Automating Coding Workflows with AI

I used to think AI was 10x-ing my productivity. I was wrong. I was just 10x-ing the static prompting. I came across Michal Malewicz's medium article on "Vibe Coding is over".  ( link in comments ) It talks about how rapid AI-generated software development, has reached a 𝘀𝗮𝘁𝘂𝗿𝗮𝘁𝗶𝗼𝗻 𝗽𝗼𝗶𝗻𝘁, shifting the focus from mere speed to quality, taste, and craft. It hit me right there. Even as part of various dev/AI communities, I have constantly observed a stillness in the past few months. This stillness stems from prompts that are 𝗹𝗼𝘄 𝗲𝗳𝗳𝗼𝗿𝘁, 𝗳𝗹𝗮𝗸𝘆 and includes "𝗩𝗲𝗿𝗶𝗳𝘆 𝗮𝗻𝗱 𝗺𝗮𝗸𝗲 𝗮 𝘀𝗺𝗼𝗼𝘁𝗵 𝗱𝗲𝘀𝗶𝗴𝗻 𝘄𝗶𝘁𝗵 𝗮𝗻 𝗲𝗮𝘀𝘆 𝘁𝗼 𝘂𝘀𝗲 𝗶𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲". The "𝗶𝗻𝘁𝗲𝗻𝘁𝗶𝗼𝗻" behind vibe coding is falling steeply. The products are becoming similar and creativity is in the hands of AI. So here is what I call my "𝗧𝗵𝗲 𝗜𝗻𝘁𝗲𝗻𝘁𝗶𝗼𝗻𝗮𝗹 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸": 1. 𝗧𝗵𝗲 𝗕𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁 : Create a clear feature outline while keeping target end users in mind before initiating vibe coding. 2. 𝗧𝗵𝗲 𝗠𝗼𝗼𝗱-𝗯𝗼𝗮𝗿𝗱: Analyze the current creative mock designs, libraries and create an inspirational document which highlights all the changes I'd like to add or use voice tools to dictate my vision and then form a mock application creative. 3. 𝗧𝗵𝗲 𝗟𝗶𝗯𝗿𝗮𝗿𝘆 𝗟𝗼𝗰𝗸-𝗶𝗻: On the technical front, I would add my previously used libraries or research new libraries that are better to match my vision. Specify the technical aspects as much as possible to create a well molded version. How you blend yourself with the best of AI is the only way forward! Do you agree? What are the ways you tackle this? #MSCS #IntentionalEngineering #PromptEngineering #AI

Interesting thought I had recently 🤖 If AI works largely through prediction… then maybe coding alone won’t be the main differentiator going forward. Feels aligned with what has been saying. Writing code may become more accessible. But deciding what should be built, what trade-offs matter, and what problem is worth solving… that still feels deeply human. 🧠 Maybe coding becomes the baseline. And judgment becomes the edge. Kind of a cheeky thought 😄 Syntax may become easier. Thinking may become more valuable. Curious if others feel the same? Will AI commoditize coding… or elevate good engineers even more? 👇 #AI #Coding #SoftwareEngineering #FutureOfWork

💻 𝗦𝘁𝗶𝗹𝗹 𝗰𝗼𝗱𝗶𝗻𝗴 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗺𝗮𝗻𝘂𝗮𝗹𝗹𝘆 𝗶𝗻 𝟮𝟬𝟮𝟲? 𝗬𝗼𝘂 𝗺𝗶𝗴𝗵𝘁 𝗯𝗲 𝘀𝗹𝗼𝘄𝗶𝗻𝗴 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳 𝗱𝗼𝘄𝗻. AI coding assistants are no longer “nice to have”, they’re becoming a 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿’𝘀 𝗱𝗮𝗶𝗹𝘆 𝘁𝗼𝗼𝗹𝗸𝗶𝘁. One of the most interesting ones right now is 𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲 by Anthropic. It’s not just about generating code… It’s about 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴, 𝗱𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴, 𝗮𝗻𝗱 𝗶𝗺𝗽𝗿𝗼𝘃𝗶𝗻𝗴 𝗶𝘁 𝘄𝗶𝘁𝗵 𝗰𝗼𝗻𝘁𝗲𝘅𝘁. Here’s what makes it stand out:  • Writes code from simple instructions  • Explains complex logic clearly  • Helps debug faster  • Works well with large codebases For developers, this means: ⚡ Less time stuck 🧠 Faster learning 🚀 Better productivity We’ve broken it down in a simple carousel, 𝘀𝘄𝗶𝗽𝗲 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 to understand how Claude Code works #AI #MachineLearning #ClaudeCode #MLExperts

2021 gave us AI code completions. 2023 made coding a conversation. 2026 is agentic: models don't complete your code or pair with you, they write it, test it, and ship it. Traditional coding is ending. For fifty years, software engineering meant writing code. Teams organized around it. Careers were defined by it. Version control, testing, deployment, and observability all existed to manage code as the primary artifact. That's being handed over to the machine. Context is your new code. When an AI agent writes your application, the thing you version, review, test, deploy, and monitor is no longer the code. It's the context that governs the agent producing the code. Crafting that context is becoming the defining engineering skill of this shift. #ContextEngineering #AI #SoftwareEngineering

I just realized Claude Code can write entire features while I review. Most developers think AI coding tools are just autocomplete on steroids. They're not. Claude Code works differently. You describe what you want, and it understands context across your entire codebase. It reads your existing patterns, your architecture, your naming conventions. Then it generates code that actually fits your project—not generic boilerplate. The real shift? You move from writing code to architecting solutions. Claude Code handles the repetitive parts. You focus on design decisions, edge cases, and whether the approach makes sense for your business. I've seen teams ship features 40% faster when they use it as a thought partner, not a replacement. The trick is knowing what to delegate and what to keep for yourself. Start small. Have it build a utility function or refactor a module. See how it handles your codebase style. Then scale up. What's your biggest blocker when using AI coding tools right now? #AI #ClaudeAI #DeveloperTools #Productivity #SoftwareEngineering #Coding

I still remember the days when coding meant hours of manual labor, pouring over lines of code to identify and fix errors. As I've worked with various development teams, I've seen how tedious and time-consuming this process can be. That's why I'm excited about the potential of AI to automate coding workflows. By leveraging AI, we can significantly reduce the time spent on mundane tasks and focus on what really matters - building innovative solutions. We've already started exploring AI-powered tools that can help with code reviews, debugging, and even generating boilerplate code. The results are promising, and I'm eager to see how this technology continues to evolve. For instance, AI can help identify bugs and vulnerabilities much faster than human reviewers, freeing up our team to work on more complex and creative problems. As we move forward with adopting AI in our coding workflows, I'm curious to know: what are some of the most significant challenges you've faced in implementing AI-powered coding tools, and how have you overcome them? #AIinCoding #CodingEfficiency #SoftwareDevelopment

Most AI coding tools are optimizing for the wrong thing. They focus on generating code faster. But speed isn’t the bottleneck in software engineering. The real challenges are: • understanding large codebases • managing system complexity • maintaining long-term code quality • making architectural decisions Generating code is the easy part. Understanding systems is the hard part. That’s where things get interesting. Over the past few months at The Artificial Singularity, I’ve been experimenting with a different direction: → instead of feeding AI more context → let it discover context dynamically The idea is simple: AI shouldn’t just generate code. It should explore, reason, and build a mental model of the system first. Early experiments are showing: – drastically lower token usage – better grounding in large codebases – more reliable execution Still early. Still experimenting. But this feels like a completely different path from most AI dev tools. Curious — How do you currently deal with understanding large codebases? #AgenticAI #DevTools #AIInfrastructure #BuildInPublic #FutureOfSoftware #SoftwareEngineering

🚀🚀🚀 I just released my new course: Applied AI-Assisted Coding on Pluralsight Over the past year, I have worked with multiple teams integrating AI into real production systems. And here is what I have noticed The challenge isn’t using AI. It’s using it well. AI can generate architecture suggestions, components, tests, docs, and many more. But without the right approach, it also introduces: ❌ subtle bugs ❌ security risks ❌ fragile code ❌ false confidence This course is built around a simple idea: 👉 AI is powerful, but only when paired with strong engineering judgment. In this course, I teach: ✅ How to actually use AI in day-to-day development ✅ Working with large codebases (where most AI breaks down) ✅ Prompting strategies that improve accuracy ✅ Guardrails to safely use AI in production environments It is based on real-world experience working with teams adopting AI. If you are an architect or developer trying to figure out how AI fits into your workflow, this is for you. 🔗 Check it out here: https://lnkd.in/g9qasbie #AI #softwareengineering #Developer #coding

The Headline Hook AI isn’t failing at coding. Your engineering discipline is. Most people treat AI like a magician. The best engineers treat it like a multiplier. If you multiply a mess by 10, you just get a 10x mess. My mental model: Your feedback loop speed is your speed limit. The moment you take on a task too big to verify quickly, you’re flying blind. Here are the 6 failure modes of AI coding—and how to fix them: F1 | The "Hallucination" Gap The Fail: AI didn't do what you wanted. The Fix: You didn't know what you wanted. Reach a shared design concept before writing a single line of code. F2 | The Verbosity Trap The Fail: The AI is writing "fluff" or redundant logic. The Fix: Build a Ubiquitous Language. Use the same terms in your prompts, your docs, and your variable names. No ambiguity. F3 | The "Broken" Build The Fail: The code looks right but doesn't run. The Fix: Shorten the loop. Static types + Automated tests + Browser access. Make bugs loud and make them early. F4 | The Scope Explosion The Fail: Trying to build a whole feature in one prompt. The Fix: Small, deliberate steps. Micro-commits are your best friend when working with LLMs. F5 | The "Shallow Module" Problem The Fail: AI doesn't understand your architecture. The Fix: Deepen your modules. A great module hides complexity behind a simple interface. If the AI has to understand 10 files to change 1, your abstractions are too shallow. F6 | Cognitive Overload The Fail: Your brain hurts trying to track the AI's changes. The Fix: Grey-box thinking. Design the interface (White-box), then delegate the implementation (Black-box). Don't micromanage the logic; manage the boundaries. The Bottom Line The best codebases are the easiest to test. The best modules are deep. The best engineers invest in system design—every single day. AI won't save a bad architecture. It will only expose it faster. Bring fundamentals. #SoftwareEngineering #AI #Programming #SystemDesign #CleanCode #DeveloperExperience