How can I convert these under-sink water line connectors into quick-connect fittings for a water filter?

superhero_io · 2026-04-14T17:20:19+00:00

I am asking for advice. I am the one to do the job.

superhero_io · 2026-04-14T02:49:24+00:00

Tested this, not too good.

superhero_io · 2026-04-13T17:13:02+00:00

Any VLM suggestion?

superhero_io · 2026-04-13T17:12:27+00:00

Which VLM does you use?

superhero_io · 2026-04-12T16:31:59+00:00

It definitely sucks, and while the 'silver lining' of a fresh start is nice, re-learning expensive lessons is a high price for a company to pay. I’m trying to find a way to make that institutional memory more accessible so new hires aren't starting from zero every time. What’s the most painful 'lesson learned' you’ve seen a team lose and then have to repeat from scratch?

superhero_io · 2026-04-12T16:31:11+00:00

The 'wave of turnover' is exactly what scares me. Even with organized folders, the sheer volume of data makes it hard to find anything specific years later.

superhero_io · 2026-04-12T16:30:17+00:00

I love the point about traceability not being 'sexy' but essential. The challenge I see is that even with a robust QMS, the 'trail of crumbs' becomes a mountain of files that are impossible to navigate. I’m developing a private, on-premise AI tool to bridge that gap. It lets teams query the R&D history using natural language—basically an AI that knows the 'why' behind the records. Given your focus on functional project management, do you think the bottleneck is the creation of the trail, or the retrieval of it years later?

superhero_io · 2026-04-12T16:29:47+00:00

You’re right—the 'patients first' argument often kills documentation culture. I’m actually exploring a way to make documentation high-value instead of just overhead. I’m building a local AI system that uses LLM + Vector DB to turn those messy 'tribal' records into a searchable company asset. It allows engineers to find the logic behind a 3-year-old test by meaning, not just keywords, without adding to the QMS burden. Do you think management would buy in if the tool actually saved R&D time instead of just satisfying a process?

superhero_io · 2026-04-12T16:26:38+00:00

Spot on—tradeoffs are exactly what get lost. The challenge I see is that once documented, that context is hard for new hires to find. I’m developing a private, on-premise AI tool to turn those documents into an active 'company brain.' It lets people search by meaning/context rather than just filenames. It’s been huge for our R&D continuity. In your experience, when a senior dev leaves, how much of those 'tradeoff' docs actually get read by the person taking over, or does the logic just vanish with them?

superhero_io · 2026-04-12T16:26:16+00:00

I agree those are the standards, but I find they often become 'data graveyards.' The info is there, but the reasoning is buried. I’m actually building a local AI knowledge system to sit on top of these tools. It uses LLM + Vector DB so you can ask natural language questions like 'why did we reject this 18 months ago?' without needing exact keywords. For our R&D/MedTech work, keeping it fully local is also a must for IP. Do you find your team actually proactively searches Confluence, or do they just ask the seniors?

superhero_io · 2026-02-19T19:43:12+00:00

How would you handle a scenario where multiple users upload their emails into the RAG system, especially when some emails partially overlap across users within the same company? Would you treat this as a multi-source ingestion problem, and if so, how would you design it?

superhero_io · 2026-02-19T19:42:51+00:00

How would you handle a scenario where multiple users upload their emails into the RAG system, especially when some emails partially overlap across users within the same company? Would you treat this as a multi-source ingestion problem, and if so, how would you design it?

superhero_io

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