71–80 of 155 posts
Earlier quoted context omitted.
> Writing with an LLM is not a shame Should be "Writing with a LLM is not a shame", no reason to put a "an" here.
"el" begins with a vowel sound, so "an" is the appropriate article. Is not about the letter, it's about practical pronunciation. "An r before a u, and an m or an f".
At this point, it would be shameful to not write with LLMs. I don't want to spend time reading plain human text when improved AI text is an option.
Large Language Models (LLMs), like GPT-4, offer numerous benefits for writing tasks across various domains. Here’s a breakdown of the key advantages:
1. Enhanced Productivity
Faster Drafting: Quickly generate drafts for essays, reports, emails, blog posts, and more.
24/7 Availability: Instant support with no downtime or fatigue.
Reduced Writer’s Block: Provides starting points and creative prompts to overcome mental blocks.
2. Improved Writing Quality
Grammar and Style: Corrects grammar, punctuation, and stylistic issues.
Tone Adjustment: Adapts tone to suit professional, casual, persuasive, or empathetic contexts.
Clarity and Conciseness: Helps simplify complex ideas and remove redundant language.
3. Creativity and Ideation
Brainstorming: Assists in generating titles, outlines, metaphors, and analogies.
Storytelling: Offers plot ideas, character development, and dialogue suggestions for creative writing.
Variations: Produces multiple versions of the same message (e.g., for A/B testing).
4. Language Versatility
Multilingual Support: Translates and writes in many languages.
Localization: Tailors content for different cultural contexts or regions.
5. Research Assistance
Summarization: Condenses large documents or articles into key points.
Information Retrieval: Provides background context on topics quickly (though should be fact-checked for critical work).
Citation Help: Assists in generating citations in formats like APA, MLA, or Chicago.
6. Editing and Rewriting
Paraphrasing: Rewrites text to avoid plagiarism or improve readability.
Consistency Checks: Maintains tone, terminology, and formatting across long documents.
Content Expansion: Adds detail to thin content or elaborates on underdeveloped points.
7. Customization and Integration
Prompt Engineering: Tailors responses for specific industries (e.g., legal, medical, technical).
API Integration: Can be embedded into writing tools, content platforms, or CMS systems.
8. Cost Efficiency
Reduces Need for Human Writers: Especially for repetitive or low-complexity tasks.
Scales Effortlessly: One model can serve multiple users or projects simultaneously.
Would you like a breakdown of how these benefits apply to a specific type of writing (e.g., academic, marketing, business)?
Earlier quoted context omitted.
Even novel thoughts are rarely original. Every time I come up with an algorithm idea, or a system idea, I'm always checking who has done it before, and I always find significant prior art. Even for really niche things. I think my name Aeonik Chaos might be one of the only original, never before done things. And even that was just an extension of established linguistic rules.
My great-great grandfather was named Aeonik Chaos!
Of course, there’s no shame in using tools that are available to us. We’re a tool-using species. We’re just a bunch of stupid monkeys without tools. A lot of what we do is about using tools to free up time to do more interesting things than doing things the tools already do better than us. Like it or not, people are using LLMs a lot. The output isn’t universally good. It depends on what you ask for and how you critic…
If the tool does the task for you, then you didnt do the task. I don't keep my food cold, my refrigerator does. I just turned it on. This doesn't matter unless I am for some reason pretending I myself am keeping my food cold somehow, and then that becomes a lie. When a tool blurs the line between who performed the task, and you take full credit despite being assisted, that is deceitful. Spell checking helps us all pr…
Riding a bike with training wheels is also not a shame. If you need the training wheels, by all means feel free to use them. But LLMs are training wheels being forced on everyone, including experienced developers and we are being gaslit that if we don't use them, we are getting behind. In reality, however, the only study up to date shows 19% decline in productivity for experienced devs using LLMs. I don't mind folks…
My workflow right now is to use AI for rough draft and developmental editing stages, then switch AI from changing files to leaving comments on files suggesting I change something. It is slower than letting it line/copyedit itself, but models derp up too much so letting them handle edits at this stage tends to be 2 steps forward 2 steps back.
At this point, it would be shameful to not write with LLMs. I don't want to spend time reading plain human text when improved AI text is an option.
I don’t know you, don’t trust you, and if you write with AI nobody else will get to know you or trust you, either, unless they fall for your false AI mask.
My main problem with AI usage is that people use it and turn their brains off. This isn't a new problem, but it is a new scale. People mindlessly punch numbers into a formula, run software they don't understand, or read a summary of a complex topics declaring mastery. The problem is sloppiness and our human tendencies to be lazy. Lazy by focusing on the least amount of energy at the moment, not the least amount of energy through time. That's the critical distinction. Slop is momentary laziness while thoughtfulness is amortized laziness.
The problem is in a way not the AI but us and the cultures we have created. At the end of the day no one cares if you wrote AI code (or docs or whatever), they care about how well it was done. You want to do things fast, but speed is nothing if the quality suffers.
I really like how Mitchell put it in this Ghostty PR[0,1]. The disclosure is to help people know what to pay more attention to. It is a declaration of where you were lazy or didn't have expertise or took some shortcut. It tells us what the actually problem is: slop isn't always obvious.
A little slop generally doesn't do too much harm (unless it grows and compounds), but a lot of slop does. If you are concerned about slop and the rate of slop is increasing then it means you must treat everything as potential slop. Because slop isn't easily recognized, it makes effort increase, exponentially. So by producing AI slop (or any kind of slop) you aren't decreasing the workload, you're outsourcing it to someone else. Often, that outsourcing produces additional costs. It only creates the illusion of productivity.
It's not about the AI, it is about shoving your work onto others. Doesn't matter if you use a shovel or bulldozer. But people are sure going to be louder (or cross that threshold where they'll actually speak up) if you start using a bulldozer to offload your work to others. The problem is it makes others have to constantly be in System 2 thinking all the time. It is absolutely exhausting.
A relevant satirical post I stumbled on today much about the same subject: https://medium.com/@Justwritet/stop-competing-with-the-machi...