Evaluating GenAI Platforms with Scoring Framework

The next generation of data infrastructure won't be built for analysts. It'll be built for agents. And honestly, most data platforms aren't ready for that yet. They were designed for humans- people who slow down, double-check, and ask for sign-off before anything hits production. Agents work differently. They move fast, they iterate, and they need infrastructure that can keep up without breaking things. That means Python-native, isolated by default, atomic merges, instant rollbacks. Not manual guardrails. On April 21 at 9am PT, we're showing what that looks like in practice- live, in #Python, with our friends at dltHub. Looking forward to seeing you there :)) Register: https://lnkd.in/eqVQ5C5n

Excited to share ParaLLeM developed by Galen Wei, a Beckman Scholar in our group mentored by Shone Ran!!! ParaLLeM is a Python library for building and running agentic LLM workflows at a large scale with 50% reduced token cost. Galen adopts a developer-first design: you keep orchestration logic in plain Python, then switch between sync and batch execution in one line. ParaLLeM is built around the Batch API, supports OpenAI, Anthropic, and Google models, and includes features such as structured output, function calling, web search, multimodal input, detailed logging, and reproducible traces. It is designed to help teams move from prototyping to scalable, production-grade workflows without rewriting their code, while batch mode can also reduce token cost by up to 50% for the same application. Github: https://lnkd.in/ekmu9AU7 Web: https://parallem.org/

I built 4 AI agents from scratch. No LangChain. No AutoGen. Just the Anthropic SDK and Python. Agent 01: answered a Kubernetes question. Cost $0.002. Agent 02: searched the web in real time. Cost $0.18 — 90x more. Here's why that matters. Agent 03: connected to my live Kubernetes cluster and ran kubectl commands autonomously. Agent 04: created a full K8s stack from one text description. The difference between a chatbot and an agent is one while loop. Full breakdown in the article 👇

Spent some time recently improving test stability in a small Python project. What looked like “random failures” at first turned out to be a mix of: - leftover state between runs - timing issues - and assumptions that were true locally but not always in CI Nothing unusual — but a good reminder how fragile systems can become when small things accumulate. The interesting part wasn’t fixing individual tests, but understanding *why* the system behaved differently depending on the context. In the end, the most valuable changes were: - making state explicit instead of implicit - isolating test cases properly - and improving observability (logs > guesswork) Not a big project, but a good example of how reliability issues are often less about tools and more about system behavior. Curious how others approach this — do you treat flaky tests as isolated problems, or as signals of deeper issues?

I've been running a public AI-assisted delivery pipeline for Python for months now - research, OpenSpec planning, TDD, evidence recording, pre-commit gates, PR review, release. The interesting part: the tool enforces the same workflow it uses itself. Full walkthrough coming soon 👇

One misconfiguration in a GitHub Actions workflow. Fast forward to Friday evening and we’re tracking six throwaway GitHub accounts, 500+ malicious PRs, and an attacker who spent three weeks iterating on payloads before anyone noticed. Always a pleasure digging into these with Rami McCarthy, Benjamin Read and Scott Piper 🔍

I’ve spent the last few weeks moving beyond just analyzing data to actually building the systems that create it. It’s been a massive learning curve, but I’ve finally started to wrap my head around:   𝗠𝗼𝗱𝗲𝗹𝘀 & 𝗔𝗱𝗺𝗶𝗻: How the database actually structured.   𝗩𝗶𝗲𝘄𝘀 & 𝗧𝗲𝗺𝗽𝗹𝗮𝘁𝗲𝘀: Passing variables between the backend and the UI.   𝗣𝗲𝗿𝗺𝗶𝘀𝘀𝗶𝗼𝗻𝘀: Managing users and groups.   𝗕𝗼𝗼𝘁𝘀𝘁𝗿𝗮𝗽: Keeping things clean and responsive. Huge shoutout to my mentor Rathan Kumar for the guidance. Check out the live site - https://lnkd.in/g-jkXR5x On to the next build! #Django #Python #DataEngineering #Learning #FullStack

I had the chance to delve into the Noise2Void codebase from zero and I must say I absolutely adored the way it was organized. The project has the workflow decoupled into very specific parts such as data handling, masking, and model training, thus making it easier for the reader to grasp the contents of the code. What I found to be the most remarkable part is the trick that the code employs through the use of custom functions and training logic to hide pixels and let the network predict from surrounding context. This is an excellent project that exhibits how well clean Python structure and TensorFlow-based modeling can work together in a smart self-supervised learning environment. Another thing I found useful was the codebase with its logic being modular, which gives the pipeline a practical and well-engineered feeling instead of being too complicated. Repo: https://lnkd.in/eF7nZXxX

The "Shadow" Fix: Python Version Compatibility **Hook:** Building for the "Latest & Greatest" is easy. Building for the "Real World" is where the engineering gets messy. **Body:** While finalizing my Enterprise RAG pipeline, I hit a silent production-breaker: A `TypeError` buried deep in a third-party dependency. The culprit? The `llama-parse` library uses Python 3.10+ type union syntax (`|`), but the production environment was locked to Python 3.9. Result: Immediate crash on boot. Instead of demanding a system-wide upgrade—which isn’t always possible in locked-down enterprise environments—I implemented a **Graceful Fallback Logic**: ✅ **Dynamic Imports**: Wrapped the cloud-parser initialization in a guarded `try-except` block. ✅ **Smart Routing**: If the Python environment is incompatible, the system automatically redirects to a local, high-fidelity `PyMuPDF` parser. ✅ **System Resilience**: The app stays online, the UI remains responsive, and 99% of RAG functionality remains available without a single user noticing a failure. Real Engineering isn't just about using the best tools—it’s about writing code that doesn't break when the environment isn't perfect. #Python #SoftwareEngineering #RAG #AIEngineering #SystemDesign #Resilience

Build a production AI agent in 10 lines of Python. Strands Agents SDK: ```python from strands import Agent from strands.models.bedrock import BedrockModel from strands_tools import calculator, web_search agent = Agent( model=BedrockModel("anthropic.claude-sonnet-4-20250514-v1:0"), tools=[calculator, web_search] ) response = agent("What's the GDP per capita of the top 5 economies?") ``` That's it. Tool calling, conversation management, streaming, multi-turn context is all handled. Why Strands over LangChain for AWS: - Built for Bedrock integration (not retrofitted) - Works with any Bedrock model - ToolSimulator for agent testing (just released) - Strands Evals for evaluation pipelines - Open-source, runs locally For production: pair with Bedrock AgentCore for managed deployment, guardrails, and observability. The stack: Strands (build) → ToolSimulator (test) → Strands Evals (evaluate) → AgentCore (deploy) Full lifecycle. Open-source foundation. Managed deployment when ready. Start: https://strandsagents.com #AWS #AIAgents #Python #Strands #Bedrock