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i am trying to automate back office ops and was learning claude and asked to very vague prompt to build 19 worksflows across different departments … but not sure weather if I can productive all of them n deply to agentcore

so instead of going on creating auth keys from zendesk, hubspot , stripe , gmail, calendar and slack.

I asked agent to verify and show me proof against fetchsansbox managed relay for webhooks and behavioral verification of agent written code

wonderibf what are you guys doing for automating business ops

A pattern that comes up in a lot of content automations: search for recent posts or news on a topic, scrape the full pages, then make a header image for a summary.

That's three providers, three accounts and three bills, for something that might run twice a week.

I can see arguments both ways. Picking the best provider for each step usually gets better results, because a great scraper isn't necessarily a great search engine. But for low volume, the overhead of three accounts can be bigger than the actual usage.

For people running this kind of chain: did you go with one service that covers all three, or the best tool for each step and live with the extra accounts?

Curious how common this is.

Most automations use a handful of paid APIs, but only one or two of them get hit a lot. The rest get called a few times a week, and many of those providers still want a monthly plan or a minimum top-up. So people end up paying a full monthly plan for a scraper that does twenty pages a week.

What I've seen people do about it:

- drop to the smallest plan and hope it's enough

- share one account across several projects

- switch to providers that charge per call

- route the occasional tools through something that bills per call (I work with the Agentik team, which does this)

What's the one you pay for every month and barely touch?

i've been looking at everyday tasks that could be handled with a basic script instead of paying for another tool. weekly meal planning is a good example because most of the information is already sitting in a spreadsheet somewhere

a script could pick meals from a saved list, avoid repeating dishes from the previous week, and build a grocery list from the ingredients. the tricky part isn't generating the plan. it's dealing with missing data, ingredients used across multiple recipes, and meals that keep getting picked because the selection logic is too simple.

i'm interested in automations that stay useful without constant maintenance. a setup that takes ten minutes to update is fine. one that needs debugging every sunday defeats the purpose

what's a personal automation you've built that still works months later without needing much attention?

When people talk about automation, the conversation usually jumps to big systems and complicated workflows. The thing that has helped me most was tiny.

Every Monday I used to spend about 40 minutes copying numbers from a few emails into a spreadsheet and then writing a short summary for my team. It was dull and I made small mistakes when I was tired. I set up a simple flow that pulls those numbers into the sheet automatically and drafts a first version of the summary. I still read and edit it before sending, because the draft sometimes misses context. But that 40 minutes is now about 5 and I’m much less likely to get a number wrong.

What surprised me is how much the small stuff adds up. I used to think automation was only worth it if it replaced a whole job, but shaving off the repetitive 10 or 40 minutes a week turned out to be the part I appreciate most.

I’d love to hear yours. What’s the smallest automation that gave you the biggest return and what tools or approaches did you use? Did you ever automate something and then realize it wasn’t worth it, or that it broke in a way you didn’t expect? And is there something repetitive in your week you’ve been meaning to tackle but haven’t started?

Details on what went wrong are welcome too, since those stories usually teach the most.

We build market intelligence engines that track buying signals across 75+ channels, including reddit, github, linkedin, web activity, and public registers and we've been doing this for a ton of companies and wanted to try it with this community.

If you've an active B2B SaaS, i'll research:

-- buyer pain: reddit threads where people discuss the problems you solve or look for alternatives
-- competitor signals: companies and products that keep coming up in relevant conversations
-- buying triggers: signals that could help you identify the right time to reach out
-- potential accounts: 5 companies worth looking into with the signals behind each one

to get started, share your website, ideal customer profile (ICP), and main competitor.

This is best suited for B2B SaaS companies with an active product and some traction but if you're a pre-launch, this won't be helpful for you.

edit: if you want the full report and buyer graph delivered directly, drop your details here buyer pain finder and we'll send the research directly to you. it's completely free.

I’ve been trying to use ChatGPT Plus to extract data from my bank statements.

Right now I crop out the section showing the balances, save it as an image, and ask ChatGPT to turn it into a CSV.

The problem is that the accuracy is hit or miss, and there is still a ton of manual work involved.
What I really want is to build an automated workflow, and this is the part I need help figuring out.

Ideally it would:

1. Go through my email and download bank statements automatically.
2. Go to my other bank accounts, ask me to log in when needed, and download the statements from there too.
3. Open the statements, including some password-protected PDFs.
4. Strip out everything I don’t need and extract things like account balances, spending, payments, etc.
5. Export everything into a clean CSV or spreadsheet that I can analyze every month.

I assume some combination of ChatGPT, Python, OCR/PDF extraction, and browser automation could do this, but I’m not sure what the best setup would be.

I've tried working and because it doesn't support direct access to my banks it's not really that helpful. Plus I want to use this as a learning opportunity. Any guidance would be appreciated. No

Edit:
1. live outside the United States and my overseas bank doesn't have a option to download things as anything except for an encrypted PDF which allows me to use a password to read but not edit (OCR) it. In between the table that shows all my transactions they will put ads for their products and the entire PDF will be eight pages long with half a page of the data. I need extracted on three of those pages. I've tried commercial PDF extraction, Tools and none of them work. ChatGPT used to do a good job of converting the images to a CSV but lately it has been making so many mistakes that the data is unusable.

1. I have severe ADHD and going to the website and trying to remember which file I already downloaded and which one I need to download next is hell for me because the website assigns random names to each PDF file as opposed to something logical talking about the statement date.

2. One of my US banks only allows me to download the past nine months of data into Excel and I frequently forget to do this.

I’m temporarily helping a PPC team that recently lost a few key members. They spend a lot on Google and Microsoft, and performance is somewhat unpredictable. The team is now tiny, junior, and one new member still needs onboarding.

I noticed they're not strong when it comes to attention to detail: exclusions mapped to the wrong campaign, irrelevant traffic getting targeted, audiences poorly built etc.. I think algorithms can flag most of this for review, especially when new campaigns are built or something gets changed.

I’d like to set up AI processes that help the team review exceptions daily or every other day. For example, a search term report that flags irrelevant terms, alerts on sharp changes in CTR, impression share or conversion rate etc.

I don’t think there's a need to build this from scratch, I’m sure plenty of people already have workflows. Have you built any that worked? LinkedIn posts are full of Google Ads automations, but I suspect a lot of it is garbage to gain followers. Did you try any?

Thanks for your help

Trying to decide how to set up multichannel outbound.

1 - I can take an all-in-one tool that does linkedin and email, like Expandi, Dripify or Skylead
2 - choose a a dedicated Linkedin tool plus a dedicated email tool, for example Linked Helper and Instantly.

wdyt?

A simple html markdown reader. Open in any browser drag on your MD file and read it properly formatted.

Github. weellio/supermarkdown

I've been building a morning account digest in n8n that combines company updates, employee changes, and recent CRM activity. Claude turns everything into a short brief that gets sent to the account owner in Slack before their first call.

For the data side, I use Coresignal for company and employee updates and pull recent interactions from our CRM. It's useful when an account has just a couple of things happening.

The problem is accounts with a lot more activity. When there are eight or ten events, the summary either gets too long or Claude compresses it so much that the important details get lost.

I've tried changing the prompt and asking for a fixed number of bullet points, but that doesn't completely solve it. Sometimes the model picks the wrong things to prioritize.

I'm considering splitting this into two steps: first rank the signals by relevance, then generate the summary from the top few. I could also do some of the filtering in Python before calling Claude, which might make the results more predictable.

For those who've built similar workflows, what worked best? Multiple LLM calls, rule-based filtering, or a better prompt?

I just want the summary to be something people can skim in Slack without having to stop and read a wall of text.

The usual way to try a new provider in a workflow (a different search API, an enrichment tool, a scraper) is: sign up, add a card, often pick a monthly plan, and then find out after ten calls it wasn't the right one.

For a quick comparison that feels backwards. Paying a few cents per call to run the same inputs through three providers side by side would tell you far more.

The options I know of:

- free tiers, when they're big enough to test properly
- trials, which tend to expire before you've decided
- a service that already has several providers and bills per call (I work with the Agentik team, which works this way)

How many test runs do you usually need before you trust a new API enough to build on it?

The 16th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMusArt 2027) is accepting paper submissions.

When: 31 March – 2 April 2027
Where: Mainz, Germany
Paper Submission deadline: 1 November 2026

EvoMUSART focuses on AI and computational approaches to creativity, covering a broad range of topics, with a particular emphasis on research exploring how AI systems generate, support, evaluate, interpret, and collaborate in creative processes.

Accepted papers will be presented at the conference and published in a dedicated LNCS volume.

If you are working at the intersection of AI and creative practice, this may be relevant to your research. Please share with others who might be interested.

Six months ago the startup I worked for closed our Lahore office, partly because "AI is doing a lot of the work now". Since then I've been building things and making short videos about dev tools. Recording takes twenty minutes. Editing took me hours and still looked bad.

So I automated it with ffmpeg, whisper.cpp running locally, and Claude Code picking the cuts from the transcript. My first raw take was 118 seconds and the cut came out at 59.5, because 42% of it was pauses and retakes.

Cutting turned out to be the easy part. Most of my time went to renders that finished without an error and came out wrong:

1. Phone footage reports 1920x1080 but decodes as 1080x1920 because of a rotation flag. If you don't read the display dimensions, every vertical video gets letterboxed.
2. When you change speed, -ss and -t both have to go before -i. After -i, -t applies to the sped-up timeline and cancels the speed change.
3. Every cut pops unless you fade the audio for 30ms at each segment boundary.
4. Overlays at a different frame rate get resampled and judder on exactly the moves you designed.

Now I pull frames at every cut and measure audio levels before I trust any render.

The full list is in HARD-RULES.md in the repo (open source, I built it). The sub blocks links, so search i-hate-editing on GitHub if you want it.

What's the worst silent failure you've hit automating media?

As an accountant, I was a little wary of using AI at first and saw it as a bit of a threat for a long time. But once I started messing around with some of the newer bookkeeping and accounting tools, I started to appreciate what they actually bring to the table without trying to take over the entire workflow.

For example, some of the more traditional platforms, like QBO and Xero, incorporate AI now. Other platforms are built with AI from the start, such as Digits with its agentic general ledger and Ask Digits assistant.

After trying a few of these modern tools and flashy AI features, here’s my takeaway: many things can (and should) be automated with a relatively high degree of accuracy. Not everything is a great fit for it, though.

What can be automated

• Invoice data extraction: This is one of the most common automations I’m seeing in action, leveraging OCR software to extract invoice data so you don’t have to input everything manually. There will always be some situations where an invoice is so sloppily formatted (even handwritten) that OCR doesn’t work, but that’s less and less common these days. 
• Invoice matching: Many platforms can match the PO, invoice, and receipt on their own, with 2, 3, and 4-way invoice matching able to be completely automated. 
• Categorization: There’s no need to manually go in and code each transaction yourself these days; AI can now categorize transactions under revenue, expenses, COGS, etc. 
• Journal entries: When you import data from your payment processor, your accounting software can automatically generate journal entries for each transaction. 
• Bank reconciliation: Much of the month-end close process, including bank recs, can be fully automated.
• Report generation: This is another one that’s kind of a given if you’re using any sort of AI accounting or automation tool. They update financial statements in real time, so you can see where you’re at, pull your P&L and balance sheet, and keep an eye on KPIs whenever you need to.

What still needs humans

• Client communication: Automation can handle stuff like invoice follow-ups, but it won’t build relationships for you, answer nuanced onboarding questions, help implement new processes, and so on. 
• Tax strategy: This is a broad category, but much of tax strategy requires human judgment. While AI can give you some of the basic facts, it can’t make the decision of whether you should go with an S corp or an LLC to lower taxes, it can’t tell you exactly which deductions you can take (though it can give you an idea), and it can’t file your tax return for you.  
• Lease accounting: Amortization schedules can be easily automated, but you don’t want AI running wild when you’re dealing with lease identification, subleases, and legal issues.
• Fair value measurement and valuation: This is another area where AI/automation can help, but it can’t do everything. A lot of times, you’ll need humans involved in valuation, whether you’re looking at stocks, patents, goodwill, or most anything else. 
• Cash flow strategy: You can get forecasts and explore different scenarios pretty easily with AI, but ultimately, humans are the ones deciding where to set prices, how to respond to unexpected events (like natural disasters and trade wars), and whether to use a credit card or pay cash. 
• Complex judgment calls: This is kind of a catch-all, but accounting is filled with ambiguous and confusing situations. Sometimes, it’s due to an inherently complex situation, like a merger. Other times, people are the ones contributing to much of the confusion. Either way, you can’t fully automate your way out of a complicated task. 

For any of my fellow accountants, I’d be interested to know what your lists would look like compared to mine. What are you automating vs. what are you still doing manually, and which tools are you using? 

A quote gets useful during the conversation after it is generated. “Make that six locations.” “Remove the weekend coverage.” “What happens if we double the volume?”

Put those choices in editable inputs and keep the pricing logic in formulas. The output becomes a working calculation the client can explore.

Univer has a spreadsheet engine with formulas, validation and an editable browser interface. Its Workspace also supports agent-generated mini-apps whose web controls, metrics and charts are bound to spreadsheet cells. A quotation screen can expose the few inputs a customer needs while keeping the calculation in the workbook.

For example, store location count, monthly volume and service level as inputs; calculate setup cost and recurring cost separately. Let the agent prepare the initial quote and explanation. The customer then changes an input and sees what it does to the calculation.

That gives the sales conversation a concrete object to work with. The next question is about a visible assumption or line item, and the accepted scenario can move into the team's review process.

We’re evaluating AMRs for transporting electronics inside an ESD-protected area. Some vendors call their robots “ESD-safe,” but mainly mention conductive wheels and antistatic materials.

1. What evidence should we request to verify this claim?
2. Which standards and test methods apply?
3. Should the complete AMR be tested while moving on the factory’s actual ESD floor?
4. What resistance or voltage-generation results should the vendor provide?
5. Is third-party testing normally required?

I’d appreciate any real-world experience with qualifying AMRs or AGVs for electronics manufacturing.