What does annoy you the most with AI today? For me it’s having to explain myself every time I swap providers. Claude has no clue to what happened on ChatGPT |
What does annoy you the most with AI today? For me it’s having to explain myself every time I swap providers. Claude has no clue to what happened on ChatGPT |
My biggest annoyance is hiding Thinking tokens; I have little trust in these aliens, and seeing how the sausage was made helped me to be more comfortable with eating it. Anthropic was the biggest provider that did not do this until recently, and they give a good rationale for the switch but that doesn’t make it less annoying. I also dislike the UX they put around it, “Hmm.”, “I should think about this” etc.
I think in the end it all boils down to a trust issue on the big labs
Edit: I shouldn’t admit this, but I even have an ontology defined - RDF and all - for some of my LLM tasks. Its classes contain examples, and so is like a few-shot instruction, and it’s working scarily well for structuring tasks.
- 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 definite risk for environmental impact - just like during our last industrialization push where we poisoned much of the country, leading to a massive rollout of environmental protections in the 1970s and 1980s - Students are taking advantage of LLMs to shirk school responsibilities. Whether this is damaging or not is subjective until proven, and AI may not be causal here (students may not have been getting the expected value from their education without LLMs, again remains to be proven) - Many companies have used AI as a justification for layoffs, who knows what’s actually true though. There is a very real fear across society that it will continue to impact jobs, and senior AI company leaders are fueling this with public predictions of massive labor shifts. Again, maybe they are lying, but can you blame anyone for worrying?
There are counterarguments to all of these, but dismissing the fear as uneducated or brainwashed reveals your own priors and ignores all of these facts. It’s healthy to ingest OP’s criticisms - especially on a form populated most by Smart People (tm).
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. Extend the timeframe of your argument. Do you think it holds 20 years from now when we have more efficient algorithms and energy generation technologies? I don’t think so.
Not to even mention looking at solutions for most basic things like prompt injections. Frankly laughable efforts. No where near what I would consider sufficient...
And somehow they are trying to push this crap to everywhere... Before you even have these things in place...
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!
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.