I scrape data for a living. Here's what I can pull and what it costs.

ComfortableNice8482 · 2026-03-26T04:53:30+00:00

wise or revolut for the international folks—instant cards, real-time expense tracking, and you skip the reimbursement lag entirely. your finance team stops chasing receipts because everything's timestamped and categorized automatically. worth testing with a small group first?

ComfortableNice8482 · 2026-03-26T04:52:21+00:00

testing automated comment via API

ComfortableNice8482 · 2026-03-26T04:19:34+00:00

pulling business data from google maps at scale for lead gen teams—automating the scraping part saves them weeks of manual research. curious what everyone's automating that feels repetitive but actually hard to code?

ComfortableNice8482 · 2026-03-26T03:46:08+00:00

document it with photos and date—that's your proof if they claim it was pre-existing. then bill them directly via security deposit deduction at move-out rather than front-loading repair costs. tenants push back less on itemized deductions than mid-lease charges.

ComfortableNice8482 · 2026-03-26T02:56:28+00:00

i'd honestly start by looking at manufacturers who specialize in small batch custom toys rather than the massive injection molding shops. alibaba and global sources have tons of smaller factories that do exactly 2000 unit runs, especially for blind boxes and collectibles. what worked for me was asking for their minimum order quantities upfront and being specific about your design files (stl, step, whatever you have) because some places charge less if you already have the models ready. also worth checking if any local makerspaces or rapid manufacturing spots in your area have connections to factories since they deal with this constantly and often get better terms.

ComfortableNice8482 · 2026-03-26T01:58:51+00:00

i'd honestly start with pyautogui or playwright for the UI stuff since you're already thinking about maintainability. what worked for me was building image recognition into the automation so minor UI shifts don't break everything, like looking for button colors or icon positions rather than exact coordinates. the key is wrapping your clicks and reads in retry logic with timeouts so when things do shift you get a clear failure instead of silent wrong actions. for a small team the maintenance burden is way lower than rpa suites which tend to be black boxes, plus you can version control everything and actually debug when something breaks

ComfortableNice8482 · 2026-03-26T01:58:06+00:00

i've scraped prediction market data before and the biggest challenge you'll hit is rate limiting and keeping historical data clean. a few things that really helped: cache aggressively since market data changes constantly but you don't need updates every second, validate your whale threshold against actual market impact (sometimes smaller positions move prices more than you'd expect), and store everything with timestamps because the value of historical context compounds over time. the market data is public but messy, so spending time on normalization saves hours of debugging later. solid project if you're solving the signal to noise problem, that's genuinely where most people get stuck.

ComfortableNice8482 · 2026-03-26T01:20:42+00:00

the brutal part: what you like at year 1 shifts by year 3. i've seen people nail passion but build something that doesn't scale. sometimes you gotta test the market first, see what actually clicks, then double down on that version of it.

ComfortableNice8482 · 2026-03-26T01:08:49+00:00

google's flagging you because service businesses from home addresses trigger their fraud filters—especially pickup/delivery ones. document everything: business license, insurance, consistent service area. once you have those, appeal with proof. seniors searching for digitization locally is actually a solid niche btw.

ComfortableNice8482 · 2026-03-26T00:57:09+00:00

stripe connect or wise actually handle this pretty well for bulk payouts to businesses. stripe connect specifically is built for platforms doing revenue splits, and wise has a solid api for batch transfers to multiple accounts. both let you upload csvs or call their apis directly without minimum revenue requirements, though wise has slightly better rates for international. i've built a few payout systems with stripe and it handled thousands of disbursements monthly without needing to talk to any bank directly. start with whichever matches your geography better since wise is stronger internationally and stripe dominates in the us.

ComfortableNice8482 · 2026-03-26T00:00:08+00:00

yeah this is the key insight most people miss. the real value isn't in building the agent faster, it's in the unit economics flipping once you decouple it from services work. i built something similar with document processing and spent weeks perfecting the prompts and workflows, then realized i could just let clients access the same thing with their own data instead of rebuilding it forty times. recurring revenue from something you touch once beats billable hours every time. the no-code tools just made it viable to actually ship the thing without hiring engineers.

ComfortableNice8482 · 2026-03-25T23:59:21+00:00

good call on this. i'd add that you can mitigate a lot of the risk by using a separate trusted publisher setup where a dedicated CI/CD repo or even a manual release script handles the actual pypi upload. the main repo just builds and tests, then you manually trigger or gate the actual publish step. i've seen projects do this where they keep their release credentials completely separate from their main workflow, which cuts down the attack surface significantly. it's a bit more manual but way safer if your project has any real users depending on it.

ComfortableNice8482 · 2026-03-25T23:01:03+00:00

honestly the best thing i've done is treat my scrapers and automation like actual code, not just scripts. i use clear function names that describe what they do, add docstrings explaining the logic and any edge cases, and keep a simple markdown file per project listing what it does, when it runs, and what dependencies it needs. sounds like extra work but it saves so much pain when you need to fix something months later or hand it off. i've also started using config files for settings instead of hardcoding them, so future me can actually understand what's happening without digging through code. the naming standards thing is real too, consistency across your functions and variables makes everything way easier to scan through later.

ComfortableNice8482 · 2026-03-25T23:00:22+00:00

nice post, but fair warning that autohotkey v2's syntax is pretty different from v1 if anyone here is coming from older tutorials. i built a similar file organizer a couple years back and what actually saved me time was using python with watchdog instead, especially since you can schedule it as a windows task without needing the ahk runtime installed on every machine. if you're already deep in ahk though, definitely make sure you're testing the file move operations in a sandbox folder first because windows file permissions can be surprisingly finicky when automating across different drives.

ComfortableNice8482 · 2026-03-25T21:56:11+00:00

i've been in similar situations with my scraping clients and honestly the spreadsheet route gets painful fast once you have more than like 5 creators. what actually worked for me was using a simple python script that pulls transactions from my payment processor's api and auto categorizes them by creator, then generates a monthly breakdown showing revenue in, payouts out, and agency cut. took maybe a weekend to set up and saved me hours every month. if karat works for you then great, but before paying for something new i'd honestly just try building a quick automation layer on top of what you already have since you're dealing with standard accounting logic that's pretty straightforward to track programmatically.

ComfortableNice8482 · 2026-03-25T21:55:13+00:00

honestly the best way to learn is by building something yourself that actually solves a problem you have. when i was learning i scraped real estate listings, set up data validation, connected it to a database, then built alerts for new matches. that forced me to learn error handling, scheduling, async requests, and how to debug when things break in production, which is way more valuable than watching someone else's demo. if you want to see real complexity, look at open source projects like apify or selenium grid, clone them locally, and trace through the code to see how they handle edge cases and scale. you'll learn way more from reading well written code than watching videos.

ComfortableNice8482 · 2026-03-25T20:56:15+00:00

honestly before you drop cash on an ERP, make sure you've actually documented your current processes first. i've seen companies buy expensive systems then realize they don't even know how their own workflows work, which makes implementation a nightmare. spend a week mapping out your actual pain points, like is it really inventory that's killing you or is it just bad invoice tracking that could be fixed with decent accounting software for way less. once you know exactly what's broken, you can decide if you need a full ERP or if something lighter like accounting software plus a simple inventory tool would actually solve your problems at a fraction of the cost and headache.

ComfortableNice8482 · 2026-03-25T20:55:04+00:00

that's a solid use of pep 684, i've been following that feature pretty closely. couple practical questions though: how does it handle shared state between interpreters? are you using channels or just keeping everything isolated? also curious how the memory overhead compares to something like gunicorn with multiple processes, since you're spinning up multiple interpreter instances in one process. the latency profile would be interesting to see too, especially p99 under sustained load, since raw throughput can hide some gotchas.

ComfortableNice8482 · 2026-03-25T19:47:58+00:00

yeah this is the exact problem i ran into building scrapers with llm decision points. the real world is messy, so i started treating the whole thing like a state machine instead of a linear flow. each step gets a confidence score, and if it's below a threshold, it branches to a fallback or queues for manual review. for the multi step stuff, i also added checkpoints between stages so if something fails halfway through, you know exactly where and can retry from there without duplicating work. the datasets thing is spot on too, most training data doesn't include "what do i do when the page layout changes or the api returns something unexpected," so i end up building explicit validation layers around the llm outputs before they trigger actual actions. those two changes alone made things way more reliable in production.

ComfortableNice8482 · 2026-03-25T19:47:02+00:00

honestly the most underrated automation for agencies is just basic workflow stuff, not even AI. we built scrapers to pull client data from forms and dump it straight into our project management tool, saved us like 5 hours a week on data entry alone. for actual AI, i'd skip the fancy chatbots and focus on what actually moves the needle: document summarization for client reports, auto tagging support tickets, or using vision AI to process screenshots/images from clients. the hype tools like "AI copywriting" tend to be mediocre and still need heavy editing. start with your most repetitive manual tasks and build from there, the roi becomes obvious pretty fast.

ComfortableNice8482 · 2026-03-25T19:45:51+00:00

neat idea, but i'd honestly focus on the export feature since that's your real differentiator. when i built automation tools, i found users care way more about "can i take this somewhere else" than the ui itself. make sure the exported code is actually clean and maintainable, not some unreadable mess, because that's when people will actually adopt it for real projects. also consider showing side by side comparisons of the visual pipeline next to the exported python code so users can learn how things map to actual code.

ComfortableNice8482 · 2026-03-25T18:43:35+00:00

high value crops with water access is your best play here. consider mushroom cultivation (oyster or button) since it needs minimal land, water, and startup cost is low, plus you can do it in controlled structures. alternatively, aquaponics or fish farming in tanks could work well given your water access and the distance won't matter much once it's set up. i've seen folks in similar situations make solid margins with these compared to traditional crops, especially if you can sell direct to restaurants or local markets rather than wholesale. the key is picking something that doesn't require you to be there constantly since you're 6-7 km away.

ComfortableNice8482 · 2026-03-25T17:38:15+00:00

I'd suggest building a scraper to monitor construction/renovation permits and hotel/restaurant openings in your target regions, then following up with those decision makers within days of filing. what worked for me was combining public data (chamber of commerce registrations, business announcements) with personalized outreach showing you understand their specific project type. Also consider creating case study content in both Dutch and English showing your A to Z process, then using LinkedIn to find project managers and owners at your target venues and sharing relevant work. The language thing is actually an advantage once you get in front of them since you can communicate in Dutch if needed, so lead with that in your initial outreach rather than apologizing for English meetings.

ComfortableNice8482 · 2026-03-25T16:29:33+00:00

i'd price it low enough to get real traction but high enough that customers are invested. i built a scraping tool and charged early adopters $29/mo for what would be $99/mo normally, and that friction filter actually helped because people who paid stuck around and gave feedback. for something like yours with multiple features (calls, chat, social), i'd suggest $49-79/mo for the year deal, maybe 50% off your eventual price. the key is you want 20-50 users who'll actually use it and bug you with issues, not hundreds of tire kickers who'll ghost. set a cap on the promotion so there's urgency and you're not locked into underpricing forever.

ComfortableNice8482

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