Telling your agent “no” is a moat now, rearward deployed engineers, and harnessing the context for your SDLC
Dev Interrupted

Telling your agent “no” is a moat now, rearward deployed engineers, and harnessing the context for your SDLC

B2Upper-intermediateAugust 14, 202634 min
  • Technology

This week on the Friday Deploy, Ben and Andrew explore Uber's strategy of "rearward deploying" engineers to spread agentic AI workflows into departments like legal and marketing. They also dive into Anthropic making Claude Code's Auto Mode the default, Meta's new on-device Muse Glimmer model, and Tim O'Reilly's case for an open source AI ecosystem. Finally, they break down context engineering for the SDLC and examine new research showing why generalized agent skills outperform personalized ones. Register: Dev Interrupted Presents: The Software Factory Roundtable Follow the show: Subscribe to our Substack Follow us...

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I think that's two weeks in a row now.

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0:00

Oh, so Andrew, we got a new life beyond token maxing story yet again for this week. I think that's two weeks in a row now. But it's not a different kind of life beyond token maxing story than what we're typically used to.

0:19

We're used to people revolting against token maxing and then putting all these restrictions on it. We have Uber, who's back in the news, who's known as one of the organizations that token masked earlier this year.

0:32

There's CTO's out there sharing some new ideas on how they're deploying their agents, their engineers tokens. Now, and it's actually a really interesting idea. They're now sending their engineers out into other teams within the company, within Uber,

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