{
  "timestamp": "2026-10-20T03:32:59.868760+00:00",
  "python": "3.12.11",
  "count": 21,
  "checks": 6220,
  "seconds": 57.65,
  "examples": {
    "adaptive-neural-infra": {
      "file": "adaptive_neural_infra.py",
      "checks": 8,
      "output": "MoE initial/final regression MSE: 3.9880120754241943 0.042410463094711304\nScalar state for [1,0,0,2]: ([1.0, 0.5, 0.25, 2.125], 2.125)\nAda's value: Python\nAnother user's value: None\nValid top-1 routing statistic below 1: 0.9359999299049377\nThese primitives are educational; the recurrence is not a full Mamba implementation.\n",
      "standalone": true
    },
    "automata-theory": {
      "file": "automata_theory.py",
      "checks": 510,
      "output": "Divisible by 3: [('', True), ('110', True), ('111', False)]\nNFA trace for 1101: [('start', ['A']), ('1', ['A']), ('1', ['A']), ('0', ['A', 'B']), ('1', ['A', 'C'])]\nAccepted: True\n",
      "standalone": true
    },
    "cfg-pda": {
      "file": "cfg_pda.py",
      "checks": 254,
      "output": "Balanced: [('', True), ('(()())', True), ('())(', False), ('(', False)]\nCYK aabb accepted: True\nFull-span variables: ['S']\nLength-2 middle span ab: ['S']\n",
      "standalone": true
    },
    "channel-capacity-codes": {
      "file": "channel_capacity_codes.py",
      "checks": 131,
      "output": "Sent: (0, 1, 1, 0, 1, 1, 0)\nReceived: (0, 1, 0, 0, 1, 1, 0)\nCorrected: (0, 1, 1, 0, 1, 1, 0)\nTwo flips recover original: False\nBSC(.1) capacity: 0.531004 bits/use\nRate of Hamming(7,4): 0.5714285714285714\n",
      "standalone": true
    },
    "cnn": {
      "file": "cnn.py",
      "checks": 6,
      "output": "Training images: 256; independent test images: 64\nInitial/final cross entropy: 0.6973921656608582 2.9766015359200537e-05\nTest accuracy: 1.0\nParameters: 1282\nThis demonstrates the training pipeline, not performance on natural photos.\n",
      "standalone": true
    },
    "compression-in-practice": {
      "file": "compression_in_practice.py",
      "checks": 29,
      "output": "RLE runs: [(5, 65), (3, 66), (2, 67), (4, 65)]\nBAC interval: (Fraction(31, 50), Fraction(13, 20))\nDecode midpoint: BAC\nzlib raw bytes: 9600 encoded bytes: 84 round trip: True\nbz2 raw bytes: 9600 encoded bytes: 94 round trip: True\nlzma raw bytes: 9600 encoded bytes: 136 round trip: True\nExact Fractions explain intervals; production coders use finite-word renormalization.\n",
      "standalone": true
    },
    "entropy-source-coding": {
      "file": "entropy_source_coding.py",
      "checks": 14,
      "output": "Codes: {'D': '111', 'C': '110', 'B': '10', 'A': '0'}\nEncoded: 0000000010101010110110111111 length: 28\nDecoded: AAAAAAAABBBBCCDD\nEntropy: 1.75 bits/symbol\nAverage code length: 1.75 bits/symbol\nModel and message framing costs are not included in the payload length.\n",
      "standalone": true
    },
    "kl-cross-entropy": {
      "file": "kl_cross_entropy.py",
      "checks": 14,
      "output": "H(p), CE(p,q), KL(p||q) in nats: 1.0397207708399179 1.2130075659799042 0.17328679513998635\nStable softmax: [0.665241, 0.244728, 0.090031]\nCE gradient at target 0: [-0.334759, 0.244728, 0.090031]\nKL([1,0] || [.9,.1]): 0.10536051565782628\nReverse KL: inf\n",
      "standalone": true
    },
    "kolmogorov-complexity": {
      "file": "kolmogorov_complexity.py",
      "checks": 1005,
      "output": "repeated raw bytes: 8000 zlib bytes: 34\nrandom raw bytes: 8000 zlib bytes: 8011\nA short exact program for the repeated string is: b'0110' * 2000\nCompressed lengths include headers; they are not Kolmogorov complexity.\nGamma stream: 001010100111 decoded: [5, 2, 3, 1]\nNCD repeated vs itself: 0.2059\nDecoder/program/framing cost must be included in theoretical description bounds.\n",
      "standalone": true
    },
    "lambda-calculus": {
      "file": "lambda_calculus.py",
      "checks": 6,
      "output": "Identity application: a\nCapture-avoiding result: (lambda y_. y)\nDiscard divergent argument: z\nreduction budget exhausted; termination remains unknown\n",
      "standalone": true
    },
    "llm-tech-talk": {
      "file": "llm_tech_talk.py",
      "checks": 5,
      "output": "Vocabulary: '\\n .acdeghnorst'\nInitial/final next-character loss: 2.84539532661438 0.053476203233003616\nGenerated: 'the dog ran.\\nthe cat sat.\\nthe dog ran.\\nthe c'\nThe small corpus demonstrates teacher forcing and causality; it does not establish general language ability.\n",
      "standalone": true
    },
    "monads-python": {
      "file": "monads_python.py",
      "checks": 38,
      "output": "Successful result: Ok(value=0.25)\nInvalid input: Err(message='not an integer')\nZero input: Err(message='division by zero')\nA present None differs from absence: Just(value=None) Nothing()\nReusable Reader: Hello, Ada Hola, Ada\n",
      "standalone": true
    },
    "p-vs-np": {
      "file": "p_vs_np.py",
      "checks": 42,
      "output": "SAT witness: (False, False, False) verified: True\nReduction: 9 vertices; 15 edges; target 3\nIndependent-set witness: (0, 4, 6)\nExhaustive small-instance solving does not settle P vs NP.\n",
      "standalone": true
    },
    "pinn": {
      "file": "pinn_heat_equation.py",
      "checks": 4,
      "output": "Generated synthetic observations: heat_equation_data.csv\nDevice: cpu; training observations: 16; physics points/step: 1000\nepoch     1 | total 7.932e+00 | physics 2.151e-02 | initial 5.412e-01 | boundary 2.189e-01 | data 3.100e-01\nepoch   250 | total 1.272e-01 | physics 9.053e-02 | initial 2.010e-03 | boundary 1.539e-03 | data 1.145e-03\nepoch   500 | total 2.256e-02 | physics 1.734e-02 | initial 2.420e-04 | boundary 2.614e-04 | data 1.805e-04\nepoch   750 | total 6.185e-03 | physics 5.030e-03 | initial 4.702e-05 | boundary 6.327e-05 | data 5.199e-05\nepoch  1000 | total 1.599e-03 | physics 1.409e-03 | initial 7.001e-06 | boundary 1.134e-05 | data 7.341e-06\nepoch  1250 | total 9.164e-04 | physics 8.333e-04 | initial 2.689e-06 | boundary 5.238e-06 | data 3.789e-06\nepoch  1500 | total 5.176e-04 | physics 4.649e-04 | initial 1.842e-06 | boundary 3.232e-06 | data 1.931e-06\nepoch  1750 | total 5.849e-04 | physics 3.257e-04 | initial 1.237e-05 | boundary 1.242e-05 | data 1.119e-05\nepoch  2000 | total 2.600e-04 | physics 2.144e-04 | initial 1.986e-06 | boundary 2.423e-06 | data 1.542e-06\nepoch  2250 | total 2.111e-04 | physics 1.800e-04 | initial 1.275e-06 | boundary 1.768e-06 | data 7.180e-07\nepoch  2500 | total 2.006e-04 | physics 1.500e-04 | initial 2.515e-06 | boundary 2.323e-06 | data 2.269e-06\n{\n  \"relative_l2\": 0.00376442214474082,\n  \"max_absolute_error\": 0.003982432186603546,\n  \"residual_rmse\": 0.011977210640907288\n}\nSaved model, figures, and logs in output/\n",
      "standalone": true
    },
    "python-101": {
      "file": "python_101.py",
      "checks": 6,
      "output": "Records: 4\nbooks: USD 15.00\nfood: USD 20.50\ntravel: USD 4.25\nTotal: USD 39.75\nTry another CSV in the REPL: summarize(parse_expenses(csv_text))\n",
      "standalone": true
    },
    "pytorch-101": {
      "file": "pytorch_101.py",
      "checks": 5,
      "output": "Initial/final MSE: 3.848921298980713 1.0434358932442933e-09\nLearned w,b: 1.9999468326568604 0.9999997615814209\nPrediction for x=.5: 1.999973177909851 ; target=2\nCrossEntropy accepts float soft targets: 0.7076059579849243\nA shape is a contract: inputs and regression targets here are both (64,1).\n",
      "standalone": true
    },
    "randomized-algorithms": {
      "file": "randomized_algorithms.py",
      "checks": 389,
      "output": "Quicksort: [1, 2, 3, 3, 5, 7, 8, 9]\nReservoir: [11, 12, 17, 10]\nFalse-negative amplification: True\n19 independent runs reduce a 1/3 miss probability to 8.603915972377315e-10\n",
      "standalone": true
    },
    "science-of-programming": {
      "file": "science_of_programming.py",
      "checks": 660,
      "output": "divide(17,5): (3, 2)\ndivide(0,5): (0, 0)\nfirst_index([4,8,8],8): 1\nfirst_index([4,8,8],7): 3\n",
      "standalone": true
    },
    "science-of-programming-python": {
      "file": "science_of_programming_python.py",
      "checks": 2304,
      "output": "lower_bound([1,3,3,7],3): 1\nsort_colors([2,0,1,2,0]): [0, 0, 1, 2, 2]\nmax_subarray([-2,3,-1,4,-5]): 6\n",
      "standalone": true
    },
    "turing-machines": {
      "file": "turing_machines.py",
      "checks": 257,
      "output": "111 + 1 = 1000\nTrace (step,state,head,read): [(0, 'scan', 0, '1'), (1, 'scan', 1, '1'), (2, 'scan', 2, '1'), (3, 'scan', 3, '_'), (4, 'carry', 2, '1'), (5, 'carry', 1, '1'), (6, 'carry', 0, '1'), (7, 'carry', -1, '_')]\nShort budget: budget exhausted; halting status remains unknown\n",
      "standalone": true
    },
    "weakest-preconditions": {
      "file": "weakest_preconditions.py",
      "checks": 533,
      "output": "Safe starting balances in 95..105: [100, 101, 102, 103, 104, 105]\n12//x==3, safe x in -5..8: [4]\nPython index -1 is defined: True\n",
      "standalone": true
    }
  }
}