Over the last few days, a small AI experiment has been circulating across LinkedIn and Reddit: open a completely fresh ChatGPT session, ask for a random number between 1 and 100, and observe what happens. Surprisingly often, the answer is 73. Sometimes it is 42. In English sessions, 79 also appears more often than true […]
Understanding Randomness in LLMs: Why ChatGPT Often Picks 73, 42, or 79 Weiterlesen »
You open your editor, describe what you want, and a few seconds later there is code on the screen. It is not perfect, but it is usually good enough to keep moving — especially when you are exploring something new or trying to get to a first working version. That removes a lot of friction,
Coding Agents Feel Cheap. That Might Not Last. Weiterlesen »
Over the last couple of days, my LinkedIn feed has been flooded with euphoric posts about “Code AI” and “local coding assistants”. Screenshots of terminals, bold claims about productivity exploding, and the familiar undertone that if you are not running an LLM locally via Ollama, OpenCode, or Copilot, you are already falling behind. I know
Running Code AI Locally: An Engineering Reality Check Weiterlesen »
Why the future of AI depends on more than fancy prompts and flashy demos? A Useful Tool—But Not a Mastermind Let’s start here: I use GenAI every day. It drafts outlines, rewrites emails, summarizes documents, and helps me explore code ideas at 2 a.m. when my brain stalls. I value the convenience, the speed, the
GenAI Is the Loudest Kid in Class—Not the Smartest Weiterlesen »
For years, the AI industry followed a fairly simple story: bigger models required more GPUs, more GPUs required larger data centers, and larger data centers required more electricity, more capital and ever more expensive infrastructure. If you wanted to compete at the frontier of artificial intelligence, the assumption was that you needed access to enormous
The $5.6M AI Training Run That Shook $1 Trillion of AI Stocks Weiterlesen »
In the landscape of AI and machine learning, Amazon Web Services (AWS) has introduced two major services—SageMaker and Bedrock—that cater to the needs of developers and businesses seeking to deploy machine learning (ML) models at scale. Although both services enable the integration of AI into various applications, their use cases and functionalities differ significantly, warranting
Navigating AWS SageMaker and Bedrock: Understanding Their Differences and Use Cases Weiterlesen »