I’ve always been a hacker at heart. Not the Hollywood kind, but the kind that sees friction and immediately thinks “there has to be a better way.”
Growing up, I was that kid writing IRC bots that kept channels in order, hosted quick games, and managed file sharing. I’d automate homework submissions, script my way out of repetitive tasks, and build tools just because manual processes felt… wrong. Small automations, big outcomes. This wasn’t a phase—this is who I am.
That instinct, removing friction with code, has been my north star through more than a decade in my career in tech. Whether it was streamlining deployment pipelines, building developer tools, or creating monitoring systems that actually helped rather than hindered, I’ve always gravitated toward making my and other developers’ lives easier.
Over the last two years, something clicked. I started introducing AI, automations, and playbooks not just to my personal workflow, but to my professional life. I embedded them directly into the Software Development Lifecycle (SDLC) at work. What started as personal efficiency hacks became company-wide force multipliers.
Little did I know, the industry would soon start calling this approach “agentic SDLC”, but we’ll dive into that evolution later on a different article I’ll post.
Why I’m sharing this journey#
Here’s the thing: friends and colleagues keep asking where my “knowledge” comes from. How do I know which tools will stick? Which AI integrations actually move the needle? Why do my automation experiments seem to work when theirs don’t?
The truth is, I read eagerly (articles, blogs, and technical docs), I watch talks, jump into deep discussions, lean heavily on my professional network, and attend meetups religiously. I’m constantly experimenting, failing fast, and iterating. When I share my sources, people often feel overwhelmed by the sheer volume.
This blog is my way of giving back. One place to curate what matters, explain why it matters in the context of real development work, and show exactly how to apply it: whether you’re an individual contributor trying to code faster or a manager trying to help your team ship better software.
I’ve lived through the transition from manual everything to AI-augmented development. This is the story of that journey, and where it’s heading next.
What you’ll get here#
🚀 Playbooks you can ship next week#
- AI‑assisted PR review with context awareness
- Incident summaries from chat transcripts
- Ticket triage automation with confidence scoring
- Release‑notes generation from merged PRs
- Docs drift detection via commit analysis
- Always with humans in the loop
📋 Frameworks & checklists#
- Choosing the right use cases (impact × confidence × ease)
- Setting proper guardrails and boundaries
- Defining “done” with measurable outcomes
📊 Metrics that matter#
- Cycle time reduction tracking
- Time‑to‑signal in code review
- Change‑failure rate attribution
- How to measure gains from specific interventions
🎯 Curation without overwhelm#
A concise feed of high‑signal resources with:
- One‑line “why this matters”
- Small “try this” experiment attached
👥 Both lenses covered#
- Manager‑level: adoption patterns and team dynamics
- IC‑level: ergonomics (latency, caching vs. correctness, testability)
How I work#
I like systems thinking more than tool chasing. AI is leverage, not replacement.
Core principles:
- Prompts are software—they need versions, tests, and owners
- Privacy is a product feature, not an afterthought
- If an intervention doesn’t move a metric or improve the developer experience, it’s decoration
A starter checklist you can use this week#
1. Identify your bottleneck#
Name the bottleneck: handoffs, ambiguity, or toil. Pick one.
2. Choose your experiment#
Select a high‑leverage experiment (impact × confidence × ease):
- 📝 Release notes from merged PRs and commits (human‑in‑the‑loop)
- 🔍 Incident timelines and summaries from chat transcripts
- 👀 Diff‑aware PR review that references tests touched and likely risk
3. Establish baseline#
Baseline before rollout: know your cycle time and review latency now.
4. Set guardrails#
- Human approvals for critical decisions
- Diff awareness for context
- Comprehensive logging
- Clear fallbacks when AI fails
5. Measure & share#
A simple before/after on the exact metric you targeted.
What I won’t publish#
❌ Clickbait prompts
❌ Tool‑of‑the‑week roulette
❌ “10×” claims with no baseline
✅ Expect: real experiments, honest retrospectives, and numbers.
The road ahead: Agentic SDLC and beyond#
Remember that “agentic SDLC” I mentioned? We’re living through a fundamental shift in how software gets built. What I’ve been experimenting with—embedding AI throughout the development lifecycle—is becoming the new standard. But most teams are approaching it wrong.
They’re treating AI as a fancy autocomplete tool instead of intelligent agents that can understand context, make decisions, and act autonomously within guardrails. I’ve been building and refining these patterns for two years. Now I’m ready to share the playbook.
What’s coming next:#
- Diff‑Aware AI for Code Reviews: How I built context-aware review agents that understand your codebase, not just syntax
- Incident Intelligence: Turning post-incident chaos into structured learning—automatically
- The Agentic SDLC Playbook: From my experiments to your production systems
- Docs vs. Reality: Why documentation drift detection is your secret weapon for team onboarding
- Prompt Testing Is Just Testing: How I learned to treat prompts like the critical code they actually are
- From Prototype to Production: The privacy, caching, and observability lessons that took me too long to learn
Keep me honest—and join the journey#
That kid writing IRC bots never imagined he’d be helping teams build agentic development workflows. But here we are. I’m still that same person who sees manual work and thinks “there’s got to be a better way”—I just have better tools now.
I’ll keep learning, building, and optimizing—in production systems, on personal projects, and in everyday life. You’ll get the distilled version: working code, battle-tested playbooks, and real metrics from systems that actually ship to users.
This is just the beginning. The line between human creativity and AI capability is blurring, and we’re the generation that gets to define how that collaboration works.
Ready to be part of this transformation?
Subscribe, follow on Telegram and LinkedIn, and bring your toughest problems. Let’s turn overwhelm into leverage, together.