Quartz sync: Mar 11, 2024, 10:14 PM

LICENSE.txt

@@ -1,3 +1,15 @@
+** CONTENT **
+
+Copyright (c) 2024 bfahrenfort.
+
+Written content (under the content/ folder) is my sole intellectual property and unauthorized reproduction/use is prohibited. I specifically authorize citing my work in your own with a deep link to the page, and the limited reproduction necessary to effectuate the non-reproduction/primary purpose of your work.
+
+** CODE **
+
+The generator and all code and configuration is MIT licensed. Copyright notice for the parts that I created and the contributions of others is reproduced below.
+
+** CODE **
+
 MIT License
 
 Copyright (c) 2021 jackyzha0

content/Essays/ai-infringement-drafts.md

@@ -0,0 +1,21 @@
+---
+title: New Note
+tags: 
+date: 2024-03-07
+lastmod: 2024-03-07
+draft: true
+---
+## Putting your work "out there" on the internet
+Artist's will, don't exploit
+### Detour: plagiarism
+There's also the problem of correctly sourcing information used in forming an opinion.
+
+One proposed "solution" to AI use of copyrighted works is interestingly to attribute that those works were used in the first place.
+## Economics
+WIP
+## The enforcement problem
+WIP
+## Building universal truth
+WIP
+## Why is piracy ethical, but not AI training?
+WIP

content/Essays/ai-infringement.md

@@ -13,7 +13,7 @@ One ticket to the original, authorized, or in the alternative, properly licensed
 
 *A film roll clatters to the ground from underneath a suspiciously camera-shaped bulge in the figure's oversized trench coat.*
 
-> [!info] I’m looking for discourse!
+> [!info] I’m looking for input!
 > Critique my points and make your own arguments. That’s what the comments section is for. 
 
 > [!warning]
@@ -35,13 +35,13 @@ I also discuss policy later in the essay. Certain policy points are instead made
 
 In short, there's a growing sentiment against copyright in general. Copyright can enable centralization of rights when paired with a capitalist economy, which is what we've been historically experiencing with the advent of record labels/publishing companies. It's even statutorily enshrined as the "work-for-hire" doctrine. AI has the potential to be an end-run around these massive copyright repositories' rights, which many people see as beneficial.
 
-However, this argument forgets that intangible rights are not *yet* so centralized that independent rights-holders have ceased to exist. While AI will indeed affect central rights-holders, it will also harm individual creators and the bargaining power of those that choose to work with the central institutions. For those against copyright as a whole, this is a neutral factor to the disestablishment of copyright. Due to my roots in the indie music and open-source communities, I'd much rather keep their/our/**your** rights intact.
+However, this argument forgets that intangible rights are not *yet* so centralized that independent rights-holders have ceased to exist. While AI will indeed affect central rights-holders, it will also harm individual creators and the bargaining power of those that choose to work with the central institutions. For those against copyright as a whole, I see AI as a neutral factor to the disestablishment of copyright. Due to my roots in the indie music and open-source communities, I'd much rather keep their/our/**your** rights intact.
 
 Reconciling the two views, I'm sympathetic to arguments against specific parts of the US's copyright regime as enforced by the courts, such as the way fair use is statutorily worded. We as a voting population have the power to compel our representatives to enact reforms that take the threat of ultimate centralization into account, and can even work to break down what's already here. But I don't think that AI should be the impetus for arguments against the system as a whole.
 ## The Legal Argument
 Fair warning, this section is going to be the most law-heavy, and probably pretty tech-heavy too. Feel free to skip [[#The First Amendment and the "Right to Read"|-> straight to the policy debates.]] The field is notoriously paywalled, but I'll try to link to publicly available versions of my sources whenever possible.
 
-Please don't criticize my sources in this section unless a case has been overruled or a statute has been repealed/amended (ie, I **can't** rely on it). This is my interpretation of what's here (also again not legal advice or a professional opinion). Whether a case is binding on you personally doesn't weigh in on whether its holding is the nationally accepted view.
+Please don't criticize my sources in this section unless a case has been overruled or a statute has been repealed/amended (*i.e.*, I **can't** rely on it). This is my interpretation of what's here (again, not legal advice or a professional opinion. Seek legal counsel before acting/refraining from action re: AI). Whether a case is binding on you personally doesn't weigh in on whether its holding is the nationally accepted view.
 
 For all of the below analysis, assume that the hypothetical model in question has been trained on some work which has a US copyright registered with the original author.
 
@@ -52,10 +52,10 @@ One common legal argument against training as infringement is that the AI extrac
 
 <img src="/Attachments/common_crap.svg" alt="Common Crawl logo edited to say 'common crap' instead" style="padding:0% 5%">
 
-Everything AI starts with a dataset. And most AI models will start with the easiest, most freely available resource: the internet. Hundreds of different scrapers exist with the goal of collecting as much of the internet as possible to train modern AI (or previously, machine learners, neural networks, or even just classifiers/cluster models).
+Everything AI starts with a dataset. And most AI models will start with the easiest, most freely available resource: the internet. Hundreds of different scrapers exist with the goal of collecting as much of the internet as possible to train modern AI (or previously, machine learners, neural networks, or even just classifiers/cluster models). I think that acquiring data without authorization to train an AI on is copyright infringement standing by itself.
 
-Acquiring data for training is an unethical mess. **In human terms**, scrapers like Common Crawl will take what they want, without asking (unless you know the magic word to make it go away, or just [[Projects/Obsidian/digital-garden#Block the bot traffic!|block it from the get-go]]), and without providing immediately useful service in return like a search engine. For more information on the ethics of AI datasets, read my tidbit on [[Essays/plagiarism#AI shouldn't disregard the need for attribution|🅿️ the need for AI attribution]], and have a look at the work of [Dr. Damien Williams](https://scholar.google.com/citations?user=riv547sAAAAJ&hl=en) ([Mastodon](https://ourislandgeorgia.net/@Wolven)).
-- Sidebar: and acquiring this data is copyright infringement too, as unlicensed copying. The case is tremendously stupid: [*MAI Systems v. Peak Computer*](https://casetext.com/case/mai-systems-corp-v-peak-computer-inc) holds that RAM copying (ie, moving a file from somewhere to a computer's memory) is an unlicensed copy. As of today, it's still good law, for some reason. Note that every single file you open in Word, a PDF reader, or your browser is moved to your memory before it gets displayed on the screen. Bring it up at trivia night: just using your computer is copyright infringement!
+Acquiring data for training is an unethical mess. **In human terms**, scrapers like Common Crawl will take what they want, without asking (unless you know the magic word to make it go away, or just [[Projects/Obsidian/digital-garden#Block the bot traffic!|block it from the get-go]]), and without providing immediately useful services in return like a search engine. For more information on the ethics of AI datasets, read my take on [[Essays/plagiarism#AI shouldn't disregard the need for attribution|🅿️ the need for AI attribution]], and have a look at the work of [Dr. Damien Williams](https://scholar.google.com/citations?user=riv547sAAAAJ&hl=en) ([Mastodon](https://ourislandgeorgia.net/@Wolven)).
+-  Sidebar: and acquiring this data is copyright infringement too, as unlicensed copying. The case is tremendously stupid: [*MAI Systems v. Peak Computer*](https://casetext.com/case/mai-systems-corp-v-peak-computer-inc) holds that RAM copying (ie, moving a file from somewhere to a computer's memory) is an unlicensed copy. As of today, it's still good law, for some reason. Note that every single file you open in Word or a PDF reader; or any webpage in your browser, is moved to your memory before it gets displayed on the screen. Bring it up at trivia night: just using your computer is copyright infringement! It's silly and needs to be overruled going forward, but it is good law.
 
 But then a company actually has to train an AI on that data. What copyright issues does that entail? First, let's talk about The Chinese Room.
 
@@ -64,47 +64,62 @@ But then a company actually has to train an AI on that data. What copyright issu
 Searle's exercise was at the time an extension of the Turing test designed to refute the theory of "Strong AI." At the time that theory was well-named, but today the AI it was talking about is not even considered AI by most. The hypothetical Strong AI was a computer program capable of understanding its inputs and outputs, and importantly *why* it took each action to solve a problem, with the ability to apply that understanding to new problems (much like our modern conception of Artificial General Intelligence). A Weak AI, on the other hand, was just the Chinese Room: taking inputs and producing outputs among defined rules. Searle reasoned that the "understanding" of a Strong AI was inherently biological, thus one could not presently exist.
 - Note that some computer science sources like [IBM](https://www.ibm.com/topics/strong-ai) have taken to using Strong AI to denote only AGI, which was a sufficient, not necessary quality of a philosophical "intelligent" intelligence.
 
-Generative AI models from different sources are architected in a variety of different ways, but they all boil down to one abstract process: tuning an absurdly massive number of parameters to the exact values that produce the most desirable output. (note: [CGP Grey's video on AI](https://www.youtube.com/watch?v=R9OHn5ZF4Uo) and its follow-up are mainly directed towards neural networks, but do apply to LLMs, and do a great job illustrating this). This process requires a gargantuan stream of data to use to calibrate those parameters and then as both input and target output to test the model. 
+Generative AI models from different sources are architected in a variety of different ways, but they all boil down to one abstract process: tuning an absurdly massive number of parameters to the exact values that produce the most desirable output. (note: [CGP Grey's video on AI](https://www.youtube.com/watch?v=R9OHn5ZF4Uo) and its follow-up are mainly directed towards neural networks, but do apply to LLMs, and do a great job illustrating this). This process requires a gargantuan stream of data to use to calibrate those parameters and then test the model. How exactly it parses that incoming data suggests that, even if the method of acquisition is disregarded, the AI model still infringes the input.
+#### The Actual Tech
+At the risk of bleeding the [[#Generation]] section into this one, generative AI is effectively a very sophisticated next-word predictor based on the words it has read and written previously. If some words being associated with others is more popular historically, then that association is more "correct" to generate in a given scenario than other options. As this relates to training, **the only data for that correctness determination is corpus training input**. This means that training doesn't have some external indicator of semantics that a secondary natural-language processor on the generation side can incorporate: an AI trains only on the words as they are on the page. Training thus can't be analogized to human learning processes, because when an AI "reads" something, it isn't reading for the forest—it's reading for the trees. Idea and expression in training data are indistinguishable to AI.
 
-Modern generative AI, like the statistical data models and machine learners before it, is a Weak AI. And weak AIs use weak AI data.
-- Sidebar: this point doesn't consider an AI's ability to summarize a work since the section focuses on how the *training* inputs are used rather than how the output is generated from real input. It's confusing, but these are two linked concepts when talking about machine learning rather than direct results of each other. Especially when you introduce concepts like "temperature", which is a degree of randomness added to a model's (already variant) choices in response to an input to simulate creativity.
+As such, modern generative AI, like the statistical data models and machine learners before it, is a Weak AI. And weak AIs use weak AI data. Here's how that translates to copyright.
+- Sidebar: this point doesn't consider an AI's ability to summarize a work since the section focuses on how the *training* inputs are used rather than how the output is generated from real input. This is why I didn't want to get into generation in this section. It's confusing, but training and generation are merely linked concepts rather than direct results of each other when talking about machine learning. Especially when you introduce concepts like "temperature", which is a degree of randomness added to a model's (already variant) choices in response to an user in order to simulate creativity.
 - ...I'll talk about that in the next section.
+#### "The Law Part"
+All of the content of this section has been to establish how an AI receives data so that I can reason about how it *stores* that data. In copyright, reproduction, derivatives or compilations of works without authorization can constitute infringement. I believe that inputting a work into a generative AI creates a derivative representation of the work. Eventually, the model is effectively a compilation of all works passed in. And finally (on a related topic), there is nothing copyrightable in how it's arranged the works in that compilation even if every work trained on is authorized. 
+- ==but because training is deterministic, there's not even any expression in how the data is arranged in its model-representation==
+
+And more cynically, I don't think any of this could be workable in a brief. Looking at how much technical setup I needed to make this argument, there's no way I could compress this all into something a judge could read (even ignoring court rule word limits) or that I could orate concisely to a jury. I'm open to suggestions on a more digestible way to go about arguing the principles I'm concerned about based on this technological understanding of AI.
 #### Detour: point for the observant
 The idea and expression being indistinguishable by AI may make one immediately think of merger doctrine. That argument looks like: the idea inherent in the work trained on merges with its expression, so it is not copyrightable. That would not be a correct reading of the doctrine. [*Ets-Hokin v. Skyy Spirits, Inc.*](https://casetext.com/case/ets-hokin-v-skyy-spirits-inc) makes it clear that the doctrine is more about disregarding the types of works that are low-expressivity by default, and that this "merge" is just a nice name to remember the actual test by. Confusing name, easy doctrine.
 ### Generation
-
-### Fair Use
-
+WIP
 #### Detour: actual harm caused by specific uses of AI models
-My bet for a strong factor when courts start applying fair use tests to AI output is that the use in the instant case causes or does not cause harm. Here's a quick list of uses that probably do cause harm.
+My bet for a strong factor when courts start applying fair use tests to AI output is harm, in that the AI use in the instant case causes or does not cause harm (and I actually wrote this before the [[Essays/no-ai-fraud-act|No AI FRAUD Act]] 's negligible-harm provision was published. -ed.). Here's a quick list of uses that probably do cause harm.
 - Election fraud, including even **more** corporate influence on US elections ([not hypothetical](https://www.washingtonpost.com/elections/2024/01/18/ai-tech-biden/) [in the slightest](https://openai.com/careers/elections-program-manager), [and knowingly unethical](https://www.npr.org/2024/01/19/1225573883/politicians-lobbyists-are-banned-from-using-chatgpt-for-official-campaign-busine))
 - Other fraud, like telemarketing/robocalls, phishing, etc
-- Competition with actual artists and authors (I am VERY excited to see where trademark law evolves around trademarking one's art or literary style). 
+- Competition with actual artists and authors (I am VERY excited to see where trademark law evolves around trademarking one's art or literary style. Currently, the arguments are weak and listed in the mini-argument section). 
 - Obsoletes human online workforces in tech support, translation, etc
 - [[plagiarism##1 Revealing what's behind the curtain|🅿️ Reinforces systemic bias]]
 ### Where do we go from here?
-Well, getting to evaluation of the above by courts would be a start. Right now, courts are ducking AI issues left and right on standing and pleading grounds. ==say more right now==
+Well, getting to evaluation of the above by courts would be a start. Right now, courts are ducking AI issues left and right on standing and pleading grounds. Once there's more solid (or honestly *any*) coverage of the legal arguments on the merits, the reasons why the law should be enforced that way as a matter of policy will become more important.
 # Policy
+These arguments will be more or less persuasive to different people. I think there's a lot more room for discussion here because they become relevant to the future direction of the law as well as current enforcement. The most important debate is up first, but the others are not particularly ordered.
+
+> [!info] Section Under Construction
+> More topics under this section forthcoming! I work and edit in an alternate document and copy over sections as I finish them.
+
+## Fair Use
+WIP
 ## The First Amendment and the "Right to Read"
-WIP
-## Putting your work "out there" on the internet
-Artist's will, don't exploit
-### Detour: plagiarism
-There's also the problem of correctly sourcing information used in forming an opinion.
+This argument favors allowing GAI to train on the entire corpus of the internet, copyright- and attribution-free, and bootstraps GAI output into being lawful as well. The position most commonly taken is that the First Amendment protects a citizen's right to information, and that there should be an analogous right for generative AI.
 
-One proposed "solution" to AI use of copyrighted works is interestingly to attribute that those works were used in the first place.
-## The enforcement problem
-WIP
-## Building universal truth
+The right to read, at least in spirit, is still being enforced today. Even the 5th Circuit believes that this particular flavor of First Amendment claim will be likely to succeed on appeal after prevailing at the trial level. [*Book People v. Wong*](https://law.justia.com/cases/federal/appellate-courts/ca5/23-50668/23-50668-2024-01-17.html), No. 23-50668 (5th Cir. 2024) (not an AI case). It also incorporates principles from intellectual property law. Notably, that you can read the content of a work without diminishing the value of the author's expression (i.e. ideas aren't copyrightable). As such, the output of an AI is not taking anything from an author that a human wouldn't take when writing something based on their knowledge. 
 
-## Why is piracy ethical, but not AI training?
+I take issue with the argument on two points that stem from the same technological foundation.
 
+First, as a policy point, the argument incorrectly humanizes current generative AI. There are no characteristics of current GAI that would warrant the analogy between a human reading a webpage and an AI training on that webpage.
+
+Second, more technically, [[#Training|the training section]] above is my case for why an AI does not learn in the same way that a human does in the eyes of copyright law. ==more==
+
+But for both of these points,  I can see where the confusion comes from. The previous leap in machine learning was called "neural networks", which definitely evokes a feeling that it has something to do with the human brain. Even more so when the techniques from neural network learners are used extensively in transformer models (that's those absurd numbers of parameters mentioned earlier).
 ## Mini-arguments
-A list of little statements that would cast doubt on the general legitimacy of the AI boom that I found compelling. Most are spread across the fediverse; others are blog posts/articles. 
-
+A list of smaller points that would cast doubt on the general zeitgeist around the AI boom that I found compelling. These may be someone else's undeveloped opinion, or it might be a point that I don't think I could contribute to in a rigorous way. Many are spread across the fediverse; others are blog posts or articles. Others still would be better placed a Further Reading section, but I don't like to tack on more than one post-script-style heading.
 - [Cartoonist Dorothy’s emotional story re: midjourney and exploitation against author intent](https://socel.net/@catandgirl/111766715711043428)
 - [Misinformation worries](https://mas.to/@gminks/111768883732550499)
+- Stronger over time
+	- One of the lauded features of bleeding-edge AI is its increasingly perfect recall from a dataset. So you're saying that as AI gets more advanced, it'll be easier for it to exactly reproduce what it was trained on? Sounds like an even better case for copyright infringement.
 - Inevitable harm
 	- Temperature and the very fact that word generation is used mean that there's no way to completely eliminate hallucination, so truth in AI is unobtainable. [Xu, et al.](https://arxiv.org/abs/2401.11817)
+- Unfair competition
+	- This doctrine is a catch-all for claims that don't fit neatly into any of the IP categories, but where someone is still being wronged by a competitor. I see two potential arguments here. 
+		- First, you could make a case for the way data is scraped from the internet being so comprehensive that there's no way to compete with it by using more fair/ethical methods. This could allow a remedy that mandates AI be trained using some judicially devised (or hey, how about we get Congress involved if they don't like the judicial mechanism), ethical procedure. The arguments are weaker, but they could be persuasive to the right judge.
+		- Second, AI work product is on balance massively cheaper than hiring humans, but has little other benefit, and causes many adverse effects. A pure cost advantage providing windfall for one company but not others could also be unfair. Again, it's very weak right now in my opinion.
 
 ==TODO analyze and applaud https://www.techdirt.com/2023/11/29/lets-not-flip-sides-on-ip-maximalism-because-of-ai/ ==

content/Essays/law-school.md

@@ -97,11 +97,5 @@ I recognize that there's an argument for a stressful, competitive law school exp
 
 I'm not sure whether this essay functions more as an introduction to the pitfalls of law school or an important consideration for those already interested in it. But I do know one thing: **Law school is broken.**
 ## Homework/Further Reading
-For those considering law school, I'd like to suggest two resources to you.
-
-- During my undergraduate studies, I stumbled across an excellent account by Rhett Campbell, a retired energy bankruptcy attorney. I don't know where I found these, probably on Reddit (I've ignored r/LawSchool and r/lawschooladmissions in this essay because all of Reddit is toxic and those two subs are no exception). At the time I found these (and presumably when they were updated), he was the CEO of a nonprofit called the Terry Foundation. A lot of his opinions hold up, and I've uploaded them here as PDFs at <a class="internal" href="https://be-far.com/Attachments/why-not-to-go-to-law-school.pdf" target="_blank" rel="noopener noreferrer">Why Not to Go to Law School</a> and <a class="internal" href="https://be-far.com/Attachments/law-study.pdf" target="_blank" rel="noopener noreferrer">Guide to Making Good Grades in Law School</a>. All credit goes to Campbell for these resources. If you only take two things from these documents, let them be "**law school is hell**" and "**outline early, outline often**."
-	- Sidebar: I do agree with Campbell's view that there's a certain "fire in the belly" that you need to be a lawyer. I think I satisfied this because reading these documents made me excited, not stressed.
-	- Sidebar x2: The resources he recommended weren't that helpful to me. The real value of his writings is his firsthand experience. 
-- During the application cycle, I also enjoyed Kathryne Young's book [How to be Sort of Happy in Law School](https://www.goodreads.com/book/show/35793679-how-to-be-sort-of-happy-in-law-school), and I think it provides a realistic expectation of what it means to be a law student while also being a person. Some of what I talk about in the [[#Detour Constitutional Law|detour on con law]] comes straight from her book.
-
-And finally, my only advice to prospective law students is do some soul searching on what you *really* want to be doing in 3 years. If that's either practicing law or working in policy/advocacy, only then should you choose law school. You don't need to know an exact field, but I love my job and I think I'm the exception for that. There is something to be said for a meaningless 9-5 surrounded by hobbies you truly enjoy, but the law takes too many of your hours in a day for it to not interest you. Any further questions or ways I can help, contact me!
+- Feel free to peruse [[Resources/law-students|Resources for Law Students]] if you're considering law school.
+- Also geared to law students, but applicable elsewhere: [How to be Sort of Happy in Law School](https://www.goodreads.com/book/show/35793679-how-to-be-sort-of-happy-in-law-school) by Kathryne Young provides subject-matter testimony from law students that gives a sense of what it's really like on many personal, professional, and emotional fronts. Some of what I talk about in the [[#Detour Constitutional Law|detour on con law]] comes straight from her book.

content/Essays/plagiarism.md

@@ -52,6 +52,7 @@ First, AI holds itself out as authoritative. Wrongfully so, due to incessant "ha
 
 Second and perhaps most importantly, because of the actual issue of AI bias, transparency in what an AI was trained on is paramount. As a society, the ability to question the source of some facts presented to us is already beneficial (as discussed elsewhere in this essay). But for AI, we need to ensure that the generated statements are not only correct, but not disregarding other positions categorically because they were made by sources that the AI incorrectly considers non-authoritative. An AI model could look at two positions, one with many more datapoints supporting it, and thus completely ignore the second position in its answer to a prompt. Now imagine that the former is a white man's perspective, and the second a black woman's. It's not inconceivable that an AI could enshrine systemic bias. Attribution allows people who've made careers in this field to critically examine a dataset and look for this sort of gap. In that way, it makes a **better** AI model (assuming the goal of AI is to be accurate) because of more community oversight, not just one that's more ethically trained.
 - Sidebar: huh, turns out that this argument parallels the open-source philosophy.
+- Countless actual examples exist, too many to list. I documented one incident [here](https://social.treehouse.systems/@be_far/111990173625090669).
 ### #2: \[citation needed\] for responses to prompts
 Not to be confused with Molly White's [excellent newsletter](https://citationneeded.news/). This requirement is a more fine-grained mitigation for the transparency issues present in the dataset at large. It also provides evidence for potential copyright infringement lawsuits if the AI has also copied the expression of the paper it sourced. Note that this isn't the be-all, end-all solution to the problem of copyright infringement by AI. Read more of my take on that [[Essays/ai-infringement|🤖 here]].
 ## To-be-written

content/Essays/we-started-the-fire.md

@@ -0,0 +1,8 @@
+---
+title: "We Started the Fire: Federal Branches"
+tags: 
+date: 2024-03-05
+lastmod: 2024-03-05
+draft: true
+---
+I'm debating whether to write this because it's kind of depressing, but I don't really like the current state of any of our branches of federal government. Much of my view stems from the apparent power of the judicial branch (and acquiescence by the other branches to what I see as an overreach). There are also independent concerns.

content/Projects/rss-foss.md

@@ -1,13 +1,32 @@
 ---
-title: The Future of RSS
+title: Toward RSS
 tags:
   - foss
   - project
   - seedling
 date: 2024-02-14
+lastmod: 2024-03-10
 ---
-RSS is the best and most private way to subscribe to a website, social media account, or more. No site analytics, no page loading, no Javascript, no ads. All the website can see is that you pulled one plaintext file from it.
-## Vision
-RSS shouldn't just be a one-feed thing. Granularity is key.
+RSS (+ derivative Atom) is the most convenient and privacy-conscious way to subscribe to a website, social media account, and more. No site analytics, no page loading, no Javascript, no ads. All the website can see is that you pulled one file from it, but you can still read all the content that you wanted from the website in a compact format. RSS isn't dead (despite [suggestions to the contrary haha](https://rss-is-dead.lol)), but it's woefully underused. Admittedly, it lacks **economic incentives** for major websites to adopt it because it runs contrary to the modern idea of a content farm. But the **convenience for users** in its familiarity and centralization both greatly increase the quality of a user's experience on the internet. 
 
-More to come when I get time haha
+I'm a contributor on [[Projects/Obsidian/digital-garden|Quartz]]'s RSS feature, and I personally use a selfhosted feed reader for a lot of my news. It's streamlined my day because everything I want to see is all in one place.
+## Vision
+These are my personal directional goals for RSS contribution. As a whole, I want RSS to be used more frequently by everyone, websites and users alike. It should be accessible to those less familiar with the underlying tech, but still provide powerful customization for power readers.
+
+On the implementation side, RSS is traditionally a one-site-one-feed feature. I want this to change, and granularity is key. In line with improving the user experience, I'd like to present the user with only what they want to see. If I could do something dynamic with custom feed concatenation, I absolutely would, but it's pushing the limits of the static-site medium that I use as my playground.
+
+And when designing a feed reader, user convenience is paramount. I think that if there's a use case that's sufficiently intuitive to someone curious about RSS, users will demand proper integration from larger sites. Otherwise, the sites risk losing large swaths of revenue to competing sites, which would provide the economic incentive necessary for change. More than any interesting implementations web developers add to the feed generation itself, *this* is what would reverse the decline in usage.
+### Use Cases and the Case from Users
+What any individual (developer, user, or both) cares about in an RSS integration is bound to differ. I'm not a fan of HN for actually resolving the issue in discussion in a thread, but there are some comments that evaluate different implementations for different use cases (read: bikeshed) that we can pay attention to when implementing others. [Ask HN: Is RSS Dead?](https://news.ycombinator.com/item?id=22497184)
+### Detour: Evolving Standards?
+I'm less certain that there's a need for an RSS 3.0 or similar evolution. RSS Module syntax allows pretty robust extensions for use cases that weren't concrete at the time of RSS 2.0. 
+
+> [!info]
+> **Development - Implementation** section to be written.
+
+## Roadmap
+Here's what I'm doing and what I will be doing in future.
+- [ ] **Right now:** get [#866 - Per-Folder RSS Feeds (Quartz)](https://github.com/jackyzha0/quartz/pull/866) features implemented and merged
+- [ ] Convert feed generation for Quartz from summaries into full-text HTML content items
+- [ ] Deeper study into user preferences to determine a direction
+- Connect with others passionate about reversing the RSS decline

content/Resources/law-students.md

@@ -0,0 +1,20 @@
+---
+title: Resources for Law Students
+tags:
+  - resources
+  - misc
+  - legal
+  - seedling
+date: 2024-03-07
+lastmod: 2024-03-07
+---
+For those considering law school, I'd like to suggest two resources to you.
+- During my undergraduate studies, I stumbled across an excellent account by Rhett Campbell, a retired energy bankruptcy attorney. I don't know where I found these, probably on Reddit (I ignored r/LawSchool and r/lawschooladmissions in my [[Essays/law-school|essay on law school]] because they're just as toxic as all of the rest of the site). At the time I found these (and presumably when they were updated), he was the CEO of a nonprofit called the Terry Foundation. A lot of his opinions hold up, and I've uploaded them here as PDFs at <a class="internal" href="https://be-far.com/Attachments/why-not-to-go-to-law-school.pdf" target="_blank" rel="noopener noreferrer">Why Not to Go to Law School</a> and <a class="internal" href="https://be-far.com/Attachments/law-study.pdf" target="_blank" rel="noopener noreferrer">Guide to Making Good Grades in Law School</a>. All credit goes to Campbell for these resources. If you only take two things from these documents, let them be "**law school is hell**" and "**outline early, outline often**."
+	- Sidebar: I do agree with Campbell's view that there's a certain "fire in the belly" that you need to be a lawyer. I think I satisfied this because reading these documents made me excited, not stressed.
+	- Sidebar x2: The resources he recommended weren't that helpful to me. The real value of his writings is his firsthand experience. 
+- During the application cycle, I also enjoyed Kathryne Young's book [How to be Sort of Happy in Law School](https://www.goodreads.com/book/show/35793679-how-to-be-sort-of-happy-in-law-school), and I think it provides a realistic expectation of what it means to be a law student while also being a person. 
+
+My personal advice to prospective law students is do some soul searching on what you *really* want to be doing in 3 years. If that's either practicing law or working in policy/advocacy, only then should you choose law school. You don't need to know an exact field, but I love my job and I think I'm the exception for that. There is something to be said for a meaningless 9-5 surrounded by hobbies you truly enjoy, but the law takes too many of your hours in a day for it to not interest you. Any further questions or ways I can help, contact me!
+
+### Detour: Finals tip
+You'll be taught final exam strategy early on, and it's going to be highly professor dependent. However, one thing I learned late into law school is what you should actually *do* when the law runs out on a question. Previously, I've just identified the fact that it could go either way and analyzed both potential paths (known as a "fork" if you spend too much time on Reddit). My new approach is to go back to policy before the fork. Using the goals underlying the usual rule in an instance, you first fashion a new rule, then apply the new rule and conclude one way. Only then do you analyze the other side of the fork. Feel free to give it a try if you think your professor would appreciate it.

content/Updates/2024/mar.md

@@ -6,8 +6,13 @@ tags:
 date: 2024-03-01
 ---
 ## Housekeeping
+Howdy, y'all. *Trump v. Anderson* got a decision, and it's about what I expected. I'm not a Supreme Court scholar (I just moonlight as a reactionary haha), so here's someone who **is** to explain the effects. Unfortunately on Substack, [Steve Vladeck's One First: The Shoddy Politics of Trump v. Anderson](https://stevevladeck.substack.com/p/70-the-three-biggest-problems-with)
 
+I'm also trying to improve my writing style, because I struggle with conveying high-tech, informed entertainment for all audiences. Suggestions appreciated, so please consider my more verbose opinions on tech in this garden a continuing work-in-progress until I find a voice I'm happy with.
 ## Pages
-- 
+- Making significant headway on the AI infringement essay. **Status 80%**, I might just publish soon after some heavy edits to curb verbosity.
+- Seedling: [[Resources/law-students|Resources for Law Students]] (extracted from [[Essays/law-school|Law School is Broken]], new section added)
+- Content update: [[Projects/rss-foss|Toward RSS]]
 ## Status Updates
-- 
+- Fixed the license on the repo, it mistakenly identified my written content as MIT-licensed.
+- Added RSS feeds to the homepage's metadata, which should allow better integration with auto-discovery tools such as [RSS Is Dead](https://rss-is-dead.lol).

content/bookmarks.md

@@ -9,7 +9,10 @@ lastmod: 2024-01-14
 ---
 One of the core philosophies of digital gardening is that one should document their learning process when trying new things. As such, here's my very disorganized to-dos and to-reads in the form of a public bookmark list. This page will change very often.
 
+- [Academish Voice](https://inkandswitch.notion.site/Academish-Voice-0d8126b3be5545d2a21705ceedb5dd45)
 - https://www.shuttle.rs/
+
+## Historical
 - [lazy.nvim plugin spec](https://github.com/folke/lazy.nvim#-plugin-spec)
 - [3D printer troubleshooting](https://www.simplify3d.com/resources/print-quality-troubleshooting/)
 - [List of attorneys on Mastodon](https://www.lawstodon.org/)