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System Design: Scaling Authentication to 100 Million Users
How would I design a login system for an application with 100 million registered users?
I'd start with the fundamentals: indexed database lookups, connection pooling, rate limiting, secure password hashing, and measurable latency targets.
Then I'd investigate whether a probabilistic data structure could reduce unnecessary database traffic.
The approach: Bloom Filter pre-check
A Bloom Filter can quickly identify usernames that are definitely absent from the registered-user set.
For 100 million entries with a target false-positive rate of 1%:
- Raw bit-array memory: approximately 114 MiB
- Optimal number of hash functions: approximately 7
- False positives: possible
- False negatives: none under correct initialization and maintenance
Proposed request flow
Login Request → Bloom Filter → Indexed User Lookup → Password Verification → Session Issuance
If a username is definitely absent, the system can avoid a database lookup. If it might exist, the request proceeds through the normal authentication process.
Production considerations:
- Maintain filter consistency as users are created or deleted.
- Prevent account enumeration through error messages and timing differences.
- Apply rate limits and abuse detection.
- Benchmark database latency and password-hashing throughput under realistic peak traffic.
- Add caching, replicas, or sharding only when measured bottlenecks justify them.
The key takeaway: 100 million registered users alone does not determine the architecture. Peak login traffic, request distribution, latency requirements, and database capacity do.
A Bloom Filter is an optimization for a specific bottleneck, not a replacement for a properly designed authentication system.
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Very clean design
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A real-time network monitoring dashboard designed to help campus IT teams observe network health, monitor interfaces, and identify potential issues from a single interface.
What I built
Real-time network monitoring with live interface and system status
Network interface analytics showing traffic and connection activity
Historical monitoring data for analyzing network behavior over time
Live WebSocket communication for continuously updated monitoring data
Network controls to start and stop monitoring directly from the dashboard
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network metrics
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Why I built it
The goal was to create a practical campus network monitoring system that demonstrates how real-time networking data can be collected, processed, and presented through a modern web interface.
cool project bro. live data + history in one place is useful. how do you handle reconnecting when the websocket drops?
A system I designed for local service businesses that consistently lose out on Google reviews. Full build in progress
Automated Review Requests for Local Service Businesses
The Problem:
Google reviews are one of the strongest signals for local search ranking, this is well established across HVAC companies, cleaning services, salons, contractors, and landscaping businesses. Every owner in this space knows reviews matter. Very few of them ask for one consistently.
The reason isn't a lack of care, it's a lack of a system. Asking every customer, right after every job, every single time, is a small task with no natural trigger behind it. It depends entirely on someone remembering in the moment, usually right when the business is busiest and least likely to remember. The result is a steady stream of satisfied customers who were never actually asked, and a review count that grows far slower than it should.
The Solution👍
I designed a system that removes the dependency on memory entirely, so a review request goes out every time, without anyone having to think about it.
The moment a job is marked complete in the CRM, the system picks it up automatically. It waits a short period first, so the request doesn't feel rushed or robotic, the customer gets a little time to settle in before being asked. Then it sends a review request by SMS or email.
From there, the system tracks what happens next. If the customer clicks through and leaves a review, that's logged as a win and the process ends there, no further messages, no risk of annoying someone who already did what was asked. If there's no response after a few days, the system sends exactly one follow-up, never more than that, since a second or third nudge tends to do more harm than good. Whatever the outcome, sent, clicked, or reviewed, it's recorded in a tracking sheet, so the business owner always has a clear, accurate picture of where every customer stands.
The result is simple: every completed job gets a consistent, well-timed ask, with no manual list to maintain and no customer slipping through the cracks.
What's Next
The full workflow build, including the architecture and the tools behind it, is going up in the next few days.
That email trigger right after a job finishes should cut out the manual reminder step and keep reviews coming in.