You can now generate a complete visual walkthrough of any codebase and save it to README in less than 5 minutes. Why? You will be able to understand complex software logic up to 3x faster than asking AI agents, relying on documentation, or tracing lines of code. Step 1: Paste any GitHub repo link (public or private) at code-canvas.com Step 2: Get a complete visual map of the codebase's business use-cases to their underlying source code that implements them. Step 3: Save the interactive diagram to your README #AI #DeveloperTools #Codex #Cursor

ScaleAPI/Scale-Agentex: An Open Source Codebase for Scale Agentex https://lnkd.in/gAnVQ6st Unlock the Future of AI with Agentex 🚀 Explore the five levels of AI agent capabilities, from simple chatbots to autonomous systems. Agentex empowers you to build, deploy, and scale agents at any level (L1–L5), ensuring flexibility as your requirements grow. Key Features: Future-Proof Architecture: Seamlessly transition between agent levels without altering your core systems. Comprehensive Resources: Access documentation, Python SDK tutorials, and a full development UI to support your journey. Zero-Ops Deployment: Share hundreds of agents enterprise-wide, hosted on cloud-agnostic infrastructure. Getting Started: Local Setup: Spin up your first agent effortlessly. Interactive Development: Utilize a user-friendly UI to enhance agent functionality. Explore Complexities: Start with a simple agent and progress to advanced models, including async capabilities. Dive into the world of agentic AI today! Don’t forget to share your thoughts in the comments! 👇 #ArtificialIntelligence #TechInnovation #AIAgent Source link https://lnkd.in/gAnVQ6st

AI can help you write code faster — but it can’t replace understanding your domain. In his latest post, Vaughn Vernon breaks down the hard truths about Gen AI and software design: - AI accelerates coding, but abstraction can hide complexity - Real software skill comes from knowing your domain - Principles like DDD remain essential “AI tools are powerful, but the real challenge is understanding your domain and designing systems that last.” — Vaughn Vernon 📖 Read the full post here: https://lnkd.in/gDbDK5TB We’ll dive deeper into these ideas in December 8–11’s live Implementing DDD workshop — last public session of 2025. #DDD #DomainDrivenDesign #SoftwareArchitecture #AI #SoftwareDevelopment #Microservices

Today, it’s possible to run your own personal LLM-powered agent locally on consumer-grade hardware. Depending on your use case, this could mean a standard chat interface for private conversations over your own documents. Or AI-powered Python notebooks to speed up data processing. You could also run a fully local coding agent, or set up complex workflows that run automatically or on demand. Until recently, voice was out of reach… but now, it too can run locally, and it’s surprisingly fast. Technology making this possible: ~ Standard Chat → Open WebUI ~ AI Python Notebooks → Marimo Notebooks ~ CLI coding agent → OpenCode ~ Complex Systems → LangGraph ~ Voice Agent → Pipecat Takeaway: You can already experience what AI has to offer → privately, locally, and under your control.

\*\*Code LLMs in 2025: Beyond Autocomplete 🚀\*\* Large Language Models for coding are rapidly evolving from basic assistance to full-fledged software engineering systems. By 2025, these advanced LLMs will tackle complex tasks like fixing GitHub issues and refactoring multi-repo backends. Understanding which model fits specific constraints is now crucial for engineering teams. How will AI reshape software engineering practices? \* AI agents will autonomously resolve real-world GitHub issues. \* Tailored AI solutions will optimize complex multi-repo refactoring. \* This marks a new era for AI as an integrated engineering partner. What challenges are you most eager to see AI tackle in your codebase? Learn more about this news in the full article: https://lnkd.in/ddkjECZk #CodeLLMs #SoftwareEngineering #GenerativeAI #AIinSoftwareDev #AutonomousAgents #FutureofCoding

Droid + OpenRouter: The Dream Team Crushing AI Coding in 2025 🚀🤖 Droid from Factory AI is the undisputed champ, topping Terminal-Bench at #1 with agentic wizardry for end-to-end dev: clean code, auto-testing, deployments that just work. Devs call it "the best engineer in the world" for slashing SDLC time by 3x. Pair it with OpenRouter 🔌✨: The slick LLM broker unlocking 400+ models (Gemini 2.5, Claude, open-source gems) via one API—blazing ~25ms latency, zero infra drama, and mix-match freedom without lock-in. Privacy-first, multimodal magic for any workflow. Together? Unstoppable productivity boosters. Who's building with them? 👇 https://lnkd.in/gKpa_snd PS: Ask me in a couple of hours and my response might have already changed! #AICoding #DevTools #DroidAI

𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗴𝗲𝘁 𝘆𝗼𝘂𝗿 𝗩𝗶𝗯𝗲 𝗖𝗼𝗱𝗲 𝗽𝗮𝘀𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝘁𝗵𝗲 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹𝗶𝘁𝘆 𝗯𝘂𝘁 𝗮𝗹𝘀𝗼 𝗵𝘂𝗺𝗮𝗻 𝗽𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀 𝗰𝗵𝗲𝗰𝗸𝘀? This paper from 𝗚𝗼𝗼𝗴𝗹𝗲 𝗗𝗲𝗲𝗽𝗠𝗶𝗻𝗱 introduces "𝗩𝗶𝗯𝗲 𝗖𝗵𝗲𝗰𝗸𝗲𝗿" to align code evaluation with human preference e.g. solution should feel right, read cleanly, preserve intent and remain correct.  • VeriCode Taxonomy - 30 verifiable code instructions covering style, logic patterns, documentation, error handling and API constraints with deterministic verifiers  • BigVibeBench & LiveVibeBench - Testbeds that augment established benchmarks to measure human preferences. Most teams are still using pass@k to evaluate AI code generation. When Google DeepMind tested leading models, they found that adding just 5 non-functional instructions dropped success rates to 46.75% meaning half your "working" code might be failing real developer standards. The gap costing engineering teams time in code reviews, refactoring, and tech debt. 𝗔𝗿𝗲 𝘆𝗼𝘂 𝘀𝘁𝗶𝗹𝗹 𝘀𝗵𝗶𝗽𝗽𝗶𝗻𝗴 𝗰𝗼𝗱𝗲 𝘁𝗵𝗮𝘁 𝗷𝘂𝘀𝘁 "𝘄𝗼𝗿𝗸𝘀," 𝗼𝗿 𝗮𝗿𝗲 𝘆𝗼𝘂 𝗿𝗲𝗮𝗱𝘆 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘁𝗵𝗮𝘁 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗮𝗻𝘁 𝘁𝗼 𝘂𝘀𝗲? #vibecoding #vibechecker #largelanguagemodels #texttocode #llm

𝐂𝐥𝐚𝐮𝐝𝐞 𝐂𝐨𝐝𝐞 𝐋𝐚𝐮𝐧𝐜𝐡𝐞𝐬 𝐨𝐧 𝐭𝐡𝐞 𝐖𝐞𝐛 𝐏𝐚𝐫𝐚𝐥𝐥𝐞𝐥 𝐀𝐈 𝐂𝐨𝐝𝐢𝐧𝐠, 𝐒𝐢𝐦𝐩𝐥𝐢𝐟𝐢𝐞𝐝 Anthropic has officially introduced Claude Code for the web, bringing a new era of parallel AI coding designed for speed, efficiency, and smarter collaboration. Developers can now write, debug, and optimize code directly in the browser powered by Claude’s advanced reasoning and contextual understanding. The tool supports real-time assistance across multiple files, making complex coding workflows faster and more intuitive. With Claude Code, Anthropic aims to simplify software development and empower engineers to focus on creativity rather than repetitive tasks marking a major leap in the evolution of AI-assisted programming. #AIFirst #ai #aicommunity #Claude

For coding agents sync/async is the wrong way to look at them, let's explain this with Semi Async valley of death(with overlayed models) For easy problems fast and cheap models are needed but For harder problems we still need fast models to stay in the mental productivity window of the flow state and deep work. For super hard problems its okay for them to work in background and take hours. Frontier labs are flexing hard multi day problem solving capacity the coding agent labs are focusing on the felt experience of using a coding agents. To explain this idea better I improved this diagram to include actual models where on the left you see models like GLM 4.6 which have incredibly fast performance with Cerebras hosted infra of 2000 tokens per second so you get magically fast responses but as you move further to the right on the axis you see higher intelligence with sonnet 4.5 and Minimax M2 and then even higher intelligence with GPT-5-Codex high at the expense of higher latency. Notice that on the right side of the graph I don't really mention anything because to me there are very few cases of models taking over 10-20 minutes and still perfectly solving the problem. As a user this leads to a mental roadblock where in the middle x axis of this graph you have models that take a lot of time to solve even simple problems so now you are forced to use different models for the different sets of problems which to me is the fastest way to solve different spectrum of problems daily.

Been testing how far AI can actually go in software engineering, and we’re already past the “copilot” phase. Tools like Claude Code and Chrome DevTools (MCP) aren’t just helping you code faster; they’re beginning to understand systems, context, and intent. That changes everything. The role of an engineer is evolving. It’s no longer about writing every line of code; it’s about designing how humans and AI work together to build reliable, scalable systems. If you understand the fundamentals, architecture, patterns, deployment, and performance, AI becomes a powerful extension of your thinking. If not, it’s just a smarter autocomplete. The future of software engineering isn’t about syntax. #ai #programming #softwareengineering #DevTools