Portfolio
Developer Portfolio Examples
Three worked examples at different levels, with the actual copy written out and an honest note on what each one would still improve. Structure you can copy; content you cannot.
What separates a good portfolio from a bad one?
One clear claim about what you build, backed by two or three projects a reviewer can verify by clicking, each with one technical decision explained, and consistency with the CV and GitHub it links to. Visual design barely registers — the best-looking portfolio with nothing deployed loses to a plain one with three working links.
How to read these
Each example gives the person’s situation, their target role, the header copy they would actually use, their projects with the technical decision each one leads on, then what works and what still would not be good enough.
That last part matters. Every real portfolio has gaps, and a model that pretends otherwise is not useful — you would compare your unfinished work against an impossible standard and conclude you are further behind than you are.
Beginner: One deployed project, documented properly
Situation. Second-year student. Three university assignments, no personal projects, nothing deployed. Applying for a first summer internship.
Target role. Front-end or full-stack internship
The header
Alex Moreau — front-end developer
Second-year computer science student. I build web applications in TypeScript and React. Looking for a summer 2027 internship.
The projects
Module Deadline Tracker
A deadline tracker for university coursework, used by about 20 people on my course.
Stack. React, TypeScript, Vite, deployed on Vercel
Evidence shown. Live link, 30-second demo GIF, source repository
Technical decision it leads on. Stored dates as UTC ISO strings after a bug where deadlines shifted by an hour when the clocks changed. Added a test that runs the list across a DST boundary.
Library Lending System
A second-year Java assignment, extended with a REST API, a web front end and tests.
Stack. Java, Spring Boot, PostgreSQL, deployed on Railway
Evidence shown. Live API with OpenAPI docs, source repository
Technical decision it leads on. Moved loan-limit enforcement from the service layer into a database constraint after finding two concurrent requests could both pass the check.
Why it works
- The header answers who, what and what they want in one sentence.
- One project has real users — twenty people on a course is more convincing than a hypothetical market.
- Both projects are deployed and clickable. Neither requires the reviewer to clone anything.
- Each project names one specific technical decision with the reason behind it.
- The assignment is upgraded rather than hidden, and the upgrade is the interesting part.
What it would still improve
- Only two projects. A third small one — a CLI tool or a published package — would add breadth cheaply.
- No tests mentioned on the first project.
- The About section is still slightly generic.
The ten-second read. A second-year student who ships things and understands why concurrency matters. Worth a first-round conversation.
Intermediate: Three projects with a consistent back-end story
Situation. Final-year student with a part-time support job. Several assignments and one side project. Applying for graduate back-end roles.
Target role. Graduate back-end engineer
The header
Priya Raman — back-end developer
Final-year computer science student. I build APIs and data pipelines in Python and Go. Available from July 2027.
The projects
Society Events Platform
Event sign-ups for three university societies. Ran a full term with about 400 registrations.
Stack. Django, PostgreSQL, Redis, deployed on Fly.io
Evidence shown. Live site, source, architecture diagram in the README
Technical decision it leads on. Used a database-level unique constraint plus a transaction for capacity, after load-testing showed two simultaneous sign-ups could both take the last place.
Log Analyser
Ingests and queries server logs larger than available memory, with a dashboard for error rate over time.
Stack. Go, SQLite, HTMX
Evidence shown. Demo video, benchmarks in the README, source
Technical decision it leads on. Streamed the file line by line rather than reading it into memory, which took peak usage from 1.2 GB to 40 MB on a 2 GB log.
devkit
A small CLI of everyday developer utilities. Published to Homebrew.
Stack. Go, Cobra, GitHub Actions
Evidence shown. Install command, 90% test coverage, CI badge
Technical decision it leads on. Chose Go over Python so the tool ships as a single binary with no runtime dependency.
Why it works
- Three projects that tell one coherent story: this person does back-end work.
- Every project has a number attached to it — registrations, memory usage, coverage — and none of them look invented.
- The published CLI is installable in one command, which is unusually easy to verify.
- The technical decisions all name the alternative that was rejected.
- There is real evidence of process: CI, tests, load testing, benchmarks.
What it would still improve
- No writing. One post explaining the memory fix would extend the reach of the same work.
- The site itself is plain, which is fine for back-end but a small missed opportunity.
The ten-second read. A final-year student with a clear back-end direction and evidence of engineering judgement, not just completed features.
Advanced: One deep project carrying the portfolio
Situation. Self-taught, changing career after four years in another field. No degree, no commercial experience. Applying for junior roles at product companies.
Target role. Junior software engineer
The header
Sam Okonkwo — software engineer
Self-taught developer, previously a logistics analyst. I build distributed back-end systems in Go. Open to junior roles.
The projects
queuelite
A distributed task queue with retries, backoff and a dead-letter queue. Used in production by two of my own services.
Stack. Go, PostgreSQL, Docker Compose
Evidence shown. Source, architecture write-up, chaos-test results, a dashboard demo video
Technical decision it leads on. Used SELECT ... FOR UPDATE SKIP LOCKED in Postgres rather than adding Redis, which removed an entire piece of infrastructure at the cost of throughput I did not need.
Observability stack
Metrics, logs and distributed tracing across my own services, plus a written post-mortem from a deliberately broken deploy.
Stack. OpenTelemetry, Prometheus, Grafana, Tempo
Evidence shown. Dashboards, the incident write-up, configuration repository
Technical decision it leads on. Sampled traces at 10% after finding full tracing added 8ms of p99 latency on the queue's hot path.
Why it works
- One genuinely deep project carries the whole portfolio, which is the right strategy without a degree to lean on.
- The two projects are connected — the observability work instruments the queue — so it reads as a system rather than a list.
- The incident post-mortem is an artefact almost no junior candidate has.
- Previous career is stated plainly as context rather than apologised for.
- Every performance claim comes with a number and a method.
What it would still improve
- Nothing user-facing. One small front-end piece would answer the 'can they build a UI' question that will otherwise come up.
- Two projects is thin if one of them is not read closely.
The ten-second read. Someone who has built real infrastructure and can reason about failure. The lack of a degree stops being the first thing you notice.
What all three have in common
The three differ in level, direction and how much material each person had. What they share is more instructive than what separates them.
- One claim, stated in the first sentence. Role, technologies, availability. None of them opens with a personality description.
- Every project is verifiable. A live link, an install command, a demo video. Nothing requires the reviewer to imagine anything.
- Each project names the alternative it rejected. “Postgres rather than adding Redis”. “Go over Python so it ships as one binary”. This is the most consistent pattern in the set.
- Numbers that sound real. 400 registrations, 40 MB, three societies, one term. None of them are impressive in isolation, and all of them are more convincing than a percentage.
- Fewer projects than you would expect. Two, three and two. Not eight.
- Nothing is hidden or apologised for. The assignment is presented as an upgraded assignment. The career change is stated as context.
Applying this to yours
Take your own portfolio and answer the same four questions:
- What is your one-sentence claim? Role, technologies, what you are looking for. If it takes more than one sentence, it is not a claim yet.
- Can a reviewer verify each project without asking you anything? If not, deploying one thing is your highest-value next action.
- What technical decision does each project lead on? With the alternative you rejected. If you cannot name one, that is the gap.
- What is your honest ten-second read? Show it to someone outside your course and ask what they think you do. If the answer does not match your target role, the positioning is the problem.
None of those four require writing more code. They are the parts of a portfolio that are usually missing, and they are the parts that take an evening rather than a month.
Common questions
What makes a developer portfolio example good?
Not the visual design. A good portfolio makes one clear claim about what you build, backs it with two or three projects a reviewer can verify, explains at least one technical decision per project, and stays consistent with the CV and GitHub it links to. The best-looking portfolio with no deployed projects loses to a plain one with three working links.
Should I copy a portfolio I like?
Copy the structure, not the content. The five-section layout is essentially solved and there is no advantage in inventing a new one. What must be yours is the claim, the projects, the technical decisions and the voice — those are the parts a reviewer is actually assessing, and a copied About section reads as exactly what it is.
Are these real people's portfolios?
No. They are constructed reference examples written to show the standard at each level. Real portfolios that get shared publicly are usually either exceptional outliers or anonymous screenshots with no explanation of why they work, and neither is much use as a model.
How different should a portfolio look for a back-end role?
The structure is the same; the emphasis changes. A back-end portfolio leans on the README, the architecture, the data model and evidence about failure handling — the site itself can be plain and it costs nothing. A front-end portfolio has to be good-looking and accessible, because the site is itself a work sample.
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