AWS Launches Conversational App Builder in Amazon Q, Raising Governance Questions

·1 views

Amazon announced on September 1, 2026, that users can build apps inside Amazon Q (Quick) by describing them in natural language, targeting the widespread problem of unversioned spreadsheets driving critical business decisions. The new tool combines conversational authoring, QuickSight visual embeds, external API connectors, built-in AI inference, and persistent key-value storage, making it closer to a stateful application than a dashboard. AWS App Studio, a competing natural-language builder generally available since November 2024, targets more technical users and connects to over 200 AWS services, charging $0.25 per user-hour for published apps. A custom-coded build using Amplify, AppSync, Lambda, and DynamoDB remains the most auditable option but requires significant engineering investment. Analysts caution that replacing ungoverned spreadsheets with ungoverned apps carrying write-path connectors introduces new risks, and that change-board-grade environments will require clear ownership, versioning, and rollback plans for any app built through these tools.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

GrapheneOS confirms Pixel 11 will support Memory Tagging Extension

GrapheneOS, the privacy-focused Android fork, has announced that Google's upcoming Pixel 11 will include support for Memory Tagging Extension (MTE). MTE is a hardware security feature that helps detect and prevent memory safety vulnerabilities at runtime. The confirmation came via a GrapheneOS post on its Mastodon-based social account. This is notable as there had apparently been prior uncertainty about whether the Pixel 11 would carry MTE support. The feature is considered significant for hardening device security against exploitation of memory-related bugs.

0 comments Read more at Hacker News

ProgrammingHacker News ·

Three Websites Generated 215,000+ Fake 'Best Software' Pages Cited by Perplexity AI

A report by Trellner has revealed that just three websites manufactured over 215,128 pages purporting to list the 'best software' for various queries. These pages appear to have been created specifically to game AI-powered search and recommendation systems. Perplexity, an AI-based answer engine, has been found citing these fabricated sources in its responses. The findings raise serious concerns about the reliability of AI-generated recommendations when underlying sources can be so easily manipulated at scale.

0 comments Read more at Hacker News

ProgrammingDEV Community ·

How to Build Production Observability for Google AI Agents Using OpenTelemetry

Debugging AI agent failures in production is difficult because traditional logs record what executed but not why decisions were made, leaving engineers without causal context. A structured observability approach using OpenTelemetry spans and events can reconstruct an agent's full decision path, from model calls and tool executions to policy checks and retries. During local development, visualizing runs as a tree helps expose missing steps, unexpected retries, and direction changes before issues reach production. Rather than logging raw prompts, the recommended method attaches structured reason codes at consequential decision boundaries, making agent behavior aggregable and auditable. Frameworks like Google ADK, Genkit, and Gemini can feed this telemetry pipeline into Cloud Trace, Logging, and Monitoring for alerts and dashboards.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Dirty AI Context Causes Bad Code Suggestions — Here Is How to Fix It

AI coding assistants often produce incorrect suggestions not due to model weakness but because they receive cluttered, irrelevant context from large repositories. A workflow published on DEV Community proposes a context isolation layer that limits what an AI agent can see to only the files changed in a git diff, a fixed allowlist, and the latest relevant test output. The approach treats context selection as a routing problem, decided by the developer before the model processes anything, rather than relying on better prompts. The guide uses MonkeyCode's open-source project, which offers free model access and a free local server with an OpenAI-compatible endpoint, to demonstrate the pipeline. The author disclosed the article was prepared as part of MonkeyCode's product outreach, though the core principles are described as applicable to any AI coding tool.

0 comments Read more at DEV Community