I built a small browser engine from scratch in C++

Hacker News

Hi HN! Korean high school senior here, about to start CS in college. I built a browser engine from scratch in C++ to understand how browsers work. First time using C++, 8 weeks of development, lots of debugging—but it works! Features: - HTML parsing with error correction - CSS cascade and inheritance - Block/inline layout engine - Async image loading + caching - Link navigation + history Hardest parts: - String parsing(html, css) - Rendering - Image Caching & Layout Reflowing What I learned (beyond code): - Systematic debugging is crucial - Ship with known bugs rather than chase perfection - The Power of "Why?" ~3,000 lines of C++17/Qt6. Would love feedback on code architecture and C++ best practices! GitHub: https://github.com/beginner-jhj/mini_browser

Visit product View on Hacker NewsJan 28, 2026

Share card

Actual performance

146points

45comments

Made the leaderboard

Launch Intel predictions

Analyze your own launch →

Hacker NewsStrong engagement from HN community · Strong signals: 000, io · Missing: https docs, excited, just released

61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

49%49% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: using, code · Missing: mac, agents, macos

45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

API monetization with its own billing engine built from scratch51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

API monetization with its own billing engine built from scratch

Hacker News1

I built a small interpreted programming language from scratch53%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I built a small interpreted programming language from scratch

Hacker News1

Implement a toy video encoder from scratch in browser65%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Implement a toy video encoder from scratch in browser

Hacker News4

I launched Claritask (built from scratch in the open; not a programmer)60%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I launched Claritask (built from scratch in the open; not a programmer)

Hacker News15

Nested VMs from Scratch in C66%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Nested VMs from Scratch in C

Hacker News4

A debugger from scratch – part 166%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A debugger from scratch – part 1

Hacker News3

Javadoc Rewritten from Scratch61%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Javadoc Rewritten from Scratch

Hacker News9

MMOBomb 4.0 – I rebuild it from scratch61%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

MMOBomb 4.0 – I rebuild it from scratch

Hacker News1

GCounter CRDT Implemented from Scratch in Golang56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

GCounter CRDT Implemented from Scratch in Golang

Hacker News1

The CEO's Brainpan27%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

An itch that I can't quite scratch.

Indie Hackers24communication

Browse all products Analyze your launch