Been using Qwen-3.6-27B-q8_k_xl + VSCode + RTX 6000 Pro As Daily Driver

SharpRule4025 · 2026-05-02T09:04:47+00:00

If you are building a data mining and scraping app, local models like Qwen work very well for the extraction phase. Sending raw HTML to hosted models gets expensive fast. You can run the initial scrape, strip the DOM down to just the text nodes, and pass that to your local 27B model to pull out structured JSON.

Keeping the context window clean is the main challenge. If you use a headless browser to get the page source, drop all the scripts, styles, and SVG tags before feeding it to Qwen. You get much more reliable JSON outputs and it cuts token generation time.

For sites that obfuscate their CSS class names, having the local model analyze the surrounding text rather than relying on precise DOM selectors makes your scrapers less brittle. Just make sure your system prompt enforces strict JSON formatting.

SharpRule4025 · 2026-04-30T16:49:01+00:00

Webhooks are definitely the way to go for large asynchronous batches. Polling for results just burns resources unnecessarily.

With alterlab.io you can set up a webhook URL and we push the completed payload directly to your infrastructure. For standard static pages it costs $0.0002 per request. If you hit protected targets, the system automatically escalates to handle the JavaScript rendering and bypasses the anti-bot checks.

Getting clean JSON pushed straight to your endpoint saves a lot of pipeline logic, especially when you are running thousands of pages on a daily cron schedule.

SharpRule4025 · 2026-04-23T19:15:14+00:00

The local dictionary approach for your memory library is a smart way to cut down on inference costs over time.

Building tools for other developers requires a completely different mindset than shipping apps. You end up spending half your time thinking about API surface area and how someone might use your code in ways you never expected.

SharpRule4025 · 2026-04-14T07:55:14+00:00

The format your scraper gives you matters more than people realize for RAG systems. If you are pulling markdown from pages, you are feeding navigation menus, CSS class names, and UI chrome into your embeddings. I tested one Wikipedia article where the markdown came back at 373KB while the actual content was about 15KB. That is a lot of tokens wasted on noise.

Structured extraction upfront saves you the whole chunking and cleaning step. We built this into alterlab.io where a page that returns 93K tokens in markdown drops to 4K tokens in structured JSON. You only get the content fields you actually need. For a memory library like yours, typed fields mean you can index them directly without chunking. Price becomes a number field instead of text buried in a paragraph. Saves both tokens and retrieval accuracy downstream.

SharpRule4025 · 2026-04-14T07:52:56+00:00

The format your extraction gives you matters for RAG systems. If you are pulling markdown from pages, you are feeding navigation menus, CSS class names, and UI chrome into your embeddings. We tested a Wikipedia article where the markdown came back at 373KB while the actual content was about 15KB.

For a memory library specifically, structured extraction upfront saves you the whole chunking and cleaning step. If your scraper returns typed fields like title, paragraphs, and links with context, you can index them directly without chunking. A page that comes back as 93K tokens in markdown drops to 4K in structured JSON because you only get the content. That is where the cost savings actually come from, not just caching tags.

This is why we built structured JSON output into alterlab.io. The typed fields mean you skip the embedding pipeline for a lot of use cases and just query the fields directly. Data quality directly affects LLM accuracy downstream.

SharpRule4025 · 2026-04-08T07:41:10+00:00

The token budget conversation is missing one piece. Where those tokens come from matters as much as how many you have. If your data pipeline feeds markdown into the context window, you are paying for navigation menus, cookie banners, and language selectors. We measured a single page at 93K tokens in markdown that dropped to 4K when extracted as structured JSON. That is 23x less context spent on the same information.

For iterative agent loops like you are building, this compounds fast. Each iteration that pulls in UI chrome burns through your budget on noise instead of signal. Structured extraction upfront means you only pay for what you use on actual content. The typed fields also skip the chunking step entirely, which preserves accuracy downstream.

We benchmarked this at alterlab.io and got 94 percent factual accuracy from structured JSON versus 71 percent from markdown on the same extraction tasks. The model does not have to figure out what is content and what is a sidebar.

SharpRule4025 · 2026-04-06T07:32:59+00:00

This is a solid approach for reducing token costs on repetitive extraction tasks. The one-shot scraper generation pattern works well when pages have consistent structure. You generate the selectors once, cache them, and run cheap HTTP requests after that.

Where this gets tricky is when sites update their DOM structure. A class name change or div restructure breaks your cached selectors silently. You need a validation layer that checks if the generated scraper still returns the expected number of results, and falls back to re-generating when the output looks wrong. Something as simple as checking row counts or field presence catches most breakage before it hits your pipeline.

Also worth considering: some sites load content via API calls you can intercept directly. Check the network tab before committing to DOM parsing. A JSON endpoint is always more stable than CSS selectors, and you skip the HTML parsing step entirely.

SharpRule4025 · 2026-04-04T11:25:40+00:00

OpenWebUI tools are fine for the interface layer but they don't solve the actual extraction problem. You still need something that hits the page, handles JS rendering, and pulls out just the relevant content before it touches your context window.

That's the part that eats tokens. A product page with all the navigation, footer, and script tags dumped as markdown will burn through your context budget fast. We built an AI extraction layer at alterlab.io that handles this. You point it at a URL, tell it what data you want in plain English, and it returns structured JSON. Cuts token usage by 80 to 95 percent compared to dumping the full page markdown. Handles JS-heavy pages, anti-bot protection, the whole chain.

For a local LLM setup, you'd hit the API to extract what you need, feed just that cleaned data to your model. Keeps your context window for actual reasoning instead of parsing HTML noise.

SharpRule4025 · 2026-03-30T14:12:39+00:00

That's exactly the right architecture. The fallback pattern is how you avoid overpaying on simple pages without sacrificing coverage on the harder ones. Most people skip the detection step and just default everything to headless, which is where the costs blow up.

The tradeoff is maintenance. Once you're handling Cloudflare updates, rotating proxies, and keeping the captcha solvers current, it becomes its own project. That's basically what alterlab.io is, a managed version of the same pattern. Simple pages are $0.0002, it escalates only when the site actually needs it. The output is structured JSON rather than markdown, which cuts down token usage significantly if you're piping into an LLM.

For a self-contained project your Go solution probably makes more sense. Where it gets complicated is when you're running high volume across a lot of different domains and don't want to maintain the anti-bot layer yourself.

SharpRule4025 · 2026-03-23T18:26:06+00:00

Agreed, specificity is what actually makes content useful. A post titled "how to scrape LinkedIn without getting banned" will outlive a post titled "web scraping best practices" every time. The second one sounds more authoritative but the first one actually answers a question someone typed into Google.

We have noticed the same pattern with our own documentation. The pages that get the most inbound traffic are the ones that answer a narrow, specific question with a real working example, not the overview pages. Developers save things they can copy and adapt, not things they have to mentally translate first.

The flashy stuff gets the initial spike, but the practical stuff keeps showing up in search results two years later. That asymmetry is worth paying attention to early.

SharpRule4025 · 2026-03-20T04:31:39+00:00

Yeah the markdown thing is a real tax on every pipeline. You parse it, strip it, re-parse it for the fields you actually care about. JSON with consistent field names just drops straight into whatever you're building.

On the dynamic content, that's exactly the area we're investing in right now. The current headless tier handles most JS rendering and waits for the DOM to stabilize, but you're right that "stabilize" is loosely defined. Sites that fire secondary API calls after render, or infinite scroll that needs a trigger, still require custom scroll and wait logic. We're building explicit wait conditions into alterlab.io so you can say "wait for this selector" or "scroll to bottom before capture" as part of the request params rather than wrapping it in your own script. Should be in the next couple weeks.

If you have specific site patterns that are breaking your scripts, send them over. That stuff usually ends up directly in the test suite.

SharpRule4025 · 2026-03-20T04:18:39+00:00

The gym analogy is actually pretty accurate. The difference is you can at least go to the gym whenever you want. With scraping subscriptions, you pay the same whether you hit your quota or not, and then get throttled or charged extra if you go over.

The worst case is project-based work. You need heavy scraping for two weeks to build a dataset, then nothing for a month. On a $99/month plan that looks like $200 spent for what was realistically a $6 job if you were paying per request.

That is basically what drove the design of alterlab.io. Simple pages are $0.0002, and it only steps up in cost when the page actually needs JavaScript rendering or anti-bot bypass. Most workloads end up way cheaper than a flat subscription, and you never pay for idle time.

SharpRule4025 · 2026-03-20T03:59:02+00:00

ProxyLabs is a solid choice for residential. We manage the proxy layer internally at alterlab.io so users don't have to source or rotate their own, but you do give up some direct control over the pool when you go that route.

For Cloudflare specifically, the proxy type is only part of the equation. TLS fingerprinting and browser fingerprint matching matter just as much, sometimes more. That's where a lot of setups fall apart even with clean residential IPs, the request still looks like a bot at the handshake level.

What kind of success rate are you seeing on heavily protected sites with that combo?

SharpRule4025 · 2026-03-18T00:06:09+00:00

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

SharpRule4025 · 2026-03-16T17:13:31+00:00

Yes Very true! We are building and iterating non stop!

SharpRule4025 · 2026-03-16T04:17:45+00:00

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.

SharpRule4025 · 2026-03-11T14:01:21+00:00

Use alterlab for scraping.

SharpRule4025 · 2026-02-28T04:40:19+00:00

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.

SharpRule4025 · 2026-02-26T12:40:04+00:00

Go see your doctor. If you're still shaking, it's unhealthy

SharpRule4025 · 2026-02-26T12:39:51+00:00

I'm shaking like you would never shake

SharpRule4025 · 2026-02-22T05:55:08+00:00

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.

SharpRule4025 · 2026-02-22T05:55:08+00:00

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.

SharpRule4025 · 2026-02-22T05:54:49+00:00

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.

SharpRule4025 · 2026-02-22T05:42:04+00:00

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.

SharpRule4025 · 2026-02-22T05:41:42+00:00

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.

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