DEV Community: Abu Rayhan Alif

Introducing InterviewLint: Technical Interview Practice Beyond Coding Problems

Abu Rayhan Alif — Sat, 31 Oct 2026 16:54:05 +0000

Technical interviews are about more than correct code. InterviewLint is a different way to prepare.

Most developers preparing for technical interviews follow a familiar routine: solve problems, study algorithms, memorize patterns, repeat.

That work has value. It still leaves a gap.

The moment an interviewer asks: “Why?”

Why that algorithm? Why is it correct? What happens at ten times the scale? Why that backend shape — and what would you change if the requirements shifted?

Knowing the answer and being able to explain, justify, and defend it are not the same skill.

That gap is what InterviewLint is built for.

What Is InterviewLint?
InterviewLint is an AI-assisted technical interview practice platform. It helps you strengthen reasoning, communication, and technical judgment — not just recall.

The emphasis is on how you arrived at an answer: trade-offs, assumptions, edge cases, and the story behind your decisions. The goal is practice that feels closer to a real interview conversation — explaining, analyzing, and defending — not only producing a solution.

InterviewLint is not meant to replace coding practice. It focuses on a part of preparation that often gets less deliberate practice: the ability to explain your thinking.

Why I’m Building It
Traditional prep can widen the distance between “I can implement this” and “I can show engineering judgment.”

A candidate who writes binary search from memory may still stumble when asked:

What invariant makes the loop correct?
What changes with duplicates?
Why is binary search appropriate here — and when is it not?
The interview stops being a typing exercise. It becomes a reasoning exercise.

The same pattern shows up beyond algorithms: a strong project on GitHub but a weak architecture walkthrough; REST knowledge but no clear story when debugging a broken service; system design vocabulary without comfort defending trade-offs.

Those are the muscles InterviewLint is designed to help you train.

How Practice Works
The loop is simple:

Practice → Explain → Get feedback → Improve

You work through realistic scenarios, put your reasoning into words — problem, assumptions, approach, alternatives, limits — and use AI-assisted feedback as a practice signal: where reasoning is thin, where edge cases are missing, where explanation could be clearer. Not a hiring verdict — a mirror for the next rep.

The point is not to finish more items. It is to get better at questions you have not seen before, because you have practiced explaining under pressure.

Coding platforms belong in serious prep. InterviewLint is not trying to win with another endless puzzle list.

The usual question is: “Can you solve this?”

InterviewLint also asks: “Can you explain why your solution makes sense?” And, outside pure algorithms: “Can you defend the engineering choices you made?”

That shift — from pattern recognition toward technical reasoning — is the product bet.

Who It’s For
InterviewLint is for anyone who wants to sound as capable in conversation as they feel in their head:

students heading into first interviews, aspiring backend and software engineers, career switchers, and experienced developers who want structured explanation practice alongside coding drills.

First internship or next role — the underlying skill is the same: communicate what you know and how you think.

InterviewLint Is in Beta
The platform is live for early exploration and still evolving. Flows will change; rough edges are expected. That is why honest feedback matters now.

You do not need a long review. Spend 15–20 minutes and tell me three things:

What confused you? Anything hard to find or understand?
What felt useful? What made you think differently?
What broke or missed the mark? Bugs, unclear copy, or feedback that did not help?
Blunt notes beat generic praise at this stage.

Try it:

interviewlint.com

If you have ever understood something perfectly in your head and struggled to say it out loud, I would especially like to hear from you.

The Bigger Vision
Interview prep should make people better technical thinkers, not only better at recognizing familiar prompts.

Explaining solutions, reasoning about constraints, investigating failures, and defending decisions matter after the interview too — in code review, incidents, architecture discussions, and day-to-day collaboration.

InterviewLint is an early step in that direction. Still a work in progress — but the line is clear:

Don’t just practice finding the answer. Practice explaining why it’s the right one.

Meet Django SaaS Kit.Stop Wiring Django SaaS Infrastructure from Scratch — I Built a Production-Ready Starter Kit

Abu Rayhan Alif — Tue, 26 May 2026 16:54:13 +0000

Just open-sourced a project I’ve been working on recently: Django SaaS Kit.

Most Django SaaS starters stop at authentication and a landing page.

I wanted something closer to what real production SaaS products need:

Multi-tenancy
RBAC
Billing
Async processing
Audit logs
CI/CD
Dockerized infrastructure
Type-safe architecture

So I built a production-ready backend starter with:

Django + DRF
PostgreSQL + Redis
Celery + Channels
Stripe billing
Docker Compose
GitHub Actions
mypy strict mode

The goal was to create a backend foundation where developers can immediately start building product features instead of spending weeks wiring infrastructure together.

Repo:
github.com/abu-rayhan-alif/django-saas-kit

Would appreciate feedback, ideas, or feature suggestions from other backend developers.

Automating Django Security Audits with SARIF Support ,Meet Django-Security-Hunter.

Abu Rayhan Alif — Fri, 10 Apr 2026 19:38:40 +0000

In many Django + DRF projects, the same security and configuration issues show up again and again during PR reviews.
To address this, I built django-security-hunter — a lightweight CLI tool that surfaces common security risks and Django/DRF misconfigurations before code reaches production. It’s designed for teams that want automated checks in local development and CI, not just during review.
Coverage (high level): • Settings & DRF: production Django settings and REST framework defaults / API exposure hints (when you pass --settings so Django loads).
• Code & templates: risky patterns — XSS-style footguns, SSRF heuristics, unsafe deserialization, secrets in logs, hardcoded secret-like names, and SQL-injection heuristics.
• Reliability / performance hints: concurrency and ORM-style patterns where applicable rules fire.
• Optional: pip-audit, Bandit, and Semgrep when enabled in config or environment (external tools may need to be installed and on your PATH).
See docs/rules.md in the repository for details and rule IDs — findings are heuristic, so please triage before changing code or configuration.
Product features: • CLI-first with CI-friendly exit codes
• SARIF output (GitHub Code Scanning integration)
• GitHub Action available on the Marketplace
Quick start:
pip install django-security-hunter
django_security_hunter scan -p . -s yourproject.settings -y -f console
Use the same --settings value as DJANGO_SETTINGS_MODULE so settings-based rules (Django + DRF) run; many file-based checks still run without it.
Goal: make security checks faster and part of everyday development.
Note: Static analysis can produce false positives — always verify findings before taking action.
-Found a bug or potential security issue in the tool? Please open an issue in the repository.
-Contributions are welcome — PRs, issues, and feedback help improve the tool for everyone.
Repo: https://lnkd.in/g3vd_RqU
PyPI: https://lnkd.in/gkFDFAKt