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24 articles in Code and Tech · matching “engineering”

Code and Tech2026-07-31 · 5 min read
The Developer’s Advantage: Why Modern Product Marketing Requires Code
In tech, there’s an old assumption that marketing and software engineering exist in two separate universes. Product managers and engineers write the code; marketers write the slogans and launch the campaigns. That boundary has officially dissolved. As software products become more technical—ranging from API-first platforms to developer tools and AI models—the way we market them has fundamentally shifted. Modern product marketing isn't just about compelling messaging anymore; it's about code, developer experience (DX), and technical empathy. Whether you're marketing a developer-focused platform like Stripe or leveraging data to drive growth in a SaaS business, understanding the coding side of product marketing is quickly becoming a cheat code for growth.
- tech
- coding
- trending
- information
- +1
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Code and Tech2026-07-27 · 7 min read
The Evolution of Vibe Coding: Now vs. 2025
Explore how 'vibe coding' is transforming from aesthetic setups to a holistic, AI-augmented developer experience. Discover what coding feels like today and what to expect by 2025.
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Code and Tech2026-07-27 · 7 min read
Beyond the Keyboard: How AI is Reshaping the Developer's Day
The daily life of a developer is rapidly evolving from manual typing to sophisticated prompting, critical reviewing, and strategic steering of AI tools. Explore how this shift impacts productivity, skill sets, and the future of software development.
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Code and Tech2026-07-27 · 8 min read
Beyond Syntax: Architecture Judgment, Code Review at Scale, and the Art of Taste
As software development matures, core skills evolve. Learn why architecture judgment, efficient code review, and cultivating 'taste' are becoming indispensable for senior developers and how to master them.
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Code and Tech2026-07-25 · 5 min read
The Death of "Vibe Coding" and the Rise of Systemic AI Engineering
There is a noticeable shift happening in modern software engineering. For the past year or two, developer culture was dominated by "vibe coding"—a approach where developers prompted AI agents, let LLMs write raw blocks of code, accepted PRs based on visual intuition, and relied on voice-to-text rants to dump context into models. It felt fast, magical, and transformational. However, engineering teams are encountering an inevitable wall: unmaintainable architecture. When AI handles implementation details in isolation, codebases slowly degrade into fragmented, untraceable structures. The defining challenge in modern engineering is no longer how fast can an AI write a function, but how effectively can humans govern the context and architecture that AI agents operate in.
- tech
- coding
- career
- backend
- +1
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Code and Tech2026-07-24 · 5 min read
Building Resilient Event-Driven Backend Pipelines with Go and NATS JetStream
The Problem: Direct, synchronous HTTP/gRPC microservice calls create tight coupling and cascading failures when downstream services lag or fail. The Solution: Event-Driven Architecture (EDA) using NATS JetStream decouples API handlers from heavy background jobs using persistent, low-latency messaging streams. Core Setup: Streams: Persist events to disk (ORDERS.*) with defined retention policies. Producers: Publish JSON payloads synchronously to guarantee NATS receives the write. Durable Pull Consumers: Fetch batches of messages, execute processing, and explicitly send ACK upon success or NAK to request a retry. Production Best Practices: Transactional Outbox: Prevents database state and message publishing from drifting out of sync. Idempotency: Protects against duplicate event execution using unique order IDs in a cache like Redis. Dead Letter Queues (DLQ): Prevents permanently failing or malformed messages from blocking consumer workers.
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Code and Tech2026-07-24 · 7 min read
Infrastructure as Code: Lessons from Our First Terraform Disaster
Learn from our mistakes as we recount a critical incident caused by a misstep with Terraform, highlighting key lessons for robust Infrastructure as Code practices.
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Code and Tech2026-07-24 · 5 min read
Building Agentic Workflows in Python: Beyond Basic RAG with LangGraph
Here is a quick breakdown of what the article covers: * **The Shift:** Traditional RAG follows a rigid, linear pipeline (`Retrieve -> Generate`). **Agentic RAG** introduces dynamic, self-correcting loops using the LLM as a reasoning engine within a state machine. * **Why LangGraph:** Unlike standard DAG (Directed Acyclic Graph) pipelines, LangGraph supports **cyclic workflows** (loops), enabling self-evaluation, error handling, and tool re-invocations. * **Core Architecture:** Built around three elements: * **State:** A shared dictionary (`AgentState`) tracking history and variables across steps. * **Nodes:** Python functions that execute steps (agent reasoning, evaluation, web search). * **Edges:** Conditional routes that direct execution flow based on state evaluation. * **Production Best Practices:** Always enforce **iteration limits** to prevent infinite loops, use **lightweight models** for evaluators to reduce latency, and keep evaluation separate from tool execution.
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Code and Tech2026-07-22 · 8 min read
How to Use AI Coding Agents Without Wrecking Your Codebase
Discover best practices for integrating AI coding agents into your development workflow safely. Learn to leverage AI for productivity without introducing technical debt or breaking changes.
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Code and Tech2026-07-22 · 7 min read
Mastering AI Code Generation: The Only Prompting Techniques You Need
Unlock the full potential of AI code generation with these proven prompting techniques. Learn how to craft effective prompts that deliver accurate, high-quality, and production-ready code.
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Code and Tech2026-07-22 · 7 min read
Why Most 'AI-Generated' Code in Production Is Technical Debt in Disguise
While AI tools promise faster development, blindly integrating AI-generated code into production often creates hidden technical debt. Learn why and how to leverage AI responsibly to avoid future headaches.
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Code and Tech2026-07-22 · 7 min read
How to Use AI Coding Agents Without Wrecking Your Codebase
Learn best practices for integrating AI coding agents into your development workflow safely. Discover strategies for version control, code review, and prompt engineering to leverage AI without compromising code quality.
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Code and Tech2026-07-22 · 7 min read
The New Developer Stack: Mastering Prompts, Not Just Syntax
Explore how prompt engineering is revolutionizing the developer stack, shifting focus from pure syntax to effective communication with AI. Learn the essential skills for modern software development.
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Code and Tech2026-07-22 · 7 min read
Stop Calling It Vibe Coding — It's Just Bad Code Review Practices
Dismissing subjective code review feedback as 'vibe coding' often masks deeper issues in a team's review process. Learn how to transform vague 'vibe' comments into actionable, constructive feedback that elevates code quality and fosters team growth.
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Code and Tech2026-07-22 · 7 min read
The 55% Productivity Claim: Fact-Checking AI Coding Tools
Examine the real impact of AI coding tools on developer productivity, dissecting the often-cited 55% claim and offering a balanced perspective on their benefits and limitations.
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Code and Tech2026-07-19 · 7 min read
Beyond Prompt Engineering: Building Systematic LLM Evaluation Pipelines at Scale
Discover how to move past ad-hoc prompt engineering to robust, scalable evaluation pipelines for large language model outputs. Learn about key metrics, datasets, and automated tools for consistent quality.
- llm
- evaluation
- prompt engineering
- ai
- +4
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Code and Tech2026-07-19 · 7 min read
The Allure and Pitfalls of Vibe Coding: Why Production Systems Demand More
Explore the rising trend of 'vibe coding' and its appeal for rapid development, contrasting it with the crucial demands of maintainability, scalability, and robustness required for production-ready software.
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Code and Tech2026-07-19 · 7 min read
Beyond Autocomplete: How AI Coding Agents Are Reshaping the Developer Workflow
Explore the revolutionary impact of AI coding agents like Claude Code and Devin-style tools, moving past simple autocomplete to fundamentally transform how developers build, debug, and deploy software.
- ai
- coding
- developer tools
- workflow
- +4
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Code and Tech2026-07-13 · 7 min read
AI Agents Explained: A Beginner's Guide to Autonomous Systems
Discover what AI agents are, how they work, and their potential applications in a clear, beginner-friendly explanation. Understand the core components that drive these intelligent systems.
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Code and Tech2026-07-12 · 8 min read
Boost Your Productivity: The Best AI Coding Assistants for Developers
Explore the top AI coding assistants revolutionizing software development. Discover tools like GitHub Copilot, Tabnine, and ChatGPT that enhance code generation, debugging, and overall developer efficiency.
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Code and Tech2026-07-09 · 8 min read
Mastering Clean Code: A Developer's Guide to Maintainable Software
Learn the essential principles of writing clean code to improve readability, reduce bugs, and build more maintainable and scalable software projects. This guide covers practical tips for every developer.
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Code and Tech2026-07-09 · 7 min read
Mastering the Code: Strategies to Accelerate Your Programming Learning Journey
Discover effective strategies and practical tips to significantly speed up your programming learning. From hands-on projects to smart debugging, learn how to become a more efficient and capable developer.
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Code and Tech2026-07-09 · 8 min read
Python Best Practices in 2026: Elevating Your Code for the Future
Explore the essential Python best practices for 2026, covering everything from modern dependency management and type hinting to asynchronous programming and robust testing, ensuring your code is efficient, maintainable, and future-proof.
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Code and Tech2026-07-03 · 5 min read
Moving Beyond Autocomplete: The Shift from AI Copilots to Multi-Agent Systems (MAS)
The article outlines a major shift in software development: moving from AI Copilots to Multi-Agent Systems (MAS). While traditional copilots simply act as smart autocomplete assistants requiring line-by-line human prompts, Multi-Agent Systems deploy networks of specialized AI agents (e.g., product, coding, testing, and security agents) that work together autonomously to solve complex tasks. Instead of replacing developers, this shift elevates them from manual code-writers to high-level system architects and AI orchestrators.
- tech
- coding
- backend
- it
- +1
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