DEV Community: Balle Tan

Switched to a new residential proxy pool — success rate finally stabilized

Balle Tan — Tue, 11 Aug 2026 08:25:24 +0000

After the project data volume increased, the proxy pools I was using before started dropping
frequently and the success rate kept falling. Retry logic was getting more and more complicated.

Residential proxies for autonomous agents – what’s holding up lately?

Balle Tan — Fri, 07 Aug 2026 08:07:36 +0000

Been messing around with a couple of autonomous agents that need to pull fresh pages for RAG. Datacenter proxies get flagged way too fast, so I moved the residential traffic over to Helodata last week. So far the sessions look cleaner and I’m not hitting as many CAPTCHA walls. Still figuring out the best rotation interval though — too aggressive and it wastes bandwidth, too slow and some sites start acting weird.

Anyone else running agents that constantly hit the open web? What’s working for you on the proxy side these days?

My Web Scraping Journey: From Simple Scripts to Reliable Systems

Balle Tan — Tue, 04 Aug 2026 09:11:41 +0000

When I first started building web scrapers, I thought the hardest part was writing the crawler logic.

Parsing pages, extracting data, and saving results seemed straightforward.

But after increasing the number of requests, I found that the real challenge was not the code itself. It was keeping the scraper stable.

Some common problems appeared:

Requests timing out
Too many failed responses
Different results from different locations
Temporary access restrictions

A scraper that works well locally does not always work well in production.

Separate Logic From Infrastructure

One improvement that helped me a lot was separating the crawler logic from the network layer.

Instead of putting everything into one script, I divided the system into different parts:

Crawler
|
Request Manager
|
Network Layer
|
Target Website

The crawler focuses on collecting data, while the network layer handles connection management, retries, and request distribution.

This makes the system easier to debug and maintain.

Handle Failures Properly

Failed requests are normal in web scraping.

A good scraper should be able to handle:

Timeout errors
Server errors
Rate limits
Temporary connection problems

Adding retry logic, reasonable delays, and proper logging can greatly improve stability.

Monitor Performance

Another lesson I learned is that monitoring is important.

I usually track:

Success rate
Response time
Error types

For example, a sudden increase in 429 errors may mean the request frequency needs adjustment, while frequent timeouts may indicate network issues.

Final Thoughts

Web scraping is not only about extracting data.

Building a reliable scraper requires good architecture, error handling, and stable infrastructure.

The goal is not just collecting more data, but creating a system that can keep running consistently over time.
This text was written with the assistance of AI.

What I Learned from Scaling Web Data Collection

Balle Tan — Mon, 03 Aug 2026 09:14:16 +0000

What I Learned from Scaling Web Data Collection
When a data project grows from a few thousand requests to millions, the biggest challenge is usually not the code itself, but maintaining consistent access quality. Things like IP reputation, geographic coverage, and request stability can directly affect the final data quality.
Recently, I’ve been exploring different proxy infrastructures for automation and AI-related data workflows. One service I tested was Helodata, mainly because it provides a large residential IP network and flexible API access. So far, it has helped simplify some of the operational work behind data collection. Still exploring different setups and would like to hear how others handle proxy management at scale.

Why Proxy Quality Matters More Than Proxy Quantity

Balle Tan — Fri, 31 Jul 2026 03:26:02 +0000

When building automation workflows, many people focus on how many IPs a proxy provider offers. But in real projects, IP reputation, connection consistency, and location accuracy often matter more.
A large IP pool doesn’t always mean better results. A smaller but cleaner and more reliable network can save a lot of time dealing with failed requests, blocks, and unstable sessions.
Recently, I’ve been exploring different proxy solutions for AI-related data tasks and web automation. One provider I tested was Helodata, mainly because they focus on residential and ISP proxy infrastructure rather than just selling large IP numbers.
Still comparing different providers, but the biggest lesson so far is that proxy performance should be measured by actual success rate, not only by marketing numbers

Technical Evaluation of Helodata: Testing a Proxy Infrastructure for Data-Driven Workflows

Balle Tan — Wed, 29 Jul 2026 09:49:04 +0000

I work with large-scale data collection, automation systems, and web infrastructure, with a focus on building reliable workflows for extracting and processing online data.

In data-driven projects, proxy infrastructure is often one of the most critical components. The quality of proxies directly affects data accessibility, request success rates, system scalability, and operational efficiency.

Over the years, I have evaluated different proxy providers across multiple scenarios, including web scraping, competitive intelligence, AI dataset preparation, ad verification, and automated data pipelines.

When testing a proxy solution, I usually evaluate several technical aspects:

IP pool quality and geographic coverage
Connection reliability and latency consistency
Session management capabilities
API flexibility and integration efficiency
Scalability for large-volume requests
Cost efficiency under real workloads

Recently, I conducted a practical evaluation of Helodata to understand how it performs in production-like environments.

Helodata provides a complete proxy infrastructure including residential proxies, ISP proxies, mobile proxies, and datacenter proxies. The platform is designed to support data-intensive applications where stable and diverse IP resources are required.

During the testing phase, I focused primarily on residential proxy performance. The integration process was straightforward, with clear API documentation and a user-friendly management dashboard. Proxy configuration and resource management were relatively efficient compared with some solutions that require more manual setup.

From a performance perspective, Helodata demonstrated consistent connection stability during data collection tasks. The residential IP resources provided good geographic flexibility, which is important when working with region-specific datasets or localized web environments.

Another notable aspect is the combination of different proxy types within one platform. Different projects have different requirements — residential IPs may be preferred for higher trust environments, while datacenter proxies can be more suitable for high-speed operations. Having multiple options allows better infrastructure planning.

In terms of overall evaluation, Helodata provides a balanced approach between reliability, scalability, and operational cost. While proxy performance can always vary depending on target websites, traffic patterns, and project requirements, the platform has shown strong potential for developers and teams building data collection systems.

Based on my testing experience, Helodata is a solution worth considering for organizations that require stable proxy infrastructure for web data workflows, AI applications, and automated data operations.Text drafted with the help of AI.

The Future of Web Data Access in the AI Era

Balle Tan — Fri, 24 Jul 2026 05:15:50 +0000

Over the past few years, the rapid growth of AI applications has significantly shifted the demand for web data.

In the past, teams primarily collected data for SEO, price monitoring, or market research.

Today, an increasing number of use cases rely on real-time data access:

AI agents require access to external information
Enterprises need real-time market insights
Automated systems require continuous data access capabilities

This has made data access infrastructure increasingly critical.

While many still view proxies simply as basic networking tools, they are increasingly becoming an integral part of the data infrastructure layer in modern workflows.

In my experience, the following challenges frequently arise:

How can long-term, stable data access be ensured?
How can access requirements across different regions be met?
How can maintenance costs be reduced in high-volume request scenarios?
How can the reliability of automated systems be improved?

I have recently been exploring various data access solutions—including proxy infrastructure providers like Helodata (which seems promising so far;
My focus has shifted away from mere IP counts toward the overall experience: reliability, ease of management, and developer-friendliness.

I’d love to hear your thoughts:

As AI agents and automation tools continue to evolve, do you think proxy infrastructure will become a standard component of future AI applications?

I underestimated how important proxy quality was for scraping projects

Balle Tan — Thu, 23 Jul 2026 03:32:21 +0000

When I started working on a data collection project, I thought the biggest challenge would be optimizing the code.
Later, I realized that the proxy layer was often the real bottleneck.
The same scraper can perform very differently depending on the proxy quality:
Different success rates
Unstable response times
Difficulty accessing certain regions

Building AI Agents? The Hard Part Might Not Be The AI Model

Balle Tan — Wed, 22 Jul 2026 02:34:02 +0000

I have been exploring AI agents and automation workflows recently, and I noticed something interesting:
The hardest part is not always the AI model.
In many real projects, the bigger challenge is getting reliable data.
A demo may work perfectly, but production brings new problems:
Data sources change
Websites behave differently by region
Requests get limited at scale
Information becomes inconsistent
An AI agent can only be as good as the data it can access.
I think the next stage of AI development is not only about smarter models, but also about better infrastructure connecting AI systems with real-world data.
Curious to know:
What is the biggest challenge you have faced when building AI agents?

This agent is running a promotion lately; has anyone used them?

Balle Tan — Fri, 17 Jul 2026 05:37:16 +0000

I have been looking for an proxy recently.

Balle Tan — Thu, 16 Jul 2026 03:47:54 +0000

I'd like to ask if there are any good proxies available.

Balle Tan — Tue, 14 Jul 2026 10:11:50 +0000

I'm curious what everyone is using now.
My requirements are pretty simple:

Stable residential IPs
Good success rate
Fair pricing
Easy dashboard I've tried a couple of providers. Right now I'm testing Helodata, and it's been working well so far. Setup was straightforward and performance has been solid. Here's the one I'm testing: [https://helodata.com?ref=7s9k2g]

Would love to hear what everyone else recommends.