AI Agent Stacks Revolutionize Dev Workflow

Stop relying on one AI model for code 🚀 Stop letting AI hallucinate your bugs. 🛑 Using just one model creates a dangerous echo chamber. 🧠 Most developers make the mistake of having the same AI plan, write, and review their code. That is exactly how vulnerabilities slip through the cracks. 🏗️ The solution is the multimodel paradigm. 💡 By pairing Claude’s high-level architectural planning with OpenAI Codex for execution and adversarial review, you get two distinct vantage points on your project. 🤝 It is simple to set up: 1. Use Opus for system design and planning. 🗺️ 2. Use Codex to handle the high-volume boilerplate. ⚡ 3. Run an adversarial review where Codex critiques the code produced by Opus. 🔍 This setup drastically reduces costs by offloading work from expensive tokens to lower-cost models while actually increasing your code quality. 💰 You stop the echo chamber and gain enterprise-grade auditing directly in your CLI. 🛡️ Full Video Here: https://lnkd.in/g52MFDg4 Are you still using a single model for your coding workflow, or have you started testing a multimodel approach? Let me know below. 👇 ♻️ Repost this to help your network build more secure and cost-effective AI coding pipelines. ➕ Follow Deven Goratela [https://lnkd.in/dQwsb2jA) for the latest strategies on staying ahead in AI and automation. #AI #Coding #SoftwareDevelopment #Automation #TechTips #DevOps

🚨🚨AI coding assistants don’t fail. Models aren’t the problem. Repos are.🚨🚨 ⚠️ The real problem No architecture docs No naming conventions No workflow definitions AI reads raw code → guesses → hallucinates ❌ Why docs don’t scale Moment code changes → docs go stale Auto-generated summaries stay surface-level Miss: • error flows • edge cases • service dependencies 💡 Different approach Stop summarizing. Make AI interrogate the codebase. 🚀 Introducing Playbook (Open Source) AI doesn’t read code. It asks questions. Explores architecture Traces workflows Finds hidden conventions Maps failure paths 🧠 What you get Architecture maps Workflow documentation Convention files Error-handling references ✅ Impact Better Copilot context Fewer hallucinations Faster onboarding AI understands multi-service systems ⚙️ Built with PowerShell Copilot CLI Zero infra Open Source 🔗 GitHub: https://lnkd.in/gcVGMG59 This isn’t theoretical. This is how AI should work with real codebases. #AI #GitHubCopilot #DeveloperExperience #AgenticAI #SoftwareEngineering

Something we kept running into with AI tooling. We have multiple repos that are deeply interdependent — shared behaviour, common config, services that affect each other. Not a monorepo. But not truly independent either. Every time we used AI for debugging or understanding the codebase, it could only see one repo at a time. The suggestions were always missing half the picture. And that half is usually where the actual answer lives. The single repo context wasn't going to cut it. But dumping everything into one place wasn't the answer either. So our architect designed an approach around this. A common main repo that represents the complete flow — with a top-level context covering how everything connects. Each individual repo keeps its own scoped context. And when you open the main repo in VSCode, a workspace task automatically clones all dependent repos into a local deps folder. Git ignored. Never committed. Just there locally so your AI tool can see the full picture when it needs to. The idea is simple — interdependent repos need interdependent context. But you don't need to change your repo structure to achieve that. You just need to be deliberate about how context is assembled. Haven't fully rolled it out yet. But the approach feels right. #DevOps #AI #PlatformEngineering #DeveloperExperience

𝗔𝗜 𝗶𝘀 𝗼𝗻𝗹𝘆 𝗮𝘀 𝘀𝗺𝗮𝗿𝘁 𝗮𝘀 𝗶𝘁𝘀 "𝘀𝗵𝗼𝗿𝘁-𝘁𝗲𝗿𝗺 𝗺𝗲𝗺𝗼𝗿𝘆." 🧠 We talk a lot about how powerful AI is, but in 2026, every dev eventually hits the same wall: 𝗧𝗵𝗲 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗪𝗶𝗻𝗱𝗼𝘄. Think of it like a desk. No matter how smart the person sitting at the desk is, they can only work with the papers they can see at that moment. Once the desk is full, they start "forgetting" the papers at the bottom of the pile. 📄🗑️ I ran into this while working on a complex refactor recently. I was feeding the AI multiple files to help me map out some logic, and suddenly, it started hallucinating. It "forgot" a core utility function I’d defined 10 minutes earlier. 😅 Here’s how I’m learning to work around the "Memory Wall": • Modularity is key: If your code is a giant "spaghetti" file, the AI will lose the plot halfway through. Breaking things into small, clean modules makes it easier to feed the AI only what it actually needs to see. 🧩 • The "Context is King" Rule: I’ve started using better documentation and clear interfaces. If the "contract" of a function is clear, the AI doesn't need to read the whole codebase to understand how to use it. • Be the Architect: You can't just dump 1,000 lines of code and expect magic. You have to guide the AI, giving it the right "papers" at the right time. The future of dev isn't just about "prompting"—it's about managing context. The better you organise your project, the better the AI can help you build it. 🛠️ Has anyone else noticed the AI "getting confused" once your files get too big? How are you handling the memory limits? 👇 #AI #SoftwareEngineering #ContextWindow #LLM #WebDev #CodingTips

People keep calling AI usage “vibe coding.” That’s only true if you use it as a crutch. The real problem isn't the AIit’s the workflow. Working on a few different projects lately, I’ve realized that as soon as logic gets complex complex state management, data flows, or dependency trees you can’t just “generate code” and hope it works. You have to own the architecture. Here are the best ways I’ve found to use AI agents without trading speed for knowledge: 1. Ask for the map, not the car. I’ve stopped asking for code first. I ask for the logic. Before a single bracket is typed, I ask the AI to map the flow: "What are the dependency risks here?" or "Outline the module structure for this feature." If I don’t understand the plan, I don't let the AI drive. 2. Set constraints early. I explicitly define rules like: "Use Clean Architecture" or "Follow DRY." Without constraints, AI takes shortcuts. Those shortcuts are just technical debt in disguise. 3. The "Delete and Rewrite" Rule. If I can’t explain a line of code, I don’t commit it. For core logic, I’ll read the AI’s output, delete it, and rewrite it myself from memory. It’s slower in the moment, but it’s the only way to ensure the syntax actually sticks in my brain. 4. AI as a Critic, not a Creator. This was my biggest shift. I’ll write a manual solution and ask the AI to roast it: "What would a Senior Dev hate about this?" or "Where is the technical debt hiding here?" The feedback is usually more valuable than the code itself. The Reality: AI writes code faster, but it often trades quality for time. My goal isn't to avoid AI that’s unrealistic. I’m just making sure it’s making me a better engineer, not just a faster one. #BuildInPublic #SoftwareEngineering #JuniorDev #GithubCopilot #CleanCode #TechLearning

Last week, I was asked to review a “vibe-coded” codebase that was built in a weekend using AI. The demo worked and the code looked clean, but the usual issues were present: - No shared patterns - Fragile state - No tests or types While these issues are not surprising, the real problem was more significant. The entire codebase relied on how the AI generated it. This led to several challenges: - Changing one thing caused something unrelated to break - Adding a feature increased duplication - Fixing a bug revealed new edge cases The code was not just messy; it lacked any structural integrity. Most cleanup efforts today tend to focus on surface fixes, such as: - Adding types - Adding tests - Refactoring components While these actions are helpful, they do not address the core issue. The system itself was never designed with structure in mind. What truly matters is: - Defining clear boundaries - Reducing everything to a few stable patterns - Making state predictable - Deciding what should never be generated Without these foundational elements, the codebase will continue to drift, regardless of how much cleanup is done. It seems that the real challenge is not just cleanup, but providing structure to something that never had it. #frontend #vibecoding #engineering #ai

🚀 CODEX vs CLAUDE — The AI Coding Battle is Real! The rise of AI coding agents is transforming how we build software. Two major players leading this shift are OpenAI Codex and Claude Code — and both bring something powerful to the table. Here’s my quick take 👇 🔹 CODEX (OpenAI) Strong at structured, well-defined tasks Writes code that passes tests and follows specifications Great for automation, backend logic, and fast execution Focuses on getting things done efficiently 🔹 CLAUDE (Anthropic) Excellent at reasoning and understanding large codebases Produces cleaner, more maintainable code Strong in refactoring, architecture, and complex problem-solving Focuses on thinking before coding 💡 Real Insight: There’s no “one-size-fits-all.” Use Codex when speed + execution matters Use Claude when depth + design matters 📊 Both tools are evolving rapidly, and the gap between them is narrowing as models improve across reasoning, context handling, and autonomy. 👉 The future? Developers won’t choose one — they’ll use both strategically. 🔥 My Take: AI won’t replace developers — but developers who use AI will replace those who don’t. #AI #Codex #Claude #SoftwareDevelopment #ArtificialIntelligence #Developers #TechTrends #FutureOfWork

There's a skill shift happening in how developers work with AI tools, and it's not about learning better prompts. It's about context engineering. A year ago, the advice was: "learn how to prompt AI well." That's still useful. But once you move from asking AI one-off questions to letting it run multi-step tasks across your codebase, prompting alone breaks down. The agent doesn't remember your conventions. It doesn't know your architecture. It doesn't know what not to do. Context engineering is what determines whether an agent delivers consistent results across a full project — or produces excellent-sounding code that breaks your conventions. The practical version of this is simpler than it sounds. Tools like Claude Code use a CLAUDE.md file, Cursor uses .cursorrules. A good context file covers your tech stack, naming conventions, constraints, and what NOT to do. That file travels with every session. The agent reads it before it does anything. The results are real. Teams that maintain context files consistently report 40% fewer "bad suggestion" sessions. Context is not an issue with model intelligence — it's an issue with information design. The model is capable. It just doesn't know your codebase unless you tell it. If you're using AI coding tools daily and not maintaining some form of project context file, you're probably spending more time fixing AI output than you need to. That's the gap context engineering closes. #SoftwareDevelopment #AITools #ContextEngineering #WebDevelopment #DeveloperProductivity #ClaudeCode #Cursor #MachineLearning

Building AI agents is easy. Building 'useful' AI agents is a nightmare. I realized this the hard way. Most agents fail because they lack "context." They hit three massive walls: → Writing custom API code for every single tool. → Hallucinating because they weren’t trained on your data. → Wasting tokens by repeating the same instructions. This is why MCP, RAG, and Skills are now non-negotiable. 1\ MCP (Model Context Protocol) Stop writing custom code for every integration. MCP is the universal plug. Your agent connects to Slack, Brave, or a DB through one standard protocol. Plug and play. Not build and pray. 2\ RAG (Retrieval Augmented Generation) Without RAG, your agent is just guessing. It takes your data → chunks it → stores it. When you ask a question, it retrieves the truth first. Then it reasons. Accuracy > Hallucination. 3\ Agent Skills Stop bloating your prompts. Skills are reusable actions (Git, Docker, Python). The agent only loads what it needs, when it needs it. It saves tokens. It stays focused. The "Context Era" of AI is here. We aren't just prompting anymore. We are architecting. I’m building, I’m learning, and I’m not stopping. #AIAgents #ArtificialIntelligence #BuildInPublic #MCP #RAG #TechTrends #SoftwareEngineering

"83% of developers wonder if there's a 'best' AI agent framework. Is there really one?" In the AI landscape, choosing the right framework for your agent can feel overwhelming. LangChain, CrewAI, or a homegrown orchestration? Here's the dilemma: Each offers unique strengths but also some caveats. LangChain is robust for those who appreciate modularity and easy chaining of components. Its community support is a notable plus. CrewAI shines with its streamlined processes and user-friendly interfaces, especially if you're gearing toward rapid deployment. But what if neither fits your exact needs? That's when custom orchestration might be your best bet. Crafting a bespoke solution allows for precision-tuned performance and flexibility, but it demands more effort and expertise. I've explored all three paths, and something interesting emerged. Using vibe coding, I could rapidly prototype a custom solution that surpassed my expectations in just a few hours. The flexibility it offered was unmatched, although it came at the cost of more initial setup time. Here's a simple Python example to illustrate one aspect of custom orchestration: ``` from custom_framework import Agent class MyAgent(Agent): def run(self, input_data): # Custom logic here processed_data = some_custom_process(input_data) return processed_data agent = MyAgent() result = agent.run(input_data) ``` At the end of the day, it boils down to your project demands, team expertise, and resource availability. So, which path do you think best aligns with your goals and capabilities? How have your experiences shaped your choice of AI agent frameworks? #AI #MachineLearning #GenerativeAI #LLM