Software Development Lifecycle Best Practices for Startups

Just ship it! Test in production.... It'll be ok! Shipping secure software at high velocity is a challenge that many smaller, fast-paced, tech-forward companies face. When you're building and deploying your own software in-house, every day counts, and often, the time between development and release can feel like it's shrinking. In my experience working in these environments, balancing speed and security requires a more dynamic approach that often ends up with things happening in parallel. One key area where I've seen significant success is through the use of automated security testing within the Continuous Integration and Continuous Development (CICD) pipelines. Essentially, this means that every time developers push new code, security checks are built right into the process, running automatically. This gives a baseline level of confidence that the code is free from known issues before it even reaches production. Automated tools can scan for common vulnerabilities, ensuring that security testing isn’t an afterthought but an integral part of the development lifecycle. This approach can identify and resolve potential problems early on, while still moving quickly. Another great tool in the arsenal is the Software Bill of Materials (SBOM). Think of it like an ingredient list for the software. In fast-paced environments, it's common to reuse code, pull in external libraries, or leverage open-source solutions to speed up development. While this helps accelerate delivery, it can also introduces risks. The SBOM helps track all the components that go into software, so teams know exactly what they’re working with. If a vulnerability is discovered in an external library, teams can quickly identify whether they’re using that component and take action before it becomes a problem. Finally, access control and code integrity monitoring play a vital role in ensuring that code is not just shipping fast, but shipping securely. Not every developer should have access to every piece of code, and this isn’t just about preventing malicious behavior—it's about protecting the integrity of the system. Segregation of duties between teams allows us to set appropriate guardrails, limiting access where necessary and ensuring that changes are reviewed by the right people before being merged. Having checks and balances in place keeps the code clean and reduces the risk of unauthorized changes making their way into production. What I’ve learned over the years is that shipping secure software at high speed requires security to be baked into the process, not bolted on at the end (says every security person ever). With automated testing, clear visibility into what goes into your software, and a structured approach to access control, you can maintain the velocity of your team while still keeping security front and center. #founders #startup #devops #cicd #sbom #iam #cybersecurity #security #ciso

I burned 600 million tokens building software with LLMs in one week. Here’s what I learned: Plan like a tyrant, ship like a minimalist • Start with a markdown implementation plan. • Trim the wishlist. Ship in tiny vertical slices. • Mark sections “done,” commit, then move on. Treat Git like life support • Fresh branch per feature. • When the AI hallucinates, git reset --hard HEAD. • Re-implement clean once you find a fix. Test what users touch • Prefer end-to-end flows over unit navel-gazing. • Click through like a real user. Catch regressions before they catch you. • Don’t proceed until green. Debug with discipline • Paste exact error messages, ask for 3 causes, not 1. • Add logging first, then fix. • If stuck, reset and try another model. Optimize the AI, not just prompts • Keep instruction files in-repo (cursor.rules, agent.md, claude.md). • Download API docs locally for grounding. • Generate multiple outputs and pick the best. Build complexity out-of-band • Prototype hairy features in a clean repo, then integrate. • Maintain stable external APIs with room for internal change. • Prefer modular services over a monolith you’ll fear to open. Choose boring tech on purpose • Established frameworks win on convention and training data. • Many small files > one thousand-line blob. • Avoid giant files the models can’t reason about. Beyond code = unfair advantage • Let AI handle infra scripts, DNS, and hosting configs. • Use it for docs, marketing drafts, and design polish. • Screenshots > paragraphs when reporting UI bugs. • Voice input speeds up iteration. Improve forever • Refactor regularly once tests exist. • Ask the model to propose refactors. • Try new releases, but keep a model roster by task. Result: faster cycles, fewer rewrites, and token spend that actually buys progress. P.S. DM to discuss what you are building

My co-founder Aashish Parikh and I were looking at our codebase and realized something terrifying and amazing: with just 3 developers in 3 months, we have built a platform that would have historically taken many more developers a full year to ship. That is the power of Vibe Coding (AI-assisted development). It creates an illusion of infinite speed. You feel invincible. You ship feature after feature and can’t get away from Claude Code. But then, the hangover hits. When you move that fast, you aren't just accumulating features; you are accumulating "Vibe Debt." AI writes code that works, but it doesn't always write code that lasts. It doesn't care about your future self who has to debug it at 2 AM. We realized that to sustain this speed, we had to shift gears from "Prompt & Pray" to good engineering processes. Here is how we are managing the Vibe Coding lifecycle without drowning in debt: 1. Plan and Prepare: Do not just open the chat window and start typing. 80% of the work is defining the architecture before the first prompt. If you can't articulate the logic, the AI can't code it. 2. Refactor and Modularize: AI loves to write monolithic, repetitive code. We aggressively refactor generated code into reusable modules. We treat the AI's output as a "Rough Draft," not the "Final Production Release." 3. Code Review: Just because an AI wrote it doesn't mean it skips review. In fact, AI code needs more scrutiny for logic gaps and security flaws than human code. We are also using tools like CodeRabbit to automate reviews 4. Test: Vibe coding creates edge cases especially when we are using LLMs in our product. Automated testing and evals are must to work with non-deterministic AI behavior. As a small team and a fast moving startup, we are not yet fully applying all these, but important to build rigorous processes early on to avoid the tech debt. What tech debt are you seeing in your assisted coding? Azentic AI

The Process of Custom Software Development (Everyone skips the most vital step.) Not the coding part. Not the testing stage. Not even the launch moment. The skipped step comes earlier. Long before any code appears. I learned this building custom tools. Many projects failed before starting. → 1. Deep discovery first — Sit with users patiently. — Hear real pains fully. — Understand their everyday struggles. —This builds real clarity. → 2. Shape the blueprint — Sketch simple flows. — Pick the right stack. — Map needs with care. → 3. Build in small cycles — Write clean focused code. — Share progress often. — Adjust using real feedback. → 4. Test like real usage — Break things on purpose. — Check load early. — Fix issues fast. → 5. Launch with steady care — Prepare servers well. — Move data safely. — Support users calmly. → 6. Improve after launch — Patch bugs quickly. — Add features slowly. — Grow with real demand. Great software begins long before coding starts. Discovery isn’t optional magic. It’s the heartbeat of success. Skip it, Everything slips. Honor it, Everything flows. P.S. Do most teams skip discovery — yes or no?

Had a fun conversation with a friend who is looking to start his own company as a technical co-founder. We talked about my learnings as a technical founder and how different it is building in a startup vs a large company like Google where we both worked before. Here are some of my lessons I shared with them: 1) Don't write any code until your CEO gets paying customers. Many people avoid selling and justify that by claiming the product isn’t ready. Focus on verifying the demand and getting paying customers first. Find a way to serve customers manually to better understand their problem. 2) Don't worry about the scalability of your service until you reach PMF. Docker containers deployed on EC2 and a simple CI/CD pipeline based on GitHub Actions will work just fine for most startups. We started with ECS/RDS/LB from the beginning, and now, looking back, we see it as a mistake. 3) Don't use microservices before you have a large team (think of 50-100 people). A monolith architecture works just fine for most startups, and microservices only help when creating a release becomes hard due to multiple blocking issues. That's when you can consider microservices. 4) Invest a little bit of time and money early into a simple CI/CD pipeline to automate testing, releases, and rollbacks. It pays off and feels great when you press a button and your production is updated in 5 minutes. 5) Do code reviews in the team. They help. My co-founder and I do non-blocking code reviews (reviews are often done after PR merges) to move fast, and we require that code created by contractors be reviewed before it's merged for security reasons. Code reviews help share the knowledge within the team and make sure the code is maintainable. 6) Separate writing code and making decisions by writing short design docs. They help discuss solutions before they are implemented and avoid back and forth. It saves a lot of time. 7) Automate testing. There is a lot of bad advice on LinkedIn where people share that they never write tests and don’t even have a STAGING environment. This is wrong, stressful and won't work unless you have a small CRUD project. Our clients make $1-3M decisions based on the data we provide. We feel responsibility and we do a lot of testing. 8) Always set a budget limit with your cloud provider (AWS, Google Cloud) to avoid surprises at the end of the month, and review your cloud spending regularly. Especially if you train your models or have a serverless architecture. That's where most of the surprises come from. As you can see, many of the practices we followed at Google are ones we continue to use in our company. This is because, when it comes to engineering, Google gets a lot of things right. However, there are key things people need to watch out for when transitioning from a large company to a startup: over engineering and writing code too early. Startups are fun!

I once looked at my early startup’s codebase and realised something uncomfortable… It wasn’t code. It was panic-driven typing disguised as progress. If you’ve ever hacked your way through a product deadline, you know this feeling. You move fast. You tape things together faster. And suddenly the whole system feels like a fragile Jenga tower held together by hope and coffee. The 6 rules I wish I had learned earlier, the ones that stop you from cleaning up your own chaos later. 1. Separation of Concerns When one function tries to do everything, it ends up doing nothing well. It’s like getting stock tips, relationship advice, and fitness routines from the same friend. Split responsibilities. Clean code starts with clean boundaries. 2. Document Your Code A comment today is a gift to your future self. Because your future self will have zero memory of the clever thing you wrote at 2am. Don’t make debugging a crime scene investigation. 3. Don’t Repeat Yourself (DRY) Copy-paste feels fast. It’s not. Every duplicate is a future bug waiting for its moment. Write once. Reuse everywhere. Let functions do the heavy lifting. 4. Keep It Simple Complex code looks impressive, until you’re the one maintaining it. The real flex is clarity. Readable > clever. Understandable > magical. 5. Test Driven Development (TDD) TDD is like writing the exam before studying. The test fails → you add logic → the test passes → you clean up. It forces discipline. It prevents surprises. It builds confidence you can’t get from vibes and manual testing alone. 6. YAGNI (You Ain’t Gonna Need It) Founders love planning for the future version of the product. Except most of those imagined features never ship. Focus on what users need now. Earn the right to build more later. So, treat your codebase like a campsite: Leave it cleaner than you found it. Your team and your roadmap will thank you. P.S. What’s the most chaotic codebase sin you’ve ever seen… that still haunts you to this day?

Innovation isn't about chasing the next shiny tool. It's about building systems that outlive the hype cycle. You can chase every new framework that drops... Or you can architect something that actually scales. It all starts with the principles you choose to follow, And the discipline you bring to implementation. 🚫 Trend-driven development is fragile and short-lived. ✅ Principle-based systems are resilient and proven. Future-proof architecture compounds over time making your: 🧘 Codebase easier to maintain. 🔪 Decisions clearer under pressure. ⭐️ Team more productive across every sprint. Technical debt, not features, is your biggest liability. Instead of wasting cycles rebuilding from scratch, Invest in these 9 principles for lasting systems: 1. Design for change, not for current requirements. ↳ Tomorrow's pivot shouldn't require a rewrite. ↳ Build abstractions that flex with business needs. ↳ Avoid hardcoding assumptions about today's reality. 2. Prioritise observability from day one. ↳ You can't fix what you can't see. ↳ Logs, metrics, and traces aren't optional extras. ↳ Production issues reveal themselves when you're watching. 3. Write code that explains itself. ↳ Your future self will thank you at 2am. ↳ Comments age poorly, clear naming doesn't. ↳ Complexity should live in the problem, not the solution. 4. Test the behaviour, not the implementation. ↳ Tests should survive refactoring. ↳ Brittle tests kill momentum faster than no tests. ↳ Focus on what the system does, not how it does it. 5. Decouple early, integrate carefully. ↳ Tight coupling is technical debt in disguise. ↳ Services should communicate, not depend. ↳ Boundaries today prevent rewrites tomorrow. 6. Automate the repetitive, document the critical. ↳ Humans make poor robots. ↳ Automation scales, manual processes don't. ↳ Save mental energy for problems that need creativity. 7. Choose boring technology for core systems. ↳ Stability compounds, experimentation costs. ↳ Proven beats cutting-edge for infrastructure. ↳ Innovation belongs in your product, not your database. 8. Build for the team you'll have, not the one you want. ↳ Clever code creates bottlenecks. ↳ Complexity should match team capability. ↳ Simple systems scale with junior developers. 9. Measure what matters, ignore vanity metrics. ↳ Track outcomes, not activity. ↳ Lines of code mean nothing. ↳ User impact and system reliability tell the real story. The systems that survive don't just launch well. They're built on principles that outlast trends... And become the foundation others build on. ♻️ Repost to help your network build better systems. And follow Aditya for more.

So, you've just closed your Series A. Congrats! Now what? I've mapped out critical product milestones post-funding below. Having helped hundreds of startups through this journey, here's what I consistently tell founders: 1️⃣ Prioritize features that drive user adoption and market differentiation. 2️⃣ Identify core functions that can be modularized. Start with high-impact, independent services turned into microservices. 3️⃣ Set up end-to-end testing for critical user flows. Set a target code coverage percentage and increase it incrementally. 4️⃣ Automate deployments to cut down the time from commit to production without compromising reliability. 5️⃣ Migrate to container orchestration (e.g., Kubernetes). Implement autoscaling based on your app's metrics. 6️⃣ Implement distributed tracing. Set up alerts for performance degradation in critical paths of your app. 7️⃣ Conduct frequent load tests, simulating traffic spikes to uncover and resolve bottlenecks early. 8️⃣ Use centralized logging that everyone can access to foster a culture driven by data and collaboration. Of course, every journey is unique—adjust accordingly. If you found this useful, please share! ➕ Follow me for more startup and tech insights! #Startups #Fundraising #Product #Technology

I’ve been working as a contractual Program/Project Manager on complex projects for the past 7 years, most of which followed Agile methodologies. While the Software Development Life Cycle (SDLC) is designed to reduce risk, poor implementation can have the opposite effect. If not executed properly, it significantly increases the risk of project failure. Here’s a quick ranking of critical failure points that commonly derail software projects: 🔴 1. Unclear or Changing Requirements Poorly defined needs or constant scope changes break alignment early and often. ✅ Fix: Involve stakeholders early, use user stories and clarify DoD (definition of done), and validate frequently; another advice: make sure to define change request in the initial contract with the client. 🔴 2. Inadequate Planning & Estimation Unrealistic timelines or budgets create pressure that leads to shortcuts and burnout. ✅ Fix: Buffer for unknowns, involve tech leads in estimation. 🟠 3. Ineffective Communication Team silos and misalignment cause costly rework and delays. ✅ Fix: Daily stand-ups, shared documentation, clear ownership. The tech team needs to understand the functional requirement to be able to implement it technically. 🟠 4. Weak Design & Architecture Hasty or shortsighted technical decisions lead to rework and scalability issues. ✅ Fix: Involving a software architect who could support drafting the best scalable architecture choices within the available projects needs, constraints and budget 🟠 5. Insufficient Testing & QA Testing cut short = bugs in production, bad UX, security holes. ✅ Fix: Invest in a QA strategy to identify tests to be run by type of release, and automate critical time-consuming tests 🟡 6. Lack of Stakeholder Involvement Software built in isolation rarely meets business goals. ✅ Fix: Demo regularly (ideally after each milestone), build feedback into the cycle. 🟡 7. Poor Change & Config Management Inconsistent environments and chaotic updates derail progress. ✅ Fix: Version control, CI/CD, and clear change protocols. 🟡 8. Inadequate Risk Management Unexpected issues become blockers when risks aren't flagged early. ✅ Fix: Ongoing risk logs, contingency planning. 🟢 9. Neglecting Post-Launch Support No plan for support = user churn and poor adoption. ✅ Fix: Monitor performance, address issues fast. 🟢 10. Lack of DevOps & Automation Manual processes delay releases and increase error rates. ✅ Fix: Embrace CI/CD and infrastructure-as-code. Strong software isn’t just about great code—it’s about clarity, communication, and continuous feedback. A strong Project Manager implements the right processes and follows each step methodically to spot weak links early and address them proactively. And when issues do arise (as they often do), they stay calm, communicate transparently, and ensure all stakeholders remain aligned throughout the journey. #SoftwareDevelopment #SDLC #TechLeadership #ProjectManagement #Agile #DevOps #ProductDelivery

As a Fractional CTO across many businesses over the years, here are some of the common issues I find in engineering organizations and tech stacks: 𝗟𝗮𝗰𝗸 𝗼𝗳 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗮𝗻𝗱 𝗧𝗲𝗰𝗵 𝗚𝗼𝗮𝗹𝘀 → Engineering teams are often working in silos. → Business objectives get lost in translation. 💡Solution: Regular syncs between engineering and business teams to ensure alignment. 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗗𝗲𝗯𝘁 𝗔𝗰𝗰𝘂𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 → Quick fixes and hacks lead to longterm issues. → Scaling becomes a nightmare due to accumulated debt. 💡Solution: Implement a strategy to pay down technical debt incrementally. 𝗜𝗻𝗮𝗱𝗲𝗾𝘂𝗮𝘁𝗲 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 → New hires struggle to get up to speed. → Knowledge is often tribal and not formally captured. 💡Solution: Invest in comprehensive documentation from the getgo. 𝗢𝘃𝗲𝗿𝗹𝘆 𝗖𝗼𝗺𝗽𝗹𝗲𝘅 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 → Engineers often overengineer solutions. → Complexity leads to more bugs and slower development. 💡Solution: Aim for simplicity and clarity in your tech stack. 𝗣𝗼𝗼𝗿 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 → Teams often rely on inefficient methods for updates. → Important information gets lost or delayed. 💡Solution: Encourage sharing information early and often (and reward people for raising issues versus shooting the messenger). 𝗜𝗻𝘀𝘂𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗤𝗔 → Bugs make it to production far too often. → Customer experience suffers as a result. 💡Solution: Invest in QA automation tools and build your tests to run automatically once versus manually testing everything with your QA team and Dev team every release. 𝗟𝗮𝗰𝗸 𝗼𝗳 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 → Tech evolves rapidly, but teams often don't. → Skills and knowledge become outdated quickly. 💡Solution: Foster a culture of continuous learning and improvement. 𝗜𝗻𝗳𝗹𝗲𝘅𝗶𝗯𝗹𝗲 𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸𝘀 → Older technologies often hinder innovation. → Migration to new stacks is seen as too disruptive. 💡Solution: Plan for gradual migration to more flexible technologies. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗢𝘃𝗲𝗿𝘀𝗶𝗴𝗵𝘁𝘀 → Security is often an afterthought. → Vulnerabilities can lead to significant business risks. 💡Solution: Make security a foundational aspect of your development lifecycle and build it into your deployment pipeline. 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀 → Teams are often under resourced. → Leads to burnout and turnover. 💡Solution: Track metrics like team velocity and apply pragmatism to workload assigned each sprint or development cycle. These issues are common but solvable. What challenges have you faced in your engineering organization? Let’s discuss!