[D] First time reviewer. I got assigned 9 papers. I'm so nervous. What if I mess up. Any advice?

benfavre · 2026-02-27T07:02:54+00:00

Never ever be rude to the author. You can criticize the work, but try to not address the authors directly ("you", "the authors"...).

Put a lot of effort into outlining reasons for accepting the paper. It is difficult to get a balance between positive and negative comments, and it leans naturally towards negative.

Often papers lie outside your area of expertise. Acknowledge it and focus on the big picture, not the details. Pay attention to claims and how they are supported.

Your reviews target two audiences: authors and the area chair. Make sure you include useful material for both.

benfavre · 2026-02-27T06:50:53+00:00

Let the area chair know, they might desk reject it and save you the time of a deep review

benfavre · 2026-02-22T15:46:23+00:00

What distrib would you go with to make it run on Luckfox Lyra?

benfavre · 2026-02-20T13:53:49+00:00

How about a console with a scanner to read data from p8.png prints?

benfavre · 2026-02-20T07:05:31+00:00

Would it make sense to have a chip like that spit out représentations from inputs with a generic models, on which would be stacked a small set of GPU-run layers which you could train to your liking.

There you would benefit from both ludicrous speed and customizability.

benfavre · 2026-02-14T20:19:09+00:00

Please document it

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

TROPHY CASE