decision paralysis between foxsell and simple bundles. i've read the listings, i want lived experience. two things matter most to me: inventory that doesn't drift, and being able to change how bundles look without filing a dev ticket every time. which one's less painful on those two fronts?
My Italian skills currently:
Ordering pasta? dangerous confidence. Recognizing words in songs? sometimes. Reading simple stuff? okay-ish. Actually answering a normal human? absolutely not.
Someone says:
“Da quanto tempo studi italiano?”
and suddenly I’m mentally scrolling through every verb tense I’ve ever abandoned.
I think I accidentally learned “Italian as vibes” instead of Italian as a usable speaking skill.
So I’m trying a very simple 30-day thing:
No new random vocab unless I use it out loud.
Current setup:
Rule:
If I learn “mi sono reso conto che…” I need to actually say:
“Mi sono reso conto che capisco più di quanto parlo.”
not just admire it in Anki like art.
Anyone else stuck in this fake-comfort zone where Italian feels familiar but speaking is still painful?
I’m in a stupid zone with Mandarin.
I’m not a total beginner anymore. I know basic grammar. I can recognize a decent number of words. I can read slow stuff with lookups. I can do app exercises. I can even understand some learner podcasts if they’re not trying to murder me.
But conversation?
Nope.
Someone says something normal-speed and my brain starts doing airport security on every syllable.
“Was that second tone?”
“Is that shì or shí?”
“Wait did they say xiǎng or xiàng?”
“Why did I just forget the word for yesterday?”
“Why am I sweating?”
So for the next month I’m trying a very boring anti-freeze plan:
Rule: if I learn a sentence, I have to say a version of it about my own life.
Anyone else hit this stage where Mandarin exists in your head but refuses to come out your mouth?
I used to think news aggregation was basically:
pull articles summarize show clean UI done
lol no . We’re building CuriousCats: https://curiouscats.ai
It’s a news briefing app, but the part that has been way harder than expected is not the AI summary. It’s figuring out when 50 different articles are actually the same story . Example:
One source posts a headline. Another adds a quote. Another adds political angle. Another republishes wire copy. Someone makes a YouTube video. People on X start reacting to a claim that was not even in the original story. Then 6 hours later one tiny update changes the whole thing.
A normal feed shows this as 20 separate items.
But as a user, I don’t want 20 items. I want to know:
what happened what changed what is new vs repeated what is confirmed what is opinion what should I ignore where should I click if I want the original source
That’s the thing we’re trying to build.
Not “AI writes news for you.”
More like “AI cleans the messy desk before you read.”
The current product groups stories, shows briefing/timeline/context, pulls from a lot of sources, and has audio briefings too.
But positioning is confusing.
If we say “AI news digest,” it sounds like every other generic AI wrapper.
If we say “Google News alternative,” people understand it but it undersells the timeline/context part.
If we say “story intelligence,” it sounds like VC brain damage.
So I’m asking here because r/SideProject is usually better for harsh product feedback than launch platforms.
What would you call this?
Also, if you were using this, what would make you trust it?
Source links? Bias labels? Timeline? Publisher controls? RSS import? No infinite feed? Daily email? Widget?
Brutal feedback welcome. Especially on the landing page. I know it probably sounds too “AI app” right now.
When people say “supports diarization,” I don’t know what that means anymore.
Diarization in a 2-minute clean demo?
Or diarization after:
40 minutes
4 speakers
one person joins late
two voices sound similar
people interrupt each other
someone is far from the mic
short “yeah / right / exactly” interjections
one speaker dominates the first 20 minutes
background noise changes halfway through
Because the painful diarization bug is not “it labeled Speaker 1 and Speaker 2.”
It’s:
Speaker 2 slowly becomes Speaker 4. The action item is assigned to the wrong person. A short correction is attributed to the wrong speaker. A late joiner breaks the rest of the transcript.
I’m thinking of doing a pyannote-assisted annotation workflow where each STT API gets scored on:
speaker count
speaker swap rate
speaker drift over time
word accuracy per speaker
action item attribution
timestamp alignment
late-joiner behavior
overlap handling
Smallest AI Pulse includes diarization, so for me the question is not “does it have the feature?”
The question is:
can the speaker labels survive a messy real call long enough to trust the summary?
Anyone running diarization in production? What broke first?
3 years on kite connect. love the docs and community, genuinely. but the expiry day latency and the 3000 instrument cap have become real problems for what im running now.
what are people moving to these days. need it to actually hold up on expiry and handle bigger chains.
Hello, I hope you’re doing well. My dad’s birthday is coming up , planning to get mini pc as a gift, with a budget of around 200$ to 400$. I saw a lot of people online saying mini PCs are super worth it for home setups, and I watched this acemagic R2544 which perfectly fits my budget. Only for everyday business work. I looked up the specs and it features an AMD Ryzen R2544 with an upgraded efficient cooling fan running at 28W TDP, plus a reliable Gigabit LAN port, supports triple 4K displays and has dual SO DIMM RAM slots dual M2 interfaces. Any recommendations or things I should watch out for?
Hi everyone! So I was just looking through deals the other day, and I saw this discount on the acemagic R2544, the price to performance ratio looked so crazy.
I do now understand why these little machines are kind of legendary, being able to push console emulation all the way up to 8th gen gaming with the Wii U at a locked 60 FPS, upgraded Radeon graphics, which deliver up to 35–50% better performance than entry level N series processors.
The R2544 has a super small form factor, featuring a type c port that supports PD power and 4K display. I think I'm just going to flash it with a pure Linux OS since I don't like dealing with dual boots or VMs. This Mini PC will get me back into Linux.
30M IT, salary 1.1L. Have Axis Magnus and Amazon Pay ICICI already. Magnus does my online travel, Amazon Pay handles Amazon. Both fine. Issue is offline UPI (35-40k/month on kirana, fuel, food, autos) doesn't get rewarded on either. Looking for a third LTF card to plug that gap. Cashback preferred over points.
getting possession of a rented flat and realised there is no purifier. society water looks okay but i don’t want to rely on luck.
buying RO, renting RO, water cans, local purifier guy, too many options. what is the sensible route if stay is not fixed?
Shayad kuch
been leaning slightly bullish on nifty so put on a bull put spread, sell 23250 PE buy 23150 PE. credit is decent but the risk reward is roughly 1:0.87 which means im risking a bit more than i can make. POP shows ~51% so its basically a coin flip with slightly worse payout than risk.
is this just the nature of credit spreads this close to the money, or would you push the short strike further OTM for a better r:r even if it lowers the credit? trying to figure out if im overthinking the risk reward on defined risk trades.
Clearing a shelf and re-sniffing caps I forgot I owned made me realize I’ve been rating too many budget bottles on first spray and box longevity claims instead of whether they’re still wearable at hour 2.
My lazy summer test now is 2 sprays on skin, 1 on cotton, check at 15 min / 2 hr / 5 hr, then one step back at each point to judge projection. In warm weather, a lot of sweet amber stuff projects hard for maybe 45-90 mins then gets dense or flat, fresh woods can sit closer by hour 2 but still smell cleaner, and leather/oud often starts rough then gets better after the settle. For me “good performer” is basically: still smells balanced at 2 hr, still detectable at 5 hr, not just loud in the opening.
Across houses like Lattafa, Armaf, Afnan and Ajmal, I keep noticing the same pattern: the winners aren’t always the loudest openers. Some Armaf-style freshies fade faster but stay elegant. Some heavier amber bombs feel huge early and then turn sticky in heat. A couple leather/oud profiles from my shelf were almost a write-off at 10 mins and much better at 90. That’s also why I’ve been judging gifting differently too: if it’s for self-wear I’ll take better drydown over bottle; if it’s for a gift, packaging probably becomes a legit tiebreaker once the scent itself is solid.
There’s also a retail angle here with beauty brands leaning harder into presentation lately, not just formula
So actual question: when you’re buying budget to mid stuff, what matters more to you after the first wear test: hour-2 smell quality, projection, or bottle/presentation? And which houses overperform after drydown vs winning only on first spray, especially in heat? I’ve been testing RiiFFS alongside the usual Lattafa/Armaf/Afnan rotation lately and had the same mixed result depending on profile, so I’m curious what others are seeing.
One thing nobody warned me about when our team hit ~10-15 people: hiring developers really becomes a part‑time job.
For the first few hires it felt manageable. few resumes. A couple calls. Done.
Then suddenly we were hiring 1–2 devs every quarter and the process quietly exploded.
Manual resume reviews. HackerRank screen. Take‑home project. 2/3 interviews/week. Sometimes a system design round. Suddenly I was spending 8–10 hours a week on hiring.
A lot of those interviews happened late evening after sprint reviews or after getting the kids to bed.
Our funnel is usually around 70–100 applicants per role. Maybe 25 get the coding test. About 10-15 make it to interviews.
The coding test is typical stuff. HackerRank/Leetcode ..Short algorithm problems just to filter.
Then a take‑home project that usually takes candidates 3–4 hours. Our senior devs hate reviewing those btw,
The bigger problem though is getting accurate signals.
One hire in particular really stuck with me. This candidate crushed the algorithm test. Clean solutions. Great interview performance.
Two weeks after joining he got stuck debugging a very basic API issue. Logs were literally showing the failing dependency and he just kept rewriting code instead of tracing it.
That was the moment it clicked that we were evaluating the wrong things.
Most real tech work is debugging messy systems, not solving puzzles.
So we started experimenting with something different. Instead of puzzles we gave candidates a small broken service and asked them to fix it.
The setup was simple. A 30–45 minute live prod-environment where an API endpoint was failing. Candidates had logs, the codebase, and could redeploy once they patched it.
At first we hacked this together internally using Docker containers. Later we tested a couple prod setup environments too, including a sandbox platform called Utkrusht just to see if running these scenarios externally was easier.
What surprised me was what actually predicted good devs. It was rarely the cleanest code.
The strongest candidates read logs carefully, reproduced the bug quickly, tested assumptions, and used tools effectively. A few used ChatGPT during the task and honestly that was interesting signal too.
We ran about 180 candidates through this over a few months.
Our interview hours dropped by like 50% because fewer weak candidates made it to live interviews. The people who passed this step were usually worth the deeper conversations.
Another interesting difference was senior vs junior behavior.
Senior candidates almost always started by exploring logs and dependencies first. Juniors tended to jump straight into rewriting functions.
Hiring still takes time obviously. But this change removed a lot of noise from the early stages.
how have other founders here handled this once teams grow past the first 10 tech people? Do you still rely on coding tests and take‑homes, or do you plan to change your process too?
Looking for tools that can create high-fidelity mobile app mockups from a prompt.
Use case:
I want to test an app idea before spending weeks building it.
Need:
onboarding screens
home screen
core feature screen
pricing/paywall screen
maybe App Store screenshots
Not looking for full app builders yet. More like idea → mobile UI → mockups → feedback.
Tools I’ve seen:
Has anyone used these for app validation or pre-selling?
The simulator lies and I'm tired of it.
Our RN app's tests pass green on iOS simulator and Android emulator. Then real devices:
simulators/emulators catch logic bugs. they miss the entire category of "real device hardware/OS behavior" bugs, which is where our actual crash reports come from.
what's everyone's real-device testing setup for RN? specifically for catching this stuff before it ships, not after the 1-star reviews.
I noticed this while clearing shelf space because the ugly bottles always end up in the back.
If two scents are both decent in the same price band, do you guys actually use bottle/presentation as a tiebreaker or do we just pretend not to? I’m not even talking luxury. More the Lattafa/Armaf/Afnan/Ajmal zone, and even stuff like RiiFFS, where part of the blind-buy appeal is obviously that it looks better on a shelf or works as a gift.
For me there’s a real cutoff. Around the same budget, a nicer bottle matters if the scent quality is close and performance is in the same rough lane. Example of the tradeoff I keep making: if one does 6-8 hours, sprays well, cap doesn’t feel flimsy, and looks giftable, I’ll forgive it not being the absolute best scent per rupee. But if the bottle is doing all the work and the juice is just mid after the first hour, that fancy presentation starts feeling like a red flag.
Real case for me: I’ve kept cheaper stuff with worse bottles out of sight and reached for better-presented ones more often, even when the smell gap was small. Not because the bottle changes the scent, obviously, but because blind-buy confidence and gifting confidence are part of the hobby too.
My lazy rule now is: for self-wear, smell wins unless the difference is tiny. For gifting, presentation gets way more weight. Curious where your cutoff is. When does “nice bottle” become a legit buying factor vs a warning sign?
