Usability Heuristics Explained

☂️ Designing For Edge Cases and Exceptions. Practical design guidelines to prevent dead-ends, lock-outs and other UX failures ↓ 🚫 People are never edge cases; “average” users don’t exist. ✅ Exceptions will occur eventually, it’s just a matter of time. ✅ To prevent failure, we need to explore unhappy paths early. ✅ Design full UI stack: blank, loading, partial, error, ideal states. ✅ Design defaults deliberately to prevent slips and mistakes. ✅ Start by designing the core flow, then scrutinize every part of it. ✅ Allow users to override validators, or add an option manually. ✅ Design for incompatibility: contradicting filters, prefs, settings. 🚫 Avoid generic error messages: they are often main blockers. ✅ Suggest presets, templates, starter kits for quick recovery. ✅ Design extreme scales: extra long/short, wide/tall, offline/slow. ✅ Design irreversible actions, e.g. Delete, Forget, Cancel, Exit. ✅ Allow users to undo critical actions for some period of time. ✅ Design a recovery UX due to delays, lock-outs, missing data. ✅ Accessibility is a reliable way to ensure design resilience. Good design paves happy paths for everyone, but also casts a wide safety net when things go sideways. I love to explore unhappy paths by setting up a dedicated design review to discover exceptions proactively. It can be helpful to also ask AI tooling to come up with alternate scenarios. Once we start discussing exceptions, we start thinking outside of the box. We have to actively challenge generic expectations, stereotypes and assumptions that we as designers typically embed in our work, often unconsciously. And to me, that’s one of the most valuable assets of such discussions. And: whenever possible, flag any mentions of average users in your design discussions. Such people don’t exist, and often it’s merely an aggregated average of assumptions and hunches. Nothing stress tests your UX better then testing it in realistic conditions with realistic data sets with real people. Useful resources: How To Fix A Bad User Interface, by Scott Hurff https://lnkd.in/ecj6PGPU How To Design Edge Cases, by Tanner Christensen https://lnkd.in/ecs3kr8z How To Find Edge Cases In UX, by Edward Chechique https://lnkd.in/e2pfqqen Just About Everyone Is an Edge Case, by Kevin Ferris https://lnkd.in/eDdUVHyj Edge Cases In UX, by Krisztina Szerovay https://lnkd.in/eM2Xynba Recommended books: – Design For Real Life, by Sara Wachter-Boettcher, Eric Meyer – The End of Average, by Todd Rose – Think Like a UX Researcher, by David Travis, Philip Hodgson – Mismatch: How Inclusion Shapes Design, by Kat Holmes #ux #design

Resilience is a design choice, not a lucky break. Two reads worth your time from Cloudera on a theme that matters right now: keeping data and critical services available when things go wrong. First, architecting for data resilience and business continuity. The core idea is to design for disruption. Build clean failover paths. Keep data portable across on-prem, edge, and cloud. Test recovery, do not assume it. When incidents happen, the difference between minutes and hours shows up in revenue, trust, and compliance. Second, why hybrid needs multi-cloud resilience. Outages are inevitable. Single-cloud dependencies turn into single points of failure. A consistent platform across environments lets teams move workloads where they can run, without big rewrites. Governance and controls must travel with the data so handoffs stay compliant and auditable. Why this matters: - Incidents are normal. Downtime should not be. - Portability gives you options under pressure. - Practiced recovery beats theoretical plans. - Consistent governance speeds safe failover. Practical next steps: - Map tier-1 workloads and their failover targets. - Define RTO and RPO you can actually meet. - Run recovery drills and measure the results. - Reduce hard vendor lock-ins where they hurt resilience. Full reads: Architecting for Data Resilience: https://lnkd.in/dERfqTBv Hybrid Needs Multi-Cloud Resilience: https://lnkd.in/dHCURFWY If you own a data platform or lead operations, this is a good checklist to pressure test your plan before the next incident. #data #ai #cloudera #resilience #hybrid #iceberg #governance #cloud #theravitshow

It’s 2:07 a.m., when Murphy loves to visit. A storm rolls in, a node naps, and yet the customer never notices. That’s the goal: “boringly brilliant” resilience. Reliable enough to be forgettable. Amit Meshram wrote about how we’re approaching resiliency at Chase in a new post on the Next at Chase blog. A few takeaways that resonated for me as an architect: • Resilience is the product. Not a feature we bolt on. We design for the inevitable, failures happen; losing customer trust cannot. Every choice in our data architecture starts with that lens. • Choose your two, deliberately. CAP tradeoffs are leadership moments. For transactional integrity, we lean into consistency and partition tolerance with relational stores; for high-velocity, geo-distributed workloads, we bias toward availability and partition tolerance with distributed NoSQL. Clarity of purpose beats wishful thinking. • Practice makes reliable. Policy-driven failover, multi-region replication tailored per workload, chaos drills and GameDays under real load. Recovery should be predictable, not surprising. • Backups are a product feature. Strong, immutable, multi-region, with rehearsed restores and verified RPO/RTO. Telemetry as an early warning system. Think self-cleaning oven for databases: quietly isolating, rerouting and replacing before anyone feels heat. • Measure what matters. SLOs on critical paths, error budgets and burn rates, hot-partition detection, restore verification, and time-to-first-byte on recovery. We improve what we instrument. • Govern the blast radius. Architecture reviews that make CAP choices explicit, define failure domains, and test data correctness. Post-incident reviews that feed patterns, not blame. If we encode resilience into our blueprints, our controls and our culture, customers experience what we’re aiming for: everything just works. If this topic resonates, I highly recommend reading the full piece. There’s a lot more detail on how we design, test, and operate for “boringly brilliant” resilience. Give it a read and let me know your takeaways in the comments.

The AWS downtime this week shook more systems than expected - here’s what you can learn from this real-world case study. 1. Redundancy isn’t optional Even the most reliable platforms can face downtime. Distributing workloads across multiple AZs isn’t enough.. design for multi-region failover. 2. Visibility can’t be one-sided When any cloud provider goes dark, so do its dashboards. Use independent monitoring and alerting to stay informed when your provider can’t. 3. Recovery plans must be tested A document isn’t a disaster recovery strategy. Inject a little chaos ~ run failover drills and chaos tests before the real outage does it for you. 4. Dependencies amplify impact One failing service can ripple across everything. You must map critical dependencies and eliminate single points of failure early. These moments are a powerful reminder that reliability and disaster recovery aren’t checkboxes .. They’re habits built into every design decision.

Retries are baked into most of our calls, but on their own, they’re selfish. They help you succeed, but they can push others over the edge. Retries don’t create resilience unless you combine them with timeouts, backoff, jitter, and idempotency. Without those, retries don’t fix problems; they amplify them. Here’s the basic playbook: 1. Timeouts Waiting forever is a trap. If a request drags on, cut it off. The longer it lingers, the more resources it hogs: - Threads - Memory - DB connections. Enough of those, and everything slows down. 2. Backoff Don’t hammer a struggling service. Spread out your retries. Give it room to recover. 3. Jitter If everyone retries at the same time, it’s a stampede. Add randomness. Spread the load. Be kind to your backend. Way safer. 4. Idempotency Some APIs have side effects, like sending emails or charging cards. If retries run wild, things break. Make those endpoints idempotent: Same input → same result → no duplicates. Retries should work with the system, not against it. If your retries add pressure, you're part of the problem. Design for recovery, not retries.

𝐖𝐡𝐲 𝐂𝐥𝐨𝐮𝐝 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 𝐅𝐚𝐢𝐥 𝐢𝐧 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 Most failures are not cloud provider issues. They are system design failures. After leading cloud transformations processing billions of transactions daily, here’s what actually breaks in production and how teams fix it. 1. Configuration Management ❌ Configuration drift, manual changes, undocumented settings ✅ Infrastructure as Code, GitOps, automated drift detection 2. Deployment Strategy ❌ Big-bang releases, no rollback plan, manual deployments ✅ Blue-green deployments, canary releases, automated rollbacks 3. Observability & Monitoring ❌ No logs, no traces, reactive monitoring, alert fatigue ✅ Distributed tracing, centralized logging, proactive monitoring 4. Security & Compliance ❌ Hardcoded secrets, misconfigurations, compliance gaps ✅ Secrets management, automated compliance checks, security scanning 5. Scalability & Performance ❌ No autoscaling, ignored resource limits, single points of failure ✅ Horizontal scaling, load balancing, resilience testing 6. Disaster Recovery ❌ No DR plan, untested backups, single-region setups ✅ Multi-region architecture, automated failover, regular DR drills 7. Feedback Loop ❌ No post-mortems, repeated mistakes, siloed teams ✅ Blameless post-mortems, incident reviews, continuous improvement Cloud outages rarely happen because a service goes down. They happen because systems are built without feedback, resilience, and visibility. If you’re designing cloud systems today, design for failure first. ♻️ Repost if you found it valuable ➕ Follow Jaswindder for more insights

Stop trying to be break-proof. Build for bounce back. We’re taught to design systems that are fail-safe. But resilience isn’t about never falling down. It’s about how fast we get back up. It's what we learn in the process. Speed & adaptability matter most. When things break, there’s a window, a threshold. - If recovery begins quickly, momentum builds. - If it drags, damage compounds. This applies to both people and teams. When a setback occurs, the first 24-72 hours determine whether we stabilize or spiral. - In systems, it’s the critical recovery rate. - In leadership, it’s the response rhythm. 5 qualities of highly resilient people/teams: (1) Design for recovery, not perfection. Ask this question: "If we fail tomorrow, how do we restore 80% of capacity in one day?" Create fallback plans, reroute paths, and playbooks for quick resets. (2) Move fast to regain momentum. Recovery is like a muscle; it strengthens through use. After a disruption, run short, visible wins to restore confidence and signal progress. (3) Know thresholds. Systems collapse when they cross invisible lines. People do too. Identify your "too broken" point before you reach it, and build early warning signals around it. (4) Don’t bounce back. Bounce forward. True resilience isn’t returning to what was. It’s transforming into what’s next. Every crisis is data, and every disruption is feedback. (5) Build recovery habits. Don't be caught off guard. Reflect even for small setbacks. Ask "What was surprising?" and "What supported a fast recovery?" Resilience isn’t a trait. It’s a design choice. We can’t eliminate failure, but we can build systems and teams that learn, adapt, and come back stronger every time. The goal isn’t to be unbreakable. It’s to be unstoppable. ♻️ Share if this resonates. ➕ Follow Elise Victor, PhD for mindset and growth insights.

Users make mistakes—it’s inevitable. But what happens when they come across an error message that’s vague or confusing? Picture this: 👉 you’re trying to reset your password, and all you get is, 'Invalid entry.' but what does that even mean? 🤔 No clue what’s wrong, no hint on how to fix it. Now users are stuck, just guessing their way through. And yeah, those moments? 😩 They kill trust and tank user engagement real quick. Instead of leaving users frustrated, let's go over some key tips for handling errors the right way in your design. 1️⃣ Minimize Errors: The best error message? The one you never have to show! Focus on preventing errors before they even happen. 2️⃣ Make Errors Obvious: When things go off track, ensure users notice it right away. Use clear visuals, icons, or animations to grab their attention. 3️⃣ Explain What Went Wrong: Don’t just say “Error.” Clearly explain what happened and why, using simple, straightforward language. Avoid jargon that could confuse users even more. 4️⃣ Help Users Fix It: Be proactive—provide clear, actionable steps to guide users through resolving the issue. 5️⃣ Be Kind, Not Blameful: Mistakes happen, but it’s not the user’s fault. Use a friendly, positive tone in your error messages. This goes a long way in making users feel respected and supported. 6️⃣ Consistency is Key: Keep error messages consistent throughout your platform. Track these errors to identify patterns for continuous improvement. 7️⃣ Test with Real Users: Always test your error messages in real-world scenarios to see how users respond. Feedback is your best friend for refining your UX! On the other hand, errors aren't just interruptions; they're opportunities to build trust and improve your app's user experience. 👉 Did you find these tips helpful? let's share them with your friend #ui #ux #fikristudio #designtips #uitips #uxtips #fikrimicroblog

Why sometimes Outage Recovery Fails We saw retries to handle network blips before. But naive retries lead to a Cascading Failure. When a service outage occurs, your retry logic turns your own users into a DDoS attack. The Retry Storm This is the Unbounded Feedback Loop described in DDIA. Imagine your Database pauses for 10 seconds (GC pause or failover). - T=0: 10,000 requests time out. - T=1: Clients retry simultaneously. - T=2: The Database comes back up, faces 2x load (new requests + retries), and crashes immediately. This is the Thundering Herd. Your system isn't down because of load; it is down because of Synchronization. The problem arises when 10,000 clients enter a retry loop at T=0s, and they all run sleep(2000ms), they will all wake up at T=2s. Even exponential backoff without jitter just delays the impact. To fix this, we must inject randomness (Entropy). We stop treating wait_time as a constant. - Deterministic solution is  sleep = min(cap, base * 2 ** attempt) - Jitter on the other hand is sleep = random_between(0, min(cap, base * 2 ** attempt)) By adding randomness, you desynchronize the requests. Instead of 10,000 requests in a 10ms window, they are distributed across a 5-second window. Now the sudden spike is not a spike but a curve on the metrics. Trade Off: - It prevents the spiral. It is the only way to cold-start a system under high load. - We deliberately make some users wait longer than mathematically necessary. so we tend sacrifice individual speed for aggregate survival. Are you using a deterministic delay? If yes, you might want to build a recovery mechanism. #HardEngineering #DistributedSystems #SRE #SystemDesign #DDIA #CascadingFailure

The one thing I wish I knew about system design interviews before my first FAANG offer. Stop designing for perfection. Design for failure. I bombed my first Amazon system design interview spectacularly. Drew beautiful diagrams. Perfect load balancers. Elegant microservices. Textbook caching layers. The interviewer asked one question that killed me: "What happens when your whole region goes down?" I froze. "Well... we'd have backups?" Wrong answer. Here's what I learned after failing that interview and eventually getting offers from Microsoft, Amazon, and Oracle: What most engineers do in system design: ❌ Design the happy path first ❌ Add failure handling as an afterthought ❌ Focus on scaling to billions (when asked about thousands) ❌ Memorize architecture patterns What actually gets you hired: ✅ Start with "What could go wrong?" ✅ Design for the disaster scenario (expect the improbable) ✅ Show you've dealt with real production fires ✅ Think like someone who's been paged at midnight The mental shift that changed everything: 🎯 Every component you draw will fail. Now what? When I redid that Amazon interview months later, I started differently: "Before we scale this, let's talk about failure modes. Database goes down? Here's our fallback. Network partition? This is how we handle split-brain. Region outage? Let me show you our DR strategy." The interviewer's eyes lit up. Because here's the truth: Anyone can design a system that works when everything's perfect. FAANG companies need engineers who can design systems that work when everything's on fire. My framework for system design interviews now: 1. Start with failure scenarios "What are the top 3 ways this system could fail?" 2. Design in degraded mode "If we lose 50% capacity, what features do we sacrifice?" 3. Make failure visible "How do we know something's wrong before customers complain?" 4. Plan for recovery "When things break, how fast can we recover?" That shift in thinking took me from rejection to multiple FAANG offers. Stop memorizing architecture patterns. Start thinking about what happens at midnight when everything breaks. Because in production, everything eventually does. --- Want to master the system design interview mindset that actually gets offers? Let's talk.