u/FakeGreatness

Hello! I'm in no way a researcher, just recently discovered the passion for local AI so I started doing some testing. Not sure if this will help in any way shape or form but I'm here with my results.

I'm using a Macbook Pro M4 Pro (20 cores) 48GB RAM. Nothing has changed between the tests, every model was cold started when the test started.

Harness: OpenCode

Electricity usage: 100-110Wh on baseline, 160Wh when the models were working.

Models were loaded through oMLX. All models used were from llm-community.

Prompt used: "Create a HTML5 canvas-based game with JavaScript in a .js file, CSS in a .css file and HTML in index.html. The game should be the classical snake game, it should have a menu, a score, a way to replay it. The focus is on creative and interesting design and also functionality. The game must be fully functional and playable without console errors. Complete the implementation end-to-end and verify that it works"

I've also attached screenshots from the games the tests have created.

These are my code-related findings:

Qwen 3.8 27B xHigh ranked 1st because it had the strongest overall architecture and correctness: explicit state management, a fixed-timestep loop with clamping, correct input queuing and tail collision, protected storage access, focus-loss pausing, and proper handling of full-board victory conditions.

Qwen 3.8 Low ranked 2nd because it was simpler but still very robust, with centralized state, correct collision logic, delta-time clamping, and clean separation of most responsibilities (also MUSIC). Qwen 3.8 Medium ranked 3rd: it was more polished in areas like DPI scaling and rendering, but its unlimited catch-up loop and unbounded while(true) food search introduced more serious runtime risks.

Qwen 3.6 27B ranked 4th because it had the most fundamental issues: incorrect tail collision handling, less reliable food spawning, weaker state management, unprotected localStorage, duplicated render logic, and a less stable setInterval-based game loop. This doesn't mean 3.6 was bad... just not better than the rest of them.

Other cool findings:
Seems like 3.8 required less RAM? 3.6 rose the RAM usage to around 90% while 3.8 to only 70-80%.

TLDR: 3.8 xhigh > low >= medium > 3.6 but tbh low and medium were really close, the differences between them weren't that big so idk how to place them

This was my first local test and I'm using it to learn and understand how LLMs work. Looking forward to learn a little bit more so if you got any suggestions I'm more than happy to learn. And yes, I've used GPT to generate that table picture.

Vă salut. Împreună cu un prieten am construit https://companiidestat.ro. Reprezintă o radiografie a celor 1502 companii de stat ale României. Am folosit date publice de pe data.gov.ro, amepip.gov.ro și bvb.ro.

Pe scurt:

• Vizualizări pe date oficiale AMEPIP
• Hartă pe județe cu distribuția companiilor pe pierdere; click pe județ deschide lista companiilor din acel județ
• Profilul individual pentru fiecare companie cu evoluție pe ultimii 6 ani, număr angajați, membri CA și salariile lor (acolo unde acestea sunt publice)
• Evoluția companiilor pe pierdere, pe sectoare (2019-2024)
• Vizualizare interactivă salariu director vs performanța financiară a companiei
• Șapte exemple pozitive de companii listate la bursă cu evoluția profitului și prețului acțiunii post-listare
• Un scoreboard al celor 10 obligații legate de companiile de stat din "Programul de Guvernare 2025-2028"
• Countdown PNRR până la termenul jalonului 443
• Plus o pagină în care am explicat exact cum au fost extrase datele

Pe partea tehnică folosim vanilla js, css, un custom build de D3.js, niște scripturi de python care extrag datele din xlsx-ul AMEPIP și două pdf-uri (indemnizații + fișe de evaluare). Când AMEPIP publică un raport nou, un pipeline rulează și regenerează datele. Pentru hosting folosim momentan GitHub Pages.

Dacă aveți feedback, întrebări sau nelămuriri, vă răspundem cu drag.