mirror of
https://github.com/mruwnik/memory.git
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250 lines
18 KiB
Python
250 lines
18 KiB
Python
import hashlib
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import pathlib
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from bs4 import BeautifulSoup
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from markdownify import markdownify
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from PIL import Image
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DATA_DIR = pathlib.Path(__file__).parent
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SAMPLE_HTML = f"""
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<html>
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<body>
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<h1>The Evolution of Programming Languages</h1>
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<p>Programming languages have undergone tremendous evolution since the early days of computing.
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From the machine code and assembly languages of the 1940s to the high-level, expressive languages
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we use today, each generation has built upon the lessons learned from its predecessors. Languages
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like FORTRAN and COBOL pioneered the concept of human-readable code, while later innovations like
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object-oriented programming in languages such as Smalltalk and C++ revolutionized how we structure
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and organize our programs.</p>
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<img src="{DATA_DIR / "lang_timeline.png"}" alt="Timeline of programming language evolution" width="600" height="400">
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<p>The rise of functional programming paradigms has brought mathematical rigor and immutability
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to the forefront of software development. Languages like Haskell, Lisp, and more recently Rust
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and Elm have demonstrated the power of pure functions and type systems in creating more reliable
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and maintainable code. These paradigms emphasize the elimination of side effects and the treatment
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of computation as the evaluation of mathematical functions.</p>
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patch
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<p>Modern development has also seen the emergence of domain-specific languages and the resurgence
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of interest in memory safety. The advent of languages like Python and JavaScript has democratized
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programming by lowering the barrier to entry, while systems languages like Rust have proven that
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performance and safety need not be mutually exclusive. The ongoing development of WebAssembly
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promises to bring high-performance computing to web browsers in ways previously unimaginable.</p>
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<img src="{DATA_DIR / "code_complexity.jpg"}" alt="Visual representation of code complexity over time" width="500" height="300">
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<p>Looking toward the future, we see emerging trends in quantum programming languages, AI-assisted
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code generation, and the continued evolution toward more expressive type systems. The challenge
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for tomorrow's language designers will be balancing expressiveness with simplicity, performance
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with safety, and innovation with backward compatibility. As computing continues to permeate every
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aspect of human life, the languages we use to command these machines will undoubtedly continue
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to evolve and shape the digital landscape.</p>
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<p>The emergence of cloud computing and distributed systems has also driven new paradigms in
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language design. Languages like Go and Elixir have been specifically crafted to excel in
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concurrent and distributed environments, while the rise of microservices has renewed interest
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in polyglot programming approaches. These developments reflect a broader shift toward languages
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that are not just powerful tools for individual developers, but robust foundations for building
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scalable, resilient systems that can handle the demands of modern internet-scale applications.</p>
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<p>Perhaps most intriguingly, the intersection of programming languages with artificial intelligence
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is opening entirely new frontiers. Differentiable programming languages are enabling new forms of
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machine learning research, while large language models are beginning to reshape how we think about
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code generation and developer tooling. As we stand on the brink of an era where AI systems may
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become active participants in the programming process itself, the very nature of what constitutes
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a programming language—and who or what programs in it—may be fundamentally transformed.</p>
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</body>
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</html>
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"""
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SECOND_PAGE = """
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<div>
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<h2>The Impact of Open Source on Language Development</h2>
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<p>The open source movement has fundamentally transformed how programming languages are developed,
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distributed, and evolved. Unlike the proprietary languages of earlier decades, modern language
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development often occurs in public repositories where thousands of contributors can participate
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in the design process. Languages like Python, JavaScript, and Rust have benefited enormously
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from this collaborative approach, with their ecosystems growing rapidly through community-driven
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|
package managers and extensive third-party libraries.</p>
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<p>This democratization of language development has led to faster innovation cycles and more
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|
responsive adaptation to developer needs. When a language feature proves problematic or a new
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|
paradigm emerges, open source languages can quickly incorporate changes through their community
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|
governance processes. The result has been an unprecedented period of language experimentation
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and refinement, where ideas can be tested, refined, and adopted across multiple language
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communities simultaneously.</p>
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<p>Furthermore, the open source model has enabled the rise of domain-specific languages that
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might never have been commercially viable under traditional development models. From specialized
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|
query languages for databases to configuration management tools, the low barrier to entry for
|
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language creation has fostered an explosion of linguistic diversity in computing, each tool
|
|
optimized for specific problem domains and user communities.</p>
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<p>The collaborative nature of open source development has also revolutionized language tooling
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|
and developer experience. Modern languages benefit from rich ecosystems of editors, debuggers,
|
|
profilers, and static analysis tools, all developed by passionate communities who understand
|
|
the daily challenges faced by practitioners. This has created a virtuous cycle where better
|
|
tooling attracts more developers, who in turn contribute improvements that make the language
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|
even more accessible and powerful.</p>
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<p>Version control systems like Git have enabled unprecedented transparency in language evolution,
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allowing developers to trace the reasoning behind every design decision through detailed commit
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histories and issue discussions. This historical record serves not only as documentation but as
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a learning resource for future language designers, helping them understand the trade-offs and
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|
considerations that shaped successful language features.</p>
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<p>The economic implications of open source language development cannot be overstated. By removing
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licensing barriers and vendor lock-in, open source languages have democratized access to powerful
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programming tools across the globe. This has enabled innovation in regions and sectors that might
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otherwise have been excluded from the software revolution, fostering a truly global community of
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software creators and problem solvers.</p>
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</div>
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"""
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CHUNKS: list[str] = [
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"""The Evolution of Programming Languages
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======================================
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Programming languages have undergone tremendous evolution since the early days of computing.
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From the machine code and assembly languages of the 1940s to the high\\-level, expressive languages
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we use today, each generation has built upon the lessons learned from its predecessors. Languages
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like FORTRAN and COBOL pioneered the concept of human\\-readable code, while later innovations like
|
|
object\\-oriented programming in languages such as Smalltalk and C\\+\\+ revolutionized how we structure
|
|
and organize our programs.
|
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|
|
The rise of functional programming paradigms has brought mathematical rigor and immutability
|
|
to the forefront of software development. Languages like Haskell, Lisp, and more recently Rust
|
|
and Elm have demonstrated the power of pure functions and type systems in creating more reliable
|
|
and maintainable code. These paradigms emphasize the elimination of side effects and the treatment
|
|
of computation as the evaluation of mathematical functions.
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Modern development has also seen the emergence of domain\\-specific languages and the resurgence
|
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of interest in memory safety. The advent of languages like Python and JavaScript has democratized
|
|
programming by lowering the barrier to entry, while systems languages like Rust have proven that
|
|
performance and safety need not be mutually exclusive. The ongoing development of WebAssembly
|
|
promises to bring high\\-performance computing to web browsers in ways previously unimaginable.
|
|
|
|
Looking toward the future, we see emerging trends in quantum programming languages, AI\\-assisted
|
|
code generation, and the continued evolution toward more expressive type systems. The challenge
|
|
for tomorrow's language designers will be balancing expressiveness with simplicity, performance
|
|
with safety, and innovation with backward compatibility. As computing continues to permeate every
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aspect of human life, the languages we use to command these machines will undoubtedly continue
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to evolve and shape the digital landscape.""",
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"""
|
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As computing continues to permeate every
|
|
aspect of human life, the languages we use to command these machines will undoubtedly continue
|
|
to evolve and shape the digital landscape.
|
|
The emergence of cloud computing and distributed systems has also driven new paradigms in
|
|
language design. Languages like Go and Elixir have been specifically crafted to excel in
|
|
concurrent and distributed environments, while the rise of microservices has renewed interest
|
|
in polyglot programming approaches. These developments reflect a broader shift toward languages
|
|
that are not just powerful tools for individual developers, but robust foundations for building
|
|
scalable, resilient systems that can handle the demands of modern internet\\-scale applications.
|
|
Perhaps most intriguingly, the intersection of programming languages with artificial intelligence
|
|
is opening entirely new frontiers. Differentiable programming languages are enabling new forms of
|
|
machine learning research, while large language models are beginning to reshape how we think about
|
|
code generation and developer tooling. As we stand on the brink of an era where AI systems may
|
|
become active participants in the programming process itself, the very nature of what constitutes
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a programming language—and who or what programs in it—may be fundamentally transformed.""",
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]
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TWO_PAGE_CHUNKS: list[str] = [
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"""
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The Evolution of Programming Languages
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======================================
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Programming languages have undergone tremendous evolution since the early days of computing.
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From the machine code and assembly languages of the 1940s to the high\-level, expressive languages
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we use today, each generation has built upon the lessons learned from its predecessors. Languages
|
|
like FORTRAN and COBOL pioneered the concept of human\-readable code, while later innovations like
|
|
object\-oriented programming in languages such as Smalltalk and C\+\+ revolutionized how we structure
|
|
and organize our programs.
|
|
|
|
The rise of functional programming paradigms has brought mathematical rigor and immutability
|
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to the forefront of software development. Languages like Haskell, Lisp, and more recently Rust
|
|
and Elm have demonstrated the power of pure functions and type systems in creating more reliable
|
|
and maintainable code. These paradigms emphasize the elimination of side effects and the treatment
|
|
of computation as the evaluation of mathematical functions.
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Modern development has also seen the emergence of domain\-specific languages and the resurgence
|
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of interest in memory safety. The advent of languages like Python and JavaScript has democratized
|
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programming by lowering the barrier to entry, while systems languages like Rust have proven that
|
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performance and safety need not be mutually exclusive. The ongoing development of WebAssembly
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promises to bring high\-performance computing to web browsers in ways previously unimaginable.
|
|
|
|
Looking toward the future, we see emerging trends in quantum programming languages, AI\-assisted
|
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code generation, and the continued evolution toward more expressive type systems. The challenge
|
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for tomorrow's language designers will be balancing expressiveness with simplicity, performance
|
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with safety, and innovation with backward compatibility. As computing continues to permeate every
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aspect of human life, the languages we use to command these machines will undoubtedly continue
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to evolve and shape the digital landscape.
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""",
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"""
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As computing continues to permeate every
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aspect of human life, the languages we use to command these machines will undoubtedly continue
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to evolve and shape the digital landscape.
|
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The emergence of cloud computing and distributed systems has also driven new paradigms in
|
|
language design. Languages like Go and Elixir have been specifically crafted to excel in
|
|
concurrent and distributed environments, while the rise of microservices has renewed interest
|
|
in polyglot programming approaches. These developments reflect a broader shift toward languages
|
|
that are not just powerful tools for individual developers, but robust foundations for building
|
|
scalable, resilient systems that can handle the demands of modern internet\-scale applications.
|
|
Perhaps most intriguingly, the intersection of programming languages with artificial intelligence
|
|
is opening entirely new frontiers. Differentiable programming languages are enabling new forms of
|
|
machine learning research, while large language models are beginning to reshape how we think about
|
|
code generation and developer tooling. As we stand on the brink of an era where AI systems may
|
|
become active participants in the programming process itself, the very nature of what constitutes
|
|
a programming language—and who or what programs in it—may be fundamentally transformed.
|
|
The Impact of Open Source on Language Development
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-------------------------------------------------
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|
The open source movement has fundamentally transformed how programming languages are developed,
|
|
distributed, and evolved. Unlike the proprietary languages of earlier decades, modern language
|
|
development often occurs in public repositories where thousands of contributors can participate
|
|
in the design process. Languages like Python, JavaScript, and Rust have benefited enormously
|
|
from this collaborative approach, with their ecosystems growing rapidly through community\-driven
|
|
package managers and extensive third\-party libraries.
|
|
This democratization of language development has led to faster innovation cycles and more
|
|
responsive adaptation to developer needs. When a language feature proves problematic or a new
|
|
paradigm emerges, open source languages can quickly incorporate changes through their community
|
|
governance processes. The result has been an unprecedented period of language experimentation
|
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and refinement, where ideas can be tested, refined, and adopted across multiple language
|
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communities simultaneously.""",
|
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"""
|
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The result has been an unprecedented period of language experimentation
|
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and refinement, where ideas can be tested, refined, and adopted across multiple language
|
|
communities simultaneously.
|
|
Furthermore, the open source model has enabled the rise of domain\-specific languages that
|
|
might never have been commercially viable under traditional development models. From specialized
|
|
query languages for databases to configuration management tools, the low barrier to entry for
|
|
language creation has fostered an explosion of linguistic diversity in computing, each tool
|
|
optimized for specific problem domains and user communities.
|
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The collaborative nature of open source development has also revolutionized language tooling
|
|
and developer experience. Modern languages benefit from rich ecosystems of editors, debuggers,
|
|
profilers, and static analysis tools, all developed by passionate communities who understand
|
|
the daily challenges faced by practitioners. This has created a virtuous cycle where better
|
|
tooling attracts more developers, who in turn contribute improvements that make the language
|
|
even more accessible and powerful.
|
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Version control systems like Git have enabled unprecedented transparency in language evolution,
|
|
allowing developers to trace the reasoning behind every design decision through detailed commit
|
|
histories and issue discussions. This historical record serves not only as documentation but as
|
|
a learning resource for future language designers, helping them understand the trade\-offs and
|
|
considerations that shaped successful language features.
|
|
The economic implications of open source language development cannot be overstated. By removing
|
|
licensing barriers and vendor lock\-in, open source languages have democratized access to powerful
|
|
programming tools across the globe. This has enabled innovation in regions and sectors that might
|
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otherwise have been excluded from the software revolution, fostering a truly global community of
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software creators and problem solvers.
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""",
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]
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SAMPLE_MARKDOWN = markdownify(SAMPLE_HTML)
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SAMPLE_TEXT = BeautifulSoup(SAMPLE_HTML, "html.parser").get_text()
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SECOND_PAGE_MARKDOWN = markdownify(SECOND_PAGE)
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SECOND_PAGE_TEXT = BeautifulSoup(SECOND_PAGE, "html.parser").get_text()
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def image_hash(image: Image.Image) -> str:
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return hashlib.sha256(image.tobytes()).hexdigest()
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LANG_TIMELINE = Image.open(DATA_DIR / "lang_timeline.png")
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CODE_COMPLEXITY = Image.open(DATA_DIR / "code_complexity.jpg")
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LANG_TIMELINE_HASH = image_hash(LANG_TIMELINE)
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CODE_COMPLEXITY_HASH = image_hash(CODE_COMPLEXITY)
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