AI in Coding and Development

I tried EVERY major AI Coding tool so you don’t have to. Here’s what I learned about each one - and which one’s the best for your particular use case 👇 After an entire weekend of hands-on testing 15+ AI coding assistants, building the same real-life application (tax comparison calculator), and documenting every step - here's the comprehensive breakdown to separate the signal from the noise: 🏆 Best Overall: Cline - 100% open source and free version of Cursor + Windsurf that’s a simple VS Code extension - Truly thoughtful agentic coding with extensive tool use (terminal, computer use, websites, etc) - Wrote the best code with fewer mistakes, better self-healing, but no inline chat 🎨 Best for Non-Technical Users: Vercel V0 - Fast, Easy, intuitive UX - Strong community and templates - Component-specific editing via AI is magical ⚡Best for Quick Prototypes: Anthropic Claude 3.5 Sonnet - Fast & clean responses - Great reasoning & logic clarity - Artifact is great for prototyping, with ability to publish and share Replit: Good for full-stack cloud development, but sits in an awkward spot—too complex for beginners, too constrained for advanced users. StackBlitz Bolt.new: A standard cloud IDE with AI codegen, but nothing special. Lovable: Similar to Bolt, but unreliable AI-generated code, hard to toggle/see code. Cursor: Great Copilot alternative, but lacks extensive agentic capabilities like Cline. Codeium Windsurf: Strong agent mode but agent was sometimes lazy and incomplete. GitHub Copilot: Good for simple inline edits, but lacks full agentic workflow (though an agent mode was recently released). Aider: Terminal & keyboard only. Feels like Vim/Emacs on steroids. Too hardcore. OpenHands: Open-source and free Cognition Devin with strong agentic coding, but SaaS version is unstable. OpenAI (o3-mini-high): Good logic depth but lacks a coding canvas. Anthropic (Claude 3.5 Sonnet): Fast + clean. Artifact is great for prototypes, but can’t edit code directly inside it. Google Gemini 2: Poor experience—lazy, incomplete code. Generated separate files that I had to manually combine. DeepSeek AI R1: Strong long reasoning chains, but gets a lot of logic wrong. Tempo (YC S23): Promising PRD → Design → Code → Deploy workflow, but still in early stages. Onlook: Strong for design-first workflows but inconvenient for direct code editing. Reweb: Generates only UI components, not code with logic. My Final Recommendations: - For non-technical users: Vercel V0 is the best no-code/low-code option. - For cloud-based development: Try Bolt. - For local AI-powered coding: Cline is free and outperforms Cursor/Codeium. - For rapid prototyping: Claude 3.5 Sonnet is fast and effective. - For designers: Tempo or Onlook provide a strong UI-first workflow. Do you want to see a full write up of my AI coding experiences? Let me know if I should make a full post comparing AI Coding tools in detail by sharing this post and commenting below.

I’ve been running a quiet experiment: using AI coding (Vibe Coding) across 10 different closed-loop production projects — from minor refactors to major migrations. In each, I varied the level of AI involvement, from 10% to 80%. Here’s what I found: The sweet spot? 40–55% AI involvement. Enough to accelerate repetitive or structural work, but not so much that the codebase starts to hallucinate or drift. Where AI shines: - Boilerplate and framework code - Large-scale refactors - Migration scaffolds - Test case generation Where it stumbles: - Complex logic paths - Context-heavy features - Anything requiring real systems thinking [and new architectures etc]. - Anything stateful or edge-case-heavy I tracked bugs and % of total dev time spent fixing AI-generated code across each project. Here's the chart. My learning is that: overreliance on AI doesn’t just plateau, it backfires. AI doesn't write perfect code. The future is a collaboration, not a handoff. Would love to hear how others are navigating this balance. #LLM #VibeCoding #AI #DeveloperTools #Dev

"Vibe Coding !== Low Quality Work: a guide to responsible AI-assisted dev" ✍️ My latest free article: https://lnkd.in/gjMdjMWV The allure of "vibe coding" – using AI to "move faster and break even more things" – is strong. AI-assisted development is undeniably transformative, lowering barriers and boosting productivity. But speed without quality is a dangerous trap. Relying uncritically on AI-generated code can lead to brittle "house of cards" systems, amplify tech debt exponentially, and introduce subtle security flaws. Volume ≠ Quality. A helpful mental model I discuss (excellently illustrated by Forrest Brazeal) is treating AI like a "very eager junior developer." It needs guidance, review, and refinement from experienced hands. You wouldn't let a junior ship unreviewed code, right? So how do we harness AI's power responsibly? I've outlined a field guide with practical rules: ✅ Always review: Treat AI output like a PR from a new hire. ✅ Refactor & test: Inject engineering wisdom – clean up, handle edge cases, test thoroughly. ✅ Maintain standards: Ensure AI code meets your team's style, architecture, and quality bar. ✅ Human-led design: Use AI for implementation grunt work, not fundamental architecture decisions. The goal isn't to reject vibe coding, but to integrate it with discipline. Let's use AI to augment our craft, pairing machine speed with human judgment. #softwareengineering #programming #ai

Anthropic 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝗮 𝗱𝗲𝗻𝘀𝗲 𝗮𝗻𝗱 𝗵𝗶𝗴𝗵𝗹𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗿𝗲𝗽𝗼𝗿𝘁 𝗼𝗻 𝗵𝗼𝘄 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 — 𝗽𝗮𝗰𝗸𝗲𝗱 𝘄𝗶𝘁𝗵 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀: ⬇️ Not just marketing, BUT a real, practical blueprint for developers and teams building AI agents that actually work. It explains how Claude Code (tool for agentic coding) can function as a software developer: writing, reviewing, testing, and even managing Git workflows autonomously. BUT in my view: The principles and patterns described in this document are not Claude-specific. You can apply them to any coding agent — from OpenAI’s Codex to Goose, Aider, or even tools like Cursor and GitHub Copilot Workspace. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 7 𝗸𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗼𝗿 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗯𝗲𝘁𝘁𝗲𝗿 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 — 𝘁𝗵𝗮𝘁 𝘄𝗼𝗿𝗸 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝘄𝗼𝗿𝗹𝗱: ⬇️ 1. 𝗔𝗴𝗲𝗻𝘁 𝗱𝗲𝘀𝗶𝗴𝗻 ≠ 𝗷𝘂𝘀𝘁 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 ➜ It’s not about clever prompts. It’s about building structured workflows — where the agent can reason, act, reflect, retry, and escalate. Think of agents like software components: stateless functions won’t cut it. 2. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗶𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 ➜ The way you manage and pass context determines how useful your agent becomes. Using summaries, structured files, project overviews, and scoped retrieval beats dumping full files into the prompt window. 3. 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝗶𝘀𝗻’𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹 ➜ You can’t expect an agent to solve multi-step problems without an explicit process. Patterns like plan > execute > review, tool use when stuck, or structured reflection are necessary. And they apply to all models, not just Claude. 4. 𝗥𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗮𝗴𝗲𝗻𝘁𝘀 𝗻𝗲𝗲𝗱 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘁𝗼𝗼𝗹𝘀 ➜ Shell access. Git. APIs. Tool plugins. The agents that actually get things done use tools — not just language. Design your agents to execute, not just explain. 5. 𝗥𝗲𝗔𝗰𝘁 𝗮𝗻𝗱 𝗖𝗼𝗧 𝗮𝗿𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀, 𝗻𝗼𝘁 𝗺𝗮𝗴𝗶𝗰 𝘁𝗿𝗶𝗰𝗸𝘀 ➜ Don’t just ask the model to “think step by step.” Build systems that enforce that structure: reasoning before action, planning before code, feedback before commits. 6. 𝗗𝗼𝗻’𝘁 𝗰𝗼𝗻𝗳𝘂𝘀𝗲 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝘆 𝘄𝗶𝘁𝗵 𝗰𝗵𝗮𝗼𝘀 ➜ Autonomous agents can cause damage — fast. Define scopes, boundaries, fallback behaviors. Controlled autonomy > random retries. 7. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝘃𝗮𝗹𝘂𝗲 𝗶𝘀 𝗶𝗻 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 ➜ A good agent isn’t just a wrapper around an LLM. It’s an orchestrator: of logic, memory, tools, and feedback. And if you’re scaling to multi-agent setups — orchestration is everything. Check the comments for the original material! Enjoy! Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents!

🧠 12 open-source GenAI tools that actually deliver (and scale) Not every tool with a GitHub repo deserves your trust. These ones do. 👉 If you're building real GenAI systems—not just demos—save this list. I grouped them into Build, Orchestrate, and Monitor so you know when to use what. GenAI AgentOS: (NEW) 📎 Agent registry → memory handoff → orchestration layer → HITL toggle ✅ Focused on production reliability and audit trails ⭐ https://lnkd.in/gyzMnnjw 🔧 BUILD – For devs building GenAI-powered apps LangChain – The Swiss army knife for chains, RAG, agents, and tools. ⭐ 70k+ stars | https://lnkd.in/gun-rmdj LlamaIndex – Clean integration layer between LLMs and your data. Great for structured docs + flexible vector backends ⭐ 30k+ stars | https://lnkd.in/gW-iBKR2 Flowise – Drag-and-drop LLM orchestration (perfect for demos & MVPs) UI-first, deploy fast, iterate even faster ⭐ 19k+ stars | https://lnkd.in/gA8J3Tr5 Embedchain – Minimalist RAG framework that just works Perfect if you’re tired of config overkill ⭐ 8.5k+ stars | https://lnkd.in/g8DnHQg2 RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. 🔁 ORCHESTRATE – For managing agents, workflows & system logic LangGraph – Declarative, stateful agent workflows built on top of LangChain Role-based agents + memory + edge control ⭐ 2.5k+ stars | https://lnkd.in/gveKVfE4 Superagent – Plug-and-play LLM agent framework API + UI, works with OpenAI, Claude, Mistral ⭐ 5.5k+ stars | https://lnkd.in/gtsy5CQ3 CrewAI – Multi-agent task planning + collaboration Gives each agent purpose, tool access, and autonomy ⭐ 9k+ stars | https://lnkd.in/gUpwvbn9 📊 MONITOR – For logging, debugging, and scaling safely Langfuse – Logging, tracing, and evals for GenAI pipelines Inspect every token and decision ⭐ 4.5k+ stars | https://lnkd.in/g6BEnVyA Phoenix – Open-source observability for LLM workflows Error tracking, token usage, monitoring ⭐ 3k+ stars | https://lnkd.in/gT3ERHgm PromptLayer – Prompt logging + analytics Simple but powerful tracking for prompt performance ⭐ 4k+ stars | https://lnkd.in/gGSRRBrH Helicone – Open-source alternative to OpenAI’s usage dashboard Understand cost, latency, and user behavior ⭐ 6k+ stars | https://lnkd.in/gCgcy7Kd 🔍 Why these matter: Too many GenAI teams waste time gluing together 20 tools, only to discover they can’t scale. These 12 tools are: ✅ Well-maintained ✅ Actively used in production ✅ Community-supported ✅ Actually helpful when you go beyond a chatbot Don’t just play with LLMs. Build systems that can grow. 🔖 Save this. ♻️ Repost this.

RIP coding? OpenAI has just introduced Codex — a cloud-based AI agent that autonomously writes features, fixes bugs, runs tests, and even documents code. Not just autocomplete, but a true virtual teammate. This marks a shift from AI-assisted to AI-autonomous software engineering. The implications are profound. We’re entering an era where writing code can be done by simply explaining what you want in natural language. Tasks that once required hours of development can now be executed in parallel by an AI agent — securely, efficiently, and with growing precision. So, what does this mean for human skills? The value is shifting fast: → From execution to architecture and design thinking → From code writing to problem framing and solution oversight → From syntax knowledge to strategic understanding of systems, ethics, and user needs As Codex and other agentic AIs evolve, the most critical skills will be, at least for SW tech roles: • AI literacy: knowing what agents can (and cannot) do • Prompt engineering and task orchestration • System design & creative problem solving • Human judgment in code quality, security, and governance It’s a new world for solo founders, tech leads, and enterprise innovation teams alike. We won’t need fewer people. We’ll need people with new skills — ready to lead in an agent-powered era. Let’s embrace the shift. The real opportunity isn’t in writing code faster — it’s in rethinking what we build, how we build, and why. #AI #Codex #FutureOfWork #SoftwareEngineering #AgenticAI #Leadership #AIAgents #TechTrends

There’s no shortage of “AI coding tools” out there. Most can autocomplete a function, suggest a snippet, or even generate a CRUD app. But few — very few — actually understand your system. Over the last week, I’ve been experimenting with Qoder — an Agentic Coding Platform that moves beyond code generation to system-level reasoning. And I have to say, it genuinely feels like the next step in how AI and developers will collaborate. What makes Qoder different While most tools assist line-by-line, Qoder operates at the architectural level. It becomes aware of your entire codebase, your repository structure, and the relationships between components. It doesn’t just “generate.” It analyzes, reasons, and orchestrates. Here’s what stood out for me: Conversational Pair Programming — you can discuss design decisions, not just syntax. Repo Wiki — Qoder automatically documents architecture, data flow, and APIs — keeping knowledge in sync with code. Quest Mode — lets it autonomously complete long-running engineering tasks with traceable reasoning steps. Codebase Awareness — it understands the context of your system and ensures every new file fits perfectly. What I built with it To test it, I built a Smart Email Orchestrator Agent — from scratch. I started with a simple scaffold, connected it to Qoder, and watched it come alive: It created missing modules and test files Fixed broken imports Generated a structured Repo Wiki And even prepared the deployment configuration It was the first time I’ve seen an AI tool think like an engineer — aware of dependencies, architecture, and flow. Why this matters This is what Agentic Coding really means. We’re entering an era where developers won’t just “prompt” models — they’ll collaborate with intelligent systems that reason, design, and execute. The value isn’t in speed. It’s in understanding — in how these systems maintain architectural integrity and engineering quality at scale. If you’re building with AI, take a moment to explore https://lnkd.in/dC3fPTTP. This isn’t another code generator — it’s a glimpse into the future of how real software will be built.

AI field note: AI is great at writing code but that's just one part of building software. At PwC we accelerate the whole lifecycle, end to end. Let's dive in. Enterprise software development is more complex than ever. As systems expand and intertwine, it's a lot. Documentation lags, business requirements drift from implementation, and technical debt piles up. While code-focused AI assistants have emerged, they only address a fraction of the challenge—missing the full context and scale that enterprise applications demand. Enter PwC Code Intelligence, a capability that redefines how enterprises understand, maintain, and evolve their software. Code Intelligence sees the big picture; by treating source code as the single source of truth and combining compiler techniques with generative AI, it builds a deep, contextual understanding of your entire software system. This understanding powers a suite of specialized AI agents working in concert to tackle engineering challenges at scale: 🧩 The Context Service forms the foundation, maintaining total recall of every line of your enterprise codebase and its interconnections. 📖 DocGen automatically keeps documentation accurate and up-to-date as your code evolves. ✅ ReqGen ensures business requirements remain aligned with implementation throughout development. 🧪 TestGen builds comprehensive test suites that validate both technical and business requirements. ⚙️ CodeGen implements features and modernizes code with a deep understanding of your enterprise patterns. Let's connect the dots. Consider modifying a mission-critical payment system—Context Service provides every agent with complete understanding of database dependencies, compliance requirements, and business logic. DocGen updates documentation instantly, ReqGen verifies requirements alignment, TestGen ensures full test coverage, and CodeGen implements changes while maintaining enterprise standards. What once took weeks of careful coordination now happens automatically—with enterprise-grade quality assured. Early adopters of Code Intelligence are seeing remarkable results: ✈️ A major U.S. airline achieved 50% efficiency gains in modernizing a critical legacy application. Code Intelligence delivered clarity on business rules, regulatory compliance, and code relationships—accelerating development while reducing costs. 📞 A leading telecom provider used Code Intelligence to migrate mission-critical data management applications from mainframes. AI-driven insights mapped complex dependencies, generated documentation, and automated migration scripts—cutting months of manual effort while improving quality. 💡 PwC's own Commercial Technology & Innovation team processed over 15 million lines of code, achieving documentation and test coverage levels beyond traditional capabilities. We couldn't be more excited by the opportunity and impact from Code Intelligence so far. We're ready to do more. Drop me a line if interested.

🦸🏻 Accessibility Skills For Accessible AI Interfaces (https://lnkd.in/envRSTuT), a thorough set of accessibility-focused instructions and guidelines for AI tools to build with accessibility in mind to meet WCAG 2.2 AA and ADA standards — even when dealing with quick fixes. Put together by Felipe A. Carriço. --- 🔹 1. AI-Generated Interfaces Are Inaccessible By Default Most AI systems model what code *looks* like, not what code *means* to assistive technologies. Accessible code is quite underrepresented in training data, and feedback loops typically involve visual output, not semantic failures. As a result, most LLMs optimize for visual output while generating near-zero semantic information for the layer that assistive technologies actually read. Improvements are slowly coming in, but they are inconsistent, and the default output remains inaccessible enough to require systematic enforcement. You can find a 5-layer enforcement system of prompt constraints — with examples and useful pointers — in a neat article “AI-Generated UI Is Inaccessible by Default” (https://lnkd.in/eirA7xSd) by Durgesh Rajubhai Pawar. --- 🔸 2. Inclusive Design Skills If you are looking for AI skills specifically focused on inclusive design, Marie-Claire Dean has put together Inclusive Design Skills (https://lnkd.in/exsrRuDA). It's a set of 40 skills and 18 commands across 6 plugins, covering cognitive accessibility, inclusive interactions, accessible content, inclusive personas, adaptive interfaces and accessibility decisions (!). --- 🔺 3. AI Can’t Replace Manual Accessibility Testing It’s always worth noting that no level of automation can replace human accessibility testing. Even if a product is fully compliant, it doesn’t mean that it’s usable for people with various accessibility needs. The best way is to regularly bring people with accessibility needs for testing. No magical Claude skill can be more efficient than that. And as always, thanks to everyone putting these wonderful little helpers together to benefit from. 🙏 ↓

The open-source AI agent ecosystem is exploding, but most market maps and guides cater to VCs rather than builders. As someone in the trenches of agent development, I've found this frustrating. That's why I've created a comprehensive list of the open-source tools I've personally found effective in production. The overview includes 38 packages across: -> Agent orchestration frameworks that go beyond basic LLM wrappers: CrewAI for role-playing agents, AutoGPT for autonomous workflows, Superagent for quick prototyping -> Tools for computer control and browser automation: Open Interpreter for local machine control, Self-Operating Computer for visual automation, LaVague for web agents -> Voice interaction capabilities beyond basic speech-to-text: Ultravox for real-time voice, Whisper for transcription, Vocode for voice-based agents -> Memory systems that enable truly personalized experiences: Mem0 for self-improving memory, Letta for long-term context, LangChain's memory components -> Testing and monitoring solutions for production-grade agents: AgentOps for benchmarking, openllmetry for observability, Voice Lab for evaluation With the holiday season here, it's the perfect time to start building. Post https://lnkd.in/gCySSuS3