1. Distributed systems lessons I wish I learned earlier
There are a few distributed systems lessons I wish I learned much earlier in my career. They would’ve saved me countless outages, late‑night incidents, and “why is this happening” moments.
Here are the big ones:
1. The network is always the bottleneck.
2. Everything fails eventually.
3. Consistency is a tax.
4. Latency is a feature.
5. Observability is not optional.
Distributed systems aren’t about complexity.
They’re about humility.
The sooner you accept that the system will misbehave, the better you’ll design it.
Primary hashtags:
#DistributedSystems #SystemDesign #SoftwareEngineering #Scalability #TechLeadership
Boost hashtags:
#CloudNative #BackendEngineering #HighAvailability #SRE #TechCareers
π₯ 2. How to design systems that survive failure
The real test of a system isn’t how it behaves when things go right — it’s how it behaves when everything goes wrong.
Here’s how I design systems that survive failure:
1. Assume every dependency will fail.
2. Build graceful degradation paths.
3. Add timeouts everywhere.
4. Retry with backoff, not brute force.
5. Make failure visible.
Resilience isn’t an add‑on.
It’s a mindset.
Systems don’t need to be perfect.
They need to be prepared.
Primary hashtags:
#ResilienceEngineering #SystemDesign #SRE #DistributedSystems #TechLeadership
Boost hashtags:
#CloudArchitecture #DevOpsCulture #HighAvailability #EngineeringBestPractices
π₯ 3. Why simplicity is the ultimate architecture
The longer I’ve been an engineer, the more I’ve realized one truth:
Simplicity is the ultimate architecture.
Simple systems:
Fail less
Scale better
Are easier to debug
Age gracefully
Complex systems:
Fail in weird ways
Require tribal knowledge
Slow teams down
The best architects weren’t the ones who drew the most boxes — they were the ones who removed the most.
Simplicity isn’t the absence of features.
It’s the absence of unnecessary friction.
Primary hashtags:
#Architecture #EngineeringLeadership #SoftwareEngineering #Simplicity #TechCulture
Boost hashtags:
#CleanCode #DesignPrinciples #TechStrategy #DeveloperExperience
π₯ 4. How to scale a service from 1k to 1M users
Scaling from 1k to 1M users isn’t magic — it’s discipline.
1. Measure everything.
2. Cache aggressively.
3. Reduce synchronous calls.
4. Split hot paths from cold paths.
5. Optimize for the 99th percentile.
6. Automate recovery.
Scaling isn’t about servers.
It’s about strategy.
Primary hashtags:
#Scalability #HighPerformanceSystems #CloudNative #DistributedSystems #BackendEngineering
Boost hashtags:
#SystemDesign #TechLeadership #PerformanceEngineering #DevOps
π₯ 5. The trade-offs behind microservices nobody talks about
Microservices are powerful — but the trade-offs are real.
1. You trade simplicity for autonomy.
2. You trade local bugs for distributed bugs.
3. You trade single deployments for orchestration complexity.
4. You trade monolithic performance for network latency.
5. You trade shared ownership for fragmented accountability.
Microservices aren’t bad.
They’re just expensive.
Choose them intentionally.
Primary hashtags:
#Microservices #Architecture #SystemDesign #SoftwareEngineering #TechLeadership
Boost hashtags:
#CloudArchitecture #DistributedSystems #DevOpsCulture #EngineeringBestPractices
π₯ 6. Caching strategies that actually work in production
Caching looks simple until you run it in production.
1. Cache the result, not the object.
2. Use TTLs that match business reality.
3. Cache at the edge whenever possible.
4. Bust caches intentionally.
5. Monitor cache hit ratios.
Caching isn’t a performance hack.
It’s a design decision.
Primary hashtags:
#Caching #PerformanceEngineering #BackendEngineering #DistributedSystems #Scalability
Boost hashtags:
#SystemDesign #CloudNative #HighPerformanceSystems #TechLeadership
π₯ 7. What I learned after applying to 100+ roles
After applying to 100+ roles, here’s what I learned:
1. Your resume matters less than your narrative.
2. Recruiters respond to momentum.
3. Referrals outperform applications by 10x.
4. You need a system, not hope.
5. Rejection is not feedback — conversations are.
Job searching is a skill.
And like any skill, it gets better with structure.
Primary hashtags:
#JobSearch #CareerGrowth #TechCareers #SoftwareEngineering #JobHunt
Boost hashtags:
#CareerAdvice #InterviewTips #LinkedInTips #VancouverTech
π₯ 8. The truth about technical interviews in 2026
Technical interviews in 2026 have changed.
1. AI hasn’t replaced interviews — it’s raised the bar.
2. System design is now the real differentiator.
3. Communication matters more than correctness.
4. Companies want engineers who can reason.
5. Collaboration beats performance.
Interviews aren’t harder.
They’re just different.
Primary hashtags:
#TechInterviews #SoftwareEngineering #CareerGrowth #SystemDesign #TechCareers
Boost hashtags:
#InterviewPreparation #EngineeringLeadership #AIInTech #VancouverTech
π₯ 9. How I track my job applications (with a custom dashboard)
One of the biggest unlocks in my job search was building my own job application dashboard.
It tracks:
Stage
Due date
Next action
Recruiter
Follow-ups
Notes
Why it works:
If you’re job searching, build a dashboard.
Your future self will thank you.
Primary hashtags:
#JobSearch #Productivity #CareerGrowth #TechCareers #SoftwareEngineering
Boost hashtags:
#DashboardDesign #OrganizationTips #VancouverTech #LinkedInTips
π₯ 10. Why rejection is data, not failure
Rejection used to feel personal.
Now it feels like data.
1. Every rejection tells you something about the market.
2. Every rejection sharpens your narrative.
3. Every rejection improves your targeting.
4. Every rejection builds resilience.
Rejection isn’t failure.
It’s feedback.
And feedback is fuel.
Primary hashtags:
#CareerGrowth #Mindset #JobSearch #TechCareers #Resilience
Boost hashtags:
#Motivation #ProfessionalDevelopment #VancouverTech #CareerAdvice
One of the most meaningful engineering shifts I’ve experienced recently was integrating AI directly into our GSD (Get Stuff Done) project.
Not as a bolt‑on feature.
Not as a “cool demo.”
But as a core engineering capability.
Here’s what AI actually enabled for us:
1. Turning vague requirements into actionable tasks
We used AI to break down high‑level product ideas into structured, prioritized work items.
No more guessing.
No more misalignment.
Just clarity.
2. Automating the “glue work” engineers usually hate
AI handled:
Drafting PR descriptions
Generating test scaffolding
Suggesting edge cases
Creating initial API contracts
This freed us to focus on architecture, reliability, and customer impact.
3. Making system design faster — and better
We used AI to explore alternative designs, compare trade-offs, and validate assumptions.
It didn’t replace our judgment.
It amplified it.
4. Improving developer experience inside the repo
AI became part of the workflow:
5. Creating a feedback loop between engineering and product
AI helped us map customer pain → engineering tasks → measurable outcomes.
It made the team more customer‑obsessed, not less.
The biggest lesson?
AI didn’t make us better engineers.
It made us more effective engineers.
The GSD repo wasn’t an AI experiment.
It was proof that AI can be a force multiplier when it’s embedded into the engineering process — not slapped on top of it.
And this is just the beginning.
#AI #SoftwareEngineering #DeveloperExperience #SystemDesign #TechLeadership #Productivity #VancouverTech #EngineeringCulture
In our GSD workflow, AI didn’t replace any engineer — but it did replace hours of repetitive work.
For example, instead of manually drafting PR descriptions or writing boilerplate tests, AI generated the first pass.
We still reviewed everything, but the time savings were massive.
It let us focus on architecture, reliability, and customer impact — the things AI can’t do.
Once we started writing prompts like design docs — with constraints, context, and trade-offs — the quality jumped.“Latency must be under 200ms.”“Cost must stay under X.”“No new dependencies.”AI thrives when the boundaries are clear.Constraint thinking is the new engineering superpower.
AI generated scaffolding, tests, documentation, and alternative designs.
It felt less like automation and more like having a junior engineer who works at superhuman speed
One of the most meaningful engineering shifts I’ve experienced recently was integrating AI directly into our GSD (Get Stuff Done) project.
Not as a bolt‑on feature.
Not as a “cool demo.”
But as a core engineering capability that changed how we design, plan, and execute work.
Here’s what AI actually enabled for us — with real examples from the repo:
1. Turning vague requirements into actionable tasks
We often start with high‑level product ideas like:
“Improve onboarding flow” or “Optimize search relevance.”
Before GSD, this meant long meetings, clarifications, and manual breakdowns.
With AI, we fed the requirement into our workflow and got back a structured task breakdown:
Example:
AI took a vague requirement — “Improve search latency” — and produced a first-pass breakdown including:
Identify slowest query paths
Add tracing to search pipeline
Propose caching layer options
Suggest percentile-based SLO targets
We still refined it, but the clarity boost was huge.
2. Automating the “glue work” engineers usually hate
AI handled the repetitive but necessary engineering tasks that slow teams down.
Examples from GSD:
Drafting PR descriptions based on diffs
Generating initial unit test scaffolding
Suggesting integration test cases
Creating API contract stubs
Writing first-pass documentation updates
This wasn’t about replacing engineers — it was about removing friction.
3. Making system design faster — and better
When we explored new features, AI generated multiple architecture options for us to evaluate.
Example:
For a new service integration, AI proposed three designs:
A synchronous API call path
An event-driven queue-based path
A hybrid model with caching + async fallback
We didn’t blindly accept any of them.
But reviewing them helped us surface trade-offs faster.
AI didn’t design the system.
It accelerated our thinking.
4. Improving developer experience inside the repo
AI became part of the repo’s workflow itself.
Examples:
Inline suggestions for refactoring
Auto-generated comments explaining complex logic
Intelligent linting based on project patterns
Summaries of large PRs for faster reviews
Engineers spent less time deciphering context and more time making decisions.
5. Creating a feedback loop between engineering and product
AI helped us map customer pain → engineering tasks → measurable outcomes.
Example:
For onboarding friction, AI analyzed user feedback and produced:
A list of top pain points
Suggested UX improvements
Technical tasks tied to each pain point
Metrics to track improvement
This made our team more customer‑obsessed, not less.
π΅ The biggest lesson?
AI didn’t make us better engineers.
It made us more effective engineers.
The GSD repo wasn’t an AI experiment.
It was proof that AI can be a force multiplier when it’s embedded into the engineering process — not slapped on top of it.
And this is just the beginning.
#AI #SoftwareEngineering #DeveloperExperience #SystemDesign #TechLeadership #Productivity #VancouverTech #EngineeringCulture
Spec‑Driven Development is a development methodology where:
You write a detailed spec first
The spec defines behavior, interfaces, and acceptance criteria
Implementation strictly follows the spec
Tests are derived from the spec
The spec becomes the contract
It’s closer to:
But with more emphasis on formal specification.
Structured Design Docs — a Microsoft‑style engineering practice focused on:
It’s essentially a thinking framework that forces clarity before implementation.
