Software Engineering Principles

I like setting up agent teams. I just open-sourced one of the methodologies I use to build AI-native applications. Free. MIT license. 36 files. Every template, guide, script, and Claude Code configuration I run on real projects. Here's the insight that drove it: The bottleneck in AI-assisted development isn't code generation. It's specification quality. Every time Claude Code asks you a clarifying question, your spec failed. Every loop, every rework, every "almost right but not quite" That's a documentation problem, not a capability problem. So I built a system around that insight. The BHIL AI-First Development Toolkit is a full development methodology for teams using AI coding agents as primary implementors. It covers the complete lifecycle — and every artifact is designed to feed the next one in a traceable, machine-actionable chain: PRD → SPEC → ADR → TASK → CODE → REVIEW → DEPLOY What makes this different from a folder of templates: → EARS-format PRD template (the notation NASA and Airbus use for unambiguous requirements — adapted for AI agents) → Three AI-native ADR types that don't exist anywhere else: Model Selection, Prompt Strategy, and Agent Orchestration — each with evaluation criteria, cost projections, and mandatory review triggers → Claude Code configuration layer: CLAUDE.md, three skills (new-sprint, new-feature, new-adr), two subagents (spec-writer, code-reviewer), path-scoped rules, and lifecycle hooks → RuFlo and RuVector integration guides for multi-agent orchestration and persistent cross-session memory → Probabilistic acceptance criteria templates — because "works correctly" is not a test for a non-deterministic system → LLM evaluation suite template (Promptfoo-compatible), guardrails specification, and GitHub Actions CI that validates every artifact's traceability chain The observed leverage ratio for solo practitioners using this approach: 20–30× on human hours. One documented case: ~35 hours of human effort producing what would have taken ~800 hours without AI. That's not marketing. That's what happens when specifications become the product and code becomes the output. The toolkit is live on GitHub now. Link in the comments. If you adapt it for your stack, language ecosystem, or industry: I'd genuinely like to see it. PRs and forks welcome. #Agentic #OpenSource

I have seen more systems struggling because of wrong code than slower ones. The fact remains, most engineers optimise too early. About 8 years ago, my principal engineer once told me: Performance is almost always the last thing you should be thinking about. As an SDE-2, this did not make sense :) After a few follow-ups, I understood why he meant that. The order that actually matters is this. First, is the code correct? Does it do what it is supposed to do? Second, can someone maintain it six months from now without wanting to quit? Third, is it fast to read and write? Only after all three does performance even enter the conversation. The reason this order exists is simple. A fast, unmaintainable codebase is a liability. A performant-but-wrong system is worse than a slow, correct one. You cannot optimise your way out of a bug. Now, this is not universally true. Databases, high-frequency trading systems, and real-time embedded software are domains where performance is a first-class concern from day one. But those are the exceptions, not the default assumption you should bring to every PR. What is certainly true is that for most codebases, premature optimisation adds complexity, reduces readability, and solves a problem that does not exist yet. So, write correct code first. Then clean it. Then, only if the profiler gives you a reason, make it fast.

Dear Software Engineers, If your app serves 10 users → a single server and REST API will do If you’re handling 10M requests a day → start thinking load balancers, autoscaling, and rate limits /— If one developer is building features → skip the ceremony, ship and test manually If 10 devs are pushing daily → invest in CI/CD, testing layers, and feature flags /— If your downtime just breaks one page → add a banner and move on If your downtime kills a business flow → redundancy, health checks, and graceful fallbacks are non-negotiable /— If you're just consuming APIs → learn how to handle 400s and 500s If you're building APIs for others → version them, document them, test them, and monitor them /— If your product can tolerate 3s of lag → pick clarity over performance If users are waiting on each click → profiling, caching, and edge delivery are part of your job /— If your data fits in RAM → store it in memory, use simple maps If your data spans terabytes → indexing, partitioning, and disk I/O patterns start to matter /— If you're solo coding → naming things poorly is just annoying If you're on a growing team → naming things poorly is a ticking time bomb /— If you're fixing bugs once a week → logs and console prints might do If you're running production → you need structured logs, tracing, alerts, and dashboards /— If your deadlines are tight → write the simplest code that works If your code is expected to last → design for readability, testability, and change /— If you work alone → "it works on my machine" might be fine If you're in a real team → reproducible builds and shared dev setups are your baseline /— If your app is new → move fast, clean up later If your app is in maintenance hell → you now pay interest on every rushed decision People think software engineering is just about building things. It’s really about: – Knowing when not to build – Being okay with deleting good code – Balancing tradeoffs without always having all the data The best engineers don’t just ship fast. They build systems that are safe to move fast on top of.

Clean code isn't just about readability —it's about creating maintainable, scalable solutions that stand the test of time. When we prioritize readability, simplicity, and thoughtful architecture, we're not just making our lives easier; we're creating value for our teams and organizations. A few principles that have made the most significant difference in my work over years: • Meaningful naming that reveals intent • Functions that do one thing exceptionally well • Tests that serve as documentation and safety nets • Consistent formatting that reduces cognitive load The greatest insight I've gained is that clean code is fundamentally an act of communication—with future developers, our teammates, and even our future selves. The time invested upfront pays dividends during maintenance, debugging, and onboarding. What clean code practices have transformed your development experience? I'd love to hear about the principles that guide your work. Image Credit - Keivan Damirchi

On Protecting the Data Privacy of Large Language Models (LLMs): A Survey From the research paper: In this paper, we extensively investigate data privacy concerns within Large LLMs, specifically examining potential privacy threats from two folds: Privacy leakage and privacy attacks, and the pivotal technologies for privacy protection during various stages of LLM privacy inference, including federated learning, differential privacy, knowledge unlearning, and hardware-assisted privacy protection. Some key aspects from the paper: 1)Challenges: Given the intricate complexity involved in training LLMs, privacy protection research tends to dissect various phases of LLM development and deployment, including pre-training, prompt tuning, and inference 2) Future Directions: Protecting the privacy of LLMs throughout their creation process is paramount and requires a multifaceted approach. (i) Firstly, during data collection, minimizing the collection of sensitive information and obtaining informed consent from users are critical steps. Data should be anonymized or pseudonymized to mitigate re-identification risks. (ii) Secondly, in data preprocessing and model training, techniques such as federated learning, secure multiparty computation, and differential privacy can be employed to train LLMs on decentralized data sources while preserving individual privacy. (iii) Additionally, conducting privacy impact assessments and adversarial testing during model evaluation ensures potential privacy risks are identified and addressed before deployment. (iv)In the deployment phase, privacy-preserving APIs and access controls can limit access to LLMs, while transparency and accountability measures foster trust with users by providing insight into data handling practices. (v)Ongoing monitoring and maintenance, including continuous monitoring for privacy breaches and regular privacy audits, are essential to ensure compliance with privacy regulations and the effectiveness of privacy safeguards. By implementing these measures comprehensively throughout the LLM creation process, developers can mitigate privacy risks and build trust with users, thereby leveraging the capabilities of LLMs while safeguarding individual privacy. #privacy #llm #llmprivacy #mitigationstrategies #riskmanagement #artificialintelligence #ai #languagelearningmodels #security #risks

Software engineers often underestimate how a single line of code can impact the company's profits. And it could be a trivial log line to print information for debugging. 😫 😫 Few years ago, my team was owning an AWS Lambda that worked very well and required minimal intervention. One day my Manager asked me why is the CloudWatch cost $15,000 but Lambda's cost was $1,200 only. 😱 😱 I decided to root cause this issue and finally figured out the main culprit was redundant log lines in the lambda. Eliminating the log lines bought down the costs by 10x. 🚀 🚀 What was the main issue for high CloudWatch costs ? 👉 CloudWatch charges $0.5/GB for ingestion and $0.03/GB for storage 👉 Our AWS Lambda was logging close to 5MB data per second. 👉 It was logging the request and a huge response payload (~100KB) 👉 As a result, the overall log ingestion cost was high. How did we debug the issue ? We used the CloudWatch log metrics to check the data usage. And identified the log group that was resulting in increased bill amount. CloudWatch console tool helped in debugging the root cause. How can we prevent such issue in the future ? ✅ Only log useful information i.e exceptions, critical errors, etc. Avoid logging everything. ✅ Use log levels such as Debug, Warn, Info, Error, etc. ✅  Add filtering to filter only the Error/Warn logs before ingesting into CloudWatch ✅  Review the code carefully and assess the impact of log line on the costs. Treat debug lines like a vulnerability. ✅  Continuously monitor the CloudWatch costs and set alarms to warn the team of any high costs. One of the key takeaways from this story is that engineers must know what impact each line of code will have on the overall business. And accordingly adopt best practices to prevent high costs. In case you have experienced a similar issue in the past, you can post in the below comments what best practices you are following. 👇 👇 #tech #aws #cloud #cloudcomputing

If you are an organisation using AI or you are an AI developer, the Australian privacy regulator has just published some vital information about AI and your privacy obligations. Here is a summary of the new guides for businesses published today by the Office of the Australian Information Commissioner which articulate how Australian privacy law applies to AI and set out the regulator’s expectations. The first guide is aimed to help businesses comply with their privacy obligations when using commercially available AI products and help them to select an appropriate product. The second provides privacy guidance to developers using personal information to train generative AI models. GUIDE ONE: Guidance on privacy and the use of commercially available AI products Top five takeaways * Privacy obligations will apply to any personal information input into an AI system, as well as the output data generated by AI (where it contains personal information).  * Businesses should update their privacy policies and notifications with clear and transparent information about their use of AI * If AI systems are used to generate or infer personal information, including images, this is a collection of personal information and must comply with APP 3 (which deals with collection of personal info). * If personal information is being input into an AI system, APP 6 requires entities to only use or disclose the information for the primary purpose for which it was collected. * As a matter of best practice, the OAIC recommends that organisations do not enter personal information, and particularly sensitive information, into publicly available generative AI tools. GUIDE 2: Guidance on privacy and developing and training generative AI models Top five takeaways * Developers must take reasonable steps to ensure accuracy in generative AI models. * Just because data is publicly available or otherwise accessible does not mean it can legally be used to train or fine-tune generative AI models or systems.. * Developers must take particular care with sensitive information, which generally requires consent to be collected. * Where developers are seeking to use personal information that they already hold for the purpose of training an AI model, and this was not a primary purpose of collection, they need to carefully consider their privacy obligations. * Where a developer cannot clearly establish that a secondary use for an AI-related purpose was within reasonable expectations and related to a primary purpose, to avoid regulatory risk they should seek consent for that use and/or offer individuals a meaningful and informed ability to opt-out of such a use. https://lnkd.in/gX_FrtS9

Everyone talks about scale. But what kind of scale are you really designing for? When we were building Sprinto, scalability did matter, but not in the way you'd think. Most early-stage founders worry about computational scale: Can the system handle a million users? Will the infra hold up? But here’s what kept Raghuveer Kancherla and I up at night: Can 50 developers work on this codebase simultaneously without stepping on each other’s toes? Having run a software company before, I knew the bottlenecks don’t always come from users, they come from your own team as you grow. One day you have 3 engineers and the next, you’re onboarding your 20th hire. If you haven’t designed for collaboration, modular code, separation of concerns, test infra, deployment scaffolding, you end up in gridlock. Code reviews slow down, testing becomes painful, debugging takes longer than building, and then velocity stalls. So from Day 1, we asked ourselves: What makes this product collaboration-scalable, not just user-scalable? That’s where our energy went, because the moment you start thinking this way, you stop writing clever code and start writing clear code. And that is what scales.

The cheapest place to fix a mistake in a software project is on a piece of paper, not in six months of code. Writing a clear requirement is a great start. But the real skill, the thing that separates a good project from a great one, is actively trying to break the logic before you build it. Here at Full Metal, we call this pre-emptive debugging. We map out the "happy path," where the user does everything perfectly. But then we spend more time on the "unhappy paths." We ask a series of 'what if' questions. For a simple password reset feature, we'll ask: ❌ What if the user enters an email that isn't registered? ❌ What if they click the reset link after it has expired? ❌ What if they try to reuse an old password? Each of those 'what ifs' becomes a new requirement, closing a loophole that could have caused problems down the line. It's about finding flaws where they're free to fix. This also helps us avoid common pitfalls I've seen time and again. The biggest is the ambiguity trap: using fuzzy words like "fast" or "easy." My "fast" is not your "fast." Instead of "The system should be quick," we define it: "The system shall return a response within 500ms." One is a wish; the other is a testable fact. This meticulous approach might seem like a lot of work up front, but it saves a fortune in rework and frustration later on. We explore these common pitfalls and how to avoid them in our latest blog for SME leaders. Find the blog here: https://lnkd.in/eptHVTKA Have you ever had a project go a bit pear-shaped because of a single, unasked 'what if' question? #SoftwareEngineering #RiskManagement #DigitalTransformation

Why Debugging Makes You a Better Engineer Writing features feels rewarding but debugging is where real engineering growth happens Because debugging forces you to: • Understand systems deeply • Read unfamiliar code • Think in edge cases • Stay patient under pressure In production, problems rarely come with clear answers Sometimes: • Logs are incomplete • Errors are misleading • The issue is happening somewhere completely unexpected And that’s where debugging changes your mindset Over time, debugging improves something more important than coding speed: engineering intuition You begin noticing patterns faster You ask better questions You think more systematically A lot of great engineers aren’t just good at building things they’re exceptionally good at figuring out why things broke in the first place