I've been using LLMs to create visualizations for math papers I come across. Prompting "Create a visualization for each segment of this article in the style of a 3 brown 1 blue video using manim." has yielded impressive results.
It helps me digest the content faster and allows me to read more articles than I otherwise would.
Thinking Machines, the started founded by former OpenAI CTO Mira Murati. The interaction models demo’s in their videos imo breaks the awkward turn-based barrier. Returning responses quickly reaches a threshold where it starts to feel like a natural conversation. Their approach to solving this problem is rather clever.
To help learn I use LLMs to generate practice exams for whatever I'm trying to learn, then on the questions I struggle with have the LLMs explain the logic and point out my mistakes. I haven't been in college for over a decade, this is just for topics I'm curious about and want to learn. For any serious topic I recommend auditing the practice exams with a different LLM than the one used to generate to help reduce hallucinations. Seems to work well for me. I quite like reading the "thought" processes shown by DeepSeek.
Not necessarily a bad thing, I'd rather see attempts at innovation or moonshot ideas than sitting on the cash. These are calculated business moves targeting ideas that have a reasonably high chance of success. Isn't Google's graveyard an example of too much cash chasing too few ideas?
Were you auto-committing everything without reading the generated code? and if you read it but didn't understand it why not just ask for detailed comments for each output? Knowing that a larger codebase causes it to struggle means the output needs to be increasingly scrutinized as it becomes more complex.
People noted similar issues ever since LLMs came out, but the rate at which they have been rapidly improving on all of these is significant. Documentation being 4x too long could probably be fixed with a rule instructing the agent to keep it concise and no longer than 2-3 paragraphs.
In my mind, the idea of AGI running amok isn't literal, instead what it enables;
Optimizing & simulating war plans, predicting enemy movements/retaliation - prompting which attacks are likely to produce the most collateral damage or political advantage. How large of a bomb? which city for most damage? Should we drop 2?? Choices such as drone striking an oil refinery vs bombing a children's hospital vs blowing up a small boat that might be smuggling narcotics.
When Apple does eventually produce larger than 16GB / 2TB / 2-port Macs I hope they don’t name them MacBook Pro Max. As long as they are called Macs this naming convention seems silly. I’d prefer that we don’t have any Max Macs.
It helps me digest the content faster and allows me to read more articles than I otherwise would.