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KNOWLEDGE, REFINED • Builders 🔧 • September 22, 2026
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📊 Scanned 8,125 articles • Selected top 12 role-critical insights for today.
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🤔 CONTRARIAN TAKEAWAY
The other side of ai market outlook
599 articles say ai boom, but 213 argue ai bubble.
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🚀 AI & Innovation
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🚀 AI & Innovation
via Vercel News • 9 min read
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Giving your agents access to your tools, data, and services is what makes them useful. As agents perform deeper work across systems, authenticating and authorizing that access becomes central to your application architecture.Today, agent access is usually granted through long-lived provider tokens stored in your environment variables, provisioned for everything your agent might need. These tokens are shared across every user, never expire, and give your agent full reach across every task, no matter how small the job.A vault makes that token harder to steal. It doesn't make it less dangerous. The problem is what happens when the token leaks: everything it can touch is now exposed.We built Vercel Connect to solve this problem. Now in Public Beta, Vercel Connect replaces the stored token with runtime credential exchange. You register a connector once. When your agent has work to do, your app proves its identity to Vercel Connect and gets back a short-lived credential, scoped to the task. Everything you used the token for still works. The agent just requests access each time instead of holding it.Register a connector once, then reuse it across projects and environmentsA connector is a reusable connection between your Vercel team and a provider like Slack or GitHub. You create it once from the dashboard or the CLI, then attach it to the projects and environments that need it, with project-level access controls.The relationship with the provider becomes a single entity you can see and manage, not something scattered across a dozen environment variable panels where a rotation means hunting down every copy.Your coding agent can run this setup too. Install the vercel-connect skill with npx skills add vercel/vercel-plugin --skill vercel-connect, and it can create and attach connectors for you.Request scoped tokens at runtimeWith a connector in place, the agent asks for a credential only when it has work to do. The @vercel/connect SDK returns a token you use immediately against the provider API, and no provider secret lives in your app.Tokens are short-lived, with a lifetime that depends on the provider. The SDK refreshes them automatically, so you never rotate a secret by hand. That leaves one question. If your app holds no secret, what proves it's allowed to ask?The app proves its identity with OIDCThe proof is an identity your app already has. Every deployment on Vercel gets an OIDC identity, and when your app or agent requests a token, the SDK presents that identity to Vercel Connect. Vercel Connect verifies it, checks that the project and environment are allowed to use the connector, and returns the provider credential. That round trip is the runtime credential exchange.The same identity is available during local development through vercel link and vercel env pull, and outside Vercel, the SDK accepts a Vercel access token. Either way, there is no provider secret in your app to leak, commit, or copy between environments.Scope each token to exactly what the task needsNot every task needs the same reach, even within a single agent. One step might read a repository while the next opens an issue. Each requests exactly the access it needs, and the request itself sets limits. A request can include:Provider scopesAn installation IDResource restrictionsProvider-specific authorization detailsGitHub is the sharpest example because it can restrict a token to specific repositories and permissions.The deployment agent can read that one repository and do nothing else. A fine-grained GitHub App install can be narrow too, but an install is a standing grant, set up once and trusted from then on. This limit exists for one request, one task. Least privilege becomes the shape of the request.Act on behalf of a specific user, with per-user token scopingA shared bot token gives every user's request the same identity and reach. Vercel Connect lets you set that identity. Switch subject from the app to a named user, and the token acts on that user's behalf, scoped to what that user authorized.When a user first grants access, startAuthorization runs the consent flow through a callback URL, a webhook, or a device code. After that, the agent requests tokens as that user.Contain access by environment, and revoke it when you need toA connector is attached to the projects and environments you choose, so you can run a separate connector for development, preview, and production instead of pointing one at all three. When each environment has its own connector with an authorization grant and scopes, a credential compromised in development cannot be replayed against production.Separate connectors limit where a credential works, but they don't pull back access already issued. That's normally the painful part. With a stored token, that means a rotation. You mint a new secret, update every place the old one lived, and redeploy whatever depended on it. With Vercel Connect, you revoke the connector's tokens, either your own or all of them.What revoking does depends on the provider. Where the provider supports revocation, Vercel Connect revokes the token at the provider. Where it does not, Vercel Connect stops issuing new tokens for that grant, and a token already issued stays valid at the provider until it expires. That is a real limit on any provider without a revocation API, and the shorter the provider keeps its tokens, the smaller that window is.Drive event-driven agents from verified Slack triggersSo far, your agent has been the one reaching out. It requests a token and calls a service when it has work to do. Triggers run the other way. A connected service sends an event to your app, and your agent responds.Vercel Connect receives the provider's webhook, verifies it, and forwards it to your project. Trigger forwarding is in beta and supports Slack, GitHub, and Linear today. A Slack connector can forward its verified webhooks to up to three of your projects, so a message in Slack can wake an agent that acts on it.The flow runs end to end without a provider secret in your app:A user posts a message in Slack.Slack sends the event to Vercel Connect.Vercel Connect verifies the event against the Slack signing secret it holds, then forwards it to your Vercel app, re-attested with its OIDC identity.Your app verifies that attestation, then requests a scoped runtime token.The agent acts and responds.The Slack signing secret does not disappear. It moves server-side to Vercel Connect, which verifies the upstream webhook and re-signs the forwarded request with an identity your app can check. Your app holds no bot token to act with and no signing secret to verify against.Vercel Connect meets your code where it already isUnderneath everything is one call. Whether your agent is built on the AI SDK, runs as a background job, or is a loop you wrote yourself, it asks for a token the same way, with getToken. Around that call are adapters for the stack you already run. Better Auth (@vercel/connect/betterauth) and Auth.js (@vercel/connect/authjs) get provider configs in the shape they expect, and @vercel/connect/ai-sdk and @vercel/connect/mcp do the same for AI SDK tools and MCP clients. The Nuxt starter gives you a working app to build on, with GitHub and Linear connected, no provider secret, and no OAuth refresh token stored in its database.Frameworks can take this further and make the connection itself declarative. In eve, the open-source agent framework by Vercel, a connection is one file, and the @vercel/connect/eve adapter supplies that connection's credential.There is no token handling in the agent's code, because connect maps the consent flow, refresh, and error cases onto eve. Any MCP server that supports OAuth can become a connector by its URL, which is how mcp.linear.app ends up with the same scoped-token model as Slack or GitHub.The same adapter wires a Slack channel. One connectSlackCredentials call covers both directions: the bot credentials for sending and the webhook verification for receiving.The two secrets a Slack integration usually keeps in your environment, SLACK_BOT_TOKEN and SLACK_SIGNING_SECRET, are gone from your app. There is nothing left to provision, store, or rotate.Access becomes something you request, scoped to the taskAn agent becomes more useful the more it can reach, which is exactly why access is the part to get right. Every system the agent can touch is a system someone could reach through a leaked token. With runtime credential exchange, nothing is provisioned for everything. Nothing is shared by every user. Nothing lasts forever. Nothing reaches past the task in front of it.Credential management used to be architecture. It was rotation scripts, secrets copied between environments, and bot tokens broad enough that you hoped no one leaked them. Now you store none of it. You request access the moment the agent needs it, scoped to the task.Start building with Vercel ConnectRegister a connector, request your first runtime token, and connect an agent to Slack or GitHub without storing a provider secret.Coding agents just need a prompt:Frequently asked questionsWhat is Vercel Connect?Vercel Connect lets your agents and services access external systems on behalf of your users and teams. Instead of storing provider credentials in long-lived environment variables, you request user-authorized tokens at runtime with project-level access controls.What problem does Vercel Connect solve?It removes long-lived third-party secrets from your runtime while still letting agents act on external APIs. You register a connector for a provider, link it to projects and environments, and request provider tokens at runtime.When should I use Vercel Connect instead of Integrations?Use Vercel Integrations for marketplace-managed installs and provider-managed products in the Vercel Marketplace. Use Vercel Connect when you need delegated runtime credentials and user authorization for agent workflows, such as an agent that needs project-scoped access to a Slack workspace.Which connectors are available?Vercel Connect supports generic OAuth and API key connectors, as well as managed connectors for Slack, GitHub, Linear, and other platforms. It also includes support for 100+ preset connectors, such as Notion, Shopify, Resend, Sanity, and Workday.How does pricing work?Pricing is based on token requests. The Hobby plan includes 5K token requests per month at no additional cost. On Pro and Enterprise plans, token requests are billed at $3 per 10K token requests.What are the current Beta limitations?Trigger forwarding is limited to Slack, GitHub, and Linear, connector branding fields cannot be fully cleared after you set them, and token revocation, token lifetime, and scope granularity depend on provider support. Read more
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💡 WHY THIS MATTERS FOR YOUR ROLE
Secure and manageable agent integration is key to unlocking the potential of AI in enterprise applications.
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🚀 AI & Innovation
via The GitHub Blog • 1 min read
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A rewrite this size wasn't affordable before agents. Here's what porting the Copilot agent runtime to 800,000 lines of production Rust actually took. The post Migrating the GitHub Copilot runtime to Rust, using Copilot appeared first on The GitHub Blog.
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💡 WHY THIS MATTERS FOR YOUR ROLE
Real-world examples of using AI to improve core engineering processes and infrastructure are critical for future development.
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🚀 AI & Innovation
via Vercel News • 4 min read
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Zo Computer on Vercel20x reduction in retry rate (7.5% → 0.34%)99.93% chat success rate (up from 98%)P99 latency cut 38% (131s → 81s)New models added in less than 1 minuteEvery company has servers that store data, run services, and do work around the clock. Consumers just have apps. Rob Cheung, co-founder of Zo Computer, is closing that gap. Zo is a personal AI cloud: your own servers and data that power an always-on agent."Cloud is one of the best computing models of all time, and consumers have zero direct access because it's so complicated," explained Rob Cheung, co-founder and CEO of Zo. "Now, with AI, it's finally possible for all of us to have cloud computers." Zo is a full computing environment, not just a chatbot. Rob laughs about his mom running servers and databases without knowing it. People use Zo to manage small businesses, do research, organize finances, and track health data.The 8-person company is two and a half years old and they have an ambitious goal: to onboard one million new users to personal cloud computing in 2026. That means millions of AI model calls every day, and when Zo users text their agent like a friend, they expect the same responsiveness.Death by a thousand adaptersZo gives users access to any model they want, and supports bring-your-own-key. That means their backend has to talk to every major provider: OpenAI, Anthropic, MiniMax, GLM, Fireworks, and more.Before they moved to Vercel, that meant custom adapter code for each model. Every provider required different handling for images, different key management, and different edge cases. On top of the code complexity, Zo's team was managing retries, provider routing, and fallback logic themselves.Every time a provider shipped a new model, an engineer had to write a new adapter, test the edge cases, and run the deployment pipeline. With new models released weekly, it was a constant drag on a small team building a consumer product, and their users felt it.Zo's baseline for AI model calls was a 98% success rate with a 7.5% retry rate. That means 1 in 50 messages failed or retried, adding up to tens of thousands of model fallbacks every day.AI SDK + AI Gateway: two layers, one integrationZo moved to Vercel's AI SDK and AI Gateway, which solved two distinct problems.AI SDK replaced the custom adapter code. Instead of per-provider implementations with bespoke edge case handling, Zo's engineers got a unified interface for every model, from image support to response format normalization.AI Gateway replaced the infrastructure-level complexity. Retries, fallback routing, provider health monitoring, and uptime were all handled at the routing layer in Vercel instead of in Zo's codebase. Rob's co-founder built APIs at Stripe, where developer experience was the product. He describes the combined effect of AI SDK and AI Gateway the same way: everything just works, and the pieces you don't see matter most. New model support went from an hour-long, multi-file code change to adding a config string in 30 seconds. The day MiniMax shipped M2.7, Zo had it live for users immediately. No adapter code, no edge case testing, no deploy cycle.For an 8-person team focusing on onboarding their first million users to personal cloud computing, cutting out interruptions for model support has been a huge relief.20x improvement in reliabilityDuring the rollout, Zo ran Vercel and non-Vercel routes simultaneously, creating a live A/B comparison under identical production conditions.The results:PeriodRoutePOST errorChat successRetry rateAvg attemptsBefore switchNon-Vercel4.59%99.73%7.52%1.12After switchNon-Vercel10.38%97.86%17.07%1.29After switchVercel0.45%99.93%0.34%1.00The non-Vercel route actually degraded during the same period that Vercel held steady. Retry rate dropped from 7.5% to 0.34%, a 20x improvement. Average attempts per chat hit 1.00, meaning virtually every request succeeded on the first try.On MiniMax M2.5, Zo's most-used model, the latency improvement was significant. In an apples-to-apples comparison over the same window, Vercel handled 18,139 chats versus 21,105 on non-Vercel and still performed better across the board:Average latency improved 25.7%P95: 46s → 34s (25% improvement)P99: 131s → 81s (38% improvement)For Zo's users, the P99 number matters most because they text their agents constantly throughout the day. A 131-second worst-case wait breaks that experience completely, but now 99% of requests complete in under 81 seconds.By the end of the test, 91.88% of Zo's traffic routed through Vercel, handling 3.3x larger context windows (42,500 average input tokens vs. 12,700) at a lower error rate than the non-Vercel path.Scaling to a million personal cloud ownersVercel handles Zo's AI layer through AI SDK and AI Gateway and hosts their public-facing marketing site. With reliable AI infrastructure and no adapter code to maintain, the team can focus on the product instead of the plumbing.With the pace of model developments in AI, Rob used to worry about the work required to keep up. “Now I don’t worry about it,” he said, “because with Vercel, the infrastructure just works.”Zo Computer is a personal AI cloud platform that gives every user their own cloud computer, housing data, services, and a personal agent. Users interact through a conversational interfaces like iMessage, or log in and use the environment directly. Founded two and a half years ago, Zo is an 8-person team based in New York City. Learn more at zo.computer. Read more
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💡 WHY THIS MATTERS FOR YOUR ROLE
Demonstrates tangible performance and reliability gains for AI applications on a modern development platform.
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☁️ Enterprise Tech
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☁️ Enterprise Tech
via AWS News Blog • 1 min read
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Amazon EC2 R9g and R9gd instances powered by AWS Graviton5 are now generally available, delivering up to 25% better compute performance than R8g, ideal for databases, in-memory caches, and real-time analytics.
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💡 WHY THIS MATTERS FOR YOUR ROLE
Understanding the latest AWS compute offerings is crucial for optimizing infrastructure spend and performance.
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☁️ Enterprise Tech
via AWS News Blog • 1 min read
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DynamoDB now supports native vector search with single-digit millisecond latency at 99%+ recall. It is designed for any scale, even trillions of vectors and requires zero infrastructure management.
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💡 WHY THIS MATTERS FOR YOUR ROLE
Native vector search in a managed database simplifies building AI-powered features and reduces operational overhead.
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☁️ Enterprise Tech
via Vercel News • 3 min read
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Notion Workers let you write and deploy code to give Custom Agents new powers: sync external data, trigger automations, call any API. With Workers, developers can build agents that sync CRM data on a schedule, open issues when error rates spike, and turn Slack threads into formatted content. Under the hood, every Worker runs on Vercel Sandbox.The problem: safely running code from any developer or agentNotion wanted to let anyone extend their platform with custom code. That's a hard infrastructure problem, but an even bigger security problem. Every Notion Worker runs arbitrary code generated by a third-party developer or agent, on behalf of a Notion user, potentially inside an enterprise workspace.Without proper isolation, a Worker would run in the same environment as the Custom Agent, with access to its secrets, permissions, and everything else in that execution context. A single prompt injection could exfiltrate credentials or access another user's data.The requirements were clear:Hard isolation: One Notion Worker can never access another's data or stateCredential security: Notion Workers need API keys to talk to external services, but those secrets can never be exposed to the code itselfNetwork controls: Enterprise customers need guarantees about the external services a Worker is allowed to reachScale: Workers need to support millions of users running concurrent executions without performance degradationState preservation: Workers need fast cold starts, which require the ability to snapshot and restore filesystem stateEconomics: A billing model that is built for agents with low CPU utilization ratesWhy Vercel SandboxVercel Sandbox runs each Notion Worker in an ephemeral Firecracker microVM. Every VM boots its own kernel, providing stronger isolation than containers. Each execution gets its own filesystem, its own network stack, and its own security boundary. When the Notion Worker finishes, the microVM is either destroyed or snapshotted for later retrieval.To support workloads like Notion Workers at scale, Vercel Sandbox provides several critical capabilities:Credential injection. Sandbox's firewall proxy can intercept and inject API keys into outbound requests at the network level, so credentials never enter the execution environment. For agent-driven workloads, this eliminates the most dangerous prompt injection vector: an agent being tricked into exfiltrating secrets. (We wrote about this architecture in depth in security boundaries in agentic architectures).Network policies. Sandbox supports dynamic network policies that can be updated during runtime without restarting the process: start with internet access to install dependencies, then lock down egress before running untrusted code. Platform builders can pass these controls through to their own customers.Snapshots. Install dependencies once, snapshot the filesystem state, and resume from that snapshot on subsequent invocations. Combined with active-CPU billing, where CPU costs only accrue when your code is actually executing, not waiting on I/O, this keeps costs predictable as usage scales.The bigger picture: Notion as a developer platformNotion Workers isn't a one-off feature. It's the beginning of Notion becoming a developer platform.This shift requires infrastructure that Notion shouldn't have to build. Secure code execution, credential management, network isolation, file-sytem based snapshotting: these are hard problems that compound as the platform scales. Vercel Sandbox handles the infrastructure complexity so Notion can focus on the developer experience. What developers are building with Notion WorkersNotion Workers support three main patterns: third-party data syncing, custom automations, and AI agent tools. Developers use them to sync external data, such as CRM records, analytics, and support tickets, into Notion on a schedule. A Worker can also be attached to a button, triggering arbitrary code with a single click. And when Notion's custom agents invoke Workers as tool calls, they become far more capable than agents limited to pre-built integrations.Extend your platform with Vercel SandboxNotion Workers requires the same capabilities as other agent platforms. Any platform that wants to let users or agents run custom code faces the same set of problems: isolation, credential security, network controls, and scale.Vercel Sandbox provides these as capabilities out of the box. If you're building a platform that needs to run untrusted code, whether for AI agents, developer plugins, or workflow automation, then this is how you do it. Read more
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💡 WHY THIS MATTERS FOR YOUR ROLE
Securely executing arbitrary code is a fundamental challenge for building extensible and safe AI-powered platforms.
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☁️ Enterprise Tech
via Vercel News • 5 min read
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To a user, an app with a Next.js frontend and a FastAPI backend feels like one product, and the same should be true for the engineers who build it. Instead, the two pieces are often deployed across different clouds with different development and deployment workflows.Today we're introducing Vercel Services in public beta, which lets you run multiple frameworks in one Vercel Project. This unlocks:Atomic deployments: Your frontend, backend, and other services stay in sync and deploy or roll back togetherShared preview deployments: See how any change affects all your servicesInternal service communication: Services can talk to each other without routing through the public InternetVercel handles the rest: routing, builds, deployments, and auto-scaling in production. The developer experience you already know from Vercel now covers your entire application.Compose applications with Vercel ServicesDeclare your services under the services key in vercel.json keeping the routing configuration explicit:Services can be mounted to a shared routing table without the need for a reverse proxy or CORS.The services configuration is recognized at multiple levels of the Vercel platform:The Deployments panel shows a visualization of services graphThe Logs UI allows filtering by individual serviceThe vercel dev CLI automatically runs all services giving you a production-like environment locallyService bindingsServices can talk to other services internally, without routing through the public Internet. Here's an example how the new bindings configuration key enables that:Now the JavaScript frontend code can talk to the Python service internally via the URL stored in the BACKEND_INTERNAL_URL environment variable:Service-to-service traffic stays on the Vercel network rather than egressing to the public internet. Many independent services can become one application connected by the same wiring, rather than separate deployments that you stitch together across hosts.Framework-defined infrastructureMost frameworks run on Vercel with zero configuration. Framework-defined infrastructure means that each service's framework is auto-detected and auto-provisioned, from FastAPI and Flask for Python to Express and Hono for TypeScript, with first-class support for Go and Rust servers.Services run on Fluid compute, autoscaling with traffic, while you pay only for active CPU time. But framework-defined optimizations go even deeper. With Django, for example, we automatically detect where static assets live and serve them from the CDN.The full stack platformVercel Services gives Vercel a structured way to build and run your application.Those services run on a platform that includes everything any backend could need, whether you're building APIs, agents, or background workers: compute, data, networking, background work, and secure connections to external services.Isolated compute for agent servicesAgents can read files, run commands, and write code. But that capability needs to be secure. Vercel Sandbox gives each agent its own Linux computer: a filesystem, a shell, Docker support, and its own kernel, completely isolated from your deployments. Agents can execute code, grep files, and spin up Redis or Postgres as dependencies. Nothing inside the sandbox can touch your production environment.With automatic persistence, state carries between sessions, and on Pro, sandboxes can run for up to 24 hours.Real-time backends with WebSocket supportVercel Functions handle persistent WebSocket connections in every Vercel runtime, whether your backend is written in Node.js, Python, or Go. They work with standard WebSocket libraries like Socket.IO, and because connections run on Fluid compute with Active CPU pricing, you only pay for the time spent processing messages, not the time a connection sits idle.Reach external services with Vercel ConnectAgents and backends need to reach services outside your project (Slack, GitHub, managed databases), which usually means storing a long-lived secret in your environment. Vercel Connect replaces that with a short-lived credential your app requests at runtime, scoped to the task in front of it. There is no long-lived secret left to leak.Databases and storage for your servicesYou can provision a database from the Vercel Marketplace in a few clicks, with credentials injected for you, from providers like Neon, Supabase, and from AWS with Aurora PostgreSQL, Aurora DSQL, and DynamoDB. Amazon OpenSearch Serverless adds full-text and vector search, and Vercel Blob handles object storage. You can query and manage any of it without leaving Vercel, and both Blob and the AWS integrations authenticate with short-lived OIDC tokens rather than stored secrets.Run durable workflows and background jobsVercel Queues processes background jobs off the request path. Vercel Workflow handles durable multi-step processes that survive crashes and redeploys. And Vercel Cron runs work on a schedule. This is the part of a backend that outlives a request, and it runs on the same platform as everything else, so the long-running side of your backend needs no separate infrastructure.Connect privately and run on the right computeWhen a backend needs to reach a private database or an internal network instead of a public service, Secure Compute, static IPs, and VPC peering open that path.Functions run for up to 30 minutes on Pro and Enterprise, and Python backends deploy with up to 500mb of dependencies.Underneath all of it, Fluid compute is the default for new projects, and Active CPU pricing bills for the time your code is actually running rather than the time it waits, the right shape for idle-heavy backend and AI work.A single platform for everything you shipVercel Services brings your frontend, backend, and supporting services into one project. They build together, preview together, deploy together, and communicate internally by default.Combined with Vercel's built-in primitives for compute, data, queues, workflows, cron, secure networking, and sandboxed agent environments, you can run the full stack without stitching together separate platforms.To start composing services, read the Vercel Services documentation and the routing and communication guide. If you are new to running a backend on Vercel, the zero-config backends post is the place to begin. Read more
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💡 WHY THIS MATTERS FOR YOUR ROLE
Simplifying full-stack deployment and management is a constant goal for engineering leaders aiming for efficiency.
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☁️ Enterprise Tech
via Vercel News • 1 min read
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Vercel has a new low-level untrusted and ephemeral compute platform—designed to give us the control needed to securely and efficiently manage and run builds. Since November 2023, this compute platform, internally codenamed "Hive", has powered Vercel’s builds, enabling key improvements like enhanced build machines and a 30% improvement in build performance.The platform operates on the fundamental assumption that we’re executing potentially malicious code on multi-tenant machines, requiring it to be safe, reliable, performant, and cost-effective. It’s architected to handle multiple use cases and can be composed in different ways depending on what’s needed. Most recently, Hive allowed us to reduce provisioning times for Secure Compute customers from 90 seconds to 5 seconds, while also improving their build speeds.We built Hive because we needed finer control and more granular management to continuously improve Vercel’s infrastructure, to meet the growing demands of our customers and to fulfill our vision of delivering the best development experience in the world. Read more
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💡 WHY THIS MATTERS FOR YOUR ROLE
Understanding the underlying infrastructure of popular platforms can inform internal build system strategies.
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