I have done a few experiment, ChatGPT and Google AI mode and publicity and find those LLM models citing differently. If only looking at chatGPT, whats a good GEO optimization strategy that works - not theory rather proven methods
Hello everyone,
I’m completely new to the topics of native memory and interoperability.
I’m trying to create a Java library (using the Adapter pattern) to connect several mathematical solvers together. I’ve found that most solvers on the market have C APIs (e.g., HiGHS).
I decided to use Project Panama (Java 22’s FFM API) for communication between Java and C. However, I understand that if I pass standard Java arrays to my C solver, the JVM will copy this data into memory. Since solvers handle a lot of data, I’m worried that the time spent copying will completely ruin performance.
So I wanted to know:
Thank you very much for your patience and advice!
The idea was to use it for my event planning startup, but that didn't work out anyway, so I decided to open source this work. Getting or-tools to compile as it is a bit difficult, it's not super portable. Getting it to work multi-threaded across new and old browsers took a lot of work, and since my event planning tool never took off, I thought I would open source this part. You should be able to run everywhere, but tell me if it doesn't work. I haven't benchmarked again the original yet, but WASM tends to be 60% the speed of native. The one catch is that the web workers take forever to spin up, so for small tasks it's often run quickest on 1 thread.
Open to thoughts, feedback, ideas!
Hey everyone, I’m trying to push my ultra-budget potato laptop to its absolute limits to get it running as smoothly as possible (mostly trying to stop micro-stutters in lighter competitive shooters and keep the OS responsive). I’ve already gone through the standard optimization routines, but I feel like I've hit a wall and want to know if there are any deeper, next-level debloating steps or registry tweaks I should look into.
Here is what I’ve done so far:
Used the Chris Titus Tech Windows Utility to strip out the heavy stuff. Completely removed Microsoft Edge, Bing, and disabled Windows Search. Disabled VBS (Virtualization-Based Security) / Core Isolation.
Unlocked and enabled the Ultimate Performance power plan, and heavily tuned my AMD Radeon software settings for maximum performance over quality.
Replaced a bunch of default Windows apps with lightweight, open-source alternatives to keep RAM usage down.
My Specs: CPU: AMD 3020e RAM: 8GB DDR3 (5.88GB usable) OS: Windows 10 Home 22H2 Graphics: AMD Radeon Graphics (Integrated)
The laptop runs significantly better than it did out of the box, but when you're working with these specs, every single megabyte of RAM and every CPU cycle counts. Are there any deeper services I can safely disable, specific registry modifications, or hardware-level tweaks that actually work? I'm trying to avoid completely breaking the OS, but I'm fine with aggressive tweaks if they yield a noticeable performance bump. Appreciate any advice or guides you guys can point me toward!
Hi, I am new to optimization. I am reading an introduction to optimization by P.G. Ciarlet. It gives me all theorical aspects.
In parallel, I would like to learn how to use optimization. I might implement the algorithms in Python for the practical aspect. But I guess that in general, we use library to solve optimization problems. Where should I look ? Are there any library to know how to use ?
What are some cool projects I could do by myself to add to my GitHub? How do I get started or even potentially do my own research? I want to do this throughout the summer.
Any advice will be appreciated.
Hi everyone,
I’m currently working on a game called Arrow Puzzle Escape. It’s a kind of puzzle/maze game where you solve levels by “pulling out” arrow chains and figuring out how they interact to complete the puzzle.
Right now I’m developing a procedural level generator for the game. The idea is that the generator takes a user-selected area on a grid and fills it with arrow-based structures to create a playable puzzle.
The generator works in two main phases:
The main issue I’m running into is that the first phase doesn’t currently include any logic to prevent “deadlocks” — situations where arrows end up blocking each other in a way that makes the level impossible to solve.
For small grids (like 10x10 or 30x30), everything works fine. However, for larger grids (40x40, 50x50 and above), the generation time grows exponentially, and the number of invalid layouts increases significantly. At that point, generation becomes practically unusable.
I feel like the issue is not just optimization, but more about the structure of the algorithm itself. I’m looking for ideas on how this approach could be redesigned so it can reliably generate larger solvable levels without exponential retries or deadlock situations.
Also, stepping back a bit — I’m starting to wonder if this kind of approach is even fundamentally scalable in general. Is it actually possible to design a procedural system like this that reliably produces valid 50×50+ solvable levels without falling into exponential failure rates?
I’ve attached a video showing a quick demonstration of how the generator currently works in Unity. If anyone is interested in digging deeper or experimenting with ideas, I can also share the source code of the project.
Any suggestions or feedback would be greatly appreciated. Also, I would be very happy to credit anyone who helps significantly with improving or solving this algorithm inside the game.