Телеграм канал «ProxySeller»

Buying data or collecting it yourself — where does real value come from?

Companies spend thousands — sometimes millions — on data, but ready-made datasets often show the same picture to everyone.

In this video, we compare data providers vs. web scraping from a business perspective — cost, speed, relevance, scalability, and control.

We explain:
- why provider data is fast but often outdated or shared;
- how web scraping delivers real-time, customized insights — and what infrastructure it requires;
- where each approach works best across e-commerce, fintech, and travel;
- and why many companies use a **hybrid strategy** in practice.

We also cover the role of proxies — the layer that makes large-scale data collection stable and predictable.

If data drives your decisions, this video helps clarify which approach actually delivers value.

🎥 Watch the full video on our YouTube channel

AI agents are moving from pilots to production.

Modern AI agents integrate with CRMs, ERPs, BI tools, internal APIs, and external data sources — executing tasks, coordinating workflows, and supporting decisions across teams.

In our article, we explain what AI agents are in practice, how they differ from classic chatbots, and what enterprises need to consider when deploying them at scale.

We cover:
- how AI agent architecture works in real business systems;
- the role of LLMs, orchestration, memory, and tooling;
- common agent types and concrete B2B use cases;
- where network infrastructure becomes a limiting factor;
- and why proxies matter once AI agents interact with the web.

👉 Read the full article

AI models don’t collapse overnight.
They slowly degrade when their data pipelines do.

In 2025, the biggest risk for AI teams isn’t model architecture — it’s unstable, unverifiable, and uneven training data. As data sources fragment by region, regulation, and availability, quality drops long before teams notice.


In this video, we look at how AI platforms lose accuracy when data collection lacks provenance, observability, and regional balance — and why regulations like the EU AI Act make this impossible to ignore.
We break down a real case of an AI platform processing over 25 TB of data monthly, where incomplete access caused falling success rates, skewed training samples, and rising compute costs — until the data layer was rebuilt with controlled, consent-based access.

This isn’t about scraping more.
It’s about knowing where data comes from, how it’s collected, and whether every request is valid.

Reliable data pipelines don’t just protect compliance.
They protect model quality.

🎥 Watch the full video on our YouTube channel

Manual GitHub workflows don’t scale across teams and repositories.
When projects grow, routine actions — tracking issues, monitoring activity, syncing repos — quickly turn into overhead.

The Python GitHub API helps automate these tasks and keep workflows consistent.

In our tutorial, we explain how to work with the GitHub API using Python — from initial setup to stable production use.

Inside the guide:
- when GitHub automation makes sense for teams;
- how to authenticate safely with personal access tokens;
- a PyGithub example for managing repositories and issues;
- common API pitfalls and rate-limit handling;
- best practices for secure tokens and request control.

Automation reduces friction only when it’s implemented correctly.

👉 Read the full article

Most competitive analysis doesn’t fail because of bad strategy.
It fails because the data is incomplete.

In 2025, competitive analysis isn’t about price tables or a few screenshots.

It’s about access to live, geo-specific data.

In this video, we explain why most companies lose at competitive analysis — and how teams working with complete data avoid these mistakes.

Key blind spots we break down:
- relying only on public pages and top-10 results;
- ignoring regional price and SERP differences;
- missing ads, creatives, and catalog changes by GEO;
- working with stale or partial data without observability.

We also show how proxy infrastructure changes the picture:
- access to local markets as real users see them;
- higher valid response rates and fewer blind spots;
- real-time monitoring instead of assumptions.

Competitive analysis fails not because of tools —
but because of incomplete access.

🎥 Watch the full video on our YouTube channel

SERM has become harder: stricter rate limits, CAPTCHAs, and changes in Google SERPs reduce access to real, localized results. Pagination is unstable, results vary by GEO, and automated collection is increasingly restricted.

Without the right infrastructure, brands lose control over how they appear in SERPs.

In our article, we explain how proxies support modern SERM workflows and help teams maintain accurate, scalable reputation monitoring.

Inside the guide:
- what SERM is and how it works beyond classic SEO;
- why recent Google changes complicate reputation tracking;
- how proxies enable stable, localized SERP collection;
- which tools and parsers teams use in practice;
- and which proxy types fit different SERM tasks.

Reputation management now depends on technical resilience as much as strategy.

👉 Read the full article

Shared proxies are chosen for speed and scale — not for perfection.

When teams need a large IP pool quickly and at a lower cost, shared proxies are often the first option. They fit tasks where throughput matters more than individual IP reputation.

In this video, we explain how shared proxies work, where they perform best, and where they create risks instead of value.

What we cover:
— how traffic is distributed across shared proxy pools;
— differences between IPv4, mobile, and semi-dedicated proxies;
— typical use cases: scraping, automation, testing, geo checks;
— trade-offs between cost, stability, and IP reputation.

Shared proxies are neither universal nor specialized.

They work when chosen for the right tasks.

🎥 Watch the full video on our YouTube channel