When to Use Claude Code and When to Use Codex
Learn where which coding agent is best The post When to Use Claude Code and When to Use Codex appeared first on Towards Data Science.
Category: AI
Quantization and Pruning Methods to Make Your LLM Leaner
This article walks through what each technique actually does, why skipping them costs real money and real latency, and then gets hands-on with five specific methods people are running in production right now.
The Local AI Stack for Productive SLMs
A practical framework for choosing the right tools at each layer of your local AI setup, from model serving to context retrieval.
Human-in-the-Loop Without Killing Throughput
How we stopped reviewing every agent action and started routing human attention where it actually mattered The post Human-in-the-Loop Without Killing Throughput appeared first on Towards Data Science.
From One Agent to a Team: Understanding Codex Subagents
A hands-on guide to defining specialist agents and coordinating their work in the Codex CLI The post From One Agent to a Team: Understanding Codex Subagents appeared first on Towards Data Science.
Our decision on Cursor following its acquisition by SpaceX
Our decision to wind down our contract providing OpenAI models to Cursor following its acquisition by SpaceX.
Why Claude Code Time Estimates Are Poor
Be a better communicator with LLM programming The post Why Claude Code Time Estimates Are Poor appeared first on Towards Data Science.
Connecting My LangGraph AI Agent to Postgres
How to run the backend locally with Docker or in the cloud The post Connecting My LangGraph AI Agent to Postgres appeared first on Towards Data Science.
CIFQA: A Deterministic Tool-Grounded Multi-Agent LLM Framework for Financial Query Answering
arXiv:2608.26114v1 Announce Type: new Abstract: Calculation-intensive financial question answering requires exact reasoning over structured rates, temporal conditions, numerical formulas, and rule-based constraints. Although Large Language Models (LLMs) perform strongly on natural language tasks, they often produce numerically incorrect yet plausible answers when solving multi-step financial calculations. To address this limitation, we introduce CIFQA (Calculation-Intensive Financial…
PICasso: An AI-Enabled Design Framework for Autonomous Optimization of Silicon Photonic Devices
arXiv:2608.26113v1 Announce Type: new Abstract: We present PICasso, an AI-assisted framework for automated synthesis, verification, and optimization of photonic integrated circuits (PICs) from natural-language specifications. PICasso couples a structured NL -> YAML -> GDS generation pipeline with PDK aware knowledge injection, automated placement and routing, DRC/LVS validation, and SAX-based photonic simulation. To systematically evaluate AI-driven…