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

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.

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

Github. weellio/supermarkdown

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?