Channel
@fullStackDevelopment55
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
62subscribers
-1 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1002360371645 |
|---|---|
| Type | Channel |
| Username | @fullStackDevelopment55 |
| Created | Between 1 September 2024 and 31 March 2025 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 10 August 2026 |
| Last confirmed live | 30 August 2026 |
| Measurements held | 3 |
| Confirmed unchanged | 2 times, most recently 30 August 2026 |
| On Telegram | t.me/fullStackDevelopment55 |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 23 Aug 2026, 17:16 | 62 | -1 |
| 10 Aug 2026, 03:30 | 63 | no change |
| 7 Aug 2026, 14:17 | 63 | first reading |
20 posts held, back to 10 January 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 page of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 20 posts for this entry, the most recent from 1 May 2026. An engagement rate over an empty window would be a number about nothing.
Measured directly from 1 video with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
38 reactions across 15 posts, in 5 distinct kinds. The most used accounts for 39.5% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 15 | 39.5% | |
| ❤ | 9 | 23.7% | |
| 🔥 | 8 | 21.1% | |
| 🤯 | 5 | 13.2% | |
| 🤔 | 1 | 2.63% |
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 15 of the 20 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 38 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 10 January 2026 to 1 May 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
Конец ? Да, но только для этого канала. Данный ряд постов, была тестовая для этого канала, и подобного кантетна тут больше не будет, подобное перемещается на другой канал https://t.me/freyzanIT. Тут же возвращается контет связанный именно по full stack разработке.
👍2
Реализация в коде: Интепритатор(core.intepritator.py): from typing import Dict class IRInterpreter: def __init__(self): self.variables: Dict = {} self.temps: Dict = {} def get_value(self, name): if isinstance(name, (int, float)): return name if name in self.temps: return self.temps[name] if name in self.variables: return self.v…
🔥2❤1
Этап Code Generation - самый интересный этап, давайте освежим в памяти весь pipline: 0. Source code 1. Lexer 2. Parser 3. AST 4. Semantic Analysis 5. IR 6. Optimization 7. Code Generation * Есть несколько вариантов генерации: - Assembly (x86-64) - Bytecode (как у JVM) - WebAssembly - Интерпретация IR Мы возьмём вариант Интерпретация IR. IR Interpreter: выполняет инструкции по типу таких IRInstruction(op='STORE', a…
🔥1
Origin IR: IRInstruction(op='LOAD_CONST', arg1=3, arg2=None, result='t1') IRInstruction(op='STORE', arg1='t1', arg2=None, result='b') IRInstruction(op='LOAD_CONST', arg1=10, arg2=None, result='t2') IRInstruction(op='ADD', arg1='t2', arg2='b', result='t3') IRInstruction(op='STORE', arg1='t3', arg2=None, result='a') IRInstruction(op='LOAD_CONST', arg1=2, arg2=None, result='t4') IRInstruction(op='MUL', arg1='a', arg2='t…
❤1👍1🔥1🤯1
Кодавая реализация на python: 1. Constant Folding core.optimizations.constant_folder.py from core.ir import IRInstruction class ConstantFolder: def optimize(self, instructions): constants = {} optimized = [] for instr in instructions: if instr.op == "LOAD_CONST": constants[instr.result] = instr.arg1 optimized.append(instr) elif i…
👍1🔥1🤯1
1. Constant Folding тут мы заранее оптимизируем константы и их вычисления до код: int a = 2 * 3 до IR IRInstruction(op='LOAD_CONST', arg1=3, arg2=None, result='t1') IRInstruction(op='LOAD_CONST', arg1=2, arg2=None, result='t4') IRInstruction(op='MUL', arg1='t1', arg2='t2', result='t3') После код: int a = 6 После IR IRInstruction(op='LOAD_CONST', arg1=6, arg2=None, result='t1') 2. Constant Propagation Если переме…
Следующий этап "Оптимизация". Оптимизации (Compile-time optimizations) - это процесс когда мы, улучшаем скорость работы программы, на уровне компиляции, при этом сохраняем правельность работы программы. Это преобразования программы, которые: * выполняются во время компиляции * не меняют результат программы * улучшают производительность / размер / читаемость IR Мы проведём самые базовые 3 вида оптимизаций в нашем …
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Реализация на python core.ir.py @dataclass class IRInstruction: op: str arg1: str | None = None arg2: str | None = None result: str | None = None core.ir.py class IRBuilder: def __init__(self): self.instructions = [] self.temp_count = 0 def new_temp(self): self.temp_count += 1 return f"t{self.temp_count}" def build(self, nodes): for node in node…
❤1👍1🔥1🤔1
После проверки программы на осмысленные конструкции, идёт следующий этап Intermediate Representation (IR) - данный слой решает проблему, связанную с тем что AST является удобным для анализа Напомню AST: [VarDeclaration(var_type='int', name='b', value=Number(value=3)), VarDeclaration(var_type='int', name='a', value=BinaryOp(left=Number(value=10), operator='+', right=Identifier(name='b'))), VarDeclaration(var_type='i…
🔥1
Реализация на python core.semantic.py class SemanticAnalyzer: def __init__(self): self.symbol_table = {} def analyze(self, nodes): for node in nodes: self.visit(node) def visit(self, node): method_name = f"visit_{type(node).__name__}" method = getattr(self, method_name, self.generic_visit) return method(node) def generic_visit(self, node): …
🤯2👍1
После проверки структуры кода и создания AST идёт этап Семантического анализа(Semantic Analyzer) Semantic Analyzer - это процесс когда код программы, проверяется на осмысленность программы, к примеру: ———— Код: int a = b + 10; Ошибка Variable 'b' not defined ———— Код: int a = 10; int a = 20; Ошибка Variable 'a' already declared ———— и еще довольно большой раяд подобных проверок. На выходе данного layer мы получ…
👍3
реализация на python: main.py from core.lexer import Lexer from core.parser import Parser def main(): code = """ int a = 10 + b; int c = a * 2; """ lexer = Lexer(code) tokens = lexer.tokenize() for token in tokens: print(token) parser = Parser(tokens) ast = parser.parse() print(ast) if __name__ == "__main__": main() ast.py from dataclasses import dataclass…
❤2
Showing the 12 most recent of 20 posts we hold for @fullStackDevelopment55. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Names
Channels on the register whose handles appear in this channel's posts.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 23 August 2026 — this entry's latest reading, not the date you are reading this.
“Full stack dev” (@fullStackDevelopment55), 62 subscribers as measured 23 August 2026. Telegram Register, tgregister.com/channel/fullStackDevelopment55.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.