feat: first commit

.gitignore

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+data
+data/**

bigint/bigint.nim

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+import bigints
+
+let x = initBigInt("810896098877923596128062174904361421866219158")
+echo x
+

bigint/parseutils.nim

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+# import parseutils
+
+# echo parseInt("810896098877923596128062174904361421866219158")
+# var res = 0
+# discard parseSaturatedNatural("810896098877923596128062174904361421866219158", res)
+# echo res

index.md

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+## Dependencies
+
+nimble install https://github.com/nim-lang/bigints
+https://nimdocs.com/nim-lang/bigints/bigints.html
+
+## To do
+
+- [ ] Exploration of OEIS data
+- [ ] Subdivide subsequent tasks into steps
+
+--- 
+
+An implementation of Infrabayesianism over OEIS sequences.
+<https://oeis.org/wiki/JSON_Format,_Compressed_Files>
+
+Or "Just-in-Time bayesianism", where getting a new hypothesis = getting a new sequence from OEIS which has the numbers you've seen so far. 
+
+Implementing Infrabayesianism as a game over OEIS sequences. Two parts:
+1. Prediction over interleaved sequences. I choose two OEIS sequences, and interleave them: a1, b1, a2, b2.
+  - Now, you don't have hypothesis over the whole set, but two hypothesis over the 
+  - I could also have a chemistry like iteration:
+    a1
+    a2 b1
+    a3 b2 c1
+    a4 b3 c2 d1
+    a5 b4 c3 d2 e1
+    .................
+  - And then it would just be computationally absurd to have hypotheses over the whole 
+
+2. Game where: You provide a deterministic procedure for estimating the probability of each OEIS sequence giving a list of trailing examples.

jit_bayes.nim

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+import strutils
+import sequtils
+import bigints # type: BigInt
+
+# let x = initBigInt("810896098877923596128062174904361421866219158")
+
+let file_path = "./data/stripped"
+
+let f = open(file_path)
+var line : string
+var i = 0
+while f.read_line(line):
+  if i > 3:
+    let arr = split(line, ",")
+    let l = arr.len
+    let nums = arr[1..(l-2)] # .map(proc (x: string): BigInt = initBigInt(x))
+    # echo nums
+  i = i + 1
+
+f.close()