DEV Community: Shri Nithi

I Confused Agentic AI with GenAI for 6 Months (Cost Me a Promotion)

Shri Nithi — Tue, 24 Mar 2026 12:07:04 +0000

Manager: "Difference between Agentic AI and Generative AI in testing?"
Me: "Uh... both AI, right?"
Promotion: someone else.
TestLeaf guide cleared it - Agentic AI vs Generative AI.
Difference
Generative AI:

Give prompt → Get content → Use it

Agentic AI:

Give goal → Plans steps → Executes autonomously

Example
GenAI
Me: "Create login tests"
ChatGPT:

Valid creds → Success
Invalid → Error
Locked → Message I copy, run manually. GenAI = Assistant Agentic AI Me: "Test checkout" Agent: Planning scenarios... Creating tests... Executing... Found 3 bugs. Agentic = Autonomous Tester Gap AI for software testing (GenAI): Generate content faster AI in testing (Agentic): Execute tests autonomously GenAI helps you write. Agentic acts like a tester. Use Cases GenAI:

Draft scenarios
Generate code
Create data
Summarize bugs

Agentic AI:

Plan strategy
Execute suites
Self-heal scripts
Detect/retry failures

Comparison
GenAIAgenticRoleCreatorActorInputPromptGoalDecisionsNoneAdvancedHumanHighLow
Tools:
GenAI: ChatGPT, Copilot
Agentic: AutoGPT, LangChain
Learning Path
M1-2: GenAI prompts, speed
M3-4: Agentic agents, autonomy
Career
Before: "I use ChatGPT for testing" → Generic skill
After: "GenAI for speed, Agentic for autonomy" → Strategic understanding
Result? Promotion next cycle.
Pattern
L1: Manual
L2: Automation
L3: AI-assisted (GenAI)
L4: AI-autonomous (Agentic)
Most: L2-3. Future: L4.
Avoid
Don't conflate.
GenAI ≠ Agentic.

GenAI: Productivity
Agentic: Autonomy

Future
2026:

GenAI writes
Agentic executes
Humans strategize

Both. Not either/or.
Lesson
"AI" knowledge insufficient.
Which AI for what matters most.

TestLeaf.
Using or understanding? 🧠

ai #testing #career

I Tried 12 AI Testing Tools. Only 2 Actually Mattered

Shri Nithi — Fri, 20 Mar 2026 11:58:33 +0000

Three months testing every "AI-powered" QA tool.
Testim. Mabl. Functionize. Applitools. Katalon. Sauce. More.
Each: "AI revolutionizes testing!"
Most: Vendor lock-in.
This TestLeaf guide -Best AI Testing Tools in 2026, changed everything.
Realization
AI in software testing isn't tools.
It's two capabilities:

Intelligence (GenAI)
Execution (Playwright)

What Works
GenAI = Intelligence
AI for software testing means:
Test Design:
"Generate edge cases login SSO + MFA" → 20 scenarios
Failure Analysis:
"Analyze stack trace + screenshots" → root cause seconds
Code Assist:
"Convert to Playwright" → test skeleton
Bug Reports:
"Summarize 15 failures" → clear report
Playwright = Execution
AI in testing needs reliability.
GenAI generates fast. Playwright executes reliably:

Modern browsers
Auto-waiting
Network mocking
Debug traces

Pattern
Before: Buy tool → platform lock → fight limits
After: GenAI thinking → Playwright execution → own capability
Example
Task: Test checkout with 10 payment methods
Old (AI platform):

Platform generates tests automatically
Tests break on dynamic elements
No debug control
Wait for vendor fixes

New (GenAI + Playwright):
javascripttest('checkout visa', async ({ page }) => {
await page.fill('[data-test="card"]', '4242424242424242');
await expect(page.locator('.success')).toBeVisible();
});
GenAI = 10 scenarios in 2 minutes.
Playwright = reliable execution I control completely.
Why Matters
Platforms: "We do everything!"
Reality: Limited, dependent, expensive.
GenAI + Playwright: Control, open source, deep debug, free GenAI.
Changed
Stopped chasing tools completely.
Built capability:

GenAI: scenarios, analysis
Playwright: stable automation
Human: judgment

Foundation
GenAI: Think faster, cover edges, debug smart
Playwright: Automate reliably, debug deep, scale
You: Strategy, risk, judgment
Insight
Best 2026 AI testing?
Not tools.
Intelligence (GenAI) + Execution (Playwright) + Judgment = sustainable.
Platforms fade. Skills stay.

Every Test Passed. Users Said the Mobile Site Was "Broken.

Shri Nithi — Wed, 18 Mar 2026 10:13:45 +0000

Friday 4 PM. Deploy. All green.
Monday 9 AM. Tickets flood.
"Checkout button invisible iPhone."
"Menu overlaps iPad."
"Pricing broken Android."
Every test passed.
This TestLeaf guide showed what I missed.
Blind Spot
Tests validated:

Button exists ✅
Click works ✅
Navigation succeeds ✅

Didn't catch:

Button clipped mobile ❌
Menu overlap tablet ❌
Layout shifts ❌

Functional ≠ Visual
Wake-Up
Learn Playwright became urgent.
Users don't care cy.get('.button') passes.
They care if they can see it.
What Changed
Screenshot diffs + device emulation.
Screenshot Diffs:
javascriptawait expect(page).toHaveScreenshot('checkout.png');
Catches layout breaks, CSS regressions, visual bugs.
Device Emulation:
javascriptawait page.setViewportSize({ width: 375, height: 667 }); // iPhone
await page.setViewportSize({ width: 768, height: 1024 }); // iPad
Same test. Multiple devices.
Strategy
Not everything. Strategic coverage.
Screenshot high-risk screens:

Landing pages
Checkout flows
Dashboards
Pricing components

Test critical breakpoints:

Desktop (1920px)
Tablet (768px)
Mobile (375px)

Three viewports = comprehensive device coverage.
Real Example
Before:
javascriptawait expect(page.locator('.checkout-btn')).toBeVisible();
Passed desktop. Failed users (button below fold).
After:
javascriptawait page.setViewportSize({ width: 375, height: 667 });
await expect(page).toHaveScreenshot('checkout-mobile.png');
Caught: clipping, overlap, shifts.
Pattern
Functional: Does it work?
Visual: Can users see it?
Both needed.
Playwright Automation Tool Advantage
Built-in screenshot diffs.
Built-in device emulation.
No extra platforms.
Visual testing becomes normal workflow.
Learned
Green tests ≠ good UX.
Responsive bugs hide at specific breakpoints.
Screenshot baselines = quality contracts. Update deliberately.
Playwright course online would've saved me weeks of painful debugging and user complaints.
Shift
From: "Tests passed, ship."
To: "Tests + visuals match devices, ship."
Modern QA.
Insight
Behavior alone incomplete.
What users see matters.

TestLeaf guide - "Why Playwright Screenshot Diffs and Device Emulation Matter".
Testing visuals or luck? 🎯

playwright #testing #qa

I Thought AI Would Write My Tests. It Did Something Way Better

Shri Nithi — Tue, 17 Mar 2026 09:57:02 +0000

200 automated tests. 40% flaky. 3 hours daily triaging failures.

"Let's try AI," my manager said.
I expected magic: "AI writes tests, we go home early."
Reality: way more interesting.
What I Got Wrong
Thought AI in software testing meant:

Auto-generate all tests
Zero maintenance
Perfect coverage

Got something different.
What AI Actually Did
Found this TestLeaf article that shifted my understanding completely.
AI didn't replace my test writing.
It eliminated the waste around testing.
Before AI
Test Design: Stare at blank Jira ticket 20 minutes
Maintenance: Hunt locator changes manually
Triage: Read 200 stack traces, group similar failures by hand
Prioritization: Run everything, hope important stuff passes first
After AI
Test Design: AI drafts scenarios from requirements, I validate
Maintenance: AI suggests locator fixes, highlights instability patterns
Triage: AI clusters failures, summarizes logs, points to likely causes
Prioritization: AI flags changed areas, risk zones, historically flaky flows
The Real Shift
Not "AI writes tests."
AI removes testing waste.
That's AI in testing that actually matters.
Where It Helped Most

Draft Test Ideas Input: User story about checkout Output: 15 edge cases I hadn't considered Saved: 30 minutes of blank-page staring
Locator Intelligence AI: "This button's xpath changed 3 times in 2 weeks" Me: "Let's use data-testid instead" Result: 40% less maintenance
Failure Clustering Before: 47 failures, 3 hours triaging After: AI groups into 4 root causes, 30 minutes
Risk Prioritization AI: "Login flow changed + historically unstable + user-critical" Me: "Run this first" Where It Still Fails AI doesn't understand your product. Example: AI generated test: "Verify checkout button exists" Reality: Button exists but payment gateway is broken AI checks presence. Doesn't validate business logic. The Framework I Use Trust Ladder:

AI generates → I validate
AI suggests → I review
AI clusters → I investigate
AI drafts → I refine

Never blindly trust AI output.
What Changed
Before: 40% time writing tests, 60% maintenance/triage
After: 70% time writing tests, 30% maintenance/triage
Not "AI replaced me."
AI for software testing amplified me.
The Bigger Idea
Future isn't "AI writes scripts."
Future is quality intelligence.
AI helping answer:

What changed that matters?
Which failures are noise?
Where's real risk building?

That's the shift.
My Advice
Don't chase "AI auto-generates everything."
Use AI to:

Reduce blank-page effort
Speed up maintenance
Improve triage
Prioritize smarter

Keep judgment. Add AI leverage.
The Truth
AI won't make bad testers good.
It'll make good testers faster.

TestLeaf guide - AI-powered test automation explained what I missed.
Using AI to eliminate waste or just to write more scripts? 🤔

ai #testing #automation #qa

I Ignored AI Skills for 2 Years. Then My Job Got Optimized

Shri Nithi — Fri, 13 Mar 2026 09:44:48 +0000

Senior QA. 8 years. Solid Selenium.
Manager introduced "testing assistant"—an AI tool.
Three months later: "Restructuring. Your role optimized."
Translation: replaced.
This TestLeaf guide - "Top Skills and Careers in Artificial Intelligence (AI)", showed what I missed.
Wake-Up
Thought AI was hype.
Thought "testing needs humans."
Wrong.
AI in software testing isn't replacing QA.
It's replacing QA who don't learn AI.
Skills That Matter
For QA
AI in testing:

Model validation
Prompt engineering
AI-assisted test generation
Log analysis
Predictive flaky detection

Core Technical
AI for software testing:

Python (TensorFlow, PyTorch)
ML fundamentals
Data analysis
GenAI tools
MLOps basics

Opportunity
QA has edge.
We know:

Test design
Edge cases
Failure modes

AI knowledge = AI Test Engineer.

Validates AI models. Tests intelligent apps.
My Transition
Month 1: Python basics, ML fundamentals
Month 2: Prompt engineering, GenAI for test generation
Month 3: Built AI-assisted test framework
Month 4: First AI model validation project
Month 6: New job as AI Quality Engineer. 40% raise.
Not theory. Real skills. Real results.
Roadmap
Foundations: Python, statistics, data
ML Basics: Learning types, training, scikit-learn
GenAI: Prompting, test generation, evaluation, bias detection
Production: MLOps, cloud, monitoring
Applications
Test Generation:
pythonprompt = "Generate login tests: valid, invalid, locked, XSS"

AI generates suite

Log Analysis: AI clusters patterns
Flaky Detection: ML predicts intermittent fails
Pattern
Traditional: Manual expertise.
AI-era: Manual + AI leverage.
Augmentation, not replacement.
Changed
Feared AI → Learned AI.
"Will AI replace me?" → "How can AI amplify me?"
Insight
AI won't take your job.
Someone who knows AI will.
Better: You who knows AI takes better jobs.

All My Selenium Tests Passed. Then Users Said UI Was Broken

Shri Nithi — Thu, 12 Mar 2026 10:33:50 +0000

Last sprint: 100% pass rate.
Green build. Confident deploy.
Monday: 47 support tickets.
"Button hidden on mobile."
"Form overlaps footer."
"Can't click submit on iPad."

Selenium automation testing passed. UI broken.
This TestLeaf breakdown explained what I missed.

The Problem
Selenium confirmed workflow worked.
Login, checkout, dashboard: ✅
None proved UI was usable.
What I Missed
Software testing with Selenium verifies:

Element exists
Workflow completes
Assertions pass

Doesn't verify:

Element visible
No overlaps
Mobile layout works
Text readable

False Confidence
Tests:
element.isDisplayed() // true
element.click() // success
Users saw:

Submit behind sticky header
Fields overlapping on mobile
Cards misaligned
Text unreadable

Why Matters
Mobile accounts for 51% of global web traffic.
India? 68% mobile dominance.
My tests ran on desktop viewports. Most actual users accessed on mobile devices.
I tested functionality. They experienced layout and visual presentation.
Gap between testing and reality.
Changed
Selenium training in Chennai taught visual quality.
Learned:

Visual regression
Responsive validation
Viewport coverage
Layout stability

New Approach
Functional (Selenium): Workflow? Logic? Data?
Visual (Added): Layout stable? Elements visible? Responsive? No overlaps?
Example
Before:
javadriver.findElement(By.id("submit")).click();
// Passes if exists
After:
javadriver.findElement(By.id("submit")).click();
driver.manage().window().setSize(new Dimension(375, 667));
takeScreenshot();
compareBaseline();
// Catches mobile issues
Insight
Green build ≠ usable UI.
Functional = it works.
Visual = users can use it.
Questions
Not "Did it complete?"
But:

Button visible?
Layout stable across viewports?
Touch-accessible?
Responsive works?

Pattern
Strong QA = functional + visual.
Selenium → workflows
Visual regression → experience
Responsive checks → real devices
Combined = release confidence.

I Automated 2,000 Tests Then Regretted Everything

Shri Nithi — Mon, 09 Mar 2026 08:44:19 +0000

Two years Selenium automation testing.
2,000+ tests. Custom framework. POM. CI/CD integrated.
Manager: "Why are 40% flaky?"
Couldn't answer.
This TestLeaf guide - "Automation Testing Pros and Cons in 2026", showed what I got wrong.
The Mistake
Automated everything.
Login, UI text, one-time prototypes.
More automation = better QA?
Wrong.
Real Pros
Software testing with Selenium has real advantages:
Speed: 3-day manual regression → 2 hours automated
Consistency: Same test, same steps, same assertions. Every time. No human error.
CI/CD Integration: Pipeline fails before bad code reaches production. Fast feedback.
Coverage: Tested 12 browsers, 5 environments simultaneously. Impossible manually.
Reusability: Good framework = reusable page objects, utilities, fixtures that compound over time.
What Nobody Tells You
"Disadvantages" aren't bugs. They're discipline signals.
Setup Takes Time: Yes. That setup enables scale.
Requires Skills: Yes. Raises technical bar.
Maintenance Pressure: Teaches better architecture.
Flaky Tests: Reveal race conditions, timing issues, unstable deps. System problems, not testing problems.
Can't Automate Everything: Forces prioritization. Good.
New Approach
After Selenium training in Chennai teaching strategy:
Automate:

Regression (stable, high-value)
Smoke tests
API validations
Cross-browser
Data-driven

Don't:

Changing prototypes
One-off validations
Subjective UX
Early exploration

Insight
Automation ≠ "manual but faster."
Different skill.
Manual: Intuition, exploration, judgment
Automation: Speed, repeatability, scale
Best teams combine.
Changed
Before: Automate everything, more = better, flakes = automation problem
After: Strategic, right tests, flakes = diagnostic
2,000 tests → 400 high-value.
Fewer tests. Better coverage. Zero flakes.
Pattern
Good automation needs:

Clear strategy
Strong architecture
Disciplined maintenance
Realistic boundaries

"Disadvantages" teach these.
Questions
Not "Can I?"
But:

Should I?
Will it stay stable?
Fast, reliable feedback?
ROI worth maintenance?

I Bombed My Tech Mahindra Interview (Here's What They Actually Asked)

Shri Nithi — Tue, 03 Mar 2026 11:46:47 +0000

Three years Selenium automation testing.

Custom framework. TestNG. POM.
Failed first round.
Not because I didn't know Selenium. Because I couldn't explain why I made design choices.
This TestLeaf guide - Tech Mahindra selenium interview questions showed what I missed.

Questions That Killed Me
"What framework?"
Me: "Hybrid with POM and TestNG."
Them: "Why hybrid? Why not pure data-driven?"
Silence.
"Multiple TestNG suites?"
Me: "Yes, testng.xml."
Them: "Configure smoke, sanity, regression in parallel for CI/CD. Show me."
More silence.
The Pattern
Software testing with Selenium isn't about tools.
It's design decisions.
Every question: What → Why → How → Trade-offs
Not "Know TestNG?"
But "Why @BeforeMethod instead of @BeforeClass here?"
Real Questions
Framework: Why hybrid over data-driven? POM structure? Test data handling?
TestNG: Multiple suites, grouping, DataProvider vs Excel, parallel execution
Java: Overloading vs overriding, interface vs abstract, collections, OOPS
Scenarios:

500 tests, need smoke after builds. How?
Login fails. Thread.sleep or explicit wait?
Validate dropdown duplicates. Which collection?

What Changed
Found Selenium training in Chennai teaching architecture, not syntax.
Learned:

Design patterns (why POM)
Framework trade-offs
Real project structure
CI/CD integration

Now I Ace
Hybrid vs Data-Driven: Hybrid = POM + data + utilities + reporting. Data-driven = only data separation.
Multiple Suites: with tags, or Maven Surefire triggers.
Grouping: @test(groups={"smoke"}) runs specific packs.
Overloading/Overriding: Overloading = same method, different params (compile). Overriding = child redefines parent (runtime).
New Answer
Question: "Explain framework."
Before: "Hybrid with POM."
Now: "Hybrid combining POM for maintainability, DataProvider for data separation, custom utilities for WebDriver wrappers, Extent Reports for visibility, Maven for CI/CD. Chose hybrid for scalability and flexibility—pure data-driven wouldn't handle complex page interactions."
The Gap
Most know Selenium.
Few explain:

Why you chose this
Trade-offs made
How to scale
When to change

That's "knows tools" vs "engineers systems."

TestLeaf.
What caught you? 🤔

selenium #testing #interview

I Watched $13B Vanish Because of Claude Code (Here's Why)

Shri Nithi — Thu, 26 Feb 2026 11:52:05 +0000

IBM: -13% in one day.
Cybersecurity: -11%.
Not earnings. Not scandal.
An AI update.
This TestLeaf breakdown explains - Claude Code vs. GitHub Copilot vs. Cursor what I missed.
What Changed
Used Copilot 2 years. Cursor 6 months. Thought revolutionary.
Then Claude Code.
Copilot: Line-level autocomplete
Cursor: File-level editing
Claude Code: System-level understanding
Category shift.
Testing Breakthrough
AI in software testing gets wild here.
Claude scanned entire codebase. Found 500+ real vulnerabilities in open-source projects.
Not "issue at line 47."
"Architectural vulnerability spanning 12 files, here's the complete fix, here's the security impact."
QA Shift
Traditional AI for software testing: I write test cases, AI helps execute them.
Claude approach: AI understands system behavior, suggests risk-based testing strategies, self-heals failures with audit trails.
Old: Test what should happen
New: Evaluate what could happen
Deterministic validation → probabilistic assessment.
Why Markets Panicked
Security Threat
Claude does what security companies do:

Scan code
Detect vulnerabilities
Suggest patches
Prioritize risk

Cybersecurity: -11%.
Legacy Modernization
Understands COBOL. Modernizes systems.
IBM (legacy business): -13%.
SaaS Fear
If AI writes, debugs, maintains autonomously...
Need SaaS tools? Large teams?
Software: Sell.
Difference
Copilot → write faster
Cursor → edit smarter
Claude → understand systems
QA Changes
AI testing shifts everything.
Old: Write cases, validate, fix.
New: Evaluate AI tests, validate uncertainty, monitor behavior.
Test execution → system intelligence.

My Workflow
Before: Copilot suggests, I write, tests validate.
After: Claude analyzes risk, suggests priorities, self-heals with audit.
200 tests in 30 min vs 2,000 in 4 hours. Same coverage.
Insight
Not "better autocomplete."
Autonomous engineering.
AI in software testing meant faster execution.
Now: intelligent evaluation.
Systems that understand, prioritize, adapt, learn.
Markets saw: Not assistance.
Infrastructure.

TestLeaf.
Still autocomplete? 🤔

ai #testing #claude

My Manager Asked One Question That Made Me Realize I'm Testing Like It's 2015

Shri Nithi — Mon, 23 Feb 2026 12:03:09 +0000

"Can you predict which builds will fail before we deploy?"

2,000 automated tests. Custom framework. 95% pass rate.
Couldn't answer.

This TestLeaf blog - Real AI Use Cases in Software Testing, woke me up.

The Problem
I optimized execution. But AI in software testing isn't faster tests.
It's intelligence replacing blind execution.
Use Cases That Changed Everything
Predictive Defect Analytics
AI analyzes code changes, commits, complexity.
Says: "This PR: 73% defect probability."
My team: 200 high-risk tests in 30 min vs 2,000 in 4 hours. Same coverage.
Self-Healing (With Governance)
UI changes broke 50+ tests weekly.
AI testing detects shifts, suggests locators, adapts.
Critical: self-healing without governance = dangerous.
We log every auto-fix. AI suggests, humans approve.
Synthetic Data
Privacy regs killed production cloning.
AI generates realistic data. No PII. Production-like scenarios.
Legal + QA happy.
Flaky Test Intelligence
47 flaky tests destroying CI trust.
AI clustered patterns. Classified issues. Suggested fixes.
Now: confidence scoring, not pass/fail.
Conversational Debugging
LLMs summarize logs. Explain traces. Suggest causes.
Time-to-fix dropped 60%.
Testing AI Systems
App embeds ML now.
Traditional testing misses bias, fairness, drift.
AI for software testing requires testing AI. New frameworks needed.
Won't Change
AI won't replace:

Release decisions
Domain judgment
Risk trade-offs
Exploratory testing

Hybrid intelligence: AI handles patterns, humans handle context.
My Workflow
Before: Write → Run all → Fix → Deploy
After: AI predicts → Run high-risk → Self-heal + audit → Score → Deploy
The Shift
Stopped: "Better scripts?"
Started: "Intelligent systems?"
Modern QA: managing complexity with intelligence.
Maturity
L1: AI-assisted (docs, basic)
L2: AI-augmented (predictive, synthetic, self-healing)
L3: AI-orchestrated (autonomous, scoring)
Most: L1-L2. Next decade: who reaches L3?

TestLeaf.
Running all tests equally? 🤔

ai #testing #qa

I Wasted a Month Testing AI Models for QA (Here's What Works)

Shri Nithi — Thu, 19 Feb 2026 11:39:49 +0000

Last month: testing every AI model for QA.

GPT-4, Claude, Gemini, Copilot—all for test generation, logs, defect prediction.

Some generated beautiful tests. Others hallucinated locators. One created tests for non-existent features.
This TestLeaf guide - Best Generative AI Models in 2026 for QA Engineers, saved weeks of trial-and-error.

The Problem
AI in software testing has different needs:

Deterministic tests?
Hallucinated locators?
Process huge logs?
Understand frameworks?

Generic rankings don't answer these.
What Works
GPT-4o/5: Automation Workhorse
Best: Selenium/Playwright scripts, user stories → test cases
Gotcha: Hallucinates locators without context. Always validate.
I use it for scaffolding, then manually verify every selector.
Gemini: UI Specialist
Best: Screenshot analysis, multimodal UI validation
Gotcha: Automation precision varies. Analysis > production scripts.
Perfect for cross-device UI consistency checks.
Claude: Log Analyzer
Best: Massive test reports, compliance documentation
Gotcha: Less aggressive in code generation.
Debugging flaky tests with 50MB logs? Claude wins.
Copilot: IDE Companion
Best: Writing tests in IDE, refactoring suites
Gotcha: Limited to project scope.
My daily driver for incremental test development.
Evaluation Framework
AI for software testing needs:

Code reasoning accuracy
Hallucination risk assessment
Context window size
Multimodal capability
Enterprise deployment readiness

My Workflow
Generation: GPT-4 → manual validation
Logs: Claude (large), GPT (summaries)
UI: Gemini screenshots
Daily: Copilot in IDE
The Mistake
Blind trust.
AI in testing can:

Generate wrong locators
Assume missing logic
Oversimplify edges
Create brittle tests

Augmentation, not replacement.
Changed
Not "which is best?"
But:

Best for what task?
Hallucination risk?
How validate?
Operational cost?

My workflow: GPT-4 scaffolds, I validate, Copilot refactors.
10x better than one model blindly.

The ML Algorithm Trap I Fell Into (And How You Can Avoid It)

Shri Nithi — Thu, 12 Feb 2026 13:05:31 +0000

Last year, I wasted weeks building fraud detection.

Picked XGBoost because "everyone uses it." Got 94% accuracy. Shipped.
Two months later: catching nothing. Fraud patterns shifted. Model useless.

This TestLeaf blog changed how I think about ML.

The Real Problem
Wrong question: "Which algorithm is best?"
Right question: "What's the simplest model that meets my metric and stays reliable?"
Best model in 2026: Not the fanciest. The one that doesn't break in production.

The Workflow
Start Simple
Baseline: linear regression or logistic regression.
Fast, stable, interpretable. If it works, done. If not, benchmark.

Upgrade Thoughtfully
Tabular? Random Forest, then boosting.
Text? Naive Bayes, then upgrade if ROI justifies.
Images? Neural networks—if you have data/infrastructure.
Prevent Leakage
Clean splits > fancy algorithms.
One leaked feature = perfect testing, production failure.
Monitor Drift
Models degrade as the world changes.
Track metrics. Watch segments. Plan retraining.
Learning Types
Supervised: Have labels. Start here.
Unsupervised: No labels. Clustering, anomaly detection.
Reinforcement: Learn by acting. Complex for business.

Algorithms
Linear: Baseline.
Random Forests: Strong tabular default.
Gradient Boosting: Highest accuracy, sensitive to leakage.
Neural Networks: Unstructured data only.
k-NN: Similarity tasks, slow inference.
Naive Bayes: Fast text baseline.
My Process

Define task/metric/failures
Clean splits
Linear baseline
One robust upgrade
Compare across segments
Pick simplest
Deploy with monitoring

Use Cases
Churn: Logistic regression worked.
Fraud: Boosting helped, but threshold tuning mattered more.
Segmentation: k-means sufficient.
Anomaly: Isolation Forest + calibration.
What Changed
Stopped chasing "best" and started asking:

Meets metric?
Explainable?
Reliable under drift?
Operational cost?

Rebuilt fraud model: simpler boosting + better monitoring. Stable 8 months.

Want to detailed study, go through testleaf blog - Machine learning algorithms list