Microsoft AI just announced its first text-to-image generator, MAI-Image-1, designed and developed in-house. The tech giant, which recently announced its first in-house Microsoft AI models, called the new image generator “the next step on our journey.”
Microsoft says it sought feedback from creative professionals in order to avoid “repetitive or generically-stylized outputs.” MAI-Image-1 “excels” at photorealistic imagery like lightning, landscapes, and more, the company claims. And it can process requests and produce images faster than “larger, slower models.” The model has already secured a spot in the top 10 of LMArena, the AI benchmark site where humans compare outputs from different systems and vote on the best one. — Read More
Daily Archives: October 14, 2025
InferenceMAX™: Open Source Inference Benchmarking
LLM Inference performance is driven by two pillars, hardware and software. While hardware innovation drives step jumps in performance every year through the release of new GPUs/XPUs and new systems, software evolves every single day, delivering continuous performance gains on top of these step jumps.
… [The] pace of software advancement creates a challenge: benchmarks conducted at a fixed point in time quickly go stale and do not represent the performance that can be achieved with the latest software packages.
InferenceMAX™, an open-source automated benchmark designed to move at the same rapid speed as the software ecosystem itself, is built to address this challenge. — Read More
Technological Optimism and Appropriate Fear
I remember being a child and after the lights turned out I would look around my bedroom and I would see shapes in the darkness and I would become afraid – afraid these shapes were creatures I did not understand that wanted to do me harm. And so I’d turn my light on. And when I turned the light on I would be relieved because the creatures turned out to be a pile of clothes on a chair, or a bookshelf, or a lampshade.
Now, in the year of 2025, we are the child from that story and the room is our planet. But when we turn the light on we find ourselves gazing upon true creatures, in the form of the powerful and somewhat unpredictable AI systems of today and those that are to come. And there are many people who desperately want to believe that these creatures are nothing but a pile of clothes on a chair, or a bookshelf, or a lampshade. And they want to get us to turn the light off and go back to sleep.
In fact, some people are even spending tremendous amounts of money to convince you of this – that’s not an artificial intelligence about to go into a hard takeoff, it’s just a tool that will be put to work in our economy. It’s just a machine, and machines are things we master.
But make no mistake: what we are dealing with is a real and mysterious creature, not a simple and predictable machine. — Read More
The Claude Code SDK and the Birth of HaaS (Harness as a Service)
As tasks require more autonomous behavior from agents, the core primitive for working with AI is shifting from the LLM API (chat style endpoints) to the Harness API (customizable runtimes). I call this Harness as a Service (HaaS). Quickly build, customize, and share agents via a rich ecosystem of agent harnesses. Today we’ll cover how to customize harnesses to build usable agents quickly + the future of agent development in a world of open harnesses. — Read More
MIT report: 95% of generative AI pilots at companies are failing
The GenAI Divide: State of AI in Business 2025, a new report published by MIT’s NANDA initiative, reveals that while generative AI holds promise for enterprises, most initiatives to drive rapid revenue growth are falling flat.
Despite the rush to integrate powerful new models, about 5% of AI pilot programs achieve rapid revenue acceleration; the vast majority stall, delivering little to no measurable impact on P&L. The research—based on 150 interviews with leaders, a survey of 350 employees, and an analysis of 300 public AI deployments—paints a clear divide between success stories and stalled projects. — Read More