Kubera doesn’t have an iPhone or Android app in 2026, only the website, which you can add to your home screen. I was looking for a way to track my net worth and investments in one place, and no mobile app was a dealbreaker for me. So I spent a few hours going through the alternatives people recommended in the earlier thread. Everything is in the tables below, so compare and pick what fits you.

🆓 free version available
✅ iPhone and Android
⚠️ Apple only
❌ no mobile app

Kubera and net worth trackers

Other apps that came up

These have net worth features too, but they’re built for something else first: budgeting, retirement planning, or investing your money for you.

Prices and regions change, so double check on each app’s site before you pay. If I got something wrong, tell me and I’ll update the tables.

Do you guys use spreadsheets or something else?

Just asking

Жыве!

Folks, my name is Artem and I built aidictation.com: ai dictation software built for people mix english, Belarusian, Ukrainian and Russian in at work. For those who are not satisfied with current state of ai dictation software and want one that built by their own folk and specifically for them.

The app is completely free for the first 5000 words which is plenty (depending on how much you type daily up to a month).

1. Can you download and test and tell if it works for you and what you'd change
2. If you use dictation already, does ai dictation handle your language better than existing software?
3. If you like it, please rate it on app store and google play!

I've seen a version of this happen a few times with B2B products lately, so, you start with a dashboard because customers need a handful of obvious metrics. like, nothing fancy:

/api/stats
     to
React
     to
4 charts

and that is probably the right architecture at the beginning. then the requests start accumulating, exampels:

“Can I change the date range?” “Can this chart use our fiscal quarter?” “Can we break this down by region?”“Can this customer define revenue differently?”, and at some point you stop adding charts and start building some kind of metric/semantic layer so the frontend isn't carrying all of the reporting logs, which.. still feels manageable! the point I'm less sure about is what happens next. eventually, sooner or later, an enterprise customer asks for something along the lines of: can our users build their own report inside the product? \at this moment, suddenly the requirement is in things like: choose dimensions/measures, filters, pivoting, saved reports,exports, permissions, tenant isolation, probably scheduled reports, ideally without exposing your underlying schema at all, which is suspiciously close to building a small BI tool. and it also becomes noticeably less about analytics. so we've been looking at whether it makes more sense to embed an existing report-builder surface on top of the same semantic model rather than implementing all of that UI ourselves. now using cube dev in our stack, so its embedded creator/reporting UI is one option we're evaluating, but I'm more interested in the architectural decision than the specific vendor. that being said, where do people normally draw this boundary?

and do you deliberately keep product analytics opinionated and refuse self-service? build the report builder yourself because it's core UX? or, embed something? or just send the power users to a separate BI product? my instinct is that “native charts” and “user-authored analytics” may actually be 2 pretty different product problems, even though they tend to get put under the same analytics roadmap

Hi,

I'm relatively new to the world of transcription. As a ESL person i'm looking for state of the art model for bi-lingual transcription. Currently focusing on russian/english pair but also interested in spanish/english and chinese/english in the futre.

On top of that I'm interested in these models to be steerable with a prompt (i.e. asking to format the content in a certain way or remove filler words)

Third most important parameter is latency (ideally looking at least 500ms response time).

So far I've found most reliable

OpenAI gpt-transcribe and gpt-live-transcribe, also older gpt-4o-transcribe are most reliable for russian/english pair and transcription speed.

I've looked at gemini 3.5 flash transcribe - but the latency isn't that great.

Anything else i should be aware of?

https://preview.redd.it/3xtc124hgslh1.png?width=400&format=png&auto=webp&s=505d77ff756b989fa48c59999c510105de032c17

Here is the raster logo i have, I need to vectorize it. What is the best way?

There are what feels like a million AI transcription apps now, but competing with Wispr Flow and superwhisper on latency is genuinely hard.

I build AI Dictation. At the beginning of this week, our end-to-end transcription latency was around 4 seconds. We’re now below 700 ms p90.

I started by asking Codex to reverse-engineer how Wispr Flow and superwhisper achieve their performance.

What I observed:

• Wispr Flow appears to use a custom gRPC endpoint and starts streaming text while you’re still speaking.
• superwhisper seems to use ordinary HTTP requests, without a fancy protocol.
• Both appear to run multiple instances around the world, ping them, and select the lowest-latency endpoint.
• superwhisper also warms up the connection before it is needed. I could verify the early TLS connection, but not whether it also warms GPUs.
• Both seem to have a very direct path to their ASR and LLM infrastructure. I suspect Wispr Flow may use a fine-tuned model, but that is only a guess.

Here’s what I implemented:

1. I stayed with HTTP instead of moving to gRPC.
2. I added connection warmup and polling to the macOS client. TLS is now established before the first voice request. This alone saved roughly 50–150 ms.
3. Our endpoints run on Vercel, so I deployed the transcription endpoint to more regions. That saved up to another 70 ms.
4. I added server-side connection pooling between Vercel and Groq, our ASR and LLM provider.
5. I switched the cleanup step from an OSS 120B model to OSS 20B for faster inference.

We’re now below 700 ms p90 end to end.

The remaining latency looks roughly like this:

• ASR: ~200 ms
• LLM cleanup: ~200–300 ms
• Everything else: the remaining gap

With the current architecture, the apparent floor is around 400–500 ms. That’s good, but I still think it can be better.

Has anyone managed to push ASR below 200 ms without noticeably hurting accuracy?

Has anyone successfully started LLM cleanup from partial ASR output instead of waiting for the full transcript? How do you handle revisions to earlier tokens?

For cleanup and grammar correction, have smaller quantized or task-specific models beaten general-purpose LLMs for you?

Anything else I should investigate if the goal is to cut another 100–200 ms without sacrificing transcription or cleanup quality?

Building something new

WDYT guys?

i know people spend hours debating voo vs vti or splitting hairs over three basis points on an expense ratio, but then duplicate the exact same three-fund portfolio inside every single account they own... so when you first start investing, everyone tells you to buy a simple broad index fund and chill. that is great advice, but the rookie mistake almost everyone makes is buying the exact same fund across their regular brokerage, their 401k, and their roth ira.

what people actually own is called asset allocation, but where you own it is called asset location. if you mix them up like i did and not once, you may end up paying taxes every single year on money that could have grown tax-free.

here is the simple way to think about where the money should sit, i try to implement this now:

1. taxable brokerage account: keep low-turnover index funds here (like voo or vxus). these throw off qualified dividends and long-term capital gains, which get taxed at lower rates, plus you can use tax-loss harvesting when the market drops.
2. tax-deferred accounts (traditional 401k / traditional ira): put your bond funds and high-dividend assets (like reits) here. bond interest and reit payouts get taxed as regular income, so letting them sit inside a traditional account protects you from getting hit with high income tax brackets every year.
3. tax-exempt accounts (roth ira / backdoor roth): put your highest-growth investments here. since qualified roth withdrawals are 100% tax-free in retirement with zero mandatory distributions, you want your fastest-growing assets compounding here so uncle sam never touches that growth

for 2026 planning, i try to keep the updated annual contribution limits on your radar: 401(k) elective deferrals are capped at $24,500, the overall 415(c) limit is $72,000, and standard ira limits are $7,500 ($8,600 if 50+)

to make this work, i try to look at all accounts of mine as one single big balance sheet instead of separate little silos. whether i track things in a python script or monitor total allocation drift across brokerages with tools like tradingview, 8figures, or wealthfront, maybe-maybe i should stop copy-pasting the exact same 3-fund mix into every single bucket! do you agree with this all? would be glad to discuss

i think most residential real estate investors evaluate deals strictly on Cash-on-Cash (CoC) yield. But CoC only captures immediate cash flow relative to out-of-pocket cash, and it often ignores mortgage principal paydown, tax shields via depreciation, and the mathematical divergence between levered and unlevered IRR

so consider a $500,000 property purchased with 20% down ($100k equity, $400k debt):
If the property value appreciates 10% to $550,000, the asset gained $50k.
but on your $100k invested capital, that $50k gain represents a 50% return on equity, minus debt service and transaction costs

funny thing, leverage amplifies returns on the way up, but it cuts both ways when cap rates expand or vacancy hits. Furthermore, when comparing direct ownership against liquid vehicles like REITs (which distribute 90%+ of taxable income and offer institutional triple-net lease structures), the active management friction of physical rentals (turnover, maintenance, 10–30% property management fees) often narrows the net return gap

when analyzing a multi-asset portfolio, evaluate properties using true internal rate of return (IRR) including debt amortization schedules... whether modeling deals manually in Excel, using DealCheck for property cash flows, or syncing Zillow estimates with automated debt tracking in something akin to 8figures, and you should always measure both levered and unlevered IRR against a baseline index

Both ChatGPT and Claude are banned from these.

many traders around me treat sentiment indicators like directional buy/sell buttons, so, when VIX pops, they panic; when it drops, they get greedy. sentiment analysis is an odds-management tool. some see it as magical crystal ball which it surely is not. In my experience, to read positioning exhaustion accurately, you may want to watch four distinct gauges in a kind of a cluster:

1. VIX Context: yes, low VIX isn't inherently bullish; it reflects cheap portfolio insurance where participants are complacent. high VIX indicates de-risking where protection is expensive but downside selling is largely exhausted
2. AAII Sentiment Spread: maybe look for multi-week clusters of extreme pessimism/optimism, not single-week surveys. persistent bearishness indicates retail dry powder has already moved to cash.
3. equity vs. Index Put/Call Ratios: critical! when the single-stock equity put/call ratio sinks (retail aggressively buying call options on high-beta tech) while the index put/call ratio rises (institutions buying broad SPX puts to hedge), you may have a fragile market with narrow leadership
4. breadth Internals: measure whether advances are backed by equal-weight participation (RSP) or just mega-cap capitalization weights

tracking these positioning signals alongside your portfolio’s sector weights, using platforms i tried, like maybe thinkorswim for options flow, Cboe for index volume, and 8figures or custom scripts for asset allocation drift, itkeeps you from reacting emotionally to market headlines. that's all not advice really as you need to test different things by yourself and the point also is when indicators show extreme greed and narrow breadth, it may be a time to try trimming outsized winners, tightening stops, and hedging through lower-cost defined-risk spreads. people try to short market leaders which backfires but maybe this 4-step plan is a bit more productive. but what opinion do you have on this? would like to discuss

localization drift is something that I have experienced and it happens quietly, for example english metadata changes then screenshots update. pricing copy shifts. onboarding gets a new step. support templates change. local versions lag behind.

and then users say 'not translated' even though the app technically has the language.

am i wrong for thinking that localization drift should be treated as a product bug, not a content chor?

the check is..... store metadata, screenshot text, first session, maybe paywall, help text, very often, review replies too, revealing the drift with external resources, or something appfollow-like can help especially when local review themes start mentioning translation, unclear pricing, or foreign-feeling copy after a release.

BUT the fix is process: every meaningful product change needs a localization impact check, so a translated app can still feel unlocalized and therefore no problem has been solved...

Got cut off at 2pm on a Tuesday. Four hour cooldown, halfway through untangling a service I'd already been at for an hour. Dumped the whole thing into another tab, spent twenty minutes rebuilding the context by hand, got a worse answer than the one I'd been cut off from.

Sat there annoyed enough to actually go count. OpenRouter lists 411 models right now. Thirty five of them turned up in the last month. This year has already put out more than the whole of last year and it's only August.

They keep getting cheaper too. GPT-4 was thirty dollars a million tokens when it launched, Turbo is still ten. Gemini Flash is thirty eight cents. There are about a hundred models under twenty cents that'll still swallow a 100k context, and eighteen that cost nothing at all.

So the twenty dollars a month I hand over covers something like fifty times the tokens it did in 2023. Doesn't feel that way from where I'm sitting. The seat costs what it cost three years ago, still has the cooldown on it, and none of that moves when the models get cheaper.

I realise this is a slightly ridiculous thing to still be annoyed about hours later.

What's everyone actually running? Genuinely asking. Stay on the flat plans because the apps around them are nicer. Go API and put up with a bill that moves. Use one of those frontends that stick a pile of models behind one key, though I've no idea which of them are any good.

Writing most days, some code, occasionally images. Mostly I want to stop rebuilding my setup every time something new drops.

Hey a few weeks ago I asked some advice how to do ASO, got recommended AppFollow, AppTweak and Claude code "vibe ASO". Ultimately I asked claude to do ASO via AppFollow AND AppTweak, updated my descriptions and titles...

And got a good jump of downloads doing nothing.... how to figure out why?

https://preview.redd.it/1ssb3e6ybnih1.png?width=2280&format=png&auto=webp&s=bd1382f4381061fb7a8d0f1774cd7d16f269556b

marketers love pre-install language: keywords, ad copy, landing pages, screenshot text

reviews are post-install language, and users are less polite there

'finally simple' is kinda positioning and when 'too many steps' it is onboarding feedback. also 'subscription trap' is pricing trust, i classify it that way... 'doesn’t do what screenshots show' is promise mismatch which is mega bad. 'better than x for invoices' may be competitor positioning and is useful for app marketing too

i do not think every review should become ad copy. that gets gross fast. but repeated review wording can show which promise users believed, which one disappointed them, and which words feel natural in the category, when volume gets annoying though, i also believe things like appfollow exist and can group themes. before that, a doc with repeated phrases is enough so that's a scaling issue. get basics right

sometimes the best copy is not invented. it is extracted from what users keep saying after they tried the product.

no-code makes shipping easier and so do many of the wonders/horrors beyond human comprehension, but it does not remove the need for a feedback loop

i guess that after launch, you still need to know what users repeat.
like, confusing setup, missing integration, pricing surprise, broken flow, unclear value, slow support. for mobile apps like the ones i try publishing lately, store reviews seems to be one of those feedback sources. had multiple ideas about working with them, as well as i’ve seen appfollow used when review volume becomes hard to manage, but , frankly, early no-code projects can start with a simple table and that would be much-much better than not tracking reviews at all

when i started out i've put there columns such as: source, raw quote, theme, severity, owner, status.

in practice that is enough to keep feedback from becoming vibes if and when at low scale still

the no-code trap is building another feature before learning whether the first version matched the promise. what do you think about this?