TGScope maps 3 million Telegram channels in an interactive visualization consuming only 90 KB

On October 21, 2026, the team behind the TGScope project published the technical documentation for a tool that solves one of the biggest problems in messaging platforms: organic content discovery. It is a semantic map that positions 3 million public Telegram channels on a single visual plane, organizing them by subject similarity. The platform's great strength, hosted for free at tgscope.io/map, is its performance engineering: the first screen of the map loads with just 90 KB of data and renders in less than a second, operating smoothly even on limited mobile connections.

The tool marks a paradigm shift in how users interact with the Telegram ecosystem, replacing traditional search based on names and exact links with exploration driven by meaning and context.

Context and background of discovery on Telegram

Historically, locating communities on Telegram relied on third-party catalogs based on static lists or the application's own native search bar, which prioritizes direct text matches. This model favors giant channels or those that practice keyword optimization tactics in their titles, burying niche creators in unreachable positions.

The TGScope project was developed to bypass this barrier through spatial artificial intelligence. The fundamental technical requirement stipulated by the engineers from the conceptual phase was strict: the map needed to open instantly in a mobile browser. This restriction immediately ruled out the market-standard approach of sending the coordinates of millions of points directly to the client, which would crash the device's processor and require megabytes of initial download. The solution required the adoption of traditional digital cartography techniques, adapted for data visualization via graphics hardware (GPUs).

Performance Engineering: Quadtrees and WebGL 2

To deliver 3 million points on mobile devices, TGScope's architecture divides georeferenced information using a data structure called a quadtree — the same progressive loading logic employed by Google Maps and OpenStreetMap.

In this model, the user's browser never downloads the entire map. Channels are sliced into tiles, where each tile stores a rigid limit of up to 4,096 channels. The root tile focuses solely on the platform's largest communities, establishing the main silhouette and the borders of major thematic categories. As the user zooms into the interface, the client dynamically requests only the smaller data blocks that intersect with the visible area of the screen.

The optimization reaches the scale of bytes. The system rigorously allocates 8 bytes per channel:

• Coordinates (X and Y): Two uint16 values (16-bit unsigned integers), determining the exact position within the corresponding tile.
• Vital metadata: One byte for the channel category, one byte for the primary language, and one extra byte classifying the subscriber range.

The application processes a binary typed array, reducing the workload on the JavaScript processor. Thus, the network budget to render the first image in the browser totals exactly 90 KB, fragmented into three initial requests: the manifest file containing indexes and categories (1.3 KB), the dictionary of navigation labels (59 KB), and the root tile itself (32 KB).

Rendering work is pushed entirely to the graphics card via WebGL 2. The software uses a single draw call per tile. Calculations regarding colors, point sizes, and language filters happen directly in the GPU shaders. This means that if a user decides to filter the entire map to display only Portuguese channels, the application changes a constant variable (uniform) on the graphics card, updating the interface instantly without any new data download.

Spatial organization based on meaning

The positioning of each point on the graph is not random, nor does it depend on the channel's social connections (such as who refers whom). TGScope does not consume subscriber lists, sent messages, or engagement data; the processing focuses surgically on the channel's static public profile.

The technical classification methodology follows a rigorous flow:

1. The indexing bot extracts the title, category, declared topics, and channel description. In the semantic weighting algorithm, topics receive a 60% weight, while the description makes up the remaining 40%. This disparity aims to mitigate noise, as administrators often use the description to post contact links, ad price tables, and URL spam, which would pollute the affinity classification.
2. The captured text data is injected into a multilingual embeddings model. This artificial intelligence translates the profile text into complex mathematical vectors. The practical result is that a news channel about technology in Portuguese and a hardware forum in Russian receive very similar vector coordinates, as their fundamental meanings are congruent, regardless of the alphabet or the absence of common words.
3. To transpose this vector of hundreds of dimensions onto the browser screen (a two-dimensional environment), the system uses the UMAP (Uniform Manifold Approximation and Projection) algorithm. UMAP preserves the proximity of highly similar points, "squashing" the data so that it becomes readable. The developers perform a 0.1% trim of extreme points positioned at the edges of the graph; without this, excessively bizarre channels or those with atypical statistical behaviors would push the vast majority of the useful ecosystem into a tiny cluster in the center of the map.

The visual taxonomy generated features labels assigned mathematically through the statistical dominance of the areas. The map subdivides the 3 million channels into exactly 25 macro-regions (such as cryptocurrencies, games, and news), unfolding into 250 intermediate themes and, at the deepest level of zoom, detailing 2,500 hyper-focused sub-themes.

Concrete operation data

The numbers extracted from the snapshot processed on the server outline the project's scope:

• Total indexed channels: 3,000,000 (all configured as public in the application).
• Data processing date: October 10, 2026.
• Technology reveal date: October 21, 2026.
• Usage cost: Free in the current web interface, accessible without the need for account creation.
• Extraction security: Channel names are not packaged in the batch downloads of tiles; they are resolved individually by the server only when the user interacts via click or mouse hover. This prevents unrestricted theft of the database through network traffic capture.

The briefing notes the existence of parallel Russian material published on the Habr aggregator referencing a project named "TGpages," which features a similar interactive map covering a "chart with 500,000 channels." Although they deal with the same analysis niche of Pavel Durov's application, these are initiatives with technically distinct sampling volumes: while the first project indexes half a million communities, TGScope covers a sample six times larger, encompassing 3 million points tracked globally.

The impact on the technology and communication sector

The launch directly affects three main groups.

For end-users, it represents a leap in auditing and discovery capabilities. The visualization immediately reveals language borders — which form their own "islands" in the virtual environment — allowing users to break out of centralized recommendation bubbles to investigate how hobby niches operate in countries with less representation in traditional search tools.

For content creators and market intelligence departments, TGScope acts as an X-ray of the competition. Brands can map their market density by identifying "semantic vacuums" (areas of the map with presumed demand but a scarcity of corresponding giant channels/points). If a brokerage wants to sponsor finance outlets, it can use the visual layers of sub-themes to find micro-influencers grouped in a connected manner, ignoring metrics corrupted by subscriber automation.

Finally, for corporate web developers, the architectural solutions disclosed in the technical paper serve as a practical framework for big data visualization in the browser (data-viz). The strategy combining 8-byte binary arrays and the delegation of rendering to WebGL shaders could impact methodologies for creating financial dashboards, geology software, and real-time analytical applications for urban infrastructure, which face performance bottlenecks similar to large listings in the HTML DOM.

The system's analytical limitations lie in its own principle of speed. Because intelligence extraction stops at the descriptive surface, the mapping completely ignores the actual activity temperature of the community. A channel categorized as sports-focused, but which in the last six months has operated primarily by publishing copyrighted material under a guise or is abandoned with ghost traffic, remains positioned side-by-side with serious sports journalism outlets of the same textual taxonomy.

What is still unknown

Despite the transparency in the technical architecture described in the October 21, 2026 material, critical operational elements remain hidden:

• Financial sustainability: Hosting 3 million multilingual embeddings and handling global traffic of dynamic API calls for username resolution requires ongoing server and bandwidth costs. The original document does not mention business report pricing plans, advertising inserted into the platform, or venture capital funding, leaving monetization as an unknown.
• Update frequency: Current data derives from a closed snapshot from October 10, 2026. It is unknown what the computational complexity is to incorporate real-time vectors, nor if there will be automatic quarterly position recalibrations as channels change purpose or shut down.
• Legal scraping limits: The document lacks express policies regarding channel privacy or opt-out guidelines (removal upon creator request), relying strictly on the public nature of the mapped URLs and profiles, which often clashes with the gray areas of European and local laws involving unsolicited metadata.

What to expect next

As reported in the original disclosure, the creators requested the involvement of the engineering community focused on tile compression. The developers hope to garner methodological contributions that allow for further reduction of record loads to values lower than the current 8 bytes per channel without causing degradation of the accuracy of X and Y coordinates.

At the same time, the interactive tools hosted in the initial version — which include language filters and individualized sharing cards — tend to be gradually appropriated by integrating platforms, possibly converting into an infrastructure in a commercial API format for marketing agencies that analyze trends within the messenger.

Frequently Asked Questions

Why are two channels that have no direct relationship together on the TGScope map? Because the positioning is not based on recommendations or link exchanges, but on the vector processing (embeddings) of the main topic. If the descriptions address the same fundamental topic — regardless of language or if they have never cited each other — the system considers them semantically identical and positions them side-by-side.

Don't I need a powerful phone to load such a detailed chart? No. The interface consumes about 90 KB initially, downloading strictly the geographic block (root tile) in focus. All formatting and point coloring processing occurs rapidly using dedicated hardware routines for graphics acceleration via WebGL 2 technology, without overloading the portable device's memory.

How does the map handle private data and deleted messages? The tool strictly reads and categorizes titles, predefined categories, and the public descriptive paragraphs openly available to any unauthenticated Telegram visitor. The index does not scan closed groups, personal profiles, history messages, or the platform users' databases.