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Chain-of-Thought Prompting: Getting AI to Reason Step by Step — Prompt to Profit · Day 11 of 30

Chain-of-Thought Prompting: Getting AI to Reason Step by Step — Prompt to Profit · Day 11 of 30

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The single technique that separates AI users who get plausible answers from those who get genuinely intelligent ones. Welcome to Week 3. For the past two weeks, you’ve been building your foundation — prompting structure, templates, roles, workflows. Today we shift to intermediate territory, and we start with the most significant single technique upgrade you can make to any prompt. Chain-of-Thought prompting. It sounds academic. It isn’t. It’s the difference between asking AI a question and having AI actually think before […]

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Quiz: How to Use GitHub Copilot Code Review in Pull Requests

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In this quiz, you’ll test your understanding of How to Use GitHub Copilot Code Review in Pull Requests. By working through this quiz, you’ll revisit how to request a review from Copilot on your pull requests, apply or push back on its suggestions, configure automatic reviews, and use custom instructions to make Copilot’s feedback follow your team’s conventions. [ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your […]

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The Challenge of Vanishing/Exploding Gradients in Deep Neural Networks

The Challenge of Vanishing/Exploding Gradients in Deep Neural Networks

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Training deep neural networks often encounters challenges like the vanishing gradient and exploding gradient problems. These issues can hinder learning by either shrinking gradients to near-zero or causing them to grow uncontrollably. This article explains the problem of exploding and vanishing gradients while training a deep neural network and the techniques that can be used to overcome this impediment. In this article, you will get a clarification about vanishing and exploding gradients and their problem and solutions. With […]

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Built an RL framework for training LLMs where you can actually understand what is going on!

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RL is a weird creature. It is hard to make work, and even when the implementation looks correct, training can still go sideways for some random reason. Training LLMs with RL makes this even messier. Now you have the RL algorithm, distributed training, rollout engines, reward computation, weight syncing, orchestration, and a bunch of small implementation details that can quietly break everything. That was the motivation behind FeynRL (pronounced “FineRL”), a framework I built and recently released. The […]

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Walmart’s AI workflows meet the realities of the balance sheet

Walmart’s AI workflows meet the realities of the balance sheet

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Walmart has reportedly begun limiting employees’ use of an internal AI assistant called Code Puppy after demands placed on the LLM backing the tool were higher than expected. Employees of Walmart were encouraged to use Code Puppy without any stricture or stipulations as to the limits of use, but Walmart is now assigning employees a fixed number of AI tokens, which limits how much it can be used. Code Puppy was publicised as being able to help with […]

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MiniMax M3 Decodes 1M Tokens 15x Faster — and It Shouldn’t Be This Cheap

MiniMax M3 Decodes 1M Tokens 15x Faster — and It Shouldn’t Be This Cheap

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Author(s): Chew Loong Nian – AI ENGINEER Originally published on Towards AI. MiniMax M3 Decodes 1M Tokens 15x Faster — and It Shouldn’t Be This Cheap On June 1, a Shanghai lab quietly shipped a model that decodes a 1-million-token context 15.6x faster than its own previous generation — and charges you roughly 8% of what Claude Opus costs to do it. I spent two days poking at MiniMax M3 through the API, and the part that actually […]

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Using Amazon SQS for AI Agent Orchestration

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Author(s): Pallav Kant Originally published on Towards AI. Using Amazon SQS for AI Agent Orchestration As AI agents become more capable, organizations are moving beyond standalone chatbots and building systems where multiple agents work together to complete complex tasks. A single request may involve one agent gathering information, another analyzing data, a third generating content, and a fourth validating the results. Coordinating between these agents to work asynchronously requires a reliable way to exchange information, hand off work, […]

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