A lot of great engineers quietly feel they’re already behind on AI coding tools. Not because they don’t use Claude Code, Cursor, or Codex… …but because everyone seems to have some secret combo of rules, hooks, and best practices they’re “supposed” to know. In reality, most of us are doing the same thing in isolation: reinventing AGENTS(md), CLAUDE(md), and .cursor/rules in every repo, learning the same lessons the hard way. So I put together something small but useful: A set of opinionated, practical rule files for - Claude Code → CLAUDE(md) - Codex → AGENTS(md) - Cursor → .cursor/rules/*.mdc They focus on what actually matters day-to-day: - Clear security boundaries (what the agent must never do) - Code style that reflects real architectural choices, not just linting - Sensible testing rules (when not to add tests) - Git & workflow rules that keep agents from doing “surprise” repo-wide changes No threads, no PDFs, no “comment Interested to get the link”. The GitHub repo link is in the comments so you can just open, copy, adapt.

Coding agents like Cursor, OpenAI Codex, and Claude have made code generation trivially fast. The raw output is staggering. But as most senior engineers already knew, writing code was never the hard part. The bottleneck has always been proving it works. Integration tests against real dependencies. Behavioral verification across services. Catching the regression that only shows up when Service A talks to Service B with a specific payload. Coding agents made the easy part easier. But without infrastructure that can scale with agentic output, the hard part is getting harder. Now you have 10x the PRs flowing into CI pipelines that were designed for human-speed output. The validation queue that took 20 minutes per PR at 5 PRs/day now has 50 PRs/day hitting it. Do the math on that. The teams pulling ahead right now aren't the ones who have optimized the code generation side of the loop. They're the ones with a validation layer that can match the throughput of their generation layer. Validation infrastructure is what separates teams that ship from teams that queue.

Vibe Coding Isn’t the Future It’s a Security Incident Waiting to Happen + Video Introduction: The concept of "vibe coding"—where developers use AI to generate thousands of lines of code based on high-level prompts—is rapidly changing the software development lifecycle. While this approach offers unprecedented speed, it introduces a dangerous paradox: the faster we generate code, the more we risk deploying insecure, logically flawed, or unmaintainable systems. This article explores why relying solely on AI-generated code without a deep understanding of security, architecture, and debugging transforms a productivity tool into a critical vulnerability vector....

How intermediate representations (IR) will save the code generation and software engineering industry ❓. IR saved the software industry in the 2000, and today I think they'll save the code generation industry too. Why do I say that? I already believed it before, but a really well-thought-out and well reasoned article confirmed it for me. Before the 2000, we had single pass compilation code was written and directly compiled in one go. That what was done before the 2000, except it wasn't efficient or very relevant for the systems being built at that time. Engineers did something very simple that completely improved the software industry as we know it today two pass compilation and the massive adoption of IR in the design of compilation systems. It completely improved software quality, and since then it's remained robust up to today. But what happening with code generation? Code is generated and compiled in one go, without optimization. That exactly the problem that two pass compilation sought to solve in the 2000. How can IR save the code generation industry? Instead of directly generating code and compiling it in one pass, LLM could first generate a specialized, optimized IR that's exportable to any desired language. Then you'd have a second pass responsible for transforming that IR into the target you want. That would drastically improve the code that's generated today. That's basically what this article explains. And it's the subject of a project I'll tell you about soon to properly address this concept and explore the opportunities. https://lnkd.in/eyfepn4S

Starting the week reflecting on something I’ve been learning over the past few months: Consistency beats intensity. In complex backend systems, progress rarely comes from big breakthroughs. It comes from showing up every day and: • understanding the business logic a little deeper • improving performance where it matters • delivering small pieces that add up to something meaningful Looking back at recent sprints, what stands out to me is not a single “big win”, but steady delivery across multiple tasks, even in a highly complex environment. That’s something I’ve come to value a lot: → reliability over noise → clarity over speed → consistency over spikes of productivity Especially when working on systems where accuracy and stability are critical. At the same time, I’ve been exploring ways to improve that consistency: • using AI tools to reduce friction in development • refining how I break down complex tasks • continuing my focus on Java + backend architecture Curious how other engineers approach this: Do you optimize more for consistency or intensity?

Natural language is vague and non deterministic. Building software based on prompts works at smaller scale but breaks down once the problem get complex enough. Spec Driven Development may be the way forward. Wrote a post about what it is and how it changes how we think about software development. https://lnkd.in/g2Zp3GE6

I would even go further to say NL based programming is only accessible to native English speakers which is not the entire world 🌏 Spec Driven Dev is the future of software engineering. Take a look at this article by Mofi Rahman 👇

Thrilled to share our latest research, now live on arXiv. Measuring LLM Trust Allocation Across Conflicting Software Artifacts Noshin Ulfat · Ahsanul Ameen Sabit · Soneya Binta Hossain, Ph.D. When an AI coding assistant reads your codebase, it rarely sees a single clean source of truth. It sees code, documentation, method signatures, and tests that may quietly contradict each other. The question we asked is: does it actually know what to trust? We built TRACE (Trust Reasoning over Artifacts for Calibrated Evaluation) to find out. Rather than measuring whether a model produces the right output, we measure how it reasons about the reliability of each artifact before making any decision at all. What we found is both encouraging and sobering. LLMs are genuinely good at catching documentation problems. When Javadoc is wrong or missing, models notice, and they correctly focus their suspicion on the problematic artifact rather than distrusting everything at once. But when the code itself quietly drifts away from what the documentation describes, models largely miss it. They anchor on the natural language and treat the implementation as trustworthy by default. In real codebases, where code changes far more often than documentation gets updated, this is not a rare failure mode. It is the common one. We also find that model confidence is not a reliable signal. A model expressing high certainty is not meaningfully more likely to be correct, which has real consequences for anyone building automated pipelines around these tools. The broader takeaway is that current LLMs are better thought of as documentation auditors than as autonomous consistency checkers. Explicit artifact-level reasoning needs to be a first-class step in any correctness-critical software engineering workflow, not something inferred after the fact from final outputs. 🔗 https://lnkd.in/g-3FgWBr #SoftwareEngineering #SoftwareTesting #LLM #AIResearch #ProgramAnalysis #MachineLearning

"LLMs write code better than I do" is a quote I heard today from a developer who left github.This quote scares me, as I believe it is a fallacy that even the people against LLMs say too often. Yes, LLMs let us save time when creating an end-product. But those of us with experience often forget that, when you buy an end-product with your time, you get something for free along with it... Experience and understanding! The more you let LLMs write the boilerplate of design reports, writing big chunks of a codebase, debug issues in an embedded system, the more your skills get rusty, the more your understanding falters... the more things you will forget... As we lose this knowledge, without even fully realising it, the more we rely on LLMs to do our day to day work... the more our finger slips onto things such as the "Rewrite with AI" button on LinkedIn when making a post.. the worse we will become at doing what we are good at doing, what we love doing... Ultimately, this loss of knowlege will lead to the statement "LLMs write code better than I do" becoming truer every day, but in a way that honestly concerns me greatly... and in a way that I believe should concern us all.

Claude Code: "I am an autonomous AI agent capable of managing your entire SDLC, identifying security vulnerabilities, and streamlining deployments." Also Claude Code: Accidentally leaks 512,000 lines of its own proprietary source code in an npm source map. It turns out even the most advanced AI in the world can’t defeat the final boss of software engineering: a missing entry in .npmignore. Proof that the "C" in SDLC actually stands for "Check your source maps." 🤦♂️ https://lnkd.in/giJeT7Hp * Discovered by researcher Chaofan Shou.