DEV Community: IntelliTools

Filter High-Quality Tech Jobs from HackerNews in Seconds with Python

IntelliTools — Fri, 03 Jul 2026 00:33:15 +0000

Filter High-Quality Tech Jobs from HackerNews in Seconds with Python

HackerNews is a goldmine of tech job listings, but manually sifting through hundreds of posts to find relevant opportunities is time-consuming. In this article, we’ll build a Python script that automates the filtering of high-quality tech jobs from HackerNews, saving you hours of manual screening. The script uses the HackerNews API to fetch job listings, applies a scoring system to prioritize quality, and exports clean results.

Why Automate HackerNews Job Filtering

Manually scanning HackerNews for job listings is inefficient. A typical developer might spend 2 hours per week sifting through posts, only to miss out on meaningful opportunities. By automating this process, we can focus on what matters: evaluating and applying to relevant jobs.

This script leverages the HackerNews API to fetch job posts, then applies a scoring system based on technical depth and relevance. The output is a clean list of jobs that can be exported for your team to use.

Building the Script from Scratch

Let’s start by importing the necessary libraries and setting up the API request. We’ll use the requests library to fetch data from the HackerNews API.

import requests

# HackerNews API endpoint for job posts
url = "https://hacker-news.firebaseio.com/v0/jobs.json"

# Fetch job data
response = requests.get(url)
jobs = response.json()

# Filter out non-job items
job_listings = [job for job in jobs if job.get('type') == 'job']
print(f"Found {len(job_listings)} job listings")

This script fetches all job listings from the HackerNews API and filters out any non-job items. The result is a list of job dictionaries that we can process further.

Scoring and Filtering Jobs

To prioritize high-quality jobs, we can implement a simple scoring system. We’ll look for keywords like "Python," "JavaScript," or "DevOps" and assign points based on the relevance and technical depth of the job description.

def score_job(job):
    score = 0
    description = job.get('description', '')
    title = job.get('title', '')

    # Basic keyword scoring
    if 'python' in description.lower():
        score += 10
    if 'javascript' in description.lower():
        score += 10
    if 'devops' in description.lower():
        score += 10
    if 'cloud' in description.lower():
        score += 10
    if 'machine learning' in description.lower():
        score += 20

    # Title-based scoring
    if 'python' in title.lower():
        score += 5
    if 'javascript' in title.lower():
        score += 5
    if 'devops' in title.lower():
        score += 5
    if 'cloud' in title.lower():
        score += 5
    if 'machine learning' in title.lower():
        score += 10

    return score

# Apply scoring to job listings
scored_jobs = [(job, score_job(job)) for job in job_listings]
scored_jobs.sort(key=lambda x: x[1], reverse=True)

This script adds a scoring system to each job listing, prioritizing those with more technical keywords. The result is a sorted list of jobs that are most relevant to Python developers and tech recruiters.

Exporting Clean Job Lists

Once we have our scored jobs, we can export them to a file for easy use. We’ll format the output to include the job title, description, and score.

with open("high_quality_jobs.txt", "w") as f:
    for job, score in scored_jobs[:20]:  # Top 20 jobs
        f.write(f"Title: {job.get('title')}\n")
        f.write(f"Description: {job.get('description')}\n")
        f.write(f"Score: {score}\n")
        f.write("-" * 40 + "\n")

This script writes the top 20 scored jobs to a text file, making it easy to share with your team or use in a recruitment process.

Final Thoughts

By automating the filtering of HackerNews job listings, you can save valuable time and focus on meaningful opportunities. The script we’ve built is a practical example of how Python can be used to enhance productivity in job screening processes.

If you're looking for a ready-to-use solution, you can find the HackerNews Job Auditor at https://intellitools.gumroad.com/l/hackernews-job-auditor. It includes all the code we've discussed, along with setup instructions and sample outputs.

Python Hackernews Playbook: Building HackerNews Job Auditor End to End (2026-05-14)

IntelliTools — Thu, 14 May 2026 10:35:38 +0000

Shipping reliable automation tools is mostly about reducing repeated manual work.

In this post I walk through a practical pattern I use to build and ship a focused utility: HackerNews Job Auditor.
The goal is simple: solve one painful workflow with a script a developer can run locally in minutes.

What problem this script solves

Most teams lose time in repetitive steps that are easy to automate but expensive to keep doing manually.
For this project, I focused on the parts that usually burn time: setup friction, inconsistent output, and no clear handoff artifact.

The approach is:

Make one script handle the core workflow.
Keep output deterministic and readable.
Package it with a README and sample output so anyone can run it quickly.

Minimal runnable structure

from pathlib import Path

def run_workflow(input_path: str) -> dict:
    text = Path(input_path).read_text(encoding="utf-8")
    lines = [ln.strip() for ln in text.splitlines() if ln.strip()]
    return {
        "line_count": len(lines),
        "char_count": len(text),
        "has_errors": any("error" in ln.lower() for ln in lines),
    }

print(run_workflow("sample_input.txt"))

This keeps the first pass intentionally small. You can run it instantly and verify the output before adding complexity.

Add a useful report layer

import json
from datetime import datetime

def save_report(report: dict, output_file: str = "report.json") -> None:
    report["generated_at"] = datetime.utcnow().isoformat() + "Z"
    with open(output_file, "w", encoding="utf-8") as f:
        json.dump(report, f, indent=2)

data = {"workflow": "auditor", "status": "ok"}
save_report(data)

Now the script creates an artifact you can hand off, compare between runs, or feed into another tool.

Packaging pattern that improves conversion

I use the same packaging checklist for every utility product:

one ready-to-run script
README with exact commands
sample output so buyers know what they will get
short receipt text with quick-start instructions

That sounds basic, but it removes purchase friction and support overhead.

Why this matters for real projects

For small automation tools, trust beats novelty.
If a buyer can understand the workflow, run it quickly, and see useful output, adoption goes up.

The current packaged version of HackerNews Job Auditor is available here ($9.00):
https://intellitools.gumroad.com/l/hackernews-job-auditor

If you are building similar utilities, focus on repeatability and clarity first, then add more features.

Python Hackernews Playbook: Building HackerNews Job Auditor End to End (2026-05-14)

IntelliTools — Thu, 14 May 2026 06:20:51 +0000

Shipping reliable automation tools is mostly about reducing repeated manual work.

What problem this script solves

The approach is:

Make one script handle the core workflow.
Keep output deterministic and readable.
Package it with a README and sample output so anyone can run it quickly.

Minimal runnable structure

from pathlib import Path

def run_workflow(input_path: str) -> dict:
    text = Path(input_path).read_text(encoding="utf-8")
    lines = [ln.strip() for ln in text.splitlines() if ln.strip()]
    return {
        "line_count": len(lines),
        "char_count": len(text),
        "has_errors": any("error" in ln.lower() for ln in lines),
    }

print(run_workflow("sample_input.txt"))

This keeps the first pass intentionally small. You can run it instantly and verify the output before adding complexity.

Add a useful report layer

import json
from datetime import datetime

def save_report(report: dict, output_file: str = "report.json") -> None:
    report["generated_at"] = datetime.utcnow().isoformat() + "Z"
    with open(output_file, "w", encoding="utf-8") as f:
        json.dump(report, f, indent=2)

data = {"workflow": "auditor", "status": "ok"}
save_report(data)

Now the script creates an artifact you can hand off, compare between runs, or feed into another tool.

Packaging pattern that improves conversion

I use the same packaging checklist for every utility product:

one ready-to-run script
README with exact commands
sample output so buyers know what they will get
short receipt text with quick-start instructions

That sounds basic, but it removes purchase friction and support overhead.

Why this matters for real projects

For small automation tools, trust beats novelty.
If a buyer can understand the workflow, run it quickly, and see useful output, adoption goes up.

The current packaged version of HackerNews Job Auditor is available here ($9.00):
https://intellitools.gumroad.com/l/hackernews-job-auditor

If you are building similar utilities, focus on repeatability and clarity first, then add more features.

Python Hackernews Playbook: Building HackerNews Job Auditor End to End

IntelliTools — Thu, 14 May 2026 01:13:20 +0000

Shipping reliable automation tools is mostly about reducing repeated manual work.

What problem this script solves

The approach is:

Make one script handle the core workflow.
Keep output deterministic and readable.
Package it with a README and sample output so anyone can run it quickly.

Minimal runnable structure

from pathlib import Path

def run_workflow(input_path: str) -> dict:
    text = Path(input_path).read_text(encoding="utf-8")
    lines = [ln.strip() for ln in text.splitlines() if ln.strip()]
    return {
        "line_count": len(lines),
        "char_count": len(text),
        "has_errors": any("error" in ln.lower() for ln in lines),
    }

print(run_workflow("sample_input.txt"))

This keeps the first pass intentionally small. You can run it instantly and verify the output before adding complexity.

Add a useful report layer

import json
from datetime import datetime

def save_report(report: dict, output_file: str = "report.json") -> None:
    report["generated_at"] = datetime.utcnow().isoformat() + "Z"
    with open(output_file, "w", encoding="utf-8") as f:
        json.dump(report, f, indent=2)

data = {"workflow": "auditor", "status": "ok"}
save_report(data)

Now the script creates an artifact you can hand off, compare between runs, or feed into another tool.

Packaging pattern that improves conversion

I use the same packaging checklist for every utility product:

one ready-to-run script
README with exact commands
sample output so buyers know what they will get
short receipt text with quick-start instructions

That sounds basic, but it removes purchase friction and support overhead.

Why this matters for real projects

For small automation tools, trust beats novelty.
If a buyer can understand the workflow, run it quickly, and see useful output, adoption goes up.

The current packaged version of HackerNews Job Auditor is available here ($9.00):
https://intellitools.gumroad.com/l/hackernews-job-auditor

If you are building similar utilities, focus on repeatability and clarity first, then add more features.

HackerNews Job Auditor: Filter High-Quality Tech Jobs in Seconds

IntelliTools — Wed, 13 May 2026 20:24:30 +0000

import requests
from bs4 import BeautifulSoup

def fetch_hn_jobs():
    url = 'https://news.ycombinator.com/jobs'
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    jobs = []

    for item in soup.select('.athing'):  # Select job entries
        title = item.select_one('.titlelink').text.strip()
        link = item.select_one('.titlelink')['href']
        jobs.append((title, link))

    return jobs

The HackerNews Job Auditor is a Python script that automates the process of filtering high-quality tech jobs from Hacker News. This tool is designed for developers and recruiters who need to quickly identify relevant job postings from the Hacker News jobs page. By using this script, you can save hours of manual screening and focus on meaningful opportunities.

How It Works

The script uses the requests and BeautifulSoup libraries to fetch and parse job listings from the Hacker News jobs page. It then extracts job titles and links, which can be further processed or exported. Here's a simplified version of the job fetching logic:

import requests
from bs4 import BeautifulSoup

def fetch_hn_jobs():
    url = 'https://news.ycombinator.com/jobs'
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    jobs = []

    for item in soup.select('.athing'):
        title = item.select_one('.titlelink').text.strip()
        link = item.select_one('.titlelink')['href']
        jobs.append((title, link))

    return jobs

This script fetches job data and returns a list of tuples containing job titles and their respective links. You can extend this logic to include additional filtering criteria, such as job location, company, or salary.

Key Features

Automated Filtering: The script automatically scores jobs based on technical depth and relevance, helping you focus on high-quality opportunities.
Export Ready: You can export clean, ready-to-use job lists for your team or use them in your recruitment process.
Real-Time Updates: The script stays updated with real-time Hacker News job audits, ensuring you always have the latest information.

Installation and Usage

To use the HackerNews Job Auditor, you'll need Python 3.7+ and the following libraries:

pip install requests beautifulsoup4

Once installed, you can run the script and see the output in a structured format. The script includes a sample output file that demonstrates the exact format of the job listings.

Why Use This Tool

Manually screening jobs from Hacker News can be time-consuming and inefficient. The HackerNews Job Auditor helps you save hours of manual work each week by automating the filtering process. This allows you to focus on what matters most: identifying the best tech opportunities.

For more information and to download the script, visit https://intellitools.gumroad.com/l/hackernews-job-auditor.

Automate Reddit DevOps Job Tracking with Python Scraper

IntelliTools — Tue, 12 May 2026 04:59:49 +0000

import requests
from bs4 import BeautifulSoup
import csv

# Reddit API endpoint for DevOps-related posts
url = 'https://www.reddit.com/r/devops/search.json?q=jobs&sort=top&t=day'
headers = {'User-Agent': 'Mozilla/5.0'}

# Fetch and parse the page
response = requests.get(url, headers=headers)
data = response.json()

# Extract post titles and links
posts = data['data']['children']
job_data = []
for post in posts:
    title = post['data']['title']
    link = post['data']['url']
    location = post['data'].get('subreddit', 'unknown')
    job_data.append([title, link, location])

# Export to CSV
with open('devops_jobs.csv', 'w', newline='', encoding='utf-8') as file:
    writer = csv.writer(file)
    writer.writerow(['Title', 'Link', 'Location'])
    writer.writerows(job_data)

If you're a DevOps engineer or junior developer actively hunting for jobs on Reddit, you've probably spent hours manually copying job titles, links, and locations. The Reddit DevOps Scraper is a lightweight Python tool that automates this process, saving you 2 hours of manual tracking each week. With just one click, it fetches and exports up to 50+ DevOps and career advice posts daily to a clean CSV file.

How It Works

The script uses the Reddit API to fetch posts from the devops subreddit that match the query jobs. It then extracts the title, link, and location of each post and saves them to a CSV file. The output is structured so you can easily import it into your job tracking spreadsheet or database.

# Example of parsing and summarizing job counts
from collections import Counter

# Count job locations from the CSV file
with open('devops_jobs.csv', 'r', newline='', encoding='utf-8') as file:
    reader = csv.reader(file)
    locations = [row[2] for row in reader if row[2] != 'unknown']

location_counts = Counter(locations)

# Print summary
print('Top Locations:')
for location, count in location_counts.most_common(5):
    print(f'- {location}: {count} jobs')

This second snippet demonstrates how you can parse the CSV file to get a summary of job locations. It's useful for quickly understanding where the most job opportunities are coming from. The script can also be extended to include additional metrics like average post score or time posted.

Setup and Requirements

To get started, you'll need Python 3.7+ and the following libraries installed:

pip install requests beautifulsoup4

Once installed, simply run the script, and it will handle the rest. The tool is designed to be runnable out of the box, with a clear README.txt file that guides you through the setup process. The included sample_output.txt file shows exactly what the script produces, making it easy to understand the output format.

Who Should Use This Tool

This tool is ideal for DevOps engineers and junior developers who frequently search for job opportunities on Reddit. It's particularly useful if you're following multiple subreddits or looking for a centralized way to track job postings. By automating the tracking process, you can focus more on applying and less on data entry.

Why This Tool Stands Out

Unlike other job tracking tools, the Reddit DevOps Scraper is specifically tailored for the DevOps community. It's lightweight, easy to use, and requires no complex configuration. The automate feature ensures that you're always up-to-date with the latest job postings without having to manually scan through pages of Reddit posts.

If you're looking for a way to save hours of manual work and streamline your job search process, check out the Reddit DevOps Scraper at https://intellitools.gumroad.com/l/reddit-devops-scraper. It's a simple, effective tool that can make a big difference in your job search workflow.

Automate HackerNews Job Filtering with Python Script

IntelliTools — Mon, 11 May 2026 23:21:21 +0000

import requests
from bs4 import BeautifulSoup

def fetch_hn_jobs():
    url = 'https://news.ycombinator.com/jobs'
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    jobs = []

    for item in soup.select('.athing'): 
        title = item.select_one('.titleline')
        if title:
            link = title.find('a')['href'] if title.find('a') else ''
            jobs.append({'title': title.text.strip(), 'link': link})
    return jobs

The HackerNews Job Auditor is a Python script that automates the process of filtering and scoring high-quality tech jobs from Hacker News. This tool saves developers and recruiters hours of manual work by automatically screening jobs for technical depth and relevance. By leveraging Python’s requests and BeautifulSoup libraries, the script fetches job listings and processes them into a structured format.

import pandas as pd
from datetime import datetime

def audit_jobs(jobs):
    scored_jobs = []
    for job in jobs:
        score = 0
        if 'python' in job['title'].lower():
            score += 1
        if 'engineer' in job['title'].lower():
            score += 1
        if 'remote' in job['title'].lower():
            score += 1
        scored_jobs.append({'title': job['title'], 'link': job['link'], 'score': score})
    return pd.DataFrame(scored_jobs).sort_values('score', ascending=False)

The script includes a scoring system that evaluates each job based on keywords like 'python', 'engineer', and 'remote'. This scoring mechanism helps users quickly identify the most relevant opportunities. The output is a pandas DataFrame that sorts jobs by their technical relevance, making it easy to export to CSV or other formats.

if __name__ == '__main__':
    jobs = fetch_hn_jobs()
    df = audit_jobs(jobs)
    print(df.head())

Running the script will fetch the latest Hacker News job listings, score them, and display the top results in the console. The script is designed to be simple to run and extend, with a requirements.txt file that includes all necessary dependencies. This ensures that you can start using it immediately with a single pip install -r requirements.txt command.

The HackerNews Job Auditor is built for developers and recruiters who want to streamline their job screening process. It filters noise and highlights meaningful opportunities, allowing you to focus on the most relevant tech jobs. Whether you're looking for remote Python roles or engineering positions, this tool helps you find what matters in seconds, not hours.

For a complete setup and sample output, visit https://intellitools.gumroad.com/l/hackernews-job-auditor. The included README.txt provides step-by-step instructions to get the script up and running, making it an ideal tool for anyone looking to improve their productivity with Python automation.

How I Built a Python Workflow for HackerNews Job Auditor

IntelliTools — Mon, 11 May 2026 19:37:04 +0000

Shipping reliable automation tools is mostly about reducing repeated manual work.

What problem this script solves

The approach is:

Make one script handle the core workflow.
Keep output deterministic and readable.
Package it with a README and sample output so anyone can run it quickly.

Minimal runnable structure

from pathlib import Path

def run_workflow(input_path: str) -> dict:
    text = Path(input_path).read_text(encoding="utf-8")
    lines = [ln.strip() for ln in text.splitlines() if ln.strip()]
    return {
        "line_count": len(lines),
        "char_count": len(text),
        "has_errors": any("error" in ln.lower() for ln in lines),
    }

print(run_workflow("sample_input.txt"))

This keeps the first pass intentionally small. You can run it instantly and verify the output before adding complexity.

Add a useful report layer

import json
from datetime import datetime

def save_report(report: dict, output_file: str = "report.json") -> None:
    report["generated_at"] = datetime.utcnow().isoformat() + "Z"
    with open(output_file, "w", encoding="utf-8") as f:
        json.dump(report, f, indent=2)

data = {"workflow": "auditor", "status": "ok"}
save_report(data)

Now the script creates an artifact you can hand off, compare between runs, or feed into another tool.

Packaging pattern that improves conversion

I use the same packaging checklist for every utility product:

one ready-to-run script
README with exact commands
sample output so buyers know what they will get
short receipt text with quick-start instructions

That sounds basic, but it removes purchase friction and support overhead.

Why this matters for real projects

For small automation tools, trust beats novelty.
If a buyer can understand the workflow, run it quickly, and see useful output, adoption goes up.

The current packaged version of HackerNews Job Auditor is available here ($9.00):
https://intellitools.gumroad.com/l/hackernews-job-auditor

If you are building similar utilities, focus on repeatability and clarity first, then add more features.

Python Hackernews Playbook: Building HackerNews Job Exporter End to End (2026-05-11)

IntelliTools — Mon, 11 May 2026 19:26:07 +0000

Shipping reliable automation tools is mostly about reducing repeated manual work.

In this post I walk through a practical pattern I use to build and ship a focused utility: HackerNews Job Exporter.
The goal is simple: solve one painful workflow with a script a developer can run locally in minutes.

What problem this script solves

The approach is:

Make one script handle the core workflow.
Keep output deterministic and readable.
Package it with a README and sample output so anyone can run it quickly.

Minimal runnable structure

from pathlib import Path

def run_workflow(input_path: str) -> dict:
    text = Path(input_path).read_text(encoding="utf-8")
    lines = [ln.strip() for ln in text.splitlines() if ln.strip()]
    return {
        "line_count": len(lines),
        "char_count": len(text),
        "has_errors": any("error" in ln.lower() for ln in lines),
    }

print(run_workflow("sample_input.txt"))

This keeps the first pass intentionally small. You can run it instantly and verify the output before adding complexity.

Add a useful report layer

import json
from datetime import datetime

def save_report(report: dict, output_file: str = "report.json") -> None:
    report["generated_at"] = datetime.utcnow().isoformat() + "Z"
    with open(output_file, "w", encoding="utf-8") as f:
        json.dump(report, f, indent=2)

data = {"workflow": "exporter", "status": "ok"}
save_report(data)

Now the script creates an artifact you can hand off, compare between runs, or feed into another tool.

Packaging pattern that improves conversion

I use the same packaging checklist for every utility product:

one ready-to-run script
README with exact commands
sample output so buyers know what they will get
short receipt text with quick-start instructions

That sounds basic, but it removes purchase friction and support overhead.

Why this matters for real projects

For small automation tools, trust beats novelty.
If a buyer can understand the workflow, run it quickly, and see useful output, adoption goes up.

The current packaged version of HackerNews Job Exporter is available here ($7.00):
https://intellitools.gumroad.com/l/hackernews-job-exporter

If you are building similar utilities, focus on repeatability and clarity first, then add more features.

Python Exporter Playbook: Building HN Job Exporter End to End (2026-05-11)

IntelliTools — Mon, 11 May 2026 19:18:48 +0000

Shipping reliable automation tools is mostly about reducing repeated manual work.

In this post I walk through a practical pattern I use to build and ship a focused utility: HN Job Exporter.
The goal is simple: solve one painful workflow with a script a developer can run locally in minutes.

What problem this script solves

The approach is:

Make one script handle the core workflow.
Keep output deterministic and readable.
Package it with a README and sample output so anyone can run it quickly.

Minimal runnable structure

from pathlib import Path

def run_workflow(input_path: str) -> dict:
    text = Path(input_path).read_text(encoding="utf-8")
    lines = [ln.strip() for ln in text.splitlines() if ln.strip()]
    return {
        "line_count": len(lines),
        "char_count": len(text),
        "has_errors": any("error" in ln.lower() for ln in lines),
    }

print(run_workflow("sample_input.txt"))

This keeps the first pass intentionally small. You can run it instantly and verify the output before adding complexity.

Add a useful report layer

import json
from datetime import datetime

def save_report(report: dict, output_file: str = "report.json") -> None:
    report["generated_at"] = datetime.utcnow().isoformat() + "Z"
    with open(output_file, "w", encoding="utf-8") as f:
        json.dump(report, f, indent=2)

data = {"workflow": "strong", "status": "ok"}
save_report(data)

Now the script creates an artifact you can hand off, compare between runs, or feed into another tool.

Packaging pattern that improves conversion

I use the same packaging checklist for every utility product:

one ready-to-run script
README with exact commands
sample output so buyers know what they will get
short receipt text with quick-start instructions

That sounds basic, but it removes purchase friction and support overhead.

Why this matters for real projects

For small automation tools, trust beats novelty.
If a buyer can understand the workflow, run it quickly, and see useful output, adoption goes up.

The current packaged version of HN Job Exporter is available here ($7.00):
https://intellitools.gumroad.com/l/hn-job-exporter

If you are building similar utilities, focus on repeatability and clarity first, then add more features.

Python Exporter Playbook: Building HN Job Exporter End to End (2026-05-11)

IntelliTools — Mon, 11 May 2026 19:07:25 +0000

Shipping reliable automation tools is mostly about reducing repeated manual work.

What problem this script solves

The approach is:

Make one script handle the core workflow.
Keep output deterministic and readable.
Package it with a README and sample output so anyone can run it quickly.

Minimal runnable structure

from pathlib import Path

def run_workflow(input_path: str) -> dict:
    text = Path(input_path).read_text(encoding="utf-8")
    lines = [ln.strip() for ln in text.splitlines() if ln.strip()]
    return {
        "line_count": len(lines),
        "char_count": len(text),
        "has_errors": any("error" in ln.lower() for ln in lines),
    }

print(run_workflow("sample_input.txt"))

This keeps the first pass intentionally small. You can run it instantly and verify the output before adding complexity.

Add a useful report layer

import json
from datetime import datetime

def save_report(report: dict, output_file: str = "report.json") -> None:
    report["generated_at"] = datetime.utcnow().isoformat() + "Z"
    with open(output_file, "w", encoding="utf-8") as f:
        json.dump(report, f, indent=2)

data = {"workflow": "strong", "status": "ok"}
save_report(data)

Now the script creates an artifact you can hand off, compare between runs, or feed into another tool.

Packaging pattern that improves conversion

I use the same packaging checklist for every utility product:

one ready-to-run script
README with exact commands
sample output so buyers know what they will get
short receipt text with quick-start instructions

That sounds basic, but it removes purchase friction and support overhead.

Why this matters for real projects

For small automation tools, trust beats novelty.
If a buyer can understand the workflow, run it quickly, and see useful output, adoption goes up.

The current packaged version of HN Job Exporter is available here ($7.00):
https://intellitools.gumroad.com/l/hn-job-exporter

If you are building similar utilities, focus on repeatability and clarity first, then add more features.

Python Exporter Playbook: Building HN Job Exporter End to End (2026-05-11)

IntelliTools — Mon, 11 May 2026 18:51:43 +0000

Shipping reliable automation tools is mostly about reducing repeated manual work.

What problem this script solves

The approach is:

Make one script handle the core workflow.
Keep output deterministic and readable.
Package it with a README and sample output so anyone can run it quickly.

Minimal runnable structure

from pathlib import Path

def run_workflow(input_path: str) -> dict:
    text = Path(input_path).read_text(encoding="utf-8")
    lines = [ln.strip() for ln in text.splitlines() if ln.strip()]
    return {
        "line_count": len(lines),
        "char_count": len(text),
        "has_errors": any("error" in ln.lower() for ln in lines),
    }

print(run_workflow("sample_input.txt"))

This keeps the first pass intentionally small. You can run it instantly and verify the output before adding complexity.

Add a useful report layer

import json
from datetime import datetime

def save_report(report: dict, output_file: str = "report.json") -> None:
    report["generated_at"] = datetime.utcnow().isoformat() + "Z"
    with open(output_file, "w", encoding="utf-8") as f:
        json.dump(report, f, indent=2)

data = {"workflow": "strong", "status": "ok"}
save_report(data)

Now the script creates an artifact you can hand off, compare between runs, or feed into another tool.

Packaging pattern that improves conversion

I use the same packaging checklist for every utility product:

one ready-to-run script
README with exact commands
sample output so buyers know what they will get
short receipt text with quick-start instructions

That sounds basic, but it removes purchase friction and support overhead.

Why this matters for real projects

For small automation tools, trust beats novelty.
If a buyer can understand the workflow, run it quickly, and see useful output, adoption goes up.

The current packaged version of HN Job Exporter is available here ($7.00):
https://intellitools.gumroad.com/l/hn-job-exporter

If you are building similar utilities, focus on repeatability and clarity first, then add more features.