randomdrake

Why wouldn’t they? After reading Careless People I’m not surprised anymore when Meta sinks to lows like this.

Why? They’ve done it before. The punishments did not outweigh the advantages. Don’t forget Myanmar, or the United States, or countless other countries where meddling has occurred in relation to public change or elections.

I deleted all Meta products from my phone and am waiting on my account export before removing Meta from my life completely, after nearly 20 years.

They could’ve become something very different. I celebrated Zuckerberg ringing the opening bell thinking, “finally, a geek got his day.” Little did we know.

They chose this.

Yes! I used to love watching the visualizations for this. You’ve got the right idea. If it truly is AI processing to help us eventually create this virtual biology, why not our idle machines with models running on them?

You’re right, of course. I never made it out of PoC. But I also implemented something with locally running models to do the same thing. Request comes in, gets forwarded to a machine not currently busy, it serves.

It would be great if we could use the credits we pay for and don’t use on our subsidized subscriptions as well. But, yes, a matter of “when” not “if” for your account getting shut down.

I’ve been toying with the idea of a SETI@Home successor that uses leftover subscription credits from the (currently) subsidized AI providers to help contribute to a collective goal like this.

I wonder if there will be any opportunities to combine resources collectively like that again in the future.

I built a small prototype that would let people in less-developed countries get AI responses from it for free. But it’d be super neat to work together and put our laptops and subscriptions to work on something like this.

How can we determine whether it’s negligence, or advertising? Do we give the benefit of the doubt? Apply Hanlon’s razor? What would we lose, in terms of capabilities, if the underlying behavior wasn’t allowed to accidentally happen? Would we have worse models than we do? There are so many questions. I’m afraid that, until publicly disclosed discovery occurs in one of these cases, we’re just guessing for so many important conclusions.

I wonder if this extends to training models on new content as well. Are we creating a cyclical information-consumption and training situation in which models being trained are more likely to pick up on and reference content created by themselves or by other LLMs than by other humans?

  Location: Tigard, OR (Portland Metro)
  Remote: Yes
  Willing to relocate: No
  Technologies: TypeScript, Python, Go, PHP, AWS, Kubernetes, React, Angular, Postgres, MySQL, Claude / MCP infrastructure
  Résumé/CV: https://davidbyrondrake.com/david-drake-resume.pdf
  Email: [email protected]

Engineering leader with nearly 20 years building teams that ship. Looking for Engineering Manager, Director, or VP roles.

Most recent: DevOps and Engineering Manager at Counterpart (insurance), where I led two full-stack teams plus DevOps and built an MCP infrastructure connecting GitHub, Linear, Salesforce, Snowflake, and Slack across the full insurance lifecycle. Agile success rate peaked at 89% against an 80% industry benchmark. A git-tracked Salesforce deployment pipeline I built on GitHub Actions saved roughly $20k a year and made deploys boring again.

Before that: Director of Engineering at Flourish Software (up to 15 engineers, 60% improvement in on-time delivery, 50% reduction in support load), Senior Full Stack at Expensify (moved millions of dollars a day, six-stack infrastructure), CEO of RedDep Labs (17 people, $8.6M valuation), CTO at iCracked (Y Combinator, 400+ technicians, dispatching system).

How I lead: I hire people who care about each other's success as much as their own, set clear commitments, and then trust the team to do the work. I trained as an actor and was a special education teacher before I wrote code, which is how I keep technical conversations legible to non-engineers and how I'm comfortable in any room.

Best fit: a team that values shipping over performance, has real users, and wants a leader technical enough to read the code and grounded enough to keep the team steady when things get hard.

I got a paper route just to get a hold of the dx2.

It was a life-changing machine.

Ordered, I believe, from the depths of a Computer Shopper magazine.

When I was in college, we were required to take a business class (Business 101) that mandated a finished business plan as part of the project.

It had to be long, in-depth, and include everything you mentioned.

I was incredibly surprised when I entered the tech and startup workforce that these were generally absent.

I had misunderstood the class and instructor and thought that you couldn't even start a business without one.

Then, when I started raising money for my own venture, I thought for sure a complete business plan was a prerequisite.

Nope. A few graphs, preferably hockey-shaped, and a good story were all that was necessary.

My venture failed, of course. But if I were to do it again, I would do myself the favor of having a complete plan. It would definitely save a lot of headaches and guessing in the moment.

Thank you for your post. Very informative. Why is it too early for AI? It’s clearly an emergent cultural evolutionary byproduct that’s been many years in the making and quite mature. Perhaps your own bias is limiting you to imagine what AI is truly capable of?