15 posts tagged with "Python"

Streaming an LLM to a user is easy. Consuming the stream on the server — token deltas, reasoning deltas, tool-call chunks, per-node state, subgraph events, usage metadata — is the part that turns into a pile of if chunk["type"] == ... branches. I shipped a streaming endpoint last week on LangGraph version="v2", because that is what's installed (1.1.8 locally, 1.2.4 on the server). The hand-rolled consumer was about twenty lines of fragile branching, a keepalive hack to stop a proxy from dropping the connection during DeepSeek's silent reasoning phase, and a manual accumulator that reset whenever langgraph_node changed.

LangGraph's version="v3" event-streaming API is what I'd reach for next, and the diff is the interesting part: it deletes most of that parsing. Instead of one undifferentiated event firehose you branch on, v3 gives you typed, per-channel projections you iterate independently, built on a content-block protocol that makes text, reasoning, tool-call, and multimodal boundaries explicit. v1 and v2 are unchanged. This is a walk through what v3 actually is, what it removes from your code, and where it still leaves work for you.

TL;DR — CrewAI's real uniqueness is that it models problems as "build a team of people" rather than "build a graph of nodes" (LangGraph) or "build a conversation" (AutoGen). The Crews + Flows dual-layer architecture is the core differentiator. The role-playing persona system and autonomous delegation are ergonomic wins, not technical breakthroughs. The hierarchical manager is conceptually appealing but broken in practice. This post separates what's genuinely novel from what's marketing.

We needed a pipeline that ingests hundreds of job postings from ATS platforms (Greenhouse, Lever, Ashby), enriches each posting with structured data from their public APIs, and then classifies whether a job is a fully remote EU position — all running on Cloudflare's edge with zero GPU costs.

This article walks through the architecture and implementation of process-jobs, a Cloudflare Python Worker that combines langchain-cloudflare with Cloudflare Workers AI, D1, and Queues to build a production classification pipeline.

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In the realm of quantitative finance, machine learning and deep learning are revolutionizing how researchers and traders discover alpha, manage portfolios, and adapt to market shifts. Qlib by Microsoft is a powerful open-source framework that merges AI techniques with end-to-end finance workflows.

This article demonstrates how Qlib automates an AI-driven quant workflow—from data ingestion and feature engineering to model training and backtesting—using a single YAML configuration for a LightGBM model. Specifically, we’ll explore the AI-centric aspects of how qrun orchestrates the entire pipeline and highlight best practices for leveraging advanced ML models in your quantitative strategies.

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Qlib is an AI-oriented, open-source platform from Microsoft that simplifies the entire quantitative finance process. By leveraging PyTorch, Qlib can seamlessly integrate modern neural networks—like Multi-Layer Perceptrons (MLPs)—to process large datasets, engineer alpha factors, and run flexible backtests. In this post, we focus on a PyTorch MLP pipeline for Alpha360 data in the US market, examining a single YAML configuration that unifies data ingestion, model training, and performance evaluation.