Ask any teacher what actually eats their time, and it's rarely the hours in front of students. It's everything that happens before and after: building a quiz from scratch on a Sunday night, formatting flashcards for that same unit the next day, then doing it all again for the next lesson. Edcafe AI claims it can shrink that entire cycle into one workspace by handling planning, creating, saving, assigning, and assessing classroom materials instead of making you bounce between a slide tool, a… [...]
Training AI reasoning models demands resources that most enterprise teams do not have. Engineering teams are often forced to choose between distilling knowledge from large, expensive models or relying [...]
In building LLM applications, enterprises often have to create very long system prompts to adjust the model’s behavior for their applications. These prompts contain company knowledge, preferences, a [...]
LinkedIn is a leader in AI recommender systems, having developed them over the last 15-plus years. But getting to a next-gen recommendation stack for the job-seekers of tomorrow required a whole new [...]
When enterprises fine-tune LLMs for new tasks, they risk breaking everything the models already know. This forces companies to maintain separate models for every skill.Researchers at MIT, the Improbab [...]
As agentic AI workflows multiply the cost and latency of long reasoning chains, a team from the University of Maryland, Lawrence Livermore National Labs, Columbia University and TogetherAI has found a [...]
Anthropic on Monday released Code Review, a multi-agent code review system built into Claude Code that dispatches teams of AI agents to scrutinize every pull request for bugs that human reviewers rout [...]
AI agents choose tools from shared registries by matching natural-language descriptions. But no human is verifying whether those descriptions are true. I discovered this gap when I filed Issue #141 in [...]