Out of curiosity, what app are you thinking of? Of all of types of software used with classic Mac OS (INITs, CDEVs, FKEYs, Desk Accessories, Drivers, etc.), apps would be the least likely to rely on implemention quirks.
Macintosh Common Lisp - at least the versions floating around Mac Garden and such - seems to refuse to run on anything besides accurate emulators and real hardware.
From a project perspective, is this for fun or is it meant to be a production solution? If the latter, what problem(s) are you trying to solve that established solutions like fnox don't? https://github.com/jdx/fnox (I'm an fnox user who's unfamiliar with this space, and am curious what your critiques would be.)
Both, honestly. Fun and production intent. But `production` here is very specific, embedded in a single Go binary, a single *.db not a CLI tool (the cli you see there is just for inspection) for developer or CI.
The problem fnox solves is great, unified access to secrets across dev, CI, prod with cloud backends. That's a different layer of the stack.
Keeper solves a lower-level problem: you have a Go process (a load balancer, a control plane, a daemon) that needs to store secrets inside its own database not in a separate file, not in a cloud vault, not in env vars. Secrets that need per bucket isolation, audit trails, and crash-safe rotation.
Here is my thinking :
- fnox = how your CLI and deploy scripts get secrets
- Keeper = how your running binary stores secrets at rest
Different problems, Could I build Keeper on top of fnox? Probably. But then I'd have a file on disk with secrets that fnox manages which is exactly the problem I wanted to eliminate.
This isn't a good test for any model since LLMs can't math (even though frontier models can sometimes correctly simulate mathing), which is why one would always use a tool for this.
I personally found it confusing and un-Mac-like that quitting the configuration app also now stops the Tailscale service. It was unfortunate to discover this while I was AFK.
My recommendation is to rethink it to work like apps like 1Password, Default Folder, Keyboard Maestro, Ice, etc., where I can always easily open a configuration app, but the service must be intentionally/knowingly quit via either the configuration app or the menu bar utility.
TLDR: Please separate the service from the new configuration app.
Maybe not scratch scratch: "And under the hood, EmDash is powered by Astro…"
> It's built on top of, and seems to perfer you use CF proprietary capabilities (D1 Databases, R2 for image/media storage, their workers for running things.
D1 is SQLite, R2 is S3, and there are other ways to securely run plugins. If it was designed to only be possible to deploy on Cloudflare, they didn't do a very good job.
I'm building a commercial SaaS product on Workers. Although I've barely scratched the surface of what Cloudflare offers¹, so far it's been great. The value proposition is effectively the same as serverless in general: You worry about the product, they worry about deployment. Note that Cloudflare Workers is just one (albeit important) star in their constellation of capabilities.
This seems a great solution, and I'll definitely be trying it. I feel like monospace fonts are the Roman roads → horse ruts → rail gauge of our industry.
The title and of this article is Don't Let AI Write For You, when its point seems to be closer to Don't Let AI Think For You (see "Thinking").
This distinction is important, because (1) writing is not the only way to faciliate thinking, and (2) writing is not neccessarily even the best way to facilitate thinking. It's definitely not the best way (a) for everyone, (b) in every situation.
Audio can be a great way to capture ideas and thought processes. Rod Serling wrote predominantly through dictation. Mark Twain wrote most of his of his autobiography by dictation. Mark Duplass on The Talking Draft Method (1m): https://www.youtube.com/watch?v=UsV-3wel7k4
This can work especially well for people who are distracted by form and "writing correctly" too early in the process, for people who are intimidated by blank pages, for non-neurotypical people, etc. Self-recording is a great way to set all of those artifacts of the medium aside and capture what you want to say.
From there, you can (and should) leverage AI for transcripts, light transcript cleanups, grammar checks, etc.
> Audio can be a great way to capture ideas and thought processes ... This can work especially well for people who are distracted by form and "writing correctly" too early in the process, for people who are intimidated by blank pages, for non-neurotypical people, etc. Self-recording is a great way to set all of those artifacts of the medium aside and capture what you want to say.
Yes, this is my process:
Record yourself rambling out loud, and import the audio in NotebookLM.
Then use this system prompt in NotebookLM chat:
> Write in my style, with my voice, in first person. Answer questions in my own words, using quotes from my recordings. You can combine multiple quotes. Edit the quotes for length and clarity. Fix speech disfluencies and remove filler words. Do not put quotation marks around the quotes. Do not use an ellipsis to indicate omitted words in quotes.
Then chat with "yourself." The replies will match your style and will be source-grounded. In fact, the replies automatically get footnotes pointing to specific quotes in your raw transcripts.
This workflow may not save me time, but it helps me get started, or get unstuck. It helps me stop procrastinating and manage my emotions. I consider it assistive technology for ADHD.
Yeah this is my problem. I can come up with ideas, but in writing my ideas never come out well. AI has helped me to express my ideas better. People who write well or are successful at writing sometimes fail to understand how uncommon is it to actually be good at writing. Shit is hard.
I would count direct dictation (eg someone writes down what you say, and that is the final text), as writing, in the context of producing a document (book, etc) that you intend others to read.
It's not the same thing as talking to someone (or a group) about something.
I do this a lot. Start by telling the AI to just listen and only provide feedback when asked. Lay out your current line of thinking conversationally. Periodically ask the AI to summarize/organize your thoughts "so far". Tactically ask for research into a decision or topic you aren't sure about and then make a decision inline.
Then once I feel like I have addressed all the areas, I ask for a "critical" review, which usually pokes holes in something that I need to fix. Finally have the AI draft up a document (Though you have to generally tell it to be as concise and clear as possible).
I usually create a document/folder with my thinking on what I want to do, any background information that is relevant, conversations on the topic, technical manuals, links etc. Then enter a conversation and explore the problem space and do something very similar to what you are doing.
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