Data/AI/Growth Engineering at Rippling. All views my own.
San Francisco, CA
Joined April 2022
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We need to know how this quick POC goes…
I don't think we should fight. The two companies are complementary. Since you're not doing this AI stuff, I really think Snowflake should consider getting its AI tokens through Databricks. You can get both cheap OSS models as well as frontier (OAI/Ant/Gemini/Grok) models from our platform. It all goes through Unity Gateway which has Smart Routing, Budgets, and Access Control. You can even use Omnigent with any harness, including CoCo. Most of our customers used this to reduce token spend ~30% last few months while token count went up. Genie lets you then track, control, manage that spend, all your financials, marketing, etc. Quick POC?
john kutay retweeted
I want to give a shout out to @parkerconrad at @Rippling . We've been squeezed by @Workday for years. And the antiquated platform that @Workday had which we put in place in the process towards going public led to a ton of broken processes and extra staffing of workday consultants and vendors on top. Millions of dollars of total burn on a system which we used 30% of. I was told we would be forced to renew for another year with Workday 2 weeks ago. Gun to our head. No choice. I reached out to @parkerconrad on a saturday afternoon on linkedin and had a response back from him in less than 30 min. It was amazing. We moved over to @Rippling today. We are going to save money, time and brain damage and streamline our operations dramatically in finance and HR as a result. Thank you @Rippling team and @parkerconrad for showing us how a large company can move fast when its founder led. @aneelb your team should take note. $BETR
The year is 2026. You can run your entire business using Rippling AI on a foldable iPhone.
Chat with @Rippling AI, book travel, submit expenses, and schedule time off in spacious luxury!! using the latest release of the Rippling app on your bendy new iPhone Duo.
Most companies are terrible at measuring ROI for their large investments. This is why vendors bring in 3P analysts to make these case studies with their customers.
Huge red herring to act like this is unique to AI.
"Despite massive investments and widespread hype around artificial intelligence, the technology has delivered zero financial payoff to corporate America outside of tech."
spr.ly/6014BH5uME
Subagents are just subprocesses with context windows for people who never wrote orchestration code before 2026.
Anthropic is now recommending that companies REMOVE unnecessary subagents from their AI systems.
This is a pretty significant shift in agent architecture, with their team finding that a single agent equipped with skills can frequently outperform complex multi-agent systems, and one customer seeing roughly 2x faster execution.
The emerging rules of thumb:
→ Skills: Teach the agent HOW to do something, including procedures, expertise, and instructions.
→ Tools / MCP: Give the agent the ability to DO things through APIs, actions, and live data.
→ Subagents: Use for parallel work, isolated context, or tasks a single agent struggles with.
The interesting part is progressive disclosure. Instead of spawning specialized agents or stuffing everything into a giant prompt, one agent discovers and loads skills and tools as needed.
Their advice is to fold existing subagents into the main loop, turn their instructions into skills, expose their actions as tools, and run evals to see if performance holds up.
As models have gotten smarter, improving agent architecture is becoming less about what you can add and more about what you can delete.
claude.com/resources/article…
Avoidant is the perfect word for this.
“What’s your plan for growth?”
“Hiring a CRO”
“then what?”
“Then I can focus on coding and fun technical problems again!”
We are in the post-stability, post-security era of software
We are currently shutting down Postiz for all customers. We are afraid @Railway has been compromised; they won't tell us.
We have received emails from Anthropic and Stripe that our keys have been compromised.
We are digging into it and will get back to you ASAP!
System 1 was a vague/opaque name when psychologists coined it but it’s a more accurate definition for what Jev is good at. “Decision model” is also not perfect because other model types make decisions too…Jev is just more suited for a category of common, high frequency decisions.
“Hardest problems in CS is naming” etc
