newsletterJune 5, 2026·Towards Data Science

The Fundamental Choice in Reinforcement Learning: On‑Policy vs. Off‑Policy

How a simple choice shapes exploration, safety, and efficiency The post The Fundamental Choice in Reinforcement Learning: On‑Policy vs. Off‑Policy appeared first on Towards Data Science.

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•How a simple choice shapes exploration, safety, and efficiency The post The Fundamental Choice in Reinforcement Learning: On‑Policy vs
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My AI Couldn’t See My Files — I Built a Zero-Dependency MCP Server

I got tired of copying files into an AI chat just to get feedback. So I built a pure Python MCP server that gives AI tools direct access to my local project—no frameworks, no dependencies. It runs over stdio for local use and switches to HTTP/SSE for concurrent clients with a single flag. The result

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Automate Writing Your LLM Prompts

Using DSPy to automatically create, evaluate, and optimize your prompts The post Automate Writing Your LLM Prompts appeared first on Towards Data Science.

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How to Fine-Tune an SLM for Emotion Recognition

Python tutorial for fine-tuning a Mistral Small 3.1 on an imbalanced training set to classify 15 emotions in social media communication The post How to Fine-Tune an SLM for Emotion Recognition appeared first on Towards Data Science.

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How to Navigate the Shift from Prompt-Based Tools to Workflow-Driven AI

Abacus.AI and the case for unified AI workflows The post How to Navigate the Shift from Prompt-Based Tools to Workflow-Driven AI appeared first on Towards Data Science.

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The Fundamental Choice in Reinforcement Learning: On‑Policy vs. Off‑Policy

Key Takeaways

Related Articles

My AI Couldn’t See My Files — I Built a Zero-Dependency MCP Server

Automate Writing Your LLM Prompts

How to Fine-Tune an SLM for Emotion Recognition

How to Navigate the Shift from Prompt-Based Tools to Workflow-Driven AI

Discussion

The Fundamental Choice in Reinforcement Learning: On‑Policy vs. Off‑Policy

Key Takeaways

Related Articles

My AI Couldn’t See My Files — I Built a Zero-Dependency MCP Server

Automate Writing Your LLM Prompts

How to Fine-Tune an SLM for Emotion Recognition

How to Navigate the Shift from Prompt-Based Tools to Workflow-Driven AI

Discussion

Related Articles

Towards Data Science
My AI Couldn’t See My Files — I Built a Zero-Dependency MCP Server
I got tired of copying files into an AI chat just to get feedback. So I built a pure Python MCP server that gives AI tools direct access to my local project—no frameworks, no dependencies. It runs over stdio for local use and switches to HTTP/SSE for concurrent clients with a single flag. The result

Towards Data Science
Automate Writing Your LLM Prompts
Using DSPy to automatically create, evaluate, and optimize your prompts The post Automate Writing Your LLM Prompts appeared first on Towards Data Science.

Towards Data Science
How to Fine-Tune an SLM for Emotion Recognition
Python tutorial for fine-tuning a Mistral Small 3.1 on an imbalanced training set to classify 15 emotions in social media communication The post How to Fine-Tune an SLM for Emotion Recognition appeared first on Towards Data Science.

Towards Data Science
How to Navigate the Shift from Prompt-Based Tools to Workflow-Driven AI
Abacus.AI and the case for unified AI workflows The post How to Navigate the Shift from Prompt-Based Tools to Workflow-Driven AI appeared first on Towards Data Science.