Graphify Turns Codebase into Queryable Knowledge Graph

"AI will replace developers." I hear this every week. Here's what I actually see after using AI tools daily for 18 months: AI replaced: ✅ Boilerplate code (90% faster) ✅ Writing unit tests (from "I'll do it later" to "done in 2 minutes") ✅ Documentation first drafts ✅ Regex and complex LINQ queries ✅ Code review prep (catching obvious issues) AI did NOT replace: ❌ Architecture decisions ❌ Understanding business requirements ❌ Debugging production issues at 3 AM ❌ Explaining trade-offs to stakeholders ❌ Knowing WHEN to say "we shouldn't build this" The developers who are scared of AI are the ones who only write code. The developers who embrace AI are the ones who solve problems. Code is the output, not the value. The value is understanding what to build, why, and how it fits together. AI makes good developers faster. It doesn't make bad developers good. How has AI changed YOUR daily workflow? #AI #SoftwareDevelopment #FutureOfWork #CoPilot

My AI/ML Journey — Exploring LangChain & LLM Applications Today was all about understanding how to build real-world AI applications using LangChain and LLMs. Here’s a quick breakdown of what I explored: -> LangChain (Framework for LLM Apps) - Helps build applications powered by Large Language Models - Supports multiple LLMs and tools - Simplifies development with ready integrations - Key insight: Focus on workflow orchestration, not just model calls -> Core Components of LangChain - Models → LLMs & Embedding Models - Prompts → Guide model behavior and output - Chains → Create step-by-step pipelines - Memory → Maintain conversation context - Indexes → Connect external data (PDFs, DBs, websites) - Agents → AI that can reason and use tools -> Prompt Engineering - Dynamic & reusable prompts using templates - Few-shot prompting (example-based learning) - Role-based prompts (system, human, AI messages) - Key insight: Better prompts = better outputs -> Chains (Pipelines) - Sequential chains (e.g., Translate → Summarize) - Parallel chains (multiple LLMs → combine output) - Conditional chains (different outputs based on logic) - Automates multi-step workflows without manual coding -> Memory Handling - Buffer memory (stores recent chats) - Window memory (limits stored interactions) - Summary memory (compresses past conversations) - Custom memory for advanced use cases -> Big Takeaway Building AI apps is not just about models — it's about connecting models, data, and logic into intelligent workflows. #GenAI #LangChain #AI #MachineLearning #LLM #LearningInPublic #TechJourney

“We’re not debugging code anymore… we’re debugging AI that debugs code.” 🤯 This week quietly marked a shift most people aren’t fully processing. With Codex 3.0 + GPT-5.5, AI just moved from assistant → execution layer. Not autocomplete. Not suggestions. But a system that can: → Build → Test → Debug → Iterate …like a real developer. Here’s what actually changed: • AI now interacts with apps like a user (clicks, navigates, tests flows) • It uses browser + vision to validate real-world behavior • Debugging happens via logs, errors, and live feedback loops • It doesn’t stop at code → it completes the full dev cycle Why this matters: We’re entering the era of the autonomous dev loop. Where the bottleneck is no longer: “Can we build this?” But: “Should we build this?” A founder recently shipped 30,000+ lines of production code using Codex. But here’s the real insight: 👉 The value wasn’t code generation. 👉 The value was momentum. AI didn’t just write code. It kept the system moving. My takeaway: The role of developers is evolving fast. From: Writing every line To: Designing systems, making tradeoffs, and guiding AI execution The real skill now? Thinking > Typing And maybe the biggest question: If AI can execute the entire dev loop… what becomes your unfair advantage? Drop your thoughts below 👇 #ArtificialIntelligence #AI #SoftwareDevelopment #DataEngineering #AIAgents #FutureOfWork #TechTrends #Developers #Automation #GenAI #ProductEngineering #Coding #Innovation #AnkitAbhishek #BuildInPublic

How to get AI to produce high quality results per tokens you burn? I was working on couple of days in creating a C++ framework. I completed the 1st version of the framework in 2 big sessions and generated 18000 lines of code. At no point in time I felt it generated any slop. The framework is tested rigourously with the same standards I use to test human written code. It just works. What I wrote was only well defined prompts of what I want. NO CODE. Here are the steps that you can take to get most out of your AI. 1) Consider AI to be a super speedy typist. DONT out source your brain to AI. 2) At any point of time, give it exact directions, constraints and validation strategy. 3) Create regression tests and ask it to perform them after every major feature. Validate everything aggressively. 4) Dont let it generate huge chunks in one go by giving open ended prompts. 5) Let multiple agents review your code and give critical feedback. Focus on getting the intersection of these points right. 6) Most Important: Once you are done take a break and manually review everything without AI. 7) Create your observations, review comments, questions, etc in a txt file and ask AI to analyze the code base and respond to your comments. 8) Repeat until you achieve your definition if done. If you lack knowledge of the field you are developing in, 1st get this knowledge using traditional methods or independent AIs. If you use AI to gain knowledge on a topic, you will need to have conversation on a topic from multiple angles. multiple times and capture the common themes and internalize them. My history is that I waited to use AI until almost end of last year to observe how it evolves. It is just over 5 months of using AI. I am yet to see it failing miserably or generating pure slop. What I have observed is that if your AI is producing slop, it is because your instructions are slop. P.S Let it type the code. Your job is to enforce extremely tight and specific requirements for testing what it types. #AI #Strategy #MyObservations #Coding #C++

👉 “Most Developers Are Using AI Wrong" They ask: 👉 “Write code for X” Smart developers ask: 👉 “Improve THIS code with constraints” Here are 3 practical ways I use AI daily (real examples 👇) 1️⃣ Debug faster (not guess faster) Instead of: “Why is this not working?” I paste: ✔ Error ✔ Code snippet ✔ Expected output 👉 AI gives targeted fixes, not random guesses 2️⃣ Code review assistant Before pushing PR: “Act as a senior reviewer. Find edge cases, performance issues.” 👉 It often catches: Null issues Missing validations Bad naming Hidden bugs 3️⃣ Convert logic across stacks Recently: Converted a LINQ query to SQL using AI 👉 Saved 30+ minutes of manual rewriting ⚠️ Reality check: AI is not your replacement. It’s your accelerator. If you don’t understand the output :- You’re just copy-pasting bugs faster. 💡 Rule I follow: “Use AI for speed. Use your brain for decisions.” How are you using AI in your daily dev workflow? #AI #SoftwareDevelopment #DotNet #Developers #Productivity #Coding

Stop letting AI break your code. 🛑 Vibe coding is powerful, but only with strict rules. 💡 Here is how to get consistent results from AI coding assistants. 🛠️ 1. Keep context clean 🧹 Remove outdated rules from your config files so the AI stays focused. 🎯 2. Reset between features 🔄 Stick to one feature per session and use the reset command often. ⏳ 3. Always plan first 📝 Ask the AI for a clear plan before it writes a single line of code. 🧠 4. Write constrained prompts 🎯 Define your exact goal and clearly list what the AI should not touch. 🚧 5. Provide actual examples 📄 Give the AI a real code file instead of abstract descriptions. 💻 6. Review every diff 🔍 Never accept blindly and always check for unwanted deletions or API changes. 🕵️♂️ 7. Test after every change ✅ Run your tests and linters immediately after accepting new code. ⚙️ 8. Set strict boundaries 🛑 Document where sensitive data lives and forbid the AI from altering it. 🔒 9. Demand migration plans 🗺️ Read a summary of any schema changes before the code is generated. 📊 10. Save repetitive prompts 📂 Build a library of your best prompt patterns to standardize your work. 📈 Vibe coding is a repeatable workflow that requires your active guidance. 🚀 It is about steering the AI with precise context and strict boundaries. 🎯 Which of these practices will you use in your next coding session? Let me know below. 👇 ♻️ Repost to share these best practices and help your network write bug-free code with AI. ➕ Follow Deven Goratela https://lnkd.in/dVt7VtDu as your go-to authority for staying ahead in AI and automation. #VibeCoding #ArtificialIntelligence #SoftwareEngineering #CodingBestPractices #DeveloperProductivity #TechTips

When asking an AI to simplify complex topics like Big O notation, do you often get a textbook definition that barely scratches the surface, leaving you no closer to understanding its practical implications for your systems? I used to hit this wall constantly, finding generic explanations that never addressed the "why" behind an algorithm's performance. My experience shows that simply asking "Explain time complexity" results in high-level summaries that are often devoid of practical utility. To genuinely leverage AI for understanding, you need to treat it less like a search engine for definitions and more like a junior colleague you're guiding. It needs specific context, clear constraints, and a defined goal for the explanation. Instead, I've found success by structuring my prompts. I explicitly specify the target level of detail, the intended audience, and crucially, the impact I'm trying to understand. For instance, I might ask: "Explain O(n log n) complexity specifically for a distributed data processing workflow. Detail how it impacts resource scaling and API response times when handling millions of records, contrasting it with O(n) and O(n^2) scenarios." This shifts the AI from merely defining terms to analyzing their practical implications. It's not just about knowing what O(n) means in theory, but understanding why O(n log n) is often the sweet spot for many sorting algorithms with large datasets, or how ensuring O(1) for critical operations like hash map lookups can literally define the performance ceiling for high-throughput microservices. 💡 One lesson I learned the hard way was assuming a simple, broad prompt would yield practical, nuanced advice; it rarely does without specific framing. The AI will often omit critical practicalities, like how constant factors or even cache locality can sometimes make a theoretically "slower" algorithm perform better for certain small 'n' or specific hardware. The key is to guide the AI towards actionable insights, not just theoretical knowledge. We're looking for the considerations that influence system design, resource allocation, and ultimately, our ability to build resilient, performant systems, far beyond just passing a coding interview. When prompting AI for deep technical concepts, what specific framing or contextual details do you include to move beyond generic explanations and get truly actionable insights? #TimeComplexity #AIEngineering #SystemDesign #SoftwareArchitecture #PromptEngineering

Most people are thinking about AI coding tools completely wrong. They obsess over: → “Which tool is best? Claude? Cursor? Windsurf? Codex?” But that’s not the real problem. The real problem is 𝐜𝐨𝐧𝐭𝐞𝐱𝐭. All these tools perform significantly better if context is managed correctly. Here’s what actually happens: AI coding agents do NOT: - read your whole repo every time - understand your architecture automatically - magically infer how your system works Instead, they rely on: 1. What’s currently open in the workspace 2. What they can retrieve from files 3. Explicit instruction files (like CLAUDE.md, AGENTS.md, rules, etc.) If your context is weak → your results will be weak. No matter how good the model is. The key insight: You don’t need a better AI tool. You need a better context system. What I’m doing now: Instead of relying on scattered READMEs and assumptions, I built a structured setup: → One workspace repo that contains: - global architecture - system boundaries - rules for AI agents → Multiple real repos (frontend, backend, database, docs) → One canonical context file: 𝐀𝐈_𝐂𝐎𝐍𝐓𝐄𝐗𝐓.𝐦𝐝 → Thin adapters per tool: - CLAUDE.md - AGENTS.md - .cursor/rules All of them point to the same source of truth. Result: The AI finally: - understands the system as a whole - respects boundaries between repos - produces consistent answers and decisions Takeaway: Stop switching tools. Start designing your context layer. That’s where the real leverage is. Curious how others are handling multi-repo context for AI agents. #AI #SoftwareEngineering #DeveloperTools #LLM #AIEngineering

AI just killed the 'Software Developer' role, and it’s the best thing to ever happen to human wisdom. The new 'Software Builder' role is focused on high-level judgment, product thinking, and AI orchestration. They define the what and the why, while AI handles the how. With the rapid advancement of AI tools like Claude and Cursor, coding syntax is rapidly becoming a commodity. The machine has mastered the "how." We are now witnessing a massive pivot from 'Knowledge Work' to 'Wisdom Work.' The premium is no longer on the person who writes the code, but on the visionary who orchestrates it. The critical new skill is no longer writing the code itself, but designing the context—building the precise information layers that allow AI to reason effectively. #FutureOfWork #SoftwareEngineering #ai #leadership #TechTrends #TheSoftwareBuilder #wisdomworker