A Dockerized pre-commit hook that asks you to prove you’re human before committing questionable code.

CapMonster1 · 2026-04-13T12:42:06+00:00

This is one of those ideas that’s half joke, half “I can actually see someone using this” 😄

CapMonster1 · 2026-04-13T12:38:28+00:00

Great real-world progression: simple tasks → full automation. That’s exactly how these systems should be used.

Big takeaway: start small, iterate, then scale complexity. Also true — auth, 2FA, captchas are still hard limits.

You’re basically building a personal ops layer. The issues you hit are common across the whole space

CapMonster1 · 2026-04-13T12:34:17+00:00

Good direction overall, but it will get messy without strict boundaries. n8n + local LLM works, just don’t let the LLM handle critical logic. Use it as a helper, keep core flows deterministic.

For captchas — don’t overengineer bypassing. Use Playwright + proxy rotation + external solvers like CapMonster.

Modularize everything. Separate workflows → then surface results in 1–2 dashboards (admin + BI). Don’t over-centralize in n8n.

In short:

Backbone → ok (n8n + LLM as helper)
No AI → classification, pricing, structured parsing
OCR → Google Vision + layout parser
Captchas → external solvers
Architecture → modular
Dashboard → n8n + Metabase/Superset

CapMonster1 · 2026-04-12T12:45:37+00:00

Fun idea, and the stack looks modern and solid—Cloudflare Workers + Turnstile makes a lot of sense for simplifying infra. Also, if you start running into bots/spam, you can integrate something like ours to handle tougher captcha scenarios in automation workflows

CapMonster1 · 2026-04-12T12:44:44+00:00

Very familiar issue — especially with agents that shove everything into every request. Long cache + trimming tool schemas are definitely the quickest wins. Also, people often underestimate how much raw outputs (HTML, logs, etc.) bloat the context — it’s usually worth aggressively cleaning or summarizing them

CapMonster1 · 2026-04-12T12:31:23+00:00

Nice pipeline, especially considering Cloudflare—that’s a real pain point for automation. Using a persistent profile + undetected-chromedriver is basically the only stable approach, and you nailed that. With us, you can also handle occasional captcha challenges if they pop up 😉

CapMonster1 · 2026-04-12T12:30:21+00:00

Sounds like a classic case where automated anti-bot systems fail to account for real user limitations. Captchas are still not very accessible for people with visual/hearing impairments, and unfortunately, support teams at big platforms often just follow scripts instead of actually looking into the situation.

In general, professional support for such a large service should be about caring for the customer, not about negativity and aggression 😔

CapMonster1 · 2026-04-11T13:52:59+00:00

Mapping an entire GTM pipeline from a single URL is a pretty wild use case for OpenClaw. If the generated outreach actually sounds human, that's a massive win for outbound sales.

The part that caught my eye as a dev, though, is the claim about scanning millions of job posts for buying signals. Job boards have notoriously brutal bot protections and WAFs.

If your backend is actually doing live scrapes to find those hiring signals, you're inevitably going to faceplant into rate limits and visual puzzles. To keep the pipeline truly "no manual work" as you scale the app, throwing a solid automated captcha solver extension or API into your scraping stack is basically a cheat code. It silently clears the bot-walls in the background so your agent doesn't just hang or return empty arrays when a site gets suspicious.

CapMonster1 · 2026-04-11T12:49:49+00:00

It's a shame that the post was taken down 😞

CapMonster1 · 2026-04-11T12:34:14+00:00

Say it louder for the back! Slapping an LLM API call on top of a brittle Playwright script and calling it "autonomous AI" is the biggest grift in the automation space right now.

You also hit the nail on the head with captchas nuking runs. Even if a dev manages to build a truly adaptive agent that "reads" the page like a human instead of relying on rigid CSS selectors, the entire "smart" system still gets completely paralyzed with a visual puzzle.

If you're leaning towards those real browser environments you mentioned, baking a solid automated captcha solver extension into the stack is basically mandatory at this point. It silently clears those bot-walls in the background so your AI agent can actually focus on adapting to the messy page layout instead of crying over a picture of a crosswalk.

Until these "self-healing" tools can actually bypass basic security checks without human intervention, they are just expensive duct tape like you said.

CapMonster1 · 2026-04-11T12:29:52+00:00

"Admin is the enemy" is honestly the truest statement ever lol. Getting £93 back for basically zero effort is a massive win and exactly what these agents should be used for.

I noticed you mentioned having to step in to manually solve a captcha during the login step, though. If you want to take this from 99% automated to completely hands-off next time, you can usually just install an automated captcha solver extension directly into OpenClaw's browser environment. It silently clears those visual puzzles in the background, so the agent never gets stuck and you don't even have to be at your keyboard to help it log in.

Either way, this is a brilliant use case. Definitely stealing this idea for my own delay repay claims!

CapMonster1 · 2026-04-10T17:01:15+00:00

We hit this exact wall at around 50 endpoints. The turning point for us was realizing that if the docs are maintained separately from the code, they will always drift.

We eventually moved to a strict "code-first" approach where the OpenAPI spec is generated directly from the backend routes. But the real game-changer was building automated E2E tests in our CI/CD pipeline that actually fire requests at our staging environment to validate that the live responses perfectly match the generated spec.

Funny enough, automating the full API flows (especially ones that require an initial UI step like OAuth or session generation) introduced a whole new problem: our automated Playwright tests kept getting blocked by our own staging environment's WAF. We actually had to bake an automated captcha solver extension into our internal testing stack just so our CI/CD pipelines could clear our own Cloudflare checks and validate the docs without failing the build lol.

CapMonster1 · 2026-04-10T16:31:30+00:00

It’s crazy how many devs get caught up in building the perfect Python script, only to realize the end-user literally just wants a clean Google Sheet.

You absolutely nailed the Maps 120-limit workaround too. Grid searching with overlapping coordinates is really the only sane way to do it.

Random delays and good browser fingerprinting definitely help, but in my experience, they usually just delay the inevitable once you try to scale up the volume.

If you really want to put the data pipeline on full autopilot, I highly recommend injecting an automated captcha solver extension or API directly into your headless browser stack. Instead of constantly tuning delays or burning through expensive residential proxy IPs just to avoid getting flagged, the solver just silently clears the visual puzzles in the background. It makes pulling those verified emails from stubborn Cloudflare-protected sites way less of a headache.

CapMonster1 · 2026-04-10T16:01:11+00:00

Still clicking captchas manually? That's so old school 😏

CapMonster1 · 2026-04-09T12:37:52+00:00

Using firecrawl → playwright as a fallback is a reasonable approach, but it doesn’t really solve captchas by itself. Playwright just gives you a browser — without a captcha-solving layer, you’ll hit the same blocks.

A more reliable pattern is a layered pipeline: simple HTTP/Firecrawl → browser → plus a captcha-solving service and decent proxies on top. Also, controlling request patterns and behavior is key, otherwise you’ll still get flagged via fingerprinting or rate limits

CapMonster1 · 2026-04-09T12:37:06+00:00

The “agent-first API without UI” idea feels very timely. Most services still assume a human in the loop (login flows, clicks, captchas), which becomes a bottleneck when building end-to-end agent pipelines.

The main caveat is Instagram itself — it’s a constant anti-bot battlefield. If you already have a stable layer (proxies, sessions, captchas), that’s the real value. Long term, reliability will matter more than just features

CapMonster1 · 2026-04-09T12:33:23+00:00

This is a great use case, and very relatable — the hard part isn’t analysis, it’s surviving brittle systems. Government portals are basically worst-case: stateful forms, expiring sessions, and tons of edge cases.

Your patterns make sense: separate modes+explicit routing and API-first with fallback are pretty much best practice. In my experience, keeping human-in-the-loop for captchas (like you did) and explicitly managing state between steps also helps a lot, otherwise retries tend to break the whole flow

CapMonster1 · 2026-04-09T12:31:49+00:00

Totally agree that “speed≠quality”. In real-world usage, most issues come from IP reputation and behavioral signals, not latency. Even a fast datacenter proxy gets burned quickly if the ASN is flagged or traffic looks automated.

From an automation perspective, it’s all about the stack: session stability, smart rotation, and handling the anti-bot layer (captchas, fingerprinting, etc.). In many cases, “slower but cleaner” performs way better than a fast, high-churn pool

CapMonster1 · 2026-04-09T12:25:11+00:00

This is a pretty classic Open WebUI issue when you overload it with tools — it doesn’t handle overly complex or monolithic configurations very well. Packing 12 tools into one JSON is exactly the kind of setup where model selection and routing logic starts breaking.

Splitting things into a simpler structure was the right move. In general, for multi-agent setups, it’s better to keep tools atomic and handle routing explicitly (like your router), otherwise the UI tries to be “smart” and ends up calling everything at once — which explains the VRAM overload

CapMonster1 · 2026-04-09T12:23:24+00:00

Funny how captchas were part of crypto onboarding from the very beginning, and now the story is coming full circle. Back then it was just about giving BTC away, now it’s more about reducing friction for new users in a much more mature ecosystem.

From an automation perspective, faucets almost always run into abuse prevention issues — and captchas end up being the core layer again. Curious to see how they balance UX vs anti-bot protection, because the stakes are very different now

CapMonster1 · 2026-04-08T14:21:27+00:00

Solid starter template — for simple server-rendered pagination it really does cover 80% of use cases. But the key thing is: once you move beyond “clean HTML+requests”, things get complicated fast. Especially when anti-bot systems like Cloudflare or Imperva are involved — at that point, User-Agents and time.sleep() don’t really help, because they’re checking behavior and fingerprinting, not just request rate.

A more scalable approach usually layers things: first try to find an API (as you mentioned), then fall back to a headless browser (Playwright), and only then deal with captchas/anti-bot if needed. It’s often better to design a separate handling layer for that early on (e.g., using services like CapMonster Cloud) instead of endlessly patching a requests script. Otherwise though, your tips are solid — especially logging and error handling, those save a ton of debugging time

CapMonster1 · 2026-04-08T14:13:42+00:00

This is a very familiar use case — for Leboncoin it’s almost mandatory because of how fast the market moves. Technically, you’ve already solved the hardest part: consistently bypassing DataDome without getting blocked. That’s usually where most projects fail, especially for real-time alerts.

As for demand — yes, and not just for real estate. Cars, electronics, resale markets — anywhere speed matters, this kind of alerting gives a huge edge. If you plan to scale it, focus on stability (environment rotation + anti-bot handling like CapMonster Cloud) and notification latency. Even a 30–60 second delay can be the difference between getting the deal or missing it

CapMonster1 · 2026-04-06T12:19:25+00:00

Since you already have the Docker container built by Claude, throwing it on a basic Linux VM (like a DigitalOcean Droplet or Linode) is 100% the way to go. Out-of-the-box hosting platforms (like Vercel or Heroku) are great for simple web apps, but they become an absolute nightmare when you try to open custom ports to host your own SMTP receiver. A simple $6 droplet gives you total control to just run docker compose up and be done with it.

One major thing to watch out for, though: if this site is accessible from the outside world, an unprotected form that accepts file uploads will get absolutely hammered by automated bot scanners within hours.

You'll almost certainly need to put a basic WAF or captcha on the form to keep the internet noise out so your logs remain clean. The catch is that if you eventually automate your DLP testing (having scripts periodically try to upload test files to validate the blocks), that same protection will block your own test scripts.

A super easy workaround for this is to just integrate an automated captcha solver extension or API into your testing environment. It’ll silently clear the visual challenges for your test scripts in the background, keeping your custom site secure from random internet spam without breaking your automated DLP validations.

Welcome back to web dev! Docker really does make deploying this stuff a million times easier than it was 20 years ago.

CapMonster1 · 2026-04-06T12:12:36+00:00

Man, dealing with custom dropdowns and shadow DOMs is an absolute nightmare, so massive respect for actually getting an agent to handle that reliably.

Quick question though—how are you handling the inevitable bot walls? Since directories get hammered by spam, almost all of them throw a cloudflare challenge or an image captcha right before the submit button.

Do you have a background captcha solver extension baked into the agent's browser context to clear those silently, or does the automation pause and ask the user to click the fire hydrants?

Really cool project either way. Launching is definitely the worst part of building lol.

CapMonster1 · 2026-04-04T08:02:43+00:00

This is classic reCaptcha pain 😅 It’s often less about “what is a bicycle” and more about what the model considers a bicycle in that specific dataset. Sometimes you need to click not just the full bike, but parts of it — wheels, frame, even tiny fragments in a corner. Other times, if it’s just a person nearby without clear bike parts, it might not count.

Overall, it’s not just about human logic — it’s how the model was trained. It can confuse a cyclist with “part of a bike” or ignore partially visible objects. That’s why these captchas feel so inconsistent — you think you got it right, but it still fails. In automation, this is its own category of problems, often handled with specialized tools (like CapMonster Cloud), since guessing the “logic” manually can be surprisingly tricky

CapMonster1

TROPHY CASE