π Building a Telegram AI that actually feels more like me
This project is still a work in progress, but the goal has changed from simply building an AI auto-responder to something more interesting: Making the AI understand how I communicate and gradually make its responses feel more like my own.
I don't want it to just answer correctly. I want it to understand the context, relationship, tone, wording, and style behind a conversation.
π§ What I'm currently working on:
Personal response style
- Using examples from my real Telegram conversations to help the model understand how I naturally write, including my Amharic-Latin mix, slang, short replies, and casual expressions.
Context + memory
- Combining recent conversation history with contact-specific memories so the response isn't generated from the current message alone.
RAG for conversational style
- Retrieving relevant examples from my previous conversations and using them as context rather than simply telling the model "talk like me."
Deterministic routing
- Not every message needs an LLM. Some situations can be handled directly, making responses faster and avoiding unnecessary AI-generated replies.
Response validation
- I'm also working on catching responses that feel too AI-like, repetitive, unnatural, or inconsistent with how I would actually respond.
Model routing
- Different models behave differently when it comes to reasoning, system-prompt adherence, and natural conversation, so I'm testing model selection as part of the architecture.
βοΈ Current stack
- Node.js
- GramJS / MTProto
- @xenova/transformers
- MiniLM
- RAG
- Contact Memory
- Custom LLM Gateway
The interesting challenge isn't:
It's:
That's the part I'm experimenting with now. Still building. Still testing. Still finding things that sound way too AI π If you have idea let me know it might be helpful for my project.
ππ Silicon Valley
π₯ More of the architecture and experiments coming soon.
#NodeJS #Telegram #GramJS #MTProto #RAG #AI #LLM #AgenticAI #Project
This project is still a work in progress, but the goal has changed from simply building an AI auto-responder to something more interesting: Making the AI understand how I communicate and gradually make its responses feel more like my own.
I don't want it to just answer correctly. I want it to understand the context, relationship, tone, wording, and style behind a conversation.
π§ What I'm currently working on:
Personal response style
- Using examples from my real Telegram conversations to help the model understand how I naturally write, including my Amharic-Latin mix, slang, short replies, and casual expressions.
Context + memory
- Combining recent conversation history with contact-specific memories so the response isn't generated from the current message alone.
RAG for conversational style
- Retrieving relevant examples from my previous conversations and using them as context rather than simply telling the model "talk like me."
Deterministic routing
- Not every message needs an LLM. Some situations can be handled directly, making responses faster and avoiding unnecessary AI-generated replies.
Response validation
- I'm also working on catching responses that feel too AI-like, repetitive, unnatural, or inconsistent with how I would actually respond.
Model routing
- Different models behave differently when it comes to reasoning, system-prompt adherence, and natural conversation, so I'm testing model selection as part of the architecture.
βοΈ Current stack
- Node.js
- GramJS / MTProto
- @xenova/transformers
- MiniLM
- RAG
- Contact Memory
- Custom LLM Gateway
The interesting challenge isn't:
"How do I make an AI reply to my Telegram messages?"
It's:
"How do I make an AI understand enough about me that its response feels like something I would actually send?"
That's the part I'm experimenting with now. Still building. Still testing. Still finding things that sound way too AI π If you have idea let me know it might be helpful for my project.
ππ Silicon Valley
π₯ More of the architecture and experiments coming soon.
#NodeJS #Telegram #GramJS #MTProto #RAG #AI #LLM #AgenticAI #Project
π₯2
Forwarded from Launch.et
β¨ Just listed: Veloxdb
lightweight open-source SQL client with a visual database designer with AI
Work & Productivity Β· by Veloxdb
π See it on Launch.et
ββββββββββ
Published: May 2026
Built something? Launch it
#WorkProductivity #Postgress #AdminTool #LaunchET
lightweight open-source SQL client with a visual database designer with AI
Work & Productivity Β· by Veloxdb
π See it on Launch.et
ββββββββββ
Published: May 2026
Built something? Launch it
#WorkProductivity #Postgress #AdminTool #LaunchET
Forwarded from EthioDeploy
Introducing Domain marketplace
A domain marketplace is now built into EthioDeploy.
Search, register, and point a domain at your app , without leaving your dashboard. Priced in Birr, no international card needed.
π ethiodeploy.com/domains
A domain marketplace is now built into EthioDeploy.
Search, register, and point a domain at your app , without leaving your dashboard. Priced in Birr, no international card needed.
π ethiodeploy.com/domains
Forwarded from Laravel News
NativePHP v4: Build Native iOS and Android UI in Blade https://laravel-news.com/nativephp-v4-supernative
Laravel News
NativePHP v4: Build Native iOS and Android UI in Blade
NativePHP v4 renders Blade components as real SwiftUI and Jetpack Compose views with no web view. Here is what SuperNative changes, plus how to upgrade.
Afro Dev πͺπΉπ§βπ»
π Building a Telegram AI that actually feels more like me This project is still a work in progress, but the goal has changed from simply building an AI auto-responder to something more interesting: Making the AI understand how I communicate and graduallyβ¦
π TG-Echo is now open source
A while back I posted about building a Telegram AI that feels more like me: deep personalization, RAG on my own chat history, trying to make an LLM sound genuinely like Bilal.
Update: that part didn't survive contact with reality. The style-RAG kept leaking AI-generated replies back into its own vector store, indexing was too flat to separate contacts or context, and I had hardcoded phrases patching over gaps a real personalization system shouldn't need. Instead of shipping something that fakes "sounding like me," I stripped it back and rebuilt the core agent properly.
What TG-Echo is now:
A 100% local, MTProto-based (GramJS) auto-reply engine for your personal Telegram DMs. No Bot API, no webhooks, no port forwarding, just your own account.
π§ Core pieces:
- Unified context engine: merges Telegram cloud history with in-memory session state into clean chronological turns for the LLM
- Zero hardcoded responses, zero fake RAG "facts," zero keyword-override shortcuts. Output is genuinely LLM-generated
- Security firewall: allowlist/blocklist + sensitive-keyword gating (money, passwords, private keys) before anything reaches the model
- Human typing simulation + multi-bubble message splitting, so replies don't read like a bot dumping a wall of text
- An AI-leak stripper in post-processing to catch responses that sound too obviously AI-generated
- Pairs with my AI-Gateway project for free multi-provider LLM routing across 9+ providers, so it can run 24/7 at zero API cost
The "make it sound like me" work isn't dead, it's just not something I'm willing to ship half-working. That layer needs a cleaner separation between AI-generated and human-authored data in the vector store, and real per-contact indexing instead of one flat pool. That's next.
For now, the base agent is solid, it's open source, and it's ready for people to build on.
π github.com/bilalshemsu1/TG-Echo
Pairs well with: github.com/bilalshemsu1/ai-gateway
#NodeJS #Telegram #GramJS #MTProto #OpenSource #AI #LLM #AgenticAI #Project
A while back I posted about building a Telegram AI that feels more like me: deep personalization, RAG on my own chat history, trying to make an LLM sound genuinely like Bilal.
Update: that part didn't survive contact with reality. The style-RAG kept leaking AI-generated replies back into its own vector store, indexing was too flat to separate contacts or context, and I had hardcoded phrases patching over gaps a real personalization system shouldn't need. Instead of shipping something that fakes "sounding like me," I stripped it back and rebuilt the core agent properly.
What TG-Echo is now:
A 100% local, MTProto-based (GramJS) auto-reply engine for your personal Telegram DMs. No Bot API, no webhooks, no port forwarding, just your own account.
π§ Core pieces:
- Unified context engine: merges Telegram cloud history with in-memory session state into clean chronological turns for the LLM
- Zero hardcoded responses, zero fake RAG "facts," zero keyword-override shortcuts. Output is genuinely LLM-generated
- Security firewall: allowlist/blocklist + sensitive-keyword gating (money, passwords, private keys) before anything reaches the model
- Human typing simulation + multi-bubble message splitting, so replies don't read like a bot dumping a wall of text
- An AI-leak stripper in post-processing to catch responses that sound too obviously AI-generated
- Pairs with my AI-Gateway project for free multi-provider LLM routing across 9+ providers, so it can run 24/7 at zero API cost
The "make it sound like me" work isn't dead, it's just not something I'm willing to ship half-working. That layer needs a cleaner separation between AI-generated and human-authored data in the vector store, and real per-contact indexing instead of one flat pool. That's next.
For now, the base agent is solid, it's open source, and it's ready for people to build on.
π github.com/bilalshemsu1/TG-Echo
Pairs well with: github.com/bilalshemsu1/ai-gateway
#NodeJS #Telegram #GramJS #MTProto #OpenSource #AI #LLM #AgenticAI #Project
Forwarded from Discover β’ Tech News
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A Brazilian X user said his father's friend wanted to buy mini dinosaurs from a WhatsApp ad, but his family stopped him. The price was 500 Brazilian reais (about $100).
The obviously AI-generated video, complete with a Gemini watermark, raised no suspicion.
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β€1
Discover β’ Tech News
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π1π1
Exploring Machine Learning with PHP
I've been spending some time exploring machine learning from a PHP perspective.
Usually, when people talk about ML, the first tools that come to mind are Python, scikit-learn, PyTorch, or TensorFlow. But PHP has some interesting options too.
I've been looking into libraries such as:
- PHP-ML for common machine learning algorithms and preprocessing
- Rubix ML for building more complete ML workflows in PHP
- FANN for neural network functionality through PHP bindings
I'm not saying PHP is going to replace Python for machine learning. It isn't.
But I think it's interesting to see how ML capabilities can be integrated directly into PHP applications, especially when you're already building backend systems with Laravel.
For me, the goal isn't to learn every ML library. It's to understand where these tools actually make sense and how they can be used in real applications.
Still experimenting and learning. π
#PHP #Laravel #MachineLearning #PHPML #RubixML #SoftwareDev
I've been spending some time exploring machine learning from a PHP perspective.
Usually, when people talk about ML, the first tools that come to mind are Python, scikit-learn, PyTorch, or TensorFlow. But PHP has some interesting options too.
I've been looking into libraries such as:
- PHP-ML for common machine learning algorithms and preprocessing
- Rubix ML for building more complete ML workflows in PHP
- FANN for neural network functionality through PHP bindings
I'm not saying PHP is going to replace Python for machine learning. It isn't.
But I think it's interesting to see how ML capabilities can be integrated directly into PHP applications, especially when you're already building backend systems with Laravel.
For me, the goal isn't to learn every ML library. It's to understand where these tools actually make sense and how they can be used in real applications.
Still experimenting and learning. π
#PHP #Laravel #MachineLearning #PHPML #RubixML #SoftwareDev
Afro Dev πͺπΉπ§βπ»
PAYMENT-SYSTEM-GUIDE.md
Laravel + Stripe + Chapa: A Complete AβZ Payment Integration Guide π³
I built a subscription payment system on Laravel 13 and documented every step so anyone can rebuild it from zero
What's inside (25 sections):
- π³ Stripe subscriptions with hosted Checkout (test cards included)
- π‘ Webhooks done right β HMAC signing, local testing with Stripe CLI, idempotent event handling
- π 13 payment models with copy-paste code: one-time payments, trials, upgrades/downgrades, coupons, invoices, refunds, customer portal, failed-payment dunning, metered billing & more
- πͺπΉ Full Chapa (Ethiopian gateway) integration β telebirr, CBE Birr, Amole, mPesa; HMAC webhook verification, test credentials, membership model (no recurring billing)
- π Real errors I hit + fixes (duplicate migrations, currency case, array message crashes, email validation, config path pitfalls)
- β Production deployment checklist & security best practices
Full guide in the attached file. Feedback welcome!
Doc: https://t.me/AfroCoders/1165
#Guide #Tip #Doc #Laravel #Stripe #Chapa #Payments
I built a subscription payment system on Laravel 13 and documented every step so anyone can rebuild it from zero
What's inside (25 sections):
π§ Full setup: Laravel β Laravel Cashier β Stripe dashboard β migrations β models β controllers β routes β Blade views- π³ Stripe subscriptions with hosted Checkout (test cards included)
- π‘ Webhooks done right β HMAC signing, local testing with Stripe CLI, idempotent event handling
- π 13 payment models with copy-paste code: one-time payments, trials, upgrades/downgrades, coupons, invoices, refunds, customer portal, failed-payment dunning, metered billing & more
- πͺπΉ Full Chapa (Ethiopian gateway) integration β telebirr, CBE Birr, Amole, mPesa; HMAC webhook verification, test credentials, membership model (no recurring billing)
- π Real errors I hit + fixes (duplicate migrations, currency case, array message crashes, email validation, config path pitfalls)
- β Production deployment checklist & security best practices
Full guide in the attached file. Feedback welcome!
Doc: https://t.me/AfroCoders/1165
#Guide #Tip #Doc #Laravel #Stripe #Chapa #Payments
Telegram
Afro Dev πͺπΉπ§βπ»
β€2
βοΈFor developers who need a Visa/Mastercard for developer-tool verification
If you're trying to activate free trials or verify payment methods on some developer/AI tools but don't have an international card, you can check out Grey: https://app.grey.co/
Grey provides virtual cards that can be used for online payments and, depending on the service and card eligibility, payment verification.
Important: Not every service accepts virtual/prepaid cards, and card eligibility depends on your country and Grey account.
Don't use it to bypass a service's payment restrictions or create multiple trial accounts. Use your own verified account and follow the service's terms.
If you've successfully used Grey for a specific developer tool, share your experience with the community so others know what works.
If you're trying to activate free trials or verify payment methods on some developer/AI tools but don't have an international card, you can check out Grey: https://app.grey.co/
Grey provides virtual cards that can be used for online payments and, depending on the service and card eligibility, payment verification.
Important: Not every service accepts virtual/prepaid cards, and card eligibility depends on your country and Grey account.
Don't use it to bypass a service's payment restrictions or create multiple trial accounts. Use your own verified account and follow the service's terms.
If you've successfully used Grey for a specific developer tool, share your experience with the community so others know what works.
Grey
Grey β International Money Transfers & Foreign Bank Accounts
Open a virtual global bank account for free to receive and make foreign payments or convert currencies, all in one place.
Forwarded from Opportunity Alertsπ’
#Opportunity_Alertsπ£
πCalling Young Founders: Join the Global Innovation Challenge 2026π
β¨Are you 18β30 years old and leading a startup or project that is already creating social or environmental impact? The Social Shifters Global Innovation Challenge 2026 is calling on young impact founders from around the world to apply for grant funding and support to grow their solutions.
What You Gain:
πΉGrant funding of up to $15,000 USD
πΉFree support to strengthen your application
πΉ1-to-1 pitch coaching for finalists
πΉGlobal exposure and connections
πΉAccess to funding, fellowships, training, networking and paid opportunities
Who Can Apply?
πΈFounders aged 18β30
πΈYouth-led startups or projects with clear founder decision-making authority
πΈProjects already launched and delivering measurable social or environmental impact
πΈApplicants from any country worldwide
πΈProjects contributing to at least one UN Sustainable Development Goal (SDG)
π‘Participation Fee: Free
πApply: https://www.socialshifters.co/global-innovation-challenge/
πDeadline: August 31, 2026
"If this isn't for you, please share it with others who might be interested."π
Follow usπfor more opportunities
@opportunity_alerts
πCalling Young Founders: Join the Global Innovation Challenge 2026π
β¨Are you 18β30 years old and leading a startup or project that is already creating social or environmental impact? The Social Shifters Global Innovation Challenge 2026 is calling on young impact founders from around the world to apply for grant funding and support to grow their solutions.
What You Gain:
πΉGrant funding of up to $15,000 USD
πΉFree support to strengthen your application
πΉ1-to-1 pitch coaching for finalists
πΉGlobal exposure and connections
πΉAccess to funding, fellowships, training, networking and paid opportunities
Who Can Apply?
πΈFounders aged 18β30
πΈYouth-led startups or projects with clear founder decision-making authority
πΈProjects already launched and delivering measurable social or environmental impact
πΈApplicants from any country worldwide
πΈProjects contributing to at least one UN Sustainable Development Goal (SDG)
π‘Participation Fee: Free
πApply: https://www.socialshifters.co/global-innovation-challenge/
πDeadline: August 31, 2026
Follow usπfor more opportunities
@opportunity_alerts
π₯1