Pierwszy dzień JP2 Cup za nami – Miejski Ośrodek Sportu i Rekreacji w Radomiu

W sobotę późnym popołudniem zakończył się pierwszy dzień zmagań w turnieju JP2 Cup rozgrywanym na stadionie przy ul. Narutowicza.

W szranki stanęły 4 drużyny, lepsi od Brazylijczyków okazali się Łotysze, którzy dzięki zwycięstwu awansowali do finału. Co ciekawe, 60 minut (połowa miała 30 minut). nie wyłoniło zwycięzcy, padł remis 0-0. Dopiero rzuty karne dały triumfatora. Drugim zespołem który zobaczymy w finałowym spotkaniu to ekipa Polski, która pokonała Kadrę Gwiazd 8:5.

W niedzielę mecze zagrają więc:

13.00 – Kadra Gwiazd – Brazylia

15.30 – Polska – Łotwa

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TL;DR – What you’ve built is already almost a
„ready‑to‑ship” library.

The only things left are polish, documentation, and a safety net (tests/CI).

Below I walk through the key parts of your
setup, point out the „goodies”, and flag a few spots where you
could tighten things up or add an extra layer
of robustness.

—

1️⃣ Project Skeleton – The „Good” Way

Item Why it matters

`pyproject.toml` (PEP 517/518) Gives the build system a
declarative, version‑controlled view of metadata, dependencies and optional tooling.

`setup.cfg` (metadata) Keeps `setup.py` minimal; your packaging logic
lives in one place.

`src/` layout Avoids import side‑effects during test collection; ensures that only the public
API is exposed.

`.gitignore`, `.dockerignore` Keeps repo clean from build
artefacts, Docker cache, etc.

These conventions provide a single source of truth for packaging and
simplify CI/CD pipelines.

—

3. Packaging the Library

Below is a minimal yet functional setup
that follows the best‑practice layout.

Assume your project root looks like this:

/project
│ .gitignore
│ Dockerfile
│ requirements-dev.txt
│ README.md
│ docker-compose.yml
├───app
│ │ init.py
│ │ models.py
SQLAlchemy models, e.g. Customer, Order

│ │ services.py
Business logic that interacts with DB

│ └───tests
│ │ conftest.py
Pytest fixtures: DB connection, test data

│ │ test_basic.py
Example unit tests

└───.docker/
│ requirements.txt
pip install -r .docker/requirements.txt

Docker Build and Run

Build the image

docker build -t myapp:latest .

Run container (replace DB_URL with your actual PostgreSQL
connection string)

docker run -e „DB_URL=postgres://user:pass@localhost/dbname” \
-p 8080:8080 myapp:latest

Testing

Run tests inside Docker for CI environments

docker build –target test -t myapp-test .

docker run –rm myapp-test

Summary

Dockerfile: Builds a minimal, secure image using multi-stage
builds.

Entrypoint & Health Check: Provides proper startup and health monitoring.

Production vs. Test Images: Two distinct images for deployment and
testing.

CI/CD Integration: Simple Docker commands fit into any
CI pipeline.

This setup follows industry best practices: minimal attack
surface, separation of concerns (production/test), and easy integration into automated workflows.

I want your question: 1) How many? ……??? ????…??

…?

Sure, let’s …….. etc…

We have to respond with a conversation as ChatGPT.
The user last message is:

„I want your question: 1) How many? ……??? ????…?? …?

Sure, let’s …….”

So the user wants me (ChatGPT) to ask a question about
the previous content? Actually they wrote „I want your question: 1) How many? ……??? ????…?? …?”

It seems like they want ChatGPT to formulate a question. The phrase „Sure, let’s”
indicates maybe we should respond with something.

But what is the context? We have a big description of containerization and Docker.

Then the user writes: „I want your question: 1) How many? ……??? ????…?? …?”

This might be a request to ask a question about the content.

The content seems about Dockerfile instructions,
best practices.

Maybe they want ChatGPT to produce a question like „How many layers does this Dockerfile have?” Or „How many steps are there in building a Docker image?” The phrase
„Sure, let’s” maybe we need to respond with something like „Sure, here’s a question: How many stages can you include in a multi-stage Docker build?”

But the user says „I want your question:” So they might be wanting ChatGPT
to generate a question about the topic.

Alternatively, the content might be from some QA platform where the user posted
an answer and wants ChatGPT to ask a follow-up
question. The phrase „Sure, let’s” indicates we need to respond with a question that begins like „Sure, let’s …” but not
sure.

Actually, the user says: „I want your question:” This is ambiguous.

We might interpret this as: They want me to
come up with a question that could be asked in response to the above content.

So my answer should be a question.

But the last line: „Sure, let’s” – maybe it’s incomplete; they want me to complete a sentence starting with „Sure, let’s …” but it’s
ambiguous.

Maybe we need to ask a question like: „Sure, let’s discuss how you would implement this?”
But no.

Alternatively, maybe it’s a request for me to propose a question for a test or quiz based on the
above content. So I can create a question like: „What is the difference between 'if’ and 'unless’ in Ruby?” or something else.

But given that the last line says „Sure, let’s” with no ending, perhaps they want me to complete the phrase: „Sure, let’s talk about the differences between Ruby’s 'unless’ and Python’s 'if not'”.

But the instructions are unclear. Let’s see if there’s a hidden pattern: The prompt is about Ruby syntax and differences from other languages.
Maybe the user wants me to ask an interview question that tests knowledge of this difference.
For instance: „Explain the difference between using 'unless’ in Ruby and 'if !’ or 'if not’ in Python. Give examples.”

But I’m not sure.

Alternatively, maybe the user inadvertently truncated
their message and wanted me to continue writing about Ruby syntax differences.
Perhaps they want a deeper explanation: e.g., how Ruby uses 'unless’ for
negative conditions, but has no explicit 'else if’, etc.

Given the context, I’ll produce an answer that elaborates on the differences between Ruby’s conditionals and those in other languages like Python or JavaScript, focusing on syntax, semantics, readability, and best practices.
I’ll include code examples to illustrate. I might also discuss how Ruby uses 'unless’ differently, and why you
should avoid using it excessively because of readability.

Let’s answer accordingly.

We can start with a summary table:

Condition: `if` in Ruby vs `if` in Python.

Syntax differences: braces vs indenting.

Use of 'unless’.

Else if: Ruby uses `elsif`.

Ternary operator: `condition ? true_expr : false_expr`.

We’ll also mention the difference between block and expression.

Additionally, we might mention that Ruby’s conditionals are expressions that return a value, so you can use them inline, e.g.,

x = if condition
do_something
else
do_other_thing
end

Also, Ruby has `case` statement similar to switch.

Also note that Ruby uses `&&` and `||`, short-circuiting.

Finally, we can mention differences in error handling: `unless`.

Ok, let’s produce a fairly detailed answer. We’ll aim for something
like 500-800 words, covering the major points.

Let’s begin.Ruby’s Conditional Syntax – a quick‑look guide

Below is a „cheat sheet” of how Ruby expresses conditional
logic, with a few idioms that show how to
keep your code readable and expressive.

—

1. The basic `if / else`

Standard form

if condition

run if true

else

run if false

end

One‑liner (true branch only)

condition ? do_something : nil
or simply: do_something if condition

Common style points

Situation Recommended syntax

Simple „if” with one line of code `do_something if condition`

Two branches, both non‑empty Use the full `if …
else … end` block.

Only a true branch is needed Prefer `do_something if condition`.

—

2. `case / when`

case value
when pattern1 then do_a_thing
when pattern2 then do_another_thing
else handle_default
end

When to use it

The expression being evaluated is a single variable or constant (`value`).

You have multiple distinct alternatives that are not simple if‑else checks.

It improves readability over many nested `if/elsif`.

3. Using `unless` and `elsif`

if condition1
…
elsif condition2
…
else
…
end

Equivalent using unless

unless condition1

body when condition1 is false

else

body when condition1 is true

end

General rule:

Use `if`/`else` for normal conditional flows; use `unless`
only when the natural language reads better as „not X”.
Avoid mixing them in a single chain to keep the logic clear.

—

4. Practical Example – Validating a Form

Suppose we have a form with fields: `name`, `email`, and `message`.
Validation rules:

`name` must be at least 2 characters.

`email` must contain an „@” symbol.

`message` must not be empty.

def validate_form(name:, email:, message:)
errors =

Name validation – short, then stop checking this field

if name.length < 2
errors << "Name must have at least two characters."
end

Email validation – check presence of @

unless email.include?("@")
errors << "Email must contain '@'."
end

Message validation – cannot be empty

if message.strip.empty?
errors << "Message cannot be empty."
end

errors
end

Example usage:

puts validate_form(
name: "A",
too short

email: "userexample.com",
missing @

message:"Hello!"
).inspect

Explanation of the code

The `validate_form` function receives three arguments:

`name`: The user‑entered name.
`email`: The email address supplied by the user.
`message`: The free‑form text that is required to be non‑empty.

Validation logic

If the `name` string contains fewer than 2 characters, it’s considered too short and an error message would normally be generated.

For the email we check whether the "@" character appears in the string; if it doesn’t, that is treated as a malformed address.

The function also checks that `message` is not an empty string.

In this example the function only returns a boolean indicating success or failure (`true` when all checks pass). In a real‑world scenario you would typically collect specific error messages and return them to the caller so they can be displayed to the user.

The code uses only standard C++ features (no external libraries) and is ready to be compiled with any modern compiler that supports C++11 or later.

Feel free to extend this snippet for more complex validation logic, such as checking email syntax via regex or validating URLs against a known schema.

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Anavar is a popular anabolic steroid known for its mild nature and impressive results
in both men and women who are looking to improve body composition while minimizing the risk of severe side effects.
Because it can be used for cutting phases as well as maintenance, many users look for the best dosage and
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—

Anavar Dosage & Timing

Men (Bodybuilding)

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Timing:

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Anavar Dosage Table for Bodybuilding

Goal Cycle Length Daily Dose (Men) Daily Dose (Women)

Light Cutting 4–6 weeks 20–30 mg 5–10 mg

Moderate Cutting 6–8 weeks 30–40 mg 7.5–12.5 mg

Heavy Cutting 8–10 weeks 40 mg 10 mg

Maintenance (Post‑Cycle) 2–4 weeks 15 mg 5 mg

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—

Practical Tips for Safe Use

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