प्रोजेक्ट्स बनाने और प्रोडक्शन-ग्रेड कोड के गैप को भरें। प्रैक्टिकल इंजीनियरिंग मॉड्यूल्स से system performance, refactoring और debugging सीखकर Full-Stack Application Expert बनें।
अभी शुरू करें:
#LearnCode#CodeDebugging#CodeOptimization
22 kilobytes.
I often think about the technical limitations that game designers had to work with in the 80s - both in terms of software and hardware. To me, the game that stands at the very top is Elite (1984).
Elite is smaller in data size than many modern text files or desktop icons, yet it lets you freely explore a multi-galaxy-spanning universe that felt gigantic and virtually limitless.
Think about this for a second: The core game code on the BBC Micro version has roughly 22 KB of memory. The C64 version from 1985, which I played as a kid, isn't much bigger.
Now think about what Braben and Bell turned those few kilobytes into: a massive universe with eight galaxies, each containing 256 star systems (for a total of 2,048 planets per system). Each system featured unique details: government type, economy, technology level, population, commodity prices, and even descriptive text.
Oh, and by the way, the game also rendered 3D wireframe ships, stations, and planets in real time on a 2 MHz 6502 processor.
This is no slight on today’s game designers. They work with what they have, and that's okay. But when I think about the worlds that some old school programmers created with the tools they were given, it sometimes breaks my brain trying to to even remotely understand how they did it.
Not megabytes, not gigabytes... kilobytes!
22 kilobytes.
I often think about the technical limitations that game designers had to work with in the 80s - both in terms of software and hardware. To me, the game that stands at the very top is Elite (1984).
Elite is smaller in data size than many modern text files or desktop icons, yet it lets you freely explore a multi-galaxy-spanning universe that felt gigantic and virtually limitless.
Think about this for a second: The core game code on the BBC Micro version has roughly 22 KB of memory. The C64 version from 1985, which I played as a kid, isn't much bigger.
Now think about what Braben and Bell turned those few kilobytes into: a massive universe with eight galaxies, each containing 256 star systems (for a total of 2,048 planets per system). Each system featured unique details: government type, economy, technology level, population, commodity prices, and even descriptive text.
Oh, and by the way, the game also rendered 3D wireframe ships, stations, and planets in real time on a 2 MHz 6502 processor.
This is no slight on today’s game designers. They work with what they have, and that's okay. But when I think about the worlds that some old school programmers created with the tools they were given, it sometimes breaks my brain trying to to even remotely understand how they did it.
Not megabytes, not gigabytes... kilobytes!
New series explores making your code faster & more efficient. We start with a functional snippet - can you optimize it?
Join the challenge & share your ideas on our forum!
🔗
#Xojo#CodeOptimization#ProgrammingTips#DeveloperChallenge
@elonmusk Sounds like a solid way to level up your C code. Anyone tried it yet? Would love to hear how much faster or cleaner your code got! #CProgramming#CodeOptimization
We cant wait for @fadamakis talk #CityJSAthens
🚀 Crafting a solid folder structure is key in large-scale apps!
Modularization fosters clarity, separates features, and slashes coupling. Let's dive into effective and scalable structuring! #CodeOptimization#Modularization
Get your ticket now
"The training material simply isn't good enough for high performance real-time apps such as games, rendering or physics simulation."
#LLMs#CodeOptimization
Udostępnij
Skopiowano
Sebastian Aaltonen@SebAaltonen
In my experience LLMs are bad for code optimization. When I am using LLMs, I let the LLM write the boilerplate code and then I rewrite the perf critical parts such as inner loops.
You have to understand performance to instruct LLM to write performant code for you. Super strict instructions about the algorithm and data structures. Then review and tell the LLM exactly what should be fixed and why.
Most of the code in GitHub has performance issues. Idiomatic C++ and Rust code tends to be filled with short lived single object allocations and awful fine grained locking. The training material simply isn't good enough for high performance real-time apps such as games, rendering or physics simulation. You have to be an expert of your respective area and make the architectural/performance decisions yourself. AI is great for writing glue code, gameplay code, UI code and similar, but you should write perf critical inner loops and shaders yourself (AI doesn't really understand SIMD loop/branch coherence).
In my experience LLMs are bad for code optimization. When I am using LLMs, I let the LLM write the boilerplate code and then I rewrite the perf critical parts such as inner loops.
You have to understand performance to instruct LLM to write performant code for you. Super strict instructions about the algorithm and data structures. Then review and tell the LLM exactly what should be fixed and why.
Most of the code in GitHub has performance issues. Idiomatic C++ and Rust code tends to be filled with short lived single object allocations and awful fine grained locking. The training material simply isn't good enough for high performance real-time apps such as games, rendering or physics simulation. You have to be an expert of your respective area and make the architectural/performance decisions yourself. AI is great for writing glue code, gameplay code, UI code and similar, but you should write perf critical inner loops and shaders yourself (AI doesn't really understand SIMD loop/branch coherence).
#100DaysOfCode Challenge Day 8:
- dived into the fascinating world of React hooks, and it's a game-changer! 🌟 Let's explore how hooks revolutionize state management and side effects in functional components....
#ReactHooks#WebDevelopment
Udostępnij
Skopiowano
Vixs ✨@vixs101
#100DaysOfCode Challenge Day 7: i wasn't able to do much, but i
- learned a bit about conditional rendering in react. it's quite a fun concept. (check comments for thread)
- Also worked on my personal project. i can't wait to show u guys when it's done.
#100DaysOfCode Challenge Day 7: i wasn't able to do much, but i
- learned a bit about conditional rendering in react. it's quite a fun concept. (check comments for thread)
- Also worked on my personal project. i can't wait to show u guys when it's done.
🧠 Thinking in Custom Hooks:
Custom hooks in React are a mind-bending concept! Extracting and reusing logic across components has never been this elegant. Share the love, create custom hooks! 💙🔄 #ReactCustomHooks#CodeReuse
🚀 Hooks Galore:
React hooks extend beyond state and effects. From useReducer for complex state logic to useContext for global state, each hook serves a unique purpose in streamlining your code. 🚀🔍 #ReactDevelopment#CodeOptimization
📣Deal of the Day📣 Feb 26
45% off TODAY ONLY!
Code Like a Pro in Rust & selected titles: #rustlang#CodeOptimization
Now in print! Get ready to code like a pro in #Rust with insider techniques used by Rust veterans!
Gas efficiency matters! Smart contract developers and users should strive for optimized code and transaction management to reduce gas consumption and save costs. ⚙️📉 #GasEfficiency#CodeOptimization
Our new blog is on "Bundle Splitting".
The blog covers how to load #JavaScript files faster by bundle splitting and also provides a video walk-through of the bundle splitting process and results in the neetoChat application.
#frontend#codeoptimization
#100DaysOfCode Kickoff!
Day 1 Progress:
-Problem: 2966
-DP concepts (striver )
Key Learnings:
Using AI for code analysis and optimization
#CodeOptimization