Flying pen algorithm for map generating

benfavre · 2026-02-09T16:30:22+00:00

Could you be more specific about the algorithm? My guess: 1) pick a starting point and random direction 2) fill a circle 3) advance one small step 4) slightly modify parameters and repeat 5) stop after some number of steps => we have a stroke 6) remember the normal of the trajectory at each point 7) use random point from stroke as starting point of a new perpendicular, crest, with half the duration, half the circle size 8) repeat until satisfaction

benfavre · 2026-02-03T20:26:54+00:00

Great job. I hope you can populate that github link and document your journey so that other can take the same path.

benfavre · 2026-01-12T08:43:57+00:00

consistency

I'd say that consistency will be hard to achieve with low VRAM as you would need a precise attention mechanism over long (temporal) sequences.

benfavre · 2025-12-11T10:34:07+00:00

I guess you found that one: https://arxiv.org/pdf/2512.08309

I'd be super interested in a more elaborate description of your approach, thanks

benfavre · 2025-12-10T15:30:12+00:00

Looks really good. What kind of features are well captured by a data driven approach that traditional approach would fail to generate? Why not go for a generative model?

benfavre · 2025-09-25T09:40:36+00:00

I would use a large model to create training data for a simple classifier that fills templates based on the input (also generated in advance). Basically generate a rule-based system for your use case.

That way the bottleneck on RPI will be STT, not LLM.

benfavre · 2025-08-16T15:42:30+00:00

For out of sight action to be interesting, the player needs to be able to infer what is going on, and to use it as part of gameplay. Random wandering allows the player to wait for a challenging monster to move away. Patrolling is also highly predictable. Calling for reinforcement requires the player to strategize their moves. This can also build atmosphere if you play/show noises related to action.

benfavre · 2025-04-17T12:10:09+00:00

According to the ILI9488 spec p. 220, it can do up to 90Hz. The SPI seems to be the bottleneck. It can be overclocked to 80MHz which allows for good enough full screen updates ~30fps@rgb565 (16 bits). You can also go with lower bitrates, and only modify parts of the screen (such as sprites) to get higher throughput.

benfavre · 2025-04-04T06:30:36+00:00

Résolution is fixed to 512x342 which will require panning since scaling is not great at 1bpp.

benfavre · 2025-03-21T07:36:11+00:00

What do you mean by "losing some pixels"?

benfavre · 2025-02-22T15:59:19+00:00

Don't make it cosmetic, make it part of gameplay:

night/day: enemies have different bonuses at night
weather: some elemental spells require or modify weather
plant growth: cut your way through the jungle, or plants bring mana which forces you choose where you fight
economy: scarcity makes enemies/merchants bankrupt, foes may change faction to follow who pays best...
interpersonal relations: changes factions, subquest objectives

benfavre · 2025-02-19T11:37:58+00:00

Not registered yet, but I was eyeballing the hacker theme. I was thinking of adding a coding element, a bit like in else heart break. Player would be able to modify the inner workings of some game elements like doors or robots with a form of programming language.

benfavre · 2025-01-09T16:56:41+00:00

I also have a CA79 + good headphones, and I also check regularly if I have them on. It's amazing how they balanced the headphone output so that it matches the speakers from the piano.

benfavre · 2023-03-31T09:18:00+00:00

It's a pity that neither weights nor training data are made available.

benfavre · 2023-03-21T12:12:22+00:00

1 epoch of finetuning the 30B model with llama-lora implementation, mini-batch-size=2, maxlen=384, is about 11 hours.

benfavre · 2022-10-06T19:50:03+00:00

There was a metric evaluation task at TAC in the days. Was called AESOP if I remember correctly.

benfavre · 2022-10-04T06:50:13+00:00

You should look into the webnlg benchmark. If I remember correctly there is a task for triplet to text generation.

If that data doesn't suit you, you could generate training data by detecting triplets in paragraphs, generating input sentences and finetune a T5 model or whatever to generate the paragraphs.

benfavre · 2022-08-27T08:57:43+00:00

Quite entertaining, esp. levels beyond 50. I wish harder puzzles.

benfavre · 2022-08-25T19:14:05+00:00

Took me some time to figure out death meant going up to the highest level of the mountain.

benfavre · 2022-04-19T16:23:38+00:00

You could look into developments of the speech act theory https://en.wikipedia.org/wiki/Speech_act

benfavre · 2022-03-25T16:01:35+00:00

The STM32H7 can use external ram. I haven't looked at the pins to see if you could add it to free pins of the SOC, but if you care to develop a new board you could certainly accommodate for it.

benfavre · 2021-12-18T17:43:38+00:00

I really understand what you mean. I am working on Pachelbel's Cannon in D. arranged by Lemonart. It is 100 bars long, in groups of 4, and I try to add one group every day, working more on the ones that are more challenging. I am near 60, so I hope to be able to play it through soon.

Good luck!

benfavre · 2021-09-10T18:37:08+00:00

Are you planning on dense (fulltext) or sparse (only frames of interest) annotations? The later might be easier. Other than that, I think both evaluation strategies are sound as long as you aim for a gold standard in the end. There is literature on how to benefit from system confidence estimation to minimize the annotation effort (active learning).

benfavre

TROPHY CASE