Name: The Good, The Bad, and The Future of Dev Containers
Start: 2025-02-25T17:00:00Z
End: 2025-02-25T17:00:56.000Z
Location: BrightTALK
Rating: 4.5

Coder is the AI software development company leading the future of autonomous coding. Coder helps teams build fast, stay secure, and scale with control by combining AI coding agents and human developers in one trusted workspace. Coder’s award-winning self-hosted Cloud Development Environment (CDE) gives enterprises the power to govern, audit, and accelerate software development without trade-offs. Learn more at coder.com.

Transform your development workflow with this hands-on 90-minute workshop. Discover how AWS and Coder eliminate "it works on my machine" issues while delivering AI-powered coding assistance through Amazon Q Developer and Anthropic Claude. Deploy production-ready cloud development environments on EKS that scale automatically and maintain enterprise-grade security.

• Deploy Coder on AWS EKS with high availability & security
• Create AI-powered templates for instant developer productivity  
• Build agentic workflows with Amazon Q & AWS Bedrock integration

AWS + Coder Workshop: Build Faster, Safer Dev Environments

As AI coding agents become more sophisticated, our development infrastructure needs to evolve. In this video, see why AI agents keep breaking on local setups and what actually works instead.

Related Resources:
→ ByteMonk Blog: https://blog.bytemonk.io/
→ System Design Course: https://academy.bytemonk.io/courses
→ LinkedIn: / bytemonk  
→ Github: https://github.com/bytemonk-academy

⏱️ TIMESTAMPS:
0:00 - AI Agents Keep Breaking on Local Setups
1:15 - Why Your Laptop Wasn't Designed for This
2:45 - The Solution: Cloud Development Environments
4:30 - Demo: Multiple Workspaces Running in Parallel
7:00 - Real-World Benefits & Getting Started

Why AI Coding Agents Keep Breaking on Your Laptop

AI coding agents aren’t coming — they’re here. And they’re reshaping how development happens in the enterprise.

Join Coder’s EMEA Field CTO, Eric Paulsen, and EM360Tech’s Shubhangi Dua for a deep dive into the real-world impact of AI agents in modern development environments. From bug fixes to dependency updates, AI agents are now working like junior engineers — helping developers focus on what matters most.

We’ll unpack:
- What agentic AI really means (and what it doesn’t)
- Why cloud development environments are critical to scale securely
- How financial institutions are training internal AI models to protect data and boost productivity
- What it takes to deploy AI agents without sacrificing control, compliance, or developer experience

This session is built for engineering leaders in regulated industries, platform engineers scaling modern dev environments, and security teams setting the guardrails for AI adoption. AI is no longer assistive. It’s autonomous. Enterprises need the right infrastructure to keep up.

Are AI Agents the Future of Developer Productivity in the Enterprise?

Credit Karma processes petabytes of data daily in BigQuery, powering critical insights across its FinTech ecosystem. Local hardware limits and inconsistent environments were slowing their data teams. By deploying configurable, scalable workspaces, they transformed how teams experiment, collaborate, and innovate while maintaining air-gapped security and cutting cloud costs.

Slash the environment setup from 30 minutes to seconds
Run secure AI agent prototypes in an air-gapped infrastructure
Optimize GPU usage to significantly reduce cloud spend

Join Coder VP of Product Ben Potter and Credit Karma's Amit Mishra, Engineering Manager, and Harish Gaggar, Staff Software Site Reliability Engineer, for a live look at how they're driving ROI, accelerating development, and setting the standard for secure FinTech operations.

How Credit Karma Accelerated Secure, Compliant, and Scalable Data workflow with Coder

Explore with Amazon Aurora PostgreSQL and Coder engineers how open-source-based RAG and coding agent solutions help organizations improve developer experience for production deployment. Launch GenAI prototype with a single click using Coder. Explore how it works using Aurora PostgreSQL as a knowledge base for Amazon Bedrock LLM and fully managed end-to-end RAG workflow using PostgreSQL pgvector as your vector sto re. Modify it to real-world GenAI application just in a few asks to an assistant, and perform well architected review with a focus on security of the your application.

Build prototype to production with proven workflows. Learn to build end-to-end—from a Coder template to an AWS-enabled AI agent that stores, searches, and retrieves enterprise domain specific context with Amazon Aurora PostgreSQL pgvector and autonomously executes multi-step reasoning for development workflows. Learn how to enable long-term agent memory capabilities for AI assistants and build vector similarity search at scale. Deploy Coder to enable secure, reproducible cloud development environments (CDEs) with Terraform-native provisioning and governance—purpose-built for human and AI agent teams on AWS.

Key takeaways:
Working reference architecture, demonstrable reference code, and best practices for prototyping to production deployment.

Who should attend:
• Developers and technical decision makers interested in developing and scaling AI agents
• Application teams building GenAI applications with PostgreSQL RAG/agent workflows

Data for GenAI Developers: Building agentic AI and RAG solutions with autonomous coding

Jennifer Spykerman didn’t just dust off her old dev skills—she rebuilt them using AI. After two decades in leadership, managing multi-million dollar federal programs and complex engineering teams, she opened up a Claude chat and started coding again.

Her mission: automate the mess of federal RFP responses with something smarter than copy-paste.

In this episode, the DefenseLogic AI CEO and founder shares how she shipped a working MVP that flags compliance gaps, tracks requirements, and speaks fluent bureaucrat, all using AI tooling and a custom-built stack. 

If your backlog is full of repetitive tasks that no one wants to do, this episode will shift how you think about using AI to get unblocked and ship smarter.

In this episode, you’ll learn:
1. How to use agents to get leverage in locked-down environments
2. What building with Claude and open tools actually feels like solo
3. Where today’s AI tools break and why it matters for platform teams

Things to listen for: 
(00:00) Meet Jennifer Spykerman
(02:04) Re-entering code after 20 years away
(05:03) What led her to start coding again
(06:40) The real cost of government proposals
(10:40) Why she built it herself through vibe coding
(15:01) Her AI tool stack and why it works
(19:32) Debugging Claude like a junior engineer
(22:12) Inside the architecture of her AI tool
(28:22) Mastering prompt engineering for consistency
(37:23) Handling security risks for government use
(42:30) How she cracked FedRAMP and NIST with AI’s help
(51:03) Why replacing dev teams is a bad bet

From Managing NASA Projects to AI Vibe Coding with Jennifer Spykerman

84% of devs use AI. Only 33% trust it. Why are we pretending this is fine?

Jason Baum, Project Leadership at Selenium, is here to say what most won’t: AI is creating as many problems as it solves. Development is faster, sure, but the cost is rising. More bugs. More hallucinations, too. Teams are losing time chasing the promise of AI without fixing the foundation it runs on.

In this episode, Jason and Nicky get real about the AI productivity paradox—why developers are feeling slower and what teams can do to get quality back on track without tossing the whole tech out the window.

If you’ve been asking yourself, “Am I actually building faster or just debugging more?” this is the reset you need.

In this episode, you’ll learn:
Why senior devs are the most skeptical AI users
How AI is shifting the definition of “quality” in code
What it takes to turn AI into a teammate instead of a time sink

Things to listen for: 
(00:00) Meet Jason Baum
(01:19) Developers are using AI that they don’t fully trust
(03:15) How hype outpaced AI’s actual capabilities
(05:59) The AI calculator analogy: helpful, but dangerous without skill
(08:47) Is AI improving or breaking software quality?
(11:03) The real purpose of “vibe coding” tools
(13:15) Why senior developers are wary of AI’s output
(18:45) Critical context: What AI still can’t grasp in code
(22:45) The rise of citizen developers using AI tools
(25:05) Risks and realities of using MCPs with LLMs
(31:20) Can AI-driven testing really catch what matters in code?
(34:08) Why AI is better at analyzing tests than writing them
(39:25) Why most AI projects fail before they even start
(45:12) Preserving developer intuition and problem-solving in the AI era
(51:02) Final thoughts: where developer-AI collaboration goes next

Are We Shipping Code We Don’t Trust? The AI Productivity Paradox

With great power comes great responsibility—and yes, coding agents can backfire if they’re not thoughtfully deployed. Instead of boosting productivity, they can frustrate developers, slow down progress, and even diminish your competitive edge.

We’ll unpack three strategies organizations should embrace when scaling agentic coding agents enterprise-wide. Drawing from firsthand lessons and internal experiments—where agents effectively resolved nearly half the tasks we threw at them—we’ll share real-world scenarios showcasing engineering work AI agents excelled at. We’ll also highlight where they fell short, detailing what went wrong and what we’d do differently next time.

Learn from our experiences and ensure your organization’s deployment of AI coding agents empowers your developers, ignites innovation, and solidifies your market leadership.

AI Coding Agents: Three Keys to Successfully Scaling in the Enterprise

In this episode of the AppDevANGLE, Paul Nashawaty (Principal Analyst, Application Development) and Rob Whiteley (CEO of Coder) dive deep into the challenges facing modern developers. Research shows developers spend up to 66% of their time on environment maintenance rather than innovation, highlighting an urgent need for change. Rob explains how Coder is solving this by shifting the entire software development lifecycle—including the critical step of writing code—to the cloud using secure, modular, container-based workspaces. Learn how platform engineering is evolving to support scale, security, and the future of AI coding agents.

Key Highlights
- The Developer Productivity Crisis: Over two-thirds of developer time is spent on maintenance and inefficient workflows, not innovation.
- Shifting to the Cloud: The strategy of moving the last piece of the software development lifecycle to the cloud for safety and productivity.
- Governing AI Coding Agents: The challenge of introducing AI tools like autonomous agents while maintaining security, compliance, and protecting intellectual property (IP).
- The Changing Definition of a Developer: How the rise of data scientists, low-code users, and "vibe coders" are expanding who writes applications, increasing the need for centralized governance.
- Container-Based Workspaces & Standardization: How Configuration as Code allows platform teams to standardize developer environments, dramatically accelerating onboarding and improving CICD reliability.
- Massive Cloud Cost Savings: Achieving an estimated 75% reduction in cloud spend by utilizing on-demand and auto-scaling compute to eliminate resources that sit idle 70% of the time.
- The Future of Platform Engineering: Creating secure, sandboxed workspaces where both human developers and AI agents (treated as untrusted third parties) can collaborate safely.

How to Cut Cloud Costs and Govern AI in Developer Workspaces

PsiQuantum has spent the last eight years at the forefront of fault-tolerant quantum computing (FTQC), building the systems, tools, and technical foundations needed to deliver the world’s first utility-scale quantum computers. As fault-tolerant quantum computing becomes a near-term reality, developing algorithms for practical, error-corrected quantum hardware is more important than ever for realizing the full potential of this technology.

PsiQuantum recently unveiled Construct, the industry’s most comprehensive FTQC algorithm development platform. Built to bring everything together in one place, Construct delivers visual circuit design, project storage, educational resources, and a powerful Quantum Development Environment (QDE) designed to handle billion-gate simulations.

Join us to:
1. Learn how PsiQuantum uses Coder to deliver secure, scalable development environments through Construct.
2. Discover how their team provides fast, on-demand compute power, packages complex research tools for domain-specific workflows, and gets users up and running with zero setup time.
3. See how researchers use Construct to develop and analyze the costs of state-of-the-art quantum chemistry algorithms like Quantum Phase Estimation, a technique used in estimating the ground state energy of specific chemicals on a quantum computer.

If you’re working in quantum research or building platforms for cutting-edge development, join us for a behind-the-scenes look at what it takes to support the future of computing.

How PsiQuantum Accelerates Algorithm Research with Coder

What if I told you that you are developing your applications the wrong way? Yes, even in 2025! Think about it: We host our applications in the cloud, our CI/CD pipelines run in the cloud, our code is stored in the cloud (GitHub, GitLab, etc.)... but where do we actually write and test that code? Often, it's stuck on our local machines. 

But What happens if you break your computer, well your code is safe, its stored in the cloud, but what about your dev environment. Reinstalling everything would take days and it would be a huge headache. But what if we could also put our dev environment into the cloud? How about the cloud development environment?

The Right Way to Develop Modern Applications  (You're Probably Doing It Wrong)

Is your local machine lying to you? Ben Potter, VP of Product at Coder, gets real about why AI agents aren’t living up to their potential without the right infrastructure. Developers and platform teams are dealing with bloated systems and inconsistent environments, but the real problem is a missing layer. 

In this episode, we uncover the key to making AI work: a development stack that’s secure, scalable, and reliable. If you’re tired of wrestling with broken dev environments and want AI to make more sense in your workflows, this episode’s for you. 

In this episode, you’ll learn: 
1. The real reason AI agents aren’t living up to their hype 
2. How to create a secure and scalable AI development stack 
3. Why your current environment setup is holding you back from AI success

Your Local Machine Might Be Lying. Ben Potter on AI Agents in Development

The fear of "vibe coding" (non-developers using AI to write software) mirrors historical fears about DIY home improvement. Yet Home Depot built a trillion-dollar market by recognizing that proper tools, education, and boundaries enable regular people to tackle appropriate projects safely. 

This session shows how open source communities can lead the citizen development revolution by applying the same three-pillar approach that made DIY construction successful. 

Nicky Pike, Field CTO at Coder, shares real examples of where citizen development works, where it fails, and what we learned about building guardrails that actually work. You'll see how open source principles of visibility, community review, and shared knowledge create natural boundaries that keep people in their lane while unlocking massive productivity gains. 

Beyond just faster development, we found that citizen developers actually help their IT teams get unstuck from endless backlogs and start shipping things that matter. Join me to see practical examples of how open source tools and governance models create a blueprint for democratizing software development without the disasters.

From Fear to Framework: How Open Source Enables Safe Citizen Development

Dev Containers provide a standardized way to define and share development environments. By capturing tooling, dependencies, and configuration in code, they remove inconsistencies between machines and eliminate the “It works on my machine” problem. In this session, we’ll explore how platform teams are implementing Dev Containers in practice—from initial setup to organization-wide adoption.

What we’ll cover:

- How Dev Containers boost reproducibility and teamwork
- Why use Dev Containers in Coder, not local machines
- Ways Coder supports Dev Containers, plus best practices

Join us as we walk you through how Coder moves workflows from one-off setups to fully standardized, scalable, and collaborative Dev Containers.

From Setup to Scale: Managing Dev Containers in Coder

Many engineers welcome the chance to improve legacy code, especially when they’re the ones driving it. But when priorities shift mid-stream, timelines slip, and support wavers, what started as an exciting technical investment can quickly become a source of frustration, or worse, another abandoned effort.

So how can engineering managers support meaningful refactoring without burning out their teams or losing momentum?

This panel explores how modern teams are redefining refactoring, not as a risky detour, but as a strategic, AI-assisted part of building better systems. We’ll share practical lessons on how to structure, scope, and sustain large-scale improvements, even in the face of delivery pressure.

Join us to learn:
1. How to lay the groundwork for a sustainable refactor
2. Where to leverage AI for faster, safer legacy transformation
3. How to build a culture that tackles technical debt with consistent leadership support

How to Refactor Without Killing Morale

Mastering Modern Development: A Deep Dive into Dev Containers

In a world where development efficiency and collaboration reign supreme, the spotlight is on Development Containers (Dev Containers). Dev Container is an emerging spec that promises to deliver reproducible environments to all developers. Join us as we break down the good, the bad, the future of Dev Containers.

Join us as we break down the good, the bad, the future of Dev Containers. In this webinar we’ll explore:

- Reproducible development environments. From heightened development efficiency to eradicating the age-old "It works on my machine" dilemma, Dev Containers hold the promise of a consistent and streamlined development journey.

- Dive into the popularity of Dev Containers. Understand potential roadblocks, from resource demands to learning curves. Tackle security concerns with best practices derived from the experiences of industry experts.

- Walk away with resources and discover multiple ways to leverage Dev Containers, along with potentially faster alternatives for organizations.

The Good, The Bad, and The Future of Dev Containers

In this presentation, we explore how several organizations built Internal Development Platforms (IDPs) centered around developers’ primary workspace, their integrated development environments (IDEs). Since IDEs are essential to developers’ productivity and workflows, we’ll dive into real-world strategies for gathering developer feedback, selecting pilot groups, and streamlining workflows without introducing disruptions. This session offers actionable insights into building platforms that foster efficiency and developer satisfaction.

Building the IDE Golden Path

In this demo, Staff Product Manager Bartek Gatz walks through our improved automation, detection, and compatibility with Dev Containers.

- Automatically detect Dev Containers for quick rebuilds or reconnections in the UI
- Cached Dev Containers automatically start, reducing dev setup
- Set up once, use many times configuration, with a very simple to use Dev Container resource and a sample Template.

Docs: https://coder.com/docs/admin/templates/managing-templates/devcontainers

Blog post: https://coder.com/blog/launch-week-2025-instant-infrastructure#dev-containers-improved-native-support

Dev Containers: Improved native support

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The Good, The Bad, and The Future of Dev Containers

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