thomasahle

#!/usr/bin/env python3
import json, os, re, subprocess, sys, time

MSG = """
please continue

Some general advice:
- Follow the project plan, and update the changelog regularly.
- What limitations do we still have? What features should we build?
- Don't shy away from ambitious and complicated tasks. Focus on what we need long term.

thomasahle

#!/usr/bin/env python3
import argparse
import json
import sys
import time


# Stop hook semantics:
# - exit 2: block stopping and continue
# - exit 0: allow stopping

thomasahle

RFC-001: Completeness-Based Streaming Validation

Field	Value
RFC	001
Title	Completeness-Based Streaming Validation
Author	Thomas Norman
Status	Implemented
Created	2026-01-13
Branch	fix/partial-recursive-models

thomasahle

# Simulate centered Bernoulli(p) random walk survival in [-1, 1]
# and plot the estimated survival probability.
#
# How it works
# ------------
# For X_i ~ Bernoulli(p), define S_j = sum_{i=0}^j (X_i - p).
# For a trial "survives" up to time n if, for every j in {0,...,n},
# we have -1 <= S_j <= 1. We estimate the survival probability by
# Monte Carlo over many independent trials.
#

thomasahle

# Simulate centered Bernoulli(p) random walk survival in [-1, 1]
# and plot the estimated survival probability.
#
# How it works
# ------------
# For X_i ~ Bernoulli(p), define S_j = sum_{i=0}^j (X_i - p).
# For a trial "survives" up to time n if, for every j in {0,...,n},
# we have -1 <= S_j <= 1. We estimate the survival probability by
# Monte Carlo over many independent trials.
#

thomasahle

async def run_agent(
    task: VerilogTask,
    client: LLMClient,
    tools: list[Tool],
    system_prompt: str = DEFAULT_SYSTEM_PROMPT,
    *,
    max_turns: int = 20,
    console: Optional[Union[Console, LogConsole]] = None,
) -> Tuple[Optional[VerificationResult], Dict[str, Any]]:

thomasahle

  1: def _generated_forward(i:int, k:int, _var_5305017088:torch.Tensor, _var_5305017136:torch.Tensor, _var_5305023376:torch.Tensor) -> tuple[torch.Tensor]:
  2:     sum_ = 0  # (i, k)
  3:     # Product of 2 tensors
  4:     var_x = _var_5305023376  # (i, j)
  5:     var_w = _var_5305017088  # (j, k)
  6:     # var_x: (i, j)
  7:     # var_w: (j, k)
  8:     prod_ = ctg.array_contract(
  9:         arrays=[var_x, var_w],
 10:         inputs=[('i', 'j'), ('j', 'k')],

thomasahle

  1: def _generated_forward(i:int, k:int, _var_5305017088:torch.Tensor, _var_5305017136:torch.Tensor, _var_5305023376:torch.Tensor) -> tuple[torch.Tensor]:
  2:     sum_ = 0  # (i, k)
  3:     # Product of 2 tensors
  4:     var_x = _var_5305023376  # (i, j)
  5:     var_w = _var_5305017088  # (j, k)
  6:     # var_x: (i, j)
  7:     # var_w: (j, k)
  8:     prod_ = ctg.array_contract(
  9:         arrays=[var_x, var_w],
 10:         inputs=[('i', 'j'), ('j', 'k')],

thomasahle

import numpy as np
from sklearn.linear_model import LinearRegression

# Generate some example data
np.random.seed(42)
n_samples = 100

# Create synthetic features (x1: thickness, x2: stiffness, x3: scent, x4: another property)
X = np.random.rand(n_samples, 4) * 10  # Random values between 0 and 10

thomasahle

import numpy as np
import matplotlib.pyplot as plt

# Parameters
d = 5
n = 5
maxP = 10
n_samples = 10000

def formula_p1(n, d):