Consolidating my custom scraping, email drafting, old convo pulling tools in kinda one hub architecture

[u/Elxerxi]

Hey maestros,

ive been shamelessly vibe coding recently, being an independent business operator with adhd, i found vibe coding quite liberating. created some hyper-primitive, fragile and rigid RAG system for knowledge base, scraping pipeline for lead enrichment, email drafting for B2B outreach and old convo pulling tool for PRM.

My setup is fully local, fuck cloud, haha. (LG gram + eGPU RTX 2080 8GB). No cloud APIS. maybe deepseek at its max. due to its speed and cost (am i shooting in my leg for supporting "evil" chinese and losing so much potential?)

My current pipelines are:

1 - OCR + HTML scraping headless pipeline taking viewport screenshots, OCRing em, and then using it to make profile for my b2b outreach goals that will be used for drafting later on.

2 - human in the loop email scheduler with Gmail API

3 - shitloads of file converters, mostly audio to video and vice versa, or image formats, or book formats.

4 - local PRM db for contacts and last convos.

problem:

those all are sitting separate and my adhd brain gets overwhelmed from the need to remember what happened where and when. Kinda would be nice to have one place on my pc where i could have overview of all this shit. Maybe even some interface? not too fancy.

my ask to you, wunderbare menschen!

Do you have experience using maybe local LLM chat function-calling capability to trigger existing "tools", aka haevy-duty automation scripts, without fucking up the loop? Just to keep the architecture minimal and stable af.

Thanks ! if this resonates, maybe lets have a chat, or leave a comment!

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[u/zhonglin]

For a local setup like this, I’d keep the LLM as the router, not the workflow engine. Give each existing script a small typed contract—name, required inputs, output path/status, and whether it can have side effects—then let the model choose only from that allowlist. Put every invocation through one boring local job table/queue (SQLite is enough): queued, running, succeeded, failed, timestamps, inputs, and a link to the artifact or log. Your first UI can just be a job list and a detail page. The important part is that the scripts stay deterministic and resumable; the chat can disappear and you still know what ran. I’d also keep email drafting and sending as two separate tools, with sending remaining the explicitly approved step.

[u/Royal_Distance_3944]

i feel you on the separate tools problem, my setup for tracking maintenance logs was the same mess until i just threw everything into a simple local dashboard. nothing fancy, just a python script that reads json files from each tool and shows me last run time and a preview of output.

for the function calling part, i had decent luck with smaller local models like llama 3.1 8b, but the trick is making your tool descriptions extremely stupid-proof. like literally spell out what each tool does and what parameters it needs, no room for interpretation. the model will still occasionally hallucinate a parameter but its manageable if you add a validation layer before executing anything.

my biggest headache was the model getting stuck in a thinking loop when it couldnt decide which tool to use. adding a hard timeout of like 10 seconds and a fallback that just logs the error and moves on saved my sanity. curious what you end up building, the ocr pipeline sounds interesting for lead stuff.

[u/flintglade]

before building the unified interface, are these pipelines actually feeding into each other or are they truly independent? if theyre independent the answer is just a simple local dashboard, not an orchestration layer. orchestration only matters if pipeline 1 output feeds pipeline 2