# Kiro Published articles for Kiro. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Improving HCLS AI reasoning with open-source agent skills DevFeed: [Improving HCLS AI reasoning with open-source agent skills]() Original publisher: [Read original article]() Author: Michael Hsieh Published: 2026-09-16T19:00:00Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Agent Skills](), [Artificial Intelligence](), [foundation-models](), [Open Source](), [Bioinformatics](), [Medical imaging]() Tags: [agent-skills](), [agents](), [ai](), [ai-agents](), [amazon-bedrock](), [amazon-quick-suite](), [announcements](), [artificial-intelligence](), [foundation-models](), [generative-ai](), [healthcare](), [healthcare-and-life-sciences](), [intermediate-200](), [kiro](), [life-sciences](), [medical-imaging](), [open-source](), [strands-agents]() ### AI overview This post presents 38 open-source agent skills spanning 11 healthcare and life sciences domains. The skills encode domain decision procedures for AI agents, and the reported evaluation found a 70-86% head-to-head win rate over agents without the skills. ### Source excerpt AI agents on foundation models often misapply healthcare and life sciences decision frameworks, citing the right guideline but applying it incorrectly. This post shares 38 open-source agent skills across 11 HCLS domains that close this gap, with installation steps, three worked use cases, and a 410-prompt evaluation showing a 70-86% win rate. ## Manage end-user OAuth consent for AI agents with Amazon Bedrock AgentCore DevFeed: [Manage end-user OAuth consent for AI agents with Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Swara Gandhi Published: 2026-09-14T20:35:45Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock AgentCore](), [OAuth](), [Amazon Web Services](), [AWS CloudTrail](), [GitHub](), [Slack](), [Model Context Protocol](), [ide]() Tags: [amazon-bedrock](), [amazon-bedrock-agentcore](), [announcements](), [aws](), [aws-cloudtrail](), [claude-code](), [cursor](), [github](), [intermediate-200](), [kiro](), [model-context-protocol](), [oauth](), [slack](), [visual-studio-code]() ### AI overview This tutorial explains how Amazon Bedrock AgentCore Identity's Consent portal manages end-user OAuth consent and session binding for AI agents. It covers configuring GitHub and Slack targets through an AgentCore Gateway, storing user tokens, supporting IDE and MCP clients, and reviewing activity in AWS CloudTrail. ### Source excerpt Amazon Bedrock AgentCore Identity now offers a Consent portal, a managed web experience and session binding endpoint for AgentCore Gateway. This post walks through provisioning a portal, configuring GitHub and Slack 3LO targets, and the end-user consent flow, and shows how to review activity in AWS CloudTrail. ## AWS Weekly Roundup: OpenAI GPT-6 Astra on Amazon Bedrock, Amazon Quick desktop GA, Kiro for students, and more (September 14, 2026) DevFeed: [AWS Weekly Roundup: OpenAI GPT-6 Astra on Amazon Bedrock, Amazon Quick desktop GA, Kiro for students, and more (September 14, 2026)]() Original publisher: [Read original article]() Author: Micah Walter Published: 2026-09-14T15:56:33Z Content type: news Language: en Sources: [AWS News Blog]() Topics: [Amazon Bedrock](), [Frontier Model](), [OpenAI](), [context window](), [browser](), [ChatGPT](), [codex](), [macOS](), [Windows](), [Mobile]() Tags: [agents](), [ai](), [ai-agents](), [amazon](), [amazon-bedrock](), [amazon-elastic-block-store-amazon-ebs](), [amazon-opensearch-service](), [amazon-quick-suite](), [aws](), [aws-lambda](), [aws-outposts](), [aws-transform](), [bedrock](), [browser](), [chatgpt](), [codex](), [context-window](), [frontier-model](), [google-play](), [gpt](), [kiro](), [launch](), [macos](), [mobile](), [openai](), [week-in-review](), [windows]() ### AI overview This AWS Weekly Roundup highlights the general availability of OpenAI GPT-6 Astra on Amazon Bedrock, describing its reasoning, writing, design, computer-use, browser-use, and million-token context-window capabilities. It also covers the Amazon Quick desktop app for macOS and Windows, including synchronized conversations and agents across desktop and mobile, plus other AWS launches and updates. ### Source excerpt There's a particular energy to mid-September in New York. Pumpkin spice lattes are flowing, temperatures are dropping, and it's nearly sweater weather. The city is back at full speed, and so is the AWS launch calendar. This week that energy showed up in a new frontier model on Amazon Bedrock, a desktop app for Amazon [...] ## The AI-native SDLC won't be one process DevFeed: [The AI-native SDLC won't be one process]() Original publisher: [Read original article]() Author: Anirudh Ramanathan Published: 2026-09-12T14:00:00Z Content type: opinion Language: en Sources: [The New Stack]() Topics: [AI Bots]() Tags: [agents](), [ai](), [ai-engineering](), [anthropic](), [compliance](), [devops](), [dynatrace](), [github](), [kiro](), [platform-engineering](), [post-contributed](), [sdlc](), [signadot](), [spec-driven-development](), [sponsor-dynatrace](), [sponsor-signadot](), [sponsored](), [sponsored-post-contributed]() ### AI overview The article argues that AI-native software development needs risk- and accountability-based process variants rather than one fixed, spec-driven workflow. It emphasizes deterministic policy enforcement, agent self-checks, human approvals, and auditable records. ### Source excerpt Anthropic recently published its AI-Native SDLC Playbook. Its central claim is that "code is no longer the bottleneck." When agents The post The AI-native SDLC won't be one process appeared first on The New Stack. ## Build full-stack AWS applications in minutes with AI-powered scaffolding DevFeed: [Build full-stack AWS applications in minutes with AI-powered scaffolding]() Original publisher: [Read original article]() Author: Jack Stevenson Published: 2026-09-08T21:24:59Z Content type: article Language: en Sources: [AWS Open Source Blog]() Topics: [App](), [cloud-infrastructure](), [Amazon Bedrock](), [Command-line interface](), [Kiro](), [Terraform](), [Bun]() Tags: [agentic](), [ai](), [ai-coding](), [amazon-bedrock](), [announcements](), [applications](), [aws](), [kiro](), [observability](), [security](), [technical-how-to](), [terraform]() ### AI overview The article introduces version 1.0 of the Nx Plugin for AWS, an open-source set of deterministic Nx generators for scaffolding deployable AWS application components and their infrastructure. It describes using AI assistants or a CLI to generate applications with security, observability, and type-safety practices included, and begins a quick start for an agentic application using Amazon Bedrock. ### Source excerpt AI assistants can stand up an app or website that runs on AWS in minutes. Getting to a production-ready version you would put in front of real customers is still the hard part. Security, observability, type-safety, and resilience are non-negotiable for production. An assistant rarely gets all of that right in one pass, and hardening [...] ## The oldest architecture in computing DevFeed: [The oldest architecture in computing]() Original publisher: [Read original article]() Author: werner@allthingsdistributed.com (Dr. Werner Vogels) Published: 2026-09-08T14:00:00Z Content type: opinion Language: en Sources: [All Things Distributed]() Topics: [Kiro](), [Artificial Intelligence](), [GPU](), [Open Source]() Tags: [agents](), [ai](), [gpu](), [kiro](), [memory](), [open-source](), [posts](), [research]() ### AI overview The article argues that AI may automate some work and change job roles, but people can remain relevant by adapting and learning to use new tools. It describes Kiro Crew, an open-source agent workspace used for research, schedule monitoring, cron-job management, and meeting preparation, with particular attention to how its independent agents manage memory and improve over time. The discussion connects these ideas to biological intelligence, including the energy efficiency of the human brain compared with a GPU and research on cortical columns. ### Source excerpt A GPU burns 700 watts and hundreds of thousands of tries to learn Pong. A human brain does it on 20 watts in a handful of attempts. So what can the oldest architecture in computing teach us about where agents and memory are headed? ## Advancing price-performance for developers with GPT-5.6 in Kiro DevFeed: [Advancing price-performance for developers with GPT-5.6 in Kiro]() Original publisher: [Read original article]() Published: 2026-08-24T12:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [Kiro](), [AI Development](), [Artificial Intelligence](), [OpenAI](), [Amazon Web Services](), [coding](), [Software Engineering]() Tags: [ai](), [ai-coding](), [aws](), [building](), [code](), [coding](), [developers](), [development](), [engineering](), [gpt](), [kiro](), [openai](), [product](), [software-development](), [spec-driven-development](), [testing]() ### AI overview GPT-5.6 is now available in Kiro, bringing OpenAI's latest flagship model series to development workflows for planning, coding, review, and testing. The article highlights structured, spec-driven development, complex multi-step coding, property-based testing, and reported cost reductions on Terminal-Bench 2.1. ### Source excerpt GPT-5.6 is now available in Kiro, helping developers plan, build, review, and test software with better price-performance. ## Four Days Left to Enter the Ready, Spec, Ship Hackathon DevFeed: [Four Days Left to Enter the Ready, Spec, Ship Hackathon]() Original publisher: [Read original article]() Author: John Crickett Published: 2026-08-19T08:02:04Z Content type: opinion Language: en Sources: [Coding Challenges]() Topics: [Hackathon](), [Kiro](), [agentic-coding]() Tags: [agentic-coding](), [build](), [coding](), [hackathon](), [kiro](), [project]() ### AI overview A reminder that four days remain to enter the Ready, Spec, Ship Hackathon. Participants may enter individually or in teams of up to three, submit multiple projects, and receive free Kiro credits if their entries are verified. The article also describes the $9,600 prize pool and submission deadline. ### Source excerpt What will you build and could you win a prize? ## AWS Weekly Roundup: AWS Heroes Summit, Web Search on Amazon Bedrock, Dogwood, Kiro Crew, and more (August 10, 2026) DevFeed: [AWS Weekly Roundup: AWS Heroes Summit, Web Search on Amazon Bedrock, Dogwood, Kiro Crew, and more (August 10, 2026)]() Original publisher: [Read original article]() Author: Channy Yun (윤석찬) Published: 2026-08-10T15:45:03Z Content type: article Language: en Sources: [AWS News Blog]() Topics: [Amazon Bedrock](), [Amazon Web Services](), [Artificial Intelligence](), [Amazon Bedrock AgentCore](), [Amazon DynamoDB](), [AWS Transform](), [Embeddings](), [Containers](), [Serverless](), [Mainframe](), [OpenAI]() Tags: [ai](), [amazon](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [amazon-dynamodb](), [applications](), [aws](), [aws-lambda](), [aws-transform](), [bedrock](), [containers](), [embeddings](), [kiro](), [modernization](), [news](), [open-source](), [product](), [serverless](), [week-in-review]() ### AI overview This AWS Weekly Roundup covers the AWS Heroes Summit and selected launches, including web search for OpenAI models in Amazon Bedrock, dedicated runtime instances for agents in Amazon Bedrock AgentCore, vector search in Amazon DynamoDB, and general availability of AWS Transform continuous modernization. ### Source excerpt Last week, we brought together AWS Heroes from around the world to connect, collaborate, and celebrate the builders who go above and beyond for the AWS community. The AWS Heroes Summit, an invite-only annual gathering, brings global experts specializing in fields like AI, serverless, and containers together for direct collaboration, technical deep-dives, and feedback sessions [...] ## Introducing Agent Plugins 1.0.0 DevFeed: [Introducing Agent Plugins 1.0.0]() Original publisher: [Read original article]() Author: Jonathan Hefner Published: 2026-08-06T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Agent Skills](), [Model Context Protocol](), [client](), [JSON](), [ChatGPT](), [codex](), [cursor](), [GitHub](), [Kiro](), [vs-code]() Tags: [agent](), [agent-skills](), [announcement](), [chatgpt](), [codex](), [cursor](), [distribution](), [github](), [github-copilot](), [guide](), [kiro](), [manifest](), [mcp](), [plugins](), [policy](), [portable](), [skills](), [user-experience](), [vs-code]() ### AI overview Agent Plugins 1.0.0 is an open, vendor-neutral standard for packaging Agent Skills and MCP servers into portable plugins. It defines a common root manifest and fixed component locations while allowing each compatible client to control installation, distribution, policy, user experience, and client-specific capabilities. ### Source excerpt Agent Plugins 1.0.0 is now available. It is an open, vendor-neutral standard for packaging Agent Skills and MCP servers into portable plugins. Compatible agent clients can discover and load them. Agent Plugins defines a common format: a root plugin.json manifest, plus fixed locations for portable components. Each client keeps control of installation, distribution, policy, user experience, and client-specific capabilities. At launch, Agent Plugins is supported across the following clients: ChatGPT and Codex Cursor GitHub Copilot Kiro VS Code Read the specification and implementation guides, learn more in the announcement post, and follow our guide to build a plugin on the new standard. Read more ## Ready, Spec, Ship Hackathon Announces $9,600 Prize Pool and Kiro Credits DevFeed: [Ready, Spec, Ship Hackathon Announces $9,600 Prize Pool and Kiro Credits]() Original publisher: [Read original article]() Author: John Crickett Published: 2026-08-04T15:03:54Z Content type: article Language: en Sources: [Coding Challenges]() Topics: [Hackathon](), [Kiro](), [agentic-coding]() Tags: [agentic](), [agentic-coding](), [coding](), [hackathon](), [kiro]() ### AI overview An announcement for the Ready, Spec, Ship Hackathon, sponsored by Kiro. Participants may enter alone or in teams of up to three, submit multiple projects, and receive Kiro credits for verified entries; submissions close on 23 August. ### Source excerpt Try Kiro for free and win prizes! ## AWS Weekly Roundup: One-click Lambda setup prompt, OpenAI GPT-5.6 models on Bedrock, and more (July 20, 2026) DevFeed: [AWS Weekly Roundup: One-click Lambda setup prompt, OpenAI GPT-5.6 models on Bedrock, and more (July 20, 2026)]() Original publisher: [Read original article]() Author: Channy Yun (윤석찬) Published: 2026-07-20T16:37:34Z Content type: news Language: en Sources: [AWS News Blog]() Topics: [AI-assisted coding](), [Amazon S3](), [cursor](), [GitHub Copilot]() Tags: [ai-agent](), [amazon-bedrock](), [amazon-cognito](), [amazon-dynamodb](), [amazon-s3](), [amazon-simple-storage-service-s3](), [aws](), [aws-lambda](), [kiro](), [mcp](), [news](), [openai](), [serverless](), [strands-agents](), [week-in-review]() ### AI overview AWS weekly roundup covering a one-click Lambda prompt for configuring coding agents with serverless skills and an MCP server, OpenAI GPT-5.6 models on Amazon Bedrock, and same-day Amazon S3 storage-class transitions. ### Source excerpt Last week, my team visited Seoul to meet AWS Korea User Group (AWSKRUG) leaders. AWSKRUG is the largest cloud developer community in Korea, with 20 meetup groups organized by topic and area that collectively host over 100 events each year, primarily in Seoul. My team regularly visits countries across the Asia-Pacific region, listens to feedback [...] ## Introducing MCP server for Registry of Open Data on AWS DevFeed: [Introducing MCP server for Registry of Open Data on AWS]() Original publisher: [Read original article]() Author: Guyu Ye Published: 2026-07-07T20:12:39Z Content type: article Language: en Sources: [AWS Open Source Blog]() Topics: [MCP Server](), [Model Context Protocol](), [datasets](), [Amazon Web Services](), [Open Source](), [Claude Code](), [Kiro](), [Artificial Intelligence]() Tags: [ai](), [ai-assistants](), [aws](), [claude-code](), [datasets](), [kiro](), [mcp-server](), [model-context-protocol](), [model-context-protocol-mcp](), [open-source]() ### AI overview AWS introduces an open-source Model Context Protocol server for the Registry of Open Data on AWS. It lets compatible AI assistants search datasets, inspect metadata, explore bucket contents, and sample files, helping researchers evaluate data through a conversational workflow. ### Source excerpt Today, we are launching an open source Model Context Protocol (MCP) server that brings AI-powered dataset discovery to Registry of Open Data on AWS (RODA). As of today, RODA hosts over 1,100 high-value datasets from more than 400 organizations, spanning satellite imagery, life sciences, climate, geospatial, and more. Ask a research question in Kiro, Claude [...] ## Accelerate your infrastructure deployments by up to 4x with AWS CloudFormation Express mode DevFeed: [Accelerate your infrastructure deployments by up to 4x with AWS CloudFormation Express mode]() Original publisher: [Read original article]() Author: Channy Yun (윤석찬) Published: 2026-06-30T21:30:33Z Content type: article Language: en Sources: [AWS News Blog]() Topics: [AWS CloudFormation](), [Deployment](), [Amazon Web Services](), [Infrastructure as code](), [Command-line interface](), [AWS Management Console](), [Resilience](), [SDKs](), [Amazon Simple Queue Service (SQS)](), [AWS Lambda](), [Kiro]() Tags: [accelerate](), [agents](), [ai-tools](), [amazon-simple-queue-service-sqs](), [aws](), [aws-cloudformation](), [aws-lambda](), [aws-management-console](), [cli](), [cloudformation](), [deployment](), [developers](), [infrastructure](), [kiro](), [launch](), [management-tools](), [news](), [resilience](), [sdks](), [testing]() ### AI overview AWS CloudFormation Express mode accelerates infrastructure deployments by completing when resource configuration is applied instead of waiting for extended stabilization checks. The article describes use in iterative development, production scenarios, testing, and AI-assisted infrastructure workflows, with examples involving SQS and Lambda. ### Source excerpt AWS CloudFormation speeds up infrastructure deployment with Express mode, enabling AI agents and developers to receive deployment confirmation in seconds and iterate faster. Available in all commercial Regions at no additional cost. ## Securing the AI coding ecosystem: Chainguard and the AI tools developers use DevFeed: [Securing the AI coding ecosystem: Chainguard and the AI tools developers use]() Original publisher: [Read original article]() Published: 2026-06-24T00:00:00Z Content type: opinion Language: en Sources: [Chainguard: Unchained]() Topics: [chainguard](), [Kiro](), [AI Development](), [cursor](), [Security](), [Containers](), [Open Source]() Tags: [ai](), [ai-agents](), [ai-coding](), [aws-kiro](), [chainguard](), [chainguard-ai-tools](), [chainguard-libraries](), [containers](), [cursor](), [developers](), [kiro](), [open-source](), [secure-by-default]() ### AI overview Chainguard argues that AI coding tools such as Kiro and Cursor need trusted sources for dependencies and container images. The article describes Chainguard Containers, Libraries, and a Kiro plugin intended to move projects from public registries to hardened supply-chain components. ### Source excerpt Chainguard brings secure-by-default containers and libraries to AI coding tools like Kiro and Cursor, making trusted open source the default. ## 1Password + Kiro: Trusted Access for AI-Powered Development DevFeed: [1Password + Kiro: Trusted Access for AI-Powered Development]() Original publisher: [Read original article]() Author: info@1password.com (Dennis Kromhout van der Meer and Scott Lougheed) Published: 2026-06-17T00:00:00Z Content type: article Language: en Sources: [Blog on 1Password Blog]() Topics: [Developer Tools](), [MSP MCP](), [AI Bots](), [AI Chat]() Tags: [ai](), [ai-security](), [coding](), [developers](), [kiro](), [mcp](), [secrets]() ### AI overview 1Password announces an integration between its MCP Server and Kiro that gives AI development workflows scoped, runtime credentials without exposing raw secrets in the model context. ### Source excerpt AI agents now write code, fix bugs, and ship to production. But in order to do useful work, agents require credentials. At 1Password, one of our core AI security principles is that raw credentials should never be directly exposed to LLMs, but all too often, that's exactly what happens: most teams sacrifice security for speed and hand agents secrets in plaintext. The shortcuts behind that tradeoff predate agentic development: secrets packed into an .env file, a script with hardcoded keys, a config file committed to a repo. But agentic development exponentially increases the blast radius of an exposed or misused credential, since anything in an agent's context window can be logged, echoed into output, or surfaced by an agent that's been manipulated into revealing it. Solving this tension means giving agents the access they need without directly exposing credentials to the model context. Today we're expanding the 1Password MCP Server to Kiro, making 1Password the trusted access layer for AI-powered development in Kiro. With this integration, credentials are issued at runtime, scoped to the assigned task, and never enter the model's context window. Agents get the access they need, and secrets stay where they belong. What this integration does Kiro is a software development agent, available in IDE, CLI or web experiences, bringing engineering rigor to AI-native coding via spec driven development and property-based testing. This integration connects Kiro directly to 1Password Environments using a local MCP server packaged inside our Password Manager developer tools. It's available on 1Password business and personal accounts. With this integration, developers can: Configure environments securely inside Kiro. Ask Kiro to create and configure an environment, then run applications using secrets stored in 1Password Environments instead of plaintext .env files, all without leaving Kiro. Clean up hard-coded secrets. Have Kiro find credentials in source code, move them into 1Pass ## The Trends #10: Amazon now requires senior approval for AI-assisted code from junior and mid-level engineers DevFeed: [The Trends #10: Amazon now requires senior approval for AI-assisted code from junior and mid-level engineers]() Original publisher: [Read original article]() Author: Dr Milan Milanović Published: 2026-04-09T15:02:37Z Content type: article Language: en Sources: [Tech World With Milan Newsletter]() Topics: [amazon](), [Kiro](), [AI Agent](), [agentic-coding](), [Amazon Web Services](), [Claude](), [Claude Code](), [Cloud](), [cursor](), [debug](), [Resilience]() Tags: [agent](), [agentic](), [agentic-coding](), [ai](), [ai-agent](), [amazon](), [aws](), [claude-code](), [cloud](), [cursor](), [kiro](), [outage](), [permissions](), [production](), [resilience](), [review]() ### AI overview A technology trends newsletter examines Amazon's requirement for senior approval of AI-assisted code from junior and mid-level engineers after production outages involving AI coding tools. It also previews discussions of cloud resilience, AI and mathematics, Claude Code tool selection, LLM debugging behavior, and Anthropic's research on junior developers using AI. ### Source excerpt The Trends filter tracks tech trends: what moved, why it matters, and what to watch next. ## Migrating an 11-Year-Old Blog from Jekyll to Astro with Kiro DevFeed: [Migrating an 11-Year-Old Blog from Jekyll to Astro with Kiro]() Original publisher: [Read original article]() Published: 2026-03-17T00:00:00Z Content type: opinion Language: en Sources: [Alex Korbonits]() Topics: [migration](), [Astro](), [Jekyll](), [Kiro](), [Claude](), [Artificial Intelligence](), [ide]() Tags: [ai](), [astro](), [blog](), [claude](), [commands](), [ide](), [jekyll](), [kiro](), [migration]() ### AI overview The author describes migrating an 11-year-old blog from Jekyll to Astro in one evening with help from Kiro and Claude. Kiro read the existing Jekyll files, generated Astro components, fixed build errors, and iterated on the redesign. ### Source excerpt A year ago this would have taken a week. How I migrated an 11-year-old Jekyll blog to Astro in one evening with two toddlers in the house. ## Just Launched: Neon Is Now a Kiro Power DevFeed: [Just Launched: Neon Is Now a Kiro Power]() Original publisher: [Read original article]() Author: Carlota Soto Published: 2025-12-03T17:52:39Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Kiro](), [AI Development](), [ide](), [Model Context Protocol (MCP)](), [Spec Driven Development](), [backend-development](), [Deployment]() Tags: [agentic-ai-development](), [ai-development](), [backend-development](), [deployment](), [ide](), [kiro](), [mcp](), [product](), [spec-driven-development]() ### AI overview The article announces Neon as an early launch partner for Kiro powers. It describes a Neon power that lets developers deploy Postgres databases, create production-like branches, run migrations and tests in isolated environments, and revisit past states from within the IDE. It also explains how Kiro powers provide domain-specific tools and knowledge through MCP servers, steering files, and hooks. ### Source excerpt Kiro just announced powers at re:Invent, a new way for developers to access a curated set of tools (each packaged with domain knowledge and best practices) directly from the IDE. Neon is one of the first launch partners, alongside companies like Figma, Stripe, Supabase, Postman,... ## The new Supabase power for Kiro DevFeed: [The new Supabase power for Kiro]() Original publisher: [Read original article]() Author: Matt Rossman Published: 2025-12-03T07:00:00Z Content type: release Language: en Sources: [Supabase Blog]() Topics: [AI-assisted coding]() Tags: [agent](), [ai](), [cli](), [database](), [edge](), [ide](), [kiro](), [mcp](), [model-context-protocol](), [postgres](), [security](), [sql]() ### AI overview Supabase announces Kiro IDE powers that connect an AI assistant to Supabase projects for schema work, edge-function review, security checks, and Postgres SQL guidance. ### Source excerpt Build full-stack applications faster with the Kiro IDE using deep knowledge of your Supabase project, best practices for database migrations, edge functions, and security policies. ## When Speed Meets Security: Snyk Studio for Kiro DevFeed: [When Speed Meets Security: Snyk Studio for Kiro]() Original publisher: [Read original article]() Author: Sarah Conway Published: 2025-12-01T05:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [AI-assisted coding](), [Vulnerabilities]() Tags: [ai](), [ai-coding](), [application-security](), [awareness](), [blog](), [code-security](), [containers](), [developer](), [ide](), [integration](), [kiro](), [related-content](), [security](), [spec-driven-development](), [vulnerabilities]() ### AI overview Snyk announces its Studio integration for Amazon Kiro, aimed at scanning AI-generated and existing code for vulnerabilities, suggesting fixes, and rescanning to validate remediation. ### Source excerpt Snyk Studio for Kiro ensures rapid, secure AI innovation. Stop security backlogs and prevent new risks at inception to ship with confidence, even with AI coding assistants. ## LLMs as Parts of Systems DevFeed: [LLMs as Parts of Systems]() Original publisher: [Read original article]() Author: Marc Brooker Published: 2025-08-12T00:00:00Z Content type: opinion Language: en Sources: [Marc Brooker's Blog](), [Marc Brooker's Blog]() Topics: [Large Language Model](), [systems](), [AI Development](), [Automated reasoning](), [Artificial Intelligence](), [Kiro](), [Amazon Bedrock]() Tags: [ai](), [ai-agent](), [amazon-bedrock](), [automated-reasoning](), [kiro](), [llms](), [reasoning](), [systems]() ### AI overview The article argues that LLMs are most powerful when combined with other system components, such as code interpreters, databases, browsers, algorithms, and SMT solvers. These combinations can solve problems that LLMs alone cannot and can deliver substantially better speed and cost efficiency. Amazon Bedrock's Automated Reasoning Checks illustrate this approach by using LLMs to extract rules and facts, then applying an SMT solver to verify logical consistency. ### Source excerpt LLMs as Parts of Systems Towers of Hanoi is a boring game, anyway. Over on the Kiro blog, I wrote a post about Kiro and the future of AI spec-driven software development, looking at where I think the space of AI-agent-powered development tools is going. In that post, I made a bit of cheeky oblique reference to a topic I think is super important. I asked Kiro to build a Towers of Hanoi game. It's an oblique reference to Apple's The Illusion of Thinking paper, and the discourse that followed it. The question of whether LLMs can scalably play Towers of Hanoi is an interesting theoretically and scientifically, but not the most important question. The more important one is can systems built with LLMs play these games?. By picking me Towers of Hanoi in that other post, I was pointing out that the answer is clearly yes. And has been for several LLM generations. As a system builder, I'm much more interested in what systems of LLMs and tools can do together. LLMs and code interpreters. LLMs and databases. LLMs and browsers. LLMs and SMT solvers. These systems can do things, today, that LLMs alone simply can't, and will never be able to do. More importantly, they can do things today orders of magnitude more cheaply and quickly than LLMs can, even in the case where they can do the same things. You know, this kind of thing: > Generate a python snippet that counts the number of rs in a string. def count_rs(input_string): return input_string.lower().count('r') Trivial? Yes. But I've now created a system that that can solve problems that this LLM can't. A better LLM can, but at about six orders of magnitude higher cost per example. Systems, fundamentally, are more than the sum of their components. A good system can do things that no component can do alone. The trivial example is trivial, but you can imagine how that power could extend to being able to use decades of progress in algorithms. And not only count, but much more powerful things like SMT solvers, or ILP approximation, or