AI-assisted development boosts prototyping speed by 3x

I used to spend hours on Data quality monitoring — automated anomaly detection in pipelines tasks. Then I tried vibe coding — letting AI handle the scaffolding while I focused on design. Result: 3x faster prototyping, same code quality. The workflow: 1. Describe the architecture in plain English 2. AI generates the boilerplate 3. I review, refactor, and optimize 4. Ship in days instead of weeks The developers who will thrive in the next 5 years aren't the ones who type the fastest. They're the ones who think the clearest. Have you tried AI-assisted development? What was your experience? #DataScience #DataEngineering #BigData

I used to spend hours on Data quality monitoring — automated anomaly detection in pipelines tasks. Then I tried vibe coding — letting AI handle the scaffolding while I focused on design. Result: 3x faster prototyping, same code quality. The workflow: 1. Describe the architecture in plain English 2. AI generates the boilerplate 3. I review, refactor, and optimize 4. Ship in days instead of weeks The developers who will thrive in the next 5 years aren't the ones who type the fastest. They're the ones who think the clearest. Have you tried AI-assisted development? What was your experience? #DataScience #DataEngineering #BigData

AI coding tools guess more than we’d like and they don’t always get it right. Cortex Code + Context Hub changes that. Agents can pull real-time docs instead of relying on stale training data, leading to more accurate outputs. See how in Dash DesAI's demo ⬇️

AI coding tools guess more than we’d like and they don’t always get it right. Cortex Code + Context Hub changes that. Agents can pull real-time docs instead of relying on stale training data, leading to more accurate outputs. See how in Dash DesAI's demo ⬇️

AI coding tools guess more than we’d like and they don’t always get it right. Cortex Code + Context Hub changes that. Agents can pull real-time docs instead of relying on stale training data, leading to more accurate outputs. See how in Dash DesAI's demo ⬇️

Every great automated workflow starts with a single trigger. well not exactly, it actually starts with choosing a task, just like a data science project life cycle starts with knowing the business problem you are trying to solve. but that's not the bone of contention today my knack for always learning new things led me to ai automation and quite frankly, i'm loving it, i meaning isn't that the whole reason for artificial intelligence, well let's get into it. In the world of low/no-code automation, a trigger is the specific event that sets your entire pipeline into motion. It could be a new form submission, an email arriving in a specific folder, or even a scheduled time of day. Once that trigger fires, the system takes over, executing a sequence of actions automatically without requiring you to write a single line of code. While we often take pride in diving deep into complex Python scripts or SQL queries to manage data, sometimes a simple visual trigger-action setup is all it takes to eliminate hours of repetitive, manual tasks. It is the fastest way to turn a bottleneck into a streamlined process. What is the most impactful automation trigger you have set up recently? Share your workflow hacks in the comments! #datascience #AI/ML #complete datascienceprojectlifecycle #Automation #NoCode #LowCode #DataDev #TechCommunity #Productivity

Six weeks to ship an AI pipeline. Or six weeks to find out why AI doesn't make it into Prod. Either way, the build log will be public (the failures as well). The biggest challenge with unstructured data extraction in finance: little public data, lots of template variation. I'm trying to build an enterprise-ready workflow that fits the regulatory shape of the domain DocExtract will pull structured data out of messy financial PDFs, the category most teams demo at AI Day and quietly skip in production. I want a system with governed outputs (typed schemas, reconciliation checks), cost discipline (~$0.05/doc, not $5), real observability, and human-in-the-loop by default until calibration earns auto-approve. Not another demo. One of the biggest failures I see with AI projects is improper scope, overengineering the MVP, and technical debt from a build-first approach (the kind vibecoding encourages). I opted for spec-based engineering, contrasting SDLC, using the agent team from my last post (CTO lead, Market Scout, Research Analyst, and Design Critic) to design a blueprint inspired by GitHub's spec-kit pattern (spec.md for the what and why, plan.md for the how, tasks.md for the steps), with my own rules.md added for non-negotiables and style. The team's biggest win was scaling down the MVP to maintain core functionality. SLM fine-tuning and a full RAG system got descoped to a smaller corpus and a tighter MVP, with future considerations moved to the next phase. Less to build, more to learn from. The architecture, what makes it production-grade, the sprint plan, and what got cut from scope are in the carousel. Next week: build update #1, data sourcing publicly available documents, parsing with Docling, classifying/extraction with pydantic and bedrock hosted Sonnet/Haiku, and vector storage. I anticipate challenges in every build log, so I would love to get feedback on my approaches (I am by no means an expert). What's the biggest problem you've seen AI initiatives hit in production? I'd love to address it in a future update. #AIEngineering #AgenticAI #SpecDrivenDevelopment #MLOps

Lately I’ve been reading Designing Data-Intensive Applications, and it’s been reinforcing something I’ve been thinking about a lot: As engineers, now is the time to sharpen our system design and architecture skills. There’s a lot of noise around AI right now. Some people are saying coding is dead. Others are saying AI is a huge risk. Personally, I think the reality is simpler than that: AI is here to stay, and like the internet, it’s going to keep changing how we work and how we live. But none of that removes the responsibility from us as engineers. AI can help speed things up. It can offload some work. But you still need to know what should be delegated and what should not. The moment you let it completely take over your workflow, you lose control. It’s no different than copying code from Stack Overflow back in the day. You still had to understand it, clean it up, and make it fit your system. And honestly, if you really stop and read a lot of AI-generated output, you’ll notice how much fluff and over-engineering it can introduce. What should’ve been 2 clean lines can easily turn into 50. That’s why I think it’s more important than ever to think like a system designer, not just someone who writes code. Because writing code was never the hardest part. Building the right system is. What data do I actually need? Where should it come from? How should it flow? Do I need SQL or NoSQL? How many users am I designing for? How consistent does the data need to be? How critical is this system? AI can help generate code, but it cannot think through your system for you. That part is still on us. The real value of an engineer isn’t just in producing code. It’s in understanding the system, the tradeoffs, and the decisions behind it. #SoftwareEngineering #SystemDesign #Architecture #DataEngineering #DistributedSystems #AI #EngineeringLeadership #SystemsArchitecture

Today I learned something that quietly changed how I think about AI tools. I've been working through the Data Engineering ZoomCamp and got into Kestra's AI Copilot feature. The idea is straightforward: instead of hand-writing YAML for your workflow configs, you describe what you want in plain English and the Copilot generates the flow code for you. But the more interesting part was understanding why it actually works well. The answer is RAG, Retrieval Augmented Generation. Without it, an AI assistant is just working from whatever it learned during training. With RAG, it pulls in live, relevant context before it responds, in this case Kestra's own documentation and workflow patterns. That's what lets it give you accurate, specific output instead of generic guesses. It clicked for me why this matters in data engineering specifically. Pipelines are detailed and unforgiving. A hallucinated config or a wrong parameter name breaks everything. Grounding the AI in real documentation before it generates anything isn't a nice-to-have, it's the whole point. Kestra recently raised $25M and reported over 2 billion workflows executed in 2025 alone, Kestra which tells you orchestration tooling is becoming serious infrastructure, not just a nice abstraction on top of cron jobs. Still early in the ZoomCamp but the depth keeps surprising me. If you're curious about the data engineering space, follow along. #DataEngineering #DEZoomCamp #Kestra #RAG #LearningInPublic #Python

Over the last few days, I spoke with data scientists across levels — via DMs, personal network, and a few direct calls — to understand how AI is actually being used in real workflows. A few clear patterns: • AI is helping… but in fragments Mostly for code snippets, quick fixes, occasional EDA → Still feels like a faster Stack Overflow with custom contexts • The core workflow hasn’t changed Data → Clean → Explore → Iterate → Debug → Repeat → Still largely manual (Jupyter / VS Code + Copilot) • Biggest time sinks are still untouched Data cleaning, iteration loops, pipeline debugging • While software engineering is seeing compounding gains from AI (with IDEs like Cursor, Windswept, Claude Code, Codex, OpenCode, etc.,) data science is getting minimal incremental gains with Hex.tech being a bit better but not up to the mark. --- My takeaway: We don’t just need AI for code — we need AI that operates across the entire data workflow. Not a better assistant, not a stack overflow alternative, but a true co-data scientist. --- 👉 If you could fully automate one part of your workflow — what would it be? Tell me your true pain at work..