21–26 of 26 posts
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Be skeptic of those telling you that technological advances are bad. They usually want something from you. And it’s usually your vote.
What political office do you believe Finnucane is running for?
That no one can shut the eff up about it. Even in the furthest spaces from tech, you hear people talking about it. There's no escape.
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I dislike the “brainwashed” comment from sibling, I believe it makes some assumptions. There aren’t any doubts that: - AI is extremely resource intensive, consuming electricity, water, silicon, etc at levels possibly never seen before in humanity’s history; whether that’s a waste or not is subjective - Massive datacenters are popping up like anthills, and coupled with R-flavored regulation rollback there is a definit…
I think you’re right. In a very narrow, short term scope. That’s the issue. The problem with this argument is that assumes the world is static. When trains were invented, they polluted a LOT. Technology evolved. Looking backwards, the amount of value unlocked by them outweighed by order of magnitude the short term pollution they generated. Inefficient in the short term. Generation changing over the longer horizon. Ex…
Said less calamitously (word?): while it’s important to be objective, objectivity is difficult when there are real existential risks.
Thank you for the frank discussion!
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I do this every day, because Codex writes my requirements and Claude implements them. Just ask it for whatever you think the next model will need, tell it to be verbose if you like, and even have a second ChatGPT check it if you are worried. You can even give it a format, going as far as providing a specification or template if you do it frequently. Stick that template in both ChatGPT and claude projects so one can w…
Holy shit. That’s scarily clever. Do you trigger it at a certain max token spend ratio? And do you think it generalizes to pass all kinds of context or its tailored for structured tasks?
I had a large context model analyze the last ten years of my notes and build an ontology, it took a lot of iterating. Examples; A software project may have Decision, Risk etc entities; Life may consist of Activities, Goals, Concerns, Problems, etc; the World has knowledge/facts (Topic Taxonomies like Wikipedia) etc. These are all joinable given the relationships are intact.
The agent put everything into a huge RDF ontology - a world model. I worked with the agent to re-frame that large ontology, to I can build a skill appropriate for a small context model to serve as an expert on the ontology, it owns it for all intents and purposes.
I then worked with an agent to define use cases using my notes, real world things I do and have done: research, project management, goal setting, “hey I found a cool project online, it would be useful for my X project which is on the back burner but I don’t want to forget about it” you get the idea. I used those use cases to build out a few skills.
These serve as the actual ontology-aligned data layer, and so have access to URIs pointing to Goal entries in Obsidian, Projects in ClickUp, my calendar etc etc. And so it knows what my most salient Concerns are, and which other entities are associated with them - projects, goals, documents etc, and utilize various MCP tools for external systems. It also creates “context packs” using its view across systems, and so I can have it export structured markdown for any arbitrary entities and their direct and transitive relationships, to facilitate some other agent performing targeted work.
This enables me to: 1. Build targeted software tools for life and work management, I bring the skills into that project which own the data layer OR export context in the form of agent-ready markdown 2. Give skills to my Claws (which aren’t Claws at all, it can be any arbitrary skill supporting agent harness like Goose or Hermes) and LLM apps like Claude Desktop.