Python's Type Safety Limitations Pose Existential Risk for Teams

Scaling Python testing isn’t just about adding more tests—it’s about keeping feedback loops tight as the codebase grows. In a large production environment with 2.5M+ lines of Python and 10,000+ tests, three metrics quickly become the pressure points: test execution time, reliability (flaky vs. consistent results), and coverage (signal quality). A practical workflow pairs Pytest + coverage reporting with CI gating (PR checks + required thresholds), treating tests as both a quality net and living documentation. When test runs started creeping past 30 minutes, the CI pipeline was optimized with a few high-leverage strategies: 1) parallelism on a single machine via pytest-xdist, and at a bigger scale, splitting across multiple runners using pytest-split—including duration-based balancing so slow tests don’t bottleneck one runner; 2) caching to cut dependency install time (pip cache keyed by requirements hash), plus faster installers like uv and prebuilt Docker images for heavy non-Python deps; 3) skipping unnecessary compute, e.g., only running certain jobs when Python files change, running linters only on touched files, and measuring coverage only for changed paths on PRs (then running full coverage on main); and 4) modern runners, including autoscaled self-hosted runners on Kubernetes/EC2 to improve price/performance. The results were tangible: the pipeline was brought down to <15 minutes, while coverage improved (e.g., moving from ~85% to ~95% over a year) without sacrificing PR safety. The operational reality also showed up: parallelization can surface flaky tests and shared-state conflicts, so retries, reporting to code owners, and quarantining blockers become part of keeping CI reliable as the suite grows. #Python #Testing #Pytest #ContinuousIntegration #CI #TestCoverage #DeveloperExperience

I just pushed a new PySpector release (v0.1.5-beta). This one, is a major architectural milestone, as we’ve officially transitioned from a partial pattern-matching engine to a full Graph-Based SAST engine! The core #Rust engine has been completely refactored, to support Inter-Procedural #Taint #Analysis. This allows PySpector (by SecurityCert) to track untrusted data across function boundaries, and between different files, something that was previously a blind spot. Some key technical updates, of this release, are: - Inter-Procedural Analysis: Propagation of taint through complex call chains and project-wide dependencies. - Flow-Sensitive CFG Engine: A new Control Flow Graph implementation that respects execution order to eliminate false positives. -Context-Aware Summaries: Precise mapping of how specific function parameters flow to return values. - Core Optimization: Maintained high-speed performance using Rayon parallelization despite the increased logic complexity. And how could i forget about the 6 new #contributors of PySpector, who in the last month decided to trust the project and dedicate their own free time to improve PySpector's #code and #documentation, your support is always much appreciated🫶 Doesn't matter if you're auditing standard Python libraries or specialized AI agents, v0.1.5 provides the depth needed to catch #critical #sinks (like Command Injections or Path Traversals) that local analysis misses, in your #Python codebase :) Check PySpector here: https://lnkd.in/dxsxJUDn

𝘼𝙨𝙮𝙣𝙘 𝙞𝙣 𝙋𝙮𝙩𝙝𝙤𝙣 𝙖𝙣𝙙 𝙍𝙪𝙨𝙩: 𝙎𝙖𝙢𝙚 𝙆𝙚𝙮𝙬𝙤𝙧𝙙, 𝘿𝙞𝙛𝙛𝙚𝙧𝙚𝙣𝙩 𝙒𝙤𝙧𝙡𝙙𝙨 I just published a new article exploring why async/await looks identical in Python and Rust but hides radically different execution models. The piece covers: → How asyncio's event loop differs from Tokio's work-stealing scheduler → The hidden costs of Python's "generous runtime" vs Rust's compile-time state machines → When to choose one over the other based on your actual problem This, as usual, wasn't just theoretical research. While working on this article, I contributed to both ecosystems: documentation improvements for Tokio's time module, and a fix for a common async anti-pattern in a Python GenAI framework, where async def without any await was silently blocking the entire event loop. Neither approach is universally "better". Python excels when domain complexity matters more than system complexity. Rust shines when execution predictability is your product. Full article in both EN/ IT below: https://lnkd.in/dqpaKCBe #AsyncProgramming #Rust #Python #SoftwareEngineering

Built Zetten - a Rust-powered task runner for Python backends. I was tired of glue code (venvs, env vars, duplicated CI scripts), so I built a tool to make running and orchestrating tasks boring again. One cool surprise: standardized task definitions make AI coding much more reliable. No guessing - it just reads the config and runs correctly. I wrote more about the journey, tradeoffs, and lessons on Substack: https://lnkd.in/gitWHmfJ If you’re into Rust, Python, or developer tooling, I’d love your feedback. Repo: https://lnkd.in/gTrzzkn4 #Rust #Python #OpenSource #BuildInPublic #DevTools

Python 3.14 is already here—and it’s finally tackling the "Performance Tax." 🐍 After 6 years in the Python ecosystem, I’ve seen my fair share of ‘slow code’ debates. But the 3.14 release (and the 2026 roadmap) is a game-changer for those of us building high-scale backends. Three features I’m currently digging into: 1. Zero-Overhead Debugging (PEP 768): We can finally attach profilers to production processes without the usual "performance hit." This is huge for diagnosing those "it only happens in prod" spikes. 2. Deferred Annotation Evaluation: Faster startup times and cleaner type-hinting without the string-quote hacks. 3. The "No-GIL" Era: We’re moving closer to true multi-core Python. If you’re still writing synchronous, single-threaded code for heavy tasks, it’s time to rethink your architecture. The takeaway? Python isn't just the "easy" language anymore; it’s becoming a performance powerhouse.

🐍 Python felt slow… until I stopped blaming Python. I had an API that worked fine in dev. Same code. Same logic. But in production? Latency spikes. Random slowness. No clear pattern. After digging deep, I found the real culprit 👇 🧠 Python wasn’t slow. Blocking I/O was. One tiny line was doing this: • Waiting for a network call • Blocking the entire worker • Holding resources idle Everything else waited. 💡 The turning point: I stopped asking “Is Python fast?” I started asking: ❓ Is this I/O-bound or CPU-bound? Once I: ✔ Used async for I/O ✔ Offloaded CPU-heavy work ✔ Stopped blocking the event loop Performance improved. Without changing languages 😌 ✨ Real backend lesson: • Python is great at I/O • Python is bad at pretending to be multi-core • Design decisions matter more than syntax Languages don’t fail systems. Architectures do. If Python ever felt slow to you, this might be why. #Python #BackendDevelopment #AsyncProgramming #SoftwareEngineering #Performance #DeveloperLearning

A practical methodology for using Claude Code to migrate a production Python codebase to Go, including what worked, what failed, and what the human still needs to own.

“Python(Basic) Questions from a HackerRank Assessment I Recently Cleared" 🔹 Problem Statement You are given a sentence consisting of space-separated words with upper and lower case English letters. Each word must be transformed using the following rules: The first character of each word remains unchanged For every next character: Compare it with the previous character (case-insensitive) If the previous character comes earlier in the alphabet → convert current character to uppercase If the previous character comes later → convert current character to lowercase If both characters are the same → keep it unchanged Spaces should remain as they are 🔹 Logic / Approach Traverse the sentence character by character Reset comparison whenever a space is encountered Keep track of the previous character Compare characters using lower() to avoid case issues Apply transformation rules and build the result step by step This ensures accurate transformation while preserving word boundaries. 🔹 Code Implementation (Python) def transformSentence(sentence): result = [] prev_char = None for ch in sentence: if ch == " ": result.append(" ") prev_char = None else: if prev_char is None: result.append(ch) else: if prev_char.lower() < ch.lower(): result.append(ch.upper()) elif ch.lower() < prev_char.lower(): result.append(ch.lower()) else: result.append(ch) prev_char = ch return "".join(result) 🔹 Sample Input a Blue MOON 🔹 Output a BLUe MOOn #Python #HackerRank #CodingPractice #ProblemSolving #LearningJourney #SoftwareEngineering

Super excited to share Archive-It's blog post (authored by yours truly) showcasing our python scripts for web archive quality assurance. If you work with web archives, this quick script pulls text from live and captured sites and compares them, highlighting differences. A bit of a game changer! https://lnkd.in/eq9a3hej

Working with our Python team at PLANEKS has shown me something that benchmarks alone never capture: code execution speed often doesn’t fully correlate with the final product performance in a real environment. Yes, C++ and Java dominate execution-speed charts, but project outcomes tell a different story. Again and again, I see Python outperform expectations because of one thing: 👉 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝘃𝗲𝗹𝗼𝗰𝗶𝘁𝘆. Python lets teams prototype, iterate, and ship solutions at a pace that directly impacts business outcomes. And when performance becomes critical, we rarely rewrite anything – we optimize the right pieces:  • offloading hot paths to C/C++ or Rust  • leveraging NumPy, Pandas, PyTorch, or CUDA for near-native speed  • using multiprocessing and async models for high-throughput workloads  • profiling systems early instead of “guessing where it’s slow.” As an outcome, systems that scale, evolve quickly, and remain maintainable, without trading off performance. What I find most exciting is that Python itself keeps getting faster. With adaptive specialization, improved bytecode execution, and ongoing work to remove the GIL, the future is shifting even closer to high-performance computing. If you’re evaluating Python for a demanding project, focus less on stereotypes and more on your actual workload, architecture, and optimization strategy. That’s where Python truly shines. 𝗪𝗲 𝗿𝗲𝗰𝗲𝗻𝘁𝗹𝘆 𝗲𝘅𝗽𝗹𝗼𝗿𝗲𝗱 𝘁𝗵𝗶𝘀 𝘁𝗼𝗽𝗶𝗰 𝗶𝗻 𝗱𝗲𝗽𝘁𝗵 𝗵𝗲𝗿𝗲 👇 https://lnkd.in/eetYGNFt If you’re navigating performance challenges or choosing the right backend architecture, PLANEKS is always happy to help. We design and optimize high-performance Python systems that scale smoothly and evolve with your product. ⚙️✨ 𝗙𝗲𝗲𝗹 𝗳𝗿𝗲𝗲 𝘁𝗼 𝘀𝗰𝗵𝗲𝗱𝘂𝗹𝗲 𝗮 𝗰𝗼𝗻𝘀𝘂𝗹𝘁𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 Yulia A. 𝘁𝗼𝗱𝗮𝘆.