17:T6e8,I built a small workspace lighting setup using LED strips and ended up learning more than I expected. On paper it was simple: stick a strip somewhere, add a diffuser, done. In practice, the stuff that mattered wasn’t the spec sheet—it was power delivery, optics, and human perception.

A few notes that surprised me:

Power planning > “just buy a bigger PSU.” Long runs behave like distributed loads. Voltage drop shows up as uneven brightness, and on RGB/RGBW it can show up as color shift (“white” gets warmer at the far end). The fix isn’t only wattage—it’s where you feed power, wire gauge, and connector losses.

Diffusion is not cosmetic. Without enough distance or diffusion, you get hotspots and glare. A cheap milky diffuser in an aluminum channel gets you most of the way there, but what helped the most was increasing LED-to-diffuser distance (depth of the channel) rather than chasing “premium” diffusers.

Indirect beats direct for comfort. Bouncing light off a wall/desk surface looked dimmer on paper but felt more usable and less fatiguing. It also hid the fact that LEDs are point sources.

Signal integrity is a separate problem (for addressable). A lot of “flicker” is actually data/ground/reference issues, not power. Short data lines, solid ground, and sometimes level shifting helped more than swapping power supplies.

Questions for folks who’ve done larger installs (10–50m) or more “production” setups:

Do you design power delivery first or physical layout first?

Any favorite diffuser/channel profiles that minimize hotspots without killing too much output?

For long addressable runs, what’s your go-to strategy for signal conditioning (buffers, differential, etc.)?18:T4a5,Every place I’ve seen run more than a couple of ML models in production ends up with a mess of bespoke inference services: different APIs, different auth, different logging, half-working dashboards, and tribal knowledge holding it all together.

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