๐Ž๐ฉ๐ž๐ง๐‚๐ฅ๐š๐ฐ ๐Ÿ๐จ๐ซ ๐๐ฎ๐ฌ๐ข๐ง๐ž๐ฌ๐ฌ – ๐ง๐จ๐ฐ ๐ก๐ข๐ซ๐ข๐ง๐ ! (Test post :))

A lot of people have been sleeping a little less lately. I know I have. But the potential upside of getting this right (along with the fun of building at this edge) makes the shorter nights worth it.

Over the past year, like so many, weโ€™ve steadily replaced core workflows at Base with AI systems built in-house. That matters to us because control over the system means control over cost, security, and capability.

In January, we shifted to building with ClawdBot. Open-source, result-driven execution, and persistence gave us flexibility and power packaged into one. It also meant not being locked into someone elseโ€™s roadmap or pricing model. But experimental technology often comes with security risks. And so did OpenClaw.

So we had to tackle security first
Then invest in infrastructure
Optimized token usage to reduce cost
Expand infrastructure
Benchmarked fully local models against hybrid performance
Also bought toys and explored fun use cases ๐Ÿ˜Š

At a certain point, it became clear this isnโ€™t just a productivity tool. It is a platform layer, capable of supporting real business operations. And as far as weโ€™re concerned, the AGI debate can wait. There is still more than enough value to be captured with secure, specialized AI that solve real business problems.

So what are we trying to get right?

Today, AI covers roughly 90 percent of nearly one-third of Baseโ€™s internal workflows. In most cases, with better consistency than manual execution. Our aim is for AI to handle 100 percent of our ๐’ƒ๐’–๐’”๐’š๐’˜๐’๐’“๐’Œ; predictable, repetitive work that takes any team member longer than 15 minutes.

We intent to achieve this…
Not by hiring more people
Not by increasing overhead
Not by adding another SaaS subscription

We want to do this in-house, with sovereign systems we fully control, at cost neutral or better. Automation only wins if it is secure, sustainable and makes financial sense.

  • On this journey, ๐จ๐ฎ๐ซ ๐ญ๐ž๐š๐ฆ ๐ข๐ฌ ๐ฌ๐ญ๐ข๐ฅ๐ฅ ๐ฅ๐จ๐จ๐ค๐ข๐ง๐  ๐Ÿ๐จ๐ซ ๐จ๐ง๐ž (๐จ๐ซ ๐ญ๐ฐ๐จ) ๐ฆ๐จ๐ญ๐ข๐ฏ๐š๐ญ๐ž๐ ๐๐ž๐ฏ๐ž๐ฅ๐จ๐ฉ๐ž๐ซ ๐ข๐ง๐ญ๐ž๐ซ๐ง๐ฌ ๐ญ๐จ ๐ฃ๐จ๐ข๐ง ๐ฎ๐ฌ until the summer (for info on a similar role see: https://lnkd.in/ecp5TFGJ) If you want to work on real operational AI systems, not just experiments (but definitely ๐š๐ฅ๐ฌ๐จ ๐ž๐ฑ๐ฉ๐ž๐ซ๐ข๐ฆ๐ž๐ง๐ญ๐ฌ ๐ฅ๐ข๐ค๐ž ๐ญ๐ก๐ข๐ฌ ๐Ž๐ฉ๐ž๐ง๐‚๐ฅ๐š๐ฐ ๐จ๐ง ๐š ๐Œ๐š๐œ ๐ฆ๐ข๐ง๐ข ๐š๐ง๐ ๐Œ๐š๐œ ๐”๐ฅ๐ญ๐ซ๐š ๐ฐ๐ข๐ญ๐ก ๐‘๐€๐˜-๐๐€๐ ๐Œ๐ž๐ญ๐š ๐€๐ˆ ๐ ๐ฅ๐š๐ฌ๐ฌ๐ž๐ฌ!) – reach out. Or please pass this along to someone who might be a fit.

If you are building practical AI systems inside your own organization, I would genuinely enjoy hearing your stories and comparing notes. What worked, what failed, what surprised you. We are always learning and are interested to find out what we are overlooking. Weโ€™re all doing our best trying to figure things out in real-time ๐Ÿ˜Š

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