# Developer Tools Programs that enable developers to create, test, and debug software. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Put Redis data and engineering guidance to work in ChatGPT Work DevFeed: [Put Redis data and engineering guidance to work in ChatGPT Work]() Original publisher: [Read original article]() Author: Olga Lopaci Published: 2026-09-11T00:00:00Z Content type: release Language: en Sources: [Redis Blog]() Topics: [Developer Tools](), [AI Chat](), [AI Bots]() Tags: [agent-skills](), [chatgpt](), [codex](), [engineering](), [observability](), [rag](), [redis](), [review](), [security](), [skills](), [tech](), [work]() ### AI overview Redis launched a development plugin for ChatGPT Work and Codex that supplies current Redis guidance for writing, reviewing, and troubleshooting code. It also describes connecting Redis data to ChatGPT Work's Data agent for plain-language exploration and investigation. ### Source excerpt Redis has launched a development plugin that brings current Redis engineering guidance into ChatGPT Work and Codex. It helps teams write, review, and troubleshoot Redis code without switching between documentation and development tools. Alongside Ope... ## Troubleshoot and secure your code faster with Datadog's Bitbucket Cloud Source Code integration DevFeed: [Troubleshoot and secure your code faster with Datadog's Bitbucket Cloud Source Code integration]() Original publisher: [Read original article]() Author: Eric Metaj; Mark Azer Published: 2026-08-31T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Developer Tools](), [iac-security](), [Vulnerabilities]() Tags: [apm](), [bitbucket](), [ci-visibility](), [cloud](), [code](), [code-profiling](), [code-security](), [devsecops](), [error-tracking](), [infrastructure-as-code](), [integration](), [observability](), [open-source](), [pull-requests](), [secret-scanning](), [security](), [software-delivery](), [test-optimization](), [vulnerabilities]() ### AI overview Datadog's Bitbucket Cloud Source Code integration links selected repositories with Datadog so teams can investigate production issues with source context and review security findings before merging code. ### Source excerpt Connect Bitbucket Cloud to Datadog to troubleshoot with source code in context and surface test, quality, and security feedback in pull requests. ## VSLive! @ Microsoft HQ: Developer Takeaways and Must-Watch Sessions DevFeed: [VSLive! @ Microsoft HQ: Developer Takeaways and Must-Watch Sessions]() Original publisher: [Read original article]() Author: James Rempt Published: 2026-08-27T14:00:09Z Content type: article Language: en Sources: [Visual Studio Blog]() Topics: [AI-assisted coding](), [.NET](), [Developer Tools](), [debugging](), [WinForms]() Tags: [ai](), [events](), [github-copilot](), [modernization](), [net](), [visual-studio]() ### AI overview A VSLive! @ Microsoft HQ recap highlights developer discussions about AI-assisted development, Visual Studio fundamentals, and .NET modernization. ### Source excerpt VSLive! @ Microsoft HQ 2026 was one of the most energizing VSLive! events I've attended. What stood out was the level of engagement over five days of learning and conversation from July 27 through July 31. Developers came ready to dig into the future of Visual Studio, .NET, and AI-assisted software development, and many Microsoft engineers and product managers were there to have those conversations directly. If you weren't able to [...] The post VSLive! @ Microsoft HQ: Developer Takeaways and Must-Watch Sessions appeared first on Visual Studio Blog. ## What's in a tag name? JavaScript, apparently DevFeed: [What's in a tag name? JavaScript, apparently]() Original publisher: [Read original article]() Author: Gareth Heyes Published: 2026-08-25T14:24:32Z Content type: article Language: en Sources: [PortSwigger Research]() Topics: [JavaScript](), [browser](), [HTML](), [Developer Tools](), [Web Development]() Tags: [browser](), [eval](), [event](), [html](), [javascript](), [payload](), [vector]() ### AI overview The article explores unusual HTML tag-name and attribute behaviors that can be combined with JavaScript features to create XSS vectors and potentially bypass web application firewalls. It discusses case transformations, event handlers, the Function constructor, eval, focusability, and alternative HTML attributes across browsers. ### Source excerpt I was on my laptop, as I often am when there's rubbish on telly, and found myself wondering what characters are allowed in a tag. I knew they had to begin with "a-zA-Z", but what about after that? I t ## The Attacker Never Sleeps, Neither Can Your Testing DevFeed: [The Attacker Never Sleeps, Neither Can Your Testing]() Original publisher: [Read original article]() Author: Manoj Nair Published: 2026-07-30T00:00:00Z Content type: opinion Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [AI-assisted coding](), [Developer Tools]() Tags: [agents](), [ai](), [application-security](), [awareness](), [blog](), [code](), [code-security](), [devops](), [devsecops](), [executive](), [finserv](), [interest](), [open-source-security](), [security](), [snyk-apprisk](), [snyk-code](), [snyk-open-source](), [snyk-platform](), [software](), [supply-chain-security](), [tech](), [testing](), [vulnerability-insights]() ### AI overview AI-driven code generation is expanding software attack surfaces while giving attackers machine-speed reasoning capabilities. The article argues that security teams need continuous testing, agent governance, and independent validation of findings. ### Source excerpt AI is accelerating software development and giving attackers machine-speed capabilities. Security teams must continuously test AI-built code, govern agents, and independently validate every finding. ## Developers are attached to tools because tools encode trust DevFeed: [Developers are attached to tools because tools encode trust]() Original publisher: [Read original article]() Author: Ryan Donovan Published: 2026-07-29T14:06:38Z Content type: opinion Language: en Sources: [Stack Overflow Blog]() Topics: [Developer Tools](), [AI-assisted coding]() Tags: [agentic](), [buiilding-software](), [dev-tools](), [developer-tooling](), [developers](), [ide](), [se-stackoverflow](), [se-tech](), [tools]() ### AI overview An opinion piece on how developers build trust in customizable tools and workflows, contrasting established tools such as Vim and Emacs with rapidly changing coding agents. ### Source excerpt The tools themselves are new and their capabilities are in constant flux. If your kitchen knife kept changing shape, weight, and edge, you'd have to relearn it every time; that's a hard tool to build trust in. But it also points to a flaw in how you use that tool, the process around it, and the way the tool reinforces the process. ## Scientific computing in the age of agentic AI DevFeed: [Scientific computing in the age of agentic AI]() Original publisher: [Read original article]() Published: 2026-07-28T17:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI-assisted coding](), [Developer Tools]() Tags: [agentic-ai](), [ai-agents](), [ai-coding](), [claude-code](), [codex](), [gpu](), [publication](), [research]() ### AI overview An exploratory field report examines eight life-science scientific-computing projects that use coding agents to maintain, optimize, migrate, and redesign research software. It reports faster development while emphasizing researchers' continuing responsibility for verification, quality, and long-term stewardship. ### Source excerpt A new field report shows how scientists use AI coding agents to modernize scientific computing, accelerating software development and discovery in genomics and beyond. ## Six Agent Harness Capabilities for Higher Model Performance DevFeed: [Six Agent Harness Capabilities for Higher Model Performance]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-07-27T09:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Bots](), [Developer Tools](), [ai observability]() Tags: [agentic-ai-generative-ai](), [ai-agent](), [architecture](), [featured](), [framework](), [llms](), [open-source](), [openshell](), [orchestration](), [performance](), [python](), [security-for-ai](), [top-stories](), [trustworthy-ai-cybersecurity]() ### AI overview The article presents NVIDIA Labs Object-Oriented Agents (NOOA), a Python-based framework for building AI agents as single classes. It outlines six harness capabilities intended to improve model performance, including typed interfaces, live object references, code-based actions, programmable loops, explicit state, and model-callable APIs. ### Source excerpt Building a great AI agent isn't just about choosing the right models. The harness is the architecture surrounding the model. How it renders context, executes... ## Separating signal from noise in coding evaluations DevFeed: [Separating signal from noise in coding evaluations]() Original publisher: [Read original article]() Published: 2026-07-08T13:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI-assisted coding](), [Developer Tools]() Tags: [agentic-coding](), [ai-models](), [benchmark](), [data-quality](), [evaluation](), [research](), [testing]() ### AI overview OpenAI audits SWE-Bench Pro and estimates that about 30% of its tasks are broken, identifying strict, underspecified, low-coverage, and misleading tests as sources of unreliable coding-evaluation results. ### Source excerpt A new analysis from OpenAI reveals issues in SWE-Bench Pro, a popular coding benchmark, raising concerns about reliability and accuracy in evaluating AI models. ## Fragments: July 6 DevFeed: [Fragments: July 6]() Original publisher: [Read original article]() Author: Martin Fowler (martin@martinfowler.com) Published: 2026-07-06T12:53:00Z Content type: opinion Language: en Sources: [Martin Fowler]() Topics: [AI-assisted coding](), [Developer Tools]() Tags: [agentic](), [architecture](), [conference](), [cost](), [design](), [developer-experience](), [software-engineering]() ### AI overview Fragmentary reflections from a software-development retreat describe agentic development moving from aspiration into production use, while noting unresolved questions about effective practices, token costs, and the role of architecture and design. ### Source excerpt Last week, Thoughtworks ran a second Future of Software Development Retreat, this time in Europe. As with the previous event, I'll be sharing some fragmentary thoughts on this. There were five parallel streams, so I could, at best, only attend ⅕ of sessions. This isn't an event that forms conclusions, rather one that allows those exploring to share what they've found, and their visions for the future. The bliki post lists all the writing I've run into on this, by myself and others. I'll be updating it as more posts appear. Giles Edwards-Alexander "noticed a real difference between the retreats": Where Deer Valley had hesitancy and a belief that there was something here even if we weren't yet sure what it was, Engelberg had confidence: the value is here. As I explained to a colleague today, this was not a conference for true believers: the evidence is in. What does the evidence say? Well, that was less clear. Some patterns and practices are emerging (one attendee had catalogued dozens of agentic engineering pattern libraries) but they are emerging. There is more work to do to truly establish what is effective, and when. Greg Herlein felt similarly: Reading the reports of the February event, when a lot of these same folks last got together, the conversation was about what agentic development might look like. Aspirational. More about what was coming. This time? Everybody in the room was doing it. Shipping it. Not slides - production. The whole debate about whether this changes software engineering is over. People have stopped arguing about whether a while ago. They're arguing about how, and the how is getting real. On a more micro level, I noted two other things. Firstly, there was much talk now about harness engineering, when that wasn't even a term in Utah - an example of how rapidly things are moving. Secondly people are now worrying about the cost of tokens, where before folks were wanting to do almost anything to incentivize people to talk to The Genie. ❄ ❄ A ques ## Introducing computer use in Gemini 3.5 Flash DevFeed: [Introducing computer use in Gemini 3.5 Flash]() Original publisher: [Read original article]() Author: Mateo Quiros Published: 2026-06-24T16:30:01Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [Google AI](), [AI Bots](), [Developer Tools]() Tags: [agentic](), [api](), [automation](), [browser](), [computer-use](), [enterprise](), [flash](), [gemini](), [none](), [safety](), [software-testing](), [tool]() ### AI overview Gemini 3.5 Flash adds built-in computer use for building agents that act across browser, mobile, and desktop environments. The release also describes safeguards for sensitive actions and indirect prompt injection. ### Source excerpt A look at the built-in computer use tool in Gemini 3.5 Flash. ## 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 ## OpenAI to acquire Ona DevFeed: [OpenAI to acquire Ona]() Original publisher: [Read original article]() Published: 2026-06-11T00:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [AI Bots](), [Developer Tools]() Tags: [agents](), [cloud](), [codex](), [company](), [developers](), [enterprise](), [openai](), [orchestration](), [production](), [security]() ### AI overview OpenAI plans to acquire Ona to add secure, persistent cloud execution and orchestration to Codex. The stated goal is to support long-running agents in customer-controlled cloud environments with security, governance, and operational controls. ### Source excerpt OpenAI plans to acquire Ona to expand Codex with secure, persistent cloud environments, enabling long-running AI agents across enterprise workflows. ## What's new in DevTools (Chrome 149) DevFeed: [What's new in DevTools (Chrome 149)]() Original publisher: [Read original article]() Author: Matthias Rohmer X GitHub LinkedIn Bluesky Published: 2026-06-02T07:00:00Z Content type: release Language: en Sources: [developer.chrome.com: Blog]() Topics: [Developer Tools](), [AI-assisted coding](), [debugging](), [Web Development]() Tags: [agents](), [ai](), [ai-coding](), [announcement](), [chrome](), [cli](), [code-completion](), [css](), [debugging](), [gemini](), [http](), [javascript](), [mcp](), [mcp-server](), [release](), [tools]() ### AI overview Chrome 149 updates DevTools for agents and AI assistance, adding stable MCP server and CLI support, experimental WebMCP debugging, and richer page-analysis features. ### Source excerpt DevTools for agents is stable, AI assistance gets major upgrade with Lighthouse and widgets, new WebMCP debugging tools. ## OpenAI and Dell partner to bring Codex to hybrid and on-premise enterprise environments DevFeed: [OpenAI and Dell partner to bring Codex to hybrid and on-premise enterprise environments]() Original publisher: [Read original article]() Published: 2026-05-18T10:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [AI-assisted coding](), [Developer Tools](), [incident]() Tags: [agentic](), [agents](), [ai](), [ai-coding](), [ai-factory](), [api](), [chatgpt](), [code](), [codex](), [coding](), [company](), [data](), [dell](), [enterprise](), [infrastructure](), [openai](), [partnership](), [workflows]() ### AI overview OpenAI and Dell are collaborating to deploy Codex in hybrid and on-premises enterprise environments. The effort centers on connecting Codex with Dell's data platform and exploring integration with Dell AI Factory for AI applications and enterprise workflows. ### Source excerpt OpenAI and Dell partner to bring Codex to hybrid and on-premise environments, helping enterprises deploy AI coding agents securely across data and workflows. ## Meta AI Developer Assistant now live for Business Messaging DevFeed: [Meta AI Developer Assistant now live for Business Messaging]() Original publisher: [Read original article]() Published: 2026-05-13T16:30:29Z Content type: release Language: en Sources: [Facebook Developers]() Topics: [Artificial Intelligence](), [Meta](), [Messaging](), [Developer experience](), [Developer Tools]() Tags: [ai](), [developer](), [developer-experience](), [developer-tools](), [messaging](), [meta](), [whatsapp]() ### AI overview Meta introduces the Meta AI Developer Assistant for WhatsApp Business developers and Tech Providers. Integrated into onboarding flows, it provides contextual guidance, troubleshooting, and answers grounded in official documentation. ### Source excerpt Today, we're introducing the Meta AI Developer Assistant for WhatsApp Business developers and tech providers. It's an AI-powered assistant that meets developers exactly where they get stuck, with contextual guidance, troubleshooting, and answers grounded in our official documentation. ## How Superset built the IDE for AI agents on Vercel DevFeed: [How Superset built the IDE for AI agents on Vercel]() Original publisher: [Read original article]() Author: Rini Vasan Published: 2026-05-10T14:00:00Z Content type: article Language: en Sources: [Vercel News]() Topics: [AI-assisted coding](), [Developer Tools](), [ci]() Tags: [agents](), [ai](), [ai-agents](), [ci](), [cloud](), [coding](), [deployment](), [ide](), [infrastructure](), [next-js](), [platform-engineering](), [vercel]() ### AI overview Superset built an IDE for coordinating up to 10 coding agents in parallel across isolated workspaces and branches. The article explains how automatic preview deployments and parallel infrastructure on Vercel support that workflow. ### Source excerpt Superset on Vercel 1,000-1,400 deployments per week ~600 preview deployments per day ~30 second average build time 57-64% week-over-week DAU growth Software development with AI started as a single engineer chatting with a single agent about a local repo. Today, developers direct fleets of agents in the cloud, but traditional tools were built for the old shape of the job: IDEs, terminals, and review systems designed for one developer moving one ticket at a time. Co-founders Kiet Ho, Satya Patel, and Avi Peltz, all former CTOs at YC-backed companies, built Superset as the IDE for multi-agent development. It runs up to 10 coding agents in parallel, each in its own isolated workspace. Developers use it to direct teams of agents generating code across multiple branches simultaneously. Running a team of agents in parallel changes what the platform underneath has to do. The product Superset offers its users only feels parallel because nothing on the platform forces the work to wait. If any layer slows down, even briefly, the parallelism on top collapses with it. Parallel agents need parallel infrastructure This workflow has a dependency that's invisible from the product surface. Every agent thread needs its own isolated environment, every branch needs a live URL, and every change needs a safe place to run. Without instant provisioning, parallel agents stop being parallel. CI pipelines have to be configured per branch, preview environments have to be managed by hand, and deploys back up behind one another. For a team running a dozen agents at once, that serialization is what breaks the product. Twelve workflows collapse into one queue, and a task that should take minutes takes hours. The developer is back to waiting, which is the exact problem Superset exists to solve. Six Next.js projects, no platform team Vercel was the default choice from the start, as all three founders had built on it at previous companies. From day one, Superset ran six Next.js projects on Vercel: the ## Running Codex safely at OpenAI DevFeed: [Running Codex safely at OpenAI]() Original publisher: [Read original article]() Published: 2026-05-08T12:30:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI Bots](), [Developer Tools](), [ai observability]() Tags: [agent](), [codex](), [coding](), [logs](), [openai](), [policy](), [sandbox](), [security](), [telemetry]() ### AI overview OpenAI describes how it runs Codex safely through sandboxing, approval policies, managed network access, and telemetry. The approach keeps routine low-risk work moving while requiring review for higher-risk actions. ### Source excerpt How OpenAI runs Codex securely with sandboxing, approvals, network policies, and agent-native telemetry to support safe and compliant coding agent adoption. ## OpenAI models, Codex, and Managed Agents come to AWS DevFeed: [OpenAI models, Codex, and Managed Agents come to AWS]() Original publisher: [Read original article]() Published: 2026-04-28T00:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [AI-assisted coding](), [Developer Tools]() Tags: [agents](), [ai](), [amazon-bedrock](), [aws](), [codex](), [gpt](), [models](), [openai](), [product](), [security]() ### AI overview OpenAI and AWS announced a limited-preview expansion that brings OpenAI models, Codex, and Amazon Bedrock Managed Agents to AWS environments. The article emphasizes enterprise use of GPT-5.5 and Codex through Amazon Bedrock for application development, software engineering, and agentic workflows. ### Source excerpt OpenAI GPT models, Codex, and Managed Agents are now available on AWS, enabling enterprises to build secure AI in their AWS environments. ## Scaling Codex to enterprises worldwide DevFeed: [Scaling Codex to enterprises worldwide]() Original publisher: [Read original article]() Published: 2026-04-21T00:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [AI-assisted coding](), [Developer Tools](), [Incident response]() Tags: [codex](), [company](), [enterprise](), [incident-response](), [openai](), [partners](), [software-development]() ### AI overview OpenAI says Codex has surpassed 4 million weekly developers and is expanding enterprise adoption through Codex Labs and partnerships with global systems integrators. The article describes uses across the software development lifecycle, including test coverage, code review, feature development, repository understanding, and incident response. ### Source excerpt OpenAI launches Codex Labs, partners with with Accenture, PwC, Infosys, and others to help enterprises deploy and scale Codex across the software development lifecycle, and hits 4M Codex WAU. ## Applications of AI at OpenAI DevFeed: [Applications of AI at OpenAI]() Original publisher: [Read original article]() Published: 2026-04-10T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI, ML & Data Engineering](), [Developer Tools](), [AI Chat](), [debugging]() Tags: [ai](), [apis](), [applications](), [automation](), [chatgpt](), [codex](), [debugging](), [developer-tooling](), [development](), [openai](), [openai-academy]() ### AI overview An overview of how OpenAI products and APIs support real-world AI use, including ChatGPT for general-purpose work and Codex for AI-assisted software development. ### Source excerpt Explore how OpenAI products like ChatGPT, Codex, and APIs bring AI into real-world use for work, development, and everyday tasks. ## Agentic Infrastructure DevFeed: [Agentic Infrastructure]() Original publisher: [Read original article]() Author: Tom Occhino Published: 2026-04-09T07:00:00Z Content type: article Language: en Sources: [Vercel News]() Topics: [AI Bots](), [Developer Tools]() Tags: [agentic](), [agents](), [ai](), [deployment](), [infrastructure](), [llms](), [vercel]() ### AI overview The article argues that coding agents and AI-native software require agentic infrastructure: programmatic, deterministic deployment surfaces that let agents build, test, verify, and ship software autonomously. ### Source excerpt Every generation of software eventually demands a new generation of infrastructure. First, we configured servers by hand. Next, the cloud turned infrastructure into APIs. Then, a more important shift: infrastructure derived from the application itself. LLMs and coding agents are driving the next transition, and it's happening fast. In the three months leading up to this post's publication, weekly deployments on Vercel doubled, with agents driving the growth. At the time, over 30% of deployments were initiated by coding agents, a 1000% increase from six months earlier. As of September 2026, that share has risen to more than 50%. Software is now agentic Agents are building, testing, and shipping AI-native software, and they're doing it at a velocity that breaks traditional operations. Vercel projects deployed by coding agents are 20 times more likely to call AI inference providers than those deployed by humans. Agents are writing software that uses AI, and agents are building agents. As the final actor shifts from human to machine, infrastructure has to adapt again. It has to work for software that acts on behalf of users, writes itself, and increasingly needs to understand its own behavior in production. This new generation of agentic software demands Agentic Infrastructure. It's not one evolution, but three: Infrastructure for coding agents to deploy to Infrastructure for building and running agents Infrastructure that itself is agentic 1. Infrastructure for coding agents to deploy to The bottleneck for agentic engineering is operational friction. When a coding agent writes a feature, it requires a place to run, test, and verify the output, which ultimately means it needs a URL. If the path from code to running system involves manual Terraform state or clicks in a cloud console UI, the autonomous loop breaks. Agents need programmatic, deterministic deployment surfaces. This is why immutable deployments, preview URLs on every commit, and instant rollbacks aren't just ## Securing the Agent Skill Ecosystem: How Snyk and Vercel Are Locking Down the New Software Supply Chain DevFeed: [Securing the Agent Skill Ecosystem: How Snyk and Vercel Are Locking Down the New Software Supply Chain]() Original publisher: [Read original article]() Author: Randall Degges Published: 2026-02-17T05:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [AI Bots](), [dev-tools](), [Developer Tools]() Tags: [agent-skill](), [ai](), [api](), [awareness](), [blog](), [cli](), [co-created](), [developer](), [interest](), [partnership](), [related-content](), [security](), [skills](), [supply-chain](), [supply-chain-security](), [tech](), [vercel]() ### AI overview Snyk and Vercel are integrating security analysis into the skills.sh marketplace to scan AI-agent skills before installation. The article explains why agent skills create a distinct software supply-chain trust risk because agents may interpret instructions and execute actions with access to developer environments. ### Source excerpt Snyk has partnered with Vercel to secure the skills.sh ecosystem, integrating real-time security scanning to detect malicious payloads and prompt injections before they reach your machine. Discover how this partnership is locking down the future of agentic AI. ## Introducing the Laravel AI SDK DevFeed: [Introducing the Laravel AI SDK]() Original publisher: [Read original article]() Author: Laravel Team Published: 2026-02-05T09:50:50Z Content type: release Language: en Sources: [Laravel Blog]() Topics: [AI Chat](), [AI Bots](), [Developer Tools]() Tags: [agent](), [ai](), [anthropic](), [api](), [laravel](), [openai]() ### AI overview Laravel introduces a beta AI SDK that provides a unified, Laravel-native API for multiple AI providers. It supports text, image, speech, embeddings, reranking, files, agent workflows, and structured JSON output. ### Source excerpt Build AI-powered Laravel applications with a unified SDK. Generate text, images, audio, and more across OpenAI, Anthropic, and other providers with clean, organized code. [Next page]()