
builder of many things // I lead tech @ resin8 // cohost of @ai_rebels
Joined June 2021
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Some more thoughts:
The core weakness is that it’s an orchestrator without the means to properly track and communicate progress on work it delegates. Links to the threads it spawns do not constitute a sufficient overview.
Unfortunately my experience with Dots has trended negative. Wanted to love them, and some aspects of the user experience (connectors to slack/gmail especially) are very well realized, but the core loop is very lackluster and leads to the Dot not following through with tasks I assign to it
Lotta people out there don’t want business to be business they want a god figure (government, AI, their mom) who coddles them
Billions must read the Zanzibar paper. Think about authorization issues at scale bc you will be facing it down soon whether you want to or not
∿spencer. retweeted
Railway has legitimately been scary the entire time. PaaS that makes you scared
∿spencer. retweeted
We've developed a new inference engine, called 'e'.
It's the closest thing to alien technology which we have, and pushes 10,500 tok/s aggregate on a single DGX Station with the new @NVIDIAAI Nemotron 3.5 Super VL model, released a few minutes ago.
Actual Computer turns your NVIDIA DGX Station into a department-scale AI factory.
Read the full case study:
actual.inc/company/blog/case…
I’m ditching CI btw I think Anthropic is right and velocity of delivery is what we should plan around
I’m very pro AI, but in terms of the Twitter software engineering discourse I see mostly people doing single person vibe coding, or AI labs with infinite compute Ralph Wigguming giant codebases.
What I don’t see are people doing AI software engineering that has multiple humans contributing to a single code base, each using AI agents, working in a non-commodified domain.
In this world I believe the AI hype bois are over their skis in terms of proclaiming the death of software engineering. Maybe in the future but for now in this 6 month gradient of the Singularity the reality on the ground are things like:
- Using CI as a harness to ensure agentic coding is correct and validated
- Balancing velocity with other concerns (reliability, choosing the right problems, quality, etc)
- Making sure the system has an architecture and philosophy so that you don’t end up with spaghetti
- Still doing code reviews (though they can be augmented with AI) to ensure you don’t end up with cognitive debt.
- Balancing AI costs, and mixing in “human tokens” where appropriate
- etc
I’d love to hear more from others in this domain! Ie those not just doing solo development projects or AI labs with infinite compute
∿spencer. retweeted
Someone could make a *lot* of money creating a curated influencer network of people who like to post about AI tinkering, put them all in touch with a roster of interested companies, and exchange resources like compute/credits in exchange for marketing they're already doing.
You actually don’t want heuristics that require sophistication of thought to apply. It’s important to have some grug brained concepts to throw around like footballs








