jaynate

RPO remaining performance obligations (not on balance sheet) unless prepaid then its deferred revenue (balance sheet). When it’s consumed it is recognized as revenue.

That’s not the way it works. Google still recognizes the revenue. The cost of goods sold and gross profit are what get hit here but it’s no different than any other partner than can sell their product through the Google marketplace and take advantage of committed spend.

>> “As engineering capacity is no longer a bottleneck, some engineering teams are starting to experience product starvation”

This has always been a problem that Marty Cagan calls “feeding the beast”, but it’s much worse now. Do engineers need to become more cross-functional themselves as well as their product mgt, UX, and quality control counterparts?

> “Then you tried to ship real client work, and the dream fell apart.”

For me it was here. I have tried for months to get AI to write for me and have now fully embraced the fact that LLMs are not ready to do that for me. None of them. I’ve gone back to writing my own content and only using the LLM to brainstorm.

The underpants gnomes. Well played!

1000%. The expense of writing software was and still is high.

Whether it’s commercial software company or an internal team writing custom software, the return on that investment depends on many things outside of the code itself.

When I initially saw this I was excited because what I’m trying to solve for is visibility of both semi and fully autonomous and agents at runtime. Marble is an improvement over chat and Claude but it’s incremental IMO.

We’ve toyed with a meta agent style interface which you chat to do everything. Add tools, connect datasets, build agents, deploy and schedule them, build skills, etc.

But at some point agents have to have an interface for human approvals, validation, training, and tuning.

I guess it’s like a spreadsheet. Ultimately agents are part of custom apps which are each going to be as unique like snowflakes. So it’s difficult to have one standard abstracted runtime interface without feeling obtuse to a business user. I think what I’m describing and wanting are new app layer building blocks vs one interface to rule them all.

All of this said, I’m all for a better personal llm harness interface (which is what this is). So keep up the great work guys!

>> Thanks for reading! Let me know if anyone's interested in more posts about this bowling nonsense.

I’d say, “yes!” judging from the upvotes.

Are you running in any enterprise environments that you know of?

Wow, can’t understand why this isn’t getting more attention.

For clarity, inference is typically a COGS and therefore hits Gross Margin vs model training which would typically be in OpEx (where R&D lives) and would hit operating margin.

Yes, and the free-for-all building of nonsense (and insecure) apps by non engineers is probably going to slow down as well.

Feels like we’re continuing to trend toward deterministic workflows which may actually be okay in 90% of cases. Reality is there’s a lot of unnecessary token burn happening right now. Simple market dynamics will solve that, i.e., when token cost subsidies begin to fade away and we face the true cost of agent applications.

Simply... amazing

Who cares how much you paid? This doesn't seem predatory, you signed up for it and you violated the TOS.

Isn’t there coverage on any other site with fewer ads and popups? I could literally barely navigate the article on my phone.

And I think it’s less about letting agents modify the product source. That’s more of a platform capability which should also be a requirement for certain types of use cases. All comes back to listening to and / or innovating for customers.

“Their relationship with the software is one of pure dependency, and when the software doesn’t do what they need, they just… live with it”

Or, more likely, they churn off the product.

The SaaS platforms that will survive are busy RIGHT NOW revamping their APIs, implementing oauth, and generally reorganizing their products to be discovered and manipulated by agents. Failing in this effort will ultimately result in the demise of any given platform. This goes for larger SaaS companies, too, it’ll just take longer.

Good engineers are way more important than they’ve ever been and the job market tells the story. Engineering job posts are up 10% year over year. The work is changing but that’s what happens when a new technology wave comes ashore. Don’t give up, ride the new wave. You’re uniquely qualified.

I think about this book often since Nov 2022. It’s a fantastic read.

> The downside is that the dryRun-flag pollutes the code a bit. In all the major phases, I need to check if the flag is set, and only print the action that will be taken, but not actually doing it.

Sounds like a case for the state machine pattern

I think we are both saying similar things here (believe it or not). Sales leaders turnover with surprising frequency - 18-24 months. About the time the sales team tells you what they “want” and you fine tune it, they will be gone. The next person will come in and probably scrap 50-75% of what the prior leader did. New requirements.

Meanwhile, besides functionality, you’ll want/need to plug in the latest and greatest go to market tools for marketing -and demand gen. But… that’ll be a custom effort, too.

Along the way you’ll also realize that you’re missing out on the most common practices in the industry because you built some idiosyncratic tool that only is relevant to your company.

History may very well prove me wrong, but I think you’re underestimating the expertise that underlies these products and platforms. It’s not just code, and the costs of getting it wrong are more than just an engineer’s time. When you waste time in GTM the impacts on the business and valuation are not linear, they’re exponential.