Software Development

If you're losing brilliant women at the final stages of hiring - this might be why... Let me talk you through a recent example where a company had a disproportionately high number of women dropping out at late interview and offer stage for their tech roles: They were offering great salaries. Flexible working. A decent benefits package. So what was going wrong? We took a look at the data. Out of 2 billion data points, a few things stood out: → Diversity is non-negotiable. Women in tech rank it 31% higher than the average candidate. If they don’t see representation in leadership, they won’t apply → Flexible hybrid work wins, because structure matters. Demand for remote-only roles is 11% below average, while core hours and in-office collaboration rank higher → Family-friendly policies trump flashy perks. Fertility leave (+41%), job sharing (+33%), and parental leave (+19%) are the real differentiators But then we dug deeper; and that's where it got really interesting: → Women in data roles showed a higher demand for in-office work - mentorship and access to resources mattered → Women in engineering & development wanted mission-driven work and career progression above all else → Women in product roles prioritised culture and flexibility more than any other group The company checked their employer brand. Their careers page talked about “great culture” and “exciting opportunities.” But it said nothing about what actually mattered to the people they were trying to hire. They weren’t losing candidates because of the salary or the benefits. They were losing them because they don't know what their target talent groups actually want. The companies getting this right aren’t guessing. They’re using data to shape their employer brand - so they attract the right people, with the right message. Download our women in tech report to access more of these insights: https://lnkd.in/enYcGpeW And tell me if you've turned down a job offer for similar reasons? #WomenInTech #Hiring #EmployerBranding #FutureOfWork #DiversityMatters

The architecture of a software system is crucial to its success The architectural style provides a blueprint for how the system is structured and how its components interact. Choosing the right style can lead to software that is scalable, maintainable, and adaptable to change. Understanding architectural styles is key for any software professional. Architectural styles emerged in the late 1960s as a way to manage complexity and tame "software crises." Early styles like structured programming enforced discipline in code. Later styles like object-oriented programming focused on modeling real-world entities. Styles evolved to enable distributed systems and microservices. Some influential architectural styles include: - Layered architecture separates concerns into hierarchical layers like presentation, business logic, and data access. This is a tried and true way to structure applications. - Event-driven architecture has become popular for highly scalable apps. Components publish and react to events from other components asynchronously. - Microkernel architecture minimizes shared core software and implements other functionality in external modules. This provides flexibility. - Space-based/actor model architecture implements objects/actors that communicate via asynchronous messaging. This is ideal for concurrent distributed systems. In 2024, architectural trends focus on scaling, resilience, and flexibility: - Serverless architectures using cloud services like AWS Lambda scale automatically without provisioning servers. - Mesh app architectures build on a distributed data layer so features can be added without monolithic rewrites. - Integrating event streaming and Complex Event Processing (CEP) enables real-time response to diverse events. - Using reactive principles and non-blocking communication facilitates resilience and elasticity. The architecture of complex modern software requires creative solutions. Understanding architectural styles equips software leaders to make optimal technical decisions as demands evolve. While foundational styles remain relevant, new innovations enable transformative capabilities. Architectural mastery will only grow in strategic importance in 2024 and beyond.

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.

There’s an underrated superpower in tech (and life): knowing who’s worth listening to. Andrej Karpathy is one of those people. Ex-Director of AI at Tesla. Founding team at OpenAI. PhD under Fei-Fei Li. If these creds don't impress you, he also coined the term 'vibe-coding'. When he took the stage at YC AI Startup School in San Francisco this week, I paid attention. Here’s what I took away: 1️⃣ Software 3.0: English as Code. He reframes software’s evolution in three eras: Software 1.0: Hand-coded logic. Software 2.0: Trained models; neural net weights are the program. Software 3.0: You program in English. Prompts are the code. Everyone who can write a clear sentence is, in theory, a coder now. 2️⃣ LLMs aren’t Utilities - they’re Operating Systems. Karpathy’s most powerful framework: we’re in the ✨ mainframe era of AI ✨ In the 1960s OS world, there was ▪️Expensive, centralized compute. Few owned mainframes, many shared them. ▪️Time-sharing. Jobs batched, users were thin clients. ▪️Command-line interfaces. No GUI, just terminals. ▪️Remote access. The computer lived in a data center, users dialed in. In LLMs today? Same story. ▪️Massive, costly, cloud-native. Nobody runs GPT-4 locally. ▪️Thin clients. We pipe requests via browser or API. ▪️No AI GUI yet. We’re typing into terminals (ChatGPT). We’re pre-personal computer. Someone still has to build the AI equivalent of the desktop, the mouse, the spreadsheet. 3️⃣ Partial Autonomy + The Autonomy Slider. Karpathy’s Tesla experience taught him what happens between flashy demos and reliable autonomy: a decade of boring, hard work. In 2013, he rode in a Waymo car that handled 30 minutes of Palo Alto driving perfectly. The demo worked. It’s 2025. We’re still debugging self-driving at scale. The same is true for AI agents. The opportunity is augmenting people with AI “Iron Man suits,” not replacing them with Iron Man robots. Cursor, Perplexity are early examples of where this is going. ▪️They package context, orchestrate multiple LLM calls, and give users GUIs to audit AI output. ▪️They offer an autonomy slider - letting humans choose how much control to give up. The future is co-pilot software - where humans steer, AI assists, and the feedback loop is fast. 4️⃣ Docs and infra need to meet AI halfway. Today’s software is built for humans and APIs. Tomorrow’s needs to be legible to agents: ▪️Ditch “click here.” Use curl. ▪️Replace PDFs with agent-friendly Markdown. ▪️Build tooling that packages context so LLMs don’t fumble their way through HTML and menus. We need to design for a new consumer: not just people, not just code, but people-like machines. We’re in AI’s mainframe era. The personal computing revolution will come. The job now is to build what comes between. And in the meantime, I guess we’ll keep typing into our terminals and hoping the prompt does what we meant.

I pitched my software idea to 47 investors across Africa in 2022. Every single one said "NO." But here's what happened next that changed everything about how I view leadership in African tech... The brutal reality nobody talks about: Starting a tech company in Zimbabwe without seed capital isn't just hard—it's like trying to build a skyscraper with your bare hands while standing in quicksand. My co-founder and I had a revolutionary AI solution for recruitment and talent matching. The problem? We were operating from a 2-bedroom flat in Harare, coding on 5-year-old laptops that overheated every 3 hours. But here's the plot twist... Those rejections forced us to pivot multiple times and become the most resourceful leaders we could ever be. The breakthrough came when we stopped pitching investors and started talking to ONE user. We couldn't afford AWS, so we optimized our code to run on potato servers. We couldn't hire senior developers, so we mentored junior talent into world-class engineers. We couldn't afford marketing, so we built solutions so good that users became our evangelists. The real turning point was when I stopped trying to be Silicon Valley 2.0 Instead, I embraced what I call "Ubuntu Leadership"—leveraging our collective strength, community networks, and solving uniquely African problems with African ingenuity. We focused on solving the recruitment problem for one company perfectly. Then we scaled that solution to similar businesses. We created new features based on real user feedback, not investor demands. The uncomfortable truth about African tech leadership: You don't need venture capital to build something meaningful. You need vision, resilience, and the courage to solve real problems for real people first. Every "disadvantage" we faced became our competitive edge. Every "no" taught us to build something undeniably valuable. Every pivot forced us to innovate beyond what well-funded competitors could imagine. To every tech leader reading this from Harare, Lagos, Nairobi, or Cape Town: Your constraints are not your ceiling—they're your creativity catalyst. The next African software giant won't be built by copying Silicon Valley playbooks. It'll be built by leaders who understand that our greatest strength isn't in mimicking others, but in solving problems that only we truly understand. What's the biggest "disadvantage" in your market that you could turn into your competitive edge? Drop your thoughts below—let's rewrite the narrative about what's possible in African tech. 🚀 #AfricanTech #Leadership #Zimbabwe #TechLeadership #AI #SoftwareDevelopment #Entrepreneurship

As a client project manager, I consistently found that offshore software development teams from major providers like Infosys, Accenture, IBM, and others delivered software that failed 1/3rd of our UAT tests after the provider's independent dedicated QA teams passed it. And when we got a fix back, it failed at the same rate, meaning some features cycled through Dev/QA/UAT ten times before they worked. I got to know some of the onshore technical leaders from these companies well enough for them to tell me confidentially that we were getting such poor quality because the offshore teams were full of junior developers who didn't know what they were doing and didn't use any modern software engineering practices like Test Driven Development. And their dedicated QA teams couldn't prevent these quality issues because they were full of junior testers who didn't know what they were doing, didn't automate tests and were ordered to test and pass everything quickly to avoid falling behind schedule. So, poor quality development and QA practices were built into the system development process, and independent QA teams didn't fix it. Independent dedicated QA teams are an outdated and costly approach to quality. It's like a car factory that consistently produces defect-ridden vehicles only to disassemble and fix them later. Instead of testing and fixing features at the end, we should build quality into the process from the start. Modern engineering teams do this by working in cross-functional teams. Teams that use test-driven development approaches to define testable requirements and continuously review, test, and integrate their work. This allows them to catch and address issues early, resulting in faster, more efficient, and higher-quality development. In modern engineering teams, QA specialists are quality champions. Their expertise strengthens the team’s ability to build robust systems, ensuring quality is integral to how the product is built from the outset. The old model, where testing is done after development, belongs in the past. Today, quality is everyone’s responsibility—not through role dilution but through shared accountability, collaboration, and modern engineering practices.

The majority of our tech team are men. Fewer women pursue technical fields because of a variety of reasons, including the dreaded imposter syndrome. This is why representation matters. It’s about showing that “someone like me” can do it too. 🎞️ I wanted to share this #insidegrab video of Pei Shan Yap. She shares about how she didn’t trust herself to have all the answers when asked to take on bigger portfolios. Well, she’s recently taken on the role of Acting Head of Cybersecurity and I hope her story inspires other women to set aside their insecurities (pun unintended!) and go after the roles they want! Honestly, I'm proud of how Grab is changing. Today, #someonelikeme can be a leader, a technologist, a driver – and we're a better company for it. ✅ Half of our country heads are women. This isn't a quota; it's a reflection of the incredible leadership they bring. ✅ We're making a conscious effort to hire more women in tech. While the talent pool is smaller, we’ve implemented a process to seek out and hire more women in our tech teams. ✅ Our year-old Women’s Drivers Programme is showing promising signs of better supporting women. Our ‘Women Passengers Preferred’ feature – built from driver feedback – has been used by 1 in 2 women driver-partners at least once. #InternationalWomensDay is not a single day of recognition for women. To me, it’s a time to reflect: are we taking consistent action to create an environment where equal opportunity is the norm, every day. I invite ideas on how we can drive more inclusion together! You can also follow InsideGrab on Instagram <https://lnkd.in/g-Z3n-pb> for more videos on Grabbers and the projects we work on.

Production changes everything. What worked in a demo starts breaking at scale. That’s where real AI systems are tested. Here are the concepts that actually matter 👇 - Prototype vs production A demo works in controlled conditions, while production systems deal with scale, failures, and messy edge cases. - Training vs inference Training happens occasionally to build the model, while inference runs continuously to serve real users. - Batch vs real-time inference Batch is cost-efficient for large workloads, while real-time is critical when user experience depends on instant responses. - Accuracy vs reliability Accuracy looks good on test data, while reliability shows consistent performance under real-world conditions. - Guardrails vs validation Guardrails prevent unsafe outputs, while validation ensures correctness. Both are needed for safe and dependable systems. - Offline vs online evaluation Offline testing uses past data, while online evaluation measures real user impact. One doesn’t guarantee the other. - Data drift vs model drift Data drift changes inputs, while model drift shows performance degradation. Detecting this early avoids silent failures. - Monitoring vs observability Monitoring tracks known issues, while observability helps you understand unknown failures and system behavior. - Model hosting vs model serving Hosting deploys the model, while serving handles scaling, routing, and real-time requests. This is where complexity grows. - RAG vs fine-tuning RAG brings in fresh external knowledge, while fine-tuning embeds knowledge into the model. One adapts, the other is fixed. - Latency vs throughput Latency is response speed, while throughput is volume. Systems often fail because latency becomes too high. - Prompting vs fine-tuning Prompting shapes behavior through instructions, while fine-tuning changes model weights. Many real systems rely more on prompting. Understanding these trade-offs is what makes AI systems actually work. Which of these has been the toughest in your production setup?

3 weeks ago, I had lunch with a founder who scaled a $2.2M ARR software company. Today, he's looking to exit - but his own software is killing 25% of his valuation. Here's how he could've avoided it: Within 6 years, my friend started a public-facing HR software that • Dominated their niche market • Steadily increased revenue • Won loyal customers Today, he's looking to exit for $8M – but he can't get that valuation. Why? Because potential buyers found out that his software's architecture wasn’t built to last. For context, there are 2 key parts to software design: • Functionality: what the software does • Architecture: whether it can keep doing it over time Like real estate, software loses value without regular maintenance and upgrades. Its functionality can last for 3-6 years. But, with the right architecture, it can last for 2-3x longer. Great architecture enables maintenance and upgrades without major rework, but most teams do not build with longevity in mind. This short-term thinking creates TECHNICAL DEBT: the hidden cost of scaling and maintaining software to make it usable. And yes, this debt hurts. It's a huge red flag for buyers or investors in the due diligence (DD) process: • If the software isn’t scalable, it’s a problem. • If it needs constant rework, it’s a liability. • If fixing it requires a rebuild, the company valuation takes a hit. Software that wasn't built well or being maintained will drag your business down. If you resonate with any of the above: • Conduct a deep review of architecture & code • Create a plan around lackluster features • Allocate resources for upgrades It's not an easy process, but it will improve your valuation, customer experience, and business longevity. Keep your software as an asset, not a liability. — P.S. If you want an assessment of where your software stands, DM me 'CTO' to get on a quick call with our CTO panel at @Incepteo. They can certainly help in assessing your strategy for the future. 

We analyzed the tech stacks of 17 successful AI startups that raised $120M+ in 2024. This is what ACTUALLY correlates with fundraising success: 𝗙𝗿𝗼𝗻𝘁𝗲𝗻𝗱: • Next.js dominated (13/17 startups) • Tailwind CSS for styling (11/17) • 4 used ShadCN UI components • 3 used Chakra UI • TypeScript was universal 𝗕𝗮𝗰𝗸𝗲𝗻𝗱: • Python with FastAPI (8/17) • Node.js with Express (6/17) • 3 used Go for performance-critical microservices • PostgreSQL was the primary database (10/17) • Most used a combination of SQL + vector DBs (Pinecone/Weaviate) 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁: • 11/17 deployed on AWS • 5 chose Vercel + AWS combination • CI/CD with GitHub Actions (14/17) • Docker was universal, Kubernetes was rare (only 3/17) • 13/17 used serverless for at least part of their stack 𝗔𝗜 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: • 14/17 used OpenAI APIs as primary models • 5 used Anthropic's Claude for specific features • 6 fine-tuned models on their own data • Only 2 deployed their own open-source LLMs Most interestingly, the data showed ZERO correlation between technology sophistication and funding success. What DID correlate? • Time to initial user feedback (strongest correlation) • Weekly deployment frequency • Time from idea to revenue Our client who raised $500K built on: • FastAPI backend with PostgreSQL + pgvector • Next.js frontend with Tailwind • LangChain for AI orchestration • OpenAI API with fine-tuned RAG • Vercel for frontend, AWS Lambda for backend Build cost: $25K Time to market: 6 weeks What they DIDN'T waste time on: • Complex microservices architecture • Training custom foundation models • Custom UI frameworks • Premature optimization for scale TAKEAWAY: The founders who raised successfully concentrated engineering hours on their core AI differentiation, not rebuilding infrastructure that already exists. What's your experience with early-stage AI stacks? Have you seen similar patterns?