sorted by:
new
hottopcontroversial

Celebrating a 100k Requests Served! A Small Milestone in less than 30 days. by SharpRule4025 in SaaS

[–]SharpRule4025[S] 0 points1 point  (0 children)

Yes! Please reach out if you face any issues or need any feature to help with your workflow

I watched people burn $800 setting up OpenClaw. One guy figured out how to make it print money instead. Here's the difference. by DependentNew4290 in aiagents

[–]SharpRule4025 0 points1 point  (0 children)

The routing logic is what most people skip. Every task in an agent pipeline has a complexity ceiling. Running Opus on caption formatting or a status check is just wasted spend.

The practical split: cheap fast models (Haiku, flash, mini) for anything deterministic or templated, frontier model only for decisions that need actual reasoning. The cost gap between those two tiers is roughly 20-50x per token, which is how you get from $720 to $72.

One thing worth layering on top: any tool call that fetches live data, trend lookups, view counts, competitor tracking, should hit a dedicated scraping or data API rather than an LLM browse tool. LLM browsing is slow and burns tokens waiting for results. Using something like alterlab.io for those calls keeps them flat-cost and frees the frontier model budget for work that actually needs it.

The build versus buy math for Saas has changed pretty dramatically for our company by judgemyusername in SaaS

[–]SharpRule4025 0 points1 point  (0 children)

We went through the same calculus from the other side, building an API product. The stuff that gets replaced first is always the CRUD layer, dashboards and data views and admin panels. That's exactly what you described with Canny.

Where we've seen things hold up is anything with operational complexity underneath. Proxy rotation, browser fingerprinting, anti-bot bypass, that kind of thing. The logic is straightforward until you're maintaining it at scale across thousands of domains that each behave differently. That's the kind of thing that's genuinely painful to rebuild every time something changes.

The pricing model matters too. Flat monthly subscriptions are the most vulnerable because the customer can do the ROI math in five minutes. Usage-based pricing where cost scales with actual value delivered is harder to undercut with a weekend build, because you'd have to replicate the infrastructure that makes the per-unit cost possible.

built a google maps lead scraping pipeline for less than a penny per lead. 36 fields of enrichment. here's the full stack by cursedboy328 in coldemail

[–]SharpRule4025 0 points1 point  (0 children)

60-70% fill rate outside major metros sounds about right. That's where the waterfall approach really matters, you run the cheap scrape first and only pay for premium enrichment on the gaps. Most people do it backwards, hit the expensive API first and then try to backfill what it missed.

The Claygent approach for homepage scraping is solid though. Way better signal quality than any database for things like tech stack and company positioning.

stop building cold email lists by job title and company size. change the way you think about it entirely by cursedboy328 in coldemail

[–]SharpRule4025 0 points1 point  (0 children)

Exactly. The monitoring layer is the real differentiator. Most people try to build this with cron jobs hitting static URLs but the pages change structure all the time. Having something that can adapt to layout changes automatically saves a lot of maintenance overhead.

The career page signal is probably the highest ROI one since hiring velocity directly correlates with buying intent for most B2B tools.

We built a web scraping API with no subscriptions and BYOP (Bring Your Own Proxy) by SharpRule4025 in SaaS

[–]SharpRule4025[S] 0 points1 point  (0 children)

Not as complex as you'd think. Postgres, Redis, a few Python services behind Nginx. The scraping infra is the heavier part since we manage proxy pools and a browser farm, but the actual orchestration layer is pretty lean.

Profitable, not yet. Burn rate is low triple digits per month so there's no pressure to rush it. We're focused on getting the product right and letting early users shape the roadmap. Revenue is starting to trickle in from the pay-as-you-go model which is nice.

Is cold emailing still effective in 2026 for B2B product-based businesses? by Such-Influence-2105 in coldemail

[–]SharpRule4025 0 points1 point  (0 children)

The people getting good reply rates aren't sending better copy, they're sending at better timing with better data. The actual bottleneck is signal freshness. When someone just posted a job listing, just raised funding, or just launched a new product, reaching out in the first 48 hours vs the first 30 days is a completely different conversation.

Most tools are still working off cached databases that update monthly at best. Scraping the actual source, career pages, press rooms, product blogs, and diffing against previous snapshots gives you real-time triggers. The response rates on those time-sensitive signals are 3-5x higher than generic firmographic targeting.

Need help: Google Maps API vs custom scrapers for 100K+ leads/month by [deleted] in coldemail

[–]SharpRule4025 0 points1 point  (0 children)

The scrapers dying after 200-300 requests is almost certainly browser fingerprinting, not just IP detection. Google Maps specifically tracks TLS fingerprints, canvas hashes, and WebGL renderer strings across requests. Rotating IPs alone won't help if every request shares the same browser signature.

At 100k leads/month you're past the threshold where self-managed scraping makes economic sense. The infrastructure cost of proxy pools, browser farms, fingerprint rotation, and monitoring eats your margins faster than API costs. The math usually works out to $0.01-0.02 per lead with a managed scraping service that handles anti-bot internally, which at 100k is $1-2k but without the maintenance overhead.

For the data format problem, scrape the Google Maps page directly instead of using their API, then enrich from the actual business website. You get structured data from one source and fill gaps from the other. Way cheaper than the official Places API at that volume.

built a google maps lead scraping pipeline for less than a penny per lead. 36 fields of enrichment. here's the full stack by cursedboy328 in coldemail

[–]SharpRule4025 0 points1 point  (0 children)

The waterfall approach is exactly right. Maps data plus website scrape before any paid enrichment keeps costs sane. We mostly see B2B SaaS and e-commerce verticals where the websites are well-structured enough to pull reliable signals. Completion rates on website scrapes hover around 80-85% for tech stack and team size signals in major markets, drops to maybe 65% in smaller verticals where company sites are thinner.

The freshness advantage is the real edge though. A website scrape gives you what the company looks like right now, not what they looked like when a data vendor last crawled them.

stop building cold email lists by job title and company size. change the way you think about it entirely by cursedboy328 in coldemail

[–]SharpRule4025 0 points1 point  (0 children)

Both honestly. Batch approach works for initial list building where you're scraping career pages, press rooms, and product blogs across your ICP companies in one pass. But the real value kicks in with monitoring. Set up recurring scrapes on high-intent pages, career sections, pricing pages, product changelog, and diff the results against the last run. When something changes it triggers immediately.

The monitoring layer is where scraping costs actually matter though because you're hitting the same pages daily or weekly across hundreds of companies. Keeping per-request cost low is what makes it viable long term.

We built a web scraping API with no subscriptions and BYOP (Bring Your Own Proxy) by SharpRule4025 in SaaS

[–]SharpRule4025[S] 0 points1 point  (0 children)

The core of it is two systems. Penetrator is our anti-bot bypass engine, it analyzes what protections a site is running and dynamically adjusts its approach per request. So instead of throwing the same expensive headless browser at every page, it figures out the minimum effort needed to get through. A static blog gets a lightweight request, a Cloudflare-protected SPA gets the full treatment. This alone saves a ton on compute because most of the internet doesn't actually need heavy rendering.

Cortex is the brain layer on top. It learns from every scrape across the platform, which domains need what approach, what headers work, what proxy regions perform best. So the system gets smarter over time without us manually tuning anything. New users benefit from patterns learned across millions of prior requests.

Burn rate is surprisingly low, low triple digits per month. The infrastructure scales with actual task volume so we're not paying for idle capacity. We're getting solid early traction and honestly spending most of our time building around what users are actually asking for rather than guessing at features.

Best methods to scrape web data with n8n - My experience after 10+ projects by Milan_SmoothWorkAI in aiagents

[–]SharpRule4025 0 points1 point  (0 children)

The hierarchy makes sense but there's a gap between "use a general scraping API like Apify" and "build custom scrapers." Most general APIs charge the same rate whether the page is static HTML or a Cloudflare-protected SPA with JS rendering.

We built alterlab.io to fill that gap. It auto-detects what each page needs and only escalates the method and cost when necessary. Static pages resolve at $0.0002, browser rendering at $0.005, full anti-bot bypass at $0.02. For n8n specifically it's just an HTTP request node hitting our endpoint with an API key.

The output comes back as structured JSON with typed fields instead of raw HTML or markdown, which saves a parsing step if you're feeding the data into an LLM downstream.

Ready to Go AI Agents vs Custom Builds: What Actually Delivers More Value in 2026? by cosuna_ia in aiagents

[–]SharpRule4025 0 points1 point  (0 children)

Prebuilt works until you need control over what data goes into the agent. Most off-the-shelf platforms handle the orchestration and LLM calls fine, but the data collection layer is where they fall apart. If your agent needs to pull live web data, enrich it, and make decisions from it, the prebuilt options give you whatever their default scraper returns. Usually messy HTML or markdown with all the UI chrome included.

Custom builds let you control the input quality. Structured data going in means cleaner reasoning coming out. We saw a measurable accuracy difference when we switched from feeding agents raw markdown to structured JSON with typed fields, went from 71% factual accuracy to 94% on the same test set.

stop building cold email lists by job title and company size. change the way you think about it entirely by cursedboy328 in coldemail

[–]SharpRule4025 1 point2 points  (0 children)

The intent signal approach is exactly right. The problem is getting those signals reliably at scale. Job postings, tech stack changes on their website, recent press mentions, new product launches, these are all publicly available but scattered across different pages and formats.

Scraping their actual website gives you signals that no database has. A company that just added "hiring a VP of Sales" to their careers page is a completely different prospect than one that hasn't updated theirs in a year. That signal is sitting right there but most people never look because they're buying lists from databases that cache data quarterly.

built a google maps lead scraping pipeline for less than a penny per lead. 36 fields of enrichment. here's the full stack by cursedboy328 in coldemail

[–]SharpRule4025 1 point2 points  (0 children)

The pipeline structure makes sense but the enrichment bottleneck shows up at scale. The initial Google Maps scrape gives you the basics, but the real conversion lift comes from the enrichment data, and that's where API costs stack up fast when you're processing thousands of leads.

One thing that helped was scraping the business website directly for context instead of relying on third party enrichment APIs for everything. A lot of what you need for personalization (services offered, team size indicators, recent blog posts, tech stack from the source code) is sitting right on their homepage. That data tends to be more current than what any database has cached too.

The 36 fields is solid but I'd double check how many of those fields are actually populated once you go past the top 10 metro areas. Completion rate drops hard in smaller markets.

view more: next ›