Interview with VentureBeat on SWE-PolyBench, a new AI benchmark

Large Language Models are powerful - but unpredictable. What if you could structure their output like any other data model? Over the past weeks, I’ve been exploring how AI Agents can actually become part of real-world software projects - not just demos, but as real agents supporting workflows. Now I’m documenting what I learn step by step, starting with Pydantic AI, a framework that brings structure, validation, and reliability into AI-driven projects. Part 1 is now live: I show how to set up your environment, create your first agent, and make its responses fully structured and reliable. If you’re building AI tools or workflows and want more control over model responses, this might be your next step. 🔗 Read it on Dev.to: https://lnkd.in/dPYT8zNa 💻 Source code on GitHub: https://lnkd.in/dQGyVi-S

1/ A new study from US universities and Google Deepmind finds that widely used tests for AI-generated code miss key qualities like style, documentation, and error handling—details that matter in real-world programming. 2/ The research team introduces new tools, the VeriCode taxonomy and the Vibe Checker testbed, to measure these overlooked aspects. Their approach matches human preferences much better than previous benchmarks. 3/ After reviewing 31 top AI models, the study shows that even the best systems have trouble following several instructions at once, underlining how important instruction following is for producing quality code. More: https://lnkd.in/eqBrs7mH

Your 180,000 line codebase won't fit in Claude's context window. By token 50,000, Claude starts forgetting your architecture decisions. By 100,000, it's rewriting patterns you established at the start. By 150,000, it's essentially working blind. This is context rot. And throwing bigger context windows at it doesn't actually solve the problem. You just hit the wall later and pay more to get there. MIT researchers published early results last week on a different approach: Recursive Language Models that treat your entire codebase like a Python variable, exploring it iteratively instead of loading it all at once. In their tests, GPT-5-mini using this approach hit 64.9% accuracy on long-context tasks versus 30.3% without it. They handled 10M+ tokens without the typical degradation. Here's what I find interesting: instead of cramming everything into context (RAG, compression, massive windows), this lets the model explore what it needs, when it needs it. But it's early research. We don't know yet how this performs across different types of codebases or tasks. The devil's in the implementation details. Still, if you're working with large codebases and fighting context limits daily, this is worth watching. I broke down the research and what it could mean for AI coding tools. What's the biggest context problem you're hitting right now?

One might be tempted to think that AI is just another abstraction above the already existing abstractions we use when writing essentially machine code, BUT this is wrong simply because coding abstractions are DETERMINISTIC, meaning they can be mathematically proved to always return the same result if ran any number of times, but AI is not, it's PROBABILISTIC! The following brief article explains this https://lnkd.in/dqati56w

This is genuinely next-level AI coding. Simon Willison challenged Claude Sonnet 4.5 to autonomously design and implement a tree-structured database feature (complete with migrations and 22 passing tests!). It nailed it. Simon is calling it the 'best coding model in the world' right now. If you're building agents or doing complex dev work, you need to see this deep dive. 👇 https://lnkd.in/grSueGXn #AI #SoftwareDevelopment #Claude #LLMs

10x productivity with AI IDEs You always hear "10x this... 10x that..." but rarely does this materialize. Until now. I catalogued my experience in using the new wave of AI IDEs and this is certainly the biggest impact of AI in my life. In my latest blog entry I compare the pros and cons of existing options for programmers: xaviercollantes.dev/10 #SoftwareEngineering #AI #LLM #Productivity #Automation #ClaudeCode #AITools #EngineeringProductivity

It’s been ages since I’ve done any article writing. Probably the last time was back when I was still involved in journalistic stuff during college. Anyway, I just wrote an article in Medium about building an AI agent for beginners using LangChain https://lnkd.in/gHM_9iZP. In the article, I used the ReAct (Reason + Act) agent architecture since it’s a simple and intuitive approach that helps you understand what’s actually happening behind the scenes. The agent runs using the AgentExecutor function in LangChain. I covered the basics of what an agent is, how to build its main components (LLM, tools, prompt engineering, and more), included practical code snippets that you can copy and run (I’ve tested them myself so they should work fine, hopefully), and even added a few advanced topics toward the end. #AI #LangChain #AIAgent #MachineLearning #LLM #GenerativeAI #Python #OpenAI

✨ Turning Dreams into Code: AI + Python in Action ✨ What happens when you give Python, the OpenAI API, and a creative constraint? You get a console app that interprets your dreams through the voices of Freud or Jung. 🚀 I recently built this project for the Programiz PRO Playground AI Challenge: 👉 Try the AI Dream Interpreter here 🔮 About the Project The AI Dream Interpreter lets you describe a dream and choose between two iconic analysts: Sigmund Freud → interprets with psychoanalysis, wish-fulfillment, and childhood symbolism. Carl Jung → interprets with archetypes, collective unconscious, and individuation. Each persona is prompt-engineered for a distinct tone and style. The result: an interactive session that feels alive. 💡 Key Highlights Pure Python – no heavy frameworks, runs in a constrained sandbox. Powered by OpenAI’s GPT-4o-mini for fast, insightful responses. Interactive persona selection and repeatable sessions. Careful prompt design to keep answers consistent, concise, and persona-driven. 🛠️ Why It Matters This project demonstrates more than just Python coding: Working effectively within tight platform constraints. Applying AI creatively to build engaging user experiences. Showcasing skills in prompt engineering, API integration, and UX thinking. 🌱 I’m excited to keep exploring how lightweight AI interpreters like this can be applied to domains such as education, healthcare, and personal development. 👉 Curious: How would you use AI interpreters outside of dream analysis? #Python #AI #OpenAI #PromptEngineering #Programiz #Innovation #CareerGrowth

🚀 Qwen3-VL Vision Model is Here! 🌍 The latest leap in multimodal AI has arrived — Qwen3-VL now combines advanced vision and language understanding with powerful scalability. Designed to process images, text, and structured data in a unified architecture, Qwen3-VL takes multimodal reasoning to an entirely new level. 🔍 Highlights:Unified visual-text backbone powered by Multimodal Diffusion Transformers (MMDiT)Smarter cross-attention mechanisms for richer visual groundingResolution-independent training for diverse real-world imagesEnhanced post-training with human-aligned preference tuning (GRPO)Open weights for developers and researchers to experiment freelyThis model continues the Qwen team’s push to democratize high-performance multimodal AI — bridging reasoning, creativity, and perception in one model. https://lnkd.in/g4HbTKKQ

AI isn't replacing developers, it's replacing bad ones. I spent time watching developers hide behind Stack Overflow copy-paste and verbose "working" code. Now AI does that in 3 seconds. If your value was being a human clipboard, you're done. But if you understand system design, trade-offs, and can architect solutions that don't collapse at scale — you're more valuable than ever. I wrote a detailed breakdown of: → What AI actually can't replace (yet) → Real code examples showing the gap → The 3 types of developers in the AI era → Practical steps to stay irreplaceable Read PDF below. Takes 2 minutes to read. #Developer #SoftwareEngineering #AI #Programming #Python #JavaScript #TechCareers #SoftwareDevelopment