SMRTR AIJun 11, 2026Hacker Noon

LLMs Shouldn’t Do Math: Why Your Agents Need Classical ML Tools

SMRTR summary

LLMs are bad at precise math and statistics, yet engineers keep forcing them to do it. A Python library called predikit solves this by wrapping classical ML models like XGBoost and scikit-learn into LLM-callable tools with auto-generated schemas, letting AI agents handle reasoning while dedicated models handle accurate predictions.

SMRTR provides this summary for quick context. The original article belongs to Hacker Noon.

Read the original article
SMRTR AI

Get the next batch of curated summaries in your inbox.

This archive is built from SMRTR newsletter summaries. Subscribe for hand-picked stories without the extra noise.

Related Stories

More SMRTR summaries that connect to this topic.

Browse AI
AIDaily.devJan 25, 2026

Getting Real With LLMs

Current Large Language Models excel at simple coding tasks with predictable outcomes and complex isolated projects, but struggle with real-world enterprise development where...

AIDaily.devApr 3, 2025

Build Your Reasoning LLM

Reinforcement fine-tuning (RFT) transforms open-source language models into reasoning powerhouses without labeled data. Using Predibase and the Countdown dataset, Qwen-2.5:7b was...