reagle

I’ve been looking forward to whenever 1.0 for a few years now. Looks like it’s actually close now!

- 2. Guiding Principles

- 2.1. Be humble

- 2.2. Be bold

- 2.3. Put humanity front and center

- 2.4. Lean into learning

- 2.5. Teach with intentionality

- 2.6. No one size fits all

- 2.7. Augmentation not automation

- 2.8. Think beyond the classroom and the campus

- 3. Recommendations

- 3.1. Adapt educational processes for an AI-aware world

- 3.1.1. Revisit course goals

- 3.1.2. Ensure durable learning through new course policies, structures, and forms of assessment

- 3.1.3. Emphasize experiential and project-based learning

- 3.1.4. Build structured in-person social learning into subjects

- 3.1.5. Preserve and expand out-of-class research and career experiences

- 3.1.6. Reconsider grades and incentives

- 3.1.7. Expand in-person spaces for labs and in-person evaluation

- 3.1.8. Provide AI use policies, with justification

- 3.1.9. Exercise caution with AI detectors and online exam platforms

- 3.1.10. Support responsible experimentation in the curriculum

- 3.2. Center people, community, and the residential experience

- 3.2.1. Define and communicate the value of residential education

- 3.2.2. Strengthen social connection and personal wellbeing

- 3.2.3. Encourage instructor disclosure around their own AI use

- 3.2.4. Teach effective, responsible, and ethical use of AI

- 3.2.5. Recognize and mitigate negative impacts of AI

- 3.2.6. Acknowledge AI use in theses and other research work

- 3.3. Build processes, teams, and tools for continuous reflection, iteration, and improvement

- 3.3.1. Establish an ongoing AI and education committee

- 3.3.2. Create school/college- or department-level AI Leads

- 3.3.3. Fund AI Fellows and an AI Implementation Team

- 3.3.4. Create an AI Pilot Fund

- 3.3.5. Provide ongoing training and instructor support

- 3.3.6. Develop metrics

- 3.3.7. Ensure equitable technology access

- 3.3.8. Protect sensitive data and preserve model choice

- 3.3.9. Establish privacy, logging, and auditing policies

- 3.3.10. Monitor AI costs and environmental impact

- 4. Conclusion

That's what I wondered.

visidata is vim influenced.

On github, I see an asciinema tag, but no explanation of the differences?

So they were the baddies?

This could be a nice pre-filter for VisiData…?

To extend your Lego metaphor to the question of “is a lot of this irrelevant?“ Does your Lego model of a city need to have interior floors, furniture, and fixtures in order to satisfy your requirements? Perhaps in some cases, but not in most.

What's so complicated about his legacy? I read the article and don't see it.

I’ve been waiting for them to release a standalone markdown editor, but it’s been a couple years already now.

You might want to check out perplexity.AI. It has access to many of these models, and it’s trivial to switch models mid conversation and ask it to repeat itself, so that you can see the differences.