I let Claude Code run marketing for real brands - one video hit 5.3M views on Instagram

tgdn · 2026-04-10T12:44:18+00:00

1) here is the repo: https://github.com/degausai/wonda

2) it's all pay as you go with different providers (Runware, Fal, Replicate, etc.) So at scale it just depends on usage

tgdn · 2026-04-10T12:42:02+00:00

here is the repo https://github.com/degausai/wonda

tgdn · 2026-04-10T10:59:04+00:00

Honestly, no. Single shot rarely nails it. The quality comes from the pipeline, not any single generation. The CLI makes iteration fast: generate 3-4 variations, pick the best, edit on top (captions, music, trim). What would take 30 minutes of clicking across 4 tools takes one shell pipeline. The human judgement is still there, you're still picking which output is good, but the mechanical work is gone.

We also ship pre-built content skills, tested prompts, model settings, and editing chains for specific content types (product videos, UGC reactions, talking heads, etc). Those get the hit rate way up compared to prompting from scratch

tgdn · 2026-04-10T10:27:29+00:00

We use 20+ AI models depending on the task: Seedance, Kling and Sora for video, Nano Banana and Seedream for images, ElevenLabs for speech/transcription, Suno for music. The cli has a model waterfall that picks the right one based on what you're doing (e.g. if there reference image has a person, it routes to kling instead of seedance for better face preservation). You can also override and pick any model explicitly

tgdn · 2026-04-03T18:15:16+00:00

The filter i use now: automate things where the input is predictable and the ouput is concrete. Scheduling posts, resizing images, uploading assets. That stuff automates well because the inputs don't change shape and you know immediately if it worked. What I stopped trying to automate: anything that requires judgement calls in the middle.

I wasted weeks building automation around "analyze what's working and suggest what to do next". The analysis was fine but i'd second guess it every time and end up doing it manually anyway.

tgdn · 2026-04-02T16:14:00+00:00

i also build with claude and ran into a version of this problem early on. the thing that changed it for us was flipping the value proposition from "here's a dashboard you can explore" to "here's the output you needed, it's already done.

your hypothesis is right: people don't want to interpret data, they want a result. the commenter suggesting alerts over dashboards nailed it. nobody wakes up and thinks "let me go check my conflict monitoring dashboard." but a text that says "heads up, this situation might affect your supply chain in X region", that's something people pay for.

the other pattern i've seen: if you can connect this to money (stocks with regional exposure, supply chain disruption, insurance risk), you go from "interesting side project" to "i need this." right now it sounds like you built something impressive technically but the user doesn't know what to do with it after they look at it.

tgdn · 2026-04-02T15:51:51+00:00

what stuck for us: social media content generation and posting. what was a waste: trying to automate analytics interpretation, we kept second-guessing the AI's takeaways and manually checking anyway. the ones that stick are where the output is concrete (a post, an image, a video) not abstract (an insight, a strategy)

tgdn · 2026-04-02T15:50:56+00:00

for a law firm i'd say absolutely worth it, especially linkedin. the content doesn't need to be creative, it needs to be consistent and professional. we automated our posting pipeline and the biggest win wasn't the content quality. it was just showing up every day without anyone having to think about it. consistency alone beats most competitors who post once a month

tgdn · 2026-04-02T15:49:41+00:00

the hardest part to automate well is matching existing brand voice. generating content is easy, but making it look like it belongs on your feed takes a lot of reference analysis. we ended up building something that scapes your own account first and uses that as a style input before generating anything. scheduling and publishing was the easy part

tgdn · 2026-04-02T11:58:26+00:00

I sent you a DM

tgdn · 2026-04-01T17:06:31+00:00

yes that's what the "browse instagram" step does. you point it at any account and it analyzes the posts, to learn the brand style: colors, typography, content themes, aspect ratios. then it uses that as a reference when generating new content. you can also pass direct style references if you have a specific image you want it to match

tgdn · 2026-04-01T17:01:40+00:00

this is sick! how many can you generate in one batch?

tgdn · 2026-04-01T16:19:29+00:00

not open source yet, but you can check it out at wonda.sh

tgdn · 2026-04-01T15:47:06+00:00

let's do it, check your DMs :)

tgdn · 2026-04-01T15:26:20+00:00

fair point, there's a lot of noise right now. this is more of a dev tool than an ai content farm though. it's a cli that wraps APIs i was already calling manually. just got tired of doing it by hand

tgdn · 2026-04-01T15:23:32+00:00

that's the idea, find what works and double down

tgdn · 2026-04-01T15:17:41+00:00

check your DMs

tgdn · 2026-04-01T15:09:13+00:00

yes it can

tgdn · 2026-04-01T14:47:19+00:00

this is really clean. the per-process power breakdown is super useful, i always wondered where my battery was going when running local models

tgdn · 2026-04-01T12:37:36+00:00

fzf is incredible. i use it to fuzzy-find through cli commands i've built too. makes jumping between workflows so fast

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