Best Tools for Completing Software Features

I've built 20+ software projects using AI this quarter. The secret? Knowing the right tool for the job. ChatGPT and Claude are 2 tools I've been testing extensively. Most people think they're interchangeable. They're not. Not even close. Let me break down where each one shines (from someone who's used them in the trenches), starting with Claude: Think about your best senior hire. The one who absorbed your entire business context in record time. That's Claude. I'm talking code, documentation, strategy docs, legal agreements—everything gets loaded into its brain. But the killer features are Claude’s Projects and content retention: ➝ Other AIs forget everything between chats ➝ Claude keeps building on its knowledge of your business. Want to onboard new clients to complex software? Need to train internal teams? Every response builds on your company's specific context. Last week I dumped 3 complex strategy docs into a Claude project. Not only did it understand each one, within minutes, but it spotted critical conflicts our entire team had missed for weeks. Now, ChatGPT is a different beast entirely. Where Claude masters context, ChatGPT (especially o1) executes with scary precision. Sure, the 4o model lets you upload docs for one-off questions. But o1 changes how you handle technical challenges. ➝ Linux debugging at 2 AM?  ➝ Complex program features? ChatGPT handles it faster and clearer than any documentation. My workflow now? 1. Use Claude to build the strategic foundation 2. Let ChatGPT execute on the details 3. Have Claude review the big picture 4. Let o1 critique and optimize Stop trying to pick one tool. Use both for what they do best.

Lately, I've been diving deep into VS Code, and I'm convinced that the right extensions aren't just helpful – they're transformative, especially when you're trying to get into that focused "vibe coding" state. 🎧✨ For anyone who wants to code faster, smarter, and with fewer interruptions, these 10 extensions are absolute game-changers. They help you stay in the flow, reduce context switching, and maintain high code quality, even when you're moving at light speed. My 𝐓𝐨𝐩 10 𝐕𝐒 𝐂𝐨𝐝𝐞 𝐄𝐱𝐭𝐞𝐧𝐬𝐢𝐨𝐧𝐬 for Peak Productivity: 𝐆𝐢𝐭𝐇𝐮𝐛 𝐂𝐨𝐩𝐢𝐥𝐨𝐭 🤖: Your AI pair programmer! Generates code, functions, and even entire files from comments or existing code. Keeps your hands on the keyboard and your mind on the logic. 𝐋𝐢𝐯𝐞 𝐒𝐡𝐚𝐫𝐞 🤝: Seamless real-time collaboration. Share your dev environment, debug, and pair program with teammates instantly, no matter where they are. 𝐑𝐄𝐒𝐓 𝐂𝐥𝐢𝐞𝐧𝐭 ⚡: Test your APIs directly within VS Code using simple .http files. Ditch external tools and keep your testing workflow integrated. 𝐏𝐫𝐞𝐭𝐭𝐢𝐞𝐫 - 𝐂𝐨𝐝𝐞 𝐟𝐨𝐫𝐦𝐚𝐭𝐭𝐞𝐫 ✨: An absolute must-have! Automatically formats your code on save, enforcing consistent style across your team and letting you focus purely on functionality. 𝐓𝐎𝐃𝐎 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭 📌: Never lose track of quick notes. Visually highlights TODO, FIXME, BUG, etc., comments so you can swiftly make mental notes and come back to them. 𝐄𝐒𝐋𝐢𝐧𝐭 / 𝐏𝐲𝐥𝐚𝐧𝐜𝐞 / 𝐆𝐨 𝐟𝐨𝐫 𝐕𝐒 𝐂𝐨𝐝𝐞 (𝐨𝐫 𝐲𝐨𝐮𝐫 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞'𝐬 𝐥𝐢𝐧𝐭𝐞𝐫) ✅: Catches errors and style issues as you type. Instant feedback prevents bugs and ensures code quality, keeping your flow uninterrupted. 𝐃𝐨𝐜𝐤𝐞𝐫 🐳: Manage Docker containers, images, and registries right from your editor. Perfect for containerized development and deployment workflows. 𝐀𝐖𝐒 𝐓𝐨𝐨𝐥𝐤𝐢𝐭 / 𝐀𝐳𝐮𝐫𝐞 𝐓𝐨𝐨𝐥𝐬 / 𝐆𝐨𝐨𝐠𝐥𝐞 𝐂𝐥𝐨𝐮𝐝 𝐂𝐨𝐝𝐞 (𝐟𝐨𝐫 𝐜𝐥𝐨𝐮𝐝 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫𝐬) ☁️: Directly interact with your cloud resources, deploy serverless functions, and manage services without leaving your IDE. Essential for multi-cloud architects! 𝐏𝐚𝐭𝐡 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐬𝐞𝐧𝐬𝐞 📂: Autocompletes filenames and paths. Small detail, huge time-saver, especially in large projects with deep directory structures. 𝐆𝐢𝐭𝐋𝐞𝐧𝐬 — 𝐆𝐢𝐭 𝐬𝐮𝐩𝐞𝐫𝐜𝐡𝐚𝐫𝐠𝐞𝐝 🧠: Provides rich Git insights directly in your editor, like who changed what line and why. Great for understanding context without breaking your coding rhythm. What are your go-to VS Code extensions that help you stay in the zone? Share your favorites below! 👇 #VSCode #DeveloperTools #CodingTips #DevOps #MultiCloud #AI #GenerativeAI #Productivity #SoftwareDevelopment #Tech

🤖 Coding just got smarter, faster, and more secure. Meet the 5 AI tools transforming software development in 2025! 1️⃣ GitHub Copilot Your ultimate coding assistant, GitHub Copilot. Key Features: 🟣Generates real-time code suggestions 🟣Easy integration with IDEs like VS Code and JetBrains. 🟣Offers custom LLM fine-tuning with personal repositories. Why Use It? 🟣85% of users feel more confident in their code quality. 🟣Tasks are completed 15% faster, with a 55% reduction in task time for Copilot users. 🟣Trusted by 55% of developers and over 50,000 businesses globally. 2️⃣ Cursor IDE A fork of VS Code with GPT-powered AI enhancements Key Features: 🟣Code Generation: Predicts and writes code blocks. 🟣Smart Rewrites: Automatically fixes syntax and formatting. 🟣Cursor Prediction: Anticipates navigation patterns for efficient coding. 🟣Integrated Chatbot: Context-aware guidance and suggestions. Why Use It? Trusted by top organizations like Samsung and OpenAI, Cursor IDE combines advanced AI features with VS Code’s flexibility, making it a strong contender in the AI-powered IDE space. 3️⃣ Tabnine If privacy and data security are a priority, Tabnine is your go-to coding assistant. Built on proprietary and external LLMs, it offers robust code completions. Key Features: 🟣Privacy-Focused: Trained on licensed code with GDPR and SOC-2 compliance. 🟣Transparent Data Use: Shares training data under NDA for added trust. 🟣Flexibility Why Use It? With over 1 million monthly users, Tabnine stands out for prioritizing security without sacrificing productivity. 4️⃣ Warp Terminal A modern twist on the CLI, Warp combines an IDE-like interface with AI-driven features to simplify terminal tasks. Key Features: 🟣Warp AI: Provides natural language command suggestions via ChatGPT. 🟣Agent Mode: Executes commands and resolves errors autonomously. 🟣Smart Command Completion: Suggests time-saving CLI commands. 🟣No-Retention Policy: Ensures complete data privacy. Why Use It? Warp is a game-changer for terminal users, offering features that save time and effort while enhancing productivity. 5️⃣ Replit Agent Replit Agent goes beyond coding assistance, acting as a virtual junior full-stack developer for building and deploying applications. Key Features: 🟣Natural Language Interface: Build complete applications with simple prompts. 🟣Infrastructure Setup: Deploy-ready configurations for various applications. 🟣Iterative Improvements: Add or modify features effortlessly. Why Use It? Although experimental and available in limited access, Replit Agent offers a glimpse into the future of AI-driven development 💡 These tools don’t just save time, they enable developers to focus on what truly matters: solving real-world problems and delivering exceptional products. #AI #SoftwareDevelopment #DeveloperTools #Productivity #TechInnovation

So many AI coding platforms, which one should you go with? The Chocolate Milk Cult spent months rigorously testing various tools for AI-assisted coding flows. We tested tools both individually and with each other across various flows involving codebase QA, targeted changes, building features from scratch, building features within existing code bases, and code review. Tool selection for coding depends on three factors: infrastructure cost versus technical complexity, collaboration dynamics, and context quality for large codebases. I won't bore you with more exposition. Here are the 4 that are worth your money, and how to best use them-- Lovable: When infrastructure cost exceeds technical complexity, use Lovable. If you want to build a simple automation web service or test an idea, the cost of setting up AWS, configuring deployment, linking services together is often higher than the actual technical work. Lovable eliminates that friction. You just build and it handles hosting, infrastructure, everything. Lovable charges a premium for this, but in many such cases, you don’t have to pay 3 SWEs part-time to set up UI, tools, and deploy, so you’re saving a lot of time and mental energy. When technical complexity is high, or when you need consistent iteration, or when multiple people are making changes, switch to other tools. CLI Tools: Claude Code & Codex. The best use of CLI tools is for microservices or standalone services. Write entire functionality end-to-end with Claude Code, spin it up as its own environment. Your larger codebase calls it as a function or service. It doesn’t integrate the code directly. This lets you make sweeping changes within the service without merge fallout in the main codebase. Claude Code is my primary interface. It has a much better search than Codex. Tool use is better, especially when it comes to autonomously working to fix things. UX is cleaner and it has a much faster speed of iteration. Codex has a higher intelligence ceiling. Code reviews are more thorough. Raw capability is higher than Claude Code. But UX is worse. Logs are hard to read. Execution is slower. I use Codex in sub-agents called from Claude Code. You get the ease of use where it matters and the intelligence ceiling when you need it. Augment Code Augment Code has the best context quality for large codebases. Claude Code and Codex are good at context but not great. Augment is better. This makes Augment the right choice for inline changes within large bases. Enterprise settings. Multi-person teams. Inflexible codebases where commits need to be small and controlled. When you’re not going to make large or complex changes per request, Augment handles targeted fixes better than CLI tools. The IDE experience is excellent. Their CLI integration is weaker. Use it in the IDE for scoped questions and targeted changes. For the best AI tools you should use, read: https://lnkd.in/eQh9vSzr

Happy Friday everyone, this week in #learnwithmz let's dive into something close to every developer's heart: 𝐀𝐈 𝐂𝐨𝐝𝐢𝐧𝐠 𝐓𝐨𝐨𝐥𝐬 As AI revolutionizes the way we write, debug, and manage code, it's important to identify which tools truly deliver value. Over the course of two weeks, I tested some of the most popular options by building a full-stack app prototype with each tool. Here's a quick breakdown to help you find the best fit for your specific needs: 🏆 Best Overall: GitHub Copilot Seamless integration with your IDE. Great for inline suggestions and debugging. New Copilot Chat feature allows conversational debugging. Learn more: https://lnkd.in/g4mdv4Ej 💡 Best for Non-Technical Users: Vercel V0 Intuitive and beginner-friendly. Component-specific editing via AI makes prototyping easier. Learn more: https://vercel.com/ 💻 Best for Full-Stack Cloud Development: Replit Ghostwriter Great for collaborative, cloud-based projects. Comes with built-in hosting capabilities. Learn more: https://replit.com/ 🚀 Emerging tool to Watch: Cursor Excellent Copilot alternative. Ideal for agent-driven workflows. Learn more: https://www.cursor.com/ 💎Notable mention: Cline Completely open-source and free alternative to Cursor + Windsurf, available as a lightweight VS Code extension. Enables agent-driven coding with advanced tool integrations. Produces cleaner code with fewer errors and improved self-correction capabilities. Lacks inline chat functionality Learn more: https://lnkd.in/gzESqien 𝐎𝐭𝐡𝐞𝐫𝐬 𝐰𝐨𝐫𝐭𝐡 𝐞𝐱𝐩𝐥𝐨𝐫𝐢𝐧𝐠 - Codeium: Strong AI assistant for codegen and refactoring. https://codeium.com/ - Bolt: Provides cloud-based development https://bolt.new/ - Tempo: PRD-to-Code workflows for designers and devs. Focused on REACT. https://www.tempolabs.ai/ 𝐖𝐡𝐲 𝐀𝐈 𝐂𝐨𝐝𝐢𝐧𝐠 𝐭𝐨𝐨𝐥𝐬 𝐦𝐚𝐭𝐭𝐞𝐫 These tools save time, reduce cognitive load, and empower developers to focus on creative problem-solving. However, the right choice depends on your use case, whether it's prototyping, debugging, or full-stack development. Which AI coding tools are you using? Let me know in the comments, and if you'd like a deeper comparison post! #AI #CodingTools #Developers #TechFriday #LearnAI #learnwithmz P.S. Image is generated via DALL·E

When I started as an SDE, my biggest bottleneck wasn't logic—it was speed. Speed in writing, debugging, and understanding code. Not because I lacked skills. But because I wasn’t using the right tools. If you’re a fresher or early SDE-1, here are some VS Code and IntelliJ extensions I wish I used from day 1 1. VS Code (JavaScript / TypeScript / React folks) ESLint – catches bugs while you write. Prettier – keeps code clean, auto-formats everything. GitLens – understand git history like a pro. Path Intellisense – auto-suggests file paths. Error Lens – highlights errors inline. TabNine / CodeWhisperer – AI suggestions that save keystrokes. IntelliJ (Java / Kotlin / Spring folks) Rainbow Brackets – makes nested code readable. Lombok Plugin – if you're using Lombok, this is a must. Key Promoter X – teaches you shortcuts as you go. Presentation Assistant – displays shortcut hints on screen. SonarLint – live code quality and security analysis. I used to think productivity was about typing faster. Now I know it’s about coding smarter. The tools you use will decide whether you debug for 3 hours or 3 minutes. Keep sharpening the axe 🔨 You'll thank yourself later. Follow Abhay Singh for more such reads. #SoftwareEngineering

The rise of Large Language Models (LLMs) has completely changed how developers write, debug, and deploy code. From generating full functions to assisting with documentation, testing, and SQL queries - today’s LLMs are not just tools; they’re intelligent coding partners. These models come in different types - open-source, commercial, coding-specific, and enterprise-grade, each designed for unique developer needs like flexibility, reasoning, and scalability. Here’s a breakdown of 20+ of the best LLMs for coding: Open-Source Models: - Starcoder: Code-focused model built specifically for developers. - Jamba: Scalable mixture-of-experts model for diverse coding tasks. - Falcon: High-performance open-source text generation model. - CODEGEN (Salesforce): Model designed for program synthesis and generation. - Mistral: Lightweight, efficient, and open-weight model. - XGen: Multi-purpose text generation model with strong reasoning abilities. - LLaMA 3: Meta’s open-source advanced reasoning model. - Vicuna: Chat-optimized conversational model tuned for open-source use. - Code LLaMA: Tailored for code generation and completion. - SQLCoder: Specialized in generating and interpreting SQL queries. - CodeTS: Transformer-based model designed for software development. - WizardLM: Instruction-tuned LLM ideal for guided code generation. - Pythia: Research model focused on LLM experimentation and studies. Commercial / Proprietary Models: - Stable Fine-Tuned: For code generation and developer productivity. - Gemini: Google’s multimodal reasoning and problem-solving model. - GPT: The versatile and most widely used language model. - Palm2: Google’s model for structured reasoning and logic tasks. - Claude: Safety-first conversational AI with strong comprehension. - Codex: Powers GitHub Copilot for seamless coding assistance. - CodeBERT: Designed for code search, understanding, and translation. - Qwen: Enterprise AI model with multilingual reasoning capabilities. - Command: Summarization and retrieval model built for business-grade use. Whether you’re writing code, automating tests, or scaling enterprise workflows, the right LLM can supercharge your development speed, accuracy, and creativity. Repost to share with others.

The 10× #ProductManager: I've tried (and broken) more tools than I can count. Here’s the #ProductAgentStack that actually works, and that you and your PM teams can get started with. 🧠 Discovery Agent 🧠 Use for Product Strategy, Opportunity Discovery, Prioritization The Insights Agent can summarize and be queried across internal and external research (a true knowledge database). Most research is scattered across SharePoint, Figma/Miro, slides, and email summaries, while market research sits externally, so connecting these sources is essential. Ideally, the agent also ingests the latest company strategy and reporting data to blend quantitative and qualitative insight. I’ve personally used #Notion to host my research, which makes querying, adding, and removing research incredibly easy. 🎨 Prototype Agent 🎨 There are many options out there, but I’ve recently fallen in love with #FigmaMaker, astonishing results, clean code, and incredibly fast iteration. It lacks some of the deployment features of V0 or Replit, but for sleek, high-quality prototypes, it’s rock solid. My typical workflow: • Define template + color/CI/fonts • Feed Maker the Discovery Output • Optimize journey by journey until the prototype feels right 🎨 Product Requirements Agent 🎨 PRDs are usually structured and repeatable, which makes them perfect for an agent. This one takes your selected opportunity and turns it into a clear, architecture-aligned requirements document. Feed the agent with your templates, but also give it access to architecture diagrams, internal naming conventions, and your code repo (GitHub), so the output matches how your engineering team actually builds. I also include Figma user journeys or process maps, especially useful for enterprise products. Even pairing it with FigmaMaker for visual process flows and journeys has been a huge time saver. Also using #Notion + #Github + #FigmaMaker to generate and refine the output. 🚚 Delivery Agent 🚚 Use for Planning and Execution While everyone talks about “AI building software end-to-end,” the only real example I’ve seen is Angel Dimitrov, and it’s still early. For now, assume you’re still working with Jira/ADO or Linear. The Delivery Agent should: • Convert PRDs into epics and user stories • Apply your story formatting standards • Suggest dependencies based on the repo • Create tasks or prep import/export files for your delivery tool 🧪 Testing Agent 🧪 This agent converts requirements and user stories into detailed test cases, generates automated tests (using Cypress and Jest), and highlights edge cases that you may not have considered. It can compare UI builds against Figma designs and flag inconsistencies, as well as identify regression risks from recent code changes. Shai Dinnar, Visheshta (Vishi) C., Josea E., Dimitrios Lippe, Eren Aldis, Scott Zachau, Adam Sareen, Emily Gao, Shivani Rathi, Justine Wang, Kushal Thakkar, Bradley Antcliff, Matt Sternberg, Anne Schmitt, Jake Medina

Using the wrong Scrum tool is like  bringing a hammer to a surgery. Here's exactly which tool to use and when." Every Scrum team has that one debate. "Should we use Jira?" "Why not Trello?" "What about ClickUp?" The answer isn't the same for everyone. The RIGHT tool depends on YOUR team. YOUR stack. YOUR scale. Here's the complete breakdown 🟢 JIRA SOFTWARE Best for: Complex projects → Deep customization → Powerful Agile boards → Perfect inside the Atlassian ecosystem If your team lives in Confluence - Jira is your natural home. 🟡 TRELLO Best for: Small to medium teams → Simple. Visual. Kanban-style. → Zero learning curve → Up and running in minutes Don't overcomplicate what's already working. Sometimes simple wins. 🔵 AZURE DEVOPS Best for: Microsoft-heavy environments → Combines Scrum with CI/CD pipelines → Seamless Microsoft integration → Built for dev teams shipping fast Code. Test. Deploy. All in one place. 🟩 VERSIONONE Best for: Large Agile enterprises → Built for scale → Supports SAFe and LeSS frameworks → Handles complex multi-team structures When Jira feels too small - VersionOne steps in. 🩵 MONDAY.COM Best for: Cross-team collaboration → Highly visual workflows → Extremely customizable → Great for teams beyond just dev Not every Scrum team is a tech team. Monday.com gets that. 🟦 CLICKUP Best for: All-in-one teams → Docs + Tasks + Communication = one platform → Strong customization → Replaces 5 tools with 1 If tool-switching is killing productivity - ClickUp fixes that. 🟠 ASANA Best for: Collaborative lightweight Scrum → Clean. Simple. Collaborative. → Task and project tracking made easy → Great for non-technical teams adopting Scrum Scrum doesn't have to be complicated. Asana proves it. 🟡 SCRUMWISE Best for: Pure Scrum focus → Dedicated Scrum tool - nothing else → Detailed Scrum metrics built in → Simple by design When you want Scrum. Just Scrum. Nothing more. 🔵 PIVOTAL TRACKER Best for: Continuous delivery teams → Lightweight Agile for software teams → Built around delivery iterations → Keeps dev teams moving fast Ship faster. Learn faster. Repeat. 🟢 TARGET PROCESS Best for: Enterprise portfolios → Enterprise-grade customization → Manages multiple teams simultaneously → Full portfolio visibility When you're managing teams OF teams - this is your command center. Here's the simple decision guide: → Small team just starting? → Trello → Complex dev project? → Jira → Microsoft shop? → Azure DevOps → Scaling enterprise? → VersionOne or Targetprocess → Cross-team collaboration? → Monday.com → All-in-one simplicity? → ClickUp → Pure Scrum metrics? → Scrumwise → Lightweight & collaborative? → Asana → Continuous delivery focus? → Pivotal Tracker The tool doesn't make the team. But the wrong tool slows the team down. Choose based on your reality. Not based on what's trending. Which Scrum tool is YOUR team using right now? And would you recommend it? Drop it below Follow for more!

Is It the Right Time to Invest in GitHub Copilot or Cursor for Your Dev Team? AI coding tools promise 2x–10x productivity gains. But should your team be using them yet? Let’s break it down 👇 1️⃣ First: Is Your Team Ready? Why this matters: ↳ AI tools amplify what already works - they don’t fix broken processes. You’re ready if: ✅ Your team has strong fundamentals ✅ Devs already use standard IDEs (VSCode, JetBrains, etc.) ✅ You follow a defined delivery process (Agile, Scrum, Kanban, etc.) ✅ Your team is open to experimenting and iterating You’re not ready if: ❌ There’s no established dev workflow ❌ Low code quality, unclear architecture ❌ Developers are overwhelmed or under-trained 2️⃣ Copilot vs Cursor: Which One? Copilot: ↳ Best for teams that want quick autocomplete, faster boilerplate, and help writing from scratch ↳ Lightweight, easy adoption Cursor: ↳ Best for mature teams working in large codebases ↳ Offers full-project understanding, AI-powered debugging, and feature scaffolding 3️⃣ Benchmarks Before Buying Set clear goals like: ↳ 30-50% faster feature prototyping ↳ 40-60% reduction in boilerplate code ↳ 20-30% fewer bugs caught in code review ↳ 5-10 hours saved per dev per week in routine tasks How to test it: ↳ Run a pilot with 2-3 high-performing devs ↳ Measure before vs after velocity, bug rate, and delivery times ↳ Collect qualitative feedback on workflow changes 4️⃣ The Bottom Line? Don’t just buy licenses and hope for magic. Make sure the foundation is strong, the benchmarks are clear, and the tool fits your context. If you're serious about boosting dev productivity in 2025, AI tooling isn’t optional - it’s strategic. Share your thoughts in the comments below 👇 #AItools #GitHubCopilot #Cursor #DeveloperProductivity #EngineeringManagement #DevEx #FutureOfWork #SoftwareDevelopment #BuildBetter #AsthaIT #AIT