Обсуждение новостей Games Workshop

[Siegemaster Durak]

Значит сёстры в разработке уже больше года.

"Шли последние приготовления"

[grisumbras]

Почитал про Виндикара и вероятность. Стало интересно посчитать.

без FNP

save: 4+; FNP: 7+

 mean: 2.1733539094650207

 median: 2

 standard deviation: 1.8133846971708114

 25% of outcomes: [1, 2]

 50% of outcomes: [0, 3]

 80% of outcomes: [0, 4]

 90% of outcomes: [0, 5]

 0 is 30.555556% of outcomes

 1 is 7.716049% of outcomes

 2 is 15.432099% of outcomes

 3 is 20.576132% of outcomes

 4 is 15.003429% of outcomes

 5 is 7.716049% of outcomes

 6 is 2.572016% of outcomes

 7 is 0.428669% of outcomes

 mode: 0


 save: 3+; FNP: 7+

 mean: 1.8111282578875172

 median: 2

 standard deviation: 1.8429163021096384

 25% of outcomes: [1, 2]

 50% of outcomes: [0, 3]

 80% of outcomes: [0, 4]

 90% of outcomes: [0, 4]

 0 is 42.129630% of outcomes

 1 is 6.430041% of outcomes

 2 is 12.860082% of outcomes

 3 is 17.146776% of outcomes

 4 is 12.502858% of outcomes

 5 is 6.430041% of outcomes

 6 is 2.143347% of outcomes

 7 is 0.357225% of outcomes

 mode: 0


 save: 2+; FNP: 7+

 mean: 1.4489026063100137

 median: 0

 standard deviation: 1.8005282802300437

 25% of outcomes: [0, 2]

 50% of outcomes: [0, 2]

 80% of outcomes: [0, 3]

 90% of outcomes: [0, 4]

 0 is 53.703704% of outcomes

 1 is 5.144033% of outcomes

 2 is 10.288066% of outcomes

 3 is 13.717421% of outcomes

 4 is 10.002286% of outcomes

 5 is 5.144033% of outcomes

 6 is 1.714678% of outcomes

 7 is 0.285780% of outcomes

 mode: 0

FNP 6+

save: 4+; FNP: 6+

 mean: 1.6057241655235481

 median: 1

 standard deviation: 1.6578488168585512

 25% of outcomes: [1, 2]

 50% of outcomes: [0, 2]

 80% of outcomes: [0, 3]

 90% of outcomes: [0, 4]

 0 is 42.129630% of outcomes

 1 is 9.112816% of outcomes

 2 is 16.454332% of outcomes

 3 is 17.064752% of outcomes

 4 is 10.079863% of outcomes

 5 is 4.034051% of outcomes

 6 is 1.004924% of outcomes

 7 is 0.119634% of outcomes

 mode: 0


 save: 3+; FNP: 6+

 mean: 1.3381034712696236

 median: 0

 standard deviation: 1.6274181278733642

 25% of outcomes: [0, 1]

 50% of outcomes: [0, 2]

 80% of outcomes: [0, 3]

 90% of outcomes: [0, 4]

 0 is 51.774691% of outcomes

 1 is 7.594013% of outcomes

 2 is 13.711944% of outcomes

 3 is 14.220626% of outcomes

 4 is 8.399885% of outcomes

 5 is 3.361709% of outcomes

 6 is 0.837436% of outcomes

 7 is 0.099695% of outcomes

 mode: 0


 save: 2+; FNP: 6+

 mean: 1.0704827770156988

 median: 0

 standard deviation: 1.5508949527019833

 25% of outcomes: [0, 1]

 50% of outcomes: [0, 2]

 80% of outcomes: [0, 3]

 90% of outcomes: [0, 3]

 0 is 61.419753% of outcomes

 1 is 6.075211% of outcomes

 2 is 10.969555% of outcomes

 3 is 11.376501% of outcomes

 4 is 6.719908% of outcomes

 5 is 2.689367% of outcomes

 6 is 0.669949% of outcomes

 7 is 0.079756% of outcomes

 mode: 0

FNP 5+

save: 4+; FNP: 5+

 mean: 1.1202560585276635

 median: 0

 standard deviation: 1.425972313382702

 25% of outcomes: [0, 1]

 50% of outcomes: [0, 2]

 80% of outcomes: [0, 2]

 90% of outcomes: [0, 3]

 0 is 53.703704% of outcomes

 1 is 10.538956% of outcomes

 2 is 15.761392% of outcomes

 3 is 12.372022% of outcomes

 4 is 5.623076% of outcomes

 5 is 1.674692% of outcomes

 6 is 0.301068% of outcomes

 7 is 0.025089% of outcomes

 mode: 0


 save: 3+; FNP: 5+

 mean: 0.9335467154397196

 median: 0

 standard deviation: 1.3670403893423693

 25% of outcomes: [0, 1]

 50% of outcomes: [0, 1]

 80% of outcomes: [0, 2]

 90% of outcomes: [0, 3]

 0 is 61.419753% of outcomes

 1 is 8.782463% of outcomes

 2 is 13.134493% of outcomes

 3 is 10.310019% of outcomes

 4 is 4.685897% of outcomes

 5 is 1.395577% of outcomes

 6 is 0.250890% of outcomes

 7 is 0.020908% of outcomes

 mode: 0


 save: 2+; FNP: 5+

 mean: 0.7468373723517756

 median: 0

 standard deviation: 1.2784682460116072

 25% of outcomes: [0, 1]

 50% of outcomes: [0, 1]

 80% of outcomes: [0, 2]

 90% of outcomes: [0, 2]

 0 is 69.135802% of outcomes

 1 is 7.025971% of outcomes

 2 is 10.507595% of outcomes

 3 is 8.248015% of outcomes

 4 is 3.748718% of outcomes

 5 is 1.116461% of outcomes

 6 is 0.200712% of outcomes

 7 is 0.016726% of outcomes

 mode: 0

Код

		   import itertools

		   import math

		   import sys

		   from functools import reduce



		   def product(seq):

			   return reduce((lambda a, b: a * b[0]), seq, 1)



		   def all_or_nothing(threshold, full, current, outcome, sequence, outcomes, continuation):

			   head = sequence[0][0]

			   tail = sequence[1:]

			   fails = threshold - 1

			   outcomes[0] += current * product(tail) * fails

			   full *= head

			   current *= head - fails

			   return continuation(full, current, outcome, tail, outcomes)



		   def roll_for_damage(full, current, outcome, sequence, outcomes, continuation):

			   head = sequence[0][0]

			   tail = sequence[1:]

			   full *= head

			   for roll in range(1, head + 1):

				   outcomes = continuation(full, current, roll, tail, outcomes)

			   return outcomes



		   def fnp(threshold, full, current, outcome, sequence, outcomes, continuation):

			   head = sequence[0][0]

			   tail = sequence[1:]

			   successes = threshold - 1

			   fails = head - successes

			   failed_outcome = outcome - 1

			   full *= head

			   if failed_outcome <= 0:

				   outcomes[0] += current * product(tail) * fails

				   current *= head - fails

				   outcomes = continuation(full, current, outcome, tail, outcomes)

			   else:

				   outcomes = continuation(full, current * fails, failed_outcome, tail, outcomes)

				   outcomes = continuation(full, current * successes, outcome, tail, outcomes)

			   return outcomes



		   def extra_damage(threshold, full, current, outcome, sequence, outcomes, continuation):

			   head = sequence[0][0]

			   tail = sequence[1:]

			   fails = threshold - 1

			   outcomes[outcome] += current * product(tail) * fails

			   full *= head

			   current *= head - fails

			   return continuation(full, current, outcome + 1, tail, outcomes)



		   def set_value(full, current, outcome, sequence, outcomes, continuation):

			   head = sequence[0][0]

			   tail = sequence[1:]

			   outcomes[outcome] += current * head * product(tail)

			   return outcomes



		   def run_sequence(full, current, outcome, sequence, outcomes):

			   if not sequence:

				   return outcomes


			   handler = sequence[0][1]

			   return handler(full, current, outcome, sequence, outcomes, run_sequence)



		   save = int(sys.argv[1]) if len(sys.argv) > 1 else 3

		   fnp_threshold = int(sys.argv[2]) if len(sys.argv) > 2 else 7

		   print("save: {}+".format(save))

		   save_threshold = 5 - save


		   roll_sequence = [

			   (6, lambda *args: all_or_nothing(2, *args)),				# to hit

			   (6, lambda *args: all_or_nothing(2, *args)),				# to wound

			   (6, lambda *args: all_or_nothing(save_threshold , *args)),  # save

			   (3, roll_for_damage),									   # damage

			   (6, lambda *args: fnp(fnp_threshold, *args)),			   # FNP

			   (6, lambda *args: fnp(fnp_threshold, *args)),			   # FNP

			   (6, lambda *args: fnp(fnp_threshold, *args)),			   # FNP

			   (6, lambda *args: extra_damage(3, *args)),				  # 3+ extra

			   (6, lambda *args: fnp(fnp_threshold, *args)),			   # FNP

			   (6, lambda *args: extra_damage(4, *args)),				  # 4+ extra

			   (6, lambda *args: fnp(fnp_threshold, *args)),			   # FNP

			   (6, lambda *args: extra_damage(5, *args)),				  # 5+ extra

			   (6, lambda *args: fnp(fnp_threshold, *args)),			   # FNP

			   (6, lambda *args: extra_damage(6, *args)),				  # 6+ extra

			   (6, lambda *args: fnp(fnp_threshold, *args)),			   # FNP

			   (1, set_value),

		   ]


		   outcomes = run_sequence(

			   1,

			   1,

			   1,

			   roll_sequence,

			   list(itertools.repeat(0, 8)),

		   )


		   total_count = sum(outcomes)

		   mean = sum([i * x for i, x in enumerate(outcomes)]) / total_count

		   print("mean: {}".format(mean))



		   half = total_count / 2

		   for i, x in enumerate(outcomes):

			   half -= x

			   if half <= 0:

				   median = i

				   break

		   print("median: {}".format(median))



		   def variance_item(x):

			   x = x - mean

			   return x * x

		   variance = sum([variance_item(i) * x for i, x in enumerate(outcomes)]) / total_count

		   s_deviation = math.sqrt(variance)

		   print("standard deviation: {}".format(s_deviation))



		   proportions = (

			   (25, 0.318639),

			   (50, 0.674490),

			   (80, 1.281552),

			   (90, 1.644854),

		   )

		   for proportion, interval in proportions:

			   min_x = math.trunc(max(0, mean - s_deviation * interval))

			   max_x = math.trunc(min(len(outcomes), mean + s_deviation * interval))

			   print("{}% of outcomes: [{}, {}]".format(proportion, min_x, max_x))



		   for i, x in enumerate(outcomes):

			   print("{} is {:2%} of outcomes".format(i, x / total_count))

		   mode = max(enumerate(outcomes), key=lambda x: x[1])

		   print("mode: {}".format(mode[0]))

Изменено 22 февраля, 2019 пользователем grisumbras

[Wlad]

Как минимум - не отрабатывается вариант нанесения д6 вундов.

Максимально можно нанести 10 вундов, а не 7.

[grisumbras]

Пардон, невнимательно правила прочитал. Чуть попозже переделаю.

[grisumbras]

Поправил. В общем, на 5 ран я бы не расчитывал.

без FNP

save: 4+; FNP: 7+

 mean: 3.2150205761316872

 median: 3

 standard deviation: 2.705648849936942

 25% of outcomes: [2, 4]

 50% of outcomes: [1, 5]

 80% of outcomes: [0, 6]

 90% of outcomes: [0, 7]

  0 is 30.56% of outcomes

  1 is 3.86% of outcomes

  2 is 7.72% of outcomes

  3 is 10.29% of outcomes

  4 is 11.36% of outcomes

  5 is 11.57% of outcomes

  6 is 11.57% of outcomes

  7 is 7.72% of outcomes

  8 is 3.86% of outcomes

  9 is 1.29% of outcomes

 10 is 0.21% of outcomes

 mode: 0


 save: 3+; FNP: 7+

 mean: 2.6791838134430725

 median: 2

 standard deviation: 2.745186987659309

 25% of outcomes: [1, 3]

 50% of outcomes: [0, 4]

 80% of outcomes: [0, 6]

 90% of outcomes: [0, 7]

  0 is 42.13% of outcomes

  1 is 3.22% of outcomes

  2 is 6.43% of outcomes

  3 is 8.57% of outcomes

  4 is 9.47% of outcomes

  5 is 9.65% of outcomes

  6 is 9.65% of outcomes

  7 is 6.43% of outcomes

  8 is 3.22% of outcomes

  9 is 1.07% of outcomes

 10 is 0.18% of outcomes

 mode: 0


 save: 2+; FNP: 7+

 mean: 2.1433470507544583

 median: 0

 standard deviation: 2.6790530832302766

 25% of outcomes: [1, 2]

 50% of outcomes: [0, 3]

 80% of outcomes: [0, 5]

 90% of outcomes: [0, 6]

  0 is 53.70% of outcomes

  1 is 2.57% of outcomes

  2 is 5.14% of outcomes

  3 is 6.86% of outcomes

  4 is 7.57% of outcomes

  5 is 7.72% of outcomes

  6 is 7.72% of outcomes

  7 is 5.14% of outcomes

  8 is 2.57% of outcomes

  9 is 0.86% of outcomes

 10 is 0.14% of outcomes

 mode: 0

FNP6+

save: 4+; FNP: 6+

 mean: 2.692222508001829

 median: 3

 standard deviation: 2.5270667273239606

 25% of outcomes: [1, 3]

 50% of outcomes: [0, 4]

 80% of outcomes: [0, 5]

 90% of outcomes: [0, 6]

  0 is 36.34% of outcomes

  1 is 4.58% of outcomes

  2 is 8.58% of outcomes

  3 is 10.77% of outcomes

  4 is 11.45% of outcomes

  5 is 11.22% of outcomes

  6 is 9.34% of outcomes

  7 is 5.14% of outcomes

  8 is 2.02% of outcomes

  9 is 0.50% of outcomes

 10 is 0.06% of outcomes

 mode: 0


 save: 3+; FNP: 6+

 mean: 2.2435187566681907

 median: 1

 standard deviation: 2.515630567590213

 25% of outcomes: [1, 3]

 50% of outcomes: [0, 3]

 80% of outcomes: [0, 5]

 90% of outcomes: [0, 6]

  0 is 46.95% of outcomes

  1 is 3.81% of outcomes

  2 is 7.15% of outcomes

  3 is 8.97% of outcomes

  4 is 9.54% of outcomes

  5 is 9.35% of outcomes

  6 is 7.78% of outcomes

  7 is 4.29% of outcomes

  8 is 1.68% of outcomes

  9 is 0.42% of outcomes

 10 is 0.05% of outcomes

 mode: 0


 save: 2+; FNP: 6+

 mean: 1.7948150053345526

 median: 0

 standard deviation: 2.4224074694235327

 25% of outcomes: [1, 2]

 50% of outcomes: [0, 3]

 80% of outcomes: [0, 4]

 90% of outcomes: [0, 5]

  0 is 57.56% of outcomes

  1 is 3.05% of outcomes

  2 is 5.72% of outcomes

  3 is 7.18% of outcomes

  4 is 7.63% of outcomes

  5 is 7.48% of outcomes

  6 is 6.23% of outcomes

  7 is 3.43% of outcomes

  8 is 1.34% of outcomes

  9 is 0.33% of outcomes

 10 is 0.04% of outcomes

 mode: 0

FNP5+

save: 4+; FNP: 5+

 mean: 2.2105052583447646

 median: 2

 standard deviation: 2.3061613042336018

 25% of outcomes: [1, 2]

 50% of outcomes: [0, 3]

 80% of outcomes: [0, 5]

 90% of outcomes: [0, 6]

  0 is 42.13% of outcomes

  1 is 5.47% of outcomes

  2 is 9.45% of outcomes

  3 is 11.13% of outcomes

  4 is 11.33% of outcomes

  5 is 10.01% of outcomes

  6 is 6.63% of outcomes

  7 is 2.85% of outcomes

  8 is 0.84% of outcomes

  9 is 0.15% of outcomes

 10 is 0.01% of outcomes

 mode: 0


 save: 3+; FNP: 5+

 mean: 1.8420877152873039

 median: 0

 standard deviation: 2.260672628028253

 25% of outcomes: [1, 2]

 50% of outcomes: [0, 3]

 80% of outcomes: [0, 4]

 90% of outcomes: [0, 5]

  0 is 51.77% of outcomes

  1 is 4.56% of outcomes

  2 is 7.87% of outcomes

  3 is 9.27% of outcomes

  4 is 9.44% of outcomes

  5 is 8.34% of outcomes

  6 is 5.53% of outcomes

  7 is 2.38% of outcomes

  8 is 0.70% of outcomes

  9 is 0.13% of outcomes

 10 is 0.01% of outcomes

 mode: 0


 save: 2+; FNP: 5+

 mean: 1.473670172229843

 median: 0

 standard deviation: 2.152077723741157

 25% of outcomes: [0, 2]

 50% of outcomes: [0, 2]

 80% of outcomes: [0, 4]

 90% of outcomes: [0, 5]

  0 is 61.42% of outcomes

  1 is 3.65% of outcomes

  2 is 6.30% of outcomes

  3 is 7.42% of outcomes

  4 is 7.55% of outcomes

  5 is 6.67% of outcomes

  6 is 4.42% of outcomes

  7 is 1.90% of outcomes

  8 is 0.56% of outcomes

  9 is 0.10% of outcomes

 10 is 0.01% of outcomes

 mode: 0

Код

#!/usr/bin/env python3



 import itertools

 import math

 import sys

 from functools import reduce



 def product(seq):

	 return reduce((lambda a, b: a * b[0]), seq, 1)



 def all_or_nothing(threshold, full, current, outcome, sequence, outcomes, continuation):

	 head = sequence[0][0]

	 tail = sequence[1:]

	 fails = threshold - 1

	 outcomes[0] += current * product(tail) * fails

	 full *= head

	 current *= head - fails

	 return continuation(full, current, outcome, tail, outcomes)



 def roll_for_damage(full, current, outcome, sequence, outcomes, continuation):

	 head = sequence[0][0]

	 tail = sequence[1:]

	 full *= head

	 for roll in range(1, head + 1):

		 outcomes = continuation(full, current, roll, tail, outcomes)

	 return outcomes



 def fnp(threshold, full, current, outcome, sequence, outcomes, continuation):

	 head = sequence[0][0]

	 tail = sequence[1:]

	 successes = threshold - 1

	 fails = head - successes

	 failed_outcome = outcome - 1

	 full *= head

	 if failed_outcome <= 0:

		 outcomes[0] += current * product(tail) * fails

		 current *= head - fails

		 outcomes = continuation(full, current, outcome, tail, outcomes)

	 else:

		 outcomes = continuation(full, current * fails, failed_outcome, tail, outcomes)

		 outcomes = continuation(full, current * successes, outcome, tail, outcomes)

	 return outcomes



 def extra_damage(threshold, full, current, outcome, sequence, outcomes, continuation):

	 head = sequence[0][0]

	 tail = sequence[1:]

	 fails = threshold - 1

	 outcomes[outcome] += current * product(tail) * fails

	 full *= head

	 current *= head - fails

	 return continuation(full, current, outcome + 1, tail, outcomes)



 def set_value(full, current, outcome, sequence, outcomes, continuation):

	 head = sequence[0][0]

	 tail = sequence[1:]

	 outcomes[outcome] += current * head * product(tail)

	 return outcomes



 def run_sequence(full, current, outcome, sequence, outcomes):

	 if not sequence:

		 return outcomes


	 handler = sequence[0][1]

	 return handler(full, current, outcome, sequence, outcomes, run_sequence)



 save = int(sys.argv[1]) if len(sys.argv) > 1 else 3

 fnp_threshold = int(sys.argv[2]) if len(sys.argv) > 2 else 7

 print("save: {}+; FNP: {}+".format(save, fnp_threshold))

 save_threshold = 5 - save


 roll_sequence = [

	 (6, lambda *args: all_or_nothing(2, *args)),				# to hit

	 (6, lambda *args: all_or_nothing(2, *args)),				# to wound

	 (6, lambda *args: all_or_nothing(save_threshold , *args)),  # save

	 (6, roll_for_damage),									   # damage

	 (6, lambda *args: fnp(fnp_threshold, *args)),			   # FNP

	 (6, lambda *args: fnp(fnp_threshold, *args)),			   # FNP

	 (6, lambda *args: fnp(fnp_threshold, *args)),			   # FNP

	 (6, lambda *args: extra_damage(3, *args)),				  # 3+ extra

	 (6, lambda *args: fnp(fnp_threshold, *args)),			   # FNP

	 (6, lambda *args: extra_damage(4, *args)),				  # 4+ extra

	 (6, lambda *args: fnp(fnp_threshold, *args)),			   # FNP

	 (6, lambda *args: extra_damage(5, *args)),				  # 5+ extra

	 (6, lambda *args: fnp(fnp_threshold, *args)),			   # FNP

	 (6, lambda *args: extra_damage(6, *args)),				  # 6+ extra

	 (6, lambda *args: fnp(fnp_threshold, *args)),			   # FNP

	 (1, set_value),

 ]


 outcomes = run_sequence(

	 1,

	 1,

	 1,

	 roll_sequence,

	 list(itertools.repeat(0, 11)),

 )


 total_count = sum(outcomes)

 mean = sum([i * x for i, x in enumerate(outcomes)]) / total_count

 print("mean: {}".format(mean))



 half = total_count / 2

 for i, x in enumerate(outcomes):

	 half -= x

	 if half <= 0:

		 median = i

		 break

 print("median: {}".format(median))



 def variance_item(x):

	 x = x - mean

	 return x * x

 variance = sum([variance_item(i) * x for i, x in enumerate(outcomes)]) / total_count

 s_deviation = math.sqrt(variance)

 print("standard deviation: {}".format(s_deviation))



 proportions = (

	 (25, 0.318639),

	 (50, 0.674490),

	 (80, 1.281552),

	 (90, 1.644854),

 )

 for proportion, interval in proportions:

	 min_x = math.trunc(max(0, mean - s_deviation * interval))

	 max_x = math.trunc(min(len(outcomes), mean + s_deviation * interval))

	 print("{}% of outcomes: [{}, {}]".format(proportion, min_x, max_x))



 for i, x in enumerate(outcomes):

	 print("{:2} is {:.2%} of outcomes".format(i, x / total_count))

 mode = max(enumerate(outcomes), key=lambda x: x[1])

 print("mode: {}".format(mode[0]))

Изменено 24 февраля, 2019 пользователем grisumbras

[Nimbar]

Поправил. В общем, на 5 ран я бы не расчитывал.

без FNP

save: 4+; FNP: 7+

 mean: 3.2150205761316872

 median: 3

 standard deviation: 2.705648849936942

 25% of outcomes: [2, 4]

 50% of outcomes: [1, 5]

 80% of outcomes: [0, 6]

 90% of outcomes: [0, 7]

  0 is 30.56% of outcomes

  1 is 3.86% of outcomes

  2 is 7.72% of outcomes

  3 is 10.29% of outcomes

  4 is 11.36% of outcomes

  5 is 11.57% of outcomes

  6 is 11.57% of outcomes

  7 is 7.72% of outcomes

  8 is 3.86% of outcomes

  9 is 1.29% of outcomes

 10 is 0.21% of outcomes

 mode: 0

Объясните кто-нть что это такое, как это читать, и где это найти...

[Senior G]

Объясните кто-нть что это такое, как это читать, и где это найти...

Это индусский код, не обращай внимание.

[grisumbras]

Объясните кто-нть что это такое, как это читать, и где это найти...

mean — математическое ожидание. Средняя температура по больнице. Если сделаешь миллиард выстрелов, сложишь все полученные раны и разделишь на миллиард, то получишь примерно это.

median — медиана. Результат ровно по середине распределения. Вероятность исходов не лучше этого - 50%, вероятность исходов не хуже этого — 50%.

standard deviation — среднеквадратичное отклонение. Характеристика разброса исходов, чем она выше — тем меньше надежность получить что-то близкое к мат. ожиданию.

N% of outcomes — диапазон в который входит мат. ожидание и еще N% возможных исходов. Иначе говоря, с вероятностью N% результат будет такой. Я тут, правда, использую допущение, что случайная величина — нормальная, а это явно не так.

mode — мода. Самый часто встречающийся результат. Здесь это бесполезный параметр, во всех случаях он равен нулю.

Изменено 25 февраля, 2019 пользователем grisumbras

[Nimbar]

Спасибо большое

[Wlad]

В общем, на 5 ран я бы не расчитывал.

Судя по твоим расчетам, шанс убрать со стола 5-вундовую пехотную модель в 4+ или хуже и без ФНП (например, Иврейн) 1м выстрелом составляет 36.22%.

Это мало?

На 5+ убрать ключевого персонажа противника... как по мне - так норм.

[Hornash]

36.22%.

Берите два, делов-то

[dimon_metalist]

и в них за кп постреляют сразу в обоих?)

[Altag]

очередной прохладный сказ

А мне понравилось.

Хаоситы режут друг друга, что может быть замечательнее.

Похоже миня варпсмита будет.

[neverhxd]

А мне понравилось.

Хаоситы режут друг друга, что может быть замечательнее.

Похоже миня варпсмита будет.

его тело умерло же) скорее всего пересадили в этого паукана

[Altag]

его тело умерло же) скорее всего пересадили в этого паукана

Хм...

А мне показалось, что в него демонца подселили.

[Locke]

i

Уведомление: Напоминаю, что для обсуждения литературы есть отдельный подфорум, в который и уехали сообщения про хоррор-линейку.

[Шикарный Манул]

очередной прохладный сказ про Виджилус

[BlackDrozD]

Какая-то новая башка из твиттера, облитератор,судя по всему.

Изменено 28 февраля, 2019 пользователем BlackDrozD

[Разжигатель Войны]

Потекло! или потекли?!

[Senior G]

Фото в цвете. Есть ещё фото в профиль.

"Раскрывающийся текст"

Изменено 28 февраля, 2019 пользователем Senior G

[Разжигатель Войны]

Первоисточник в твиттере у кого есть? Поделитесь!

[Ditrih]

А это не нойзы случаем?

[Senior G]

А это не нойзы случаем?

Сначала берсы, теперь вот нойзов увидели.

[Разжигатель Войны]

Это обли, самые что настоящие. Одна из этих голов в ролике была.

[CivilWAR]

угу врядли нойзы, рано для них пока