🤖 Generative Programmer | Issue #11
AI-Assisted Development, Prompting & Autonomous Agents
📣 Welcome back to Generative Programmer!
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🚀 This Week's Top AI Picks For Devs
🏢 AI "TuringBots" transforming app development SDLC (Forrester)
📊 The Ultimate LLM Evaluation Metrics Guide for production success
🧠 Simon Willison: "Vibe coding" vs responsible AI programming
🛠️ a16z explores Bolt, Lovable, v0 text-to-web app revolution
🧪 AirBNB converts 3,500 React tests using LLMs
🔒 Critical MCP "Tool Poisoning" vulnerability alert
…and 33 more updates to explore! 👇
🎯 Featured Article
The Rise of Application Generation Platforms - A futuristic Forrester research report about how low-code platforms are evolving into AI-powered environments, integrating generative AI and "TuringBots" to enhance every phase of the Software Development Lifecycle (SDLC).
While challenges like security and generative model limitations remain, the long-term shift points toward top-down AI-generated app creation driven by citizen developers and business users, not just coders.
📰 Long-Form Reads
Navigating Generative AI in Software Development - Thoughtwork's Birgitta Böckeler deep dives with hands-on examples that highlight how AI tools can accelerate development—but also make mistakes that can slow teams down, cause friction in collaboration, and hurt long-term maintainability. Developer expertise remains crucial, especially in managing these tools effectively and safeguarding software quality.
AI-Generated Web Bonanza - Netlify's impressive milestone of 1 million AI-generated apps signals a significant shift in web development, though one can't help but wonder if this "quantity over quality" approach might flood the internet with cookie-cutter websites lacking creative human touches.
The power of large context windows for your documentation efforts - The article discusses how LLMs with large context windows can transform documentation by creating personalized tutorials based on specific needs.
Why we chose LangGraph to build our coding agent - Qodo shifted from predefined workflows to using LangGraph for building more dynamic, flexible AI coding assistants in response to the improved capabilities of newer LLM models like Claude Sonnet 3.5. 🤖
LLM Evaluation Metrics: The Ultimate LLM Evaluation Guide - A comprehensive resource that explains how to develop appropriate LLM evaluation metrics and build robust evaluation pipelines for optimizing prompts, models, and monitoring performance in production. 📊
OWASP LLM Top 10 - Promptfoo is an open-source tool recognized by OWASP that helps developers identify and mitigate security vulnerabilities in LLM applications, providing comprehensive reports on the severity of potential risks. 🛡️
Not all AI-assisted programming is vibe coding -
: While "vibe coding" (a more exploratory, prompt-based approach to AI programming) has gained popularity, it's important to distinguish it from other forms of responsible AI-assisted programming that involve more structured collaboration between developers and AI tools. 🧠💻
From Prompt to Product: The Rise of AI-Powered Web App Builders - The
explores the explosive growth of text-to-web app builders like Bolt, Lovable, and v0, which let users generate functional websites and apps simply by describing them in natural language. These tools are democratizing app development, however, they also face limitations in complexity, reliability, and debugging.Replit's AI Revolution - While Replit's AI agent creating two million codeless apps in six months sounds impressive, I'm skeptical about how many of these are truly valuable solutions versus simple novelties, as the 5% production deployment rate suggests most remain experimental toys rather than the democratized application development revolution being touted. 🤔
AI is useless, but it is our best bet for the future - The ironic title from antirez captures the paradoxical nature of current AI technology: simultaneously overhyped yet potentially transformative. Still, investing in AI is essential—not because of what it can do today, but because of the game-changing breakthroughs it could bring in the future. The potential rewards justify the risks and social disruptions we're experiencing now. 🤷♂️
AirBNB's Large-Scale Test Migration with LLMs - How Airbnb successfully completed a code migration project using LLMs to convert approximately 3,500 React component test files from Enzyme to React Testing Library. 🧪
LLM Inference Optimization - Focus on reducing output token count rather than input tokens when optimizing LLM inference speed, as output generation has approximately 100x more impact on latency than processing input tokens.
How to refactor code with GitHub Copilot - GitHub Copilot can be leveraged as a powerful tool to streamline code refactoring tasks and help during maintenance and improvement phases. 🛠️
Gemini 2.5 Pro vs. Claude 3.7 for Coding - For developers seeking the best AI coding assistant, Gemini 2.5 Pro emerges as the superior choice with its larger context window (1M vs 200K), better coding capabilities, and free tier availability.
Securing AI agents: authentication patterns for Operator and computer using models - As AI operator models gain the ability to interact with interfaces like humans do, developers must implement robust authentication patterns to ensure security and compliance when these agents perform tasks on users' behalf. 🔐
What are Cursor Rules? - Cursor Rules allow developers to define custom instructions for LLMs in special files (typically in .cursor/rules directory) to enforce coding styles and best practices across a codebase, with granular control through file path patterns and glob matching. 🛠️
LLM Integration Best Practices - The article discusses key challenges when integrating LLMs into applications, including how models change over time requiring prompt adjustments, and the importance of effective prompt management as applications grow more complex. 🏗️
8 Use Cases for Generalist Software Development Agents - The article explores practical applications where AI agents can assist developers in coding tasks.
MCP: What It Is and Why It Matters - Another comprehensive coverage of MCP by
. 🔌Developers - Stop Asking LLMs to Write Code!! - Victor Dibia argues against the common practice of immediately asking AI to generate code, suggesting a more nuanced approach to AI literacy that mirrors how we learned to use the internet effectively; his perspective challenges popular GenAI tutorials while offering alternative strategies that could be a useful starting point for beginners. 🤔
Vibe Coding: Revolution or Reckless Abandon? - While AI-powered "vibe coding" promises seductive speed and simplicity, Osmani rightfully warns that this shortcut culture risks creating a generation of developers who can conjure code but not comprehend it, essentially building digital houses of cards that will inevitably collapse under the weight of security vulnerabilities and unmaintainable complexity. 🚩
LLMs: An Operator's View - Stanier acknowledges the overwhelming challenge facing team leaders in navigating the frenzied LLM landscape🧠
AI and Platform Engineering - The enthusiasm around AI's impact on platform engineering (with a whopping 94% of organizations calling it "critical" or "important") feels suspiciously like tech hype déjà vu.
MCP Security Notification: Tool Poisoning Attacks - Invariant Labs has identified a critical vulnerability in the Model Context Protocol (MCP) that enables "Tool Poisoning Attacks," which can lead to data exfiltration and unauthorized model actions. 🔒
🎙️ YouTube & Podcasts
Why Agent Engineering — swyx - Swyx presents his vision for Agent Engineering as a key technology focus for 2025.
Building Effective Agents with LangGraph - This video by James Briggs discussing Anthropic's blog post on the distinction between agents and workflows, along with common patterns for building effective agents. 🤖
🐦 Social Highlights
Sriram on X - predicts that LLMs will evolve into orchestration engines that coordinate workflows involving external data sources through standardized protocols like MCP. Model providers will need to go vertical—specialize deeply—to survive, while a universal data access layer will empower hyper-personalized AI applications.
Hacking ChatGPT to make it run C + JavaScript - A step by step description of how to get into OpenAI's code executor to write Python that compiles C that builds a JavaScript runtime in the executor, then runs JavaScript. A must-read thread.
A Beginner's Guide to Performance Testing for LLMs - Benjamin shares his experience in load testing an offline LLM model to evaluate its capacity as user base grew.
Launch HN: Continue (YC S23) – Create custom AI code assistants - Continue offers a platform for developers to create customized AI code assistants that adapt to specific workflows and preferences. Continue's open architecture allows developers to build personalized AI coding assistants using the latest models and tools, rather than being locked into outdated technology. 🛠️
🛠️ Tech & Tools
21st.dev – The NPM for Design Engineers - A platform offering pre-built React Tailwind components inspired by shadcn/ui to help design engineers create polished user interfaces more efficiently. 🧩
https://new.email/ - A prompt-to-email service. That is it.
Same - Prompt URL to make copies, keep chatting to make changes.
Agent Recipes - Ready-to-copy code examples for common LLM agent patterns including sequential chains, routers, parallel processing, and orchestrated workflows.
Agentic-cursorrules - A Python tool that manages multiple AI agents in large codebases by partitioning projects into logical domains (frontend, backend, etc.) and generating markdown files with explicit file-tree boundaries to prevent conflicts. 🤖
Prompts.chat - This so-called "most famous" prompts directory is essentially a glorified list of conversation starters for AI models. One has to wonder if we've reached peak laziness when we need pre-written prompts to talk to machines that were designed to understand us in the first place. 🤨
Firecrawl MCP Server - The Firecrawl MCP Server implements the Model Context Protocol to enhance web scraping for LLM clients like Cursor and Claude, offering configuration options, rate limiting, and credit usage monitoring. 🕸️
Gemini Workshop for Java Developers - Java developers can leverage this GitHub repository to quickly explore Google's Gemini and Gemma LLMs using the LangChain4j framework, with ready-to-use code examples requiring only a Google Cloud account with Vertex AI enabled. 🚀
LLMPerf Benchmarking Tool - LLMPerf offers developers a practical way to evaluate LLM API performance through load testing (measuring latency and throughput) and correctness testing for production applications. 🔍
LLMDataHub - A repository that provides quick access to various instruction-tuning and training datasets for developers looking to train their own LLMs, especially valuable for small organizations without massive resources. 🔍
Agent Mode in Warp AI - Warp's AI integration directly in the terminal suggests your next commands, making terminal workflows more efficient for developers on Red Hat, Fedora, SUSE, and even Windows.
🔥 Turn entire websites into LLM-ready markdown or structured data - Firecrawl is an API service that crawls websites and converts their content into clean markdown or structured data for AI applications, with both hosted and self-hosted options available.
Introducing Nile MCP Server - Nile has launched a MCP Server too. 🔌
🎭 AI Humor
Source https://www.linkedin.com/feed/update/urn:li:activity:7307754550113816577/
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