u/BadGeeky

hi all

been renting in phra khanong for around 2 and a bit years, one bed maybe 30 something sqm on a middle floor. Its fine but the electricity bills have been slowly getting under my skin, and after talking to a friend who lives near thonglor last month i realised how much ive been overpaying.

my building charges 8 baht per unit which is basically double what MEA would charge me on a direct meter. Asked juristic office more than once and the answer is always the same, its baked into how the building operates and you cant switch. Two of my neighbors have complained about it over the years and nothing changed. My friend in thonglor pays around 4.5 baht through a direct meter, i felt kind of stupid for not doing the math sooner.

did the math one bored sunday. aircon was probably eating 55-60% of my bill running maybe 10ish hours since i started wfh. Average bill this past hot season was around 3200 baht for one person in a small unit which for phra khanong felt too much.

Since i cant change the per unit rate, the only lever i had was the aircon itself. The one that came with the flat was a really old non inverter split, probably 8+ years old, cools ok but obviously not efficient. Talked to my landlord (surprisingly reasonable) and she agreed i could replace it at my cost if i leave it when i move out.

Spent a saturday at homepro and power buy. Daikin and mitsubishi were both quoting well above 25k for a 1.5HP inverter installed which felt like too much for a unit i'll leave behind. Ended up going with a cheaper inverter a sales guy at homepro flagged, all in about 22k with removal and new copper pipe.

3 weeks in, bills trending lower on the same usage pattern, need one more full cycle to have a real number. The new one is a Midea Celest btw. Room hits set temp way faster too, and i actually turn it off sometimes when i step out because it comes back down quickly.

Anyway thats the whole point i guess, if you cant change the per unit rate the appliance is the only lever. Also if you rent in phra khanong and pay closer to MEA rates please drop the building name because i want to know where to look when my lease is up.

Started actually trying to hit the fiber recommendation about 3 weeks ago and my gut has been actively unwell about it ever since. I'm 43, generally healthy, and until this year i was probably averaging 12-15 grams of fiber a day. Read enough about the 25g recommendation for women and decided to just do it. Beans at lunch, oats in the morning, chia in yogurt, more vegetables at dinner, psyllium scoop on days i'm falling short. Sitting around 30-32g most days now.

Mornings i'm flat. By 2pm i look 4 months pregnant. It's not painful and there's no specific food i can pin it to, just a slow midday puff that comes on no matter what i ate. Waist is up almost an inch even though the scale hasn't moved.

Things i've tried since it started.

Adding fiber even slower over 2 more weeks. Made no difference, just extended the misery.

Drinking more water. Up to about 3 liters a day now. No change.

Spreading fiber across the day instead of loading it at lunch. Slightly better in the mornings, still puffy in the afternoon.

A multi strain probiotic from the pharmacy for 6 weeks. Nothing i could point to.

Digestive enzymes with meals. Helped on two heavier dinners then went inconsistent enough that i stopped bothering.

The thing that actually started shifting things was switching probiotics. Not the multi strain grab bag from the pharmacy but wonderbiotics weight management, which actually spells out the strain codes on the label (b420 and hn019 are the two bifidobacterium ones) plus polydextrose as a prebiotic. I'd been reading around and kept seeing that specific bifidobacterium strains are one of the main things that helps the gut catch up to a bigger fiber load, and the multi strain i'd been on didn't really tell me what was in it beyond genus. About 4 weeks in the afternoon puff is maybe 40 percent smaller and my waistband is comfortable through the day again. Not going to pretend it's a complete fix. B. longum specifically isn't in it, only b. lactis which is a different species even if same genus, and the full formula has never been tested as a whole. But it's the first thing that's actually moved the needle in months of trying stuff.

Would be curious what other women in this age range doing something similar have actually landed on. Fiber source diversity is the one thing i haven't really pushed on yet, still leaning heavy on beans and oats. Suspect there's more room there than i've explored.

Small AI tooling startup out of Singapore, four of us here. Six contractors spread across Indonesia, Argentina, Vietnam, and one in Portugal. Late 2024 we started scaling and three of them independently asked if we could pay in USDC. The Buenos Aires one was the loudest, which tracks given the peso was down about 30% that year.

Deel didn't have DLUSD back then, and the crypto withdrawal setup they did have felt like an afterthought. Our setup was Deel plus Wise Business SG for SGD ops. Deel was $49 per contractor per month plus their FX markup, and then the contractor typically ate another spread converting to local currency. On a $2k invoice that adds up, especially in markets where the local rate is already bad.

First thing we tried was sending USDC directly from an exchange account. Worked but felt fragile, and after everything that happened in 2022-2023 I didn't love having company treasury sitting on a CEX.

Moved to a self-custody wallet setup instead. Company holds USDC in a multi-sig, monthly payouts go straight to contractor wallets, they off-ramp to local currency however they prefer. Settlement is basically instant, no monthly platform fee for the ones on this track, and the Buenos Aires contractor especially likes just holding the balance and spending it as needed.

Not a free lunch though. Deel is still on for anyone we might convert to full-time later (contract compliance stuff, EOR pathway if we need it). Wise Business stays for SGD ops and local invoices. We also lost some of Deel's built-in paperwork on the contractor tax side, so more of that falls on us now per jurisdiction. Haven't been audited yet, so honestly no idea if this holds up long-term.

For the wallet-plus-card layer we ended up on MetaMask for anything that needs a dapp connection, and BenPay for the card side plus cross-chain moves. The card ties into the same self-custody balance we pay contractors out of, so team spending on SaaS subs and hardware pulls from the same pot and cuts down on internal reconciliation a lot. Chain list on BenPay is shorter than what MetaMask exposes so we still bounce back for anything on a longtail chain, and its country coverage is US MSB shaped so onboarding for a couple of our contractors took some back and forth. Balance sits mostly on Base and Arbitrum for now.

MAS's stablecoin framework moving to full effect this year makes me feel better about betting on this longer term. Genuinely curious how other SG founders are handling this now that Deel has DLUSD live. Anyone moved fully over, or still running your own setup like us?

Been learning Japanese for about 20 months. Somewhere around N3 now, did Genki I and half of II, ground through the core 2k deck on Anki, watched more anime than I care to admit. On paper I was fine, grammar tests were fine, reading was slow but decent. Then I had my first actual voice chat with a Japanese person and felt like I'd been studying a completely different language.

It wasn't the vocab. Wasn't the grammar. It was that she kept making these tiny sounds every few seconds while I was talking. うん. そう. へえ. なるほど. うんうん. I kept pausing because I thought she was trying to interject, so I'd stop, wait for her to say something, and she'd just look confused. Then I'd start again and she'd do it again. A few things that took me forever to actually internalize:

そうですか versus そうなんですか. Genki taught me the first one. Almost nobody in casual conversation just says そうですか. It comes off flat, sometimes even skeptical. そうなんですか carries mild surprise, "oh really?", and it's much more common. Getting the wrong one made me sound like I didn't care what the other person was saying.

なるほど. I used this way too much for the first year because I'd heard it a lot in anime. Then a Japanese friend gently told me I probably shouldn't use it with someone older or in a work context. It has a subtle "I've evaluated your point and accept it" energy, which is fine peer to peer but weird coming from a junior. Nobody explained this to me anywhere.

へえ. This is for genuine new information. If you use it for something that's actually not surprising, you sound sarcastic. If you don't use it when hearing something interesting, you sound like you're not listening. The timing matters more than the word.

うんうん double. Way more casual than a single うん, and completely wrong in any semi-formal context. I probably used this with people I shouldn't have for like six months.

The thing I eventually figured out is that aizuchi isn't a filler. It's the listener's half of the conversation. Japanese conversation is structured so both people are actively producing sound, and if you go silent as the listener, the speaker starts feeling like they're talking to a wall. Kind of the opposite of English where interrupting with sounds is often considered rude.

None of this was in Genki. None of this was in the JLPT prep books I've used. I started actually picking it up by doing voice chats with Japanese friends I'd met on HelloTalk. Maybe an hour a week per person, half in English half in Japanese, and I gradually started copying the timing and choice of their aizuchi. Took months before I stopped feeling self-conscious about it, but eventually it became automatic and my conversations started feeling less mechanical.

The part that surprised me most was how much this changed my listening comprehension. Once I stopped pausing to figure out if the other person was trying to speak, and started expecting the constant aizuchi from them, I could actually follow much faster speech. My brain wasn't burning cycles on false interruption signals anymore.

If your grammar is fine but conversations still feel like you're doing something wrong, this might be part of it. It's one of the biggest gaps between what textbooks teach and what actual Japanese people do.

Halo. Mau diskusi soal handling PLN drop voltage, khususnya buat rumah tangga yang beban listriknya campur-campur (PC, monitor, kulkas, AC).

Setup di rumahku (Sidoarjo, R1 2200 VA). Voltmeter yang aku pasang di stopkontak sering baca 190-198V pas jam beban puncak (17:00-22:00 WIB), pas hujan lebat bisa nyentuh 185V.

Imbasnya udah kerasa. PC (PSU Corsair CX650M) pernah restart 2-3 kali pas voltase drop-nya sangat mendadak, kulkas kompresor bunyi klik-klik masuk protection mode, monitor kadang blank sekilas. Mulai worried soal long-term degradasi komponen.

Sejauh ini aku tahu ada tiga opsi umum:

1. Stabilizer eksternal servo-motor kayak Matsunaga atau Kenika. Kapasitas 1000-3000 VA harganya Rp600rb sampai Rp2 jutaan. Efisiensi sekitar 95-97%. Downside: ada bunyi mekanis dari servo, dan buat load fluktuatif kayak PC responsnya agak lambat.

2. Line interactive UPS (APC atau ICA), 650-1200 VA. Ada AVR built-in yang bisa boost voltase drop tanpa switch ke baterai. Downside: kapasitas kecil, mostly cuma cover PC + monitor, kulkas nggak masuk.

3. Ganti perangkat lama ke yang PCB input voltage range-nya udah lebar. Contoh: PSU 80+ modern rata-rata udah 100-240V full range. Kulkas inverter beberapa merk dispec 187-242V. AC inverter sekarang ada yang dispec 160-240V. Contoh yang lagi aku survey serius: Midea MSIAF-09CRDN2X seri XtremeSave, datasheet 160-240V dan pakai HyperGrapfins di outdoor buat corrosion resistance. Garansi 5 tahun sparepart, 10 tahun kompresor.

Pertanyaan yang mau aku diskusiin:

Kalau perangkat sudah punya wide-voltage PCB built-in, apakah beneran aman tanpa stabilizer eksternal, atau tetep ada risiko degradasi di komponen switching-nya (elco filter, MOSFET) kalau voltase kerja terus-terusan di batas bawah nominal?

Aku pernah baca argumen di forum luar bahwa PSU 80+ Gold meskipun rated 100-240V, kalau kerja di 180V terus-menerus efficiency-nya turun signifikan dan heat naik. Beneran atau overhyped?

Buat yang udah pengalaman pakai wide-voltage AC inverter di area yang PLN-nya sering drop, apakah beneran nggak trip lagi, atau tetep butuh stabilizer separate?

Sharing pengalaman atau data teknis dari yang udah ngalamin welcome banget. Post di sini karena ini engineering decision, bukan sekedar beli merk apa.

Started sema back in early April, been on 0.5 since mid May. So about 8 weeks at this dose. Lost 12 in the first two months then it slowed a lot, sitting at 16 down total now. SW 168, CW 152.

First month or so on 0.5 the food noise was basically gone. I'd forget lunch until my calendar reminded me. It was kind of surreal after years of the opposite. But the last three weeks something's shifted. The noise is coming back, not full volume, more like a whisper. And around 3-4pm I get an actual hungry feeling that I didn't have before. I'm not overeating, my portions are still small, but the mental quiet I had is fading.

Went in for my follow-up last Thursday and my NP suggested moving to 1.0. She wasn't pushy about it, said we could stay another month if I wanted. I asked what she thought about staying and she basically said the drug plateaus and going up usually restores the effect for a few more months.

Part of me just wants to bump and be done thinking about it. The other part doesn't want to escalate if I haven't hit the ceiling of what 0.5 can do with tighter habits. Also my insurance keeps switching pens on me and there's already been enough supply drama this year without adding a titration change on top of it.

Anyone stayed at 0.5 past the point where food noise started coming back? Did habits fix it or did you end up going up anyway?

Been at this MNC in KL for about three years. Sales role so I've got constant client dinners, taxis, occasional regional trips to SG or BKK. Standard expense pattern, nothing unusual.

The reimbursement process is what gets me. Submit the claim with all the receipts, manager approves, then finance reviews, then it sits in some queue for who knows what reason, and finally I see the money land in my account about 3-4 weeks later. Sometimes longer if finance is closing the month.

The corporate card billing doesn't care about any of this though. The cycle is 21 days. So basically every month I'm fronting RM2-3k on personal cash flow while waiting for the company to pay me back for stuff that was the company's spend in the first place.

Raised this to my manager and HR more than once. The line was always "that's just how the system works". Apparently the system is a legacy expense tool that runs fine for the SG entity but somehow KL claims always sit longer in the queue. Cross-border ones are even worse, those can take 5-6 weeks because of how the FX gets settled between two entities.

What's frustrating is this isn't unusual. Multiple colleagues have the same problem. Some have stopped expensing anything under RM200 because the cash flow tradeoff isn't worth it for small claims. Which means the company is just absorbing the cost in worse ways. People take Grab personally, dinners go unclaimed.

My finance team acts like this is fixed in stone, but my friends at other MNCs get reimbursed within a week, sometimes faster via DuitNow. So something is different about how our company specifically processes APAC claims. Just venting honestly, the credit card bill arriving before the company pays you back gets old after three years.

We run a B2B SaaS, customer-facing AI feature has been in production for a while. For most of that time we were on a shared serverless inference tier and it was fine. Latency was acceptable, billing was easy to forecast, ops overhead was basically zero.

What changed was the tail. Median stayed flat but p99 started drifting around in a way that was correlated with time of day rather than our own load. Some afternoons everything sat at baseline, other afternoons the long-tail latency would creep up enough that customers noticed. Our SLO model assumed roughly flat variance and that assumption was breaking.

We sat with it for a while because shared infrastructure is supposed to have some variance. The thing that pushed the decision was a customer call where the AI assistant felt sluggish during a live demo. You can engineer around a lot but you can't really engineer around customer demos.

Spent a few weeks looking at the options. Renting and self-hosting GPUs was off the table for a team our size. Reserved capacity on a hyperscaler had multi-month lead times for the GPU classes we wanted. What I actually wanted was dedicated inference on hardware we didn't share with anyone else, ideally without a year-long commitment.

For us that ended up being Prime Inference from GMI Cloud. They could spin up a dedicated endpoint with reserved H200 capacity in the region we needed without a long wait. What sealed it was that the open weight model we already run was on their tuned-runtime list, so we didn't have to do that engineering work ourselves.

Couple of small things I didn't expect.

First-time model upload took longer than the docs implied. We brought the same fine-tuned weights we'd been running and the first load was closer to 40 minutes than the 15-20 the docs suggested. Subsequent reloads after that were quick. Worth budgeting an extra hour on day one.

Cost is meaningfully higher than the shared tier on a per-token basis, roughly 2x at our current volume. The math gets better as utilization climbs and we'll cross break-even at higher steady state, but I want to be honest that this isn't a cost-savings story. The thing we bought is predictability, not savings.

What I'm still working out. The shared tier is genuinely cheap when it works, and most workloads probably don't need dedicated. The boundary feels like it's somewhere around "are you SLO-bound on AI latency to a customer-facing surface". If yes, the variance on shared catches up with you eventually. If no, the cost of dedicated probably doesn't justify itself. I haven't seen this written down clearly and I don't have a confident answer.

Been polishing what I'm calling movie mode for the last few weekends and getting hung up on timing. The setup is smartwings rollers on the two living room windows, three Lutron-controlled lamps, the Apple TV, and the projector. Trigger it from a voice command and on paper everything should fire at once.

The issue is the shades take about 12 seconds to fully roll down, the lamps dim instantly, and the projector takes 7 to 8 seconds to wake up and warm up. So if I fire it all at once, the room goes dark a full 10 seconds before the windows are actually covered, which looks weird at sunset when there's still real light coming through. Or the projector blasts a startup screen into a still-bright room before the shades catch up.

I've been hacking around it with hardcoded delays in the automation, fire shades first, wait 8 seconds, then dim lamps, wait 2, then projector. Works most of the time but it's not great, and on sunny afternoons when the shades seem to have a bit more friction it lags by another second or two and the whole thing falls apart.

Already thought about triggering the shade close X seconds before everything else with a separate routine, which would make the voice trigger two-step and not as clean. Other angle is sensing the actual shade position and gating the rest off that, which feels like the right answer but I have no idea how people surface position state cleanly in a scene.

Mostly trying to figure out if the right pattern here is hardcoded delays everyone tunes themselves, or if there's a state-aware approach that actually scales.

Hitting a wall on multi-turn and want to check if i'm missing something obvious.

Customer facing RAG bot on our help center, a few hundred product docs as the source. Single turn works fine, retrieval pulls reasonable chunks, answer comes back with citations, nobody complains.

The interesting failures are when a user pivots topics inside the same session. Had a transcript last week where someone asked a pricing question, got their answer, then later in the same session asked about a login issue. The bot answered the login question as if it were still a pricing question. Stuck on the previous topic, retrieval pulled chunks that didn't really make sense, but the model wove them together into a confident sounding answer anyway. Took a while staring at logs to figure out where it had gone sideways.

Underneath that there's a smaller version of the same problem, the model occasionally pulls a citation forward from an earlier turn and uses it to back something in turn three, even when the doc isn't relevant anymore. Feels like it's holding on to context the retrieval has long moved past. And in the other direction, when a follow up is actually a real continuation, retrieval sometimes treats it as a standalone query and pulls back nothing useful. "What about for enterprise" with no anchor.

We've been comparing how a few setups handle this. Testing Denser on the customer side. Some of the hosted ones do query rewriting between turns automatically, some leave it on you.

What i can't get clean is the tradeoff. Rewriting the user's query each turn helps retrieval but distorts what they actually asked. Throwing the whole conversation into the retrieval query catches more continuity but you end up dragging stale terms from earlier turns into the new search. Fixed window of N turns feels arbitrary and breaks in obvious ways.

What i'd really like to know is whether anyone's actually solved this in a way that doesn't feel like a hack. Every thing i've tried so far trades one failure mode for another.

Got pulled down a rabbit hole on dietary patterns in regions with longer healthspan and one thing kept showing up that I'd never paid attention to. Mushroom intake in parts of East Asia is dramatically higher than what most people in the West eat, even the health conscious ones. Not just shiitake, basically all kinds, often daily. There's a long-running Singapore cohort that found higher mushroom consumption tracked with slower cognitive decline in older adults.

That sent me into a tangent about why mushrooms specifically. Apparently there's a compound in them that humans have a specific transporter dedicated to, which is unusual for something that isn't strictly essential. Plasma levels drop significantly with age and lower levels correlate with worse cognitive trajectories.

What got me was I'd spent maybe four years tweaking the standard rotation, magnesium and fish oil and b vitamins, without ever asking what populations who actually age well are doing differently at the food level. Bumping mushroom intake is what I tried first but the amounts you'd realistically need to move plasma levels are kind of impractical from food alone unless you're eating them at most meals.

Curious to hear from people who went food-first before reaching for supplementation, especially for stuff that's hard to hit from a Western diet.

I've been through three bluetooth speakers in the last two years and they all have the same problem. They sound fine when I'm doing dishes or have people over, but the second I want to play something at like 20% volume late at night while reading or cooking dinner solo, everything turns into a thin tinny mess. Bass disappears, vocals get this weird hollow quality, and I end up just turning my phone speaker on instead.

I get that there's some Fletcher Munson loudness curve thing happening at low volumes but most consumer speakers don't seem to engineer for it at all. The auto loudness compensation on some of them actually makes it worse, like it's pumping fake bass that sounds boomy and disconnected from the rest of the mix.

Most of what gets recommended in this sub is built for parties or beach use. I want something specifically meant to live indoors and sound decent at conversational volume. Doesn't need to be portable, doesn't need to be waterproof, just needs to not sound like garbage at the volume I actually use it 90 percent of the time.

Mostly trying to figure out if this is a driver count thing, a tuning thing, or just a spend more money thing.

Working on a production RAG pipeline, went the standard route. BM25 plus vector ensemble with LangChain's EnsembleRetriever doing RRF fusion. Theory being keyword matching for proper nouns, version codes, exact terms, and semantic matching for everything else.

What's killing me is the weight tuning. Some queries clearly want more BM25 weight (codes, exact phrases, anything where keyword precision matters). Others clearly want more vector (paraphrased questions, conceptual stuff). Any single weight combo I lock in, half my eval set gets better and the other half regresses. Feels like there's no global optimum, every choice is a tradeoff.

One side thing that's been bugging me. RRF on its own is rank-based and shouldn't need weights at all. The original paper just sums 1/(k+rank) across retrievers. LangChain's implementation takes weights and applies them to the RRF scores, which is technically RRF plus weighted fusion combined. Works, but it means I'm tuning a parameter that the algorithm conceptually doesn't have.

Tried a couple of escape hatches. Query classification routing helps a bit (short keyword-heavy queries to BM25-weighted, long NL queries to vector-weighted), but the classifier becomes its own weak link. Dropping fusion and just using a strong reranker on a wide vector candidate set actually worked better than fusion for our data. Set vector to top-50, rerank to top-5, skip BM25 entirely. Tradeoff is reranker latency, which is real.

For people running mixed-query RAG in production, what's the strategy that survived contact with real users? Genuinely curious if anyone has a cleaner pattern than "just throw a reranker at it".