I am looking for an opportunity as a software engineer:
My tech stack is :
Node.js/Nestjs
Python
Javascript
TypeScript
tools:
docker
git/github
Backend:
Express
FastAPI
Ruby on Rails
Database:
PostgreSQL
MongoDB
Mysql
Other technologies:
Redis, TypeORM, Prisma, Next.js, React and Tailwind CSS
So If you are looking for a software engineer please DM me. 📞 +254727206415
Thank you for your attention !!
Betting backends are usually a mess—so I built a better one.
A multi-tenant engine in Go 1.26.
100k+ users | <200ms latency
K-Sorted IDs & Atomic Wallets
M-Pesa/Tax Engine integrated
Sovereign Minimalism
Clean. Fast. Production-ready.
https://t.co/PMlgLQOSMl
I am looking for someone with good frontend skills for a TW+React+Next.js for an opensource project. You will work on wiring it with a rust backend. This is a good chance for someone who is junior but meticulous and willing to accept something small on the side. No vibe coders.
Not only are MCPs awesome, but ours has access to comprehensive query insights data.
Super powerful for investigating query performance.
Just ask it "what are the slowest queries in my database" and get ripping!
B-tree database lookups are way slower without a memory cache in place + a good replacement policy for OLTP.
MySQL uses a segmented LRU whereas Postgres uses a clock-sweep algorithm. Both good choices for different reasons.
In Kafka, we have topics. Producers send data to a topic, and consumers pull data from it.
Each topic has multiple partitions. You might have heard your team lead say that we can simply increase the partition count to make our cluster more scalable.
There is no ordering guarantee across partitions for a topic, but messages within a partition are ordered.
This can be confusing initially, but think of consumer groups as subscribers: each group has multiple consumers pulling data from a topic, and the group maintains its own offset for that topic.
Check out the pinned post on my profile to understand this in detail!
As a Backend Engineer in 2026 aiming for Staff, please learn:
1. One language deeply (Go/Rust/Java)
Not “I can write APIs”, but runtime model, memory, concurrency, profiling, GC behavior (if any), and how to read stack traces like a native.
2. Data modeling and storage fundamentals
Relational modeling, constraints, isolation levels, indexes, query plans, locks, deadlocks, migrations, backup/restore, partitioning. Most “scaling” problems are schema + query shape problems.
3. Distributed systems basics that actually show up in prod
Consistency vs availability, timeouts, retries, idempotency, backpressure, message ordering, leader election, clock skew, eventual consistency, and what happens during partial failures.
4. API design and contracts
Versioning, pagination, filtering, error models, idempotency keys, rate limits, backwards compatibility, and how to avoid breaking mobile clients for months.
5. Performance and capacity engineering
Latency budgets (p50/p95/p99), tail latency causes, load testing, queueing theory intuition, connection pools, CPU vs IO bound, and capacity planning with real numbers.
6. Reliability engineering
SLOs/SLIs, incident response, postmortems, alerting that does not spam, error budgets, graceful degradation, feature flags, circuit breakers, bulkheads.
7. Observability like a pro
Structured logs, metrics, tracing, correlation ids, RED/USE metrics, sampling strategies, and how to debug “it is slow sometimes” without just guessing.
8. Security fundamentals
AuthN/AuthZ, least privilege, secrets management, token expiry, OWASP basics, SSRF, injection, secure defaults, audit logs, threat modeling for your own services.
9. Messaging and async systems
Kafka/Rabbit/SQS semantics, at-least-once vs exactly-once (and why “exactly once” is mostly a marketing term), consumer groups, retries, DLQs, replay, dedupe.
10. Caching with correctness
Cache invalidation strategies, TTLs, stampede protection, read-through/write-through, negative caching, and when caching makes bugs harder than latency.
11. Infrastructure literacy
Linux basics, networking (DNS, TCP, TLS), containers, k8s concepts, autoscaling, deployment strategies (blue/green, canary), and what your cloud bill is really paying for.
12. System design, but with tradeoffs
Designing is picking pain. Learn to write down constraints, failure modes, data growth, and operational cost. Staff is judged on tradeoffs, not diagrams.
13. Codebase leadership
Design docs, RFCs, review quality, mentorship, aligning teams, reducing complexity, owning a subsystem end-to-end, making boring systems that do not wake people at 2am.
14. Pick ONE domain to go deep
Payments, search, streaming, identity, infra, data platform, etc. Staff engineers are “the person for a hard area”, not generic API writers.
Stop hopping stacks every month. Pick a lane, build proof of reliability, and become the person people call when prod is on fire. That is Staff.
You're probably sick of me saying "B-tree" but these impact SO MUCH of database performance. They're used all over the place in Postgres, MySQL, and SQLite.
This week I broke down B-tree lookups and how the page cache makes lookups faster.
@hillarykiptoo_ @SiroDevs Nilisema hivi njaa ikanirudisha ofisi mbio 😅😅. I had to make the hard decision to either stay jobless for another unknown time or just go to the office and earn a living. So it depends.