When Your AI Can't Tell the Difference Between "Fine" and Frustration
Final Results of SER Accuracy of Gemini 2.5 Flash and GPT 4o across the two modalities.
LLM
When Your AI Transcription Turns "Tasty Burger" Into "Nasty Murder"
WER vs SNR for Transcription Models
Your Horrible Code is Making LLMs Evil: Exploring Emergent Misalignment
What is Emergent Misalignment?
One bad apple can spoil the bunch. Apparently this stands true when speaking of finetuning tasks too. A recent paper uncovered a quite interesting phenomenon: finetuning an LLM on insecure code led it to show homicidal tendencies in conversations. And this is not just a fluke,
Building and Evaluating a Reddit Insights Agent with Gumloop and Maxim AI
Reddit is one of the internet’s most valuable data sources, and also one of the most chaotic. Somewhere between the hot takes on r/technology and the unsolicited growth advice on r/marketing, there are real signals hiding in plain sight: what people are building, breaking, hyping up, or
Sure your LLM is smart, but does it really give a damn?
You can take your model to the water, but you can’t make it think.
Every frontier lab’s model drops are accompanied by boasts on improved capabilities on a dozen benchmarks. A recent study explores that the fact that a model is capable of accomplishing a task doesn’t
🐞 Building an Agentic Debugging Game: Anthropic for LLM & Maxim for Observability
Welcome! In this tutorial, we'll build a fun, interactive AI agent called "Guess the Bug." The agent will use Anthropic's Claude model to generate simple Python code snippets with hidden bugs. Your job is to find the bug, and the agent will tell you
Making Language Models Unbiased, One Vector At a Time
Introduction
AI has officially broken out of the tech bubble and into everyday workflows, boosting productivity but also raising safety concerns, especially around bias in large language models. These models inherit societal biases from internet data, and debiasing efforts by frontier labs can sometimes go too far (remember the racially