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.
Vibe coding gets you to a prototype. Spec-driven development gets you to production. As AI coding agents grow more powerful, the engineering community has quietly split into two camps: developers who prompt iteratively and hope for the best, and developers who write structured specifications first and let agents execute against them. The second group is shipping faster, with fewer regressions, and with code that survives review. This guide covers the 9 AI tools driving that shift in 2026 — from AWS Kiro's EARS-structured spec IDE to GitHub Spec Kit's 93K-star open-source workflow, to lean execution frameworks like GSD that have crossed 61K stars in under five months.
The post 9 Best AI Tools for Spec-Driven Development in 2026: Kiro, BMAD, GSD, and More Compare 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.
If you’re an aspiring AI engineer looking to sharpen your skills, building AI agents is one of the most effective ways to get hands-on experience. AI agents represent practical applications of AI across domains, from personal assistants and recommendation systems to financial traders. Here are 10 AI agents every engineer should build. For each, you’ll […]
The post 10 AI Agents Every AI Engineer Must Build (with GitHub Samples) appeared first on Analytics Vidhya.
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.
Save to Spotify is a new command-line tool designed specifically for AI agents like OpenClaw, Claude Code, or OpenAI Codex. If you're the kind of person who collects research on a topic, then feeds it through their AI of choice to create audio summaries and personal podcasts, this lets you save them right alongside the latest episode of The Vergecast and Welcome to Night Vale on Spotify.
To set it up, you need to download and install the Save to Spotify CLI from GitHub. Then you just prompt your AI agent as normal, but tack on "and save to Spotify," and it should show up right in your podcast feed. In the blog post announcing the feature, S …
Read the full story at The Verge.
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
