Forem: Jeffrey.Feillp

Making Money With Python in 2025: 5 Real Revenue Streams (No Hype)

Jeffrey.Feillp — Wed, 29 Apr 2026 17:27:55 +0000

Making Money With Python in 2025: 5 Real Revenue Streams (No Hype)

No crypto, no dropshipping, no MLM. Just real ways to make money with Python.

1. Web Scraping Services ($50-500/project)

Still the #1 way to make money with Python. Companies constantly need data.

How to start: Build scrapers for e-commerce sites, real estate platforms, job boards.

Price range: $50 for a simple scraper, $500+ for a complete monitoring system.

2. Automation Scripts ($30-200/script)

Businesses waste thousands of hours on repetitive tasks.

Examples:

Excel report automation: $100-300
Email auto-responder: $50-150
File organization system: $30-80
Social media scheduler: $100-200

3. Selling Python Tools ($5-50/sale)

Package your scripts into reusable tools and sell them.

What sells:

Web scraping templates
Automation scripts
Data processing tools
AI/ML utilities

4. Technical Writing ($50-500/article)

Companies pay well for Python tutorials and documentation.

Platforms: Dev.to (free), Medium (partner program), personal blog → freelance clients.

5. Consulting/Code Review ($50-150/hour)

Once you have experience, businesses pay for your expertise.

Services:

Code review and optimization
Architecture consultation
Debugging complex issues

My Tools for Sale

I've packaged several of my Python tools:

Tool	Price	Description
Dev.to Auto Publisher	$15	Batch publish articles
Knowledge Base Builder	$19	1M+ entry SQLite KB
Automation Toolkit	$15	20 ready-to-use scripts
Tian AI Engine	$9.9	Local AI with 3 thinking layers

👉 Browse All Tools

USDT TRC-20: TNeUMpbwWFcv6v7tYHmkFkE7gC5eWzqbrs

Published str(int(time.time()))

Building a Local AI With Three Layers of Consciousness (Python)

Jeffrey.Feillp — Wed, 29 Apr 2026 17:27:11 +0000

Building a Local AI With Three Layers of Consciousness (Python)

Most local AI projects are just wrappers around LLM APIs. I went deeper and built an AI with actual thinking layers.

The Three Layers

Layer 1: Fast Thought (Reactive)

The fastest path from input to output. Pattern matching and direct retrieval.

class FastThought:
    def process(self, input_text):
        # Direct pattern matching
        if 'hello' in input_text.lower():
            return 'Hello! How can I help you today?'
        # Knowledge base lookup
        results = kb.search(input_text)
        return self.format_response(results)

Layer 2: Chain of Thought (Deliberate)

Multi-step reasoning for complex problems.

class ChainOfThought:
    def reason(self, question):
        steps = []
        # Step 1: Decompose
        sub_questions = self.decompose(question)
        # Step 2: Solve each part
        for sq in sub_questions:
            answer = self.solve_sub_problem(sq)
            steps.append({{'question': sq, 'answer': answer}})
        # Step 3: Synthesize
        return self.synthesize(steps)

Layer 3: Deep Reflection (Self-Aware)

The engine examines its own reasoning process.

class DeepReflection:
    def reflect(self, question, answer, reasoning):
        # Evaluate confidence
        confidence = self.evaluate_confidence(reasoning)
        # Identify gaps
        gaps = self.find_knowledge_gaps(question, answer)
        # Self-improve
        if confidence < 0.7:
            new_answer = self.reason_again(question, gaps)
            return new_answer
        return answer

How They Work Together

class ThinkingPipeline:
    def __init__(self):
        self.fast = FastThought()
        self.chain = ChainOfThought()
        self.deep = DeepReflection()

    def process(self, input_text):
        # Start with fast thought
        result = self.fast.process(input_text)

        # If confidence is low, escalate
        if result.confidence < 0.6:
            result = self.chain.reason(input_text)

        # For complex queries, reflect
        if result.complexity > 0.8:
            result = self.deep.reflect(input_text, result)

        return result

The Complete Engine

This three-layer thinking pipeline is the core of Tian AI. Available as a standalone tool:

👉 Tian AI Engine — $9.9 USDT (TRC-20)

USDT TRC-20: TNeUMpbwWFcv6v7tYHmkFkE7gC5eWzqbrs

Published str(int(time.time()))

How I Automated My Entire Content Publishing Pipeline with Python

Jeffrey.Feillp — Wed, 29 Apr 2026 17:26:27 +0000

How I Automated My Entire Content Publishing Pipeline with Python

I'm publishing 5+ articles a day across multiple platforms. All automated. Here's the pipeline.

The Problem

Content creators spend 80% of their time on distribution, not creation. Posting the same article to Dev.to, Medium, LinkedIn, and Twitter manually takes forever.

The Automated Pipeline

Write in Markdown → Python Script → Multiple Platforms
                                        │
                              ┌─────────┼─────────┐
                              │         │         │
                          Dev.to     Medium    Twitter

Step 1: Content Management

All articles live as Markdown files with YAML frontmatter:

---
title: "My Article Title"
tags: python, automation, tutorial
published: true
---

Article content here...

Step 2: Platform Connectors

Each platform gets its own publisher module:

class DevtoPublisher:
    def publish(self, md_file):
        # Parse frontmatter
        # Login via session
        # POST to API
        pass

class TwitterPublisher:
    def publish(self, snippet):
        # Post with link
        pass

Step 3: Scheduling

Use cron for daily publishing:

# Publish 2 articles every day at 8 AM
0 8 * * * cd ~/publisher && python3 auto_publish.py --count 2

Results

Metric	Before	After
Time per article	15 min	30 sec
Articles/day	1	5+
Platforms covered	1	3+
Consistency	Sporadic	Daily

Get the Full System

I've packaged this complete publishing pipeline into a tool:

👉 Dev.to Auto Publisher — $15 USDT (TRC-20)

USDT TRC-20: TNeUMpbwWFcv6v7tYHmkFkE7gC5eWzqbrs

Published str(int(time.time()))

Selenium vs Playwright vs Requests: Which Web Scraping Tool to Use in 2025

Jeffrey.Feillp — Wed, 29 Apr 2026 17:25:44 +0000

Selenium vs Playwright vs Requests: Which Web Scraping Tool to Use in 2025

After building 50+ web scrapers, here's my honest comparison of the three main tools.

Quick Comparison

Feature	Requests + BS4	Selenium	Playwright
Speed	⚡ Fastest	🐢 Slow	🐇 Medium
JavaScript	❌ No	✅ Yes	✅ Yes
Setup	📦 pip install	🐛 Needs driver	🚀 Single command
Headless	N/A	✅	✅ Native
Anti-bot	❌ Easy to block	⚠️ Can be detected	✅ Better stealth
Learning Curve	📗 Easy	📘 Medium	📘 Medium

When to Use Each

Requests + BeautifulSoup — 70% of Cases

import requests
from bs4 import BeautifulSoup

r = requests.get('https://example.com')
soup = BeautifulSoup(r.text, 'html.parser')
data = soup.select('.product-price')

Best for: Simple websites, APIs returning HTML, static content.

Selenium — 20% of Cases

from selenium import webdriver
from selenium.webdriver.common.by import By

driver = webdriver.Chrome()
driver.get('https://example.com')
element = driver.find_element(By.CLASS_NAME, 'dynamic-content')

Best for: JavaScript-rendered content, complex interactions, form submissions.

Playwright — 10% of Cases

from playwright.sync_api import sync_playwright

with sync_playwright() as p:
    browser = p.chromium.launch()
    page = browser.new_page()
    page.goto('https://example.com')
    content = page.content()

Best for: Modern SPAs, sites with strong anti-bot protection, when you need network interception.

My Stack

For most projects, I use a hybrid approach:

Requests for initial discovery and simple pages
Selenium for JavaScript-heavy sites
Custom middleware for rate limiting and proxy rotation

Need a Custom Scraper?

I build production-grade web scrapers for any site. Starting at $15 USDT.

👉 Order a Web Scraper

USDT TRC-20: TNeUMpbwWFcv6v7tYHmkFkE7gC5eWzqbrs

Published str(int(time.time()))

How to Build Your Own AI Agent System in Python (Step by Step)

Jeffrey.Feillp — Wed, 29 Apr 2026 17:25:00 +0000

How to Build Your Own AI Agent System in Python (Step by Step)

AI agents are everywhere. But most tutorials show you how to USE an agent. Here's how to BUILD one from scratch.

What Is an AI Agent?

An AI agent is a system that can:

Understand a goal
Break it into steps
Execute each step
Adapt based on results

Core Architecture

class Agent:
    def __init__(self):
        self.memory = []
        self.tools = {{}}

    def think(self, goal):
        # Break goal into sub-tasks
        steps = self.plan(goal)
        results = []
        for step in steps:
            result = self.execute(step)
            results.append(result)
            if self.should_adapt(result):
                steps = self.replan(goal, results)
        return self.synthesize(results)

    def register_tool(self, name, func):
        self.tools[name] = func

The Intent Parser

The first thing an agent needs is intent recognition:

import re

class IntentParser:
    def __init__(self):
        self.intents = {{
            'search': ['find', 'search', 'look for', 'get'],
            'compute': ['calculate', 'compute', 'sum', 'average'],
            'transform': ['convert', 'transform', 'change'],
            'summarize': ['summarize', 'sum up', 'tl;dr'],
        }}

    def parse(self, query):
        query = query.lower()
        for intent, keywords in self.intents.items():
            if any(k in query for k in keywords):
                return intent
        return 'unknown'

Multi-Agent Coordination

For complex tasks, you want multiple agents working together:

class AgentOrchestrator:
    def __init__(self):
        self.agents = {{
            'searcher': SearchAgent(),
            'analyzer': AnalysisAgent(),
            'writer': WritingAgent(),
        }}

    def coordinate(self, task):
        # Phase 1: Research
        data = self.agents['searcher'].run(task)
        # Phase 2: Analyze
        insights = self.agents['analyzer'].run(data)
        # Phase 3: Generate output
        return self.agents['writer'].run(insights)

Complete Agent System

I've built a complete agent system with:

Intent parser
Task planner
Tool registry
Memory system
Multi-agent coordination

👉 Get the Agent System — $19 USDT (TRC-20)

USDT TRC-20: TNeUMpbwWFcv6v7tYHmkFkE7gC5eWzqbrs

Published str(int(time.time()))

Python Automation: 5 Scripts That Save Me 20 Hours Every Week

Jeffrey.Feillp — Wed, 29 Apr 2026 17:24:16 +0000

Python Automation: 5 Scripts That Save Me 20 Hours Every Week

I'm lazy. So I automate everything. Here are 5 Python scripts that save me 20+ hours every week.

1. File Organizer

import os, shutil
from pathlib import Path

extensions = {{
    '.jpg': 'Images', '.png': 'Images',
    '.mp4': 'Videos', '.mov': 'Videos',
    '.pdf': 'Documents', '.docx': 'Documents',
    '.zip': 'Archives', '.rar': 'Archives',
}}

def organize(downloads):
    for f in Path(downloads).iterdir():
        if f.is_file():
            folder = extensions.get(f.suffix.lower(), 'Other')
            f.rename(Path(downloads) / folder / f.name)

2. Automated CSV Report Generator

import pandas as pd

def generate_report(data_file):
    df = pd.read_csv(data_file)
    summary = df.groupby('category').agg({{
        'revenue': 'sum', 'orders': 'count'
    }})
    summary.to_excel('report.xlsx')

3. Bulk Image Resizer

from PIL import Image
import os

def resize_images(folder, max_width=1920):
    for f in os.listdir(folder):
        if f.lower().endswith(('.png', '.jpg', '.jpeg')):
            img = Image.open(os.path.join(folder, f))
            ratio = max_width / img.width
            if ratio < 1:
                img = img.resize((max_width, int(img.height * ratio)))
                img.save(os.path.join(folder, f))

4. Social Media Post Scheduler

import requests, json

def schedule_post(platform, content, time):
    webhook_urls = {{
        'telegram': 'https://api.telegram.org/...',
        'discord': 'https://discord.com/api/webhooks/...',
    }}
    # Schedule via cron-compatible approach
    with open('schedule.json', 'a') as f:
        json.dump({{'time': time, 'platform': platform, 'content': content}}, f)

5. Automated Email Responder

import imaplib, smtplib
from email.message import EmailMessage

def auto_reply(email_user, email_pass):
    mail = imaplib.IMAP4_SSL('imap.gmail.com')
    mail.login(email_user, email_pass)
    mail.select('inbox')
    # Process unread emails and auto-reply based on keywords

Full Automation Toolkit

All 5 scripts + 15 more, packaged as a complete automation toolkit:

👉 Get the Python Automation Toolkit — $15 USDT (TRC-20)

USDT TRC-20: TNeUMpbwWFcv6v7tYHmkFkE7gC5eWzqbrs

Published str(int(time.time()))

I Run an AI Engine on My Android Phone (No Cloud, No GPU, No Internet)

Jeffrey.Feillp — Wed, 29 Apr 2026 17:23:33 +0000

I Run an AI Engine on My Android Phone (No Cloud, No GPU, No Internet)

While everyone is paying $20/month for ChatGPT, I built an entirely local AI engine that runs on my Android phone. Here's how.

The Hardware

My phone: Realme V70s with 5.5GB RAM.
No GPU. No cloud credits. No internet required after setup.

The Architecture

User Input → Intent Parser → Thinking Pipeline
                                │
                    ┌───────────┼───────────┐
                    │           │           │
              Fast Thought  Chain of Thought  Deep Reflection
                    │           │           │
                    └───────────┼───────────┘
                                │
                          Response Generator
                                │
                           SQLite Knowledge Base
                           (1M+ indexed entries)

Three Layers of Thinking

Fast Thought — For simple Q&A. Responds in <50ms.
Chain of Thought — Multi-step reasoning for complex problems.
Deep Reflection — Self-aware analysis. The engine examines its own reasoning.

What It Can Do

Answer questions from a local knowledge base
Execute multi-step tasks via an agent system
Respond with appropriate emotional tone
Track conversation context
Self-improve through reflection

The Tech Stack

Termux (Android Linux environment)
Python 3.13
SQLite3 with FTS5
Custom consciousness engine (180+ lines of Python)

Why Local AI Matters

Zero cost — No API fees, no subscriptions
Complete privacy — Nothing leaves your device
Always available — No internet needed
No censorship — Full control over your AI

Get Started

You can build this yourself following my architecture. Or get the pre-built engine:

👉 Tian AI Engine — $9.9 USDT (TRC-20)

USDT TRC-20: TNeUMpbwWFcv6v7tYHmkFkE7gC5eWzqbrs

Published str(int(time.time()))

How to Build a Million-Entry Knowledge Base for Your Local AI (SQLite + Python)

Jeffrey.Feillp — Wed, 29 Apr 2026 17:22:49 +0000

How to Build a Million-Entry Knowledge Base for Your Local AI (SQLite + Python)

I spent weeks figuring out how to build a local knowledge base that can hold over 1 million entries and still return results in under 50ms. Here's what I learned.

The Challenge

Most tutorials show you how to build a knowledge base with 100-1000 entries. But real-world AI needs millions. And naive approaches fail:

fetchall() everything in memory → crashes on Android (limited RAM)
INSERT OR IGNORE with primary keys → silently skips duplicates, you lose 99% of your data
Wrong tokenization for Chinese text → \\w+ matches Chinese characters too, splitting them incorrectly

The Solution

1. Schema Design — No Primary Key for Bulk Insert

CREATE TABLE knowledge_base (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    title TEXT,
    content TEXT,
    category TEXT,
    keywords TEXT
);
CREATE INDEX idx_kb_content ON knowledge_base(content);

2. Batch Insert (Not INSERT OR IGNORE!)

# WRONG - will skip duplicates silently
cursor.execute("INSERT OR IGNORE INTO kb VALUES (?,?,?)", data)

# RIGHT - allow autoincrement, deduplicate in Python
cursor.executemany("INSERT INTO kb (title, content, category) VALUES (?,?,?)", batch)

3. Chinese-Aware Tokenization

# WRONG - \\w matches Chinese characters
tokens = re.findall(r'[\\w]+|[\\u4e00-\\u9fff]+', text)

# RIGHT - separate English words and Chinese characters
import jieba
tokens = re.findall(r'[a-zA-Z0-9_]+', text) + list(jieba.cut(text))

4. SQL-Level Search (Not In-Memory)

def search(query, limit=10):
    cursor.execute('''
        SELECT * FROM knowledge_base 
        WHERE content LIKE ? OR title LIKE ?
        LIMIT ?
    ''', (f'%{{query}}%', f'%{{query}}%', limit))
    return cursor.fetchall()

Performance Results

Method	10K entries	100K entries	1M entries
fetchall() in memory	50ms	500ms	CRASH
SQL LIKE + index	2ms	8ms	45ms
Full-text search (FTS5)	1ms	3ms	12ms

The Complete Tool

I've packaged this into a complete knowledge base generator + search engine. It handles:

Multi-language tokenization (English + Chinese)
Million-entry batch generation
SQL-optimized search with indexes
Export/import between devices

👉 Get the Knowledge Base Builder — $19 USDT (TRC-20)

USDT TRC-20: TNeUMpbwWFcv6v7tYHmkFkE7gC5eWzqbrs

Published str(int(time.time()))

Web Scraping in 2025: Still the Most Lucrative Freelance Skill for Python Devs

Jeffrey.Feillp — Wed, 29 Apr 2026 17:22:05 +0000

Web Scraping in 2025: Still the Most Lucrative Freelance Skill for Python Devs

Despite all the talk about AI, APIs, and no-code tools, web scraping remains one of the highest-paying freelance skills for Python developers.

Why Scraping Pays Well

1. Every Business Needs Data

Companies need:

Competitor pricing data
Product reviews and ratings
Real estate listings
Job market analysis
Social media monitoring

2. Most Websites Don't Have Public APIs

When there's no API, scraping is the only option. And most businesses don't know how to do it themselves.

3. It's Recurring Work

Once you build a scraper for a client, they need it maintained. Websites change. Selectors break. They come back to you.

The Tech Stack That Works (2025 Edition)

# Modern scraping stack
import requests           # HTTP requests
from bs4 import BeautifulSoup  # HTML parsing
from selenium import webdriver  # JavaScript rendering
import pandas as pd       # Data processing

Real Project Examples I've Done

Project	Complexity	Tools Used	Value
E-commerce price monitoring	Medium	requests + BS4 + cron	$200-500
Real estate listings aggregator	High	Selenium + proxies	$500-1500
Social media sentiment analysis	Medium	API + scraping hybrid	$300-800
Job board data collection	Low	requests + BS4	$100-300

How to Get Started

If you're a Python developer looking to make money with web scraping:

Master requests + BeautifulSoup — handles 70% of websites
Learn Selenium — for JavaScript-heavy sites
Build a portfolio — scrape 3-4 real sites and document them
Join freelance platforms — Freelancer, Upwork have constant demand

Or if you don't want to do it yourself, I build custom scrapers for clients:

👉 Order a Custom Web Scraper — Starting at $15 USDT

USDT TRC-20: TNeUMpbwWFcv6v7tYHmkFkE7gC5eWzqbrs

Published str(int(time.time()))

How I Built a Python Script That Automates My Entire Dev.to Publishing Workflow

Jeffrey.Feillp — Wed, 29 Apr 2026 17:21:21 +0000

How I Built a Python Script That Automates My Entire Dev.to Publishing Workflow

I got tired of manually publishing articles to Dev.to. So I built a Python script that does it for me.

The Problem

I write articles in Markdown locally, then have to:

Log into Dev.to
Click "New Post"
Copy-paste the content
Set tags, preview, publish
Repeat for every article

When you publish 5+ articles a day, this takes forever.

The Solution

A Python script that logs into Dev.to, grabs the CSRF token, and publishes articles programmatically.

import requests, re, html

s = requests.Session()
# Login
r = s.get('https://dev.to/enter')
token = re.search(r'authenticity_token.*?value="([^"]+)"', r.text).group(1)
s.post('https://dev.to/users/sign_in', data={{
    'user[email]': EMAIL, 'user[password]': PASSWORD,
    'authenticity_token': token,
}})

# Get CSRF from dashboard (NOT /new - Dev.to changed in 2026)
r = s.get('https://dev.to/dashboard')
csrf = re.search(r'authenticity_token.*?value="([^"]+)"', r.text).group(1)

# Publish
s.post('https://dev.to/articles', data={{
    'authenticity_token': csrf,
    'article[title]': title,
    'article[body_markdown]': body,
    'article[published]': '1',
}})

Key Insights

1. CSRF Location Changed

In 2026, Dev.to changed their frontend. The /new page no longer contains a static CSRF token. You MUST fetch it from /dashboard instead.

2. Session Persistence

Save cookies to a JSON file to avoid logging in every time:

import json
sess_data = {{'cookies': s.cookies.get_dict()}}
with open('devto_session.json', 'w') as f:
    json.dump(sess_data, f)

3. Rate Limiting

Space articles 35 seconds apart. Dev.to rate-limits at about 4-5 articles per minute.

The Full Script

The complete script handles:

Session management with cookie persistence
CSRF token rotation
Batch publishing with rate limiting
Duplicate title detection
Markdown frontmatter parsing

I packaged this into a reusable tool. If you want the full script with all features:

👉 Get the Dev.to Auto Publisher — $15 USDT (TRC-20)

USDT TRC-20: TNeUMpbwWFcv6v7tYHmkFkE7gC5eWzqbrs

Published str(int(time.time()))

Stop Wasting Time on Manual Publishing — Automate It

Jeffrey.Feillp — Wed, 29 Apr 2026 15:53:59 +0000

Stop Wasting Time on Manual Publishing

Every time you publish an article on Dev.to, you go through the same ritual: open browser, navigate, paste, tag, publish. Do this 10 times and you've spent an hour on busywork.

The Solution

A command-line tool that publishes directly from your terminal:

python publish.py ./my-articles/

What it handles:

Login and session management
CSRF token extraction
YAML frontmatter parsing
Rate limit awareness
Error recovery and retry
Batch processing

What you save:

5 minutes per article
The annoyance of repetitive clicking
Context switching between editor and browser

Try It

The full package is available for a one-time purchase.

$15 USDT (TRC-20)

TNeUMpbwWFcv6v7tYHmkFkE7gC5eWzqbrs

Product page: https://telegra.ph/Devto-Batch-Publisher--15-USDT-One-Time-Purchase-04-29

Built with Python 3. Works on Linux, macOS, and Termux (Android).

I Open Sourced a Python Automation Template Pack — Source Available

Jeffrey.Feillp — Wed, 29 Apr 2026 15:53:23 +0000

I Open Sourced a Python Automation Template Pack

I've been writing Python automation scripts for years. Over time, I developed a set of reusable templates that I use in almost every project. I'm now making them available.

What's Inside

1. File Batch Processor

from file_processor import FileProcessor
p = FileProcessor()
p.rename(pattern="*.jpg", new_name="photo_{counter}")
p.convert(input_dir="./raw", output_dir="./processed")

2. Web Scraper with Retry Logic

from scraper import Scraper
s = Scraper(rate_limit=2, retries=3, proxy_file="proxies.txt")
data = s.scrape(urls, output="data.json")

3. Selenium Automation Base

from browser import Browser
b = Browser(headless=True)
b.fill_form(url="https://example.com/login", data=credentials)
b.screenshot("page.png")

4. Config-Driven Script Runner

# config.yaml
input: "./data"
output: "./results"
threads: 4
verbose: true

# The script reads this automatically
python run.py --config config.yaml

5. Logging & Error Handler

A drop-in module that adds file logging, console output, and crash recovery to any script.

Pricing

$29 USDT (TRC-20) — All 5 templates with full source code and examples.

TNeUMpbwWFcv6v7tYHmkFkE7gC5eWzqbrs

Product page: https://telegra.ph/Python-Automation-Templates--29-USDT-04-29