I scaled back my ambitions from "how to write AI apps" to what I've actually learned. Key lessons: LLMs excel at condensing text, not creating it. Give them the data they need rather than relying on training. Let them self-correct. Use regular code wherever possible. And stop trying to replace humans, especially doctors and lawyers.
God Damn AI is making me dumb
It's so god damn tempting to use AI to write. Whether it is articles, code, or documents. I feel like using AI is diminishing my ability to write myself. ...
How do LLMs affect writing? Talks by visitors to the AI STORIES project | UiB
The AI STORIES project is hosting a day of research talks focused on analysing AI-generated writing, with approaches ranging from the qualitative analysis of a professor of writing to large-scale computational analysis finding that prompting an LLM to edit a text to improve its grammar leads to significiant shifts in meaning.
How to write well with AI
Why people who pledge never to write with AI are telling on themselves
The best AI apps have zero AI features
Plaintext-first apps are naturally ready for the age of AI
How I program with Agents
Personal experiences using feedback-driven LLMs to write programs.
AI coding wisdom from the people who would know
Essays and threads from experienced developers who've gone deep on AI-assisted coding.
deta/surf
Personal AI Notebooks. Organize files & webpages and generate notes from them. Open source, local & open data, open model choice (incl. local).
The only reason you’ll ever need not to write with AI — The Carlson Lab
Over the last year, our lab has been developing a policy on AI use. To do this, we did three main things: We read a lot of academic publications and tech news. We set up an #ai channel on our lab Slack to share news, experiences, and memes. We had several long and grueling lab meetings talk
Writing with AI help can shift your opinions | Cornell Chronicle
Artificial intelligence-powered writing assistants that autocomplete sentences or offer “smart replies” not only put words into people’s mouths, they also put ideas into their heads, according to new research.
AI tools make things up a lot, and that’s a huge problem | CNN Business
Artificial intelligence-powered tools like ChatGPT have mesmerized us with their ability to produce authoritative, human-sounding responses to seemingly any prompt. But as more people turn to this buzzy technology for things like homework help, workplace research, or health inquiries, one of its biggest pitfalls is becoming increasingly apparent: AI models sometimes just make things up.
Usage, Effects and Requirements for AI Coding Assistants in the Enterprise: An Empirical Study
The rise of large language models (LLMs) has accelerated the development of automated techniques and tools for supporting various software engineering tasks, e.g., program understanding, code generation, software testing, and program repair. As CodeLLMs are being employed toward automating these tasks, one question that arises, especially in enterprise settings, is whether these coding assistants and the code LLMs that power them are ready for real-world projects and enterprise use cases, and how do they impact the existing software engineering process and user experience. In this paper we survey 57 developers from different domains and with varying software engineering skill about their experience with AI coding assistants and CodeLLMs. We also reviewed 35 user surveys on the usage, experience and expectations of professionals and students using AI coding assistants and CodeLLMs. Based on our study findings and analysis of existing surveys, we discuss the requirements for AI-powered coding assistants.
Writing With AI Is Harder Than You Think
It takes rigor, judgment, and willingness to be told your work isn't good enough.
Supporting Our AI Overlords: Redesigning Data Systems to be Agent-First
Large Language Model (LLM) agents, acting on their users' behalf to manipulate and analyze data, are likely to become the dominant workload for data systems in the future. When working with data,...
Big AI is accelerating the metacrisis: What can we do?
The world is in the grip of ecological, meaning, and language crises that are converging into a metacrisis. Big AI is accelerating them all. LLM engineering sits at the core. Despite the public good motives of language engineers and the promise of LLMs, this work is being leveraged to create unprecedented wealth and power for a handful of individuals and corporations while causing existential harm to life on earth. As a profession, we urgently need to come together to explore alternatives and to design a life-affirming future for our field of natural language processing that is centered on human flourishing on a living planet.
