I spent years treating Spring Boot startup time as a fact of life, the way you treat weather. Then I found out the JVM has had a fix for a big chunk of it since Java 12; it works beautifully inside Docker, and almost nobody bakes it into their images. It is called Class Data Sharing, CDS for short, and this article shows you how to make your Docker build do the work

A safer operating model treats each agent as an isolated contributor with a dedicated Git worktree, an explicit file-level contract, deterministic validation commands, and no authority to integrate directly into the protected branch. Git worktrees provide multiple linked working trees for one repository, while modern coding-agent platforms independently reinforce the same principle through isolated sandboxes, scoped write access, and controlled network permissions. 

The repository's demo reports 30x+ actor-to-worker oversubscription for that sample workload, with sub-second activation. It is a demonstration, not a production capacity guarantee. 

In this article, we'll build a pipeline that fetches real-time air quality data from the IQAir API, stores it in Neo4j as a graph, and produces complementary views of the data:

An EA tool acts as a Central Enterprise Repository (a “single source of truth”) that architects and other stakeholders use to model both the current state of the enterprise and the desired future state.

A reliable CDC design has to account for those failure modes from the beginning. That means combining Kafka and Debezium with idempotent writes, deterministic projections, controlled replay workflows, reconciliation checks, and enough recovery evidence to explain what happened when something goes wrong.

"The daily digest the pipeline sends, with new postings badged."

I've spent 13 years as an application architect. In that time, I've watched the same situation repeat itself across teams: a query is slow, the developer who wrote it has to fix it, and the tools available to them are either way too expensive or way too generic. Enterprise monitoring agents like pganalyze or Datadog Database Monitoring cost hundreds of dollars a month and require installing an agent with full database credentials. Generic AI LLMs don't know whether you're on Oracle or MySQL. There's nothing useful in between.

If you deploy AWS Lambdas through Terraform, you almost certainly use archive_file. With enough lambdas, a single terraform apply can kill the CI runner with OOM. The trickiest part is that you will not see any errors in Terraform output and have no clue what just happened.

I noticed this when my lambdas started failing — every first terraform apply after a routine change. SIGKILL from the kernel OOM killer and nothing in Terraform logs. The strange part is that reapply sometimes worked — not always on the first try, but eventually it went through. I've named the ticket "Flaky CI," and two weeks of investigation was focused on the CI itself: runner memory, parallel jobs, Docker leaks. terraform apply was the last suspect — from my perspective, there was no way or reason for it to consume so much memory.

• In regulated industries, cloud migration success is determined less by technology selection and more by how deliberately you decouple risk vectors — compliance risk, organizational hesitation, user adoption gaps, and integration changes — so no single failure can derail the whole program.
• You can successfully migrate an application to AWS while keeping data on-premises by routing through a REST API abstraction (e.g., IBM’s DB2 REST API layer) paired with dedicated AWS security groups controlling cloud-to-on-prem traffic, allowing the data migration to proceed on its own compliance and trust-building timeline.
• The most dangerous compliance gap in regulated applications isn’t declared sensitive fields — it’s free-form text fields where users may inadvertently type SSNs, credit cards, or other regulated identifiers; proactive tokenization in the application’s write path closes this gap before any audit finds it.
• Long-tenured business users carry a decade of UX muscle memory that QA testing cannot replicate; allocating real production validation time (such as a 15-day dark deployment cohort) is essential when migrating systems users have relied on daily for 10+ years.
• Before starting a regulated cloud migration, ask which risk vector each architectural decision is decoupling and whether your team is aligned on why — this single question reframes "cloud migration" from a technology project into a coordinated risk-management exercise.

Introduction

Most published writing on legacy-to-cloud migration treats it as a technical exercise: pick the stack, plan the cutover, flip the switch. In regulated industries, that framing fails — and the failure mode isn’t a missed deployment window. It’s a stalled program, a failed compliance audit, or a client who pulls back from the cloud strategy entirely.

A cloud migration in healthcare insurance is as much about regulatory risk management, organizational trust-building, and user adoption as it is about microservices and Fargate. Get the technology right and miss the risk choreography, and the project doesn’t ship.

What first got you interested in digging into complex infrastructure and systems problems?

Can we do the same thing in Neo4j?

Autonomous AI coding agents like Goose go far beyond simple code autocompletion. Built in Rust for speed and portability, Goose runs on your local machine, inspects files, runs terminal commands, and uses tools over MCP to automate complex engineering tasks.

A Familiar Routine

If you build data pipelines for a living, you've lived this story. Your PySpark job runs perfectly in a cloud notebook. You productionize it, push it through CI, deploy it to the cluster, and it fails. A dependency mismatch. A different Spark minor version. A Delta Lake protocol feature your local wheel doesn't know about. A timezone default nobody set.

The answer determines whether the architecture that follows will be auditable or not. Teams that answer it with process — "we have policies about that" — spend the next 18 months patching access-control gaps that reopen every time a new engineer joins or a new service gets wired in. Teams that answer it architecturally spend a week setting up AWS Organizations correctly and then largely stop thinking about it.

Session capture, per-line attribution, and selection-bias-free agent benchmarks, on top of the Git host you already use.

A technical overview for engineers and engineering leaders evaluating how much of their codebase is now written by AI agents - and who is accountable for it.

]]> Mon, 24 Aug 2026 17:00:01 GMT https://dzone.com/articles/3669854 Troian Serhii https://feeds.dzone.com/link/23566/17425202/agents-tools-agentic-ai In this article, we will build a simple understanding of the following:

• What a model is
• Why a model needs tools
• What tools are
• How an agent uses tools

Model vs. ChatGPT

Before understanding agents, let's clarify the difference between a model and ChatGPT. Whatever question we type into ChatGPT is sent to a model behind the scenes, which generates the response. You can think of ChatGPT as a web or mobile application — an interface through which we interact with the underlying Model/LLM.

]]> Fri, 21 Aug 2026 14:00:00 GMT https://dzone.com/articles/3669977 Ruchi Saini https://feeds.dzone.com/link/23566/17424580/bedrock-vs-vertex-vs-foundry Every few weeks, someone on my team, or in a client meeting, asks me the same question: "Which cloud should we use for our AI workloads?" I have been building enterprise integrations for over fourteen years now, and lately most of my time goes into RAG pipelines, vector databases, and agentic orchestration on top of these platforms. So I get this question a lot, and honestly, there is no single right answer. The right cloud depends on where your data already lives, what your compliance team will accept, and which models your architecture actually needs.

In this article, I want to walk through the three big players, AWS Bedrock, Google Vertex AI, and Microsoft Azure AI Foundry, and share what I have learned working with these platforms in real enterprise settings, not just from reading marketing pages.

]]> Thu, 20 Aug 2026 15:00:01 GMT https://dzone.com/articles/3671140 Balaji Venkatasubramaniyar https://feeds.dzone.com/link/23566/17423151/docker-ai-development-platform It stopped being just a packaging tool the day our onboarding doc got shorter instead of longer.

Three weeks into a new ML platform job, I asked a coworker why the 'getting started' doc had a section called 'If conda breaks, try the alternative.' He laughed in a way that told me it wasn't a joke. Every new hire spent their first two days fighting Python versions, CUDA driver mismatches, and a vector database that someone had installed locally in 2022 and nobody dared touch. We had four individuals on the team, each with distinct working setups, and "it works on my machine" was no longer a mere punchline; it had become a regular agenda item during our daily standup meetings. 

]]> Wed, 19 Aug 2026 17:00:00 GMT https://dzone.com/articles/3663882 Pruthvi Raj Seknametla https://feeds.dzone.com/link/23566/17423066/docker-llm-containerization The first time I containerized a fine-tuned Llama model for a client's internal search tool, the build finished at 38 gigabytes. I remember staring at the terminal thinking there was no way that was right. It was right. The image included a CUDA base, PyTorch with every backend compiled in, model weights baked directly into the layer, and a pip cache that had not been cleaned. 

Pushing that to our registry took eleven minutes on a good connection. Pulling it onto a fresh node during an autoscale event took even longer, and by the time the pod was ready, the traffic spike it was supposed to handle had already passed. That's the moment I stopped treating LLM containers like regular application containers, because they are not the same animal at all.

]]> Wed, 19 Aug 2026 14:00:01 GMT https://dzone.com/articles/3663880 Pruthvi Raj Seknametla