# coding assistant Published articles for coding assistant. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Build an AI Agent (From Scratch) DevFeed: [Build an AI Agent (From Scratch)]() Original publisher: [Read original article]() Author: Nicolas Fränkel Published: 2026-09-06T00:00:00Z Content type: opinion Language: en Sources: [Nicolas Fränkel]() Topics: [AI Agent](), [Large Language Model](), [Model Context Protocol (MCP)](), [Retrieval Augmented Generation (RAG)](), [Multi Agent Systems]() Tags: [ai](), [ai-agent](), [ai-agents](), [book](), [claude-code](), [coding-assistant](), [genealogy](), [llm](), [llms](), [mcp](), [multi-agent-systems](), [rag](), [review]() ### AI overview A review of Build an AI Agent (From Scratch) by Jungjun Hur and Younghee Song describes the book's coverage of LLM agents, tool use, ReAct agents, RAG, memory, planning, code execution, multi-agent systems, and agent evaluation. The reviewer considers it a fundamental introduction, while wishing the evaluation chapter were more developed. ### Source excerpt Facts 10 chapters$29.29 (at the time of this writing)315 pages Chapters Building your first LLM agentWhat is an AI agent? ## AI-assisted genealogy DevFeed: [AI-assisted genealogy]() Original publisher: [Read original article]() Author: Nicolas Fränkel Published: 2026-08-30T00:00:00Z Content type: tutorial Language: en Sources: [Nicolas Fränkel]() Topics: [Artificial Intelligence](), [data](), [GitHub](), [Software](), [Cloudflare]() Tags: [ai](), [claude-code](), [cloudflare](), [coding-assistant](), [genealogy](), [github](), [project](), [software](), [technical]() ### AI overview The article describes using AI as a tool for genealogical research. In less than a month, the author gathered more than 600 individuals and traced 12 generations in some branches, using a software-project workflow, genealogical records, GEDCOM files, GitHub or Codeberg, and Cloudflare Pages. ### Source excerpt My son recently came to me to brag about using AI to find our ancestors. While the results were correct, I didn't learn anything new, as it stopped at my grandparents. I was never very interested in my genealogy, but I decided to see if AI would be a good tool for this. TL;DR: Yes, it is, and even more than that. In a little less than one month, I managed to gather more than 600 individuals and get back 12 generations in some branches. ## MCP Clearly Explained DevFeed: [MCP Clearly Explained]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-06-15T12:57:01Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [Model Context Protocol (MCP)](), [AI Engineering](), [Protocol (disambiguation)](), [client](), [Server]() Tags: [ai-engineering](), [coding-assistant](), [integration](), [model-context-protocol-mcp](), [protocol](), [server](), [tool]() ### AI overview A beginner-friendly explanation of Model Context Protocol (MCP), a standard interface that lets AI applications communicate with external services. It describes MCP's purpose, core roles, capability discovery, data requests, action invocation, session establishment, and compatibility features. ### Source excerpt Plus: Big upgrades are coming to LUC. Vote on what you'd like to see first. ## Harness May 2026 Product Updates: 60+ New Features DevFeed: [Harness May 2026 Product Updates: 60+ New Features]() Original publisher: [Read original article]() Author: Chinmay Gaikwad Published: 2026-06-03T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [MCP](), [MCP Server](), [Development](), [Artificial Intelligence](), [ai-coding](), [Security](), [Claude]() Tags: [2026](), [agentic](), [ai](), [ai-coding](), [ai-infrastructure](), [api](), [claude](), [claude-code](), [coding-assistant](), [connectors](), [cves](), [development](), [features](), [infrastructure](), [integration](), [mcp](), [mcp-server](), [product-updates](), [security](), [updates]() ### AI overview Harness reports more than 60 product updates shipped in May 2026 across AI-native development, software delivery, security, cost management, and engineering insights. Highlights include AI cost tracking, AI adoption metrics, Claude connector access, and expanded MCP Server capabilities. ### Source excerpt See 60+ Harness updates from May 2026 across AI-native development, software delivery, security, artifact management, cost visibility, and engineering insights. | Blog ## AI gateways: why and how DevFeed: [AI gateways: why and how]() Original publisher: [Read original article]() Author: Nicolas Fränkel Published: 2026-05-31T00:00:00Z Content type: article Language: en Sources: [Nicolas Fränkel]() Topics: [Amazon API Gateway](), [LLMs](), [Artificial Intelligence](), [Routing (disambiguation)](), [Claude](), [Security](), [litellm](), [Caching](), [Scalability](), [Load Balancing](), [observability]() Tags: [ai](), [ai-gateway](), [anthropic](), [api-gateway](), [backend](), [caching](), [claude](), [claude-code](), [coding-assistant](), [decoupling](), [devstral](), [gateway](), [litellm](), [llm](), [llms](), [load-balancing](), [mistral](), [observability](), [open-source](), [routing](), [scalability](), [security](), [technical]() ### AI overview This article explains how AI gateways apply the API gateway pattern to AI clients and LLM backends. It discusses using a gateway to centralize model and provider management, governance, cost control, routing, caching, failover, load balancing, and observability, and considers routing Claude Code requests to another provider. ### Source excerpt Before working for 2 years on the Apache APISIX API gateway, I was mainly oblivious to API gateways. It's only by working with them that I understood their value. Decoupling the client and the server unlocks a lot of options: moving authentication to the API Gateway, securing APIs, deduplicating API requests, etc. In this post, I want to describe how the same pattern applies to AI. AI gateways AI gateways work in a similar way. ## AI agent reliability requires recovery infrastructure for partial failures DevFeed: [AI agent reliability requires recovery infrastructure for partial failures]() Original publisher: [Read original article]() Author: Melanie Warrick Published: 2026-04-01T00:00:00Z Content type: opinion Language: en Sources: [Temporal Blog]() Topics: [Artificial Intelligence](), [reliability](), [Resilience](), [agent observability](), [Machine learning](), [coding assistant](), [Neural Network](), [image recognition](), [Google](), [recommendation systems]() Tags: [agent](), [agents](), [ai](), [ai-agents](), [coding-assistant](), [google](), [image-recognition](), [neural-network](), [recommendation-systems](), [reliability](), [resilience](), [temporal-voices]() ### AI overview AI agents can reason through complex tasks and run long workflows, but failures during execution can compound when systems cannot checkpoint progress, recover from partial failures, or resume work. The article argues that dependable AI requires durable infrastructure and resilience in addition to better models, placing current agent reliability challenges in the context of earlier AI failures. ### Source excerpt Smart AI agents still fail mid-workflow. Learn why solving the AI reliability gap requires durable infrastructure, not just better models. ## The AI Multiplier: Where Developers Actually Get Leverage DevFeed: [The AI Multiplier: Where Developers Actually Get Leverage]() Original publisher: [Read original article]() Author: Brian Jenney Published: 2026-03-07T21:57:25Z Content type: opinion Language: en Sources: [Brian Jenney]() Topics: [Artificial Intelligence](), [AI-assisted coding](), [coding assistant](), [coding](), [Software]() Tags: [ai](), [autocomplete](), [coding](), [coding-assistant](), [context](), [developers](), [refactor](), [tests]() ### AI overview This commentary describes how software developers can gain leverage from AI through progressively more advanced usage. It outlines coding-assistant tasks such as generating functions, refactoring code, writing tests, and explaining errors, then discusses using AI as a planning partner to analyze systems, identify risks, and propose plans before code is written. ### Source excerpt Last night I met up with Alex Lau, the author of Keep Calm, Code On, while he was in San Francisco. ## AI's impact on Tailwind Labs, revenue, and open-source sustainability DevFeed: [AI's impact on Tailwind Labs, revenue, and open-source sustainability]() Original publisher: [Read original article]() Author: TheAnkurTyagi Published: 2026-01-26T12:41:38Z Content type: opinion Language: en Sources: [ByteSizedBets]() Topics: [Artificial Intelligence](), [Tailwind CSS](), [coding assistant](), [coding]() Tags: [ai](), [ai-coding](), [coding-assistant](), [engineering](), [revenue](), [tailwind]() ### AI overview This opinion article discusses reported financial and staffing difficulties at Tailwind Labs and considers how AI coding assistants may be affecting Tailwind's adoption, monetization, and the sustainability of open-source developer tools. ### Source excerpt You didn't agree to this, but it's happening. ## Temporal is hiring an Agentic Coding PM to improve AI coding assistant performance DevFeed: [Temporal is hiring an Agentic Coding PM to improve AI coding assistant performance]() Original publisher: [Read original article]() Author: Ethan Ruhe Published: 2026-01-15T00:00:00Z Content type: opinion Language: en Sources: [Temporal Blog]() Topics: [agentic-coding](), [coding assistant](), [AI Agent](), [Benchmark](), [Developer experience](), [Development]() Tags: [agent](), [agentic-coding](), [ai-agent](), [benchmark](), [coding-assistant](), [developer](), [developers](), [development](), [temporal-voices]() ### AI overview Temporal describes how AI coding agents are changing developer experience and announces a dedicated Product Manager role focused on improving AI coding assistant performance for Temporal. The role will use benchmarks, supporting assets, and feedback from developers to measure and improve results. ### Source excerpt The world of engineering is changing in response to AI. Here's how we're adapting on our team. ## Pop quiz: what time was it? DevFeed: [Pop quiz: what time was it?]() Original publisher: [Read original article]() Author: Dave Cheney Published: 2025-12-18T00:38:52Z Content type: tutorial Language: en Sources: [Dave Cheney]() Topics: [coding](), [GitHub Copilot](), [ai-coding](), [DateTime]() Tags: [ai](), [ai-coding](), [coding](), [coding-assistant](), [go](), [time]() ### AI overview A short quiz examines incorrect advice from an AI coding assistant by asking how far apart two timestamps printed by a program will be. The provided text does not include the answer. ### Source excerpt Here's a small quiz derived from some incorrect advice from an AI coding assistant. This program prints two timestamps; will they be a. Roughly the same time (ie, the same second)b. Roughly 10 seconds apartc. Something else Answer after the fold ## Tactical Coding Assistants DevFeed: [Tactical Coding Assistants]() Original publisher: [Read original article]() Author: Pieter Tolsma Published: 2025-07-03T08:53:09Z Content type: opinion Language: en Sources: [Booking.com Development - Medium]() Topics: [Large Language Model](), [Development](), [Software Engineering](), [Architecture & Design](), [Refactoring](), [Claude](), [Artificial Intelligence]() Tags: [ai](), [claude](), [coding](), [coding-assistant](), [complexity](), [design](), [development](), [genai](), [llm](), [refactor](), [review](), [software-design]() ### AI overview The article compares tactical and strategic programming and applies that distinction to coding assistants. Drawing on the author's experience, it argues that assistants such as Gemini 2.5 Pro and Claude 3.7 can implement clearly specified tasks effectively, but may produce excessive complexity, duplicated behavior, and difficult-to-maintain code when used without architectural review. ### Source excerpt Over the past 15 months I have been actively working with LLM's in my job as a software engineer. I have had many moments where I thought singularity was here, but then almost immediately after I would see the agent fall flat on its face. I have become interested in figuring out when and why these new tools fail, in the hope that it can help me use these tools to their maximum potential while still keeping control over the quality of what I commit. In this short article, I want to expand on a recent observation. Generated using Gemini Imagen 4 In the book 'A Philosophy of Software Design' by John K. Ousterhout, a great analogy is made between two different modes of development: tactical and strategic programming. Tactical programming: this is a shortsighted approach focused on completing the immediate task as quickly as possible. Here, the primary goal is to make a feature or bug fix work in the most direct way. You can imagine what will happen if you have a team of engineers that only works like this: technical debt will pile up fast. Generated using Gemini Imagen 4 On the other side there is strategic programming: this involves a higher level of thinking. A strategic programmer is not finished when the feature is implemented. They take a step back and review the architecture, to see if any abstractions can be made. Does the current approach overcomplicate things? Can we refactor things? The focus here is on design, where the engineers main goal is to keep complexity at bay. These two analogies hooked onto what I thought about the current state of coding assistants. In my experience, some of the current (mid-2025) state of the art coding assistants (Gemini 2.5 pro, Claude 3.7) are extremely adept tactical programmers. Assuming you give them a clear description of what you need implemented, they will go ahead and do it for you (with quite a high success rate in my experience). We humans are limited by many things, such as the speed at which we can physically press k ## AI-Assisted Coding May Increase the Value of Experienced Software Engineers DevFeed: [AI-Assisted Coding May Increase the Value of Experienced Software Engineers]() Original publisher: [Read original article]() Author: Kaushik Gopal Published: 2025-06-21T00:00:00Z Content type: opinion Language: en Sources: [Kaushik Gopal's Site]() Topics: [Artificial Intelligence](), [AI-assisted coding](), [coding assistant](), [Software Engineering](), [coding]() Tags: [ai](), [ai-assisted-coding](), [coding](), [coding-assistant](), [software-engineering]() ### AI overview The author argues that AI-assisted coding is more effective for engineers with strong communication, judgment, technical fundamentals, and software craftsmanship. The article also argues that clean architecture, clear naming, and maintainable code help AI coding assistants perform better, while noting that software engineering's main value lies in decisions and tradeoffs rather than typing code. ### Source excerpt I just read a good piece by the Nilenso crew on AI-assisted coding for teams that can't get away with vibes. Blog post title aside, it helped me synthesize my own thoughts better on a question that keeps coming up: will AI replace all software engineers?". Not only won't it replace us, it'll make the best engineers even more valuable. Here's why. AI amplifies what you already have ## AI is a multiplier. To make AI good, get good yourself. AI is a multiplier. If you are a small coefficient, you won't see much gain. If you are a negative coefficient, expect negative gains. 🎯. The best engineers extract far more value from AI tools. Why? Because they already have the fundamentals that AI needs to work with: They communicate technical ideas clearly - AI responds to good prompts, and good prompts require clear thinking. They have "the mechanic's touch" - that intuitive sense for what makes good systems work. They steer AI toward better solutions. They have strong fundamentals - they can quickly evaluate AI suggestions and know when something's off. They have taste - AI mirrors your sensibilities. Experienced engineers get better results because they ask for better things. I've seen this repeatedly. Give the same AI tool to a junior and senior engineer. The senior gets dramatically better results. Not because they're smarter, but because they know what questions to ask. What helps humans helps AI ## Here's where it gets interesting. From the article: Messy code confuses AI too Our AI coding assistant struggled to complete a task of equal difficulty on the latter codebase when compared to the former! This is likely because the messier codebase was as confusing for the AI as it would be for a human. Clean architecture, good naming, clear patterns - everything that makes codebases maintainable for humans also makes them effective for AI. So if you want to be more effective with AI, you still need all the craftsmanship skills that good software engineers have always needed. T ## Not Everything is an Agent DevFeed: [Not Everything is an Agent]() Original publisher: [Read original article]() Published: 2025-04-01T06:47:17Z Content type: opinion Language: en Sources: [Ariya Hidayat]() Topics: [Large Language Model](), [Artificial Intelligence](), [LangChain](), [Ollama](), [coding assistant](), [anthropic]() Tags: [agent](), [agentic](), [ai](), [anthropic](), [code](), [coding-assistant](), [langchain](), [llm](), [ollama](), [routing]() ### AI overview This opinion argues that not every LLM-based program should be called an agent. It distinguishes simple LLM calls, retrieval, memory, chaining, and routing from systems handling complex real-world tasks, and recommends relying on successful evaluations rather than anecdotes or hype. ### Source excerpt "Agent" is likely going to be the word that will cause existential dread to true LLM enthusiasts. ## GitHub Copilot Now in Espressif-IDE with Copilot4Eclipse DevFeed: [GitHub Copilot Now in Espressif-IDE with Copilot4Eclipse]() Original publisher: [Read original article]() Author: John Lee Published: 2025-02-13T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [GitHub Copilot](), [AI-assisted coding](), [ide](), [c/c++](), [Artificial Intelligence](), [Extension](), [ESP-IDF](), [ESP32]() Tags: [ai](), [blog](), [c-c-plus-plus](), [c-plus-plus](), [code-generation](), [coding-assistant](), [debug](), [esp-idf](), [esp32](), [espressif](), [espressif-ide](), [github-copilot](), [how-to](), [ide](), [install](), [plugin](), [sign-in](), [subscription]() ### AI overview This article presents Copilot4Eclipse as a plugin that brings GitHub Copilot assistance to Espressif-IDE's Eclipse-based C/C++ editor. It describes code suggestions, completions, refactoring, Copilot Chat, and setup steps including installation and GitHub authentication. ### Source excerpt We all love GitHub Copilot as a powerful AI-powered coding assistant that enhances productivity. However, until now, it has been officially available only for VS Code and a few other editors. But here's some good news for Eclipse users! We have tried the Copilot4Eclipse plugin with the Espressif-IDE LSP C/C++ Editor, and it works amazingly well, helping to write code faster and fix bugs. ## Why Coding Assistants Need Senior Developer Oversight DevFeed: [Why Coding Assistants Need Senior Developer Oversight]() Original publisher: [Read original article]() Author: Filipe Castro Published: 2024-09-11T11:00:00Z Content type: opinion Language: en Sources: [Avenue Code]() Topics: [coding assistant](), [GitHub Copilot](), [Artificial Intelligence](), [Programming](), [Deep learning]() Tags: [ai](), [artificial-inteligence](), [autocomplete](), [chatgpt](), [coding-assistant](), [copilot](), [data-and-machine-learning](), [deep-learning](), [devops](), [github-copilot](), [programming]() ### AI overview The article argues that coding assistants can accelerate development but may generate incorrect or inefficient code. Because beginners may accept flawed suggestions, it explains why experienced developers should evaluate assistant output and introduces the deep learning process behind these tools. ### Source excerpt Coding assistant tools are popping up everywhere nowadays (Copilot, JB Assistant, ChatGPT, etc.). They ensure fast and easy coding for developers and also provide a variety of ways to solve the same problem. Normally, the developer can choose preprocessed snippets for a line or lines of their code, along with some suggestions for autocomplete. ## How SAS secures their AI-generated code DevFeed: [How SAS secures their AI-generated code]() Original publisher: [Read original article]() Author: Gerald Crescione Published: 2024-04-15T05:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [AI Development](), [Generative AI](), [Security](), [Development](), [Software Engineering](), [snyk]() Tags: [ai](), [blog](), [ci](), [ci-cd](), [cloud](), [coding-assistant](), [developer](), [developers](), [devops](), [devsecops](), [enablement](), [executive](), [generative-ai](), [interest](), [megawatt](), [security](), [software-development]() ### AI overview This article examines the security risks of generative AI coding assistants, including low-quality or insecure generated code and new attack vectors. It describes how these tools can increase developer efficiency while requiring security teams to establish precautions for safer software development. ### Source excerpt Generative AI is ubiquitous across the software industry. Learn more about its impact on today's development teams and how to use it securely.