SShortSingh.
TopTechnologyAI & MLStartupsProgrammingScienceBusinessFinanceCrypto & Web3World
Back to feed
Programming

How to Build Production-Grade Observability Using OpenTelemetry, Prometheus, and Grafana

0
·2 views

Modern software engineering teams increasingly require full observability — not just uptime monitoring — to diagnose complex failures in distributed systems. A detailed technical guide published on DEV Community outlines how to implement production-ready observability using OpenTelemetry, Prometheus, and Grafana for Node.js microservices. The approach uses OpenTelemetry's vendor-neutral SDK to instrument a sample checkout endpoint, capturing both request throughput and latency distribution with high-cardinality attributes. Metrics are exported via a Prometheus exporter and can be visualized through Grafana dashboards, giving engineering teams direct insight into root causes during incidents. The guide also recommends pairing this observability stack with an automated CI/CD validation workflow to ensure instrumentation integrity across deployments.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

How Strategic Logging Helps Developers Debug Production Issues Faster

A frontend developer building an AI startup's web interface discovered the value of strategic logging when authentication stopped working after a staging deployment. Unlike basic console.log statements, strategic logging captures data state, execution context, and action outcomes at each step of a program's flow. By adding detailed logs throughout the auth process, the developer quickly identified that API responses were returning a 500 error, pointing the problem to the backend rather than the frontend. The logs revealed that an incomplete database migration was silently crashing the login endpoint on every attempt. What could have taken days of misdirected debugging was resolved in minutes by tracing the issue to its actual source.

0 comments Read more at DEV Community
0
ProgrammingDEV Community ·

Developer builds MT5 trading bot using Python, Smart Money Concept, and machine learning

A software developer has detailed the architecture of an automated trading bot built with Python and connected to the MetaTrader 5 platform via its official Python package. The bot uses Smart Money Concept techniques — including Order Blocks, Fair Value Gaps, and Liquidity Sweeps — to identify potential trade entries instead of relying on traditional indicators. A machine learning layer, trained on over 200 logged trades, applies logistic regression to filter low-quality signals based on contextual features such as session timing, RSI, and ATR. The developer chose Python over MQL5 for its richer ecosystem, enabling easier integration with machine learning libraries, Telegram, and web dashboards. Key lessons from the project include the importance of flexible entry conditions, consistent feature engineering between training and inference, and walk-forward backtesting to prevent overfitting.

0 comments Read more at DEV Community
0
ProgrammingDEV Community ·

Ten-Layer Framework Proposed for Building AI Skills From Prompts to Business Workflows

A structured ten-layer framework for constructing AI Skills has been outlined, targeting teams building large model applications that go beyond simple conversational use. The framework progresses from the most basic single-file prompt Skill, which relies entirely on the model's language understanding, to complex multi-step business workflow automation. Early layers focus on well-crafted prompts and reference materials, while higher layers introduce decision trees, external tool calls, and end-to-end process automation. Each layer is defined by clear capability boundaries, typical file structures, and evaluation criteria to help teams identify their current maturity level. The framework aims to bridge the gap between simple prompt engineering and fully automated, closed-loop business execution.

0 comments Read more at DEV Community
0
ProgrammingDEV Community ·

Developer releases AI Guard Gateway v0.1.0 to block prompt injection and endpoint hijacking

A developer known as MagoPredator has released AI Guard Gateway v0.1.0, an open-source reverse proxy designed to secure AI inference APIs such as Ollama and LiteLLM against unauthorized access and abuse. The tool was built in response to documented vulnerabilities involving exposed AI endpoints that can be exploited without any traditional exploit, simply by accessing unsecured APIs anonymously. AI Guard Gateway addresses key threat vectors including endpoint hijacking, prompt injection, resource exhaustion, and PII leakage through components such as mandatory API key or JWT authentication, sliding-window rate limiting, and automatic redaction of sensitive data. The project was developed using Spec-Driven Development and validated through pytest test coverage, a Bandit security scan returning zero high or medium severity findings, and continuous analysis via a SonarCloud CI/CD pipeline. The gateway is available on GitHub under the AGPL-3.0 license and is intended to be deployed in front of any language model serving infrastructure.

0 comments Read more at DEV Community