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Despite all the innovation, the 2025 survey found a widespread and costly habit: the vast majority of developers are running outdated Python versions. 83% are missing out on the latest updates, with most saying their current version works just fine or that they simply haven’t had time to upgrade. Kennedy points out that this isn’t just about missing a few new features; it’s leaving serious performance gains on the table. An upgrade from Python 3.10, for example, could make code run about 42% faster without a single change.

## Limitations / Watch‑outs **Raw CPU performance**lower than C++/Rust/Go for pure compute‑heavy workloads → mitigate via native extensions, vectorization, parallelism. **Historical concurrency (the GIL)**: limits CPU-bound multi‑threading (I/O is fine). Alternatives: asyncio, multi‑process, native offload. *Free‑threaded*builds are progressing; validate case by case. **Latency & footprint**: cold starts/memory sometimes higher than Go/Node for serverless. **Typing debt**: annotations are optional → enforce mypy/pyright in CI. **Tooling fragmentation**: multiple workflows (pip/Poetry/Hatch/PDM/uv) → standardize at the team level.

You don't like Python's use of IEEE 754 float64 for its "float" type because it's already so slow that you think Python should use a data type which better fits the expectations of primary school math training. Then to demonstrate the timing issue you give an example where you ignore the simplest, fastest, and most accurate Python solution, using a built-in function which would likely be more accurate than what's available in stock Rust. … Pathologically large numerators and denominators make rationals not "just slower" but "a lot slower". > somehow every Python implementation would be compatible It's more of a rough consensus thing than full compatibility. > Python doesn't really care about compatibility Correct, and it causes me pain every years. But do note that historic compatibility is different than cross-implementation compatibility, since there is a strong incentive for other implementations to do a good job of staying CPython compatible. … In my experience it is alright to write short scripts but complexity very quickly balloons and python projects are very often unwieldy. I would write a script in python, but only if I was positive I would never need to run or edit it again. Which is very rarely the case. This is an extraordinarly common feature among scripting languages. In fact, JS is really the odd duck out.

## The Ecosystem Fatigue Is Real React’s ecosystem is huge, and for years, that was its biggest strength. Need a router? React Router. State management? Redux, MobX, Zustand, or Context. Want server-side rendering? Hello, Next.js. But over time, this "choose your own adventure" style started to feel more like a maze. … ## React Isn’t Simple Anymore Remember when React was all about simplicity? Back in the day, you wrote class components, passed some props, and called it a day. Then hooks arrived—and they were awesome—but they also came with their own complexity. Suddenly, every blog post and tutorial was about managing `useEffect` dependencies or battling React’s rendering lifecycle. And don’t get me started on server components. They’re supposed to make things easier, but they add yet another layer to an already complicated stack. At some point, React started feeling less like a “library for building user interfaces” and more like a convoluted framework trying to be everything at once. … Thank you for your reply! That's the point of my comment - many frameworks don't offer standard packages for core functionality that is a core of many apps these days (name web app without form, router; just as an example 😄). And I guess there will be a delay in those libraries' updating when a new framework version is released? Migration from one library to another brings lots of headaches, and, again, as mentioned in your post... fragmentation - each project is unique and will have its own set of libraries. Choosing a framework these days is similar to choosing the country to live in if you're not happy with the current one 🤣 … I mean yeah it really depends on what the requirements are. I suspect form-heavy features with elaborate validation rules. There's definitely a need for that in the Svelte world, SuperForms aside. I do also think that while SvelteKit is fantastic, the philosophy of sticking to native HTML and browser APIs may be cumbersome if you want 1st party components like select fields with custom option components, date pickers, and data tables. … But mostly because, if you think about it, its the same "state management issue" rehashed into an import. WHY would you even want to explicitly sync the DOM state into an "explicit" object that is often spread out across a component tree that yet again mimics the DOM tree and every so often brings up conceptually unsolvable syncing issues? … Ilya Gorenburg • Jan 14 '25 ... I work with them almost every day. And our forms are complex, it's not about comment or contact us form - they have lots of logic (i'm not talking about validators). Talking about validation - validation attributes can't cover even big portion of the things we need. Building native validators with attributes (custom, not just by by pattern) are complex thing and it works with DOM anyway. When then re-invent a bicycle every time? Framework is made to ease the pain. Isn't it? … Agree that the community has become fragmented and that will get worse. Because most apps built with React do not need good performance, a large crowd of people have become stuck on a path that leads nowhere. This has led to the development of the React compiler in React 19. This will further facilitate this split in the community. Other frameworks like Svelte have embraced zero-performance-concern from the start. That might seem cool, but ultimately leads to terrible developers who no longer realize that they're writing JavaScript code with terrible performance, because the compiler will fix it for them. … As it happens with compiled languages, it then becomes a compiler issue. Being able to write complex logic relying on a compiler optimizing stuff for you is not a bad thing, it's something we should wish for. ... I used to be a backend developer. In 2020, I was working with a company and started with angular and what i hated most is NgModules declaration for a new frontend dev this is pure illogical import statement i have seen, first import the component provide it to `@NgModules` do the `declaration`, `imports`, `providers` etc. Also the file structure `dir/component.css`, `dir/component.html`, `dir/component.ts`. You can write three things in one when you write in basic `component.html`. … You can check svelte project that I have stared over the period: github.com/theetherGit?tab=stars

# Typing Is Very Weak Python introduced types in 2015 in Python 3.5. During my assignments I never used them. Mostly because we had the requirement to use Python 2. As I developed more and more features in my project, I forgot exactly which type of parameters or variables I needed and added types. Now, the whole code is fully typed. Still, I have many runtime issues. Coming from a similar experience where JavaScript wasn't typed and TypeScript added type I was surprised to see that typing the code does not provide much defense against bad code. Python's type is mainly a documentation and helps the code editor. … Fortunately, I have several unit tests that mitigate most of the issues. However, the problem with testing was that the tests were failing not because of assertion but because the PyTest tool couldn't run the tests in some situations because of the refactoring of broken dependencies. In other cases, the unit tests would fail before hitting the assertion because a function received a wrong object and properties were missing. Another pitfall with type is asynchronous functions. Missing an `await` generates an exception instead of automatically giving the developer a hint that this does not work. In the end, the problem is that once it works, it works but nothing prevents someone from modifying the return of a function and still having the code looking fine until at runtime it fails. While unit tests might mitigate some issues, there are many scenarios where it continues to be precarious. Going back to the change of the return type, the unit tests of the modified function might fail and will require changes but every place where the code invokes the function is now failing. … # Conclusion Overall, the typing situation looks problematic as a project grows, especially if you have not written a part (or don't remember). The project is only about 7,000 lines of code, and I feel some pain and hesitation when refactoring. With a stronger typing system, moving files from one folder to another would let the code editor manage the reference automatically.

Python remains a powerhouse in 2025, driving AI, web apps, and data science. But as the language evolves, so do the pitfalls. From clinging to legacy code to botching async workflows, even seasoned developers fall into traps that sabotage performance, security, and scalability. In this guide, we’ll expose the **7 deadly sins of Python programming in 2025**—and arm you with battle-tested fixes to write code that’s fast, secure, and maintainable. **Sin #1: Ignoring Type Hints** **Why It’s Harmful:** Unreadable code, missed bugs, and angry teammates. Type hints are now **mandatory** in 2025 enterprise projects. **Fix It:** - Use `mypy`or `pyright`for static analysis. - Add hints for complex functions ``` def process_data(data: list[dict[str, int]]) -> pd.DataFrame: ... ``` **Pro Tip:** Adopt Python 3.12’s new `@override` decorator for clearer OOP. **Sin #2: Clinging to Python 2.x Legacy Code** **Why It’s Harmful:** Python 2’s EOL in 2020 didn’t stop some holdouts. By 2025, unpatched security flaws and compatibility issues will cripple your apps. **Fix It:** - Run `2to3`for basic conversions. - Refactor - Test with `tox`across Python 3.9+ environments. **Case Study:** A startup lost $200k in downtime after a Python 2.7 script failed under Py3.11. **Sin #3: Blocking the Event Loop (Async Abuse)** **Why It’s Harmful:** Async code that blocks (e.g., sync HTTP calls) throttles performance. **Fix It:** - Replace `requests`with `httpx`or `aiohttp`. - Use `anyio`for structured concurrency: … **Sin #4: Dependency Chaos** **Why It’s Harmful:** Global `pip installs` lead to version conflicts and “works on my machine” disasters. **Fix It:** **Poetry:**Manage dependencies and virtual environments. **PDM:**Modern PEP 621-compliant tool. **Dockerize:**Lock OS and Python versions. **Dependency Manager Comparison** |Tool|Best For| |--|--| |Poetry|Apps with strict deps| |PDM|Library developers| |Pipenv|Legacy projects| **Sin #5: Over-Engineering with Patterns** **Why It’s Harmful:** Forcing Abstract Factories into a 100-line script? YAGNI! **Fix It:** - Follow KISS (Keep It Simple, Stupid). - Use `dataclasses`or `pydantic`for data models. - Reserve patterns for complex domains (e.g., fintech transaction systems). **Example:** ``` # Bad: Over-engineered class AbstractReport(ABC): @abstractmethod def generate(self): ... # Good: Simple def generate_report(data: list, format: str) -> str: ... ``` … **Stat:** 60% of Python breaches in 2025 trace to `pickle` misuse (OWASP). **Sin #7: Slow Data Handling with Vanilla Python** **Why It’s Harmful:** Loops and lists can’t compete with NumPy/Polars in 2025’s data-heavy apps. **Fix It:** **Polars:**Process 10M rows in seconds. **DuckDB:**SQL-like queries on DataFrames. **Numba:**Accelerate math-heavy code. **Code Snippet:** ``` # Bad: Slow loop total = 0 for num in data: total += num # Good: Polars df.select(pl.col("values").sum()) ``` **Case Study: How Fixing These Sins Saved a Startup** **Problem:** A healthtech app crashed under 10k users due to sync I/O and untyped code. **Solution:** - Migrated to async with FastAPI. - Added type hints and `mypy`. - Switched from CSV to Polars.

Python has long been celebrated as the Swiss Army knife of programming languages—versatile, beginner-friendly, and dominant in fields like AI, web development, and data science. But by 2025, the landscape has shifted dramatically. While Python isn’t going extinct, developers face a perfect storm of challenges that make the journey frustrating. Let’s unpack why 2025 might be the year Python devs feel the squeeze. ## 1. Performance Woes in a Speed-Obsessed World Python’s Achilles’ heel—its runtime speed—has become a glaring liability. As applications demand real-time processing (think metaverse interactions or autonomous systems), competitors like Rust, Julia, and Go have stolen the spotlight. Python’s Global Interpreter Lock (GIL) remains unresolved, forcing developers to rely on workarounds like multiprocessing or outsourcing performance-critical code to other languages. Meanwhile, Julia’s dominance in scientific computing and Rust’s adoption in systems programming leave Python looking sluggish. The rise of quantum computing libraries in C++ and Fortran hasn’t helped either. ## 2. Dependency Hell 2.0: Ecosystem Fragmentation Python’s “batteries included” philosophy is now a double-edged sword. The standard library is bloated, with deprecated modules cluttering documentation. Package management is a nightmare: PyPI’s security breaches in 2024 led to strict corporate policies, forcing developers to juggle private registries and labyrinthine pip/conda workflows. Virtual environments feel archaic compared to Rust’s Cargo or JavaScript’s pnpm. Worse, critical libraries like NumPy and Pandas struggle to keep up with GPU-driven data demands, fragmenting the ecosystem into niche, incompatible forks. ## 3. The Job Market: Oversaturation and Shifting Sands Python’s accessibility flooded the market with junior developers, creating cutthroat competition for entry-level roles. Meanwhile, companies chasing performance and type safety are migrating to Go or Kotlin. AI startups now prefer Julia for prototyping and Rust for deployment, leaving Python devs to maintain legacy TensorFlow 1.x models. Salaries stagnate as demand shifts to specialists in newer languages. Even FAANG companies, once Python strongholds, now prioritize Mojo (Python’s faster cousin) for infrastructure code. ## 4. Tooling Turmoil and Python 4.0’s Identity Crisis The long-awaited Python 4.0 arrived in 2024… and it was a disaster. Intended to modernize the language, it introduced breaking changes (e.g., a new string interpreter, controversial async overhauls) that fractured the community. Migration tools like 2to3 were clunky, and many libraries lagged behind. IDEs struggle to keep up, with PyCharm plugins breaking nightly. Meanwhile, tools for Rust or TypeScript offer AI-powered codegen and flawless refactoring, making Python’s toolchain feel outdated. ## 5. Corporate Abandonment and the Open-Source Exodus Corporate backing kept Python’s ecosystem alive, but 2025 saw key players jump ship. Google shifted TensorFlow to Mojo, and Microsoft’s PyTorch began integrating with C#. Abandoned libraries litter GitHub, forcing teams to maintain forks or rewrite codebases. Even Django’s updates slowed as maintainers burned out. The result? A fragile ecosystem where updating one dependency can collapse your entire stack. ## 6. Security: The Cost of Popularity Python’s popularity made it a target. Supply-chain attacks on PyPI peaked in 2024, with malicious packages exploiting pip’s vulnerabilities. Companies now mandate expensive audits for open-source dependencies, and developers spend more time writing SBOMs (Software Bill of Materials) than code. Python’s dynamic typing also exacerbates security reviews—type hints aren’t enough for auditors demanding Rust-like memory safety. ## 7. The Rise of the Underdogs Languages like Mojo (Python’s speedier offshoot), Zig, and Julia are eating Python’s lunch. Mojo offers seamless Python interop with C-level speed, luring data engineers. WebAssembly-centric languages dominate edge computing, leaving Python struggling in IoT. Even education sectors now teach JavaScript (for full-stack) or Swift (for AR/VR), eroding Python’s “first language” advantage. ## Is Python Doomed? Not exactly. Python remains entrenched in legacy systems, scripting, and niches like bioinformatics. Its community is resilient, and projects like mypy gradual typing show progress. But in 2025, being a Python developer means grappling with stagnation, competition, and a sense that the world moved on. To survive, devs must adapt—embracing multilingualism (Python + Rust?), contributing to open-source revitalization, or pivoting to emerging tools. The golden age of Python may be over, but its legacy (and headaches) live on.

### Cost is top pain — but AI is the fix Cost overtook skills and security as the #1 challenge (42%), with 88% reporting a year-on-year rise in total Kubernetes TCO, and growth expected over the next 12 months. Yet 92% say they are investing in AI-powered optimization tools to bring bills back under control. … ... Over half say their clusters are still "snowflakes" with highly manual operations. Teams that centralize application deployment in a platform-engineering function outperform every other group on key DevOps metrics around reliability and speed.

Komodor released its *2025 Enterprise Kubernetes Report*, revealing that 79% of production outages stem from system changes and that enterprises lose an average of 34 workdays per year troubleshooting incidents. The report also highlights chronic over-provisioning, with 82% of workloads misaligned to actual resource needs. Read the full report here. … ... Komodor’s finding that 79% of issues come from recent changes underscores a common pain point: enterprises are shipping faster than they can stabilize. Even as CI/CD adoption rises (over 42% of teams have automated 51–75% of their pipelines) teams remain caught in a cycle of firefighting. Median detection times of 40 minutes and recovery times of 50 minutes show that monitoring improvements haven’t fully translated into resilience. For developers, this means that the burden of reliability often falls back on ops teams, stalling feature delivery and increasing context-switching costs. ### Why This Matters Traditionally, enterprises leaned on manual playbooks, siloed monitoring tools, and “safe” over-provisioning to prevent outages. According to theCUBE Research, 45.7% of organizations still spend too much time identifying the root cause, citing lack of visibility across multi-cluster and multi-cloud estates. Developers often relied on golden images or static resource allocations, trading efficiency for predictability. This explains Komodor’s overspend findings: 65% of workloads use less than half of their requested CPU or memory, leading to inflated cloud bills without delivering reliability. … ## Looking Ahead The Komodor report reinforces that Kubernetes is the enterprise standard, but operational gaps remain the Achilles’ heel. As organizations move deeper into AI/ML workloads, the complexity of environments will only grow, making automation and AI-assisted observability table stakes.

security enhancements, and scaling initiatives. This is particularly acute for organizations running AI/ML workloads, where storage costs (50%) have become the primary concern — reflecting the enormous data requirements of training datasets, model checkpoints, and inference results for large-scale AI deployments. 2. The AI/ML Revolution Accelerates: While databases maintain their #1 … 4. Performance Gaps Reveal Optimization Opportunities: Despite widespread adoption, performance bottlenecks persist. Storage I/O performance is cited as the primary concern, followed closely by model/ data loading times. These gaps represent both challenges and opportunities for the ecosystem to deliver better tooling, practices, and infrastructure. … revenue to these deployments. However, maturity brings new challenges. The top operational concerns are no longer about basic adoption but about optimization: performance optimization (46%), security and compliance (42%), and talent/skills gaps (40%). The skills gap is particularly acute — organizations need practitioners who understand both Kubernetes operations AND data workload optimization. … performance bottleneck (24%), followed closely by model/data loading times (23%), indicating that data access patterns are the primary constraint for DoK workloads • Organizations implement numerous storage strategies: Object storage integration (43%), local SSDs for performance (43%), caching layers (42%), block storage (42%) … Performance Bottlenecks Biggest Performance Bottlenecks What is your biggest performance bottleneck with data workloads on Kubernetes? Storage and data movement dominate the bottleneck list, validating the focus on storage acceleration techniques. 0 10 20 30 40 50 Object storage (S3, GCS, Azure Blob, etc.) … 16 Data on Kubernetes Report 2025 AI/ML Top Cost Concerns The cost landscape has shifted dramatically: Primary Cost Concerns (AI/ML Workloads) If you use AI/ML: What is your biggest cost concern with AI/ML workloads on Kubernetes? (Select top THREE) Storage costs have emerged as the dominant concern, reflecting: … 17 Data on Kubernetes Report 2025 Operational Challenges and Governance Top Operational Challenges The nature of challenges has evolved from adoption to optimization: Top 3 Operational Challenges What are your TOP 3 operational challenges with DoK today? (Select up to THREE) Performance optimization has emerged as the #1 challenge, displacing earlier concerns about basic … Top Concerns What’s your biggest concern about DoK in the next year? Security has emerged as the #1 concern, likely driven by: • High-profile Kubernetes security incidents • Complexity of securing distributed data workloads • Regulatory compliance requirements • AI/ML data sensitivity 0 10 20

Enterprises are expanding their Kubernetes footprints across clusters, clouds, and workloads. Growth brings efficiency, but it also multiplies complexity. Governance, consistency, and optimization become harder as environments scale. ... ### DZone’s report highlights one of the biggest enterprise pain points: Tool sprawl. As teams stack solutions for security, observability, networking, and deployment, the ecosystem becomes harder to manage and secure. Each tool solves a problem but together, they create friction — operational overhead, higher attack surfaces, and escalating costs. Platform engineering is emerging as the antidote.

Scale is back, whatever the cost Enterprises already run >20 clusters and >1,000 nodes, across five-plus clouds and environments, driven by multicloud, repatriation and AI imperatives. The consequence? Cost is the biggest pain across the board. Learn what enterprise K8s adoption looks like in 2025