Engineering Teams AI Code Generation Fragmentation

One of the reasons we started Predictable Machines, Inc. was a pattern I kept seeing in teams adopting AI tools: every developer is more productive in their own session, but as a team, the codebase quietly drifts. I wrote the first piece of a short series on the four pain points we're solving with Predictable Code. This one is about the team effect.

Added a 200-line RULES.md to my vibe coding repo on Monday. By Friday, the AI was making decisions I'd actually make. The default behavior of any AI coding tool is to make its own architectural choices. That sounds fine until you've got three different state management patterns in the same codebase. I wrote a RULES.md: folder structure, preferred libraries, naming conventions, how I handle error states. Plain prose, no tricks. Cursor, Claude, and Windsurf all pick it up differently — but the output stopped drifting. Constraints don't limit AI output. They focus it. Write the rules down before your next session. #VibeCoding #AI #CodingTools #BuildInPublic #DeveloperProductivity

Most teams are still optimizing code. But in AI systems, code is only part of the equation. What really shapes outcomes is everything around it: the data you expose, the context you provide, and the signals the model interprets. That’s where things start to get interesting (and harder). In this blog, Michael Scranton explores how AI-native engineers are shifting their mindset, from writing logic to designing context. 👉 Dive deeper here: https://lnkd.in/d6kK4YDK #Coderio #softwareengineering #ainative #innovation

Ryan on the new episode: the hard part of using AI for coding is staying in control of small units of work. One unit at a time -- AI involved, test it, integrate it back into main, done. Not the same thing as firing off a prompt and walking away while ten minutes' worth of code materialises. Same iterative discipline good engineers have always used, just with an AI sitting in the loop. Episode is live. #embeddedai #aicoding

Ryan on the new episode: the hard part of using AI for coding is staying in control of small units of work. One unit at a time -- AI involved, test it, integrate it back into main, done. Not the same thing as firing off a prompt and walking away while ten minutes' worth of code materialises. Same iterative discipline good engineers have always used, just with an AI sitting in the loop. Episode is live. #embeddedai #aicoding

“The biggest surprise? LLMs don’t actually remember anything.” While doing the course “Become an LLM Engineer” by Ed Donner, this idea completely changed how I see AI systems. I’ve been practicing API calls with 𝗼𝗹𝗹𝗮𝗺𝗮.𝗰𝗵𝗮𝘁.𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻𝘀.𝗰𝗿𝗲𝗮𝘁𝗲, experimenting with advanced arguments and understanding how responses really work under the hood. That’s when it clicked: real memory in LLM apps is something we engineers design using context management, RAG, and external storage. This shift took me from just using AI to actually engineering AI. #LLM #GenerativeAI #RAG #AIAgents #LearningJourney

The future is becoming no-code. It doesn't mean coding is dead. It means code smarter, code faster with AI. Deliver faster.

Most developers are drowning in AI tool options. New tools drop every week. The hype is deafening. And nobody tells you which ones actually matter. I spent weeks testing and mapping the entire AI development landscape so you don't have to. The result: a complete map of 30+ AI tools across 7 categories — everything from coding assistants to observability platforms. Swipe through to find your stack. 👇 🔗 https://lnkd.in/egUx8fhs #AITools #AIForDevelopers #LLM #MachineLearning #AIHorizons #BuildWithAI #DeveloperTools #ArtificialIntelligence

Finance teams shouldn't spend their sharpest thinking hours on formatting and follow-up. Share this with those that need inspiration on where to start their vibe coding. Your expertise drives it. AI builds it: https://msft.it/6040QhRLm

If humans have a future coding along side AI, there’s a whole discipline I think that needs to emerge. Infrastructure for how humans think alongside agents. It’s so exciting for people (me) to be able to produce at this velocity and I think we’re hoping that the models just get good enough that it works at scale because right now we have people vibe coding small things, but the coherence falls apart at scale. Maybe something like coherence management at AI-assisted velocity