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

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.

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?

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.

so i got tired of support chatbots that just make stuff up and helpdesks that take a week to set up, so i built my own. its called momo and the core is on github now.

basically you point it at your website or upload some docs, and it answers visitors from that. before it sends anything it checks the draft against your sources, and if its not sure it says so and passes the chat to a human instead of inventing a refund policy. theres an inbox built in where you can jump into any conversation, and when you reply you can hit one button to save that answer as knowledge so the bot knows it next time.

setup is docker compose up, it starts postgres, redis, web and a worker. single tenant, you bring your own model through openrouter, data stays on your server.

its the first version so dont expect too much yet. only a website widget for now, no email or slack, no api, one agent.
https://github.com/moaljumaa/momo

New York Fed President John Williams, speaking on 14 October 2026, stated AI-related supply chain constraints could persist for more than two years and left open the possibility of further interest rate increases. No price moves for specific instruments were reported in connection with his remarks.....

Am I the only one that keeps hopping around different services - VPS provider, then set up n8n, setting up a domain, sending passwords via some secure option for credentials, adding a healthcheck ping in another one and then also some 3rd party backups setup?

I came across this article today about Ramp launching Glass which from what I could gather as info is a AI workspace that lets teams turn their internal knowledge and workflows into reusable AI skills and it seems to also have persistent memory and automation features which to me at least seems like teams don't have to keep setting everything up from scratch.

I recently read a post about someone who automated their morning Slack routine using n8n and AI. They used to spend 1-2 hours scrolling through threads to figure out what shipped, what was blocked, and what needed their attention. Now, an automated workflow scans everything and delivers a one-page summary by 7 AM, saving them around 5-6 hours a week.

The interesting part was that it even caught misalignment between teams by spotting contradictions between discussions in channels like #product and #strategy. Pretty crazy how automation can sometimes catch things that you'd miss even after reading everything yourself.

So curious to the experts here, what super annoying repetitive task have you been able to eliminate with AI/Automation so far?

I’m a firefighter and I use a web-based report system (esosuite) when filling out our fire reports. I’m on a busy unit, so I end up having 10-20 reports typically each day. Each report has 40-50 items I have to enter via dropdown menu, and they are almost always the same selection.

I would LOVE LOVE LOVE some program or “thing” where, when I start a new report I could push a button and it would auto-populate those 40 odd selections. It would cut down the time I take on these reports dramatically, and help save my sanity to boot.

However I am a caveman and I have no idea how to go about setting something up like that. Could y’all point me in the right direction, preferably while using small(er) words? Thanks in advance!

curious what other developers use in their n8n workflowa? especially when automations would require to send out emails and handle incoming replies? are you sticking with gmail or do you use an email api or a dedicated service like agentmail or hostinger agentic mail? would love to know what works for you in production and what you'll avoid

All the GPT-6 Astra talk pushed me to hand one weekly chore to an agent. I don't code and had never automated anything.

Every Monday I copy payouts, fees, and refunds from my payment portal into a 12-field form for my bookkeeper. The portal and form order them differently, so by hand it takes about 40 minutes.

On my Mac, I signed into the portal through MiniMax Code's built-in browser. Each Monday the agent:

• opens the statement page and reads the figures
• fills each figure into its matching field on the form
• stops at submit and asks
• puts the week's figures on a simple page and hands me a link to look them over on my phone

First Monday ran about an hour, longer than by hand, because explaining which line goes where took the time. Second Monday, reviewing each field and pressing submit, about 10 minutes.

Two Mondays in. I still review every field and press submit myself because that's where a wrong number matters. What part of your recurring payout or invoice work do you still do yourself?

Tasket++ lets you define your own clicks, keystrokes, cursor positions and system operations, then replay them exactly when you want. It’s a simple Windows tool that handles repetitive workflows for you: auto‑send messages, paste text anywhere, take silent screenshots, launch or close apps, adjust volume or run your end‑of‑day shutdown sequence.

Everything runs locally, with no telemetry, no cloud, and no complexity.

What it can do:
- Replay user‑defined cursor positions and keystrokes
- Paste predefined text anywhere
- Perform system actions: open files/programs, change volume, take silent screenshots, shut down, files/folders operations
- Schedule tasks at a specific time, at startup, or run via desktop shortcut
- Run tasks once, in loops, or indefinitely
- Discreet mode: runs quietly from the tray
- Use command line to autorun a task : Tasket++.exe "TaskName" <delayInSeconds (>0)> <NumberOfLoop (-1 for infinite)>
- Easily share tasks between different PC : just import the .scht file located in /saved_tasks

Local, portable, free, open source. Privacy intact.

Fully fonctionnal, available now !
Microsoft Store: search for "Tasket++"
Portable (v2.1) & Source: under Github /AmirHammouteneEI/ScheduledPasteAndKeys

For feedback, help, suggestions, or other inquiries : [contact@amirhammoutene.dev](mailto:contact@amirhammoutene.dev)

Nothing is vibe coded, I never used AI (except for the choice of UI colors suggested by Copilot).
Conception is genuine, I didn't know about similar tools when I began developing it.

I'm looking for testers or quick feedback (beyond its visual aspect), please give a try and comment here :)