FlameIQ v1.0.2 Released: Open-Source Performance Regression Detection

I wrote a CLI tool to help your teams catch "slop" code before it creates a mountain of tech debt. Runs for any python projects 3.10 -> 3.13. Drop it in your CI/CD pipelines to act as a last linter for AI specific slop patterns like vague comments, util functions, and more. Check it out! https://lnkd.in/e5xNUwH2 https://lnkd.in/eT6psfZS

I put together a lightweight agent orchestration system for Claude Code called the Claude Agents Plugin. It breaks complex development tasks into tracked, parallel sub-agents using hierarchical markdown file trees. The idea is to let you describe a task naturally—like "Build a user auth system with login, signup, and JWT tokens." Claude then automatically scans your codebase, maps out the work, spawns parallel agents, and tracks everything in markdown files. A few architectural details: Context-aware: It reads your existing project before touching anything, clarifies what it will modify versus create, and never overwrites existing code. Dependency management: It builds hierarchical task trees to handle parent-child relationships and detects circular dependencies. Zero dependencies: It’s a single file relying purely on the Python standard library (Python 3.9+). It is MIT licensed. If you are building with Claude Code and want to test out structured agent orchestration, the repository is linked below. https://lnkd.in/gG3fdrmZ #ClaudeCode #Python #OpenSource #AgenticAI #DeveloperTools

𝗧𝗵𝗲 𝗗𝗕 𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗹𝗲𝗮𝗸𝗲𝗱 𝗶𝗻 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻. 𝗧𝗵𝗲 𝗳𝗶𝗹𝗲 𝗵𝗮𝗻𝗱𝗹𝗲 𝘀𝘁𝗮𝘆𝗲𝗱 𝗼𝗽𝗲𝗻. 𝗧𝗵𝗲 𝗿𝗼𝘄 𝗹𝗼𝗰𝗸 𝗵𝘂𝗻𝗴 𝗶𝗻𝗱𝗲𝗳𝗶𝗻𝗶𝘁𝗲𝗹𝘆. These aren't edge cases—they are the inevitable result of making resource cleanup the caller's responsibility. In Python, 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿𝘀 move that responsibility from the developer to the type itself. The resource becomes self-healing. 🔹 __exit__ is called even if an exception is raised—that is the safety guarantee. 🔹 @contextmanager lets you write the same protocol with 'yield'—no class needed. 🔹 Any resource with an acquire/release lifecycle belongs in a context manager. The 𝘸𝘪𝘵𝘩 statement isn't just syntactic sugar—it’s a contract. The caller writes business logic; the object handles the cleanup. #Python #SoftwareEngineering #BackendDevelopment #SoftwareArchitecture #CleanCode

~700 downloads in just 1 week of launch. Crazy I built and launched agentkube-mini, a tiny agent orchestration engine just to show how multi-agent systems actually work under the hood. Most frameworks abstract everything away. Which is great… until something breaks. Then you realize: You don’t understand the system you built. So I built something different. 𝗮𝗴𝗲𝗻𝘁𝗸𝘂𝗯𝗲-𝗺𝗶𝗻𝗶 is: • a task DAG-based orchestration engine • dependency-aware parallel execution • event-driven observability • shared memory across agents All in ~300 lines of Python. Zero dependencies. What it’s for: • understanding agent orchestration deeply • building simple, reliable pipelines • debugging multi-agent workflows • layering on top of existing systems (LangGraph, etc.) What it’s NOT: • not a full agent framework • not for complex tool loops or persistence The goal wasn’t to build the most powerful system. It was to build the clearest one. Because once you understand: 👉 agents = nodes 👉 dependencies = edges 👉 scheduler = execution You understand the core of every multi-agent runtime. Appreciate everyone who tried it, shared feedback, and pushed it forward. More coming soon 🚀 #pypi #orchestration #agentkubemini #opensource

#MorningReflections I revisited an old codebase and realized I had built a CLI I didn’t even remember. As part of my internal tooling process, argparse makes creating functional CLIs almost automagical—you set options, and it handles the heavy lifting. Don’t be a fool like me, always keep learning. 😂 #Python #InternalTools #CLIDevelopment

Making some progress on the "mom and dad" problem & introducing Tempura! This iteration of WorkflowSkill is built on Python workflows in Temporal. The combined effect is dramatic. My qualitative assessment is that authoring success has jumped from 70% to ~98%. Needs a true study, but the difference is obvious in practice. So I built Tempura, a hosted service where you describe what you want automated and it just runs, to test whether this can actually work for regular people. Running a private beta now. Would love feedback, especially from people who aren't developers. What would you automate if you could just describe it? Full article in comments.

*Debugging Lesson from a Real-World Integration* While working on a Python-based bot integration, I ran into an interesting issue that initially looked like a simple dependency error — but turned out to be a deeper compatibility problem. *The Error:* ModuleNotFoundError: No module named 'pkg_resources' AttributeError: module 'pkgutil' has no attribute 'ImpImporter' At first glance, it seemed like a missing package. But even after installing dependencies, the issue persisted. *Root Cause:* The environment was running on Python 3.12, where certain legacy components like pkgutil.ImpImporter have been removed. However, some widely used libraries (and their dependencies) still rely on these older components — leading to unexpected runtime failures. *Solution:* Instead of patching individual packages, the clean and stable solution was: - Align Python version to 3.10 - Use compatible versions of dependencies 💡 Key Takeaways: • Not every issue is a “missing install” problem • Ecosystem compatibility matters more than latest versions • Stable environments > cutting-edge versions in production Sometimes, the real bug is not in your code — but in the version mismatch between your tools. #Python #Debugging #SoftwareEngineering #BackendDevelopment #LearningJourney

Secrets Hunter v0.6.0 is here! Most secret scanners ask one question: "Does this suspicious string match a known pattern?" Secrets Hunter asks a better one: "What is the context this suspicious string was found in?" A high-entropy string alone means little. But a high-entropy string next to API_KEY= is a different story entirely. Secrets Hunter combines Shannon entropy with assignment context analysis and a bigram language model to catch secrets that no pattern library would ever cover — including custom tokens, internal credentials, and formats unique to your stack. Here's what makes it different: → Assignment context boosting — severity is determined not just by the string itself, but by what it's assigned to → Semantic false positive filtering — strings that look like human language get filtered out before they reach you → Zero dependencies — pure Python 3.11+, no binaries, no system packages, no network calls at scan time → TOML overlay config — stack team, CI, and local configs without duplicating anything → SARIF + JSON export — plugs directly into GitHub Code Scanning and any CI pipeline Built from real AppSec experience. Lightweight by design. Fully auditable. 👉 pip install secrets-hunter Visit Secrets Hunter repository: https://lnkd.in/dYNysfgW #AppSec #DevSecOps #SecretScanning #Python #OpenSource #SecurityTools

Built something this weekend. Introducing RAGPipe. Every RAG project starts the same way. Ingest data, chunk text, generate embeddings, store in a vector DB, then query. And somehow you end up stitching 4 to 5 libraries with hundreds of lines of boilerplate just to get started. So I built RAGPipe. An open-source library that makes RAG simple. - ragpipe.ingest("./docs") - ragpipe.query("your question") - ragpipe.Pipeline() Simple by default, flexible when you need control. What makes it useful: Zero config Auto detects Ollama, OpenAI, or sentence-transformers. No API keys needed to start. YAML pipelines Define your flow in a YAML file and run it with one command. CLI built for developers Clean output with progress bars and status feedback. Works with Qdrant, Pinecone, or even a JSON file for local testing. Built during Moat Weekend. Replacing scrolling with building. If you are working with LLMs, this might help. -> pip install ragpipe[cli] Opensource at https://lnkd.in/gyu_4UBr What are you building this weekend? #MoatWeekend #BuildInPublic #RAG #LLM #Python #OpenSource

⚠️ API Automation Issue i encountered While automating an API, a test kept failing intermittently even though the API looked fine. The issue was? Timing and data readiness. The API response depended on a background process that hadn’t completed yet, so the test was validating the response too early. 🔧 What helped to fix it: • Added a retry mechanism with small waits • Validated the response only after the expected data was available • Ensured test data was created before running the validation After this change, the test became stable and reliable. Sometimes failures are not bugs in the API - they’re about how we design the test logic. Have you faced similar stability issues in API automation? #softwaretesting #qaautomation #apitesting #python #testautomation