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Codex /goal Implementation

How OpenAI's Codex CLI implements the /goal slash command for persisted long-running task objectives.

The /goal command sets a persisted objective for a long-running task. It's a five-layer system: a SQLite table stores one goal per thread with status (active/paused/budget_limited/complete), optional token budget, and running usage counters. The app-server exposes thread/goal/set/get/clear JSON-RPC methods. The model sees three tools — create_goal, update_goal(complete), and get_goal — but can't pause/resume; those are system-controlled. A runtime event bus hooks into turn lifecycle to track token + wall-clock deltas for accounting, auto-pauses on interrupt, auto-reactivates paused goals on thread resume, and injects budget-limit steering into the model's response stream. The TUI handles the /goal slash command and displays goal state in the status bar.

Author: etraut-openai | 5 PRs, ~15K additions, landed in ~10 days (Apr 16–25, 2026)

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from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql import Row
from pyspark.sql.types import IntegerType

# Create the Spark session
spark = SparkSession.builder \
    .master("local") \
    .config("spark.sql.autoBroadcastJoinThreshold", -1) \
    .config("spark.executor.memory", "500mb") \

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import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.PipelineStage
import org.apache.spark.ml.Transformer
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.feature.LabeledPoint
import org.apache.spark.ml.linalg.DenseVector
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.sql.Dataset
import org.apache.spark.sql.Row

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package com.diorsding.spark.ml;

import java.util.Arrays;
import java.util.List;

import org.apache.spark.SparkConf;
import org.apache.spark.SparkContext;
import org.apache.spark.ml.Pipeline;
import org.apache.spark.ml.PipelineModel;
import org.apache.spark.ml.PipelineStage;

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import java.util.Arrays;
import java.util.List;

import org.apache.hadoop.yarn.webapp.hamlet.HamletSpec.P;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.MapFunction;
import org.apache.spark.ml.Pipeline;
import org.apache.spark.ml.PipelineModel;
import org.apache.spark.ml.PipelineStage;

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import org.apache.spark.sql.functions._
import org.apache.spark.sql.SparkSession

object DataFrameWithFileNameApp extends App {
  
  val spark: SparkSession =
    SparkSession
      .builder()
      .appName("DataFrameApp")
      .config("spark.master", "local[*]")

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# Custom history configuration
# Run script using:
# chmod u+x better_history.sh
# sudo su
# ./better_history.sh

echo ">>> Starting"
echo ">>> Loading configuration into /etc/bash.bashrc"
echo "HISTTIMEFORMAT='%F %T '" >> /etc/bash.bashrc
echo 'HISTFILESIZE=-1' >> /etc/bash.bashrc

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[alias]
	co = checkout
	cob = checkout -b
	coo = !git fetch && git checkout
	br = branch
	brd = branch -d
	brD = branch -D
	merged = branch --merged
	dmerged = "git branch --merged | grep -v '\\*' | xargs -n 1 git branch -d"
	st = status

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#!/bin/bash
set -e

##########################################################
# Install script for Docker-CE on ElementaryOS 0.4.1 Loki
# Had to update the repository to point to xenial instead
# of using 'lsb_release -cs' because there's no loki
# repository at download.docker.com.
##########################################################

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This gist includes hive ql scripts to create an external partitioned table for Syslog
generated log files using regex serde; 
Usecase:  Count the number of occurances of processes that got logged, by year, month,
day and process.
 
Includes:
---------
Sample data and structure:           01-SampleDataAndStructure 
Data download:                       02-DataDownload
Data load commands:                  03-DataLoadCommands