Programming19 articlesEN · DE
Python, Go and TypeScript in production — tooling, performance, web scraping
Good software is not clever code — it is systems that stay correct, fast and maintainable as they grow. This hub covers the three languages I ship in: Python (data models, parallelism, visualization), Go (concurrency, SQLite in production) and TypeScript (tooling migrations and UI libraries) — plus a deep track on web scraping, from Playwright on Kubernetes to buy-vs-build. Written from 12+ years of backends and platform tooling in large enterprises.
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TypeScript 7 vs TypeScript 6: What Changed, What Breaks and How to Upgrade
TypeScript 7 is the same language on a new native compiler written in Go. What changed between TypeScript 6 and 7, an own benchmark on type-fest, kysely and hono, every option that is now a hard error, the tooling that does not work yet, and a safe upgrade path through 6.0, with the 7.1 beta in view.
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shadcn/ui vs MUI vs Mantine: I Built the Same Screen Five Ways and Measured It
shadcn/ui vs MUI vs Mantine on a real benchmark: the same React screen built five ways, gzip bundle sizes, npm packages shipped, the code you end up owning, what changed when shadcn made Base UI its default instead of Radix, what MUI 9 and Mantine 9 changed, the traps I hit, and which library to pick.
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SQLite in Go: Drivers, WAL, Locking and Production Patterns
SQLite works remarkably well inside a Go service when the connection pool respects its locking model. This guide compares current drivers, measures their actual cost, and builds a production setup around WAL, bounded pools, short transactions, checkpoints, backups and observable failure modes.
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Migrating from Jest to Vitest: What Actually Breaks
Swap jest for vi and most of a Jest suite runs on Vitest untouched. The problems are not the calls that throw — they are the ones that keep passing while meaning something different. A migration guide organised by how each difference fails.
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Five Python Libraries for Data Visualization: Which One and When
matplotlib, seaborn, plotly, bokeh and altair all draw a bar chart. What separates them is where the chart is rendered and where your data ends up when it is. A practical guide to picking one, with the specific constraint each library carries.
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Scraper Studio vs Apify vs DIY: Which Web Scraping Path Fits Your Team in 2026
Three ways to build a web scraper in 2026: a managed platform like Scraper Studio, an actor marketplace like Apify, or a DIY Scrapy + Playwright stack. Here is how they really compare on speed, ownership, and the part that actually costs you — maintenance.
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I Replaced ESLint + Prettier with Biome in 2026 — Here Is the Benchmark
A hands-on 2026 look at replacing ESLint and Prettier with Biome: what it is, the one-command migration, real benchmark numbers, what you actually lose, and an honest verdict on when to switch and when not to.
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Web Scraping vs Web Crawling: The Real Difference (2026)
Everyone mixes up web scraping and web crawling. Here is the real difference in one minute, how they fit into one pipeline, why both break in production, and the tools that keep them running at scale.
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es-toolkit in 2026: The Lodash Replacement That's 2× Faster and 97% Smaller
A 2026 deep dive into es-toolkit: 2-3x faster than lodash, up to 97% smaller bundles, native TypeScript types, tree-shaking by default, and a one-line drop-in migration via es-toolkit/compat.
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Production-Grade Playwright Web Scraping on Kubernetes with Bright Data (2026)
The hard part is not writing the scraper. It is making it run reliably in production. A step-by-step guide using Playwright, Bright Data Browser API, and Kubernetes Jobs and CronJobs.
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LinkedIn Scraping vs. Buying a Dataset in 2026: 3 Days of Code I Could Have Skipped
A data engineer's review of Bright Data's Dataset Marketplace: why ready-made LinkedIn datasets can save weeks of scraping.
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Python Web Scraping Without Proxy Management: Bright Data Web Unlocker API (2026)
The hard part is not parsing HTML. It is getting a usable response from modern protected websites in the first place. A step-by-step guide using Bright Data Web Unlocker API with Python and BeautifulSoup.
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FAQ
Before you reach out
What is your engineering background?
12+ years building production backends and platform tooling at scale in large enterprise environments. I work hands-on in Python, Go, Node.js and TypeScript, and I am a Google Cloud Ambassador, 5× Google Cloud Professional certified.
Are you open to a permanent role?
Yes — I am looking for a permanent, full-time role as a senior backend or platform engineer in the DACH region (Germany, Austria, Switzerland): on-site, hybrid or remote, and ready to relocate. I work fluently in English.
How do we start the conversation?
Send me the role on the contact page — the stack, the team and the problems it owns — and I will reply with my CV and how I would approach the first months.
Open to permanent roles · DACH
Hiring a senior backend engineer in DACH?
Python, Go and TypeScript in production, 12+ years at enterprise scale. I am looking for a permanent role in Germany, Austria or Switzerland — and ready to relocate.
- Permanent, full-time
- Germany · Austria · Switzerland
- Ready to relocate