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ExternalSoftware Engineering
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Decoding the Observability Pipeline: A Java Architect's Guide to Metrics, Logs, and Traces

If you’ve spent any time modernizing a Java-based microservices architecture recently, you’ve likely hit the "Observability Wall." The ecosystem is drowning in tools. We hear about Prometheus, Loki, OpenSearch, Zipkin, Tempo, OpenTelemetry, Grafana Alloy, Datadog—the list never ends. Observability isn't about collecting tools; it’s about establishing reliable data streams that tell you exactly what your system is doing. In this article, we’re going to demystify observability architecture in the

javaobservabilityarchitecture
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ExternalSoftware Engineering
DEV Community

Best AI for Code: Top 4 Tools Tested and Ranked

Most AI coding comparisons test "Hello World" apps and call it a day. I ran every major tool through the same three-stage gauntlet: a simple build, a complex full-stack application, and multiple rounds of revisions. The best AI for code should hold up under all three. Most do not. Here is what I found, scored using a 100-point rubric across four equal categories: interface and experience, AI agent effectiveness, deployment, and pricing. No favorites going in. The scores reflect what actually hap

ainewstools
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ExternalSoftware Engineering
DEV Community

Stopping the LLM from calling the same tool twice (and other things it shouldn't)

A user gave one of our agents this query: "Get the products from our catalog, summarize them in a nice doc, share the doc with X, and send them an email asking for feedback." The agent called create_doc seven times. Seven empty Google Docs showed up in the user's Drive. No catalog summary. No sharing. No email. The trace looked roughly like this: [ { "role": "assistant", "tool_call": { "name": "create_doc", "arguments": { "title": "Product Catalog Summary" } } }, { "role": "tool", "tool

aiagentsllm
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ExternalSoftware Engineering
DEV Community

Why Your Data Lineage Is Still a Spreadsheet (and How to Fix It in 5 Minutes)

The Heisenberg Problem: Why Observing Your Data Pipeline Breaks Your Documentation Or: How to stop lying to your auditors (and yourself) There's a principle in quantum mechanics that says the act of observing a particle changes its behavior. Your data lineage documentation has the opposite problem: the moment you stop observing it, it collapses into a superposition of "probably still accurate" and "completely wrong." You know the drill. Six months ago, someone built a meticulous lineage diagra

aidevtoolsopensource
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ExternalSoftware Engineering
DEV Community

API Versioning: URI vs Header – Which Is More Practical?

What Is API Versioning? – Brief Definition and Why It Matters API versioning allows clients to consume new features without breaking existing contracts. When I added a new reporting endpoint in a production ERP system, I made versioning mandatory to avoid breaking existing integrations. In my first experience, after a week with %23 error reports, I spent an additional 2 hours on maintenance due to missing versioning. These kinds of issues echo not only in client code but also in logs and monit

tutorials
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ExternalSoftware Engineering
DEV Community

Do "Ok" ao "Termonuclear": Elevando a Barra do Code Review com IA

Se você programa profissionalmente, provavelmente já usa IA para gerar trechos de código, preencher boilerplates ou acelerar refatorações pequenas. Mas se a sua rotina de revisão automatizada ainda se resume a caçar vírgulas perdidas, discutir nomenclatura ou repetir checklist cosmético, você está deixando muito dinheiro — e muita sanidade mental — na mesa. A revisão automatizada com IA só fica realmente poderosa quando deixa de perguntar “o código compila?” e passa a perguntar “esse design mere

aicodereviewmattpocock
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ExternalSoftware Engineering
DEV Community

AI Hallucinations Are Not a Bug. They Are the Architecture. Here Is How I Deal With Them Now.

I do a lot of research. Legal documents, technical specs, academic papers, regulatory filings. For a while I thought using an LLM would cut my fact-checking time in half. It made it three times worse. Not because the models were obviously wrong. The dangerous part is how convincingly right they sound. After months of getting burned I eventually found a workflow that actually holds up, but before I get to that it is worth understanding why the problem exists at the architecture level, because tha

aillmproductivity
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