DEV Community: kk mors

I Built an AI Reddit Lead Monitor That Finds Customers Automatically

kk mors — Tue, 28 Apr 2026 12:30:08 +0000

Manual Reddit marketing is a waste of time. You scroll for hours, find 3 relevant posts, write replies, and get banned for self-promotion. I built a system that does the finding and filtering automatically.

The Problem with Reddit Marketing

Too many subreddits to monitor manually
Most posts are not relevant to your product
Self-promotion rules are confusing and vary by subreddit
Writing natural replies is time-consuming
Easy to get banned if you do it wrong

My Solution: Reddit Lead Monitor

The system has 3 layers:

Layer 1: Scanning

Uses Reddit's public JSON API (no auth needed — just append .json to any Reddit URL) to scan target subreddits every hour.

# Fetching posts is this simple
url = "https://www.reddit.com/r/SideProject/new.json?limit=25"
# No API key needed for reading

Layer 2: Filtering

Each post is scored 0-100 across 5 dimensions:

Dimension	Weight	What It Measures
Problem Match	30%	Does the post describe a problem you solve?
Intent Signal	25%	Is the author looking for a solution?
Product Fit	20%	How directly does your product help?
Engagement Potential	15%	Will your reply get visibility?
Subreddit Rules	10%	Can you self-promote here?

70+ = Reply immediately. 50-69 = Reply later. Below 50 = Skip.

Layer 3: Reply Generation

The Milestone Approach (anti-ban):

Help first — Genuinely answer their question
Then mention your product as a relevant resource
Never lead with your product link

Bad: "Check out my product! It solves your problem! [link]"
Good: "I had the same issue. What worked for me was [approach]. 
      I actually built [product] for this exact use case. [link]"

The Anti-Ban Safety System

Max 5 replies per hour across all subreddits
Always customize at least 30% of each reply
Never reply to the same user twice in 24h
Match the subreddit's tone (casual vs technical)
Track which subreddits allow self-promotion
Never use marketing language ("game-changing", "revolutionary")

Results After 2 Weeks

Scanned 12 subreddits daily
Found 15-25 high-relevance leads per day
Posted 8-12 natural replies per day
Generated 340 clicks to product pages
Converted 12 paying customers ($480 revenue)
Zero bans or warnings

Subreddit Targeting Strategy

Tier 1 (Direct fit): r/SideProject, r/imadethis, r/AlphaandBetaUsers
Tier 2 (Problem-aware): r/webdev, r/Entrepreneur, r/SaaS, r/ChatGPT
Tier 3 (Volume): r/technology, r/startups

The key insight: Tier 2 subreddits have the best ROI. People there have the problem but haven't found a solution yet.

What You Get

The Reddit Lead Monitor Pack includes:

5 AI agent skill files (works with any AI agent)
Production Python script (scan, filter, score, generate replies)
Reply templates for 4 scenarios
Subreddit targeting worksheet
No Reddit API keys needed

If you're spending more than 30 minutes a day on Reddit marketing, you need this.

What's your Reddit marketing strategy? Would love to compare approaches.

I Built an AI Product Trend Radar That Spots Winning Products Before Competitors

kk mors — Tue, 28 Apr 2026 12:01:44 +0000

Every day, thousands of products trend on Amazon and TikTok Shop. By the time most sellers notice, the early-mover advantage is gone.

I built a Product Trend Radar — an AI-powered commerce intelligence system that scans Amazon and TikTok Shop daily to find trending products, analyze competition, mine review insights, and score opportunities before they saturate.

How It Works

The radar runs on 6 AI agent skills working together:

1. Amazon Product Scout

Scans bestsellers, new releases, and movers and shakers across your target categories. Extracts ASIN, price, rating, review count, BSR, and seller information.

2. TikTok Shop Scout

Monitors trending products, viral content, and creator partnerships. Measures content velocity — the number of videos, views, and engagement a product generates in 24h/7d windows.

3. Review Miner

Deep-mines product reviews to find:

What customers love (copy these features)
What they hate (fix these pain points)
What they wish existed (build these features)
Which competitors they mention

4. Competitor Tracker

Monitors competitor pricing, listing changes, inventory signals, and ranking movements. Alerts you when a competitor drops price, adds new variations, or runs out of stock.

5. Opportunity Scorer

Multi-factor scoring engine that rates each product 0-100 across 5 dimensions:

Dimension	Weight	What It Measures
Demand Signal	25%	BSR trend, search volume, TikTok views
Competition Level	20%	Seller count, brand concentration
Review Sentiment	20%	Pain points, feature gaps
Content Velocity	15%	Social momentum, creator activity
Profit Potential	20%	Margins, shipping, seasonality

80+ = Green light. 60-79 = Investigate. Below 60 = Skip.

6. Daily Briefing Generator

Combines all signals into a concise morning report with:

Market pulse (biggest movers)
Hot opportunities with scores and margins
Competitor alerts
Prioritized action items

The Scoring Framework in Practice

# Example: Scoring a stackable bento box
product = {
    "title": "Stackable Bento Box",
    "bsr": 1923,           # Improving rapidly
    "reviews": 156,         # Low competition (few reviews)
    "rating": 3.8,          # Room for improvement
    "tiktok_views": 500000, # Viral content
    "seller_count": 5,      # Fragmented market
    "estimated_margin": 68, # Great margins
}

# Result: 84/100 — High Opportunity
# Action: Source immediately from 1688 at $2.10, sell at $19.99

What You Get

The Product Trend Radar Pack includes:

6 AI agent skill files — Ready-to-use prompts for any AI agent (Hermes, OpenClaw, Manus, ChatGPT)
3 Python scripts — Amazon stealth scraper, opportunity scoring engine, configurable settings
Templates — Daily briefing, product scorecard, competitor profile
Examples — Sample outputs for every component

Works with or without coding. The AI agent skills need zero code — just copy the prompt and run.

Target Markets

Amazon US, EU (DE/UK/FR/IT/ES), Japan
TikTok Shop (Global)
Wildberries / Ozon (Russia)
Mercado Libre (LatAm)
1688 (sourcing)

My Results

After running this radar for 3 months:

Found 12 products scoring 80+ before they peaked
Average time from detection to listing: 5 days
3 products became top-10 BSR in their categories
One product generated $8K in its first month

The key insight: speed beats scale. Finding a trending product 2 weeks early is worth more than having 100 mediocre listings.

If you sell on Amazon, TikTok Shop, or any e-commerce platform, the Product Trend Radar Pack will help you spot winners before the crowd.

What tools do you use for product research? Would love to compare notes.

Agent Command Center — Manage Multiple AI Agents from One Dashboard

kk mors — Tue, 28 Apr 2026 10:33:38 +0000

Running multiple AI agents (trading bots, content generators, monitors) quickly becomes chaos. I built a command center to manage them all from one place.

The Problem

If you're running AI agents like me:

Trading monitor that runs 24/7
Content bots that post on schedule
Scraping agents that collect data
Customer service bots

You end up with:

Processes scattered across terminals
No visibility into what's running
Hard to restart/stop individual agents
No centralized logging
Memory/CPU usage unclear

The Solution

Agent Command Center provides:

1. Dashboard

Real-time view of all running agents:

Status (running/stopped/error)
CPU and memory usage
Last action taken
Uptime tracking

2. Process Management

# Start an agent
acc start crypto-monitor

# Stop gracefully
acc stop content-bot

# Restart with new config
acc restart scraper --config new_targets.yaml

# View logs
acc logs trading-agent --tail 50

3. Alerting

Get notified when:

An agent crashes or hangs
Memory usage exceeds threshold
An agent hasn't produced output in X minutes
Error rate spikes

4. Scheduling

Built-in cron-like scheduling:

Run agents at specific times
Set up recurring tasks
Chain agents together (output of A feeds into B)

5. Logging & Analytics

Centralized log aggregation
Performance metrics per agent
Cost tracking (API calls, compute time)
Historical run data

Get the Agent Command Center and take control of your AI agent fleet.

How do you manage your AI agents? Would love to hear your setup.

Python Crypto Indicators — Production-Ready Technical Analysis Library

kk mors — Tue, 28 Apr 2026 10:28:27 +0000

I got tired of crypto trading libraries that are either too bloated or too basic. So I built a focused set of Python indicators that I actually use in production.

What's Included

Trend Indicators

EMA/SMA crossovers — With signal generation
Ichimoku Cloud — Full implementation with all 5 lines
ADX — Trend strength measurement

Momentum Indicators

RSI — With divergence detection
MACD — With histogram and signal crossovers
Stochastic RSI — Combining both for better signals

Volatility Indicators

Bollinger Bands — With squeeze detection
ATR — For dynamic stop-loss placement
Keltner Channels — Combined with BB for Squeeze play

Volume Indicators

OBV — On-balance volume
VWAP — Volume-weighted average price
Volume Profile — Identify high-volume zones

from crypto_indicators import RSI, MACD, BollingerBands

# Quick analysis
df = load_candle_data("ETH/USDT", timeframe="4h")

df["rsi"] = RSI(df["close"], period=14)
df["macd"], df["signal"], df["hist"] = MACD(df["close"])
df["bb_upper"], df["bb_mid"], df["bb_lower"] = BollingerBands(df["close"])

# Find RSI divergence
divergences = RSI.detect_divergence(df["close"], df["rsi"])
print(f"Found {len(divergences)} divergences")

Why This One?

Zero dependencies beyond numpy/pandas — No heavy frameworks
Vectorized calculations — Fast on large datasets
Signal generation built-in — Not just values, but actionable signals
Backtested — Every indicator tested against historical data
Type hints — Full type annotations for IDE support

Get the Python Crypto Indicators library.

What indicators do you rely on for crypto trading?

AI Crypto Monitor — Automated Trading Signals with Multiple AI Models

kk mors — Tue, 28 Apr 2026 10:23:16 +0000

Crypto markets never sleep, but you do. I built an AI-powered monitor that watches the markets 24/7 and sends trading signals when conditions are right.

How It Works

The monitor uses multiple AI models to analyze market conditions:

Technical Analysis Agent — Reads charts, identifies patterns (RSI, MACD, support/resistance)
Sentiment Agent — Monitors social media and news for market sentiment shifts
On-Chain Agent — Tracks whale movements, exchange flows, and DeFi metrics
Decision Engine — Combines all signals and generates a confidence score

# Signal example
{
    "token": "ETH",
    "action": "LONG",
    "confidence": 0.87,
    "entry": 3420,
    "stop_loss": 3350,
    "take_profit": 3580,
    "reasoning": [
        "RSI oversold on 4H timeframe",
        "Whale accumulation detected (3 large wallets)",
        "Sentiment turning positive on X/Twitter"
    ],
    "risk_level": "MEDIUM"
}

Features

Multi-model consensus — Not relying on a single AI's judgment
Conservative strategy — All 4 conditions must be met before signaling
Telegram alerts — Get signals instantly on your phone
Backtesting — Test strategies against historical data
Customizable parameters — Adjust risk tolerance and trading pairs

My Strategy (V2.4)

Very conservative — 4 conditions must ALL be met:

Technical indicators aligned
Sentiment confirms direction
On-chain data supports
No major risk events upcoming

This means fewer signals but higher win rate.

Get the AI Crypto Monitor and start getting smarter trading signals.

What's your approach to crypto trading? Do you use AI for analysis?

Jimeng AI Free API — Access ByteDance's Image Generation for Free

kk mors — Tue, 28 Apr 2026 10:18:06 +0000

ByteDance's Jimeng (即梦) AI generates stunning images, but there's no official free API. I reverse-engineered the web interface to create a free API wrapper.

What is Jimeng AI?

Jimeng is ByteDance's (TikTok's parent company) AI image generation model. It produces high-quality images with:

Excellent Chinese text rendering in images
Strong artistic style control
Fast generation times
Multiple model options

The Free API

I built a wrapper that lets you call Jimeng through a simple REST API:

import requests

# Generate an image
response = requests.post("http://localhost:8080/generate", json={
    "prompt": "A cyberpunk city at sunset, neon lights reflecting on wet streets",
    "width": 1024,
    "height": 1024,
    "style": "vivid"
})

image_url = response.json()["url"]

Features

REST API interface — Simple HTTP calls
Multiple models — Access different Jimeng model variants
Batch generation — Generate multiple images at once
Style presets — Photorealistic, anime, oil painting, etc.
No API key needed — Uses free web access

Limitations

Rate limited to ~20 images per hour
Requires a valid Jimeng web session (cookie)
Not for commercial use (check ByteDance's terms)

Get the Jimeng AI Free API setup guide and code.

Has anyone else worked with Jimeng or other Chinese AI image generators?

X/Twitter Automation Toolkit — Schedule Threads, Auto-Reply, and Grow Your Audience

kk mors — Tue, 28 Apr 2026 10:12:55 +0000

Growing on X/Twitter requires consistent posting and engagement. I built a toolkit that automates the repetitive parts while keeping your voice authentic.

What's in the Toolkit

1. Thread Scheduler

Write threads in advance, schedule them for optimal posting times.

Supports up to 25-tweet threads
Image attachment support
Optimal timing based on your audience's timezone

2. Smart Auto-Reply

Monitor keywords and auto-reply to relevant tweets:

// Example: Auto-reply to tweets about "AI API pricing"
const keywords = ["AI API", "API pricing", "cheap API"];
const replyTemplate = "Great point! Have you tried Chinese AI APIs? They're 80% cheaper...";

// The bot monitors your timeline and replies to matching tweets
// with natural-sounding responses (not spam!)

3. Engagement Tracker

Track who engages with your content:

Top engagers list
Reply-to-engagement ratio
Best performing tweets
Follower growth analytics

4. Content Calendar

Plan your content week by week:

Mix of original content, threads, and engagement
Template prompts for different content types
Performance tracking per content category

Important Notes

Always follow X/Twitter's terms of service
Don't spam — quality over quantity
Use auto-reply sparingly and make responses natural
The toolkit helps you be more efficient, not replace your voice

Get the X Automation Toolkit and start growing your X presence.

What tools do you use for social media automation?

I Built a Telegram Customer Service Bot with AI — Here's How It Works

kk mors — Tue, 28 Apr 2026 10:07:43 +0000

Customer service is one of the most time-consuming parts of running a business. I built a Telegram bot that handles 80% of customer inquiries automatically using AI.

Why Telegram?

Huge user base in Asia and Eastern Europe
Bot API is well-documented and free
Supports rich messages, inline keyboards, and payments
Customers are already on Telegram

How It Works

The bot architecture:

Message intake — Customer sends a message to your Telegram bot
Intent classification — AI determines what the customer wants (pricing, support, feature request, etc.)
Knowledge base lookup — Bot searches your product documentation
Response generation — AI crafts a natural response
Escalation — Complex queries get forwarded to a human

# Core bot logic
from telegram import Update
from telegram.ext import Application, MessageHandler

async def handle_message(update: Update, context):
    user_msg = update.message.text
    intent = classify_intent(user_msg)

    if intent.confidence > 0.85:
        answer = search_knowledge_base(intent)
        response = generate_response(answer, user_msg)
        await update.message.reply_text(response)
    else:
        await escalate_to_human(update, user_msg)

Features

Multi-language support — Responds in the customer's language
Knowledge base integration — Connects to your docs, FAQ, and product info
Conversation history — Remembers context across messages
Analytics dashboard — Track common questions, response times, satisfaction
Human handoff — Seamless escalation when AI can't help

If you want to set up your own AI-powered Telegram CS bot, check out the Telegram CS Bot.

Has anyone else built customer service bots? What's your stack?

AI Side Hustle OS — A Notion System for Managing Multiple AI Income Streams

kk mors — Tue, 28 Apr 2026 10:02:32 +0000

I got tired of juggling spreadsheets, notes, and random docs to track my AI side hustles. So I built a complete operating system in Notion.

The Problem

If you're running multiple AI income streams (API relay, content creation, automation services, digital products), you quickly run into chaos:

Revenue tracking scattered across platforms
No clear view of which projects are profitable
Task management mixed with business planning
No system for evaluating new opportunities

What AI Side Hustle OS Includes

Dashboard — Real-time overview of all income streams, revenue, and priorities

Project Tracker — Each side hustle gets its own page with:

Revenue and expense tracking
Time investment logging
Growth metrics
Next action items

Idea Pipeline — Evaluate new opportunities with:

Startup cost estimation
Revenue potential scoring
Time-to-market assessment
Risk evaluation

Weekly Review Template — Structured review process:

What worked this week
What didn't
Revenue vs. projections
Priorities for next week

Resource Library — Curated tools, APIs, and platforms organized by category

Who It's For

Developers running AI API businesses
Content creators using AI tools
Freelancers offering AI automation services
Anyone with multiple AI-related income streams

Get the AI Side Hustle OS and start organizing your AI income.

What tools do you use to manage your side projects?

I Built a WeChat Article Scraper API That Handles Anti-Bot Detection Automatically

kk mors — Tue, 28 Apr 2026 09:57:21 +0000

Scraping WeChat public account articles is notoriously difficult. The platform has aggressive anti-bot measures, dynamic content loading, and strict rate limiting.

After months of trial and error, I built a scraper API that handles all of this automatically.

The Challenge with WeChat Scraping

WeChat articles are behind several layers of protection:

Dynamic URL generation — article URLs change based on session
Browser fingerprinting — they detect headless browsers
Cookie-based access — you need a valid WeChat web session
Rate limiting — too many requests = instant block

The Solution

I built a REST API (Camofox-based) that:

Manages browser sessions automatically
Rotates fingerprints between requests
Handles cookie persistence
Returns clean article text, images, and metadata

# Simple API call
curl -X POST http://localhost:9377/tabs \
  -H "Content-Type: application/json" \
  -d '{"url": "https://mp.weixin.qq.com/s/xxxxx"}'

# Get the article content
curl http://localhost:9377/tabs/{id}/snapshot

What You Get

Full article text (Markdown format)
All images extracted and hosted
Article metadata (author, date, title)
Anti-bot detection bypass built-in
REST API interface for easy integration

If you need to scrape WeChat articles at scale, check out the WeChat Scraper API.

What's your experience with WeChat scraping? Would love to hear what approaches have worked for you.

Getting Blocked by Anti-Bot Measures? Here is a Scraping Methodology That Actually Works

kk mors — Tue, 28 Apr 2026 09:45:21 +0000

After months of scraping WeChat, Taobao, and other heavily-protected sites, I finally have a reliable anti-detection methodology.

The Key Insights

1. Browser Fingerprint Rotation Matters More Than Proxies

Most people focus on rotating IPs, but modern anti-bot systems fingerprint your browser. If your fingerprint stays the same, a new IP will not help.

2. Cookie/Session Management is Crucial

For persistent logins (like WeChat articles), proper cookie management is essential. One wrong header and your session is invalidated.

3. Rate Limiting That Mimics Humans

Aggressive scraping = instant ban. I use patterns like:

Random delays between 2-8 seconds
Variable request volumes per hour
Mouse movement simulation before clicks
Scroll patterns that match real users

4. Playwright Stealth Beats Selenium

Playwright with proper stealth configuration passes most bot detection systems. Selenium is too easily fingerprinted.

Technical Implementation

const { chromium } = require('playwright');

const browser = await chromium.launch({
  headless: false,
  args: [
    '--disable-blink-features=AutomationControlled',
    '--no-sandbox',
  ]
});

const context = await browser.newContext({
  userAgent: 'Mozilla/5.0 ...',
  viewport: { width: 1920, height: 1080 },
  locale: 'zh-CN',
  timezoneId: 'Asia/Shanghai',
});

What Works On

WeChat public account articles
Taobao product pages
Social media platforms
Sites with Cloudflare protection
Sites with reCAPTCHA

I packaged the complete methodology into a skill pack with Playwright stealth configs, fingerprint rotation strategies, and cookie management patterns: Anti-Detection Scraping Pack

What is your biggest scraping pain point? Always looking to improve the methodology.

I Built an AI API Relay Business in a Weekend — Here's the Full Blueprint

kk mors — Tue, 28 Apr 2026 09:39:34 +0000

The AI API relay business is booming. Developers need affordable access to GPT-4, Claude, Midjourney — and you can be the middleman with 40-60% gross margins.

The Business Model

The concept is simple:

Chinese AI providers (GLM-5.1, Qwen, DeepSeek) offer APIs at 80% less than OpenAI/Claude
You set up a relay service using open-source One-API
Developers connect to your service instead of paying OpenAI prices
You keep the margin

Real Numbers

Startup cost: One VPS + One-API = ~$20/month
Setup time: ~30 minutes with Docker
Monthly revenue: ¥2000-5000 from a single server
Maintenance: Minimal once running
Gross margins: 40-60%

Technical Setup

# Deploy One-API with Docker
docker run -d --name one-api \
  -p 3000:3000 \
  -v /home/one-api:/data \
  justsong/one-api

# Add upstream channels (Chinese AI providers)
# Set pricing for your users
# Share your endpoint with developers

One-API handles:

API key management
Rate limiting
Load balancing across providers
Usage tracking and billing
OpenAI-compatible format (drop-in replacement)

What You Need

I packaged everything into a business-in-a-box:

One-API setup guide with Docker deployment
Profit calculator to model your margins before you start
Pricing strategy based on real market data
Customer acquisition playbook

If you're interested in the API relay model, check out the API Relay Business-in-a-Box.

Happy to answer any questions about the API relay model!