I spent over a month reworking the entire team's AI Coding workflow. We brought in Amazon's aidlc-workflows and adapted it to fit our own needs. Both the team's AI development efficiency and code quality improved significantly.

Most AI coding workflows start to drift once the context gets too long. I introduced a graph engine based on a state machine to the AIDLC workflow. This engine guides the workflow's execution, which solves the drift problem. dataleadsfuture.com/your-ai-… #ai #vibecoding #coding

Each of us has a different set of strengths, and a top-tier programmer isn't necessarily better than an average one when it comes to understanding the finer details of a business. For a dev team, the real focus should be on getting everyone into the right role, not on trying to build a team of superhumans. But most Coding Agents and SDD frameworks out there tend to push one person to do everything, from requirements analysis all the way to product ops. That just doesn't make sense. So lately I've been working on building our own process-driven development workflow on top of Amazon's aidlc workflow. The goal is to hand off different parts of the product development process to the people who actually know that part best. The docs and code are still written by AI, but people are only responsible for planning and reviewing the parts they own. So far, everything's working pretty well. The docs that Aidlc produces are incredibly detailed. Even DeepSeek Flash can build a product that fully meets the requirements just by following those docs. The consistency is great too, so you don't have to worry about code and docs drifting apart after multiple rounds of iteration. The best part is that this process-oriented workflow forces every round of human-AI interaction to leave a paper trail with approval records. So when a bug shows up in production, I know exactly whose door to knock on.

The essence of prompt engineering isn't about feeding the model more information. It's about helping both the model and humans put their limited attention in the right place. dataleadsfuture.com/why-well…

Even though DeepSeek-V4 doesn't have the ability to read images, I built a small OpenCode plugin that successfully gave DeepSeek indirect image reading capabilities. dataleadsfuture.com/deepseek… #ai #vibecoding #coding

Does your AI always write garbage code? That's because your spec files have never been properly reviewed. By adding a reflection mechanism to the proposal stage of OpenSpec, I got DeepSeek-v4-pro to write code on par with Opus 4.6: dataleadsfuture.com/reflecti… #vibecoding #ai

Ride the wave of AI coding, don't get swept away by it. In my latest article, I dive into the practical details of building your own personal AI coding setup using OpenCode + Oh-My-OpenCode-Slim + OpenSpec. dataleadsfuture.com/how-i-us…

Tried RedisVL as a memory layer for AI agents: ✅ Short-term memory with MessageHistory: works great ✅ Long-term semantic memory with SemanticMessageHistory: not so much If you are thinking about RedisVL for agent memory, this will save you time: dataleadsfuture.com/build-lo…

AI agents can be smart — but without compliance guardrails, they can be risky. I used Microsoft Agent Framework middleware + AG‑UI to block unsafe prompts in a live chatbot. More on the setup + real example 👇 dataleadsfuture.com/microsof… #AI #AIAgents #LLM #DataScience

📄 Turning PDFs into usable data isn’t magic — it’s DeepSeek-OCR + docling. I tested this combo with real financial reports, added Agentic RAG, and pitted DeepSeek-OCR against PaddleOCR. The results? Eye-opening. 👀⬇️ dataleadsfuture.com/how-to-u… #AI #RAG #OCR #LLM #DataScience