I started playing around with ESP32 boards about 8 months ago, mostly because I wanted to automate a few things around the house. I thought it would be a fun weekend project

Turns out there were quite a few weekends after that lol

I made pretty much every beginner mistake you can think of:

• I bought random sensors that weren't compatible with each other
• I assumed that every ESP32 board was basically the same until I realized they arent
• Spent hours debugging code only to realize I'd selected the wrong board in Arduino IDE
• Building something that worked perfectly on my desk... and then completely failed once I put it inside an enclosure (that was the most frustrating part)

After building things like Home Assistant sensors, MQTT devices, a garage parking assistant, an e-paper dashboard, and a few environmental monitoring projects, I ended up trying boards from four different companies.

This isn't sponsored by anyone, just sharing what I've personally used and what I liked (or didn't) and the list isn't going in any particular order

If you're working on similar projects, feel free to DM me too. I'm always curious to see what other people are building

1. Soldered

I probably ended up using the NULA Mini ESP32-C6 the most because I really liked the simplicity of it

The project that really sold me on it was an e-paper dashboard that pulled data from Home Assistant over MQTT. Before that, I'd spent way too much time fighting with random hardware issues instead of actually building the dashboard I wanted

What I liked:

• Arduino and MicroPython both worked nicely
• Wi-Fi 6 is a nice bonus
• documentation answered most of the questions I actually had
• everything just felt... predictable

The biggest compliment I can give it is that I mostly forgot about the board itself and focused on the project instead

I think I paid around $17.50 for the Nula Mini, which I thought was pretty fair and decent in price

2. Espressif

I used an official ESP32 DevKit while building a small web dashboard to control a few devices around my apartment and test different Wi-Fi features.

Whenever I wasn't sure whether a problem was my code or the hardware, I'd usually grab an official Espressif dev board

I used it mostly for testing Wi-Fi features and building a simple ESP32 web server

Things I appreciated from this provider:

• official hardware that was really easy to navigate
• loads of examples online
• almost every tutorial assumes you're using one so it was pretty easy
• great for learning the ESP32 ecosystem

If you're completely new, it's hard to go wrong with an official board.

Price for this one depends on the series, but its in the $15-18 range and the one I bought cost me 18$

3. Adafruit

I picked up one of their Feather boards for a portable environmental monitor that measured temperature and humidity while running from a battery.

The hardware was solid, but honestly the documentation is what impressed me the most.

Every time I got stuck, there was usually already a guide explaining exactly what I was trying to do.

Big positives that surprised me:

• fantastic documentation
• really polished libraries
• CircuitPython support
• lots of beginner-friendly tutorials

It feels like they spend as much effort on teaching as they do on building hardware

The one I used for this project cost me $19.95 - Adafruit ESP32 Feather V2 – 8MB Flash + 2MB PSRAM

4. Seeed Studio

My experience with Seeed mostly came from putting together a quick office monitoring prototype using Grove sensors

I wanted to see how quickly I could go from "idea" to "working prototype" without spending half the evening wiring everything together

The Grove ecosystem was honestly pretty fun for that

Things I liked:

• Grove makes prototyping ridiculously fast
• huge selection of sensors
• swapping modules takes seconds
• easy to experiment with different ideas

I probably wouldn't build every long-term project around Grove, but for testing ideas it's incredibly convenient.

Anyway, those were my experiences

I'm curious what everyone else has settled on after building a few ESP32 projects

I think I spent around $50 for the initial setup (board + a few Grove modules), then most extra sensors I added later were somewhere in the $5–20 range. You can definitely build the same thing for less if you buy generic sensors, but for prototyping I really liked the convenience of just plugging everything together.

Is there one ESP32 board you keep coming back to, or do you switch depending on the project?

EDIT: I added prices to all projects because of a recommendation from a comment, thank you

I was a bit sceptical when I first read this post last week, I was honestly thinking it is a copycat website.

But - netnut has officially updated their new netnut.ai website on their official linkedin profile as well and it was confirmed verified page by linkedin 12 days ago

Screenshot:

https://preview.redd.it/y2m1cri9nxjh1.png?width=796&format=png&auto=webp&s=28b587dbaa61008658293d93c05da49c6dd1d17f

They've moved away from presenting themselves primarily as a residential proxy provider and are now positioning the company as:

• The data layer for AI
• The web data infrastructure for the AI era

When your previous domains have been seized and the industry has spent weeks questioning how your network was sourced, simply changing the story isn't enough. Customers buying infrastructure care about reliability, but they also care about transparency and confidence in the company behind it and I don't believe in this company anymore?

Companies like Bright Data (they had their own sourcing problems as well which everyone knows about), Oxylabs Nodemaven, decodo and others have all expanded beyond proxies into scraping APIs, datasets and AI-focused products. The market is slowly moving away from selling IPs and toward selling access to web data

So I guess the cycle is

1. Proxy company makes a mistake of sourcing IPs and blatantly using their customers data

2. Gets seized by FBI

3. Buys new domain

4. Gets repositioned from proxy provider to "DATA LAYER FOR AI"

Like what even is this?

How is this supposed to work this way?

A completely banned company, just used the same logo, the same mascot, the same colors, just slighlty different messaging

ANd it's back online?

Damn

When someone says to me they used 500GB or let's say 1TB of proxy traffic last month...

How much of that traffic created value and was used in workflows?

And how much was spent on:

• Retries
• Failed requests
• CAPTCHAs
• Blocked sessions
• Debugging
• Requests that never returned usable data

I think It's surprisingly difficult to answer and very few providers have this transparent overview like a monthly report where the traffic went

Because most providers show you how much traffic you consumed and very few help you understand why it was consumed.

That got me thinking, is proxy bandwidth becoming like cloud bills?

Everyone knows they're paying for it in a monthly subscription, but very few people know where it's going

Would you be interested in seeing a data-backed investigation into where proxy traffic really goes? Because I definitely would

For years, I think competition in the proxy market was relatively simple and straightforward

If someone wasn't buying from one of the biggest providers like oxylabs, they were probably buying from nodemaven, iproyal, decodo, webshare or another provider that is mentioned or an industry leader

If you win that customer, you've grown your market share, right?

But after working a lot in the scraping and automation industry and noticing the evolve over the last couple of years, I believe we should be looking at the broader scope of the whole setup

Today, companies that work and need proxies have far more options than simply choosing another proxy provider

Need search engine data? You can build your own infrastructure with proxies or you can simply use SerpAPI, DataForSEO or another managed API

Need product data? You can scrape it yourself by building a scraping bot that works relatively quickly compared to a solution from a company that has a scraping browser

Need website content? You can now build an extraction pipeline or use tools like Firecrawl, Exa or other AI-native retrieval platforms that abstract away much of the underlying infrastructure

Even browser automation is rapidly changing in this indutry as every improvement in Playwright, Puppeteer, anti-detect browsers or fingerprinting technology helps teams complete more work with fewer failed requests and fewer IPs.

That's interesting because those companies aren't selling proxies. But they're solving the same customer problem. The competition is no longer just provider versus provider

It's workflow versus workflow in my opinion

We've reached a point where customers care less about how data is collected and more about how reliably they can get the outcome they're after

If an API gives them the data faster than building a scraper, they'll use the API

If an AI search platform answers the question without scraping a website, they'll use that instead

If better browser technology reduces failed sessions by 30%, they may not need as much proxy traffic in the first place

That's a much broader competitive landscape than most people realize

I think It also changes what proxy providers should optimize for

Maybe the next generation of providers won't win because they have the largest IP pool

Maybe they'll win because they're the easiest infrastructure to integrate into modern data pipelines

Or because they provide better diagnostics, smarter routing, cleaner IP selection and better tooling around the proxy itself.

If you build scraping or browser automation systems, who do you think proxy providers are really competing against today?

And it's not because their websites are bad or something, it's because on reddit I find the most insightful information with real people sharing real experiences. Do you feel the same or trust landing pages or reviews?

Because usually, landing pages tell me just:

• How many IPs they have (which is sometimes total BS)
• How many countries they cover (and almost each proxy provider has the same country coverage)
• How much they charge (well that's probably the only thing that I look for on landing pages)

Reddit has been able to tell me answers or experiences like:

1. "We've been debugging this issue for three days with this provider"
2. "Don't pay this much to use this provider, they're not worth it"
3. "We switched from Bright data to Nodemaven or decodo because sticky sessions were more reliable for our workflow"

That's the kind of information that actually changes buying decisions.

Marketing tells you what a product is supposed to do

Users tell you what happened at 2 a.m. when production broke

I'm pretty curious about this... where do you spend more time researching infrastructure these days?

When I speak with other colleagues or when I even search for proxies myself , everyone basically just compares:

• Price
• GB (or price per GB)
• IP count (this one I don't understand because it's just a flashy marketing number)

But after being in this industry for almost 10 years I realized the real cost wasn't the proxy itself (it's a lot of things that are connected to them, but not the proxy itself)

I personally think it is hours spent debugging failed sessions, replacing accounts, rerunning jobs, and figuring out whether the issue was my setup or the provider

I've used almost all providers in the book over the past few years like Oxlyabs, Decodo and NodeMaven, and I eventually started valuing reliability and security from these providers more than the price per GB. Becuase for example sometimes I valued more cheaper proxies from providers like iproyal until I realized they weren't working for me

What hidden cost do you think people overlook the most?

That title would have probably sounded pretty ridiculous five years ago.

Back then when I was trying to get my company of the ground, most of the conversation revolved around frameworks and browser automation. Me and my team debated whether Scrapy was still relevant, whether Playwright would replace Selenium, or how much faster one parser was than another. The assumption was that if you had the right tooling, the rest of the problem would largely solve itself

I'm not sure that's true anymore honestly

Look at what has happened over the last two years. AI can now generate surprisingly capable scraping logic from a prompt. Open-source projects like Playwright, Crawl4AI, Crawlee, Camoufox and browser-use continue to improve at a pace that would have been difficult to imagine a few years ago. Every few months another project appears that abstracts away another piece of the engineering work that used to take days or weeks. At what point will we have this infrastructure that it's literally the easiest thing possible to start?

What still feels like a rare success is everything that happens after the first successful request

Keeping sessions alive over time is a big factor for me. Understanding why success rates suddenly dropped by fifteen percent overnight while I literally didn't change anything in my setup. Distinguishing between a browser fingerprint issue, an IP reputation issue, a behavioral signal or a target website that quietly changed its defenses. Building systems that continue running next month instead of simply working once on your laptop.

Those are very different engineering problems, and they're becoming a much larger share of the work

That's why I sometimes think we're watching value migrate away from the application layer and toward the infrastructure underneath it.

The code that extracts data is increasingly becoming a commodity. The environment that allows that code to operate reliably is not

You can already see this happening across the ecosystem. Open-source frameworks continue to gain adoption while companies are increasingly differentiating on infrastructure, browser identity, anti-bot research, observability and managed services rather than the crawler itself. Even commercial scraping platforms often end up competing less on whether they can scrape a page and more on how reliably they can keep doing it at production scale.

That's also why I don't really see open-source frameworks competing with companies like Decodo, Oxylabs or NodeMaven. They solve completely different problems as one provides the logic and the other provides the environment that lets the logic survive in production.

If I had to make one prediction, it would be that within a few years most production scraping stacks will look surprisingly similar. The crawling logic will be open source as AI will generate a growing percentage of the glue code, and the competitive advantage will shift further down the stack toward infrastructure, identity management and operational reliability.

I'm curious whether others working in production see the same trend, or whether you think the application layer still has plenty of room left to differentiate?

I was talking to a few people recently about proxy providers and something interesting came up which are refunds.

It seems like a lot of people only think about it after something goes wrong. You test a provider, things don’t perform the way you expected, and then you’re stuck wondering if you can even get your money back or are complaining to get your money back

From what I’ve seen, policies vary a lot. Some providers are pretty strict (no refunds once traffic is used), others are more flexible depending on the situation and sometimes can actually give you some traffic back like a quality guarantee, and sometimes supporrt dissappears when you bring it up lol

Made me curious how common this is.

A little over a month ago, I found myself dealing with a problem that most people involved in scraping, browser automation, account management, or AI agents have probably experienced at some point which are captcha isuses

At first it was just the occasional challenge but it has became a nightmare over a longer period of time

Like most people (sometimes even here in this subreddit), my first instinct was to blame the proxies

The problem was that I kept running into situations that didn't fit that explanation. Sometimes a workflow would run perfectly for days and then suddenly start accumulating a lot of captchas. Other times I would switch to a completely different residential IP and somehow end up with even more instabilities than before

For the next thirty days, I kept a log of every CAPTCHA, Cloudflare challenge, browser verification page, and unexpected access restriction I encountered. Every time a workflow started falling apart, I would go back and look at what had changed beforehand

Going into this experiment, I expected to find that most challenges were directly tied to IP quality but I found a few other things as well that might help yo uout

1. The first trigger that showed up repeatedly was something I didn't expect at all: brand-new browser profiles

For years I assumed fresh profiles were safer and always the best option, these accounts have no cookies, no browsing history, no leftover session data. Everything starts clean and controlled. That's how a lot of automation setups are built.

What I kept seeing in the logs was that some of the highest challenge rates appeared during the first few sessions of a completely new identity

Meanwhile, browser profiles that had accumulated weeks of browsing history often moved through the same websites with significantly less friction. These weren't abandoned profiles collecting dust somewhere. They were actively used identities that had built a believable trail of activity over time. They had cookies, session history, previous visits, and interaction patterns that made them look more like returning users than brand-new visitors.

2. The second trigger was aggressive rotation

This one genuinely surprised me because for years I operated under the assumption that more rotation meant more safety. If one residential IP is good, rotating through hundreds of them should be even better.

The workflows that remained stable for the longest periods usually maintained continuity. They stayed on the same identity long enough to build history. The browser remained consistent. The browsing behavior remained predictable. The session looked like a user who arrived on a website, spent time there, came back later, and continued where they left off

Some of the heavily rotated setups started looking strange when viewed as a complete journey. A session would appear from one IP, perform a few actions, disappear, reappear elsewhere, start over, and repeat the process. Every individual request looked legitimate, but the overall pattern felt unnatural and weird

3. The third trigger was signal mismatches

This was one of those things that didn't immediately break workflows, which is probably why it's easy to overlook

I had several browser profiles where the timezone, language settings, browser behavior, and network location weren't perfectly aligned. Nothing was obviously wrong. Pages loaded correctly. Requests completed successfully. The proxies themselves worked fine

Yet when I reviewed the logs, these profiles appeared disproportionately often in sessions that accumulated challenges over time

What made this interesting was that a single mismatch rarely seemed to matter. The problems started appearing when multiple small inconsistencies stacked together. A browser configured for one region, an IP from another region, and a fresh profile with no history might all be harmless individually. Combined together, they often showed up in workflows that attracted more verification checks

4. The fourth trigger was session resets

This was another finding that challenged some assumptions I had for years.

Whenever a profile suddenly lost cookies, local storage, or other accumulated session data, challenge rates often increased during the next few browsing sessions. This was especially noticeable on websites where the identity had previously established a history of normal activity.

From the website's perspective, a user that has been behaving consistently for weeks suddenly shows up as if they've never visited before. Looking back, it's not difficult to understand why that might attract additional scrutiny

5. The fifth trigger wasn't a technical setting at all. It was my online behaviour

For some workflows my behaviour was simply too efficinet. I landed directly on target pages, extracted information, moved to the next page, and repeated the process with almost no variation. Technically everything worked. Operationally everything looked correct

But when I compared those workflows against sessions that navigated more naturally, opened supporting pages, spent more time browsing, and behaved more like actual users, the difference was noticeable.

The more human-looking sessions consistently accumulated fewer challenges.

What surprised me most wasn't any individual trigger.

It was the fact that all five of them pointed toward the same conclusion.

Fresh profiles, aggressive rotation, signal mismatches, session resets, and unnatural browsing behavior all have one thing in common: they create inconsistencies in the identity.

The funny thing is that after thirty days of collecting all this data, I became less interested in arguing about which proxy provider is best. Over the years I've run workflows through various residential networks including NodeMaven, Bright Data, Decodo, IPRoyal, and several others. While there are certainly differences between providers, the largest improvements I observed during this experiment rarely came from changing providers. More often than not, the biggest gains came from eliminating inconsistencies elsewhere in the identity stack

The more I reviewed the logs, the less it felt like I was seeing bad proxies and the more it felt like I was seeing broken identities

I'm curious whether anyone else has gone looking for a proxy problem and ended up discovering an identity problem instead

Hey all, I've seen plenty of proxy comparisons over the years and now especially on reddit in recent months where everyone is focusing on "who is best provider" or "who has best proxies" which is getting really boring honestly, but no one actually posts results from real tests

I wanted to share something a bit more measurable

Over the last two weeks I purchased the cheapest residential package from 10 different providers and ran them through the same workloads. My goal wasn't to rank providers or declare a winner. I was more interested in trying to help others understand which characteristics correlate with successful scraping on websites these days

For anyone curious about the setup, everything ran from the same Hetzner Cloud VPS in Virginia. I used Playwright with Chromium 137, kept the browser fingerprint identical across all runs, disabled WebRTC leaks, matched timezone and geolocation settings, and maintained the same concurrency limits for every provide

The providers included Bright Data, Oxylabs, NodeMaven, NetNut, Decodo, SOAX, IPRoyal, Rayobyte, Webshare, and Packestream

I used Quotes.toscrape.com as a baseline sanity check because it has virtually no anti-bot protection. The goal wasn't to compare providers on this website, since nearly every provider can achieve almost perfect success rates on this. Instead, I used it to validate that the scraping setup itself was functioning correctly before moving on to more challenging website targets like Zillow and Indeed (these were my benchmark websites where I will share data below) as they have Cloudflare-protected endpoints

Each provider received roughly 10,000 requests distributed across the same workflows. For Zillow, the workflow involved browsing property listings, navigating pagination, and opening individual property pages. For Indeed, it involved keyword searches, pagination, and job detail pages

I made sure to intentionally avoid extremely aggressive concurrency because I wanted to evaluate the quality of the proxy network itself rather than DDoS-style resilience

The lowest latency network averaged 612ms response times across all requests. Another network averaged 1.74 seconds. Despite that difference, their Zillow success rates were only a few percentage points apart

Here's the sample from my dataset:

Provider	Avg latency	Zillow success	Indeed success
Bright Data	612ms	86.1%	92.4%
Oxylabs	853ms	87.5%	93.2%
NodeMaven	789ms	88.4%	94.1%
NetNut	1.15s	84.9%	91.1%
Decodo	1.12s	81.7%	88.6%
SOAX	1.03s	82.5%	90.8%
IPRoyal	1.28s	75.2%	87.4%
Rayobyte	1.41s	78.8%	89.3%
Webshare	1.36s	76.9%	88.2%
PacketStream	1.57s	80.1%	86.7%

After seeing those numbers I stopped paying much attention to latency and started looking at what was actually different between the providers that consistently performed well

One metric that ended up being surprisingly useful was ASN diversity

For every provider I collected roughly 2,500 assigned residential IPs and extracted ASN information

Also, I wasn't trying to validate marketing claims about pool size because I don't believe in marketing pool sizes and numbers of IPs that other proxy providers say. I was more interested in understanding how concentrated each network was

Here I used two metrics, unique ASNs tested and duplicate IP rate according to the IPs

Provider	Unique ASNs tested	Duplicate IP rate
Bright Data	487	7.1%
Oxylabs	452	8.4%
NodeMaven	391	6.8%
SOAX	367	9.2%
Decodo	324	12.1%
NetNut	287	10.7%
Rayobyte	241	13.4%
IPRoyal	223	14.1%
Webshare	198	15.7%
PacketStream	171	16.3%

The providers with broader ASN distribution generally performed better on Zillow and Cloudflare

That's not enough data to claim the best one, but it was one of the strongest correlations in the benchmark

Another thing I became interested in was session longevity and sticky sessions

Most benchmarks focus on raw success rates, but in real-world scraping I care more about how long an identity survives before challenges start accumulating. If I can stay on a site for 20 minutes without friction, that's often more valuable than a provider having 2% higher success rates on fresh sessions.

To measure this, I tracked how long a browsing session could continue on Zillow before challenge rates exceeded 10%.

Provider	Avg session duration before challenge accumulation
NodeMaven	21.6 minutes
Bright Data	18.2 minutes
Oxylabs	17.4 minutes
NetNut	16.4 minutes
SOAX	16.1 minutes
Decodo	15.1 minutes
PacketStream	14.8 minutes
Rayobyte	13.7 minutes
Webshare	12.9 minutes
IPRoyal	12.4 minutes

The providers that maintained longer sessions also tended to generate fewer CAPTCHA challenges and fewer abrupt session resets, this was really good

The biggest surprise of the entire benchmark came when I started testing rotation strategies

For years I've operated under the assumption that more rotation is safer. If one residential IP is good, rotating through hundreds of them should be even better.

I tested three different approaches against the same Zillow workflow: rotating every request, rotating every ten requests, and maintaining a 30-minute sticky session.

Rotation strategy	Success rate	Challenge rate
Every request	61.4%	17.9%
Every 10 requests	73.8%	11.2%
30 Minute sticky session	82.7%	4.8%

Looking back, it makes sense. A real user doesn't change cities, ISPs, and IP addresses every page load. Even if every IP individually looks legitimate, the overall identity becomes really inconsistent

At this point I thought I was getting close to identifying which providers were genuinely better

But the more data I collected, the less convinced I became that there is a universal "best" proxy network.

What I expected to be a proxy benchmark gradually turned into an identity benchmark

The providers absolutely mattered, but the gap between a well-configured browser identity and a poorly configured one was often larger than the gap between several reputable residential networks

Hope this coudl help someone

The funny thing about WebRTC leaks is that most people don't know they exist until something weird happens. Like if a a website starts throwing verification checks at you constnatly or maybe your account gets flagged or maybe your browser fingerprint suddenly looks like it belongs to two different people living in two different countries

And then begins the sacred ritual of opening 17 browser settings tabs, watching three YouTube tutorials, and questioning every life decision that led you here

The good news for me and for others? Finding a WebRTC leak is usually much easier than fixing one

I compiled a list of free tools that might be really useful for you, hope it helps

1. NodeMaven WebRTC Leak Test

If you've ever spent 20 minutes configuring a proxy, launched your browser profile, opened a leak checker, and immediately thought "well that's not good", this tool was built for you.

Most WebRTC leak tests simply dump a bunch of IP addresses on the screen and leave you to figure out what they mean. The NodeMaven WebRTC Leak Test takes a different approach. It compares your HTTP connection against the WebRTC candidates your browser exposes and tells you whether those two identities match.

The tool automatically compares your normal HTTP connection against the WebRTC candidates your browser exposes. If both identities match, you're generally in good shape. If WebRTC is exposing a different network path, you'll see it immediately.

It also checks for:

• Public IP exposure
• Local LAN IP exposure
• IPv6 leaks
• mDNS candidates
• ICE candidates
• Browser WebRTC capabilities
• SDP information

What makes it particularly useful for proxy users is that it was designed around real-world browser setups rather than generic VPN testing

If you're using antidetect browsers or any automation tools, this one is for you

And yes, it saves you from having to stare at ICE candidate logs wondering if you've accidentally become a networking expert overnight

2. Browserleaks WebRTC Test

BrowserLeaks is basically the Swiss Army knife of browser fingerprint testing

You start by checking for a WebRTC leak

Thirty minutes later you're testing your canvas fingerprint, WebGL fingerprint, fonts, audio stack, timezone, battery API, and questioning every browser setting you've ever changed.

I personally think that its WebRTC test remains one of the most detailed available online. The tool communicates with STUN servers and displays exactly what your browser exposes through WebRTC

The biggest advantage is transparency. BrowserLeaks shows raw information instead of trying to simplify everything. For technical users, that's extremely valuable

The potential downside?

Some beginners might open the page and immediately feel like they accidentally enrolled in a network engineering course lol

Still, I think if you're troubleshooting a stubborn browser profile, BrowserLeaks is one of the best resources available

3. IPLeak

I know that IPleak has been around forever, and the reason why peeople use it is because it works and it's simple, also It's pretty old school and I like it

Instead of focusing only on WebRTC, it gives you a broader picture of your privacy setup. You can test DNS leaks, IPv6 leaks, torrent leaks, and WebRTC leaks from a single page

This was really useful for me when setting up a new VPN, proxy chain, or anti-detect browser profile

One thing many users discover is that fixing a WebRTC leak doesn't necessarily mean everything is configured correctly. Sometimes DNS requests are still escaping through the local ISP, or IPv6 traffic is taking a completely different route

The interface definitely looks like it belongs to an internet era from 2000s, but that's part of its charm. Nobody visits IPLeak because it's pretty, people visit because it works

4. vpnMentor WebRTC leak test

Sometimes you don't want ICE candidates and SDP logs

You just want somebody to answer:

"Am I leaking my IP address?"

That's where vpnMentor does a good job I think

The tool checks for local IP leaks, public IP leaks, IPv6 leaks, and even identifies devices such as cameras and microphones that may be exposed through WebRTC.

The explanations are clear, beginner-friendly, and written for people who may never have heard the term "ICE candidate" before

If you're helping a teammate troubleshoot a setup or introducing someone to browser privacy for the first time, vpnMentor is often easier to understand than more technical alternatives

5.  WebRTC leak test

Hide takes the opposite approach from BrowserLeaks in my opinion

Their philosophy seems to be:

"Run the test. Get the answer. Move on with your day."

The page loads quickly, runs the check automatically, and shows whether WebRTC is exposing information that it shouldn't

There isn't a huge amount of technical detail, but that's fine if all you're looking for is a quick sanity check before launching an automation workflow or starting a browsing session

It's definitely not a full security audit, but it gets the job done

Why WebRTC leaks still matter in 2026

I have seen a lot of my colleagues and friends still believe that if their proxy or VPN changes their visible IP address, they're fully protected

My research has repeatedly shown that WebRTC can expose network information through ICE candidates even when users are connected through VPNs or other anonymity tools.

That's particularly important for:

• Multi-account management
• Browser automation
• Web scraping
• Social media management
• Ad verification
• Anti-detect browser users

In other words, exactly the people most likely to be using proxies in the first place

My recommendation

If you're a normal internet user, vpnMentor or Hide will probably give you everything you need.

If you're troubleshooting browser fingerprints, BrowserLeaks remains one of the best technical resources available.

If you want a broader privacy health check, IPLeak is still a classic

And if you're specifically running proxies, anti-detect browsers, browser automation, or multi-account setups, start with the NodeMaven WebRTC leak test because it focuses on the thing proxy users care about most: the sync between proxies and your browser

I've spent roughly 10 years around browser automation, managing multiple accounts, scraping, proxies, anti-detect browsers, and figuring out anti-bot systems. Watching the space evolve has been fascinating because I don't think either side is actually winning

For people unfamiliar with this world, there are essentially two industries growing at the same time

On one side, you have companies like Cloudflare, Akamai, DataDome, and others investing heavily in bot detection, fraud prevention, and traffic verification. Their goal is one thing only: figure out whether a visitor is a legitimate user or some form of automation (and they are constantly trying to implement new ways of figuring out who's a legitimate user lol)

On the other side, there's a rapidly growing automation ecosystem. For example companies like Bright Data, Oxylabs, and Nodemaven provide the network layer. Then companies like Dolphin anty, Octo browser and MUltilogin focus on browser identity. Browserbase and similar platforms abstract away browser infrastructure. AI agents sit on top of all of it now. They're different pieces of the stack, but they ultimately push in the same direction: making software better at operating websites that were originally designed for humans

What surprises me is that both sides seem to be growing faster than ever

The anti-bot market is obviously much larger because massive enterprises are willing to spend huge amounts of money protecting accounts, preventing fraud, and filtering traffic
But I'm noticing that demand for automation infrastructure is also exploding. I have noticed that companies now increasilgy rely on web data, automated workflows, competitive intelligence, account management, and AI-powered processes that need reliable access to websites

And now what's happening is that AI agents seem to be accelerating both markets at the same time. Every new agent framework creates more demand for automated browser interactions, while every increase in automated traffic creates more demand for detection systems

One thing I've noticed over the years is that anti-bot systems still struggle with false positives. The more sophisticated detection becomes, the higher the chance that legitimate users get caught in the process. Most people have experienced this without even realizing it. You visit a website, solve a challenge, solve another one, refresh the page, get challenged again, and suddenly you're wondering why proving you're human feels harder than logging into your bank account lol

On the automation side, the challenge is totally different though. The hardest websites are becoming increasingly expensive to automate reliably. In many cases the solution isn't some magical bypass technique anymore. It's simply throwing more resources at the problem. More powerful machines, more browser instances, higher quality proxies, better browser environments, and more engineering effort. A setup that worked comfortably a few years ago can require significantly more infrastructure today.

That's why I don't think we're heading toward a future where one side wins and the other disappears

The relationship feels almost symbiotic. Better detection systems create demand for better automation infrastructure. Better automation infrastructure creates demand for better detection systems. Both industries push each other forward and, as a result, both continue growing

My broader prediction is that the web becomes increasingly programmatically accessible, even if many platforms resist it. Companies want data and companies want automation. They want agents that can interact with software on their behalf. As AI improves and compute becomes cheaper, more web interactions will have an automated component attached to them

That was my Ted talk, thank you

Everytime I click a video or something it just stops loading, or if I want to fast forward a video, when I click on a certain part of the video, it just freezes

Hey all, I wrote this post thinking it could be really valuable who do not know about the best antidetect browser + proxy pairing, so this might be able to help you out, I have been quite extensively working on some really big multi-accounting workflows, so I hope this helps you out or at least gives you some valuable advice

I juggles between a lot of social media platforms and have a lot of ad accounts so if anyone is in the similar space as me, I am happy to help

What i mean by multi accounting is that I’ve gone through workflows such as:

• running approx hundred of browser profiles
• managing social media accounts that i managed to keep stable for a long time
• scaling automation workflows using these accounts
• testing GEO-sensitive setups using good quality IPs from various countries
• warming accounts that need to stay alive
• browser automation
• scraping social platforms
• maintaining session trust over long periods

And after A LOT of testing, I made one big conclusion which I think some people don’t think about

People massively underestimate how much the browser + proxy pairing affects stability

You might think about these things first:

“What’s the best antidetect browser?”
“What’s the best proxy provider?”

And I think that’s not really the right quesiton, I think the right question

Which combination breaks the least under your actual workflow?

Because once you scale browser-based automation, tiny problems start becoming big problems

• session invalidation
• repeated email verification
• suspicious login prompts
• failed warmups
• dead cookies
• browser memory leaks
• profile corruption
• unstable automation flows

I started testing different combinations specifically to figure out:

• which setups survive longer
• which setups feel stable after weeks
• which ones create hidden maintenance problems
• which ones scale cleanly

How I tested everything and each proider

My testing was real and detailed

I wasn’t running “speed tests” or checking homepage load times as i tested these setups in actual multi-accounting environments

Main workflows I tested

• TikTok account warmups
• Facebook account management
• Reddit automation
• X/Twitter workflows
• browser scraping
• session persistence testing
• automation through Playwright/Puppeteer
• anti-detect browser automation
• long-running login sessions
• GEO switching
• cookie persistence

What were my success metrics for each provider

I mainly tracked:

• session longevity
• account survival rate
• random re-verification frequency
• IP consistency
• browser stability over time
• memory usage
• automation interruption frequency
• login invalidation rate
• CAPTCHA frequency
• profile corruption issues

Testing duration

Most combinations were tested for:

• 2–6 weeks
• multiple browser profiles
• repeated automation cycles
• varying concurrency levels

Because if you think about it almost every setup looks “good” during the first 24 hours

The real problems usually appear later in the workflow and automation aspect

Here's my list

1. NodeMaven + Multilogin

This was probably the cleanest long-session setup I tested because it lasted the longest without any issues

And what was refreshing for me is that this combo solved one of the biggest problems I had which is session instability during long-lived browser workflows (SO CRUCIAL FOR THESE SETUPS) and it was so refreshing to have this

I mainly tested this combo for:

• TikTok warmups
• Reddit account management
• persistent social sessions
• browser automation with long login retention
• affiliate account management

The problem I had before with previous providers

With many rotating residential providers, I kept seeing:

• random session invalidation
• cookie distrust
• weird location inconsistencies
• browser sessions suddenly behaving differently
• accounts getting logged out unexpectedly

So why is their combo good?

NodeMaven’s sticky residential + ISP sessions paired really naturally with Multilogin’s browser persistence and they seem to have some sort of collaboraiton so their pairing works REALLY well together. Also what I liked is that nodemaven now providers some sort of quality guarantee, so if some of their IPs are not working well, they will give you back the money and I have experienced only one IP not working properly

Their setup actually solved a lot of problems for me which were:

• mid-session IP inconsistency
• session resets
• account distrust
• login interruptions

What stood out most is that the sessions simply behaved predictably and i could just keep the automation going

Technical observations that might interest you as well

• Lower CAPTCHA trigger rate
• Better cookie retention
• More stable long-running sessions
• Fewer browser trust resets
• Better automation continuity over multiple days

Weaknesses

The setup is not optimized for:

• hyper-aggressive rotation
• ultra-cheap scaling
• high-churn scraping

This pairing clearly favors:

• quality
• stability
• persistence

over raw volume and scraping large volume of data

2. Dolphin{anty} + Decodo

This setup felt very different operationally

IT was Much faster and muuch more rotation-oriented

I tested this combo mostly for:

• rapid account cycling
• Facebook setups
• TikTok profile scaling
• GEO testing
• account segmentation

What worked well

Dolphin’s workflow style naturally favors:

• quick profile management
• high operational speed
• fast deployment

And Decodo’s rotating residential pools paired well with that

This setup worked especially well when:

• profiles didn’t need ultra-long persistence
• accounts rotated frequently
• GEO flexibility mattered
• account quantities increased quickly

Problems it solved

Compared to stickier setups:

• easier traffic separation
• easier profile scaling
• faster environment resets
• simpler operational management

Issues I still encountered

Under longer sessions, I occasionally noticed:

• more re-verification
• shorter “trust lifespan”
• more session inconsistency over time

This setup felt really optimized for speed and scale, but it might just be my use case

3. AdsPower + Webshare

This was one of the most budget-efficient setups I tested. Probably the cheapeast option you can find but this can possibly break your workflow if it runs for too long

I used this combo for

• lightweight account farming
• testing automation flows
• burner workflows
• lower-risk scraping
• mass profile creation

What surprised me

AdsPower tolerated lower-quality proxies better than I expected

Some antidetect browsers become extremely unstable once proxy quality drops slightly

AdsPower stayed relatively usable even with:

• cheaper residential pools
• mixed IP quality
• higher profile quantities

Problems I encountered

Webshare is actually quite known for having the cheapest proxies possible on the market,g but their proxies can get quite bad during longer-period use, which can be a problem if you need to have a stable working flow of many accounts

So critical issues with this setup can be:

• longer session persistence
• trust-sensitive platforms
• older account management

I started seeing:

• more login challenges
• more verification prompts
• weaker session trust over time

BUT, if you are on budget, and just starting out, or a freelancer who doesn't simple have the money for, this is a really good combo for:

• testing
• scaling cheaply
• operational experimentation

+ and the setup honestly performed better than expected

4. Octobrowser + MarsProxies

This setup felt very natural and I was able to use it for quite a long time actually

I used them for managing multiple accounts and was able to run sessions with 15-20 accounts with ease in one go

This combo also works well with:

• browser automation at higher concurrency
• regional account testing

What stood out

MarsPorixes routing flexibility was probably the biggest strength because they are a bigger provider and were able to provide me with good stability

Especially I think thy have good:

• GEO precision
• ASN targeting
• traffic routing consistency
• large-scale reliability

Problems it solved

This setup significantly reduced:

• GEO mismatches
• localization inconsistencies
• unstable routing behavior

Especially useful when:

• testing regional content
• scraping localized SERPs
• running region-sensitive workflows

Downsides

Honestly this setup doesn't have many downsides, I didn't experience a lot of problems but like a few random proxy kills and banned IPs so for my workflow this worked like a charm

And for smaller operators, it’s definitely an overkill

5. Undetectable + Oxylabs

This setup performed extremely well under heavier automation loads as I believe oxylabs proxies are also built for that in some way. This is basically a setup for enterprise users mostly? (because I don't see any regular users or people needing tihs in really big scale)

I used this combo for:

• browser scraping
• higher concurrency automation
• Playwright integrations
• larger scraping sessions

What I liked

The setup stayed surprisingly stable under load (which is expected out of an enteprrise proxy provider any basically any antidetect browser combo)

Some combinations start degrading badly once:

• concurrency increases
• memory usage grows
• browser workers scale

This one remained relatively clean operationally

Problems it solved

• worker instability
• session crashes
• automation interruption
• browser lag under scale

Technical observations

• Good browser stability
• Lower crash frequency
• Strong scaling consistency
• Better concurrency handling

6. Incogniton + IPRoyal

This felt like the best beginner-friendly pairing because iproyal has cheaper prices, and incognitioin is quite intuitive as a browser

I used this combination for:

• smaller affiliate workflows
• basic browser automation
• moderate account quantities
• testing ideas quickly

Why I liked it

Simple to setup, it’s actually really cheap and it’s low-friction

Not every workflow needs enterprise infrastructure so the combination between incognition + iproyal gives you this

Sometimes you just need:

• acceptable stability
• decent residential IPs
• manageable costs
• enough flexibility to test ideas

And honestly, this setup delivered that pretty well

What does each setup realistically cost per month?

Setup	Browser cost	Proxy cost	Approx. monthly budget	best use case
Multilogin + NodeMaven	Multilogin Pro (10 profiles) ~$11/mo	NodeMaven 20GB plan ~$75/mo	~$86/mo	Long-term account management
Dolphin Anty + Decodo	10$ per month (for 10 profiles)	81.25$ for 20GB	~$60–120/mo	Fast account scaling
AdsPower + Webshare	9.6$ per month (for 20 profiles)	27$ per month (for 1000 proxies)	~$20–50/mo	Budget testing
Octobrowser + MarsProxies	9$ per month (for 20 profiles)	75$ for 20GB	$90+/mo	Good for multi accounting as well as the NodeMaven workflow
Undetectable + Oxylabs	11$ per month (for 20 profiles)	100$ per month	$110+/mo	Enterprise workflows and scraping
Incogniton + IPRoyal	13.99$ for 10 profiles	52.5$ for 10GB	~$30–60/mo	Beginners

One of my friends was able to build a basically ai assistant that is helping him monitor a lot of things at once, he combined a lot of his actual work workflows with openclaw and it is helping him out quite a lot with work, is it really that good? Were you able to build something with it that is worth mentioning

Hey all, I’m following this subreddit fo ra while but haven’t seen these type of post so I thought it would be useful to share it from my experience

I am a fellow multi-accounting addictive hustler who juggles between a lot of social media platforms and has a lot of ad accounts so if anyone is in the similar space as me, I am happy to help

What i mean by multi accounting hustlering is that I’ve gone through workflows such as:

• running approx hundred of browser profiles
• managing social media accounts that i managed to keep stable for a long time
• scaling automation workflows using these accounts
• testing GEO-sensitive setups using good quality IPs from various countries
• warming accounts that need to stay alive
• browser automation
• scraping social platforms
• maintaining session trust over long periods

And after enough testing, one I made one big conclusion which I think some people don’t think about

People massively underestimate how much the browser + proxy pairing affects stability

Most discussions online are focused either around

“What’s the best antidetect browser?”
“What’s the best proxy provider?”

And I think that’s not really the right quesiton, I think the right question

Which combination breaks the least under your actual workflow?

Because once you scale browser-based automation, tiny problems start becoming big problems

• session invalidation
• repeated email verification
• suspicious login prompts
• failed warmups
• dead cookies
• browser memory leaks
• profile corruption
• unstable automation flows

I started testing different combinations specifically to figure out:

• which setups survive longer
• which setups feel stable after weeks
• which ones create hidden maintenance problems
• which ones scale cleanly

How I tested everything and each proider

My testing was real and detailed

I wasn’t running “speed tests” or checking homepage load times as i tested these setups in actual multi-accounting environments

Main workflows I tested

• TikTok account warmups
• Facebook account management
• Reddit automation
• X/Twitter workflows
• browser scraping
• session persistence testing
• automation through Playwright/Puppeteer
• anti-detect browser automation
• long-running login sessions
• GEO switching
• cookie persistence

What were my success metrics for each provider

I mainly tracked:

• session longevity
• account survival rate
• random re-verification frequency
• IP consistency
• browser stability over time
• memory usage
• automation interruption frequency
• login invalidation rate
• CAPTCHA frequency
• profile corruption issues

Testing duration

Most combinations were tested for:

• 2–6 weeks
• multiple browser profiles
• repeated automation cycles
• varying concurrency levels

Because if you think about it almost every setup looks “good” during the first 24 hours

The real problems usually appear later in the workflow and automation aspect

1. NodeMaven + Multilogin

This was probably the cleanest long-session setup I tested because it lasted the longest without any issues

And what was refreshing for me is that this combo solved one of the biggest problems I had which is session instability during long-lived browser workflows (SO CRUCIAL FOR THESE SETUPS) and it was so refreshing to have this

My use case

I mainly tested this combo for:

• TikTok warmups
• Reddit account management
• persistent social sessions
• browser automation with long login retention
• affiliate account management

The problem I had before with previous providers

With many rotating residential providers, I kept seeing:

• random session invalidation
• cookie distrust
• weird location inconsistencies
• browser sessions suddenly behaving differently
• accounts getting logged out unexpectedly

So why is their combo good?

NodeMaven’s sticky residential + ISP sessions paired really naturally with Multilogin’s browser persistence and they seem to have some sort of collaboraiton so their pairing works REALLY well together. Also what I liked is that nodemaven now providers some sort of quality guarantee, so if some of their IPs are not working well, they will give you back the money and I have experienced only one IP not working properly

Their setup actually solved a lot of problems for me which were:

• mid-session IP inconsistency
• session resets
• account distrust
• login interruptions

What stood out most is that the sessions simply behaved predictably and i could just keep the automation going

Technical observations that might interest you as well

• Lower CAPTCHA frequency
• Better cookie retention
• More stable long-running sessions
• Fewer browser trust resets
• Better automation continuity over multiple days

Weaknesses

The setup is not optimized for:

• hyper-aggressive rotation
• ultra-cheap scaling
• high-churn scraping

This pairing clearly favors:

• quality
• stability
• persistence

over raw volume and scraping large volume of data

2. Dolphin{anty} + Decodo

This setup felt very different operationally

IT was Much faster and muuch more rotation-oriented

My use case

I tested this combo mostly for:

• rapid account cycling
• Facebook setups
• TikTok profile scaling
• GEO testing
• account segmentation

What worked well

Dolphin’s workflow style naturally favors:

• quick profile management
• high operational speed
• fast deployment

And Decodo’s rotating residential pools paired well with that

This setup worked especially well when:

• profiles didn’t need ultra-long persistence
• accounts rotated frequently
• GEO flexibility mattered
• account quantities increased quickly

Problems it solved

Compared to stickier setups:

• easier traffic separation
• easier profile scaling
• faster environment resets
• simpler operational management

Issues I still encountered

Under longer sessions, I occasionally noticed:

• more re-verification
• shorter “trust lifespan”
• more session inconsistency over time

This setup felt really optimized for speed and scale, but it might just be my use case

3. AdsPower + Webshare

This was one of the most budget-efficient setups I tested. Probably the cheapeast option you can find but this can possibly break your workflow if it runs for too long

My use case

• lightweight account farming
• testing automation flows
• burner workflows
• lower-risk scraping
• mass profile creation

What surprised me

AdsPower tolerated lower-quality proxies better than I expected

Some antidetect browsers become extremely unstable once proxy quality drops slightly

AdsPower stayed relatively usable even with:

• cheaper residential pools
• mixed IP quality
• higher profile quantities

Problems I encountered

The limitations became obvious during:

• longer session persistence
• trust-sensitive platforms
• older account management

I started seeing:

• more login challenges
• more verification prompts
• weaker session trust over time

But for:

• testing
• scaling cheaply
• operational experimentation

…the setup honestly performed better than expected

4. GoLogin + Bright Data

This setup felt very enterprise-oriented and brighdata requires KYC so this setup might be a good setup for enterprise scaling or some s*it like that

My use case

• larger scraping workflows
• GEO-sensitive browser testing
• browser automation at higher concurrency
• regional account testing

What stood out

Bright Data’s routing flexibility was probably the biggest strength because they are a huge provider and are able to provide these things

Especially I think thy have good:

• GEO precision
• ASN targeting
• traffic routing consistency
• large-scale reliability

Problems it solved

This setup significantly reduced:

• GEO mismatches
• localization inconsistencies
• unstable routing behavior

Especially useful when:

• testing regional content
• scraping localized SERPs
• running region-sensitive workflows

Downsides

Honestly the biggest downside this setup is quite expensive and i don’t see how it could work for my use, but for some it could work

And for smaller operators, it’s definitely an overkill

5. Undetectable + Oxylabs

This setup performed extremely well under heavier automation loads as I believe oxylabs proxies are also built for that in some way

My use case

• browser scraping
• higher concurrency automation
• Playwright integrations
• larger scraping sessions

What I liked

The setup stayed surprisingly stable under load

Some combinations start degrading badly once:

• concurrency increases
• memory usage grows
• browser workers scale

This one remained relatively clean operationally.

Problems it solved

• worker instability
• session crashes
• automation interruption
• browser lag under scale

Technical observations

• Good browser stability
• Lower crash frequency
• Strong scaling consistency
• Better concurrency handling

Incogniton + IPRoyal

This felt like the best beginner-friendly pairing because iproyal has cheaper prices, and incognitioin is quite intuitive as a browser

My use case

• smaller affiliate workflows
• basic browser automation
• moderate account quantities
• testing ideas quickly

Why I liked it

Simple to setup, it’s actually really cheap and it’s low-friction

Not every workflow needs enterprise infrastructure.

Sometimes you just need:

• acceptable stability
• decent residential IPs
• manageable costs
• enough flexibility to test ideas

And honestly, this setup delivered that pretty well

What does each setup realistically cost per month?

Setup	Browser cost	Proxy cost	Approx. monthly budget	best use case
Multilogin + NodeMaven	Multilogin Pro (10 profiles) ~$11/mo	NodeMaven 20GB plan ~$75/mo	~$86/mo	Long-term account management
Dolphin Anty + Decodo	Dolphin Free/Team plan	Decodo residential traffic plan	~$60–120/mo	Fast account scaling
AdsPower + Webshare	AdsPower Professional	Webshare residential plan	~$20–50/mo	Budget testing
GoLogin + Bright Data	GoLogin Professional	Bright Data residential traffic	$200+/mo	Enterprise workflows
Undetectable + Oxylabs	Undetectable subscription	Oxylabs residential traffic	$200+/mo	Heavy scraping
Incogniton + IPRoyal	Incogniton Starter	IPRoyal residential traffic	~$30–60/mo	Beginners

Final thoughts

The biggest lesson from all this?

There’s no universally “best” antidetect browser or proxy provider

The best setup depends entirely on

• your workflow (as in my example the best combination for was managing multiple accounts)
• your session length
• your scaling model
• your tolerance for instability
• your budget

Some setups optimize for:

• trust
• persistence
• stable identities

Others optimize for:

• rotation
• speed
• high concurrency

And once you start testing these things at scale, you realize something very quickly:

Tiny infrastructure inconsistencies eventually become operational nightmares

That’s why the pairing matters so much

I am rated 1000 elo rapid, but I have not expected to this development at bullet, I just enjoy the 1 minute games so much because they're really high in intensity and pace and predicting next moves. This was probably the fastest I've ever climbed in any chess format

https://preview.redd.it/pdn05lztxm2h1.png?width=711&format=png&auto=webp&s=1d5c9a8b055edbbdac7e7b7bd197963ac704d97f

Over the last year I started experimenting heavily with Cursor, Claude and GPT for coding

Not because I wanted to become a “real developer,” but because I kept running into scraping problems where existing tools got me 80% moving forward when I was stuck with some scraping projects

And honestly, the biggest realization for me was this:

AI coding is insanely useful for people who understand scraping workflows better than programming itself

Most of what I do is SERP/data scraping, browser automation and scraping platforms thta aggressively rate limit or fingerprint traffic

Also I wouldn’t call myself a proper Python developer. My knowledge is actually really basic

But with AI-assisted coding I’ve managed to build:

• proxy rotation systems
• local APIs
• scraping helpers
• queue-based workers
• Telegram monitoring bots
• browser automation utilities
• retry/failure handling
• image processing tools
• account management workflows

What surprised me most is that the hard part usually isn’t the code itself.

It’s:

• structuring the architecture properly
• handling retries
• avoiding race conditions
• making sessions stable
• organizing proxies cleanly
• preventing spaghetti code
• figuring out where bottlenecks actually happen

That’s where AI became really helpful for me case

For example, instead of prompting:

>“make me a scraper”

I started prompting like:

>“Build a modular Python scraping system with rotating residential proxies, retry logic, queue workers, FastAPI endpoints, logging, rate limiting and browser session management.”

The difference in output quality is massive

One workflow I’ve been using a lot lately:

• FastAPI running locally
• scraping workers communicating through localhost
• rotating residential/mobile proxies
• anti-detect browsers for harder targets
• Telegram bots for monitoring/control
• queue systems handling retries automatically

Honestly, AI feels especially powerful for writing “glue infrastructure.”

Things like:

• proxy rotation layers
• retry logic
• task queues
• logging systems
• browser wrappers
• local APIs
• automation scripts
• monitoring tools

A lot of this used to feel inaccessible unless you were a serious engineer

Now solo scrapers can prototype infrastructure ridiculously fast

I’ve also noticed stable sticky sessions matter WAY more than raw proxy speed once you scale browser-based scraping or account workflows. Been testing some setups with a few proxy providers like node maven and brightdata recently for that reason because cleaner residential sessions + an antidetect browsers reduce random failures quite a bit actually

Biggest lesson so far: don’t try to build perfect systems immediately as this will not work, at least it didn't work for me

Now I mostly:

1. Build minimum working infrastructure
2. Stress test it
3. Fix bottlenecks as they appear

Way faster than overengineering from the start

Curious if other scrapers here are using AI coding tools heavily now for infrastructure/tooling?

Feels like the barrier to building custom scraping systems dropped massively in the last year.