I just ordered a Goat A1600 LiDAR Pro and am waiting for its arrival, and I'm already overthinking about having a pet constantly running around messing with the mower's workflow. I know the A1600 claims its obstacle avoidance is great things like basketballs or toys are no problem. But dogs move unpredictably. So i want to know how mowers usually react to pets moving around the yard?
I never paid much attention to window cleaning before. If the glass didn’t look obviously dirty, I just ignored it. But earlier this year I visited a friend’s apartment with huge floor-to-ceiling windows, and the whole place felt way brighter and more open than I expected. I didn’t realize clean windows could change the feel of a space that much.
They were also using a window cleaning robot, which was the first time I’d seen one in real life instead of online. Watching it move around the glass by itself was honestly kinda interesting lol. After a while it stopped feeling gimmicky and just felt like another smart home device quietly taking care of a chore in the background. Do you guys think window cleaning robots are becoming more common in smart homes now, or are they still pretty niche?
my current setup uses reasoning models for multi-step agent tasks (scrape → extract → summarize → push). The pattern I keep hitting: step 1 is solid, by step 3 the model starts drifting, by step 5 it's solving a different problem than what I asked for.
I've been looking at Ring 2.6 1T because it has a plan-first execution mode, you give it a goal, it outputs an execution plan first, then works through each step. The idea: if the plan exists, pointing the model back should be easier than restarting.
But I haven't pulled the trigger yet. Has anyone tested this plan-first approach with Ring 2.6 1T for real agent work? Does it actually help with drift, or is the plan too vague in practice? What's the realistic failure rate?
I shoot talking-head stuff and short interview clips out of a 10x10 spare room. For a long time I ran a COB as my main key. Every session was the same negotiation: too close and the face looked carved, pull it back and I'm hunting ISO, add diffusion and I lose half the output. Coffee always cold by the time I started rolling.
The two panels I kept coming back to were the Elgato Key Light Panel and the Neewer NL480.
The Elgato gets recommended constantly on creator forums and the reason is obvious once you use it. The app integration is polished, color range is 2900 to 7000K, and the build feels solid. If you're streaming and want lighting tied to macros or scene presets, it's a genuinely good tool.
I went with the Neewer NL480 because of how my room actually works. The NL480 is physically larger, and at close range a bigger source means softer shadows without needing a modifier rig I don't have space for. Output was also meaningfully higher in comparisons I found online. The trade-off is no app integration, but I'm not syncing lights to stream alerts so it wasn't a real loss.
Setup has been boring in the best way since. Output is even, color holds once I dial it in. I rarely push the dimmer to the bottom, but it can skew slightly green-gray on fair skin at very low levels, so I keep a correction saved. I still pull the Neewer HB80C for a sharper key or rim against a dark background.
Elgato is the call if the ecosystem actually matters to you. For a tight video room where you just need a soft, even face and nothing else, I got more out of the NL480. Anyone else made a similar switch from COB to panel as a primary key in a small space, and did the workflow actually get easier or did something else come up?
If you run an ecom store you’ve probably looked into 3PLs at some point. I spent way too much time going back and forth between options and realized most of the info online doesn’t really help when you’re actually trying to decide.
After going through it myself, a few things stood out that made a bigger difference than I expected:
First is just how easy they are to work with. Some look great on paper but onboarding takes forever or you’re stuck waiting days for replies. That gets old fast.
Second is how well everything connects with your store. If the Shopify integration or inventory sync is off, you’ll feel it immediately. That’s where most of the headaches come from.
Third is consistency. Not just fast shipping, but orders going out correctly every time. One or two mistakes here and there might not sound like a lot, but it adds up.
Pricing is another one. A lot of providers look cheap upfront but then you start noticing small fees everywhere. Hard to compare unless you really dig into it.
And honestly, size matters more than I thought. Some of the bigger 3PLs seem great until you realize you’re just another account. Smaller or mid-size ones can be easier to deal with depending on where you’re at.
Best advice I got was to test before committing long term. Way easier to figure things out early than switching later.
Curious what others prioritize when picking a 3PL or if you’ve had good or bad experiences.
Hello, i haven't tested this yet, but I'd like to find a solution so that Ling-1T is configured by default, but uses Ling Flash for code agents, including scheduling.
Ultimately, I want it to only use 1T for the most complex tasks.
Have you found an effective solution for this?
Thank you.
As someone who experiences heightened sensory sensitivity, especially to noise, falling asleep at night can be a real challenge. The background hum of traffic, random household sounds, or even the ticking of a clock can keep me wide awake for hours. I’ve heard a lot about active noise cancelling sleep earbuds, and I’m curious if they could help with noise sensitivity and provide a quiet sleep environment. I’m looking for a solution that blocks out unwanted noise without adding more discomfort, but I’m not sure if sleep earbuds are worth the investment. Has anyone tried active noise cancelling sleep earbuds to help with sensory overload and sleep? Did they really reduce noise effectively, or is it more about masking sounds with white noise? I’m looking for a reliable option that won’t make my ears hurt after wearing them for hours. Any recommendations or experiences would be really helpful!
Do we lock in our opinion of open models way too early?
Feels like a lot of open models get branded in the first 24 hours.
People try a few prompts, read some reactions, decide it’s either overhyped or impressive, and then that label kind of sticks. But that seems like a bad way to judge models that may only make sense after real usage patterns emerge.
Ling-2.6-1T is one of those cases to me, because the more relevant question seems to be workflow fit and efficiency over time, not launch-day vibe.
I’m starting to wonder how many models get mis-scored because people judge them off launch-day vibe instead of where they actually fit a few days later.
Do you think the community re-evaluates enough, or do first impressions basically decide the story?
Something has been bothering me lately and I don’t think it’s getting enough attention.
At first, AI felt like a tool something you use when you’re stuck or when you need help speeding things up. But over time, it quietly shifts from being a support system to becoming the default way of thinking through problems.
Instead of sitting with a problem, people now jump straight to asking AI. Instead of forming an idea, they generate one. Instead of refining thoughts, they regenerate them.
It feels efficient in the moment but there’s a subtle tradeoff happening. The more we rely on AI for thinking, the less we practice doing it ourselves.
And the strange part is it doesn’t feel like a loss. It feels like progress.
So now I’m wondering if the real shift isn’t just about what AI can do, but what humans slowly stop doing because of it.
At what point does convenience start replacing capability?
hey,
Recently started a small ecommerce brand (Shopify) and orders are slowly picking up, but fulfillment is already becoming kind of a pain so wanna fix that.
I’ve been looking into 3PL / fulfillment centers but it’s hard to tell what’s good because of mixed reviws. And tbh i dont know which are best for lower volumes.
Anyone here been through this and found something that works? and please say what's best about it
Sentence mining from native youtube vlogs is the gold standard here, but i think we need to be honest about how miserable the workflow actually is.
pausing a video every 45 seconds to copy the subtitle, look up the grammar, and paste it into anki is soul-crushing for me. you spend 2 hours studying but only got 15 minutes of actual comprehensible input.
My current workflow is way messier but I actually enjoy watching content now. I just watch the video straight through. When it's done, I send the link to recall, which rips the transcript and auto-generates a spaced repetition quiz based on the slang used in the video. I don't make a single flashcard manually.
Is anyone else leaving manual anki decks for automated stuff, or am I missing out on some magic retention benefit by not doing the data entry myself?
I am working on a 3 bed 2 bath cosmetic rehab right now. The previous owners let the house go to foreclosure and someone stripped the outdoor AC condenser for copper before I bought it. I need to drop a new unit in before I list it next month.
I am trying to keep my margins tight. I found a costway 3 ton ducted system online that is extremely affordable and I have a guy who can braze and charge it for a fair hourly rate. My realtor is warning me that buyers might get spooked if they don't see a familiar name brand like Carrier or Lennox on the side of the box. From your experience flipping houses, do retail buyers actually inspect the equipment brand or do they just care that cold air comes out of the vents?