Everyone is talking about Claude Code. With millions of weekly downloads and a rapidly expanding feature set, it has quietly become one of the most powerful tools in a developer's arsenal. But most people are barely scratching the surface.
If you have spent time using AI coding agents — GitHub Copilot, Claude Code, Gemini CLI — you have probably run into this situation: you describe what you want, the agent generates a block of code that looks correct, compiles, and then subtly misses the actual intent. This “vibe-coding” approach can work for quick prototypes […]
The post Meet GitHub Spec-Kit: An Open Source Toolkit for Spec-Driven Development with AI Coding Agents appeared first on MarkTechPost.
How hook implementation gives Claude Code, Codex, and Cursor persistent memory via Neo4j, without locking you into any one of them.
The post Unified Agentic Memory Across Harnesses Using Hooks appeared first on Towards Data Science.
Using Claude Code in large projects can lead to skyrocketing token costs. A 2025 Stanford study reveals developers waste thousands of tokens daily, draining budgets as unchecked context limits pile up. By setting strict boundaries from the outset, teams can reduce costs without compromising code quality. Optimizing token usage and context window sizes early on […]
The post 23 Tips for Smart Claude Code Token Saving and Workflow Optimization appeared first on Analytics Vidhya.
Inference efficiency has quietly become one of the most consequential bottlenecks in AI deployment. As agentic coding systems such as Claude Code, Codex, and Cursor scale from developer tools to infrastructure powering software development at large, the underlying inference engines serving those requests are under increasing strain. The LightSeek Foundation researchers have released TokenSpeed, an […]
The post LightSeek Foundation Releases TokenSpeed, an Open-Source LLM Inference Engine Targeting TensorRT-LLM-Level Performance for Agentic Workloads appeared first on MarkTechPost.
Writing code has always been the most time- and resource-intensive task in software development. AI is changing that, and faster than most engineering organizations are prepared for. Tools like Claude Code and Cursor are already handling significant parts of code construction, freeing developers to spend more time on requirements, architecture, and design.
But that shift creates a new challenge nobody is talking about enough. As AI takes on the heavy lifting, the skills that matter most are moving upstream: how to provide the right context for a prompt, how to evaluate what the model produces, and how to understand a problem deeply enough that you can’t be fooled by a confident but wrong answer.
This piece explores those three skills and why developers who master them will have a significant edge over those who don’t.
Beyond coding: Mastering the art of the prompt
Software translation tools such as compilers and assemblers map a high-level description of code to a lower-level represent
