标签：#FDE

We have a web server container running the nginx image. The access and error logs generated by the web server are not critical enough to be placed on a persistent volume. However, Nautilus developers need access to the last 24 hours of logs so that they can trace issues and bugs. Therefore, we need to ship the access and error logs for the web server to a log-aggregation service. Following the separation of concerns principle, we implement the Sidecar pattern by deploying a second container that ships the error and access logs from nginx. Nginx does one thing, and it does it well - serving web pages. The second container also specializes in its task - shipping logs. Since containers are running on the same Pod, we can use a shared emptyDir volume to read and write logs. Create a pod named webserver . Create an emptyDir volume named shared-logs . Create a regular container in the webserver pod from the nginx:latest image named nginx-container , and an init container from the ubuntu:latest image named sidecar-container . Add the following command to the sidecar-container "sh","-c","while true; do cat /var/log/nginx/access.log /var/log/nginx/error.log; sleep 30; done" Mount the shared-logs volume in both containers at /var/log/nginx . Ensure all containers are in a running state. What is a Sidecar Container? Think of a sidecar like a motorcycle sidecar – it's attached to the main vehicle and extends its capabilities without changing the main vehicle itself. ┌─────────────────────────────────────────────────────────────────────────────┐ │ The Sidecar Analogy │ │ │ │ ┌────────────────────────────────────────────────────────────────────────┐ │ │ │ Motorcycle: The Main Vehicle │ │ │ │ - Does its primary job (serving web pages) │ │ │ │ - Doesn't worry about extra tasks │ │ │ └────────────────────────────────────────────────────────────────────────┘ │ │ │ │ │ ▼ │ │ ┌────────────────────────────────────────────────────────────────────────┐ │ │ │ Sidecar: Adds Extra Functional

A new study estimates only 2,000 U.S. engineers have the expertise to deliver meaningful AI ROI, as enterprises race to hire forward-deployed engineers to implement AI at scale.

If you've been anywhere near AI job postings lately, you've seen the title: Forward-Deployed Engineer. Sometimes it's "Deployment Engineer" or "Solutions Engineer" or "Applied AI Engineer." Sarvam is hiring more than a hundred of them. Palantir built a large part of its business on them. OpenAI, Anthropic, and a long tail of AI startups are all competing for the same people. And yet, if you ask five engineers what an FDE is , you'll get five different answers. Let's fix that. The one-sentence definition A Forward-Deployed Engineer is an engineer who is deployed forward — to the customer — to take a powerful but generic product and make it solve that specific customer's real problem. The word "forward" is borrowed from the military sense: you're not back at HQ, you're out in the field where the actual work happens. For an FDE, "the field" is the customer's environment — their data, their workflows, their constraints, their stakeholders. Think of it as part engineer, part consultant, part founder-in-the-field. You build, but you build for someone specific, sitting right next to you . Why AI companies need this role so badly right now Here's the thing about modern AI products: they're incredibly powerful and incredibly generic . A foundation model or an AI platform can do a thousand things — but an enterprise customer doesn't want a thousand things. They want their one problem solved, with their data, inside their systems, respecting their compliance rules. That gap — between "powerful generic product" and "solves my specific problem" — is exactly where deals are won and lost. And it's too custom, too messy, and too high-stakes to solve with documentation alone. So AI companies send an engineer to close the gap in person. That engineer is the FDE. One strong FDE can be the difference between a seven-figure enterprise contract signing or walking away. That's why the role is: High-leverage — your work directly moves revenue Well-paid — companies pay up for people who can

Can a handful of engineers really do the work of an army of consultants? That’s the bet behind Ode with Anthropic — the joint venture dedicated to embedding forward-deployed engineers in enterprise firms, backed by Anthropic, Blackstone, Hellman & Friedman, Goldman Sachs and others. On this episode of TechCrunch’s Equity podcast, Rebecca Bellan sits down with Ode’s leaders Chris Taylor and Eddie Siegel, who founded Fractional AI, […]