Mohamed Abdelaal’s Post

The discussion around AI-assisted development has given rise to the term Vibe Coding (popularized by Andrej Karpathy), focusing on speed, flow, and trusting the AI to generate code from high-level prompts. But as our tools mature, so must our approach. I believe we need to embrace the counter-concept: Deep Coding. Deep Coding: The Mindful Approach to AI-Assisted Development Deep Coding is the "adult version" of working with AI. It’s not about handing over control; it's about accelerating through understanding and control. If Vibe Coding is about speed and trust, Deep Coding is about Control, Architecture, and Intentionality. The Principles of Deep Coding: - Deep Architectural Understanding: The developer designs the entire system, resisting the urge to rely solely on fragmented, generated code snippets. - Process Control: The programmer reviews every diff (change), understands the logic, and maintains full command over the codebase health. - AI as an Amplifier: AI assists with implementation details and boilerplate but does not replace architectural thinking or critical design decisions. - Foundational Knowledge: Deep expertise in programming fundamentals is essential—it’s what allows a developer to effectively critique, debug, and leverage AI tools. In practice, the strongest developers are already using a hybrid model: ***Vibe Code first to quickly prototype and sketch out an idea. ***Deep Code next to structure, refine, secure, and deliver a production-ready system. We are still in the early stages of this new development era. The best practices are being written right now. It's a huge opportunity to experiment and define the most effective hybrid workflow for ourselves. What is your experience? Are you a Vibe Coder, a Deep Coder, or a Hybrid? #SoftwareDevelopment #AICoding #DeepCoding #VibeCoding #SoftwareEngineering #LLMs

Most AI coding tools amplify chaos: they generate fast, but they forget why. Codeplain is betting on a new paradigm dubbed spec-driven development (SDD), where developers write structured natural-language specs that generate, test, and validate code automatically. At the core is plain, a Markdown-style spec language designed to express intent clearly enough for both humans and machines. Think: “natural language, but with just enough structure to make it executable.” AI Native Dev caught up with Codeplain founder and CEO Dušan Omerčević to get the lowdown on what his startup is trying to achieve. A few things stand out: • Specs drive the entire lifecycle — from generation to testing. • “Micro-decisions” stabilize underspecified behavior (i.e. no more green-then-red buttons). • Developers stay central to the software development process. The question isn’t whether AI can code — it’s whether we can describe software precisely enough for it to understand. How close do you think we are to that level of precision? 🔗 Link in the comments. #AIEngineering #SpecDrivenDev #AINativeDev

Top AI Tools for Code Quality in 2025: A Quick Analysis As AI reshapes software development, choosing the right tool for high-quality code is critical. Based on recent benchmarks (HumanEval, SWE-bench) and developer feedback, here’s a snapshot of the top AI coding tools in 2025, ranked by code quality (correctness, efficiency, and clarity): Claude 4 Opus (Anthropic): 82.3% quality score. Excels in clean code and complex refactoring but comes at a premium. GPT-5 Codex (OpenAI): 80%. Fast, multilingual, powers Copilot. Watch for occasional hallucinations. Cursor: 80%. AI-driven IDE with seamless fixes, ideal for beginners and pros alike. DeepSeek R1: 77%. Open-source and cost-effective but less consistent in real-world repos. GitHub Copilot: 75%. Best for VS Code integration, though cloud-dependent. Key Insight: Claude 4 leads for enterprise-grade tasks, while open-source options like DeepSeek offer value. Always test tools on your specific use case - context is king! What’s your go-to AI coding tool in 2025? Share your thoughts below! #AI #Coding #SoftwareDevelopment

🚀 Stop Coding, Start Creating! AI Code-Gen 2.0 is Here to Revolutionize Your Workflow! The world of software development just got a massive upgrade. Forget basic autocompletion – we're talking about AI Code-Gen 2.0, the intelligent co-pilot that understands your intent, writes complex functions, detects bugs, and even generates tests! This isn't just about speed; it's about unlocking a new level of creativity and efficiency. Imagine focusing on what to build, not just how. From rapid prototyping to flawless refactoring, AI Code-Gen 2.0 is transforming how developers deliver exceptional software. Ready to see how this game-changer can supercharge your productivity and elevate your code quality? 🔗 Dive deep into the future of development in our latest blog post: https://lnkd.in/g4avuxQk #ai #codegen #dougortiz

No-Code AI Agents: How n8n Lets You Build Agentic Systems Without Coding The AI landscape is shifting fast. We’ve moved from simple chatbots to Agentic AI systems — intelligent agents that can reason, remember, and act autonomously. Traditionally, building such systems required frameworks like LangChain and lots of Python code. But now, no-code platforms like n8n are opening the doors for non-developers to build powerful AI agents using drag-and-drop workflows. This blog breaks down the No-Code Advantage in AI development — and why it matters for speed, agility, and accessibility. Not everyone building AI workflows is a software engineer. Entrepreneurs, product managers, and solopreneurs often need AI-driven automation but lack deep coding skills. That’s where no-code shines: Faster prototyping → build in hours, not weeks Lower barrier to entry → anyone can design AI workflows Agility → adapt workflows quickly without long dev cycles Open-source power → extend with code if needed (n8n is fully extensible) In short: no-code empowers more https://lnkd.in/gWYbSh-T

Breaking: Augment Code, the new AI coding agent from Sourcegraph, just launched on October 9, 2025, and it's already topping benchmarks for autonomous code generation—outpacing Cursor and Devin in real-world repo handling. This isn't your average autocomplete; Augment Code acts as an intelligent agent that ingests your full codebase, identifies vulnerabilities or inefficiencies, and deploys fixes with built-in testing—all while maintaining production-grade security. Early adopters report 2-3x faster iteration cycles, making it a game-changer for scaling AI-assisted development. For AI devs and full-stack engineers, it matters because it shifts bottlenecks from manual reviews to strategic design, potentially unlocking 50%+ productivity gains in 2025 workflows. Backed by Sourcegraph's expertise in code intelligence, it's free for 3,000 messages + 50 agent runs to start. Key features: Repo-wide context awareness for accurate, holistic edits Autonomous execution: Writes, tests, and iterates without hand-holding Benchmark leadership in speed and accuracy across 30+ languages Seamless IDE integration (VS Code, JetBrains) with CI/CD hooks Privacy-first: Runs locally or in secure environments How do you see agentic AI like Augment Code transforming your team's velocity? Drop your take in the comments.

🚀 AI Code Generation Is Reshaping Software Engineering With the rise of AI-assisted code generation tools, I’ve been seeing a real shift in how engineers work day to day. These are a few things I’ve noticed (and experienced) along the way: Throughput is way up — and so is the responsibility. LoC isn’t a perfect metric, but it’s a lot easier to write a lot of code now. The real work is making sure it actually works. That means more tests, better docs, and cleaner structure. Polyglot engineers are becoming the norm. Switching between languages is way less painful when AI handles syntax and boilerplate. It’s expected that jumping between different languages like Python or Go (or vice versa) should feel seamless. Still, what matters more is system thinking. AI still struggles with complexity. Multi-repo changes, backward compatibility, tricky architecture — these are still hard for AI. Engineers need to catch gaps early, set guardrails, and bridge the “it works” vs. “it works in prod” gap. At the end of the day, it’s still your responsibility — AI is just another tool (for now). Overall, the engineers who lean into this shift will move faster and build better. Those who don’t might get left behind. #AI #SoftwareEngineering #CodeGeneration #Productivity #DeveloperExperience #Engineering #AICoding #DevTools #SoftwareDevelopment #TechTrends

I’m super excited about AI’s future, especially how it can transform how we work! AI first nailed coding. Clean GitHub repos, clear rules for writing code (like .py or .js files), and tons of open-source projects helped AI shine. Tools like GitHub Copilot became a game-changer for developers like me—catching bugs, speeding up work, and letting me focus on building awesome, user-friendly interfaces. Now, AI’s tackling a bigger challenge: messy, unstructured data—like phone calls, emails, or random notes. It’s chaotic, inconsistent, and often sensitive. Solving this is like reinventing coding from scratch. Just as AI changed how we code, it’s now ready to replace annoying phone menus with smart, human-like conversations. By turning messy talks into clear, useful insights, AI can help us make better decisions and build stronger connections. If coding was AI’s first big win, this next step could be even huger! #AI #DataDriven #UIDesign

🤖 Seven hours. No breaks. No hand-holding. GPT‑5 Codex debugged, tested, and shipped until everything turned green. 🚀 The programming world just shifted. OpenAI's GPT-5 Codex isn't just another coding assistant—it's approaching autonomous development. Here's what changes everything: • Dynamic thinking time allocation based on task complexity • Seven hours of continuous work without human intervention • 90% reduction in task start times through Codex Cloud integration • Built-in progress tracking and contextual awareness in VS Code The productivity impact is staggering. While I'm learning HTML, CSS, and React as a frontend developer, tools like this will reshape how we approach coding challenges. For software teams, this means earlier bug detection in development cycles, reduced cognitive load on repetitive tasks, and more time for creative problem-solving and architecture decisions. The future of AI coding assistants isn't about replacing developers. It's about amplifying our capabilities and letting us focus on what humans do best—innovation and strategic thinking. Safety remains paramount with sandboxed execution and permission-based risky commands. This isn't reckless automation; it's thoughtful augmentation. As someone starting their development journey, I'm excited about a future where AI handles the debugging marathons while we architect the solutions. What's your take on AI-powered development tools? Are you embracing the change or staying cautious? #AI #SoftwareDevelopment #Programming 𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/dx9WePcZ

Every developer using AI coding tools has hit this wall: You start a session. The AI writes brilliant code. You're flying. Then you close your laptop. Come back tomorrow. And the AI has completely forgotten your architecture, your naming conventions, your entire codebase context. You're back to square one. According to the 2025 Stack Overflow Developer Survey, developer trust in AI accuracy dropped from 43% in 2024 to just 33% this year. The reason? Context management is fundamentally broken. Here's what's happening: - Context "decays" between sessions—agents forget critical architectural decisions - Developers report AI code is "almost right, but not quite" 66% of the time - Bills skyrocket from $100/month to $20-30/day as you repeatedly re-explain the same context - 65% of developers say AI misses critical context during refactoring The first company to solve persistent memory across sessions doesn't just win market share—they unlock the next generation of AI-powered development. Because the real value isn't in the AI writing code. It's in the AI *understanding your codebase* deeply enough to write the RIGHT code, every single time, without re-explanation. The context crisis is real. And it's holding back the entire industry. #AIEngineering #DeveloperTools #AIforDevelopers #SoftwareDevelopment #CodingWithAI