u/iambharatmeenaa

before getting my cockapoo I was fixated on the shedding question because I have mild dog allergies and wanted to understand what I was actually signing up for rather than just trusting the low shedding marketing, so I paid close attention for the first several months and here's what I found.

the answer is the same one that applies to most poodle crosses, it depends on coat type, curlier coats shed significantly less than wavy or straighter coats, and the distribution of coat types within cockapoo litters has real variation, asking a breeder specifically about coat outcomes from their particular lines is more useful than asking whether cockapoos shed in general.

mine has a wavy coat and sheds a small amount, nothing dramatic, I find hair on the furniture occasionally but nothing that's affected my allergies meaningfully, a curlier coated cockapoo from the same litter would likely shed less.

the allergen piece is worth separating from the shedding piece because they're related but not identical, lower shedding reduces dander distribution but the allergen is in saliva and skin secretions rather than the fur itself, for my specific allergies the reduced shedding has been sufficient but individual reactions vary and testing in person before committing is the only reliable way to know.

grooming frequency also affects how much ends up in your environment, regular brushing removes loose hair before it deposits on surfaces.

Most telehealth companies in this space operate like ecommerce storefronts. Same underlying pharmacies, same logistics, different branding and pricing. The markup is real and patients are usually paying for marketing not care.

Been evaluating AI coding assistants for rollout across a 200-person engineering org and repo graph drift is the problem that keeps disqualifying tools that look good in demos.

The pattern is consistent, the tool indexes at setup, looks great for the first few weeks, then the codebase evolves and the index doesn't keep up. Service A gets renamed. An internal SDK gets a breaking change. A shared library gets deprecated. The tool keeps reasoning from the old state and the suggestions get quietly wrong in ways that don't surface until integration or review.

Vendors use the term codebase intelligence without defining it and that vagueness is where the drift problem hides. Some mean file-level autocomplete. Some mean a one-time index of your project directory. Very few mean what I'd actually call codebase intelligence: a continuously updated understanding of your full repository graph, cross-service dependencies, and shared library state that reflects what's actually deployed right now.

For a DevOps context the third definition is the only one that matters. I'm documenting our evaluation criteria here because I suspect others have hit the same gap between vendor claims and operational reality.

some days i’ll get insanely good generations and then other days the videos barely follow

the prompt at all. feels like ai video tools are getting more unpredictable lately, especially

with motion and character consistency.

with all the sora shutdown discussions happening recently too, i’ve been trying to look at

other options more seriously. curious what people here have been using lately for ai video or

creative stuff in general.

Spilled red wine on my carpet last weekend and even after cleaning it myself the stain still shows under certain lighting. Now I’m noticing a bunch of older marks too and it’s driving me insane.

I’m renting in London and probably moving out later this year so I’m debating whether it’s worth hiring professional cleaners instead of constantly trying DIY fixes. I’ve been looking at local carpet cleaning companies online but it’s hard to tell which ones are genuinely good and which are just marketing.

For people in London do you usually hire pros for this stuff or just rent machines and handle it yourself?

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Not looking for huge life overhauls. Just simple, realistic habits that stuck and genuinely helped. Mental, physical, productivity, anything. What's something small you started doing that surprisingly paid off?

For me it's been using tDCS daily. I love my mave sessions. 

I use a Mave headset every morning for about 20 mins with my green tea and a book. It's become my favorite part of the day honestly. Not because anything dramatic happens during it but because those 20 mins are the only time my brain is not consuming something, reacting to something, or planning something. It just sits there. Those 20 mins feels like they are for me only, you know. That kind of feeling… 

But that's mine. I want to hear yours. The weirder and simpler the better. What small thing stuck for you this year?

Most algo losses I see discussed here are not strategy losses; they are execution, data, or API-assumption losses. Manual vs automated is not only about speed; it changes the failure mode. 

I was making chai while comparing docs, so this became a small checklist instead of a rant. For Zerodha Kite, Dhan, Upstox, Fyers etc, I would not ask “which is best?” first. I’d test: 100-200 small orders in UAT/live if possible, log signal timestamp, API request timestamp, order ACK timestamp, fill timestamp, rejected/modified/cancelled states, WebSocket disconnects, reconnect time, duplicate ticks, and rate-limit errors. Use p95/p99, not average. If average ACK is 80 ms but p99 becomes 2 sec on expiry, that is the real number for options adjustments.

Concrete case-style example: say a short straddle adjustment triggers at 10:15:00.000, quote stream lags 700 ms, order ACK p95 is 1.2 sec, and Nifty option moves ₹4 during that gap. On a 4-leg adjustment, even ₹2-3 per leg slippage can turn a clean hedge into a bad one. This is not a strategy backtest issue; it is execution plumbing. So before live size, I’d compare documented rate limits, WebSocket stability, sandbox/UAT parity, historical/expired options data, Greeks availability, and expiry-day behaviour.

My recommendation: beginners should first prove auth/order/position flows in sandbox; intermediate API users should run parallel paper/live logs for at least a few sessions; advanced users should monitor ACK latency and disconnects like a production service. I’m also checking newer infra-first APIs like Nubra because UAT/live parity, Greeks streaming and expired options data are hard to find together, but I’d still verify all of it with logs before trusting size.
What metrics do others here track before trusting any Indian broker API live?

Not about cheap. About the biggest difference between what you pay and what you get.

Arabian fragrance houses are the clearest example I've found. RiiFFS, Rasasi, Ajmal, Armaf. All of them deliver longevity and ingredient quality that should cost significantly more. The distribution and marketing model is just built differently than western brands. The money goes into the fragrance rather than the campaign.

The second clearest example is decant communities. Splitting a 100ml bottle of something expensive across several people brings the cost to almost nothing per wear and lets you try things you'd never risk as a full bottle.

What's your best example of this gap, in fragrance or anything else?