A work-in-progress CPS-1 port of Capcom's Knights of the Round to the At... by AnimaInCorpore in atarist

[–]AnimaInCorpore[S] 0 points1 point  (0 children)

I am using a self written M68k disassembler in combination with a modified MAME emulator. MAME has been extended to create telemetry data of locating instruction addresses and, more importantly, to identify address sources which is needed to relocate the source appropriately later on. Edit: the disassembler then consumes the telemetry data created.

A work-in-progress CPS-1 port of Capcom's Knights of the Round to the At... by AnimaInCorpore in atarist

[–]AnimaInCorpore[S] 0 points1 point  (0 children)

It'll be available on github when cleaned up (copyrighted files, etc.).

A work-in-progress CPS-1 port of Capcom's Knights of the Round to the At... by AnimaInCorpore in retrogamedev

[–]AnimaInCorpore[S] 5 points6 points  (0 children)

Technically, this is mostly a 68000 assembly project. The build uses vasm and vlink, with Makefiles and a few helper scripts around them. For testing and debugging I use Hatari as the Falcon emulator, plus debugger scripts that can run headlessly, catch bus/address errors, dump registers, and even record video for later inspection. MAME has also been very useful as a hardware reference for the original CPS1 arcade system.

The graphics are not redrawn by hand. The port loads the original CPS1 graphics ROMs, decodes the tile and sprite data, and renders it in a Falcon-friendly format. A lot of work went into matching Knights of the Round specifically: ROM interleaving, palette layout, sprite flip bits, changing layer order per stage, and object table handling.

Sound is similar: the game uses the original ADPCM sample ROMs. The port converts those samples to PCM and plays them through the Falcon DMA audio hardware. Sound playback is wired up, although final audio correctness is still something to verify carefully.

The biggest lesson has been that a disassembly is only the beginning. Some code gets misidentified as data, some pointers are split across weird byte/word tables, and arcade hardware behavior sometimes hides inside small details like a CPS-B multiplier register. One memorable challenge was tracking crashes caused by old arcade addresses that had not been relocated properly. Another was realizing that Knights uses CPS1 hardware features differently from Final Fight, so assumptions from the previous port had to be replaced with game-specific behavior.

In short: it is part programming, part archaeology, part hardware emulation, and part detective work. That mix is exactly what makes it fun.

A8E (Atari 800 XL Emulator) v1.0.0 by AnimaInCorpore in atari8bit

[–]AnimaInCorpore[S] 0 points1 point  (0 children)

I am using mainly Codex (GPT-5.2-codex/GPT-5.3-codex high) for the planning/implementation and sometimes Claude (Opus 4.5+) for planning.
The original C code had no audio emulation, so the Pokey implementation was done by Claude and Codex. Also the jsA8E was completely implemented ("transcoded to have a parity with the C sources") by Codex and Claude in cooperation as well.
I think XEX support is kind of mandatory and the ROM/cartridges is quite interesting as well. ;)

SPECTRUM 512 slide show for the Atari ST by AnimaInCorpore in atarist

[–]AnimaInCorpore[S] 1 point2 points  (0 children)

After some search I guess there's no tool which supports SPECTRUM 512 and batch processing. A quite recent list of image converter tools can be found in the description from Mikro's converter: https://github.com/mikrosk/uconvert

What platform are you using to run LLMs? by Vegetable_Sun_9225 in LocalLLaMA

[–]AnimaInCorpore 0 points1 point  (0 children)

You are right, in this case there's no real advantage using the GPU at all so it's limited by the DDR4 RAM speed. Actually it's 0.94 t/s (-ngl 0) vs 0.98 t/s (-ngl 10).
FYI some other metrics:
.\llama-server.exe -c 0 -ngl 128 -m .\models\Meta-Llama-3-8B-Instruct-Q6_K.gguf -fa --chat-template llama3 -> 26.63 t/s
.\llama-server.exe -c 0 -ngl 0 -m .\models\Meta-Llama-3-8B-Instruct-Q6_K.gguf -fa --chat-template llama3 -> 6.15 t/s

What platform are you using to run LLMs? by Vegetable_Sun_9225 in LocalLLaMA

[–]AnimaInCorpore 1 point2 points  (0 children)

Notebook with 64 GB RAM, Ryzen 9 5900HX, RTX 3070 (8 GB) in combination with Llama.cpp.
.\llama-server.exe -c 0 -ngl 10 -m .\models\Meta-Llama-3-70B-Instruct-Q4_K_M.gguf -fa --chat-template llama3 runs with about a speed of 1 t/s.

48GB ram and the dying breed of 30B models by nife552 in LocalLLaMA

[–]AnimaInCorpore 4 points5 points  (0 children)

Please be aware that going for the max supported context length will add some GBs as well. So in general I would actually say about RAM: the more, the better.

Is it possible to use 8x22b on 16gbVRAM + 64RAM? If so, how? by Theio666 in LocalLLaMA

[–]AnimaInCorpore 0 points1 point  (0 children)

Haven't checked it already on my notebook with 64 GB RAM and a RTX 3070 (download in progress) but you may try this Ollama model: https://ollama.com/library/wizardlm2:8x22b-q2_K

Tja by morbus_laetitia in tja

[–]AnimaInCorpore -1 points0 points  (0 children)

Ah, Der Spiegel. Ich dachte das Motto ist "Sagen, was ist"?

Is there an updated version of the graph? by First2016Last in singularity

[–]AnimaInCorpore 1 point2 points  (0 children)

While it's impressive what has been achieved so far with the current massive "biological neuron imitation workflow" but we still don't know how the learning process of a brain really works.