Forem: PersonymAi

How to Earn Recurring Income From Telegram Without Running a Channel

PersonymAi — Tue, 12 May 2026 10:49:44 +0000

Most people think earning from Telegram means running a channel, growing an audience, and selling ads. That's one way. There's a quieter one — and it compounds over time.

If you already know Telegram admins, work in the crypto space, run an agency, or just have connections in the community, there's a real opportunity in the PersonymAI partner program. Not a one-time referral fee. A monthly, recurring commission — on every single payment your client makes, forever.

Let me break down exactly how it works, what you're selling, and what the numbers actually look like.

What Is PersonymAI?

PersonymAI is an AI platform for Telegram channels. Three products, one ecosystem:

1. AI Comments — The system generates natural, human-like discussions under every post in your client's channel. Not templates. Over 1,000 unique AI personas, each with its own character, vocabulary, and communication style. Crypto niches covered: trading, airdrops, NFT, DeFi, memecoins, TON, news, and more. Even AI detectors can't tell the difference. Pricing starts at $49/month (Advanced) or $99/month (Simple — accounts included, plug and play).

2. ModerAI Anti-Spam — A Telegram moderation bot with a multi-layer AI detection pipeline. Not keyword lists. The system understands context: who this user is, what your group is about, whether a specific message is spam in this specific context. 99.7% accuracy. Zero false bans on real users. It detects voice spam (transcribes and analyzes audio), reads spam in images (Vision AI), catches masked spam like С🔥П🔥А🔥М and 1nv3st, and detects when users post clean messages and then edit them into spam links. Global ban network: one ban in your chat automatically propagates to all connected chats. $9/month per chat — flat fee, no limits.

3. AI Autoposting — The system reads the channel's last posts, extracts the admin's writing style (tone, phrases, emoji patterns, footer links), then publishes new posts on schedule using fresh news from the niche. The admin's voice, not a template. DM approval mode available. From $29/month as an addon.

The Partner Program Structure

When you refer a client, you earn 10% of every payment they make — monthly, recurring, for as long as they're subscribed. Not a one-time bonus. If a client pays $199/month for 18 months, you get $19.90 every single month for 18 months.

Three commission streams:

Type	Rate	When
Direct clients (you referred)	10%	Every payment, every month
Sub-partner clients (your network)	5%	Every payment from their clients
Cashback on your own payments	10%	Your own subscriptions

The sub-partner layer is where it gets interesting for anyone running an agency or managing a team.

The Sub-Partner Network — Passive Income at Scale

You can recruit other managers as sub-partners using a separate referral link. When they bring in clients, you earn 5% of every payment those clients make — without doing anything.

Your sub-partner earns their full 10% from their clients. Your 5% is added on top, separately. It's genuinely win-win: they have full incentive to sell, and you build passive income from their work.

Example:
You recruit 3 managers as sub-partners. Each has 10 clients on Simple Pro ($199/month).

30 clients × $199 × 5% = $298.50/month — from other people's work.
Plus your own direct clients on top.

This is the structure that makes the program interesting for agency owners and community managers who already have networks in the Telegram admin space.

One Client = Multiple Services

Here's what most people miss: you don't sell one product to one client. A serious Telegram admin typically has a channel, sometimes two, and a chat group. Each of those is a separate product opportunity.

A realistic single-client stack:

Service	Price	Your commission
AI Comments Simple Pro (main channel)	$199/mo	$19.90/mo
AI Comments Simple Starter (second channel)	$99/mo	$9.90/mo
ModerAI (main chat)	$9/mo	$0.90/mo
ModerAI (second chat)	$9/mo	$0.90/mo
AI Autoposting addon	$29/mo	$2.90/mo
Total	$345/mo	$34.50/mo

That's $34.50/month from a single client. Every month. If that client stays for a year, you've earned $414 from one conversation.

Why the Market Makes This a Good Bet Right Now

The competitive landscape for Telegram tools is changing fast:

Combot — the largest anti-spam bot — announced API deprecation and had a DDoS incident. Their ban system is permanent with no appeal.
ModdyAI — a direct AI moderation competitor — officially closed. Their site reads: "Project no longer active, preserved for showcase only."
Shieldy — their site returns HTTP 400. The GitHub is frozen.
GetVarta — the only real AI competitor to ModerAI — charges $49/month per chat. PersonymAI charges $9.

For AI comments, there are no direct competitors at this quality level. The niche is wide open.

What's in the Partner Dashboard

Two referral links: one for clients (10%), one for recruiting sub-partners (5% from their clients)
CRM: list of all your referrals, their activity, subscription status
Commission history: every accrual with date and amount — nothing hidden
Payout settings: add your USDT wallet (BEP20, TRC20, or ERC20)
Minimum withdrawal: $10
Processing time: 1–2 business days
Official representative listing: appear on the verified representatives page on the website

Attribution: first-click, 30 days. If someone clicks your link and signs up 3 weeks later, they're still yours. Works across web and Mini App (t.me/PersonymAI_bot/app).

The Numbers

Clients	Monthly revenue	Your commission
10 direct (Simple Pro)	$1,990	$199/mo
30 direct (Simple Pro)	$5,970	$597/mo
10 direct + sub-partner network (30 clients)	$7,960	$398–596/mo
20 clients with full stack ($345 avg)	$6,900	$690/mo

How to Get Started

No application form. No approval process.

Email: hello@personym-ai.com
Telegram: @PersonymAi_Support

Training AI on 100 Posts to Match Author's Voice — GPT 5.5 Test

PersonymAi — Tue, 28 Apr 2026 22:11:02 +0000

Built an AI writing system that learns an author's voice from their 100 most recent posts. Not "write like X" via prompt engineering — actual style extraction: vocabulary distribution, sentence length entropy, profanity scoring, emoji patterns, structural templates.
Training takes 5 minutes per author. Output: posts so close to original style that GPT 5.5 (in adversarial testing) couldn't reliably classify which posts were AI-generated vs human-written.
Built for Telegram channel admins burning out from daily posting, but the technique generalizes to any author with sufficient post history.
Anyone here tackled the "voice replication" problem differently? Curious how others approach style extraction beyond simple fine-tuning.

How We Built 1,000+ AI Personas for Telegram Comments

PersonymAi — Wed, 15 Apr 2026 15:14:56 +0000

We tried hiring humans to write comments on 20+ Telegram channels. Four teams, $200/week total. It lasted three months before human factor killed it — boredom, inconsistency, no-shows.

So we built an AI system using TDLib (Telegram's official C++ library, not Telethon) that generates contextual comments from 1,000+ unique personas. Each persona has persistent personality traits, opinion drift over time, and natural language quirks including typos and slang. 65-85% of generated comments are threaded replies where personas argue, agree, and reference real-time market data from BingX, CoinGlass, and CoinGecko.

The hardest technical challenge wasn't NLP — it was keeping accounts alive. Telegram's anti-bot systems are aggressive, and we had to build natural typing delays, randomized activity patterns, and session management on TDLib to avoid bans.

We also built ModerAI — a 15-layer anti-spam pipeline with 99.7% accuracy, including voice spam transcription and Vision AI for image spam. Have you tried building anti-spam for Telegram? What approach worked for you?

Transparent Moderation: We Now Show Why We Ban

PersonymAi — Sat, 11 Apr 2026 16:39:40 +0000

Today we rolled out a significant improvement to PersonymAi Moderator.
Every time a user is banned, the system now displays a detailed banner containing:
• The user who was banned
• The exact reason for the ban
• Spam Score (0–100%)
The message itself automatically disappears after 60 seconds to keep the chat clean, while the full information remains visible to admins.
This gives administrators complete clarity into the moderation logic without compromising chat cleanliness.
No more guessing. No black boxes. Just transparent, explainable moderation.

Transparent Moderation: We Now Show Why We Ban

PersonymAi — Sat, 11 Apr 2026 16:33:53 +0000

How We Detect Reaction Spam in Telegram Using Behavioral Scoring

PersonymAi — Tue, 07 Apr 2026 14:19:41 +0000

Most Telegram anti-spam bots are built around one assumption: spammers
write messages. So we match text against patterns, run it through NLP,
check for suspicious links. But what happens when the spammer sends no
text at all?

Reaction spam is exactly that. A bot joins your group silently, then
floods every post with 🤡, 18+, and gambling emojis — harming your
channel's reputation without triggering a single keyword filter.

Our approach at ModerAI: instead of analyzing message content, we score
behavioral signals. Things like — does this user react but never comment?
Are they reacting from outside the group via channel post comments? Does
their bio contain obfuscated text patterns? Each signal contributes to a
spam probability score. Cross a threshold — you get restricted.
No text needed.

What behavioral signals have you found most reliable for detecting
non-text spam in group chats?

Building a Product You Can Never Demo Publicly

PersonymAi — Mon, 06 Apr 2026 17:49:37 +0000

What happens when your product's core value proposition requires absolute secrecy about who uses it?

At PersonymAI, we built an AI system that generates natural Telegram comments using 1,000+ unique personas — each with distinct writing styles, opinions, and behavioral patterns. The result? Neither marketers nor advertisers can tell it apart from real conversation.

Our clients use this to maintain active-looking communities and sell advertising. But this creates a fundamental marketing paradox: showing a real client channel as a case study would immediately undermine the product's value for that client.

We chose NDA over growth metrics. Every client is protected. No public channels, no named testimonials, no before/after reveals.

How do you market a product that works best when nobody knows it exists? Curious to hear how others in the AI space handle similar transparency trade-offs.

Implementing 3-Tier Moderation for Telegram Bots

PersonymAi — Tue, 31 Mar 2026 14:23:04 +0000

Binary spam detection (spam or not spam) breaks down in active communities. A forwarded giveaway could be spam or a legitimate user sharing excitement. A message saying "write me" could be a scam CTA or an angry user. We rebuilt our Telegram moderation pipeline into three action tiers using AI for intent classification: tier 1 (ban) for clear spam with profit intent, tier 2 (mute + admin buttons) for ambiguous cases, and tier 3 (3-strike warnings) for links and forwards. The system also auto-detects chat language from linked channel posts using character-set heuristics (їєґ → Ukrainian, ыэъ → Russian). How do you handle the gray area between spam and legitimate messages in your moderation systems?

Connecting a Context-Aware Telegram Moderation Bot in 5 Steps

PersonymAi — Mon, 30 Mar 2026 10:00:11 +0000

Most Telegram moderation bots run on keyword blocklists — easy to bypass, high false-positive rate, zero context awareness. ModerAI takes a different approach: you describe your group's topic in plain text, and the NLP pipeline uses that as a context window when classifying messages. The 15-layer AI stack handles the rest — no rule-building, no regex. How does a natural-language topic description feed into a spam classification pipeline at scale?

Building a Fairer Anti-Spam System: How We Handle Links, Warnings, and New Chats

PersonymAi — Sat, 28 Mar 2026 10:54:40 +0000

just shipped three changes to our Telegram anti-spam bot (ModerAI) that fundamentally change how we handle edge cases. Here's what we built and why.

The Problem With Binary Decisions
Most anti-spam bots make binary decisions: spam or not spam. Ban or allow.

This creates two failure modes:

False positives — legitimate users banned for having a link in their bio
False negatives — spammers who learn the rules and work around them
We needed a middle ground.

Change 1: Contextual Bio Link Analysis

Before:
if "t.me/" in user.bio:
ban(user) # crude but effective... and unfair

After:
link_target = analyze_link_context(user.bio)
if link_target.category in ["spam_channel", "scam", "adult"]:
ban(user)
elif link_target.category in ["game_referral", "personal_channel", "community"]:
allow(user) # legitimate use case

AI analyzes what the link actually points to. A Hamster Kombat referral? Fine. A channel selling "guaranteed 500% returns"? Ban.

Change 2: Progressive Warning System
Instead of ban-on-first-offense, we implemented a 3-strike system:

Strike 1: delete message + warn ("у вас ещё 2 попытки")
Strike 2: delete message + warn ("у вас ещё 1 попытка")
Strike 3: ban

Exception: edited message → instant ban (no strikes)

The edit detection is key. Spammers who post "Hello everyone!" then edit to a scam link 5 minutes later get zero warnings. This pattern is always intentional.

Change 3: Fresh Chat Grace Period
When ModerAI connects to a new chat, it has zero context. Every user is "unknown."

Aggressive bio scoring on day 1 would ban half the existing members. So we added a 48-hour grace period:

chat_age = now() - chat.connected_at

if chat_age < 48_hours:
# Relaxed mode: skip suspicious bio scoring
# Still ban critical threats (adult, drugs, obvious scam)
if threat_level == "critical":
ban(user)
else:
allow(user) # gather data first
else:
# Normal mode: full scoring pipeline
run_full_analysis(user)
After 48 hours, the bot has enough context to make accurate decisions.

Results
These changes reduced false positive rate from ~0.3% to ~0.1% while maintaining 99.7% spam detection.

The key insight: fairness and accuracy aren't opposites. A system that gives legitimate users the benefit of the doubt can still be ruthless with actual spammers — you just need smarter decision-making, not stricter rules.

ModerAI: $9/month per chat. 7-day free trial.

→ personym-ai.com/moderator-ai

Questions about the implementation? Happy to discuss in the comments.

How We Built Voice and Image Spam Detection for Telegram (Technical Deep Dive)

PersonymAi — Fri, 27 Mar 2026 12:59:39 +0000

Last month we shipped three features that no other Telegram anti-spam bot has: voice message analysis, image spam detection, and anti-masking intelligence. Here's how we built them.

The Problem
Spammers evolved. Our text-based pipeline was catching 99.7% of text spam. So spammers stopped using text.

Voice messages with gambling/scam ads
Images with overlaid promotional text
Text with emoji inserted between every character
Traditional keyword and even AI text analysis is blind to all three.

Voice Message Pipeline
Architecture:

Voice message received
→ Download .ogg file from Telegram API
→ Transcribe (speech-to-text)
→ Feed transcript into existing anti-spam pipeline
→ Same AI context analysis as text messages
→ Decision: ban / warn / allow

Key decisions:

We transcribe everything under 5 minutes (covers 99% of spam voice notes)
Transcription runs async — doesn't block the moderation pipeline
The transcript gets the same 8-layer analysis as text: whitelist → global ban → reputation → trust → fingerprint → rules → AI context → decision
Language detection handles Russian, Ukrainian, and English voice messages
The result: a 15-second voice note saying "free betting tips, guaranteed profit" gets transcribed, classified as gambling spam, and the user gets banned — all within 3-5 seconds.

Image Spam Detection
Architecture:

Image received (photo or document)
→ Send to Vision AI
→ Analyze: is there promotional/spam text in the image?
→ If spam detected → classify category → ban
→ If clean → allow

What Vision AI catches:

Screenshots of fake profit/portfolio charts with channel links
Photos with overlaid text advertising gambling/crypto
Casino/betting ad graphics
Profile avatars that are literally advertisements
We only trigger Vision analysis on images from untrusted users (trust score below threshold). Trusted members' images pass through without Vision analysis — saves cost and reduces latency.

Anti-Masking
Spammers discovered they could bypass keyword filters with tricks like:

З🎰а🎰р🎰а🎰б🎰о🎰т🎰о🎰к (emoji between letters)
3аработок (number 3 instead of letter З)
Зaрaботок (Latin 'a' instead of Cyrillic 'а')
Our approach:

Raw message text
→ Strip emoji and special characters
→ Normalize Unicode (Cyrillic/Latin homoglyphs)
→ Normalize number→letter substitutions
→ Feed cleaned text into AI analysis
→ AI evaluates meaning, not characters

The AI layer is the key — even after normalization, context matters. "Заработок" in a freelance group is normal. "Заработок" in a cooking group is spam. Same word, different context, different decision.

Updated Pipeline (10 layers)

Whitelist check → free, 0ms
Global ban check → free, 0ms
Reputation auto-ban → free, 0ms
Trust system check → free, 0ms
Anti-masking normalization → free, 1ms
Fingerprint matching → free, 1ms
Rule-based detection → free, 0ms
Voice transcription → if voice message
Vision AI analysis → if image from untrusted user
AI context analysis → for edge cases
Decision → ban / mute / allow

Cheapest checks first. AI and Vision only for what cheaper layers can't decide.

Results
Voice spam: from 0% detection → ~95% detection
Image spam: from 0% detection → ~90% detection
Masked text: from ~60% → ~95% detection
Overall accuracy: maintained at 99.7%
False positive rate: still near zero
What's Next
Video message analysis is on the roadmap. Spammers will try video next — we'll be ready.

→ personym-ai.com/moderator-ai
→ Try free for 7 days

How We Increased Telegram Channel Comments by 340% in 2 Weeks (Real Examples)

PersonymAi — Thu, 26 Mar 2026 08:34:40 +0000

Every Telegram admin knows the feeling. You spend hours creating a quality post. You publish it. And then... silence. Zero comments. Maybe one emoji reaction.

Your subscribers see the silence too. And they leave.

This is the cold-start problem — and it kills more Telegram channels than bad content ever will.

The Death Spiral

Here's what actually happens:

New post → 0 comments

Subscribers see empty comment section → "dead channel"

They don't engage → less reason for others to engage

Growth stalls → subscribers slowly leave

We've seen channels with 10,000+ subscribers averaging 0-2 comments per post. The content was great. The engagement was dead.

What We Tested

We connected PersonymAI to 5 channels in different niches (crypto, news, tech, motivation, entertainment) and tracked results for 2 weeks.

Setup took 10 minutes per channel:

Connected the bot

Selected persona types that match the niche

Set comment frequency and timing

Done

The Results (2 weeks)

Metric Before After Change

Avg. comments per post 1.3 5.7 +340%

Unique real commenters per week 4 17 +325%

Avg. time to first comment 4+ hours 2 minutes -99%

Subscriber retention rate 82% 94% +12%

Why It Works

The key insight: real people comment more when they see others commenting.

Nobody wants to be the first commenter on a silent post. But when there's already a discussion happening — arguments, jokes, reactions — people naturally want to join in.

That's exactly what our AI personas do. They don't post "Great content! 👍". They have actual opinions:

Crypto channel example:

Persona "Skeptic": "BTC at 95K and people are still calling for 150K? Show me the on-chain data."

Persona "Degen": "95K is nothing, we're going to 200K easy 🚀🚀🚀"

Persona "Analyst": "Support at 92.5K looks solid. If we hold above 94K on the daily close, 100K is realistic."

Real subscriber joins: "Analyst is right, the RSI is showing..."

That last line is the magic. A real person saw a discussion and wanted to participate. That would never happen under a silent post.

What Makes Personas Feel Real

Each persona has a consistent personality that evolves over time

They argue with each other (not just agree)

35%+ of comments are short (1-5 words) like real chat

They use stickers, GIFs, and reactions

They reply in threads, not just flat comments

They reference the actual post content, not generic reactions

The Unexpected Effect

After about a week, something interesting happened across all 5 channels: organic comments started growing on their own. Even on posts where AI personas hadn't commented yet, real subscribers were more active than before.

Why? Because the channel no longer felt dead. Subscribers had been retrained to expect discussions. The culture changed from "lurking" to "participating."

Niche-Specific Results

Niche Best persona types Avg. organic comment growth

Crypto/Trading Analyst + Degen + Skeptic +420%

News Hot-take + Moderate + Cynic +280%

Tech Builder + Critic + Enthusiast +310%

Motivation Supporter + Realist + Joker +190%

Entertainment Memer + Troll + Fan +380%

Crypto performed best because the audience is already opinionated — they just needed a spark.

For Admins Who Also Deal With Spam

Half of these channels also had a spam problem. While testing the comment system, we also ran ModerAI anti-spam on the groups.

Results:

99.7% spam caught

0 false bans in 2 weeks

Admins spent zero time on moderation

ModerAI has a 7-day free trial — connect it and forget about spam.

How To Try It

AI Comments: Check plans at personym-ai.com — connect your channel and see first comments within minutes.

ModerAI Anti-Spam: personym-ai.com/moderator-ai — 7-day free trial, no credit card.

If you're an admin who cares about your community — you know the difference between a dead channel and a living one. We help you make that switch.

→ personym-ai.com

Metric	Before	After	Change
Avg. comments per post	1.3	5.7	+340%
Unique real commenters per week	4	17	+325%
Avg. time to first comment	4+ hours	2 minutes	-99%
Subscriber retention rate	82%	94%	+12%

Niche	Best persona types	Avg. organic comment growth
Crypto/Trading	Analyst + Degen + Skeptic	+420%
News	Hot-take + Moderate + Cynic	+280%
Tech	Builder + Critic + Enthusiast	+310%
Motivation	Supporter + Realist + Joker	+190%
Entertainment	Memer + Troll + Fan	+380%