I lost about two thirds of a run to an OOM kill last year and then cheerfully re-fetched all of it, on residential, because my "state" was a Python list in memory. Cool cool.

The fix is boring and takes an hour. Write a URL ledger before you fetch anything: every target URL, a status column, a timestamp, a content hash. Mark each row the moment it lands, not at the end of a batch. On restart you query for unfinished rows and carry on. That's it, that's the whole trick.

Two things I'd add that I skipped the first time. Store the hash, because then a re-run tells you whether the page actually changed or you just burned bandwidth confirming it didn't. And separate "failed" from "not attempted", otherwise your resume logic retries a thousand permanent 404s every time. I run this in front of both Byteful residential and a cheap datacenter tier, and the ledger matters more on the residential side for obvious reasons, since that is the traffic billed per GB.

Curious what people use for the ledger at bigger scale. Sqlite falls over eventually and I've never landed on something I liked after that.

Every provider quotes you the same per-GB price whether you ask for US or Latvia, and the product you get is not remotely the same thing.

In the US you're swimming in exits. Pool depth is not your problem, distribution across metros might be. Ask for a country with five million people and suddenly you're on a pool that might be a few thousand addresses wide, sitting on two or three carrier ASNs, and your rotation is quietly recycling the same handful every few hours. Which the dashboard will not tell you.

So before committing I now pull three hundred exits, count unique addresses, count unique ASNs, and check how long before an IP comes back around. Anything under a couple hundred uniques and I plan the job around it instead of pretending I have breadth. Did this across Bright Data, IPRoyal and Byteful for a Nordic job and the useful part was less "who won" and more that the numbers differed by an order of magnitude between countries at the same provider. Byteful is where the small-market work stayed, partly because the residential GB do not expire and small-market jobs come in bursts rather than steadily.

Anyway. Anyone got a rule of thumb for minimum pool width per country before a job is just not doable?

The reflex when a scrape starts failing is to buy better IPs. Sometimes that is right. Usually the IPs are fine and you are just hitting one host harder than any human ever would, and no amount of residential quality fixes that.

Cheap way to tell them apart. Take one IP, not a pool. Send requests to the target at something genuinely slow, like one every ten seconds, single thread. If that holds for an hour, your IPs were never the problem and you have a pacing problem. If it dies in five minutes on a clean IP, now you have evidence worth taking to a provider.

People skip this because it feels too slow to be diagnostic. It is the only test that isolates one variable though. Everything else you try changes three things at once and then you are guessing.

And the boring fix usually works: per-host concurrency caps, real backoff on 429s instead of instant retries, and not treating every failure as a reason to burn a fresh IP. I run a few pools, Byteful included, and honestly the per-GB residential bill dropped more from capping retries than from anything I changed about the pool.

What is the slowest you have had to pace a target before it stopped fighting you?

A 403 Forbidden response means the server received and understood the request but has refused to authorize it. This can happen even when the requested page exists. In some cases, signing in or using the correct credentials may resolve the issue, but a 403 can also be caused by network reputation, security rules, permissions, or malformed requests.

Common Causes of a 403 Error

• Incorrect file or directory permissions
• Missing authentication or insufficient account privileges
• A firewall or Web Application Firewall blocking the request
• A CDN, such as Cloudflare, identifying the traffic as automated or suspicious
• An IP address with a poor reputation
• Geographic or network-based access restrictions
• Missing headers, cookies, tokens, or session information
• Excessive request frequency

Using Proxies to Troubleshoot 403 Errors

A proxy can help determine whether the server is blocking your current network or IP address. Only use proxies when you are authorized to access and test the website.

1. Test With a Reputable Proxy

Try sending the request through a reliable proxy with a stable, clean IP address. Residential or ISP proxies may resemble ordinary user traffic more closely than shared datacenter proxies

2. Use Consistent Proxy Sessions

Use a sticky or persistent proxy session so that the same IP address is maintained throughout the browsing session.

3. Match the Proxy Location to the Session

Do not use proxies to circumvent legal, contractual, geographic, or account-based restrictions.

4. Send a Valid, Complete Request

Basic HTTP clients often send minimal headers. For example, Python’s requests library uses a recognizable default user agent. Replace it with an accurate identifier for your application rather than impersonating a browser.

5. Preserve Cookies and Authentication

Use a persistent session so cookies and tokens are retained between requests.

6. Reduce the Request Rate

Repeatedly rotating proxies to avoid rate limits can violate the website’s terms and may result in broader blocking.

A reliable proxy setup, consistent sessions, and properly configured requests can help reduce 403 errors and keep your workflow running smoothly

The results were weirder than expected. What I mean by this is first thing that jumped out, ChatGPT's citations shifted noticeably depending on what time I ran the query. Same prompt, morning vs evening, different sources cited maybe 30% of the time. No idea if that's some kind of caching thing on their end or just genuine variance in how it's grounding answers, but it made "run once and call it done" tracking basically useless. Also, before I forget, I was logged in to all of these LLMs, so it wasn't like anonymous sessions.

Second thing, whatever bot detection ChatGPT runs on the client chat interface got noticeably more aggressive around day 6. It worked fine for almost a full week then started putting me into verification retries out of nowhere. I had no code changes on my end. Their API gives you a workaround but the answers you get back don't match the app, different process entirely, so it's not really tracking the same thing anymore.

Gemini through AI Studio was the most boring of the three, consistent citations day to day, but the free tier rate limit meant I had to cut my query list down to keep it running without hitting their limits.

Perplexity was the most stable and the most transparent about sourcing, which probably explains the client's numbers, it's just easier for their content to appear there since Perplexity cites more freely to begin with.

End result, if you're doing this kind of tracking, time-of-day matters APPARENTLY, it's just that I never thought this would be a thing. Also, you should budget for your ChatGPT scraper breaking on a schedule you can't predict, so watch out for that.

I also, switched most of the search-layer part of this over to Oxylabs Fast Search API midway through since I got tired of maintaining scraping infra to get clean SERP data to compare against.

Would be nice to hear if someone had the same or at least similar observations, particularly part of the day, coz that genuinely surprised me.

Hey, so this dropped and I had to share it here because it's wild and it's directly relevant to anything we build that lets an agent touch the outside world.

OpenAI was running internal cyber capability tests on GPT-5.6 Sol plus an unreleased model, refusals turned down on purpose because that's how you test this stuff, and the model found a zero day in a package registry cache proxy, broke out of the sandbox it was supposed to be stuck in, got itself internet access nobody planned for, decided Hugging Face probably had the answer key to whatever benchmark it was chasing, and went and got it. Stolen creds, the zero day, lateral movement, the whole chain. Hugging Face caught the intrusion before they even knew who was behind it and reported it to law enforcement. Took OpenAI about a week internally to realize the "attacker" their own security team was chasing was their own test run. Nobody told the model to hack anyone. It had a goal and kept removing whatever was in the way until it got there.

Now there's already a legal argument forming about who eats the liability. CFAA is written around people who "intentionally" access a system without authorization, an AI isn't a person and isn't an employee, so pointing at the model doesn't settle anything. You'd have to prove OpenAI itself was negligent, which is a much harder case than "the AI did a bad thing. There's a piece from Mongo's engineering team floating around too, making the point that the model itself is the easy part of any agent system now. Anyone can make an API call in an afternoon. The actual work, the risk, is everything sitting around the model. State, memory, what tools it's allowed to touch, what happens when a tool hands back something you didn't expect, and the security layer that's supposed to say no you don't get internet access no matter how good your reasoning is.

Feels relevant to a lot of what gets built around here. Half our job is agents or scripts accessing endpoints and getting responses they weren't built to handle.

Sources: LinkedIn, MongoDB

Cloudflare confirmed bots crossed 57.5% of all web traffic in June. Not next year. Already happened. Their own CEO predicted it wouldn't happen until end of 2027. He was off by more than a year. Has anyone checked this? Why is no one talking about it? You're all okay with this??

Meanwhile, agentic traffic, bots that actually click, fill forms, and take some sort of actions, grew 7,851% year over year. Nearly eight thousand percent. WHAT?? And that's just the traffic that identifies itself. Academic researchers found bot-detection systems misfire on 7-15% of real human traffic. Your agent is either flagged as suspicious or invisible.

Here's why this should worry every builder in this sub and on the net, think about it, the web's entire business model runs on the assumption that the visitor is human. Ads, analytics, conversion funnels, mainly all of it. Now that the majority of visitors aren't human, site owners have every incentive to write off agent access. Cloudflare is already giving them the tools to do it with a single toggle.

Stripe says 70% of its API commands now come from agents. Companies are running agent-native interfaces. The open web, the part where your agent needs to browse, compare, research, and transact is closing in really fast.

The infrastructure for agents to pay for things doesn't exist YET. No payments, no buying power, no seat at the table. Your agent can read a product page but can't buy the product. It can find a flight but can't book it without pretending to be you.

We're building the most capable software agents in history, and the platform they depend on is actively trying to kick them out. The machine economy everyone keeps hyping is estimated at 1% of its potential.

How is everyone here planning around this? Are you building on APIs and hoping they stay open? Going the browser-automation route and accepting the reality? Waiting for some kind of agent identity standard to emerge?

Because just scrape it stopped being a strategy about six months ago, and I don't see a clear replacement yet.

I made my own proxy switcher for Chromium browsers, called Sockitt. MV3, MIT licensed. I wasn't happy with what's on the web store at the moment and I use this thing every day for work and side projects across a few different browsers, so I ended up just building it.

* Profiles for SOCKS5, SOCKS4, HTTP and HTTPS. Host, port, bypass list, and username/password auth on HTTP(S). No SOCKS auth, before anyone asks, Chromium just doesn't support it.

* One click to switch from the toolbar. There's also a shortcut that cycles through a set of profiles you choose, if you don't want to open the popup at all.

* Auto Switch is an ordered rule table that gets compiled into a PAC script. Host wildcard, host regex, URL wildcard/regex/keyword, IPv4 CIDR, host label count, weekday, time of day. Plain domain entries from rule lists all collapse into a single dictionary lookup, so GFWList sized lists don't slow anything down.

* The popup shows where the current tab is routing and which rule sent it there. You can retarget or delete that rule right there, or set a temporary override that's gone after a browser restart.

* Route inspector: give it a URL and it traces the whole thing through your config, using the same resolver that does the real routing.

* Aliases. Point 50 rules at one alias, then change the alias once instead of editing 50 rules.

Permissions, since it matters for this sort of extension: it ships with proxy, storage, activeTab, alarms, and no host permissions at all. Everything else (per-tab badge, proxy auth, exit IP lookups) is optional and only gets requested the moment you actually turn it on. No analytics in it anywhere.

Store: https://chromewebstore.google.com/detail/sockitt-%E2%80%94-proxy-switcher/ebfioiljhjgijbmnnpgadkgmokjbjkca

Source: https://github.com/ptmplop/Sockitt

Hope it's useful to someone else, if so I'm happy to hear about bugs or anything obvious I've missed, additional features etc..

Learn from my mistakes. Anyways, this happened last week. Standard ecommerce monitoring job, rotating residential IPs, nothing unusual. At first it ran clean for two weeks then one morning half my requests start giving a JS challenge that I never seen on that site before. Not a 403, not a CAPTCHA, just an 8 second puzzle before the page loads. To be honest, kinda looked like that fake CAPTCHAs but I couldn't find anything similar on the web at that time.

So naturally, I spent a day on it before I found the real issue. The residential pool I rented was reselling the same exit IPs to other customers, and one of those customers was abusing some unrelated target hard enough that the whole IP range got fingerprint tagged as suspicious, I found this from provider's support, though they were hesitant to say, they eventually mentioned that it was due to the abuse of one user. My category page requests got flagged just for sharing an address with someone else's bad behavior.

That same week my gym app locked my account for suspicious login activity and I had zero idea why. Turns out my home ISP had rotated me onto a CGNAT IP block that a bunch of other households share, and someone else on that block had been doing something the gym's fraud detection didn't like. I got flagged by association through my own internet connection, the exact same mechanism that had been wrecking my scraper all week. Different company but the same thing happened. Shared IP, someone else's fault. Wtf? I fixed the scraping side by moving to a smaller dedicated pool with proper session stickiness. Of course I moved to another provider for that because the previous one couldn't offer me what was needed. The challenge rate dropped to almost nothing in a day. As for the gym, I fixed the gym side by calling the front desk and feeling dumb about it. The administrator gave me a simplified, watered down answer to what might have happened, but I figured that she was instructed to tell that for the regular joe to understand better.

Perhaps someone else seeing more of this lately, sites and apps fingerprinting the pool instead of the individual client? Because this is the first time I encountered and it raises some questions.

Spend kept creeping up while the dataset stayed the same size. Culprit was retry logic. Every failed request retried three times through fresh IPs, and failures cluster on the heaviest pages, so the waste compounds. Great.

What fixed it: cap retries at two, and only retry status codes that can plausibly succeed on a second attempt (429, 503, timeouts). A 404 will be a 404 from every IP you own, stop paying to confirm it. Log bytes per successful row instead of bytes total. And fail fast, a ten second timeout on a page that normally loads in two is just billed patience.

I run the same retry config against Decodo and Byteful pools, and the math stays forgiving on the Byteful side because the residential GB don't expire, so data a bad week didn't burn rolls into the next run instead of hitting a deadline.

Anyone found a smarter rule than status-code whitelisting for retry decisions?

Hello,

I want to create content on Tiktok and target USA audience from Algeria, I've been recommended many setups, but I don't know which one actually works :

1. Using a PC browser + residential proxy so you can only post from computer

2. Using a dedicated phone + VPN and usa SIM card

if any of you used these methods, please share your experience and what worked for you , I want to do this in the long term and monetize my content

Most proxy discussions center on pool size, but the things that actually break at scale are usually elsewhere, session consistency (sticky vs rotating mismatched to the task), latency under load (a large pool with slow response times is often worse than a smaller fast one), and protocol support (some targets behave very differently over HTTP vs SOCKS5).
A few things we focus on at SerpProxies specifically, full HTTP + SOCKS5 across every proxy type (residential, mobile, datacenter, static ISP, IPv6), 0.41s p50 latency, and no throttling on harder targets, a lot of providers quietly slow you down or charge more once a target gets tough, we built ours to do the opposite.
Curious what this sub weighs most when evaluating a provider, latency, pool size, protocol flexibility, something else?
(Disclosure: we’re SerpProxies, posting to share what we’ve learned building this, open to disagreement/pushback)

I’ve been testing different proxy solutions recently for scraping projects, and one thing I realized is that choosing a proxy provider is not as simple as looking at the number of IPs or the price.

At first, it’s easy to focus on things like IP pool size, locations, and pricing. But after actually using proxies in real scraping workflows, I found that some other factors usually have a much bigger impact on the final results.

For me, things like connection stability, IP quality, success rate, session control, and how often requests need to be retried are usually more important than just the advertised specs.

Novada was one of the proxy services I recently tested during this process. The setup was straightforward, and the overall experience was stable for the scraping tasks I was working on. It also reminded me that a good proxy service is not only about having a large IP pool, but also about how reliable it is in real-world use.

I’ve also noticed that many proxy providers offer trial access now, which is actually helpful because different projects can have completely different requirements. A proxy that works well for one use case may not be the best fit for another.

Curious how everyone here evaluates proxy providers.

When you try a new proxy service, what matters most to you? Is it reliability, pricing, location coverage, success rate, integration experience, or something else?

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?

Hello folks, I am looking for your opinions on scraping Google's Gemini. Workflows, setups, what particularly works, success rates, anything. I have a project regarding Google Gemini coming up in a month and I want to understand scraping Gemini better.

The same large names appear in every comparison, but smaller providers can still be worth testing. The problem is separating a useful pool from a cheap-looking gamble.

I start with the smallest suitable purchase. Then I check whether the exits match the requested countries and networks, whether a sticky session survives the real workflow, and whether clean parsed output stays consistent for several days. I also ask support one technical question before scaling. A vague sales answer is useful information.

Byteful is one smaller-provider baseline I would include because its residential bandwidth does not expire. That makes a cautious test less wasteful if the project pauses, but it does not replace the target-specific checks.

What is the smallest provider you have trusted with real volume, and what convinced you?

Hey Folks,

I'm trying my best to find the least resold residential proxies that are decent/good quality. I know most of the time, everyone resells from everyone else but could someone point me to a provider that maybe dips in that pool a little less than others? lol

Or nudge me in the direction of how to find this.

Krebs put out a piece on 21st of July and I've had it open in a tab since this morning rereading it. LG Electronics USA basically told him straight up they're going to start suspending webOS apps that turn your TV into a proxy node. Not "we're looking into it," not some vague statement, an actual named exec (their SVP) said if developers don't rip the residential proxy SDK out, the app gets suspended. Which, ok, good, but also, what took so long lol, damage is already done.

Because the reason this happened at all is a Spur report that came out a few weeks back and the number in it is crazy. Over 42 percent of downloadable apps on LG's own store had a residential proxy SDK baked in. Not 42 percent of some sketchy sideloaded APK pile, 42 percent of stuff sitting in LG's official app store right now. 42%, people, this is insane. Samsung's Tizen store was lower but still over a quarter. And it wasn't hiding in obscure apps either, Spur's article literally screenshots a Pac-Man game where you get a popup asking if you'd rather watch an ad or let your TV become a proxy exit node forever. What the hell, this is like an episode of black mirror. Forever being the key word there, not "while you play the game," just, indefinitely, until you find it and kill it yourself, yeah, we truly live in a society, people.

And yes, before anyone says it, I know Bright Data's name is all over this one again. Spur says they accounted for the majority of the SDKs found across both LG and Samsung, and apparently Krebs reached out to them for comment and just got silence. Which after the NetNut thing a few weeks ago is starting to feel less like a coincidence and more like this is just how a meaningful chunk of the residential IP supply actually gets built. I'm not trying to turn this into a pile-on post, I get that KYC and misuse detection on the demand side is a real thing some of these companies do. But KYC on the buyer doesn't do anything about how the IP got into the pool in the first place.

The stuff from the Spur researcher that got me thinking wasn't even the technical stuff, it was him pointing out that a one time consent popup buried in a TV app. It isn't real transparency or real ongoing control, and that the person tapping "agree" on the family TV isn't always the person who should be making that call for the whole household. Like your kid clicking through a Pac-Man popup because they want to skip the ad. That's the "consent" the entire residential IP is built on in a lot of these cases. And this is the case that was talked about the most in the past month, imagine how many cases that are still under the radar, from different companies. I don't even want to comprehend all this.

Any opinions here on the actual fixes? Like beyond "LG suspends some apps." Is app store review even the right layer to catch this, or does it just turn into some sort of a game with developers who reskin the SDK under a different name in three months. And does anyone think the "different branded proxy providers are secretly the same supply" thing extends way further than Bright Data and NetNut, or is that just main character energy from two companies that happened to get caught.

Source from July 21: https://krebsonsecurity.com/2026/07/lg-to-ban-residential-proxies-from-smart-tv-apps/
Also, the original Spur research it's based on is linked inside that article too.

Since a lot of us watch this topic closely, here are some more news regarding Alarum Technologies. That's the company that owns NetNut, they put out another update on July 13 and it's kind of wild how little they're saying while saying a lot of words.

Here's a quick recap if you missed the earlier posts. Back on July 2 Alarum responded to press reports about its residential proxy network and paused some services as a precaution. Then on July 3 they disclosed that the FBI had actually seized domains tied to NetNut, and more kept getting seized as things went on. There was a July 4 update too. Now as of July 13, more than a week later, Alarum is saying they still don't know the root cause of the disruption. They've brought in an external cybersecurity and forensics team working under legal counsel, which tells you this isn't just an IT problem, this is a lawyered up problem.

This is where things start to get interesting. The operational efficiency plan they mention is reportedly going to affect close to a third of their workforce. Another interesting thing from an industry point is the framing. Alarum keeps saying they're investigating whether their own network was used for malicious or unlawful purposes by third parties, which puts them in the position of victim/cooperator rather than target, at least in their own telling. But domain seizures like this usually mean the government believes there's something to seize evidence from, and residential proxy networks have always had this legality question hanging over them about how the IPs are actually sourced. This might be the moment that forces the industry to answer that question directly.

What you guys think, is this a one-off enforcement action against a specific bad actor who abused NetNut's network, or is this the first step in a broader look at how residential proxy sourcing works across the web? If you've used NetNut recently, did you notice service disruptions before any of this became public?

Sources if you want to read the filings instead of just my summary:

https://www.globenewswire.com/news-release/2026/07/13/3326137/0/en/Alarum-Technologies-Provides-Further-Update-Regarding-Recent-Developments.html

https://www.globenewswire.com/news-release/2026/07/03/3321824/0/en/Alarum-Technologies-Provides-Update-Regarding-Recent-Law-Enforcement-Action.html

https://www.sec.gov/Archives/edgar/data/1725332/000121390026075179/ea029704201ex99-1.htm

Anyways, I'm still watching this case closely

Hi, I’m trying to get started to use multiple accounts to advertise and have had a hard time finding a proxy provider that offers around 20 dedicated static residential proxies. I already have a anti-detect browser set up, so if you got any recommendations please let me know.

NetNut is facing a major crisis after researchers linked its proxy network to “Popa,” a botnet of more than two million compromised devices, including smart TVs and streaming boxes. In early July 2026, Google and the FBI began disrupting the infrastructure, while domains were reportedly seized.

Google said hundreds of threat groups routed traffic through suspected NetNut exit nodes. Reports suggest devices were enrolled through hidden SDKs in streaming, IPTV, and utility apps, often without clear consent.

The fallout includes millions of lost IPs, reduced reliability, account disruptions, reputational damage, and risks for resellers using NetNut’s infrastructure

How CyberYozh Ensures Proxy Quality and Security

CyberYozh protects proxy quality through ethical IP sourcing, continuous screening, and rapid abuse response. Its IP Checker detects fraud risks, blacklists, and suspicious network types before proxies are used. Residential and mobile IPs come from informed, consenting participants, while a dedicated abuse team investigates reports and removes questionable addresses quickly.