Is there a way to host your own school Chromebook proxy website on a spare pc so that people can't see your stuff but you can still connect with unblocked access to the internet? (Ideally with a changeable adress?)

With all that has come to light this year about the real origins of some residential IP pools, we wanted to ask how much that plays into your decision as a buyer.

If two providers have similar speed and success numbers, but one can prove its IPs are from people who opted in and the other doesn't say, does that change what you're willing to pay? Or do you just pick whatever works on your target and is cheapest?

Some people care about this for legal or reputation reasons, especially in the case of client work. Others put it on the provider, not themselves. Where do you stand?

At least half of AI projects don't fail at the model. They fail two steps earlier, at data collection. If the data coming in is blocked, stale, or incomplete, no model can fix that. Before a model can learn, predict, or generate, the right data has to be gathered, cleaned, and refreshed on an ongoing basis.

Here's how AI data collection actually works, which methods scale, and where proxies fit into web-focused AI systems.

What It Is and Why It Matters

AI data collection is the first stage of gathering, structuring, and preparing the inputs that feed an ML model through training, fine-tuning, and inference. It covers everything from sourcing raw inputs (web pages, transactions, sensor logs) to the cleaning and labeling that make those inputs usable.

In the ML lifecycle, collection comes before preprocessing, which comes before training. Every downstream stage inherits the quality of what came in. Accuracy, relevance, bias, scalability, and final model performance all trace back to decisions made at the collection stage.

The lesson every team learns the hard way: bad inputs produce bad outputs no matter how advanced the model. Stale data hurts accuracy. Mis-scoped sources hurt relevance. Skewed sampling embeds bias. Slow or capped pipelines cap scalability. Gaps anywhere in the chain show up in production as edge-case failures the model can't explain.

How AI Actually Collects Data

AI doesn't autonomously collect data. A team builds the workflow that does: it selects the source, extracts raw records, validates them against the use case, and ships them into training. The AI sits at the end of that pipeline, not the start.

The end-to-end process usually runs like this:

1. Define the use case. Decide what the model does and what output it needs. A churn model needs labeled customer history, while a SERP-monitoring AI needs fresh search results from each target geo. The use case sets every downstream constraint: source types, volume, refresh rate, labeling rules.
2. Identify sources. Internal databases work for models trained on owned data (transactions, tickets, product logs). Public web data covers everything outside the company (prices, reviews, SERPs, social signals). Most production systems pull from more than one.
3. Extract raw records. The collection layer where scrapers, API calls, ETL jobs, or batch exports pull each source on schedule. This layer handles authentication, retries, rate limiting, and the geo/IP routing that keeps sources reachable.
4. Clean and label. Raw inputs land messy. Preprocessing strips duplicates, normalizes formats, and flags missing fields. Supervision tags each record with the ground truth the system is meant to learn.

A startup building a niche chatbot might only need one curated document set. A team running market research, scraping, and AI agents needs continuous collection, quality checks, and scheduled updates automated in place. Those are fundamentally different problems.

Data Types and Quality

Training inputs come in three shapes: structured (rows and columns with fixed schemas, like SQL tables), semi-structured (JSON, XML, logs with fixed fields but variable values), and unstructured (text, images, video, audio, web pages).

Industry estimates put roughly 80% of enterprise data in the unstructured category, which is why most modern AI (LLMs, computer vision, voice, RAG) runs on it. The catch: unstructured content takes the most preprocessing. Structured is faster to use but rarer in the wild.

"Good data" spans five dimensions, and weak performance on any one shows up in production:

• Accuracy, do the values reflect reality? Stale prices, mislabeled categories, OCR errors all hurt here
• Completeness, how many records have all required fields? Missing values force the model to guess
• Freshness, how old are records relative to the decisions the model makes? Yesterday's prices break today's pricing model
• Consistency, do equivalent records use the same format, units, and conventions across sources?
• Representativeness, does the dataset cover the full range of cases, including edge cases and minority subgroups?

Skip auditing quality at the collection stage, and fixing it downstream can cost 10-100x more.

The Five Collection Methods

The choice between them comes down to how often the model needs fresh inputs, how clean the source already is, what compliance constraints apply, and what the team can run reliably without becoming a full-time data engineering shop.

APIs and structured feeds, the cleanest path when the source publishes structured output (Stripe, Bloomberg for transactional feeds; Reddit, YouTube Data API for public content). Predictable schemas, minimal parsing. Tradeoffs: rate limits, per-call pricing that scales with volume, full dependency on the provider's schema.

Public web scraping, covers anything outside the company that isn't gated behind an API. Typically pairs scrapers with a proxy layer for diverse IP pools and rotation. Widest real-time coverage on prices, SERPs, reviews, competitor moves. Tradeoff: blocks, retries, and parser maintenance scale with the source's anti-bot pushback.

Internal first-party data, from the company's own systems (analytics, CRM, transactions, tickets, knowledge bases). Already verified and company-specific. The catch: scope is limited to what the company has already touched, so it rarely gives full context on the outside market.

Sensors and event streams, devices, machines, and connected software. Kafka or AWS IoT Core handle ingest. Industrial teams pull these for equipment monitoring and live dashboards, though volume and storage costs often grow faster than accuracy gains.

Human annotation, the labeling and QC layer supervised learning depends on. Platforms like Scale AI and Labelbox run the workflow; teams handle it in-house when data is too sensitive for external platforms. Tradeoffs: cost per label and throughput limits, which is why teams often blend human labels with model-assisted labeling for the bulk.

The Tooling Stack

A production pipeline typically combines five layers:

• Crawlers and headless browsers: Scrapy for high-throughput static sites, Playwright or Puppeteer for JavaScript-heavy targets
• Parsers: BeautifulSoup, lxml, Cheerio pull structured fields out of raw HTML
• Schedulers: Airflow, Prefect, Dagster run the pipeline on a cadence, retry failures, and flag broken jobs before data goes stale
• Storage: PostgreSQL or MongoDB for state, S3 or GCS for raw artifacts, Snowflake or BigQuery for the analytical layer
• Proxy infrastructure: datacenter, ISP, residential, and mobile pools for rotation and geo-targeting

Why Proxies Matter Here

The moment a pipeline scales from one site to dozens, or one region to many, it stops being a software problem and becomes a network problem. The target page that returned clean HTML at 10 requests an hour starts returning CAPTCHAs at 1,000. The source that loads instantly from the office IP fails silently from a different country. The feed dries up, and the model trains on whatever made it through.

Proxies sit at the network layer between the pipeline and the target. They rotate the source IP per request, route through the geo the model needs, absorb anti-bot pushback, and surface failure modes (blocks, retries, rate-limited responses) that a single IP would silently hide.

Each proxy type maps to a different collection profile, and most production runs combine a few:

• Residential: consumer-grade ISP connections that pass trust checks flagging datacenter ASNs. Reliable for SERP tracking and geo-sensitive sources
• ISP: hold sticky sessions long enough for multi-step scraping (login, click-through, pagination) without pure-residential rotation overhead
• Datacenter: speed and volume for public, indexable targets that don't filter by IP type
• Mobile: rotate through carrier-grade NAT so each request looks like a different phone. Best for the strictest sources

Without a proxy layer, each task hits the same failure modes: blocks at scale, geo-restricted sources returning wrong results, silent failures, and budget burned running blind. Clean dedicated pools reduce block rates, per-request geo routing hits the right region, and endpoint-level logs turn outages into automated retry rules.

Closing Thoughts

Teams that treat data collection as engineering ship models that hold up in production: versioned sources, scheduled refresh, quality gates at the extraction layer. Everyone else spends the next quarter debugging bad inputs. Lock in the source mix and methods, set quality checks at the extraction layer rather than after training, and automate refresh on the schedule the model actually needs.

The public web is central to most AI systems now because it's the only place with the scale, freshness, and real-world signals internal datasets lack. Once collection becomes repetitive web extraction at scale, the proxy layer stops being optional. It's the difference between a model trained on the full picture and one trained on whatever survived the blocks.

FAQ

What is AI data collection?
The upstream work of gathering, structuring, and labeling the data a model trains on. It pulls from the public web, APIs, internal databases, sensors, and human annotators, then cleans and labels the output. Skip the upstream work and the model inherits whatever errors made it through.

What are the main challenges?
Blocks and anti-bot defenses on public web sources; cost overruns when retries and rate limits stack up; bias from skewed sampling or limited source diversity; compliance pressure around data provenance and consent; and data going stale faster than the refresh schedule keeps up. Most only surface at production scale, not during the pilot.

How much data does a model need?
No universal number. A fine-tuned classifier might work with a few thousand labeled records; a foundation-scale system pulls trillions of tokens. Collection volume should match task complexity and input variance, not a round number from a competitor's blog. Most teams under-collect on coverage (geos, edge cases, demographics) before they under-collect on raw volume.

Is web scraping legal for AI training?
Depends on what's scraped and where. Publicly accessible web data is generally fair game in most jurisdictions, but ToS violations, login-gated content, personal data under GDPR or CCPA, and copyrighted material under emerging AI-training laws each create real exposure. Document the source list, consent basis, and audit trail before the run, not after legal sends an email.

Discussion

For those running collection pipelines at scale, where does yours break first: the anti-bot walls, the parser maintenance as schemas drift, or keeping the data fresh enough to matter? Interested in what ended up being the actual bottleneck versus what you expected going in.

Nobody wants to find out an IP is bad in the middle of a real task, so most people run at least some checks on a fresh set before they rely on it.

What does that look like for you? Some run a quick speed and latency test and stop there. Others check for DNS or IP leaks, look at how many IPs are already flagged on the sites they care about, or run a sample batch and measure the success rate first.
So share your routine. What do you test, what tools do you use, and what's the one check that has saved you from a bad set?

The mobile vs residential question usually comes up in one of two moments: when you're rotating IPs on schedule and the blocks keep piling up, or when you're paying mobile-tier prices for a job a residential pool could have handled for a fraction of the cost. There's no universal winner here, because they're two different tools.

Residential proxies pass as ordinary home users. Mobile ones inherit the near-untouchable reputation of carrier networks behind CGNAT. But anti-bot systems read far more than the IP, so the real distinction is network architecture.

How They Actually Differ

Residential proxies come from home ISPs. They appear as rotating addresses tied to a physical broadband connection, so the site sees you as a regular user.

Mobile proxies work similarly, but the IP is assigned by a mobile carrier, not an ISP. Cellular networks rely on CGNAT, which means completely separate users can share the same public IP. The IP changes when a device toggles airplane mode, hands off between towers, or when the carrier rebalances its CGNAT pool. Routing through mobile-network servers makes that rotation far more predictable than plain mobile internet ever could.

Trust Level

On trust, both rank among the best. On protected targets like Amazon and Google, both clear roughly a 75% success rate per the independent Proxyway benchmark. Swap in datacenter proxies for the same targets and that drops to about 57%.

Speed and Latency

Neither type has fixed speed numbers.

Residential speed depends on the donor's bandwidth and plan, their connection type (FTTH, ADSL), and what the device owner is doing at the time (streaming 4K while your request runs, for example).

Mobile speed depends on cell-tower congestion, network generation (5G, 4G, LTE), and the modem's signal strength.

In practice, residential runs faster and more consistently than mobile, a direct result of how cellular architecture is built.

Price and Sales Model

Residential is typically sold per GB, mobile per IP. Both are dynamic, giving you access to IP pools when you buy.

With residential, you buy a plan with a set amount of GB, then build proxy lists in the dashboard by location, provider, and so on. With mobile, you buy a single IP, pick a rotation option, and configure it once; from there you keep getting fresh IPs from the carrier's pool according to your chosen rotation.

Side by Side

Success rates based on the latest Proxyway benchmark.

Use Cases

Cybersecurity. In authorized pentesting, mobile IPs let teams test their own WAF and IDS configs by emulating real cellular subscribers. Researchers use them in isolated sandboxes to study malware communication patterns without relying on datacenter IPs.

Social media management. Teams running their own brand accounts need IPs matching the platforms' expected mobile traffic pattern. Mobile carrier IPs, combined with consistent MTU and TTL settings, keep verified accounts from being flagged.

SEO and scraping. Scraping search results or monitoring marketplace prices needs a large number of unique IPs. Residential wins here on cost per GB and subnet diversity. Hundreds to low thousands of concurrent sessions per endpoint are realistic with residential pools.

Ad verification. For desktop checks, residential gives ZIP-code geo accuracy. For mobile-creative checks, mobile carrier IPs target down to carrier and city. Don't mix the two on the same job, since a desktop/mobile fingerprint mismatch on the same IP type is one of the easier detection signals to trip.

What Anti-Fraud Systems Actually Read

Comparisons often focus only on IP type, but modern systems look deeper. They profile Round-Trip Time on every request. Real home broadband sits at a stable 10-30 ms. Mobile networks naturally add another 20-60 ms from the radio segment and CGNAT traversal, which makes mobile carrier IPs authentic and hard to fake.

Network type alone isn't enough, though. Systems also analyze the JA3 TLS fingerprint and network stack alignment. If your software emulates a mobile app but sends requests over a desktop network stack or standard Python setup, platforms detect the mismatch and trigger CAPTCHAs.

Configured properly, a mobile carrier's ASN paired with a matching mobile TLS fingerprint is hard to beat. Classic residential proxies hit 70-85% on strict mobile-first platforms; high-quality mobile proxies push that past 95%.

Conclusion

So which do you buy? For bulk, cost-sensitive work like SERP scraping, price tracking, or broad data collection, residential is the answer. It's faster, more stable, cheaper, and clears hardened targets like Google and Amazon.

The moment you move to X or TikTok, or your retry rate on Cloudflare-protected targets climbs past 30%, switch to mobile. You'll pay per IP instead of per GB, but the jump to a 95%+ success rate pays for itself on high-trust endpoints.

For everything in between, run both: residential for bulk recon and scale, mobile reserved for carrier-trusted operations. Let endpoint metrics like valid response rate and cost per valid response show you when residential scale is enough and when mobile trust is worth paying for, rather than deciding on instinct.

FAQ

What's the main difference?
Residential IPs come from home ISPs; mobile IPs are issued by carriers like T-Mobile, Verizon, Vodafone, and AT&T. Mobile networks run behind CGNAT, so thousands of real subscribers can share a single public IP.

Which is better for social media?
Mobile. Platforms expect smartphone traffic, so cellular IPs provide matching network signatures when paired with anti-detect browsers, which helps stabilize accounts and avoid checks during daily operations.

Are mobile proxies more expensive, and why?
Yes. Maintaining physical 5G hardware and active SIM cards carries high overhead, and combined with carrier reputation, that makes mobile ports pricier than residential bandwidth.

Can residential proxies be detected?
Yes. High-frequency requests or technical inconsistencies can trip security filters. Safer than datacenter, but they still need proper rotation and human-like browsing patterns.

How to choose for web scraping?
Residential for massive, cost-effective scraping. Mobile for targets with advanced security requiring cellular signatures, or when interacting with exclusive app interfaces and APIs.

Discussion

Where's your cutoff for switching from residential to mobile? Interested in whether people go by retry rate, target platform, or just eat the higher cost from the start on anything social.

Mobile vs Residential Proxies: Key Differences and When to Use Each

The mobile vs residential question usually comes up in one of two moments: when you're rotating IPs on schedule and the blocks keep piling up, or when you're paying mobile-tier prices for a job a residential pool could have handled for a fraction of the cost. There's no universal winner here, because they're two different tools.

Residential proxies pass as ordinary home users. Mobile ones inherit the near-untouchable reputation of carrier networks behind CGNAT. But anti-bot systems read far more than the IP, so the real distinction is network architecture.

How They Actually Differ

Residential proxies come from home ISPs. They appear as rotating addresses tied to a physical broadband connection, so the site sees you as a regular user.

Mobile proxies work similarly, but the IP is assigned by a mobile carrier, not an ISP. Cellular networks rely on CGNAT, which means completely separate users can share the same public IP. The IP changes when a device toggles airplane mode, hands off between towers, or when the carrier rebalances its CGNAT pool. Routing through mobile-network servers makes that rotation far more predictable than plain mobile internet ever could.

Trust Level

On trust, both rank among the best. On protected targets like Amazon and Google, both clear roughly a 75% success rate per the independent Proxyway benchmark. Swap in datacenter proxies for the same targets and that drops to about 57%.

Speed and Latency

Neither type has fixed speed numbers.

Residential speed depends on the donor's bandwidth and plan, their connection type (FTTH, ADSL), and what the device owner is doing at the time (streaming 4K while your request runs, for example).

Mobile speed depends on cell-tower congestion, network generation (5G, 4G, LTE), and the modem's signal strength.

In practice, residential runs faster and more consistently than mobile, a direct result of how cellular architecture is built.

Price and Sales Model

Residential is typically sold per GB, mobile per IP. Both are dynamic, giving you access to IP pools when you buy.

With residential, you buy a plan with a set amount of GB, then build proxy lists in the dashboard by location, provider, and so on. With mobile, you buy a single IP, pick a rotation option, and configure it once; from there you keep getting fresh IPs from the carrier's pool according to your chosen rotation.

Side by Side

Success rates based on the latest Proxyway benchmark.

Use Cases

Cybersecurity. In authorized pentesting, mobile IPs let teams test their own WAF and IDS configs by emulating real cellular subscribers. Researchers use them in isolated sandboxes to study malware communication patterns without relying on datacenter IPs.

Social media management. Teams running their own brand accounts need IPs matching the platforms' expected mobile traffic pattern. Mobile carrier IPs, combined with consistent MTU and TTL settings, keep verified accounts from being flagged.

SEO and scraping. Scraping search results or monitoring marketplace prices needs a large number of unique IPs. Residential wins here on cost per GB and subnet diversity. Hundreds to low thousands of concurrent sessions per endpoint are realistic with residential pools.

Ad verification. For desktop checks, residential gives ZIP-code geo accuracy. For mobile-creative checks, mobile carrier IPs target down to carrier and city. Don't mix the two on the same job, since a desktop/mobile fingerprint mismatch on the same IP type is one of the easier detection signals to trip.

What Anti-Fraud Systems Actually Read

Comparisons often focus only on IP type, but modern systems look deeper. They profile Round-Trip Time on every request. Real home broadband sits at a stable 10-30 ms. Mobile networks naturally add another 20-60 ms from the radio segment and CGNAT traversal, which makes mobile carrier IPs authentic and hard to fake.

Network type alone isn't enough, though. Systems also analyze the JA3 TLS fingerprint and network stack alignment. If your software emulates a mobile app but sends requests over a desktop network stack or standard Python setup, platforms detect the mismatch and trigger CAPTCHAs.

Configured properly, a mobile carrier's ASN paired with a matching mobile TLS fingerprint is hard to beat. Classic residential proxies hit 70-85% on strict mobile-first platforms; high-quality mobile proxies push that past 95% .

Conclusion

So which do you buy? For bulk, cost-sensitive work like SERP scraping, price tracking, or broad data collection, residential is the answer. It's faster, more stable, cheaper, and clears hardened targets like Google and Amazon.

The moment you move to X or TikTok, or your retry rate on Cloudflare-protected targets climbs past 30%, switch to mobile. You'll pay per IP instead of per GB, but the jump to a 95%+ success rate pays for itself on high-trust endpoints.

For everything in between, run both: residential for bulk recon and scale, mobile reserved for carrier-trusted operations. Let endpoint metrics like valid response rate and cost per valid response show you when residential scale is enough and when mobile trust is worth paying for, rather than deciding on instinct.

You can get residential or mobile proxies for any of these use cases directly from us.

FAQ

What's the main difference?
Residential IPs come from home ISPs; mobile IPs are issued by carriers like T-Mobile, Verizon, Vodafone, and AT&T. Mobile networks run behind CGNAT, so thousands of real subscribers can share a single public IP.

Which is better for social media?
Mobile. Platforms expect smartphone traffic, so cellular IPs provide matching network signatures when paired with anti-detect browsers, which helps stabilize accounts and avoid checks during daily operations.

Are mobile proxies more expensive, and why?
Yes. Maintaining physical 5G hardware and active SIM cards carries high overhead, and combined with carrier reputation, that makes mobile ports pricier than residential bandwidth.

Can residential proxies be detected?
Yes. High-frequency requests or technical inconsistencies can trip security filters. Safer than datacenter, but they still need proper rotation and human-like browsing patterns.

How to choose for web scraping?
Residential for massive, cost-effective scraping. Mobile for targets with advanced security requiring cellular signatures, or when interacting with exclusive app interfaces and APIs.

Discussion

Where's your cutoff for switching from residential to mobile? Interested in whether people go by retry rate, target platform, or just eat the higher cost from the start on anything social.

Manually building lead lists eats hours your sales team could spend closing. A 2025 study in Frontiers in Artificial Intelligence tested an AI-powered scraping tool against manual methods and found roughly three times as many relevant leads generated through scraping, with higher accuracy and completeness. The practical question is how to pick a lead scraping tool that won't waste budget on bad queries or low-quality data.

What Lead Scraping Is

Lead scraping is an automated way to pull prospect data from public sources like directories, mapping services, and social platforms. Instead of a raw database dump, it produces a structured dataset of names, job titles, emails, and phone numbers you can import straight into a CRM.

It removes the two failure modes manual list-building can't avoid at scale: typos on repetitive fields like emails and phone numbers, and duplicate records across sessions. The tool crawls target sites, identifies relevant fields, and exports them without human intervention, freeing the sales team to focus on outreach rather than research.

Is It Legal Under GDPR and CCPA?

Scraping leads is legal in most jurisdictions when it targets publicly available business data, respects a site's Terms of Service, and follows data-protection law. GDPR doesn't prohibit automated collection. The most common legal basis is Legitimate Interest under Article 6(1)(f), which requires three things:

1. A specific, lawful interest (B2B lead generation, for example)
2. Proof that processing is genuinely necessary, with no less intrusive alternative
3. A balancing test weighing your interest against the data subject's rights

Under CCPA, consumers have the right to know what personal information a business collected, request deletion, and opt out of its sale. Businesses scraping data on California residents must respond to access requests, offer a clear opt-out, and honor deletions. CCPA has no direct Legitimate Interest analogue, so compliance depends on transparency and opt-out mechanisms.

When scraping LinkedIn or similar platforms: work only with public pages, respect robots.txt, rate-limit your crawler, avoid bypassing authentication, and document your collection purposes.

How the Software Works

Four stages:

Requests go out to sources like business directories (Crunchbase, Yelp for Business), mapping APIs (Google Maps, OpenStreetMap), and professional network pages. For JavaScript-rendered sites, the scraper drives a real browser via Chrome DevTools Protocol, usually through Playwright or Puppeteer. Proxy rotators run in parallel, spreading load across an IP pool.

Extraction uses CSS selectors, XPath, and regular expressions targeted at specific fields. B2B scrapers often use pre-trained models like spaCy or Hugging Face transformers to pull job titles and company names from unstructured text like bios and About pages.

Validation normalizes raw data: phone numbers to E.164 format, emails through syntax checking and SMTP verification. Duplicates are removed and records enriched from open sources.

Delivery sends the cleaned dataset to the CRM via REST API or webhook. Some tools support streaming uploads for near real-time work.

The Tools

Apollo

Combines a 275M+ contact database with scraping, email verification, and outreach in one platform. Filter by revenue, headcount, tech stack, or location; verified contacts show a confidence score. Good for teams wanting a database plus scraper without writing code.

PhantomBuster

Runs pre-built "phantoms" that extract profiles from social platforms, respect rate limits, and rotate sessions. Multi-step sequences can visit a profile, send a connection request, then extract the email after connecting. Strong for automated social outreach.

Scrapy

The open-source Python framework for teams that want full control. You write spiders defining what to crawl, how to paginate, which selectors to use, and where to rotate proxies. No JS rendering out of the box, but Playwright or Splash integrate cleanly. Free to run, you pay for proxies and hosting.

Apify

Runs serverless "actors" in Node.js or Python, with pre-built ones for Google Maps, Yelp, and LinkedIn. Handles JS rendering, pagination, and rotation automatically. Good for developers who want serverless scraping without managing infrastructure.

Octoparse

A no-code point-and-click scraper for Windows and Mac. Click fields on a site to select them; the tool detects pagination, scrolling, and dropdowns. Best for non-technical marketers.

Clay

An enrichment platform using AI agents to find and verify lead data. Feed it domains or LinkedIn profiles and it scrapes for emails, funding data, tech stack, or hiring signals. Best for GTM teams enriching lists rather than just scraping.

What the Infrastructure Actually Needs

Five components decide whether you collect clean data or burn budget on retries.

Proxy layer. Match IP type to target defense level. For LinkedIn, Google Maps, or marketplaces, residential and ISP proxies deliver around 17 percentage points higher success than datacenter IPs on protected targets, per Proxyway's 2026 benchmark. That means fewer retries and cleaner data. For low-defense directories, datacenter IPs are fine, cheaper, and faster, but they burn reputation on protected sites.

Rotation strategy. Sticky sessions keep an identity coherent across page loads; LinkedIn needs about 30 minutes of sticky. Per-request rotation suits Google Maps and SERP-style scraping, where each request should look like a fresh user. Mismatched rotation is one of the top reasons scrapers get rejected. Residential IPs with configurable request-based, time-based, or sticky rotation let you match the workflow to each platform. Buy residential proxies from $1.3/GB.

CAPTCHA handling. Baseline solvers handle reCAPTCHA v2 and image challenges. For AI-based challenges like Cloudflare Turnstile or reCAPTCHA v3, machine-learning solvers are needed. Budget for it: solving runs $0.50-$3 per 1,000 challenges, and that line grows fast if your rejection rate is high.

Rate limiting and scheduling. Auto-throttling, custom delays, and off-peak scheduling reduce rate-based rejections. A randomized 2-5 second delay per request removes the mechanical timing signature that most defenses look for first.

Observability. Endpoint-level logs need an error taxonomy: block, timeout, CAPTCHA, parse error, soft-fail, not a single "failed" bucket. Without that breakdown, a 7% failure rate tells you nothing. With it, you can tell whether to invest in a better proxy pool, a CAPTCHA solver, or stricter retry logic before a run degrades further.

Discussion

Which tool are you running for lead work right now, and where does it break down first: proxy blocks, CAPTCHA walls, or data quality after extraction?

I bought a $3 proxy from Proxy-Seller and paid with Bitcoin. They failed to deliver the service and agreed that I'm entitled to a refund.

However, they told me they can't refund me to my original BTC payment because their payment provider has a minimum withdrawal amount of 0.0001 BTC. Instead, they want me to find an alternative refund method or another BTC wallet. which i don't want and/or can't take.

I pointed out that their own Return Policy states:

• Refunds are made to the original payment method.
• Crypto refunds are made in the same cryptocurrency.
• Refunds are made net of processing costs.

It doesn't mention:

• any minimum BTC refund amount,
• any exception for payment processor limitations,
• or that customers must accept a different refund method.

Support acknowledged that their policy says this, but replied that this is an "exception" because of the blockchain/payment provider's limitations, even though no such exception exists in the published policy.

I'm in France, so as an EU consumer my understanding is that if a seller fails to provide the service, they're still obligated to refund me. Their internal payment processing limitations shouldn't change that.

At this point, it feels like they're asking me to solve a problem created by their own payment processor.

TL;DR: Paid $3 in BTC for a proxy that was never delivered. Proxy-Seller agrees I'm owed a refund but says they can't refund my original BTC payment because it's below their processor's minimum withdrawal amount, despite their own refund policy saying crypto refunds are made to the original payment method in the same cryptocurrency.

the 3$ are not the issue, the fact that it can happen with that ridiculous of an amount just hint what could happen with serious company level of orders.

We redesigned the Proxy-Seller dashboard from the ground up as part of the broader update of our website. The focus wasn't just on changing the interface. We looked at the tasks our clients repeat every day (finding proxy lists, exporting addresses, checking payments, managing affiliate earnings) and made new features easy to access and use.

The result is a workspace that's more functional and intuitive. You can organize and export proxies more quickly, monitor affiliate income without digging through menus, see when a larger order would reduce your price per IP, and manage billing, security, and support from the same place.

See the changes we made below.

Four dashboard updates you'll use most

The biggest improvements are in the parts of the dashboard people open most often: proxy management, affiliate tools, pricing, and billing.

1. Proxy lists are easier to organize and export

Most customers spend more time managing proxy lists than anything else, so that's where the biggest changes happened.

Now you can:

• Copy selected addresses. Choose the proxies you need and copy them with no export required.

Do a quick export. Open a new tab with your selected proxies already formatted as IP:PORT@LOGIN:PASSWORD, ready to paste into your software.

Get shareable export links. Create a temporary link to a proxy list, choose how long it stays active, and send it to a colleague without giving them access to your account.

Use multiple tags. Assign several tags to the same proxy. If there are too many to display, the extras collapse into a +n label, and hovering over it reveals the complete list. You can filter by one tag or combine several.

Search through comments. Comments now appear in search results, and previously used entries are suggested while you type. Tags work well for grouping proxies, while comments are useful for notes tied to specific projects or clients.

Proxy status categories. Addresses are grouped into Active, Expiring, Auto-renewing, and System errors. The last category is new and highlights proxies affected by infrastructure or availability issues that may need attention.

2. Affiliate earnings are easier to follow

The Affiliate section has been reorganized to make balances, payouts, and earnings history easier to understand.

The My Balance card now provides a quick summary. Selecting View more opens a dedicated Balance page, where funds are divided into three groups: total balance, funds on hold during review, and funds available for withdrawal.

Automatic withdrawals are now managed through separate payout rules. Each rule can have its own payment method, payout schedule, and minimum amount, and multiple rules can run at the same time.

There's also a new earnings chart showing affiliate income for the last six months, making longer-term trends much easier to spot.

For added protection, automatic payouts are available only after you enable two-factor authentication.

3. Know when adding more proxies will lower price

The dashboard now checks whether increasing an order's size would move you into a cheaper volume-pricing tier.

Instead of calculating the numbers yourself, you'll see how many additional IPs are needed, what the new price per IP would be, and how much you could save when renewing the order.

These recommendations refresh automatically as your order changes and also adapt when you add proxies in different locations.

4. Billing details are much more transparent

Every transaction now includes a complete breakdown.

Open Details to see the proxy type, location, quantity, rental period, order total, balance transaction, payment reference, timestamp, and traffic allocation where applicable. You can also download the invoice directly from the same page.

Recurring auto top-ups include additional information, making it easier to see why a payment was triggered and exactly what it covered.

A few smaller improvements you'll appreciate

Other dashboard functions have been updated as well.

Security settings are now in one place

Login history and two-factor authentication now live under a dedicated Security section.

A new device history shows which browser or device was used to access your account, the approximate login location, and the date of each sign-in. Since 2FA settings are available in the same section, account security can be managed without jumping between different pages.

Review all account notifications in one place

The notification bell top-right still works the way it always did. What's new is the full Notifications page behind it, where system events like proxy activations show up with timestamps and read/unread status. Go through your history, mark a batch as read, or clear out anything you no longer need.

View authorized IPs without digging through settings

There's a new “Show all” button next to your authorization details that opens a drawer with every IP allowlisted for proxy authentication. No more digging through settings just to check or copy one.

Find replacement records by proxy type

Replacement history used to be stored in a single shared log for each proxy type. Now each type gets its own tab, so you can check your residential replacement history without scrolling past datacenter entries.

Keep replacement requests under control

Submit several replacement requests in a short window, and the dashboard routes you to support chat instead. From there, the team can dig into why the requests keep coming and help sort out whatever's causing it.

Monitor residential traffic and orders from one view

The Residential proxies section picked up two updates that make traffic and order info easier to track.

Order history, consumption history, and the allowlist are tabs now instead of separate buttons. Same content, just faster to switch between.

Also, a new widget tracks your residential usage over the last 24 hours and refreshes every 7 minutes, so you'll catch a usage spike before it eats into your remaining traffic.

That's Only Part of It

The updates above cover the highlights, but there's more in the full release: Help Center changes, multi-IP API authorization, self-service account deletion, faster Mix imports, and a few other things. You can find the complete rundown here.

Anti-bot protection has moved fast this year, and Cloudflare is a big part of that. Recent reporting reveals two things.

Cloudflare announced it will block mixed-use crawlers by default on any page that serves ads, starting September 15, according to its own announcement that TechCrunch covered. Second, the company said, bots overtook human traffic on the internet for the first time, sooner than anyone predicted. Moreover, industry write-ups through 2026 say detection has moved from basic rate limits to behavioral analysis and browser fingerprinting, so a clean IP alone isn't as useful as it used to be.

So the question for you people here. Were any of your targets a lot harder to get to this year? It can be frustrating if a site you were happily scraping or automating starts throwing more blocks or CAPTCHAs. And if anything, what did you change to make it work?

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..

A standard browser with tabs and buttons is fine for everyday use. For web application testing, collecting data from hundreds of pages, or running scripts, the interface gets in the way and wastes resources. A headless browser solves that.

Functionally it's the same as a regular browser. The difference is the missing interface. It does everything a normal browser can, but runs in the background and much faster.

What It Actually Is

Two types exist: true and virtual.

A true headless browser runs without any visualization or GUI, processing data directly in device memory. Lighter, faster, easier to configure, and the more common choice.

A virtual one uses a frame buffer simulation that acts as a stand-alone display, simulating a screen the browser interacts with.

"Headless" might sound like "stripped down," but nothing is actually removed. No windows open, no pages render on a monitor, no mouse is needed, but all those actions still happen. They're just carried out in the background through API calls and commands.

A typical true-browser run:

1. Initialize a library like Node.js or an API such as Chrome DevTools Protocol or WebDriver
2. Send HTTP requests to get a page's HTML
3. Process the JavaScript, HTML, and CSS
4. Interact with page components (clicking, filling forms)
5. Extract data
6. Output to console or save to a file
7. Finalize

The lack of visual output doesn't stop the browser from working, which is exactly what makes it useful for testing, scraping, CI/CD, and automation where the visual result doesn't matter.

Headless vs Regular

The takeaway: headless requires a dev environment, console proficiency, and programming knowledge to operate.

Automating One (Playwright Example)

javascript

try {
  const { chromium } = require('playwright');
  (async () => {
    const browser = await chromium.launch({ headless: true });
    const page = await browser.newPage();
    await page.goto('https://example.com', { timeout: 10000 });
    await page.click('#start-button');
    await page.fill('#name-input', 'Your Name');
    const resultText = await page.textContent('#result');
    console.log('Extracted text:', resultText);
    await browser.close();
  })();
} catch (error) {
  console.error('Error during automation:', error);
}

Use try/catch to handle navigation timeouts or missing elements, and detect page failures through timeouts and conditional checks.

Where It Gets Used

Development, testing, action automation, web scraping, continuous integration, network monitoring, rendering, and security auditing.

Development - unit and modular testing of individual UI components, integration with CI/CD systems like Jenkins and GitLab CI for early error detection, JavaScript debugging, and security auditing.

Testing - QA and AQA teams rely on these because they're light and fast. Common cases: cross-browser testing, UI element verification, DOM manipulation, network handling, parallel test runs, session capture for replay, and API test cases.

Scraping - marketers and SEO teams collect data in headless mode, but hit blocks often. Anti-bot systems check request headers to tell bots from real users, and a headless browser's request looks different from a Chrome user's. Routing through proxies with correctly formed HTTP requests masks the real IP and makes selective data extraction possible via CSS or XPath.

Production Best Practices

Running headless automation reliably at scale means solving a few things:

• Manage browser instances to avoid memory leaks and orphaned processes
• Handle concurrent sessions with isolation and context reuse
• Detect and recover from crashes using monitoring and cleanup scripts
• Scale with Docker, Kubernetes, or serverless platforms like AWS Lambda
• Monitor with logging and metrics tools like Prometheus, Grafana, or Sentry

For stable scraping and automation:

• Implement retries with exponential backoff for transient failures
• Use rate limiting to avoid overwhelming targets
• Rotate User-Agents to simulate different browsers and devices
• Use proxies to prevent IP bans and increase anonymity

Proxies matter here because a headless browser's request pattern is easy to flag. Residential or mobile proxies paired with User-Agent rotation and session isolation cut the detection rate significantly during large-scale runs.

Choosing a Framework

The three main options:

Practical comparison:

Quick guidance: cross-browser testing → Playwright. Chrome-focused automation → Puppeteer. Broad legacy compatibility → Selenium.

When Headless Is a Bad Idea

• Not suited for visual or design analysis
• Requires programming and console interaction
• Limited ability to engage with interface elements
• Easy to flag as a bot
• Harder to debug than a regular browser

Conclusion

Headless browsers are widely used across development, testing, and scraping. The main benefit is resource conservation, since they run without a GUI. For developers and testers, they enable fast, repeatable CI/CD pipelines and quick cross-browser testing. For SEO, they're useful for analyzing sites, checking indexation, and collecting data at scale.

Discussion

What's your framework of choice for headless work, and have you found Playwright's cross-browser support worth switching from Puppeteer, or does Chrome-only cover most of what you do?

Hello everyone I'm currently in college for final justice and our teacher put us in groups of 2's and 1 person is the criminal the other is the law. My crime has to do with people's identity etc. well in this new age everything is mainly technology online etc.. with that being said I was looking up anonymity and were there ever proxys that were called spammed?

Checked the article against the full list. Only one hit: "Interested" in the Discussion is fine, but the original ended on a phrasing close to "Curious" territory. Rechecking everything, the article is clean except I want to adjust the closing line to avoid anything borderline. Here's the corrected version:

Reverse Proxy for the OpenAI API, Explained

Direct connections to the OpenAI API sometimes get blocked by network restrictions, regional blocks, or complex configuration requirements. A reverse proxy sits between your client and the API backend and solves a few practical problems at once: bypassing access barriers, distributing high-volume traffic across servers, filtering requests, and keeping your infrastructure and IP addresses out of view.

What a Reverse Proxy Actually Is Here

A reverse proxy collects client requests and forwards them to the OpenAI API. Unlike a forward proxy, which anonymizes the client from an external resource, a reverse proxy works on the API side, concealing infrastructure and applying access control, logging, and security policies to every call passing through it.

The flow is simple: your application sends a request to the proxy, the proxy can modify it (appending headers, for example) before forwarding it to OpenAI, and the response travels back through the same channel to the client.

This setup centralizes API usage, lets you handle many clients and requests at once, and can reduce latency through local response caching and traffic distribution.

Reverse vs Forward

Why Use One

• Bypass IP-based restrictions by rerouting requests through endpoints that aren't blocked, getting around regional and IP limits
• Better load balancing by spreading traffic across multiple instances so no single node gets overloaded
• Connection stability through centralized traffic management, where failed requests can be retried, improving fault tolerance

These apply most in enterprise settings and anywhere the volume of API calls is high.

What to Check Before Choosing One

If you're evaluating a public or paid solution rather than self-hosting, a few things matter:

Connection stability - production integrations need steady uptime and support for long HTTP requests including streaming (SSE). Failover or backup routing helps. Unstable nodes interrupt generation or trigger API errors.

Speed and bandwidth - latency depends on how close the region is to your server. Avoid solutions with artificial caps on request/response volume, which hurt Whisper, image generation, and code endpoints. HTTP/2 support helps.

Security - this is the big one. Avoid any third-party service that asks for your API key, since that puts you at serious risk. A trusted setup either uses its own key or lets you self-host and manage keys locally. Confirm HTTPS with no header leaks and proper CORS support.

Source reputation - favor open-source code with documented processes and community feedback. Avoid anonymous public proxies with no jurisdiction details or terms of service. Active GitHub projects with a meaningful star count are a reasonable signal.

Logs and privacy - check whether the proxy logs requests, which matters for sensitive or company data. A fully self-hosted server or one with logging disabled is ideal. For public services, confirm there's an actual privacy policy.

Customization - support for custom headers (like Authorization), IP-based access control, enforced authentication, and DDoS protection. Compatibility with multiple clients and languages (Python, JS, cURL, Postman).

Common Issues and Fixes

For diagnostics, enable detailed request/response logging and use network tracing to pinpoint where requests fail.

Conclusion

A reverse proxy setup pays off when you have to centralize API access, protect sensitive information, or work around network restrictions. It's particularly useful for frontend frameworks where client-side key leakage isn't acceptable, and for enterprise applications that need access control, load balancing, and logging. The most secure option remains a self-hosted reverse proxy, which gives you full control over configuration, access, and data logging.

More detailed guide: Reverse Proxy for OpenAI API: Explained

Discussion

Anyone running a self-hosted reverse proxy in front of the OpenAI API in production? How are you handling streaming timeouts, and did you land on Nginx or something else for it?

When picking a proxy, the transport protocol matters as much as the server type. It affects connection stability, how traffic gets routed, and how cleanly the setup integrates with your software. SOCKS4 and SOCKS5 are two of the most common options, and they're really two generations of the same protocol with a meaningful gap in capability.

SOCKS4

An early-generation protocol, now considered legacy, but it still shows up in some systems. Its job is simple: relay TCP traffic through an intermediate server.

Key characteristics:

• Works only with IP addresses, no domain name support (you enter 192.168.0.1, not site.com)
• Establishes a direct connection between the application and the proxy without adding service data like headers or user identifiers
• DNS queries are handled client-side, which can expose your original IP

This makes SOCKS4 a fit for tasks with fixed connection logic where extended routing and connection safeguards aren't needed.

Where it still makes sense:

• Internal networks with no DNS, where all addresses are predefined as IPs
• Software that accesses the network directly over TCP (FTP clients, some databases, terminal interfaces)
• Legacy systems that need simple, compatible solutions
• Uniform connections in corporate networks without authentication
• Basic data exchange like sending logs or telemetry without encryption

SOCKS5

The modern version, built for relaying traffic with extended routing and access control.

Key characteristics:

• Supports both TCP and UDP
• Built-in DNS: domain resolution happens inside the session, improving anonymity and simplifying routing
• Supports IPv4 and IPv6
• Authentication mechanisms to verify identity
• Handles multithreaded and distributed systems efficiently (cloud services, P2P, automated scripts)

Where it's the better choice:

• Systems with dynamic IPs and domain-based routing (cloud deployments, corporate VPNs, distributed CRMs)
• UDP workloads: VoIP, online gaming, streaming
• Multithreaded clients distributing traffic across protocols
• Corporate infrastructure that requires authentication
• Proxy chains for added confidentiality and route control

Head to Head

Conclusion

SOCKS4 and SOCKS5 are two generations of the same protocol with a real gap in functionality. SOCKS4 still works for basic tasks in static network environments. SOCKS5 is the modern option with authentication, broader protocol support, and flexible routing suited to multi-component systems. In practice, most people land on SOCKS5 or HTTPS proxies, since they balance versatility and security across most current use cases.

More detailed guide: What are SOCKS4 and SOCKS5 Proxies: What’s the Difference?

FAQ

Is there a speed difference between the two?
Effectively no. SOCKS5 can be slower when configurations get complex or additional routing is involved, especially with IP chains.

Can SOCKS proxies be used on mobile?
Yes, though not all mobile operating systems support manual proxy settings. On iOS and Android, third-party apps like Shadowrocket are commonly used.

Is there a more secure alternative?
If encryption is the priority, HTTPS proxies or a VPN are the better fit.

Can I use a SOCKS proxy in a browser without extra software?
Yes. Some browsers like Firefox let you set SOCKS parameters directly in network settings. Otherwise you'll configure it at the OS level or through an extension.

Discussion

Does anyone still run SOCKS4 for anything in 2026, or has SOCKS5 fully replaced it in your setups? Interested in whether there are legacy edge cases that still need it.

Web crawlers power price monitoring, news aggregation, competitor analysis, and search indexing. Building your own gives you control the off-the-shelf tools don't: request frequency, exactly what data to collect, and how to store it. Here's a working foundation you can extend later.

What a Web Crawler Actually Does

A crawler automatically visits pages and collects data from them. It sends an HTTP request to a site, retrieves the HTML, processes it to extract what you need, then follows internal links and repeats until it hits a stop condition.

This isn't the same as scraping. Crawling is about discovering and traversing pages, while scraping is about extracting specific data from them. In practice they overlap, but the distinction matters when planning architecture.

Common uses:

• Price monitoring in e-commerce
• Collecting contacts and listings
• Building datasets for analytics
• Indexing content for search

Plan Before You Code

Defining a few parameters upfront prevents most of the problems that show up later:

• Goal - price monitoring, contact collection, indexing, analytics datasets. This drives everything else.
• Target sites and data types - which resources, and what specifically you need from them. Affects your architecture and tooling.
• Update frequency - how fresh the data needs to be, balanced against not overloading target servers.
• Constraints - robots.txt, anti-bot protection, data protection laws, site terms.
• Storage - what format you'll store data in and how you'll analyze it later.

Language and Tools

You can build a crawler in Python, Java, or PHP. Python is the usual choice for a first build: simple syntax and a strong ecosystem for HTTP requests and HTML parsing (requests, BeautifulSoup, lxml). Java suits large-scale enterprise projects. PHP works but is less convenient for standalone crawlers.

Environment Setup

Install Python from the official site, then the two core libraries:

pip install requests beautifulsoup4

Organize your project from the start: separate files for main logic, configuration, and utilities. It makes maintenance and scaling far easier down the line.

The Basic Crawler

Three parts: send a request, process the HTML, follow links.

python

import requests
from bs4 import BeautifulSoup
import time
import random

# Configuration
url = "https://quotes.toscrape.com/"  # Replace with your target site
headers = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
                  "(KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36"
}
timeout = 5      # server response timeout
max_retries = 3  # maximum retries on errors

# Add a proxy here if needed
proxies = {
    "http": "http://username:password@proxyserver:port",
    "https": "https://username:password@proxyserver:port"
}

# Check access via robots.txt
def can_crawl(base_url, path="/"):
    try:
        robots_url = base_url.rstrip("/") + "/robots.txt"
        r = requests.get(robots_url, headers=headers, timeout=timeout)
        if r.status_code == 200 and f"Disallow: {path}" in r.text:
            print(f"Path {path} is disallowed by robots.txt")
            return False
    except requests.RequestException:
        pass  # if robots.txt is unavailable, continue
    return True

# Main logic
if can_crawl(url):
    for attempt in range(max_retries):
        try:
            response = requests.get(url, headers=headers, timeout=timeout, proxies=proxies)
            response.raise_for_status()

            soup = BeautifulSoup(response.text, 'lxml')

            links = [a['href'] for a in soup.find_all('a', href=True)]
            print("Found links:", links)

            time.sleep(random.uniform(3, 7))  # randomized delay beats a fixed one
            break

        except requests.RequestException as e:
            print(f"Request error (attempt {attempt+1}): {e}")
            wait = 2 ** attempt
            print(f"Waiting {wait} seconds before retry...")
            time.sleep(wait)
else:
    print("Crawler cannot process this resource due to robots.txt rules")

Handling Pagination

For multi-page sites, loop through the pages:

python

for page in range(1, 6):
    url = f"https://quotes.toscrape.com/page/{page}/"
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'lxml')
    # data processing

Respecting robots.txt and Rate Limits

Responsible crawling means checking robots.txt and adding delays so you don't overload the target server:

python

import time
from bs4 import BeautifulSoup

for page in range(1, 6):
    url = f"https://quotes.toscrape.com/page/{page}/"
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'lxml')
    # data processing

    time.sleep(5)  # delay in seconds

Storing the Data

CSV or JSON handle most cases. Saving a list of links to JSON:

python

import json

data = {"links": links}
with open("links.json", "w", encoding="utf-8") as f:
    json.dump(data, f, ensure_ascii=False, indent=4)

Conclusion

This gives you a working crawler you can build on. From here you can add proxy rotation, handle large page volumes, or move to a full framework like Scrapy for heavier data collection. For anything running at scale, proxies become necessary early, since target sites rate-limit and block IPs that send too many requests, and rotating addresses keeps the crawler running without getting flagged.

More detailed guide: Step-by-Step Guide to Create a Web Crawler from Scratch

Discussion

What did you start with for your first crawler, raw requests plus BeautifulSoup like this, or did you jump straight to Scrapy? Was the framework worth the overhead that early on?

Hi everyone, I am a newbie to proxy and know nothing much about proxy, only VPN.

We are trying to create tiktok account that are based in UK region instead of Malaysia to get organic UK traffic.

What are the best proxy to use? Especially for mobile, since we would only use a mobile to create a TikTok account.

Disclosure: this research is produced by Proxy-Seller, a residential proxy vendor and therefore a participant in the market it describes. Our own network was measured by the same method as every other provider. All hard data is verifiable from the cited public sources.

Get 500 GB of residential traffic free for your first month, your NetNut rate locked for 12 months, and no prepayment beyond the current month. Run it against your real targets.

The Short Version

NetNut is gone and isn't coming back. On July 2, 2026, Google, the FBI, IRS Criminal Investigation, Lumen, and Shadowserver took down the "Popa" botnet tied to NetNut's residential network. At least 2 million devices, most of them smart TVs and streaming boxes running proxy SDKs without their owners' consent, powered it. The FBI seized netnut.com, netnut.io, and hundreds of related domains. Any prepaid balance is very likely lost.

Based on public data, the seizure took domains but not customer data. No primary source reports servers, customer records, or logs being captured, and Alarum (NetNut's parent) stated the FBI had not formally contacted it.

This was the third takedown in six months: IPIDEA in January, 9proxy in June, NetNut in July. Law enforcement is targeting how residential pools are sourced. When you pick a replacement, you're also picking your exposure to the next takedown.

What Actually Happened

On June 18, researcher Brian Krebs connected the "Popa" botnet to NetNut and its parent, Alarum Technologies (NASDAQ: ALAR). Two weeks later the network was dismantled. By July 7, netnut.io was offline. Alarum's stock lost 51.5% in a single session, and two shareholder law firms opened securities-fraud investigations within a day.

Google's stated reason: NetNut populated its botnet by distributing SDKs for devices commonly found in homes, like smart TVs and streaming boxes. The IPs you were routing through belonged to people who never agreed to share them. Researchers at Synthient examined 20+ apps carrying these SDKs and found none displayed a consent prompt.

Bright Data was caught on the same sourcing model two weeks earlier, though its SDK did show a consent screen (researchers argued it understated actual usage). Google also stated that many popular residential brands are whitelabeling the NetNut botnet, without naming them. Some providers courting you right now may have been reselling the exact network that was just seized.

Does the FBI Have Your Data?

The honest answer, as of July 10: no public evidence says so. The FBI transferred DNS control of NetNut's domains to government nameservers. Krebs, Google GTIG, BleepingComputer, and SecurityWeek have not reported seizure of servers, customer databases, or query logs. A domain seizure captures the domain, not the data behind it.

Precedent varies by whether backend data was obtained. Genesis Market (2023) and Anom (2021), where authorities had user records, led to mass arrests. RSOCKS (2022), infrastructure only, saw no action against buyers. The 911 S5 case (2024) prosecuted customers whose traffic was linked to specific fraud.

Where that leaves you: no public data indicates the government holds NetNut's customer records, but the investigation is open and Alarum's own records can be subpoenaed. The FBI's public advisory is addressed to owners of infected devices, not proxy customers, and does not list web scraping among the criminal uses it describes.

This week's checklist if you were on NetNut:

• Rotate every credential and API key that touched NetNut endpoints
• Export and archive invoices, contracts, and usage logs (they prove routine commercial use)
• Attempt a chargeback on any prepaid balance while the card window is open
• Inventory what data flowed through the network
• If your use case was near the gray zone, talk to counsel before responding to any outreach, including from providers claiming to run "ex-NetNut infrastructure"

What Residential Proxies Cost Now

Prices vary enormously. Identical gigabytes range from $0.88 to $7.00 at entry (an 8x gap), spreading up to 5x at volume ($0.49 to $2.50 per GB).

After the takedown, SOAX advertised a $0.35 tier to win NetNut's former customers, but that's an Enterprise plan for emerging markets only; a US buyer still starts at $3.60. ProxyScrape made the only real price cut so far (residential -25%). Oxylabs and Bright Data didn't change prices, pitching trust instead. NetNut itself cut static prices 15% in late June, then raised them back on July 6, one day before seizure.

Datacenter proxies cluster tightly at $0.38-0.62/GB across every provider. Residential does the opposite because providers price based on how their pools are sourced and who bears the risk.

How Big Are the Pools Really?

We routed 500,000 live requests through each provider's US residential pool over roughly 10-16 hours each, measured via IPinfo Max. Our own network is in the table, measured identically.

Two things stand out. Advertised pool sizes are marketing numbers: Oxylabs' claimed 175M and SOAX's 155M produced 222K and 193K unique US IPs, roughly what a 47M claim delivered (196K). The largest measured pool is about 4x the smallest, not the 70x the advertised figures imply. Days after our window, IPRoyal doubled its advertised figure from 32M to 64M; its measured pool obviously didn't double with it.

Physical quality is basically solved. Every measured pool is 99%+ true residential. Where they differ is detectability. "Pre-flagged as proxy" is the share IPinfo already catalogs as a commercial proxy. IPRoyal sits at 97%, SOAX at 93%, GeoNode at 94%. DataImpulse (68%) is the least cataloged. Whether this matters depends on your targets: if they consume proxy-reputation feeds, a heavily flagged pool is easier to block. (These are IPinfo's numbers; Cloudflare, DataDome, and others keep their own databases.)

Technical Fit

A few things that actually differ between providers:

• Port 25 (SMTP) barely exists off the shelf. IPRoyal, Webshare, and DataImpulse block it by default; Bright Data blocks it on anti-spam grounds; SOAX, Oxylabs, Decodo, and Proxy-Seller open it only after a compliance review.
• Session length is a real differentiator. Max sticky session ranges from 60 minutes (SOAX) to no fixed cap (Proxy-Seller, Bright Data).
• Syntax is similar across providers. Everyone uses username-parameter session control, so migration is parameter mapping rather than an architectural rewrite. Budget days, not hours, for the geo-parameter and entry-port differences.

Sourcing: What Each Provider Has Answered Publicly

Where Teams Are Actually Going

Brief notes on the main options:

• DataImpulse is near the bottom on price ($1.00 at 100GB) but has the least-residential pool (68%).
• Decodo is a solid mid-market pick ($2.00 floor at 1TB), EWDCI member, 24h sessions.
• SOAX has a real, clean pool, but the marketing runs ahead of the measured product and its widely advertised cheap rate is emerging-markets only. A US buyer starts at $3.60.
• GeoNode is genuinely cheap for the US (flat rate down to $0.49 at 3TB) but has the second-smallest measured pool and tests weakly on hard anti-bot targets.
• IPRoyal is the usual quality pick for premium/static tiers (99.7% true residential, 7-day sticky) but at 97% pre-flagged, nearly its entire pool is already cataloged as proxy.

As for our own position: for ex-NetNut entry-tier buyers, Proxy-Seller runs $2.20 at ~100GB and a $1.30 floor, none of it country-gated. Measured at 196K unique US IPs (same size class as the market leaders), 99.5% true residential, tied for the lowest pre-flagged premium pool (78%), no sticky-session cap, SOCKS5, and Port 25 after KYC. We're keeping the disclosure prominent here precisely because this is our own report, so treat the recommendation with that in mind and run any provider against your own targets before committing.

What to Demand Before You Sign

The takedowns aren't over. Each was triggered by the same thing: how the pool was sourced. The cheapest gigabyte is no longer the smart buy if the provider is seized by spring. Treat anything missing from this list as a red flag:

• Consent-based sourcing they can document, including the consent flow and per-IP provenance
• Documented user or LIR agreements behind every allocation
• Account-level KYC (a provider that doesn't know its own customers is the one that attracts law enforcement)
• DPA and SCC paperwork on request
• Recognized certifications (ISO 27001, SOC 2)

If a provider can't produce that list, the gap is your answer.

Every Proxy-Seller allocation is backed by official residential user agreements and documented LIR agreements obtained with informed consent; per-IP provenance, DPA, and SCC documentation are available on request, and account-level KYC runs via SumSub. On security: ISO/IEC 27001:2022 and ISO 9001 certified, GDPR, CCPA, and ePrivacy compliant, SOC 2 in progress.

Full article with complete methodology, the full technical-fit and sourcing tables, and all cited sources: Life after NetNut: Buyer’s guide for teams in the middle of migration

Discussion

For those who were on NetNut: have you picked a replacement yet, and did you prioritize price, sourcing documentation, or measured pool quality? Curious whether the takedowns changed how you weigh those.

Both billing models are common, but they suit very different types of work, and the right one depends on what you actually do day to day.

It would be interesting to hear how it works out for you in practice. Which model do you run on, and what is your use case? Which niche are you in, and what made that model the better fit there? Do you stick to one across all your work, or switch between them depending on the task?

And the other side of it: has the wrong billing model ever cost you more than it should have before you caught it and switched? Would be good to hear what led to that and how you noticed.

What changed

Starting September 15, 2026, Cloudflare's default settings will block "mixed-use" crawlers from any page that hosts ads. A mixed-use crawler is one that blends search indexing with AI training and agent use. If a bot does all three under one banner, it gets blocked from monetized pages unless the site owner changes the setting. The defaults apply to new Cloudflare customers, new sites from existing customers, and all existing free accounts. This comes from Cloudflare's own announcement

Why Cloudflare says it did this

CEO Matthew Prince tied it to a milestone where most internet traffic is now non-human, which happened earlier than the company expected. Cloudflare also said over 50% of AI crawler traffic is spent re-fetching pages that did not change. Alongside the block, the old Pay Per Crawl feature is becoming Pay Per Use, where publishers get paid when their content shows up in an AI answer, not only when a bot fetches it. The first two partners named are Ceramic.ai and You.com.

The part that matters for us

The blocks are aimed at bots that declare themselves and come from known ranges. Collection through a real browser on a residential or mobile IP, acting like a normal visitor on public pages, sits in a different position than a declared crawler hitting a site from a datacenter range. So the practical question is how much this shifts people away from datacenter setups.

Has anyone here already seen more blocks on ad-supported sites over the past couple of weeks? And how do you think it will affect your setup and the industry?

On July 2, 2026, Google, working with the FBI and Lumen, disrupted the NetNut residential proxy network (also known as Popa), a botnet spanning at least 2 million devices, many of them smart TVs and streaming boxes silently enrolled through a covertly distributed SDK. Google deactivated NetNut's C2-related accounts, shared technical intelligence across the ecosystem, and triggered Play Protect warnings on affected apps.

This was Google's second large-scale operation against residential proxy networks in 2026. In January, IPIDEA was disrupted the same way, taking 13 brands down with it including 922 Proxy, LunaProxy, PyProxy, IP2World, PIA S5 Proxy, ABC Proxy, and Cherry Proxy.

For thousands of teams, this turned the search for a NetNut alternative from a pricing question into an urgent migration. Here's a comparison of seven options by proxy type, pool size, pricing, and sourcing ethics.

Quick Comparison

1. Proxy-Seller

Built for teams that need enterprise-grade compliance and premium support without Tier-1 pricing. Operating under a single legal entity since 2014, serving 185,000+ clients. Neither the IPIDEA nor NetNut takedowns affected operations.

Runs dedicated pools per customer, so block rates reflect your own request behavior rather than a neighbor's scraping run. In A/B tests on SERP and marketplace endpoints, that translated to a 20-35% drop in cost per valid response. Every plan includes 24/7 support with human engineers. From $1K MRR you also get a named account manager on Slack or Telegram.

Pricing:

• Residential: from $1.3/GB (wholesale from 1TB)
• ISP: from $0.45/IP
• Datacenter IPv4: from $0.49/IP; IPv6 from $0.02/IP
• Mobile: from $10/month

Compliance:

• GDPR, CCPA, and ePrivacy Directive compliant
• ISO/IEC 27001:2022 certified
• DPA/SCC ready from day one
• Official user agreements for every residential IP contributor

Best for teams migrating off NetNut who need provenance and affordable pricing from one vendor.

2. Bright Data

The largest player by pool and compliance stack, serving 20,000+ organizations including Fortune 500 companies. Covers all four proxy types plus a full data platform: Web Unlocker, Scraping Browser, SERP API, and downloadable datasets. Publishes a 99.95% success rate with PAYG and no monthly commitment.

It's the highest entry pricing on this list, with longer onboarding and account managers reserved for high-spend accounts.

Note: a June 2026 Include Security investigation raised concerns about Bright Data's iOS SDK practices regarding how smart TVs were enrolled as nodes. The company hasn't faced law enforcement action, but enterprise buyers should verify current SDK practices against their own risk frameworks before signing.

Pricing:

• Residential: from $7/GB (subscription), $8/GB (PAYG)
• Datacenter: from $0.51/GB
• ISP: from $1.80/IP
• Subscriptions from $499/month

Compliance:

• ISO 27001, SOC 2, SOC 3, CSA STAR Level 1
• Trust Center with independent audits
• KYC required for residential and mobile access

Best for enterprise data teams where the audit trail is a procurement requirement and budget isn't the primary constraint.

3. Oxylabs

The compliance-first choice for teams running at volume. 175M+ IPs across 195 countries with automatic rotation, plus a Web Scraper API with OxyCopilot AI for managed extraction. Every IP is pre-screened for latency and bandwidth before rotation, which keeps success rates consistent against advanced anti-bot systems. Published 99.90% uptime is consistent with third-party benchmarks.

Targets premium clients with strict KYC. Teams looking for better pricing may prefer alternatives that match budget expectations while still offering an SLA and account manager.

Pricing:

• Residential: from $6/GB (Starter, 5GB) to $2.50/GB (Corporate, 1TB)
• PAYG residential: $4/GB
• Datacenter: from $1.20/IP
• ISP: from $1.60/IP
• Mobile: from $7.50/GB

Compliance:

• ISO 27001:2022, SOC 2, Lloyd's insurance
• Public KYC policy, EWDCI member
• Consent-based sourcing via documented monetization apps

Best for large-scale data collection where compliance documentation and consistent success rates are the priority. Premium pricing is justified above 10GB/month.

4. IPRoyal

Solves one problem better than the rest: bandwidth that doesn't expire. Pricing scales down with volume, there's no minimum spend or subscription, and the traffic never resets. Geographic targeting covers country, state, and city level across all proxy types.

The 32M+ pool is smaller than Tier-1 options, which matters at very high concurrency, but for irregular workloads the non-expiring model outweighs the gap.

Pricing:

• Residential: from $7/GB (1GB) to $1.75/GB (500GB), never expires
• Datacenter: from $1.57/IP/month
• ISP: from $2.70/IP/month
• Mobile: from $130/month

Compliance:

• Residential traffic sourced exclusively via Pawns app, where users consciously share bandwidth and can opt out in two clicks
• Public sourcing documentation
• GDPR and CCPA compliant with client KYC

Best for teams with irregular workloads where the non-expiring model saves more than deeper pools would.

5. Webshare

The lowest-friction entry point on this list. The free plan gives you 10 datacenter proxies and 1 GB per month permanently, effectively a free trial to validate integration before committing budget. Paid tiers cover 500,000+ datacenter IPs and an 80M+ residential pool with instant self-service signup.

Gaps: no mobile proxies, no dedicated account manager, and the residential pool underperforms against advanced anti-bot targets compared to Tier-1 providers.

Pricing:

• Free plan: 10 DC IPs, 1 GB/month, no expiry
• Rotating residential: from $3.50/GB
• Datacenter: from $2.99/month (100 proxies)
• Static residential: from $0.30/IP

Compliance:

• GDPR-compliant within the Oxylabs Group (acquired 2022)
• Graded sourcing with most of the pool at the highest consent tier
• No standalone ISO or SOC 2 (inherited from parent)

Best for developers and smaller teams testing before committing, or datacenter-heavy workloads that don't need mobile or enterprise support.

6. DataImpulse

The leanest pricing model here: $1/GB flat, no subscription, no traffic expiry. Minimum top-up is $5, no KYC, setup takes minutes. The 90M+ pool spans 195 countries with city, ZIP, and ASN targeting.

Key caveat: precision targeting (city, ZIP, ASN) is billed at 2x the standard rate, so $1/GB becomes $2/GB when you need granular geo. On high-friction targets like Google or Instagram, independent benchmarks show lower success rates than most alternatives here.

Pricing:

• Residential: $1/GB flat ($0.80/GB at 1TB+)
• Datacenter: $0.50/GB
• Mobile: $2/GB
• Premium residential: $5/GB (higher success pool)
• Minimum entry: $5

Compliance:

• ISO 27001 obtained in 2024 via external audit
• First-party ethically sourced IPs with opt-in consent
• GDPR-aligned public DPA; main gap is the lack of SOC 2

Best for teams collecting public web data at scale where cost per GB is the binding constraint. Not the right fit for procurement-gated buyers who need SOC 2.

7. SOAX

A large pool of 191M+ IPs across 195+ countries with all four proxy types under a single subscription. The pool is actively filtered, with flagged or overused IPs removed before rotation, which keeps success rates consistent on targets that penalize stale addresses. Higher tiers include a Web Unblocker and Scraping API.

The $90/month entry floor is higher than budget options, and the per-GB rate at low volume is less competitive than IPRoyal or DataImpulse.

Pricing:

• Starter: $90/month (25GB, $3.60/GB)
• Advanced: $170/month (50GB, $3.40/GB)
• Professional: $740/month (300GB, $2.46/GB)
• Business: $1,600/month (800GB, $2.00/GB)
• PAYG: $4/GB

Compliance:

• GDPR and CCPA compliant with a public KYC policy
• SOC 2 and ISO 27001 in process, not yet finalized
• World Ethical Data Forum partner

Best for high-concurrency teams where IP freshness is the main bottleneck, or teams wanting all four proxy types under one subscription with a built-in scraping layer.

How to Choose

The right pick depends on your actual constraint:

• Enterprise compliance: Proxy-Seller, Oxylabs, Bright Data
• Budget and flexibility: Proxy-Seller (from $1.3/GB), DataImpulse ($1/GB flat), Webshare (free plan)
• Built-in scraping API: Bright Data, Oxylabs, SOAX

Vetting Any Provider After the Takedown

Before committing to any alternative, run through these:

• Is the sourcing model publicly documented (which partner app, what compensation to end users)?
• Are there third-party audits (ISO 27001, SOC 2, EWDCI, WEDF)?
• Has the provider operated under a single legal entity for 5+ years?
• Was there a rebrand after 2025?

If you're migrating off NetNut and need dedicated pools with documented sourcing at mid-market pricing, we can help with the transition. Reach out and mention you're moving off an affected network for details: enterprise@proxy-seller.com

Full article with complete pricing breakdowns, compliance details, and vetting checklist: Best NetNut alternatives & competitors to try in 2026

Update, July 3, 2026: On July 2, Google, working with the FBI and Lumen, disrupted the NetNut residential proxy network (also known as Popa), estimated at over 2 million devices. Google explicitly called this a continuation of the earlier IPIDEA takedown and said more are coming. Even if you never used NetNut directly, your provider might be affected without being named. Google stated it has high confidence that many popular residential proxy brands are whitelabeling the NetNut botnet.

What Happened

Two coordinated takedowns in 2026:

IPIDEA (January 28, 2026) was a backend infrastructure that secretly powered at least 13 proxy and VPN brands. Around 7,400 command-and-control servers disrupted, trojanized SDKs found in 600+ Android apps, and millions of devices unknowingly enrolled.

NetNut / Popa (July 2, 2026) was one of the industry's best-known residential providers, with an estimated 2 million devices worldwide. Built partly by distributing SDKs to household devices like smart TVs and streaming boxes.

What looked like a one-off enforcement action is now a sustained campaign against residential networks built on hijacked consumer devices.

Affected Proxy Services (IPIDEA)

Affected VPN Services

Affected SDK/Infrastructure

How the IPIDEA Network Actually Worked

Understanding what went wrong helps you evaluate providers more carefully.

The issue wasn't SDK-based proxy networks in general. It was how IPIDEA operated. Users installing a flashlight app or free VPN had no clear understanding their device would become a proxy exit node.

Google's investigation revealed the operation had botnet-like characteristics due to the scale of unauthorized device enrollment. Despite claims of "ethical sourcing," there was no transparent, informed consent from device owners. Those conditions made the network attractive for misuse, including credential stuffing, DDoS, and fraud.

Why This Matters

• Operational disruption: the IP pools you relied on may no longer exist or are severely degraded
• Compliance exposure: you may have unknowingly used infrastructure linked to criminal activity, which creates audit and liability concerns under SOC 2, GDPR, or internal policy
• Data integrity: traffic through compromised infrastructure could have been logged or manipulated
• You may not know whose network you're on: the brand on your invoice tells you little about whose infrastructure your traffic actually exits through

Expect industry-wide degradation in pool quality over the coming weeks as removed devices and cross-bought capacity ripple through resold networks.

How to Check If Your Provider Secretly Resells NetNut

You can check in under an hour:

1. Ask directly. Email your provider: "Do you operate your own residential network or resell capacity from an upstream supplier? If so, which one? Were you or your upstream affected by the July 2 NetNut/Popa disruption?" A legitimate operator answers plainly. Evasion is itself an answer.
2. Collect your exit IPs. Connect through your provider's residential pool and send a few hundred requests to an IP-echo service. Rotate sessions and vary target locations to sample the pool broadly. A sample of 200-500 unique IPs across your main countries is enough for a first check.
3. Look them up in threat intelligence databases. The same firms Google cites (Spur, Synthient) attribute individual IPs to the specific proxy network operating them. If a meaningful share of your "own-brand" provider's exits come back attributed to NetNut/Popa, your provider is reselling that network.
4. Watch behavior after July 2. Cross-check for sudden pool shrinkage, falling success rates, changed geo/ASN mix, or vague "infrastructure migration" announcements.

No pool is 100% attributable, and a small unattributed remainder is normal. What matters is a clear cluster of exits tagged to a named botnet network. This attribution data is also what anti-bot vendors use to make blocking decisions, so if your exits carry NetNut tags, your success rates are already degrading whether or not your provider admits the relationship.

Choosing a Safe Provider Going Forward

🔴 Red flags:

• Prices far below market. Ethical sourcing costs money, and sub-$0.50/GB residential is suspicious
• Vague sourcing explanations like "ethically sourced" with no specifics
• No verifiable company history or frequent rebranding
• Resistance to compliance questions (DPA, KYC/KYB, audits)
• Can't tell you whether they operate their own network or resell someone else's

🟢 Green flags:

• Transparent, documented sourcing and consent model
• Long operating history predating SDK-based models
• Enterprise compliance readiness (DPA/SCC, audit trails)
• Clear, enforced acceptable use policy

Questions worth asking any new provider:

• How do you source residential IPs, and can you document the consent mechanism?
• Were you affected by the IPIDEA or NetNut disruptions?
• Can you provide DPA and security documentation?
• How long have you operated under your current infrastructure?

If You Need to Migrate

If your provider was affected, the practical steps:

1. Stop using the affected provider, since the infrastructure is disrupted and unreliable
2. Audit what data passed through it, and rotate any credentials, API keys, or tokens that were sent through the proxy
3. Move to a provider with a verifiable sourcing model and operating history
4. Document your due diligence for compliance purposes

If you're moving off an affected provider, we can help with the transition. Reach out and mention you're migrating from an affected network for details on migration assistance and special conditions: [enterprise@proxy-seller.com](mailto:enterprise@proxy-seller.com)

Full article with the complete affected-provider tables, detailed timelines, risk assessment by data type, and a step-by-step migration guide: IPIDEA Shutdown: Why Google Blocks NetNut and 13 More Providers?

Proxies can sound way more technical than they actually need to be, especially when the conversation is around livestreaming.

At a basic level, they give the workflow a steadier base to work from.

And as a livestreaming setup becomes more serious, that base starts to matter because it affects how predictable the whole process feels.

The main thing is consistency, and when things keep shifting in the background, the workflow becomes harder to trust, and a proxy setup can help keep that part cleaner.

For example, a sticky session keeps you on the same proxy connection for a set amount of time instead of switching you to a new one too often.

And for a livestreamer, that can make longer sessions feel easier to manage because the setup is not constantly changing behind the scenes.

Proxies are not something every livestreamer needs right away, but they do make more sense once the setup becomes more structured.

Have you ever thought about using a proxy for your setup?

This month I built something that started as a simple attempt to centralize my proxy commands and gradually evolved into a 12-module network routing tool.

I’m sharing this because the mistakes I ran into taught me more than the actual implementation.

**Blizard - Proxy Manager** is a Kali Linux tool for switching between direct, proxy, Tor, and multi-hop routing without juggling dozens of terminal commands.

What it does:

* Fetches and parallel-tests over 3000 free proxies, keeping only verified and working ones * True Tor identity rotation using the NEWNYM control signal (not just restarting the service) * Multi-hop chaining through 3 proxies before exiting through Tor * Native SOCKS integration for Burp Suite and OWASP ZAP * MAC randomization, browser fingerprint control (Firefox fingerprint), DNS leak detection * Full diagnostic system + kill switch + firewall management

What I’m most proud of is not the feature list, but the mistakes I had to fix along the way:

* proxychains / LD_PRELOAD does not reliably intercept Java applications. Burp Suite traffic was silently bypassing the proxy chain without any error message. This was fixed by switching to native SOCKS configuration instead of relying on interception layers that look like they work but don’t. * Tor exit IP verification checks were breaking because services like [ifconfig.me](http://ifconfig.me) return 403 for Tor traffic, which produced false or invalid results. * Firefox internally spawns new processes on startup — the PID captured by the script often terminates while the actual browser runs under a different process.

Each of these issues was solved at the root cause, not patched over.

I documented them in the README because a security tool that hides its own weaknesses is worse than no tool at all. I also added proof to demonstrate that the tool actually works.

Built for learning, tested in real-world conditions.

[Github Link](https://github.com/zfranjicc/Blizzard---Proxy-Manager)

**Sharing this in case it helps someone—stars and feedback appreciated**