Typescript

.gitignore

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 .DS_Store
 .hypothesis/
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+.idea/

AWS/script.py

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+import boto3
+import json
+import pulp
+from datetime import datetime, timezone
+import time
+
+# Initialize AWS clients
+cloudwatch = boto3.client('cloudwatch')
+kinesis = boto3.client('kinesis')
+
+def put_metric_data(metric_name, value, unit):
+    """Put custom metric data to CloudWatch"""
+
+    cloudwatch.put_metric_data(
+        Namespace='CustomMetrics',
+        MetricData=[
+            {
+                'MetricName': metric_name,
+                'Value': value,
+                'Unit': unit,
+                'Timestamp': datetime.now(timezone.utc)  # Fixed here
+            }
+        ]
+    )
+
+def put_record_to_kinesis(stream_name, data):
+    """Put a record to Kinesis Data Stream"""
+
+    response = kinesis.put_record(
+        StreamName=stream_name,
+        Data=json.dumps(data),
+        PartitionKey=str(datetime.now(timezone.utc).timestamp())
+    )
+    return response
+
+def log_to_cloudwatch(log_group, log_stream, message):
+    cloudwatch_logs = boto3.client('logs')
+    cloudwatch_logs.put_log_events(
+        logGroupName=log_group,
+        logStreamName=log_stream,
+        logEvents=[
+            {
+                'timestamp': int(time.time() * 1000),
+                'message': message,
+            },
+        ]
+    )
+
+def optimize_production():
+
+    """Optimize production using linear programming and log the results to CloudWatch"""
+
+    log_group = 'ProductionOptimizationLogs'
+    log_stream = 'OptimizationResults'
+
+    # Create the LP problem
+    prob = pulp.LpProblem("Production Optimization", pulp.LpMaximize)
+
+    # Define variables
+    x1 = pulp.LpVariable("Product_1", lowBound=0)
+    x2 = pulp.LpVariable("Product_2", lowBound=0)
+
+    # Define the objective function
+    prob += 20 * x1 + 30 * x2, "Profit"
+
+    # Define constraints
+    prob += 2 * x1 + 3 * x2 <= 100, "Labor_hours"
+    prob += 4 * x1 + 3 * x2 <= 120, "Material_units"
+
+    # Solve the problem
+    prob.solve()
+
+    # Prepare the results
+    status = pulp.LpStatus[prob.status]
+    product1 = x1.varValue
+    product2 = x2.varValue
+    total_profit = pulp.value(prob.objective)
+
+    # Log the results to CloudWatch
+    log_to_cloudwatch(log_group, log_stream, f"Status: {status}")
+    log_to_cloudwatch(log_group, log_stream, "Optimal Production Plan:")
+    log_to_cloudwatch(log_group, log_stream, f"Product 1: {product1} units")
+    log_to_cloudwatch(log_group, log_stream, f"Product 2: {product2} units")
+    log_to_cloudwatch(log_group, log_stream, f"Total Profit: ${total_profit}")
+
+    # Send the results to Kinesis
+    stream_name = 'ProductionOptimizationResults'
+    data = {
+        'timestamp': str(datetime.now(timezone.utc)),  # Fixed here
+        'status': status,
+        'product1': product1,
+        'product2': product2,
+        'total_profit': total_profit
+    }
+    response = put_record_to_kinesis(stream_name, data)
+    log_to_cloudwatch(log_group, log_stream, f"Optimization results sent to Kinesis. Shard ID: {response['ShardId']}")
+
+    # Return the results
+    return {
+        'status': status,
+        'product1': product1,
+        'product2': product2,
+        'total_profit': total_profit
+    }
+
+if __name__ == "__main__":
+    # Put a custom metric to CloudWatch
+    put_metric_data('MyCustomMetric', 42, 'Count')
+
+    # Put a record to Kinesis Data Stream
+    stream_name = 'MyKinesisStream'
+    data = {
+        'timestamp': str(datetime.now(timezone.utc)),  # Fixed here
+        'message': 'Hello, Kinesis!'
+    }
+    response = put_record_to_kinesis(stream_name, data)
+    print(f"Record sent to Kinesis. Shard ID: {response['ShardId']}")

COBOL/.gitignore

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+payroll
+*.txt

COBOL/README.md

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+# Code Modernization
+
+Astonishingly, 70% of all existing business logic is in COBOL, and 800 billion lines of COBOL are still in use in industry today [1].
+Many (perhaps all) companies that still use COBOL wish they could move off it, since it is massively antiquated and the only living engineers who still know it are in retirement homes.  Obviously, LLMs present a compelling solution to this problem via automated translation.  But how can you be sure the translation is correct?  This demo is meant to illustrate how Benchify can fit into the analysis of a translation from COBOL to Python.
+
+You can see the complete report [here](https://github.com/Benchify/benchify-examples/pull/134).
+
+Example usage of the COBOL wrapper:
+```
+ipython3
+Python 3.9.6 (default, Nov 11 2024, 03:15:38) 
+Type 'copyright', 'credits' or 'license' for more information
+IPython 8.18.1 -- An enhanced Interactive Python. Type '?' for help.
+
+In [1]: from script import *
+   ...: install_cobc_and_compile_script()
+   ...: 
+   ...: employee_records = [
+   ...:     EmployeeRecord(emp_id="12345", hours_worked=40, hourly_rate=20.0, tax_deduction=50.0),
+   ...:     EmployeeRecord(emp_id="67890", hours_worked=35, hourly_rate=15.0, tax_deduction=30.0),
+   ...:     EmployeeRecord(emp_id="54321", hours_worked=45, hourly_rate=25.0, tax_deduction=70.0)
+   ...: ]
+   ...: 
+   ...: payroll_results = process_payroll_cobol(employee_records)
+   ...: payroll_results
+GNU COBOL (cobc) is already installed.
+Compiling payroll.cbl...
+Compilation successful.
+GNU COBOL (cobc) is already installed.
+Compiling payroll.cbl...
+Compilation successful.
+=== Debug: Writing the following lines to input file ===
+1234504000020000005000
+6789003500015000003000
+5432104500025000007000
+=======================================================
+Running the COBOL payroll program with file: temp_payroll_input_ceacb40a807b19a0.txt
+Processing COBOL output...
+=== Raw COBOL Output ===
+EMP-ID,GROSS-PAY,NET-PAY
+12345,0000800.00,0000750.00
+67890,0000525.00,0000495.00
+54321,0001125.00,0001055.00
+=========================
+Employee ID: 12345
+Gross Pay:  $800.00
+Net Pay:    $750.00
+Employee ID: 67890
+Gross Pay:  $525.00
+Net Pay:    $495.00
+Employee ID: 54321
+Gross Pay:  $1125.00
+Net Pay:    $1055.00
+Out[1]: [(800.0, 750.0), (525.0, 495.0), (1125.0, 1055.0)]
+
+In [2]: payroll_results_2 = process_payroll(employee_records)
+Employee ID: 12345
+Gross Pay: $800.00
+Net Pay:   $750.00
+Employee ID: 67890
+Gross Pay: $525.00
+Net Pay:   $495.00
+Employee ID: 54321
+Gross Pay: $1125.00
+Net Pay:   $1055.00
+
+In [3]: payroll_results_2
+Out[3]: [(800.0, 750.0), (525.0, 495.0), (1125.0, 1055.0)]
+
+In [4]: payroll_results_2 == payroll_results
+Out[4]: True
+
+In [5]: exit()
+```
+
+[1]: https://cobolcowboys.com/cobol-today/#:~:text=70%25%20of%20all%20critical%20business,use%20today%20by%20various%20industries.