormal AI: Amazon Bedrock AgentCore for agentic email security at scale]() Original publisher: [Read original article]() Author: Aswin Vasudevan Published: 2026-09-14T21:22:45Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock AgentCore](), [threat detection](), [Amazon Bedrock](), [Security](), [Serverless](), [API](), [Amazon Web Services](), [AWS CloudTrail](), [Amazon CloudWatch](), [Amazon S3]() Tags: [advanced-300](), [agentic](), [agents](), [amazon-bedrock-agentcore](), [amazon-cloudwatch](), [amazon-s3](), [analysis](), [api](), [aws](), [aws-cloudtrail](), [code](), [customer-solutions](), [data](), [data-processing](), [security](), [serverless](), [threat-detection]() ### AI overview Abnormal AI uses Amazon Bedrock AgentCore Code Interpreter as an ephemeral, serverless compute scratch pad for real-time inline email threat detection. The article describes its sandbox isolation, networking and file-handling options, preloaded Python capabilities, observability integrations, and use at billion-message scale. ### Source excerpt Learn how Abnormal AI deployed Amazon Bedrock AgentCore Code Interpreter as an ephemeral compute scratch pad for the agents behind its real-time email threat detection at billion-message scale, plus the sandbox design decisions and practical lessons for builders deploying Code Interpreter in production. ## Unmasking Cloud Identities: From Behavioral Clustering to Automated Detection DevFeed: [Unmasking Cloud Identities: From Behavioral Clustering to Automated Detection]() Original publisher: [Read original article]() Author: Osher Jacob Published: 2026-09-14T10:00:01Z Content type: article Language: en Sources: [Unit 42]() Topics: [AWS CloudTrail](), [Cloud](), [threat detection](), [Threat Research](), [SIEM, Security, Observability](), [Machine learning](), [Algorithms](), [SQL](), [IAM](), [identity and access management](), [Amazon Web Services]() Tags: [algorithms](), [amazon-web-services-aws](), [analysis](), [aws-cloudtrail](), [cloud](), [cloud-cybersecurity-research](), [cloud-detection](), [devops](), [iam](), [identity-and-access-management](), [logs](), [machine-learning](), [post](), [sql](), [threat-detection](), [threat-research]() ### AI overview This article presents a behavioral clustering model for mapping cloud identities to functional roles using activity patterns from audit logs. It applies unsupervised machine learning with UMAP and HDBSCAN to data from more than 40,000 identities across 125 cloud environments, and shows how the resulting map can support automated threat detection. The article also explains how lightweight heuristics extracted from the map can classify identities at scale using standard SQL, reducing the need for continuous resource-intensive machine learning pipelines. ### Source excerpt We designed a behavioral clustering model to map cloud identity roles from audit logs, enabling continuous threat detection using standard SQL queries. The post Unmasking Cloud Identities: From Behavioral Clustering to Automated Detection appeared first on Unit 42. ## September 2026 Patch Tuesday: Two Exploited Zero-Days and 113 Critical Vulnerabilities Among 972 CVEs DevFeed: [September 2026 Patch Tuesday: Two Exploited Zero-Days and 113 Critical Vulnerabilities Among 972 CVEs]() Original publisher: [Read original article]() Author: Falcon Exposure Management Team Published: 2026-09-12T11:17:51.295154Z Content type: news Language: en Sources: [Blog]() Topics: [Vulnerabilities]() Tags: [ai](), [analysis](), [defender](), [exposure-management](), [microsoft](), [patch-tuesday](), [security](), [september-2026](), [updates](), [vulnerabilities]() ### AI overview Microsoft's September 2026 Patch Tuesday addresses 972 vulnerabilities, including two exploited zero-days and 113 critical issues. The article also notes a disclosed proof-of-concept zero-day exploit against Microsoft Defender. ### Source excerpt Microsoft has released security updates for 972 vulnerabilities, including two exploited zero-days and 113 critical, in its September 2026 Patch Tuesday rollout. ## Decoding cosmic signals with deep learning and Keras DevFeed: [Decoding cosmic signals with deep learning and Keras]() Original publisher: [Read original article]() Author: Yufeng Guo; Jonas Glombitza, PhD Published: 2026-09-12T11:04:33.891311Z Content type: article Language: en Sources: [Google Developers Blog]() Topics: [Deep learning](), [Keras](), [data]() Tags: [analysis](), [blog-post](), [data](), [deep-learning](), [keras](), [particle-physics](), [physics](), [research]() ### AI overview The article explains how deep learning and Keras can help analyze the enormous, complex datasets produced by astroparticle-physics observatories. These methods may improve instrument sensitivity, reveal hidden patterns, and identify anomalies in signals from cosmic messengers such as photons, neutrinos, and cosmic rays. ### Source excerpt Astroparticle physics sits at the exciting intersection of astrophysics and particle physics and stu... ## Analyze your experiments in ChatGPT with the Datadog Experiments plugin DevFeed: [Analyze your experiments in ChatGPT with the Datadog Experiments plugin]() Original publisher: [Read original article]() Author: Jonathan Fulton; Amy Zhou; Uday Tennety Published: 2026-09-11T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [experiments](), [data](), [Latency]() Tags: [ai](), [analysis](), [chatgpt](), [experimentation](), [experiments](), [openai]() ### AI overview Datadog introduces a ChatGPT Work plugin that lets teams query current experiment results in plain language while retaining Datadog's statistical methods, guardrails, and connected context. ### Source excerpt Learn how the Datadog Experiments OpenAI Data plugin lets your team read, question, and act on experiment results directly in ChatGPT. ## GuardBreaker: Derailing AI-assisted malware analysis with a code comment DevFeed: [GuardBreaker: Derailing AI-assisted malware analysis with a code comment]() Original publisher: [Read original article]() Author: Tomáš Foltýn Published: 2026-09-10T09:00:00Z Content type: article Language: en Sources: [WeLiveSecurity]() Topics: [Machine Learning, Security Attacks](), [Malware](), [Large Language Model](), [Threat Research](), [Code]() Tags: [ai](), [analysis](), [attacks](), [business-security](), [llm](), [malware](), [security]() ### AI overview The article describes GuardBreaker, a prompt-injection technique that hides a safety-triggering request in a VBScript comment to disrupt an LLM-powered malware code scanner. The comment does not affect runtime behavior, but may cause the model to stop analysis before reaching malicious code. ### Source excerpt LLM-based code scanners won't help attackers build a nuclear weapon, but that refusal could work in their favor ## How GPT-5.6 Sol helps run quantum computing experiments DevFeed: [How GPT-5.6 Sol helps run quantum computing experiments]() Original publisher: [Read original article]() Published: 2026-09-08T17:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI research agents]() Tags: [agents](), [ai](), [ai-agents](), [analysis](), [applied-ai](), [codex](), [experiments](), [gpt](), [quantum](), [quantum-computing]() ### AI overview An MIT researcher connected GPT-5.6 Sol through Codex to laboratory software for superconducting-qubit experiments. The system ran routine measurements, analyzed results, and selected next steps, reducing the need for constant supervision during calibration workflows. ### Source excerpt See how an MIT researcher uses GPT-5.6 Sol with Codex to autonomously run quantum computing experiments, analyze results, and calibrate qubits. ## A Software Contractor Is Ignored After a Key Employee Dies DevFeed: [A Software Contractor Is Ignored After a Key Employee Dies]() Original publisher: [Read original article]() Author: Ellis Morning Published: 2026-09-08T06:30:00Z Content type: opinion Language: en Sources: [The Daily WTF]() Topics: [Software](), [Development](), [coding]() Tags: [analysis](), [development](), [feature-articles](), [software]() ### AI overview An anonymous software contractor describes being hired to analyze a company's core program, then being largely ignored for about 18 months after the employee who hired them died. During that time, the contractor built a development environment and was eventually dismissed. ### Source excerpt From our anonymous submitter: Having reached the end of the road at a company increasingly swallowed up companies further east which you'd never believe were still afloat, I found myself headhunted for certain specialty software skills. I was reaching the final few years of my expected working span, so I jumped at the chance. The money was (to me at that time) spectacularly good, so I jumped into it. It started when my first day was spent by me being sent home for the weeks it was still going to take to onboard me. Not bad, engaged to wait, as it were, and the first 6 months was thus and so. The man who had interviewed me, call him Fred, was intelligent and urbane, and was a joy to meet. He and I clearly hit it off, and lo and behold I was in. It was he who gave me my first assignment, which was mathematical analysis of their core milk-cow program because they needed to find out what it did, and how it did it, so they could perhaps implement it in a more contemporary language. So I did that, and was just about to publish my findings with him, when Fred inconveniently dropped dead suddenly. In the what-are-we-going-to-do-now-our-key-man-is-no-more confusion, we contractors were forgotten. For the next 18 months or so (may have been more, may have been less) I was more or less ignored. I spent the time writing a development environment to work on any part of the program conveniently, all the while sitting next to a man who was constantly, forcefully and repetitiously speaking ill of the managers in his line structure. The ridiculously garrulous boss who inherited me thought little of me, and handed me the little work that came my way with active hostility. One or two good guys, but mostly a cabal of elderly men trying to preserve their little money-spinner as long as they could, and a johnny-come-lately trying to increase (and even introduce) automatic processes was less than welcome. During that time I spent quite some time on TDWTF, submitting a gem or two here and ## How engineering teams can measure AI coding ROI with cost attribution and outcome metrics DevFeed: [How engineering teams can measure AI coding ROI with cost attribution and outcome metrics]() Original publisher: [Read original article]() Author: Kelsey Rosen Published: 2026-09-08T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [ai-coding](), [Ground truth / benchmark quality](), [coding](), [Finance]() Tags: [ai](), [ai-coding](), [analysis](), [blog](), [coding](), [cost](), [datadog](), [engineering](), [governance](), [harness](), [incident](), [lifecycle](), [measurement](), [metrics](), [numbers](), [production](), [team](), [tokens](), [tooling](), [workflow]() ### AI overview A Harness panel discussion outlines ways engineering organizations can measure AI coding ROI. The panel recommends attributing costs to specific work, combining throughput metrics with qualitative evidence, and using governance and process changes to interpret results. ### Source excerpt Harness panelists share practical strategies for measuring AI coding ROI, from granular cost attribution and lifecycle metrics to budgeting, governance. | Blog ## Incident response guide for AWS CloudTrail investigations - Part 1 DevFeed: [Incident response guide for AWS CloudTrail investigations - Part 1]() Original publisher: [Read original article]() Author: Oscar Diaz Published: 2026-09-03T21:15:39Z Content type: tutorial Language: en Sources: [AWS Security Blog]() Topics: [AWS CloudTrail](), [Incident response](), [AWS IAM]() Tags: [advanced-300](), [analysis](), [aws-cloudtrail](), [best-practices](), [guide](), [incident-response](), [logs](), [security](), [security-blog](), [security-identity-compliance]() ### AI overview A practical guide to investigating suspicious AWS activity through CloudTrail logs. It covers scenarios such as cross-account unauthorized access, cryptocurrency mining, and AI service abuse, with investigation questions, annotated logs, and preventive lessons. ### Source excerpt AWS CloudTrail logs contain the evidence you need when investigating suspicious activity in your AWS environment, but knowing which fields matter and how to interpret them can mean the difference between surface-level analysis and uncovering the full scope of an incident. This guide walks you through real-world scenarios, showing you how to analyze CloudTrail events [...] ## Introducing agentic video understanding with Gemini DevFeed: [Introducing agentic video understanding with Gemini]() Original publisher: [Read original article]() Author: Rohan Doshi Published: 2026-09-01T17:08:51Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [Google AI](), [Google]() Tags: [agentic](), [ai](), [analysis](), [api](), [audio](), [benchmarks](), [cost](), [developers](), [enterprise](), [feature](), [flash](), [gemini](), [google-ai](), [models](), [none](), [performance](), [reasoning](), [retrieval](), [tools](), [transcripts](), [video]() ### AI overview Google launches agentic video understanding for Gemini Flash models. It uses native video tools to inspect relevant frames, audio, and transcripts, aiming to improve video-analysis accuracy while reducing token use and cost. ### Source excerpt We're launching agentic video understanding across our latest Gemini models for improved accuracy and lower costs and token usage. ## The IRR Landscape: What RPKI Can Replace DevFeed: [The IRR Landscape: What RPKI Can Replace]() Original publisher: [Read original article]() Author: Matthias Wichtlhuber Published: 2026-09-01T12:20:33Z Content type: article Language: en Sources: [RIPE Labs]() Topics: [BGP](), [Routing Security](), [Routing (disambiguation)](), [Databases](), [Internet]() Tags: [analysis](), [bgp](), [competition](), [data](), [database](), [ixps](), [measurements](), [overview](), [research](), [routing](), [routing-security](), [rpki](), [series]() ### AI overview This article analyzes which parts of the Internet Route Registry landscape can safely be replaced by RPKI using traffic and route-server data from AMS-IX and DE-CIX. It concludes that third-party ROUTE(6) objects can largely be phased out, while third-party AS-SETs remain important for BGP filter generation. ### Source excerpt In the third part of our IRR landscape series, we use traffic measurements from AMS-IX and DE-CIX to assess which parts of the IRR landscape can safely be replaced with RPKI. Third-party ROUTE(6) objects can largely be phased out, while third-party AS-SETs remain essential for BGP filter generation. ## Visualize how CUPED adjusts experiment results with Datadog DevFeed: [Visualize how CUPED adjusts experiment results with Datadog]() Original publisher: [Read original article]() Author: Tyler Buffington; Lukas Goetz-Weiss; Ryan Lucht Published: 2026-09-01T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [experiments]() Tags: [analysis](), [digital-experience-monitoring](), [experiment](), [experiments](), [how-to](), [metrics](), [product-analytics](), [visualization]() ### AI overview This tutorial explains Datadog Experiments' CUPED adjustments visualization, which breaks the difference between raw and CUPED-adjusted experiment lift into individual covariate contributions. ### Source excerpt Learn how Datadog visualizes CUPED adjustments so you can trace which covariates change experiment lift estimates and improve precision. ## GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models DevFeed: [GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models]() Original publisher: [Read original article]() Author: Naoto Usuyama, Jeya Maria Jose Valanarasu, Tristan Naumann Published: 2026-08-31T16:00:00Z Content type: article Language: en Sources: [Microsoft Research]() Topics: [foundation-models](), [datasets](), [data]() Tags: [analysis](), [data](), [datasets](), [foundation-models](), [models](), [performance](), [research](), [research-blog](), [scale](), [validation]() ### AI overview GigaPath-Flash and GigaTIME-Flash are efficient pathology foundation models designed to reduce computational demands while maintaining strong performance. They enable repeated analysis of larger cancer cohorts and support population-scale research into disease biology, biomarkers, and clinical outcomes. ### Source excerpt What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research. ## How an MIT research project became a global programming language DevFeed: [How an MIT research project became a global programming language]() Original publisher: [Read original article]() Author: Zach Winn | MIT News Published: 2026-08-31T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [The Julia Language](), [Programming language](), [Data Science](), [Complex Systems](), [Data analysis](), [Open Source]() Tags: [ai](), [ai-platform](), [alan-edelman](), [analysis](), [applications](), [artificial-intelligence](), [chris-rackauckas](), [complex-systems](), [computer-science-and-artificial-intelligence-laboratory-csail](), [deshpande-center](), [jeff-bezanson](), [julia-programming-language](), [juliahub](), [language](), [mit-schwarzman-college-of-computing](), [open-source](), [programming](), [programming-language](), [software](), [startups](), [stefan-karpinski](), [viral-shah]() ### AI overview An MIT research project created Julia, a free and open-source programming language for scientific research, data analysis, and complex-systems modeling. The article describes Julia's adoption by researchers, engineers, companies, and universities, and introduces JuliaHub's Dyad 3.0 AI platform. ### Source excerpt With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more. ## TerminalFix campaign deploys a reverse tunnel through multistage intrusion DevFeed: [TerminalFix campaign deploys a reverse tunnel through multistage intrusion]() Original publisher: [Read original article]() Author: Microsoft Security Research, Sagar Patil, Suriyaraj Natarajan and Parasharan Raghavan Published: 2026-08-29T03:43:27Z Content type: article Language: en Sources: [Microsoft Security Blog]() Topics: [High Profile Threats]() Tags: [analysis](), [c2](), [clickfix](), [cloudflare](), [microsoft](), [persistence](), [powershell](), [python](), [ransomware](), [security](), [windows]() ### AI overview Microsoft analyzes the TerminalFix ClickFix campaign, which uses a fake Cloudflare CAPTCHA to induce PowerShell execution and deploys a multi-stage intrusion chain. The chain includes DLL sideloading, steganographic payload delivery, Active Directory reconnaissance, persistence, and an encrypted reverse tunnel that can provide access into the compromised network. ### Source excerpt Microsoft Threat Intelligence provides analysis of a ClickFix campaign that uses fake CAPTCHA prompts, DLL sideloading, and a reverse tunnel, with detections and hunting guidance. The post TerminalFix campaign deploys a reverse tunnel through multistage intrusion appeared first on Microsoft Security Blog. ## Better answers, broader thinking: What students gain from ChatGPT and critical-thinking training DevFeed: [Better answers, broader thinking: What students gain from ChatGPT and critical-thinking training]() Original publisher: [Read original article]() Published: 2026-08-27T09:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Artificial Intelligence](), [OpenAI]() Tags: [ai](), [analysis](), [business](), [chatgpt](), [company](), [experiment](), [generate](), [gpt](), [marketing](), [openai](), [performance](), [reasoning](), [research](), [training]() ### AI overview Un randomized experiment with more than 1,000 first-year undergraduate students found that ChatGPT access improved the quality, coherence, logic, and professionalism of work on a real-world business assignment. Separate causal-reasoning training increased the originality and variety of ideas, and students receiving both interventions showed both benefits. ### Source excerpt A randomized study of more than 1,000 students examines ChatGPT, critical thinking, originality, and student performance on a real-world university assignment. ## Detecting multi-stage attacks on AWS: A guide to cross-service signal correlation DevFeed: [Detecting multi-stage attacks on AWS: A guide to cross-service signal correlation]() Original publisher: [Read original article]() Author: Nisha Kashyap Published: 2026-08-26T17:39:19Z Content type: article Language: en Sources: [AWS Security Blog]() Topics: [Amazon Web Services (AWS)](), [Security](), [Detection engineering](), [Amazon CloudWatch Logs](), [AWS CloudTrail](), [VPC Flow Logs](), [Amazon S3](), [data]() Tags: [advanced-300](), [amazon-cloudwatch](), [amazon-cloudwatch-logs](), [amazon-guardduty](), [amazon-route-53](), [amazon-web-services-aws](), [analysis](), [aws-cloudtrail](), [aws-security-hub](), [best-practices](), [cybersecurity](), [guide](), [incident-response](), [logs](), [s3](), [security](), [security-blog](), [security-identity-compliance](), [shared-responsibility-model](), [technical-how-to](), [threat-detection](), [vpc-flow-logs]() ### AI overview This article explains how security engineers can detect multi-stage attacks on AWS by correlating signals across services with business context. It presents examples using CloudWatch Logs Insights and discusses expanding the correlations into an automated pipeline. ### Source excerpt A single alert from one security service tells you something happened. Read that signal alongside activity from other services and your own business context, and you will know whether what happened is part of a multi-stage attack. Consider a short sequence. An identity calls GetCallerIdentity from a source address it hasn't previously used. Within minutes, [...] ## Intelligent transcription with Gemini 3.5 Transcribe DevFeed: [Intelligent transcription with Gemini 3.5 Transcribe]() Original publisher: [Read original article]() Author: Diego Melendo Casado Published: 2026-08-26T17:01:00Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [asr](), [voice ai](), [Google AI](), [API](), [real-time](), [Streaming](), [Latency](), [Artificial Intelligence]() Tags: [ai](), [analysis](), [analytics](), [android](), [api](), [audio](), [developer](), [developers](), [gemini](), [google](), [google-ai](), [latency](), [macos](), [model](), [none](), [real-time](), [speech](), [streaming](), [transcription](), [voice](), [workflows]() ### AI overview Google introduces Gemini 3.5 Transcribe, a speech-to-text model for accurate, formatted transcription in noisy and specialized audio. Developers can use it through the Gemini API in Google AI Studio and Gemini Enterprise Agent Platform for voice agents, real-time captioning, and post-call analytics. It supports real-time streaming and pre-recorded audio processing, custom vocabulary, speaker attribution, word-level timestamps, and more than 85 languages. ### Source excerpt Now you can get more intelligent speech-to-text transcription with Gemini 3.5 Transcribe. [Next page]()