DEV Community: Aleksei Aleinikov

Why Proxies Are Essential For Web Scraping And Automation

Aleksei Aleinikov — Tue, 21 Apr 2026 17:15:48 +0000

Most scraping projects do not fail because of bad code.
They fail because the internet starts noticing them.

If your scripts keep running into 403s, 429s, CAPTCHAs, unstable sessions, and inconsistent data, the problem may not be your parser at all. It may be your network layer.

In this article, I break down why proxies are one of the most important — and most underestimated — parts of modern web scraping and automation.

https://medium.com/code-your-own-path/why-proxies-are-essential-for-web-scraping-and-automation-c63a1eaa7bee?sk=7ef5acfab928fc90328514b18381aca6

Static RAG works — until your data gets outdated.

Aleksei Aleinikov — Sun, 19 Apr 2026 07:53:56 +0000

Most RAG pipelines rely on embeddings + a fixed dataset.
That’s fine for internal docs. Not fine for anything that changes daily.

The fix is simple:

👉 add a live search layer before generation

In this setup:

Python script → sends query to SERP API
gets fresh results
turns snippets into structured context
feeds it into the LLM

Now your RAG isn’t just relevant — it’s current.
You don’t need a full pipeline to start.

Even a small search → context → prompt loop already makes a big difference.

https://levelup.gitconnected.com/how-to-build-a-real-time-data-pipeline-for-rag-in-python-using-bright-datas-serp-api-dc70b32ded37

Playwright scraping is easy. Running it reliably on a schedule is the real challenge.

Aleksei Aleinikov — Sun, 19 Apr 2026 07:52:38 +0000

A script that works on a laptop can fall apart in production fast: browser startup issues, oversized images, overlapping runs, flaky retries, and JavaScript-heavy pages behaving differently under automation.

That is why I like this setup:

Playwright for automation
Bright Data Browser API for remote browser execution
Kubernetes Jobs/CronJobs for repeatable batch runs

The key shift is simple:
stop treating scraping like a script, and start treating it like a worker.

Remote browsers + Kubernetes make the pipeline cleaner, smaller, and much easier to operate at scale.

https://levelup.gitconnected.com/using-playwright-bright-datas-browser-api-in-a-kubernetes-deployed-scraping-pipeline-e914b4e1800e

The bigger the agent gets, the worse a giant prompt performs.

Aleksei Aleinikov — Sun, 19 Apr 2026 07:51:16 +0000

At some point, prompt engineering stops being enough.
What starts as one “smart” system prompt slowly turns into a brittle mess: expensive, hard to maintain, and easy to break.

That’s why agent design in 2026 is moving from prompts to architecture.

Instead of stuffing everything into one instruction block, it makes more sense to use small core prompts + modular skills + on-demand references.

The agent should carry an index, not the whole library.

https://medium.com/google-cloud/how-to-build-ai-agents-with-adk-and-skills-in-2026-from-prompts-to-architecture-2b7c5ff7003c

Firewalls still matter. They’re just no longer the center of security.

Aleksei Aleinikov — Sun, 19 Apr 2026 07:50:03 +0000

In 2026, the real security decision is increasingly made by identity, behavior, cloud context, and data sensitivity — not by network rules alone.

NGFW is becoming an enforcement point, not the main brain.

https://blog.devgenius.io/why-firewalls-are-losing-their-central-role-in-2026-3b437cb24dd6

AI code is not dangerous because it looks bad.

Aleksei Aleinikov — Sun, 19 Apr 2026 07:49:01 +0000

It’s dangerous because it often looks correct.

In 2026, better AI code doesn’t come from a magic prompt. It comes from constraints, narrow context, real validation, and strong review.

Speed helps. Control matters more.

https://medium.com/code-your-own-path/how-to-get-better-ai-code-in-2026-90b02991c7ca

Most teams don’t under-secure Vertex AI because they’re careless.

Aleksei Aleinikov — Sun, 19 Apr 2026 07:47:48 +0000

They do it because they confuse setup with a security baseline.

IAM and logging are not enough.
A real baseline means isolation, service perimeters, secrets discipline, model governance, and runtime protection.

In this post, I break down the minimum controls you need to run Vertex AI safely in production in 2026.

https://medium.com/google-cloud/how-to-secure-vertex-ai-in-2026-the-minimum-security-baseline-for-generative-ai-on-google-cloud-cf7982b9ed7a

Everyone says the new Gemini model is better. Almost no one proves it.

Aleksei Aleinikov — Sun, 19 Apr 2026 07:46:47 +0000

A newer Gemini model can sound better and still perform worse in production.
Better writing does not mean better discipline, lower cost, or safer behavior.
If you compare models by demos instead of real workflows, you are not evaluating — you are guessing.

In this post, I break down how to compare Gemini versions honestly: task success, instruction fidelity, latency, cost, and hallucination risk.

https://medium.com/google-cloud/gemini-migration-in-2026-how-to-compare-versions-without-fooling-yourself-c2c148af886f

Not Every New Technology Makes Life Better in 2026

Aleksei Aleinikov — Sun, 19 Apr 2026 07:45:25 +0000

Not every new technology makes life better. Some tools save time. Others quietly weaken attention, independence, and the ability to function without constant digital help. In 2026, the real skill is not adopting everything new — it is learning what truly deserves a place in your life.

https://medium.com/word-garden/why-not-every-new-technology-makes-life-better-in-2026-3e73bbd7ecbb

Static service account keys in CI/CD are not a convenience anymore. They are a liability.

Aleksei Aleinikov — Sun, 19 Apr 2026 07:44:02 +0000

In Google Cloud, the stronger path is clear: replace long-lived JSON keys in GitHub Actions, GitLab, and Terraform with Workload Identity Federation, short-lived tokens, and tightly scoped impersonation. Fewer secrets, smaller blast radius, better control.

https://medium.com/@aleksei.aleinikov.gr/how-to-remove-service-account-keys-from-github-actions-gitlab-and-terraform-in-google-cloud-in-a88cea0ed304

Skills are useful. MCP is necessary.

Aleksei Aleinikov — Sun, 19 Apr 2026 07:43:11 +0000

In 2026, I still do not see them as competitors. Skills help models understand context, rules, and team-specific nuance. MCP helps them actually connect to services in a clean, stable, and scalable way. The more serious the workflow becomes, the clearer that distinction gets.

https://medium.com/ai-ai-oh/why-i-choose-mcp-over-skills-in-2026-045207aadfed

Security Command Center is far more useful when you stop treating it like a findings inbox.

Aleksei Aleinikov — Sun, 19 Apr 2026 07:42:06 +0000

In 2026, the real value is in connecting AI assets, attack paths, toxic combinations, and live threat signals before they turn into incidents. If you use SCC only to collect alerts, you are missing the point. If you use it to prioritize blast radius and consequence, it becomes a real decision layer.

https://medium.com/google-cloud/how-to-use-security-command-center-in-2026-to-detect-ai-and-cloud-risks-before-they-become-62331c391b6b