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Simple questions and Help thread - Month of August




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Microsoft confirms it will make Windows 11 run better on PCs with 8GB RAM: Promises continued focus on fixing Windows' biggest issues this year

Microsoft plans to fix Windows 11's slow context menus with major overhaul designed to improve performance and reliability: New compact menu design will be faster and user customizable




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Windows 11 KB5101684 is out with reliable fixes, an appropriate file size in File Explorer (no more just KB!), and Search now handles typos and partial app names better, and more




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Microsoft announces major Windows 11 search overhaul that prioritizes clearer, local results, and removes ads: Huge effort to fix search on Windows is finally happening







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I JUST NOW realized windows have separate "power saving" PC option outside of control panel that is "always on" by default. YEARS of inexplicable random 165Hz > 10Hz frame drops on all games and days of stress and pain solved overnight. I WANT MY LIFE BACK. THIS IS CRIMINAL.
I JUST NOW realized windows have separate "power saving" PC option outside of control panel that is "always on" by default. YEARS of inexplicable random 165Hz > 10Hz frame drops on all games and days of stress and pain solved overnight. I WANT MY LIFE BACK. THIS IS CRIMINAL.
Discussion
  • r/Windows11 - I JUST NOW realized windows have separate "power saving" PC option outside of control panel that is "always on" by default. YEARS of inexplicable random 165Hz > 10Hz frame drops on all games and days of stress and pain solved overnight. I WANT MY LIFE BACK. THIS IS CRIMINAL.










Advertisement: I built a portfolio optimizer that runs Markowitz backwards. My first architecture was mathematically impossible and I didn't notice for weeks.
I built a portfolio optimizer that runs Markowitz backwards. My first architecture was mathematically impossible and I didn't notice for weeks.

I'm a solo founder building Reverse Efficient Frontier, a portfolio app that that inverts Modern Portfolio Theory — instead of picking stocks and hoping, you set a target return and risk tolerance and it finds the stock blend that matches. React Native/Expo frontend, Azure Functions + Blob Storage backend, Databricks for the optimization, Claude API for the plain-English explanations. Live on both stores.

I want to write up the architectural mistake that nearly killed it, because it's a flavor of "I did not think about scale" that I haven't seen described much.

The mistake: I assumed I could solve the optimization on demand, when the user moves the slider.

Classic portfolio optimization runs forward. You hand it a set of stocks, it solves for the optimal weights. That's a convex problem — fast, well-behaved, solvable in milliseconds. I'd read the papers, I understood the math, I felt good.

What I actually needed was the inverse: given a target return and risk, find which stocks and their weights. Those are not the same problem. Selecting k stocks from a universe of N is combinatorial, and each candidate set needs its own optimization run before you can even compare them. For 10-stock portfolios that's a number with more zeros than there are stars in the galaxy. My v1 fired off an optimizer on slider change like it was a database query. It worked beautifully in testing — because in testing my universe was twelve tickers.

Everything is obvious in hindsight.

What I rebuilt, three layers:

  1. Sample instead of enumerate. I stopped trying to find the optimal portfolio and started building a dense point cloud of good ones. Randomly sample thousands of k-stock subsets, optimize each one properly, keep the result. It's an approximation of the true efficient frontier, not the thing itself — and being honest about that in the app matters more to me than pretending otherwise.

  2. Multi-start optimization with weight bounds. Single-start SLSQP kept converging to garbage — 97% in one ticker, dust positions everywhere. Fixed it with multiple randomized starting points per subset and hard bounds on individual weights, so you can't get a "diversified" portfolio that's secretly one stock.

  3. Precompute and park. The whole thing moved to a scheduled Databricks job that writes results as JSON to Azure Blob behind a CDN. The phone doesn't optimize anything anymore. It does a nearest-neighbor lookup in return/risk space against precomputed results. All the expensive math happens once, on a schedule, away from the user.

Result: query time went from "architecturally impossible" to under a second on a mid-range phone, and compute became a fixed predictable cost instead of scaling with every slider drag. Same pattern now runs everything I build — if it's expensive, compute it on a schedule and let the app read the answer.

The app's live on [Google Play] and the [App Store] if anyone wants to poke at it ( https://frontier.chosenhomeland.com ). You get 3 free portfolio lookups, no signup, no card — enough to see whether the idea holds up.

Happy to answer anything about the optimization approach, running scipy on Databricks, precompute-and-park as a pattern, or building fintech solo. Also genuinely want feedback on the first-run experience — whether the value proposition lands before you've done anything, since that's my current bottleneck.

(Not financial advice — it's an analytical tool, and it says so in the app.)












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Microsoft wants to integrate your smartphone more deeply with Windows 11: Plans UX enhancements that will make your PC and phone more seamlessly connected







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