# AI Development The practice of developing machine-learning and artificial-intelligence applications, models, tools, and frameworks. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Building an Internal Developer Platform with Artificial Intelligence DevFeed: [Building an Internal Developer Platform with Artificial Intelligence]() Original publisher: [Read original article]() Author: Ben Linders Published: 2026-09-17T11:11:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [internal developer platform](), [AI Development](), [AI search](), [agent observability](), [OpenTelemetry](), [telemetry]() Tags: [agent](), [agents](), [artificial-intelligence](), [culture-methods](), [developer-experience](), [developer-platform](), [distributed-tracing](), [guardrails](), [internal-developer-platform](), [logging](), [logs](), [metrics](), [news](), [opentelemetry](), [platform-artificial-intelligence](), [platform-engineering](), [prometheus](), [traces]() ### AI overview This news article covers a KubeCon presentation about using AI agents as an internal developer platform. It discusses semantic search across sources such as Git, Slack, Jira, repositories, pull requests, and wiki pages; guardrails for controlling actions; and logs, metrics, and traces for understanding agent behavior. The speakers also describe OpenTelemetry conventions for GenAI and related observability tools. ### Source excerpt Agents are becoming the new developer platform, using semantic search with data from tools like Git, Slack, and Jira for context. Things to consider are setting guardrails to block or allow things, and using logs, metrics, and traces to understand agent behavior. By Ben Linders ## Our framework for reporting model misalignment DevFeed: [Our framework for reporting model misalignment]() Original publisher: [Read original article]() Published: 2026-09-16T17:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [OpenAI](), [AI Development](), [AI Strategy](), [Framework](), [Development]() Tags: [ai](), [ai-development](), [alignment](), [behavior](), [developers](), [model](), [monitoring](), [openai](), [reporting](), [research](), [standards]() ### AI overview OpenAI introduces a framework for tracking, investigating, and disclosing model misalignment, accompanied by six reports on unexpected or concerning model behavior observed over the previous six months. The framework favors disclosure even when the significance of an instance is uncertain and is intended to evolve through experience and public feedback. ### Source excerpt OpenAI shares a framework for tracking, investigating, and disclosing model misalignment, alongside six reports of unexpected or concerning model behavior. ## AI Is Changing How We Code. It's Also Changing How We Learn. DevFeed: [AI Is Changing How We Code. It's Also Changing How We Learn.]() Original publisher: [Read original article]() Author: James Rempt Published: 2026-09-16T14:00:09Z Content type: opinion Language: en Sources: [Visual Studio Blog]() Topics: [Artificial Intelligence](), [Development](), [AI Development](), [Software]() Tags: [ai](), [code](), [developers](), [development](), [learning](), [software](), [software-development](), [visual-studio]() ### AI overview The article argues that AI tools are changing software development by helping developers write and understand code, troubleshoot problems, learn frameworks, and get answers quickly. It also emphasizes the continuing value of learning directly from other developers through practical education, hands-on discussion, and professional networking at events such as VSLive!. ### Source excerpt AI is changing software development at a pace no one can ignore. Developers now have powerful AI tools that can help us write code, understand unfamiliar codebases, troubleshoot problems, learn new frameworks, and get answers in seconds. That is an incredible opportunity. But as these tools become more capable, I believe something else is becoming more valuable [...] The post AI Is Changing How We Code. It's Also Changing How We Learn. appeared first on Visual Studio Blog. ## The AI Hurricane Is Here DevFeed: [The AI Hurricane Is Here]() Original publisher: [Read original article]() Author: Manoj Nair Published: 2026-09-15T04:00:00Z Content type: opinion Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Artificial Intelligence](), [AI Development](), [Security](), [Vulnerabilities](), [Architecture & Design](), [audit trail]() Tags: [agentic-development](), [agents](), [ai](), [application-security](), [audit-trail](), [awareness](), [blog](), [code-security](), [developer](), [executive](), [security](), [security-labs](), [snyk-platform](), [snyk-security-intel](), [supply-chain-security](), [tech](), [validation](), [vulnerabilities](), [vulnerability-insights]() ### AI overview The article argues that AI is accelerating software creation and cyberattacks, widening the gap between machine-speed development and slower validation. It calls for securing agentic development, enforcing runtime controls, maintaining inventories and audit trails for production AI applications, and using independent validation. ### Source excerpt AI is accelerating software creation and cyberattacks alike. Leaders must secure agents and code at inception, enforce controls at runtime, and validate defenses independently. ## From AI demos to real work: How Engineering and Operations learn side by side DevFeed: [From AI demos to real work: How Engineering and Operations learn side by side]() Original publisher: [Read original article]() Author: Nubank Editorial Published: 2026-09-14T16:28:23Z Content type: article Language: en Sources: [Nubank]() Topics: [Artificial Intelligence](), [Generative AI](), [AI Development](), [Software Engineering](), [Development]() Tags: [agents](), [ai](), [ai-research](), [applied-ai](), [data-science-machine-learning](), [development](), [engineering](), [mcps]() ### AI overview This article describes Nubank's Ops AI Acceleration Program and an applied AI workshop with PJ Operations. The workshop combined foundational technical concepts about generative AI, agents, models, tools, context, skills, and MCPs with a real operational challenge to connect engineering knowledge with operational expertise. ### Source excerpt Building shared technical understanding so operational expertise can turn AI into practical improvements The post From AI demos to real work: How Engineering and Operations learn side by side appeared first on Building Nubank. ## Podcast: How Will We Train Developers If AI Does the Routine Work: A Conversation with Scott Hanselman DevFeed: [Podcast: How Will We Train Developers If AI Does the Routine Work: A Conversation with Scott Hanselman]() Original publisher: [Read original article]() Author: Scott Hanselman Published: 2026-09-14T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Artificial Intelligence](), [AI Development](), [Architecture & Design](), [coding]() Tags: [agentic-ai](), [agents](), [ai](), [ai-agents](), [ai-architecture](), [ai-ml-data-engineering](), [architecture](), [architecture-design](), [artificial-intelligence](), [code](), [developers](), [infoq](), [junior-developers](), [ml-data-engineering](), [podcast](), [software-engineering](), [the-infoq-podcast](), [train-developers-ai-routine-work]() ### AI overview The podcast discusses how to train software engineers when AI agents perform much of the routine work traditionally assigned to junior developers. Scott Hanselman advocates a preceptorship model, experienced engineers overseeing AI-generated work, and long-term investment in mentorship and human connection. ### Source excerpt In this podcast, Michael Stiefel spoke to Scott Hanselman about developing new software engineers when artificial intelligence agents are doing most of the work on which junior developers were trained. Hanselman suggests the software industry should adopt a preceptorship model similar to the nursing profession. By Scott Hanselman ## The Grafana AI SDK for Go: a shared foundation for building AI applications DevFeed: [The Grafana AI SDK for Go: a shared foundation for building AI applications]() Original publisher: [Read original article]() Author: Luccas Quadros Published: 2026-09-12T11:22:06.456390Z Content type: article Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [vercel ai sdk](), [SDKs](), [Go Language](), [AI Development](), [Large Language Model](), [agent observability](), [Model Context Protocol](), [React]() Tags: [agent-observability](), [ai](), [analytics](), [backend](), [building](), [go](), [grafana](), [logs](), [metrics](), [monitoring](), [observability](), [sdk](), [streaming](), [tools](), [traces]() ### AI overview Grafana Labs introduces the Grafana AI SDK for Go, an open-source shared foundation for building AI applications. The SDK standardizes model calls, streaming, tool execution, structured output, multi-step agents, workflow controls, and operational features such as retries, logging, metrics, and Agent Observability. It also supports streaming Go backends to Vercel AI SDK frontend hooks. ### Source excerpt Starting an experiment with an LLM has never been easier. Keeping a growing collection of those experiments consistent is another matter. Earlier this year, as more teams began exploring AI features here at Grafana Labs, we repeatedly encountered the same pattern: a new experiment would start, move quickly, and build its own client for whichever model provider it needed. The next experiment would do the same, with a slightly different abstraction for streaming, tools, errors, or provider configuration. This was understandable, given the circumstances. Model providers were changing quickly, our teams were learning quickly, and coding agents made it possible to turn an idea into a working integration faster than ever. But that speed also made it easier for every integration to develop its own architecture. Eventually, we were maintaining a collection of solutions to what was essentially the same problem. And since most of our backend is written in Go, we built the Grafana AI SDK for Go to give our teams a shared foundation to work from. It provides common interfaces for calling models, streaming responses, executing tools, producing structured output, and running multi-step agents. It also speaks the protocol used by Vercel AI SDK frontend hooks, so a Go backend can stream directly to useChat, useCompletion, and useObject. We built it because we needed it inside Grafana Labs, but we open sourced it last month (alongside a broader collection of tools we released for building, operating, and understanding AI systems during our first Grafana Labs AI Week) because we think other teams building AI applications in Go are likely to encounter many of the same problems. We would like to build the next part together, so in this blog I'll tell you a bit more about the project, including how you can put it to use today, as well as how you can help us improve it. What teams can build with it today The SDK supports both simple model calls and larger application workflows: Generate ## Why Go is an Ideal Language for AI-Assisted Software Engineering DevFeed: [Why Go is an Ideal Language for AI-Assisted Software Engineering]() Original publisher: [Read original article]() Author: Cameron Balahan; Richard Seroter Published: 2026-09-12T11:04:33.891311Z Content type: opinion Language: en Sources: [Google Developers Blog]() Topics: [Go Language](), [Software Engineering](), [AI Development](), [Architecture & Design](), [Tooling]() Tags: [ai](), [ai-coding](), [compatibility](), [developer-tools](), [engineering](), [go](), [software-development](), [software-engineering]() ### AI overview The article argues that AI-assisted software engineering shifts developers' work from writing boilerplate toward reviewing, verifying, maintaining, and architecting systems. It presents Go as well suited to this model because its simplicity, readability, tooling, compatibility guarantees, and team-oriented design provide consistency and guardrails for AI-generated code. ### Source excerpt As AI coding assistants shift the developer's primary role from writing boilerplate to reviewing and maintaining systems, language choice becomes critical for long-term architectural integrity. Go directly addresses this new paradigm by utilizing its strict compiler, integrated toolchain, and uncompromising readability to provide deterministic guardrails that help AI models self-correct and generate highly standardized code. By enforcing ecosystem-wide consistency and strict backward compatibility, the Go platform empowers engineering teams to efficiently verify, optimize, and maintain high-velocity, AI-generated output in production environments. ## Announcing ADK for Kotlin 1.0: Building Production-Ready AI Agents in Kotlin, Android, and Beyond DevFeed: [Announcing ADK for Kotlin 1.0: Building Production-Ready AI Agents in Kotlin, Android, and Beyond]() Original publisher: [Read original article]() Author: Guillaume Laforge Published: 2026-09-12T11:04:33.891311Z Content type: release Language: en Sources: [Google Developers Blog]() Topics: [Artificial Intelligence](), [Kotlin](), [AI Development](), [Android](), [Kotlin Multiplatform](), [multiplatform](), [Orchestration](), [Google](), [Persistence](), [Agent Skill](), [LiteRT](), [Cloud]() Tags: [agent-skill](), [agents](), [ai](), [ai-agent](), [ai-agents](), [android](), [building](), [database](), [development-kit](), [firebase](), [incident](), [kotlin](), [multiplatform](), [orchestration](), [persistence](), [production]() ### AI overview Google announces the 1.0 general availability release of the Agent Development Kit (ADK) for Kotlin, a production-ready toolkit for building multi-agent applications with Kotlin, Java, and Android. Built on Kotlin Multiplatform, it provides feature parity with the ADK 1.0 Core and adds Android-first extensions for on-device agents with LiteRT-LM and ML Kit, hybrid cloud workflows through Firebase AI Logic, and state persistence with Room and AppSearch. The release also includes type-safe, compile-time function calling through KSP and declarative agent skills. ### Source excerpt Google has officially released version 1.0 of the Agent Development Kit (ADK) for Kotlin, achieving full feature parity with the Python and Java ADK cores to enable idiomatic, multi-agent AI development. Built on Kotlin Multiplatform (KMP), the framework leverages Kotlin Symbol Processing (KSP) for zero-reflection, type-safe function calling, alongside advanced orchestration capabilities like human-in-the-loop workflows and context compaction. Additionally, the release introduces a robust suite of Android-first extensions, allowing mobile developers to integrate local models via LiteRT-LM, cloud reasoning through Firebase AI, session persistence using Room, and semantic memory powered by AppSearch. ## Learn Claude Code, evals, AI systems, and more: ByteByteGo Live is here DevFeed: [Learn Claude Code, evals, AI systems, and more: ByteByteGo Live is here]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-09-11T15:32:16Z Content type: release Language: en Sources: [ByteByteGo]() Topics: [Artificial Intelligence](), [Claude Code](), [AI Development](), [AI Engineering]() Tags: [ai](), [ai-development](), [ai-engineering](), [ai-evals](), [claude-code](), [development]() ### AI overview ByteByteGo announces ByteByteGo Live, a membership offering live courses on Claude Code, production AI systems, AI engineering, AI evaluations, cost optimization, and related topics. The announcement cites higher completion rates for live cohorts and says the membership covers courses offered over the next 12 months. ### Source excerpt Most online courses never get finished (~4% completion). Live cohorts get ~40%, roughly 10x higher. Live courses are the only courses people actually finish. So we're launching ByteByteGo Live. ## Bringing custom knowledge to agents with AutoRAG DevFeed: [Bringing custom knowledge to agents with AutoRAG]() Original publisher: [Read original article]() Author: Diego Alvarez Ponce, Diego Garcia Perez Published: 2026-09-11T13:01:50Z Content type: article Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [Retrieval Augmented Generation (RAG)](), [Model Context Protocol (MCP)](), [Databases](), [Large Language Model](), [AI Development](), [LLM evaluation / benchmarking]() Tags: [ai](), [ai-inference](), [artificial-intelligence](), [database](), [developer](), [generative-ai](), [gpu](), [guide](), [how-to](), [large-language-models-llms](), [model-context-protocol-mcp](), [rag](), [red-hat](), [terminal]() ### AI overview This guide explains how to use AutoRAG in Red Hat OpenShift AI to optimize retrieval-augmented generation for enterprise, domain-specific data. It also shows how to integrate a Model Context Protocol server that retrieves customer information from an internal database, using a fictional banking scenario. ### Source excerpt The large language models (LLMs) that power generative AI work by drawing upon the patterns and information present in their training data. Without access to the right data, LLMs struggle to comprehend context--like our internal corporate vocabulary--and as a result, start to hallucinate. The post Bringing custom knowledge to agents with AutoRAG appeared first on Red Hat Developer. ## Honoring #IconsOfQuality: Debbie O'Brien DevFeed: [Honoring #IconsOfQuality: Debbie O'Brien]() Original publisher: [Read original article]() Author: Rajrupa Roychowdhury Published: 2026-09-11T09:21:23Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Testing](), [Playwright](), [Model Context Protocol](), [AI Development](), [Bot](), [Open Source](), [Front end](), [Microsoft](), [Google]() Tags: [ai](), [ai-agents](), [developer](), [developers](), [education](), [google](), [icons-of-quality](), [mcp](), [microsoft](), [open-source](), [program](), [testing](), [web-development]() ### AI overview BrowserStack highlights Debbie O'Brien as an advocate for Playwright end-to-end testing, Playwright MCP, AI agents, applied AI, and developer education. The article discusses using AI to verify its own work, designing systems that improve agent reliability, and using bots to discover, summarize, and prioritize learning materials. ### Source excerpt To celebrate the relentless passion and invaluable contributions of leaders in software quality, BrowserStack is proud to honour Icons of Quality. ## 47,000 job listings reveal the engineering roles that AI is creating DevFeed: [47,000 job listings reveal the engineering roles that AI is creating]() Original publisher: [Read original article]() Author: Jennifer Riggins Published: 2026-09-10T13:09:28Z Content type: news Language: en Sources: [The New Stack]() Topics: [Machine Learning & Artificial Intelligence](), [AI Development](), [Machine learning]() Tags: [ai](), [ai-engineering](), [ai-strategy](), [andela](), [engineering](), [jobs](), [machine-learning](), [post](), [skills](), [sponsor-andela](), [sponsored](), [sponsored-post](), [tech-careers](), [work]() ### AI overview Andela's analysis of 47,000 Fortune 500 engineering job postings identifies emerging AI-related roles formed by combining established skill sets. The article argues that organizations should use AI to delegate suitable work while retaining human expertise and specialization. ### Source excerpt Every major transformation in tech has led to roles merging, then new ones emerging. Friction between developers and operations drove The post 47,000 job listings reveal the engineering roles that AI is creating appeared first on The New Stack. ## Will AI kill us all within the next decade? DevFeed: [Will AI kill us all within the next decade?]() Original publisher: [Read original article]() Author: Pieter Arntz Published: 2026-09-10T12:18:51Z Content type: opinion Language: en Sources: [Malwarebytes]() Topics: [Responsibility & Safety](), [AI Strategy](), [AI Development](), [Vulnerabilities](), [anthropic]() Tags: [ai](), [ai-safety](), [anthropic](), [autonomous](), [fraud](), [models](), [news](), [superintelligence](), [vulnerabilities]() ### AI overview The article discusses warnings about possible future AI risks while noting that current models are described as low risk. It argues for safeguards such as independent testing, limits on high-risk autonomous uses, transparency, and accountability, alongside action against AI-enabled cybercrime. ### Source excerpt AI researchers are warning that the technology could kill us all within the next decade, although they say the risk from current models is low. ## Article: When Spec-Driven Development Pays Off DevFeed: [Article: When Spec-Driven Development Pays Off]() Original publisher: [Read original article]() Author: Nitin Garg Published: 2026-09-10T09:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [AI-assisted coding](), [AI Development](), [Software Engineering](), [code productivity]() Tags: [ai](), [ai-assisted-coding](), [ai-coding](), [ai-development](), [ai-ml-data-engineering](), [architecture](), [article](), [artificial-intelligence](), [code-generation](), [generative-ai](), [ml-data-engineering](), [productivity](), [reasoning](), [review](), [security](), [software-development](), [spec-driven-development](), [when-spec-driven-development-pays-off]() ### AI overview The article argues that AI-assisted coding shifts the main constraint from writing code to verifying it. It presents specification-first development as a governance approach for hard, multi-constraint work, while noting its time and cost and warning that apparent gains may instead come from reasoning. ### Source excerpt AI coding assistants have become a core part of software development. AI-generated code has shown productivity gains, but it's also contributing to security weaknesses and familiar bug patterns. In this article, author Nitin Garg highlights the bottleneck has moved from code generation to code verification, and how to detect & mitigate it when the AI-generated behavior diverges from the intent. By Nitin Garg ## Is prevention essentially a solved problem? DevFeed: [Is prevention essentially a solved problem?]() Original publisher: [Read original article]() Author: Ezra Tanzer Published: 2026-09-10T04:00:00Z Content type: opinion Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [AI Development](), [Vulnerabilities](), [Architecture & Design](), [Code]() Tags: [agent](), [agentic](), [ai](), [application-security](), [awareness](), [blog](), [code](), [code-security](), [cost](), [developer](), [devops](), [devsecops](), [secrets](), [security](), [snyk-code](), [snyk-open-source](), [snyk-platform](), [vulnerabilities](), [vulnerability-insights]() ### AI overview The article argues that preventing vulnerabilities in agent-generated code is architecturally understood, but organizations must apply the appropriate controls throughout a changing development lifecycle. It says prompts can influence code but cannot reliably constrain agents, and that earlier security intervention reduces model-token cost, human attention, and the risk of an incorrect fix. ### Source excerpt Prevention in agent-generated code is architecturally solved--but choosing controls that protect security without slowing development remains the challenge. ## Rebuilding AUTOMATIC1111 with Gradio Workflow DevFeed: [Rebuilding AUTOMATIC1111 with Gradio Workflow]() Original publisher: [Read original article]() Author: yuvraj sharma; Abubakar Abid Published: 2026-09-10T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [AI Development](), [Graphs](), [stable-diffusion](), [Inference](), [Large Language Model](), [hugging face](), [vlm]() Tags: [automatic1111](), [comfyui](), [diffusion](), [flux](), [gradio](), [hugging-face](), [image-to-image](), [image-to-video](), [inference](), [inference-providers](), [llm](), [models](), [python](), [space](), [spaces](), [text-to-image](), [vlm](), [workflow](), [workflows]() ### AI overview A walkthrough of Workflow1111, a Gradio graph that recreates AUTOMATIC1111-style media pipelines with connected operator nodes for image generation, editing, prompting, and related tasks. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## NVIDIA Brings Real-Time AI to Broadcast, Sports and Global Streaming at IBC DevFeed: [NVIDIA Brings Real-Time AI to Broadcast, Sports and Global Streaming at IBC]() Original publisher: [Read original article]() Author: NVIDIA Writers Published: 2026-09-09T16:00:42Z Content type: release Language: en Sources: [NVIDIA Blog]() Topics: [AI Development](), [GPU](), [SDKs](), [Microservice]() Tags: [ai](), [artificial-intelligence](), [compliance](), [events](), [gpu](), [holoscan-for-media](), [image-to-video](), [media](), [media-and-entertainment](), [microservices](), [news](), [nvidia](), [nvidia-nim](), [pro-graphics](), [real-time](), [sdks](), [streaming](), [synthetic](), [text-to-video](), [video]() ### AI overview NVIDIA announced an expansion of NVIDIA AI for Media at IBC 2026, including GPU-accelerated SDKs and NIM microservices for media workflows. The article highlights Synthetic Video Detector integrations for assessing whether video footage may be AI-generated and for compliance review. ### Source excerpt At the IBC conference, running Sept. 11-14 in Amsterdam, the creative, technology and business communities are coming together to turn ideas into action and discuss innovations across the media and entertainment industries. More than 44,000 attendees from 170+ countries are gathering to explore 1,300+ exhibitions in 14+ halls and outdoor spaces, with over 600 speakers [...] ## dotInsights | September 2026 DevFeed: [dotInsights | September 2026]() Original publisher: [Read original article]() Author: Rachel Appel Published: 2026-09-08T14:16:12Z Content type: article Language: en Sources: [The JetBrains Blog]() Topics: [.NET](), [Software Engineering](), [AI Development](), [ASP.NET Core](), [Uno Platform](), [Model Context Protocol]() Tags: [ai](), [asp-net-core](), [c-sharp](), [community](), [dotinsights](), [mcp](), [net](), [net-11](), [net-tools](), [september-2026](), [uno-platform]() ### AI overview A dotInsights newsletter roundup of recent .NET and software-development articles, with a note about the open-source C# Roslyn compiler and links covering performance, security, AI, web development, architecture, and tooling. ### Source excerpt Did you know? Fun fact: The C# compiler (Roslyn) is open source and written in C#. It exposes APIs to analyze and generate code. Welcome to dotInsights by JetBrains! This newsletter is the home for recent .NET and software development related information. 🔗 Links Here's the latest from the developer community. ☕ Coffee Break Take [...] ## Agentic Software Engineering Platform: Definition, Capabilities DevFeed: [Agentic Software Engineering Platform: Definition, Capabilities]() Original publisher: [Read original article]() Author: Aaron Taylor Published: 2026-09-07T12:48:46Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Software Engineering](), [AI Development](), [sdlc](), [Orchestration](), [Model Context Protocol](), [CI/CD](), [real-time](), [Slack]() Tags: [agentic](), [ai](), [ai-agents](), [ci-cd](), [mcp](), [orchestration](), [platforms](), [sdlc]() ### AI overview The article defines an Agentic Software Engineering Platform as infrastructure that enables AI agents and developers to work safely across the software development lifecycle. It describes a unified engineering context layer, workflow orchestration, governance, MCP connections, CI/CD integration, and ways to demonstrate return on investment. ### Source excerpt Learn what an Agentic Software Engineering Platform is, its five core capabilities, when to adopt one, and how to measure its ROI. ## Product Engineering for PMs, Part 2: Build a SaaS App Without Coding DevFeed: [Product Engineering for PMs, Part 2: Build a SaaS App Without Coding]() Original publisher: [Read original article]() Author: Paweł Huryn Published: 2026-09-07T12:33:05Z Content type: tutorial Language: en Sources: [The Product Compass]() Topics: [Software as a service](), [GitHub](), [Supabase](), [Netlify](), [AI Development](), [Authentication]() Tags: [ai](), [authentication](), [development](), [github](), [netlify](), [saas]() ### AI overview This tutorial continues a product-engineering series for PMs by describing how to take the AskOne multi-tenant SaaS application to production. It covers production projects and deployment with GitHub, Supabase, and Netlify, along with a custom DNS domain, Clerk, analytics, and Google authentication, within a lightweight AI-assisted software lifecycle. ### Source excerpt Add a moderator role, then take a real multi-tenant SaaS app to production: GitHub, Supabase, Netlify, custom domain, Clerk, analytics, Google auth. No coding. ## Supporting independent journalism in Ukraine DevFeed: [Supporting independent journalism in Ukraine]() Original publisher: [Read original article]() Published: 2026-09-07T00:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [AI Strategy](), [AI Development](), [Responsibility & Safety]() Tags: [ai-adoption](), [api](), [global-affairs](), [openai](), [partnership]() ### AI overview OpenAI, WAN-IFRA, and AIRPPU announced a programme to help Ukrainian independent news organisations adopt AI through masterclasses, project support, implementation roadmaps, pilots, and OpenAI API credits. ### Source excerpt OpenAI, AIRPPU and WAN-IFRA launch an AI program to help Ukrainian news organizations strengthen innovation, resilience, and independent journalism. ## MCP Logging in Spring AI DevFeed: [MCP Logging in Spring AI]() Original publisher: [Read original article]() Author: Burak Gökmen Published: 2026-09-06T23:19:21Z Content type: tutorial Language: en Sources: [Baeldung]() Topics: [Model Context Protocol](), [log management](), [AI Development](), [Remote Procedure Call (RPC)](), [telemetry]() Tags: [logging](), [mcp](), [mcp-server](), [opentelemetry](), [popular](), [rpc](), [spring](), [spring-ai](), [spring-ai-mcp-popular](), [spring-boot](), [tutorial]() ### AI overview A tutorial on MCP logging in Spring AI, covering server-to-client log notifications, client-controlled severity levels, and migration guidance following the feature's deprecation. ### Source excerpt Learn about MCP logging in Spring AI, both in an MCP server and a client. The post MCP Logging in Spring AI first appeared on Baeldung. ## Coding Challenge #135 - Voice Dictation App DevFeed: [Coding Challenge #135 - Voice Dictation App]() Original publisher: [Read original article]() Author: John Crickett Published: 2026-09-05T08:01:16Z Content type: tutorial Language: en Sources: [Coding Challenges]() Topics: [Code Challenge](), [Programming](), [AI Development](), [Accessibility](), [Operating system]() Tags: [accessibility](), [ai](), [audio](), [coding](), [os](), [programming](), [ui-automation](), [voice]() ### AI overview A Coding Challenge tutorial for building a private voice dictation app that runs entirely on the local computer. It covers audio capture, local speech recognition, text cleanup, spoken formatting commands, custom dictionaries, history, and inserting dictated text into the focused application without network requests. ### Source excerpt This challenge is to build your own voice dictation app. [Next page]()