Adopter — The Complete Guide
Instead of scrolling channels all day — an agent that filters
Adopter is an autonomous AI agent that serves as my personal research assistant that never sleeps. Its mission is simple but critical in 2026: filter the daily flood of new AI, tech, and business content and decide what deserves my time and what is noise. Here is how it works in practice: Adopter follows a curated set of professional Telegram channels (currently four in my setup) in the simplest possible way — reading the public web page Telegram publishes for every open channel (no bot, no account, no login) — and sends each post to Gemini 2.5 Flash (Google's fast AI model with a generous free tier) for a quick review against four critical questions: 'how new is it?', 'how accurate?', 'can I act on it?', and 'is there risk here?'. Only posts that clear all four questions with a high score get stored in Qdrant (the network's smart memory store); the rest are dropped. Full disclosure: after a recent server migration, part of Adopter's pipeline is being rebuilt on my side — but the principles in this guide are exactly what it is built on. For you, it can point at any other content source: RSS feeds, Discord channels, Reddit forums, Twitter, mailing lists — any content firehose that needs a smart AI-based filter.
What this guide covers
What is Adopter? A personal research assistant that never stops
An automatic service that reads on your behalf, filters, and surfaces only what is truly worth your time
Adopter is an autonomous agent — a piece of software that runs in the background with no hand-holding — that I put in charge of the most tiring task in 2026 tech: tracking professional Telegram broadcast channels run by practitioners and deciding, for me, what is worth an hour and what is noise. It is written in Python, triggered by cron (the operating system's job scheduler — essentially a clock that fires it on the hour, every hour), sends every post to Gemini Flash (Google's fast, free LLM) which grades it across four criteria, and stores only the best items inside a Qdrant collection — a semantic database that remembers by meaning rather than by keyword.
The full flow — the six steps from channel to memory
The journey of a Telegram post, from the moment it is written to the moment it reaches you as an insight
This flow is a pipeline — a sequence of stages that every post passes through, one after another, until it either lands in long-term memory or is discarded. Each stage is simple on its own; the magic is in the chain. Let's walk through it end to end.
The classifier — how Adopter decides what is truly 'interesting'
The craft of prompt engineering combined with learning from your own feedback
The classifier is the beating heart of Adopter. It is the written instruction that Gemini Flash receives for every post. Classification is the technical term for automatically sorting content into predefined buckets — the same way email services sort your inbox into primary, social, or promotions. This prompt is dynamic — it improves over time because you are teaching it what counts as a miss and what counts as a hit.
Circuit Breaker — the gatekeeper that prevents overflow
Five adoptions a day is the ceiling — and that is what keeps the system sane
A circuit breaker is a metaphor borrowed from electricity — the automatic switch in your fuse box that trips under load and prevents a fire. We adopted the same principle in software: a hard ceiling that kicks in the moment something crosses the allowed threshold. In Adopter the ceiling is five adoptions per day. Without it, a single 'noisy' day on one channel could dump 50 items into memory and bury you.
Dedup — recognising a story you have already seen
A semantic search before every adoption — so information never duplicates itself
Dedup (short for deduplication) is arguably Adopter's best-kept secret. In a world where many Telegram channels cover the same news cycle, any new tool will show up in 6 different places within two hours. Without this step you would see the same insight six times. With it, you see it once — and it is linked to every source that mentioned it.
Advanced tips — lessons from real-world operation
The nuances that separate a demo project from a system that lives for the long haul
After a stretch of continuous operation, plenty of tiny adjustments to the prompt and thresholds — and a server migration that forced me to rebuild part of the pipeline — these are the things I wish I had known before I started. Every line here is worth hours of trial and error.
