I have to post it again because I’m so excited about it. I was featured in a video with @cursor_ai and I just can’t stop smiling about it.
https://t.co/j4Z0xnBv0s
We're proud to be featured in @OpenAI's Decisions API launch.
Enterprise AI gets useful when understanding a document leads to action. Our work with Box Extract and the Decisions API explores that fast-decision layer, turning document understanding into an automated workflow. Routine work automated, lower-confidence cases sent to reviewers.
when the cybersecurity team finds out there are actual vulnerabilities and they have to do their jobs instead of trying to trick someone into clicking a phishing email
Here's a new AI app stack you can dive into this week.
File systems coupled with LLMs become a powerful backend for apps.
@Box ran a hackathon with @awscloud and challenged hackers to explore this new concept.
I came out of this hackathon inspired and fired up for whats possible with this new stack.
Read about it here 👇
The countdown to BoxWorks is on, and we can’t wait to bring this community together again.
Join the brightest minds in Content + AI November 5–6 in San Francisco for keynotes, hands-on demos, and partner solutions in the AI innovation showcase. Connect with fellow builders and explore how business content can power your next AI workflow.
Bring your questions, and leave with ideas to put to work.
Register: https://t.co/ZgtYdRCDRE
The biggest misread of anyone predicting the end of software engineering was imagining that the world only needed a certain amount of software, and bringing automation to the field would reduce the need for labor.
Exactly the opposite is happening where essentially most things are actually turning into software. Because agents can use tools, work with computers, write software on the fly, and process data like humans (but far faster), we can now throw “software” at problems that never could reasonably be digitized before.
What that leads to is the role of engineering taking on greater importance in the economy, because we can now engineer far more systems and processes than were possible before.
If you’re a bank trying to do new forms of trading, engineers are often your first bet. If you are in life sciences, you expect more and more research and lab work to be engineering centric. If you’re in manufacturing, you’re trying to automate more of the supply chain, all that is engineering. More and more work is shaped like software problems.
Yes we’re not going to be coding manually in the future. But almost everything will be code, which means those that understand it are in a fantastic position.
They did it. They made a Muse open weights model, which is a huge step forward for OSS in the US.
This is going to further accelerate AI diffusion because enterprises will be able to mix and match frontier performance for orchestration and high end tasks, while being able to have low cost and tailored post trained models for repeatable workflows.
Overall this dramatically increases token consumption and workloads across the stack. Great to see Meta doing this.