www-citationneeded-news-loassa.txt (33835B)
1 [1] [citation needed] 2 a newsletter by Molly White 3 4 • [4]Archive 5 • [5]Recap issues 6 • [6]Podcast feed 7 • [7]About 8 • [8]RSS 9 • [9]Tip jar 10 • [10]Privacy policy 11 12 [12]Sign in [13]Subscribe 13 14 Sidenotes 15 16 [15][ ] Show footnotes 17 [16][ ] Show references 18 [17]( ) [18]( ) [19]( ) 19 [20]( ) [21]( ) [22]( ) 20 [23]Newsletter 21 22 AI isn't useless. But is it worth it? 23 24 AI can be kind of useful, but I'm not sure that a "kind of useful" tool 25 justifies the harm. 26 27 [24] Molly White 28 29 [25]Molly White 30 31 Apr 17, 2024 — 15 min read 32 AI isn't useless. But is it worth it? 33 audio-thumbnail 34 AI isn't useless. But is it worth it? 35 0:00 36 /1311.164082 37 [28][0 ]1×[32][100 ] 38 Listen to a voiceover of this post, [33]download the recording for later, or 39 [34]subscribe to the feed in your podcast app. 40 41 As someone known for my criticism of the previous deeply flawed technology to 42 become the subject of the tech world's overinflated aspirations, I have had 43 people express surprise when I've remarked that generative artificial 44 intelligence tools^[35]a can be useful. In fact, I was a little surprised 45 myself. 46 47 But there is a yawning gap between "AI tools can be handy for some things" and 48 the kinds of stories AI companies are telling (and the media is uncritically 49 reprinting). And when it comes to the massively harmful ways in which large 50 language models (LLMs) are being developed and trained, the feeble argument 51 that "well, they can sometimes be handy..." doesn't offer much of a 52 justification. 53 54 Some are surprised when they discover I don't think blockchains are useless, 55 either. Like so many technologies, blockchains are designed to prioritize a few 56 specific characteristics (coordination among parties who don't trust one 57 another, censorship-resistance, etc.) at the expense of many others (speed, 58 cost, etc.). And as they became trendy, people often used them for purposes 59 where their characteristics weren't necessary — or were sometimes even unwanted 60 — and so they got all of the flaws with none of the benefits. The thing with 61 blockchains is that the things they are suited for are not things I personally 62 find to be terribly desirable, such as the massive casinos that have emerged 63 around gambling on token prices, or financial transactions that cannot be 64 reversed. 65 66 When I boil it down, I find my feelings about AI are actually pretty similar to 67 my feelings about blockchains: they do a poor job of much of what people try to 68 do with them, they can't do the things their creators claim they one day might, 69 and many of the things they are well suited to do may not be altogether that 70 beneficial. And while I do think that AI tools are more broadly useful than 71 blockchains, they also come with similarly monstrous costs. 72 73 [36]Subscribe 74 75 I've been slow to get around to writing about artificial intelligence in any 76 depth, mostly because I've been trying to take the time to interrogate my own 77 knee-jerk response to a clearly overhyped technology. After spending so much 78 time writing about a niche that's practically all hype with little practical 79 functionality, it's all too easy to look at such a frothy mania around a 80 different type of technology and assume it's all the same. 81 82 In the earliest months of the LLM mania, my ethical concerns about the tools 83 made me hesitant to try them at all. When my early tests were met with mediocre 84 to outright unhelpful results, I'll admit I was quick to internally dismiss the 85 technology as more or less useless. It takes time to experiment with these 86 models and learn how to prompt them to produce useful outputs,^[37]b and I just 87 didn't have that time then.^[38]c But as the hype around AI has grown, and with 88 it my desire to understand the space in more depth, I wanted to really 89 understand what these tools can do, to develop as strong an understanding as 90 possible of their potential capabilities as well as their limitations and 91 tradeoffs, to ensure my opinions are well-formed. 92 93 I, like many others who have experimented with or adopted these products, have 94 found that these tools actually can be pretty useful for some tasks. Though AI 95 companies are prone to making overblown promises that the tools will shortly be 96 able to replace your content writing team or generate feature-length films or 97 develop a video game from scratch, the reality is far more mundane: they are 98 handy in the same way that it might occasionally be useful to delegate some 99 tasks to an inexperienced and sometimes sloppy intern. 100 101 Still, I do think acknowledging the usefulness is important, while also holding 102 companies to account for their false or impossible promises, abusive labor 103 practices, and myriad other issues. When critics dismiss AI outright, I think 104 in many cases this weakens the criticism, as readers who have used and 105 benefited from AI tools think "wait, that's not been my experience at all". 106 107 Use cases 108 109 I've found AI tools to be useful to my writing, though not for the actual 110 writing bit. When I'm writing, I often find myself with a word on the "tip of 111 my tongue" (so to speak), and I've had more success with ChatGPT than with 112 Google for these circumstances — although I can usually find the word with 113 Google if I try hard enough. 114 115 [Screenshot-2024-04-16-at-4]User: What's a word for something that will not 116 affect the final outcome Assistant: Inconsequential 117 118 Like many people, I also find it challenging to proofread my own writing, and I 119 sometimes miss typos or weird grammar accidentally left in from changing a 120 sentence halfway through. 121 122 USER Please proofread the following text and point out any spelling or grammar 123 problems, typos, and other such issues. Do not change the tone of the writing 124 or try to adjust the reading level. Do not repeat the entire corrected text 125 back to me, simply identify with brief quotes of the flawed text where changes 126 need to be made. <document> Bitcoiners are all aflutter as the halving date 127 approaches in about a week. This is very exciting for them, because the past 128 three halvings look like they've precipitated major price increases 129 (particularly if you use a log scale, which bitcoiners love). Bitcoin prices in 130 linear scale (blue) and log scale (black) since 2010, with halvings marked by 131 the red lines (chart via Bitbo) The "halving" is the moment when bitcoin block 132 rewards that is, the amount of bitcoin issued as a reward to the miner for 133 successfully mining a block are reduced by half. This is a strategy to ensure 134 the total supply of bitcoin gradually tapers... RESPONSE Here are the spelling, 135 grammar, and typo issues I found in the text: 1. "kind of the thought process" 136 should be "kind of thought process" 2. "beliefs of guaranteed double-your-money 137 returns" should be "belief of guaranteed double-your-money returns" 3. "2024" 138 is used instead of "2023" in several places, likely typos: • "Notice of appeal 139 filed on April 11, 2024." • "Letter motion filed on April 8, 2024." • "Tweet by 140 Inner City Press, April 9, 2024." • "Tweet by Inner City Press, April 12, 141 2024." • "Order filed on April 8, 2024." 4. "Committee on Committee on Banking" 142 should be "Committee on Banking" 5. "consider proposals" should be "considers 143 proposals" 6. "BitBoy" Armstrong's name is inconsistently capitalized as 144 "Bitboy" in one instance 7. "coinflips" should be "coin flips" 8. There is an 145 inconsistency in the number of Web3 is Going Just Great entries between April 1 146 and April 12. The text states "11 entries" but only 7 are listed.Proofreading a 147 newsletter post with Anthropic's Claude model 148 149 LLMs are pretty decent at proofreading, and although they sometimes spit out a 150 few false positives, this example from proofreading my most recent recap issue 151 shows where it caught several mistakes (points 1, 2, 4, and 8; point 5 was also 152 a genuine error, but it was within a quote). 153 154 However, I don't think I need generative AI to do this, either. There are a lot 155 of proofreading tools^[39]d that work quite well, and, helpfully, don't invent 156 errors that weren't in the original text (as I've found the ChatGPT models are 157 particularly wont to do). 158 159 Coding has been the far more compelling use case for me. Copilot, Github's AI 160 coding assistant, integrates directly into VSCode and other [40]IDEs. I've also 161 played with using the more general models, like ChatGPT, for coding tasks. They 162 are certainly flawed — Copilot has an annoying habit of "hallucinating" 163 (fabricating) imports instead of deferring to VSCode's perfectly good non-AI 164 auto-import, for example — but in other cases they are genuinely helpful. 165 166 I've found these tools to be particularly good at simple tasks that would 167 normally pull me out of my workflow to consult documentation or StackOverflow, 168 like generating finicky CSS selectors or helping me craft database aggregation 169 operations. On at least one occasion, they've pointed me towards useful 170 functionality I never knew about and wouldn't even think to look up. They're 171 also great at saving you some typing by spitting out the kind of boilerplate-y 172 code you have to write for things like new unit tests. 173 174 The tools can also do the kind of simple, repetitive tasks I'd previously write 175 a quick script to do for me — or they can generate that quick script. For 176 example, here's me asking ChatGPT to write a quick Python script to turn my 177 blogroll OPML file into the JSON file I wanted while I was adding a [41] 178 blogroll page to my website: 179 180 Suggest some python code to turn an OPML file like this into a JSON file with 181 fields for "text", "xmlUrl", and "htmlUrl": <opml version="1.0"> <head> <title> 182 Feeds of molly.white5 tagged as Blogroll from Inoreader [https:// 183 www.inoreader.com]</title> </head> <body> <outline text="Blogroll" title= 184 "Blogroll"> <outline text="Adactio" title="Adactio" type="rss" xmlUrl="https:// 185 adactio.com/rss/" htmlUrl="https://adactio.com/"/> <outline text="Anil Dash" 186 title="Anil Dash" type="rss" xmlUrl="https://feeds.dashes.com/AnilDash" htmlUrl 187 ="https://dashes.com/anil/"/> <outline text="Ben Werdmüller" title="Ben 188 Werdmüller" type="rss" xmlUrl="https://werd.io/?_t=rss" htmlUrl="https:// 189 werd.io/"/> <outline text="Birch Tree" title="Birch Tree" type="rss" xmlUrl= 190 "https://birchtree.me/rss/" htmlUrl="https://birchtree.me/"/> <outline text= 191 "cabel.com" title="cabel.com" type="rss" xmlUrl="https://cabel.com/feed/" 192 htmlUrl="https://cabel.com"/> 193 194 After changing the feeds.opml file path to the location of the file on my 195 computer, the code it suggested worked without any modification: 196 197 ASSISTANT python import xml.etree.Element Tree as ET import json tree = 198 ET.parse('feeds.opml') root = tree.getroot() feeds = for outline in 199 root.findall('.//outline'): feed = { 'text': outline.attrib.get('text'), 200 'xmlUrl': outline.attrib.get('xmlUrl"), 'htmlUrl': outline.attrib.get 201 ('htmlUrl') } feeds.append(feed) json_data = json.dumps(feeds, indent=4) with 202 open('feeds.json', 'w') as f: f.write(json_data) This code will read the OPML 203 file 'feeds.opml', extract the 'text', 'xmlUrl', and 'htmlUrl' attributes from 204 each outline element, and save the data into a JSON file 'feeds.json' with 205 proper indentation. 206 207 Besides my own experimentation, others are using these tools in ways that are 208 really hard to argue aren't useful. Someone I know in real life has told me 209 about creating a custom model based on their own emails, which they then query 210 as needed, or use to create some fairly boilerplate documents they previously 211 had to spend hours on. Open source developer Simon Willison has been 212 documenting his own AI coding experiments on his blog, and has described how 213 LLMs have [42]made him more ambitious with his projects and more likely to 214 embark on what he calls "[43]sidequests".^[44]e Sumana Harihareswara [45]uses 215 OpenAI's speech recognition tools to create subtitles for her videos and 216 recorded talks, or to "mine" them for material she can later reuse. Elsewhere 217 on the internet, those who speak English as a second language have spoken of 218 LLMs' usefulness in revising their professional communications. Others use it 219 to summarize meeting notes. Some use it as a starting point for documentation. 220 221 Reality check 222 223 Despite some unarguably useful features, the limitations of these tools make 224 themselves readily apparent. 225 226 When it comes to coding, while it can make for a handy assistant to an 227 experienced developer, it can't replace an experienced developer. Microsoft's 228 [46]Super Bowl commercial, which shows a person prompting Copilot to "Write 229 code for my 3D open world game", is pure fantasy. 230 231 molly Write code for my 3D open world game GitHub Copilot Used 1 reference > 232 Sorry, but writing code for a 3D open world game is a complex task that 233 requires a lot of time and effort. It's not something that can be done in a 234 single response. However, I can help you get started by creating a basic 235 structure for a game using a game development framework like Unity or Unreal 236 Engine. Here's a simple example of a Unity script in C# that moves a game 237 object: using System.Collections; using System.Collections.Generic; using 238 UnityEngine; public class PlayerMovement: MonoBehaviour { public float speed = 239 10.0f; void Update() { float moveHorizontal = Input.GetAxis ("Horizontal"); 240 float moveVertical = Input.GetAxis ("Vertical"); Vector3 movement = new Vector3 241 (moveHorizontal, 0.0f, moveVertical); transform.position = transform.position + 242 movement * speed * Time.de } } This script should be attached to the player 243 object. It takes input from the keyboard and moves the player object 244 accordingly. The speed variable can be adjusted to control how fast the player 245 moves. 246 247 And in my experience, it sometimes gets in the way more than it helps, as when 248 I experimented with it while working on a [47]Chrome extension I was writing 249 recently and ultimately had to turn it off. It constantly suggested plausible 250 but completely non-functional code, scaffolded the project in an outdated 251 format, and autogenerated CSS classes that looked like they could be Bootstrap 252 classes, but weren't. It's good at short functions and common boilerplate, but 253 it's not going to architect a project for you, and, as with writing, it's not 254 going to "think" of novel ideas. I like it for getting annoying, repetitive 255 tasks out of my way; I don't worry it's going to take my job. 256 257 Arguably the most widely-described use case for generative AI is writing. 258 Indeed, as media companies lay off journalists in droves, [48]some [49]outlets 259 are replacing their work with shoddy, AI-generated approximations. Freelance 260 writers are reporting challenges in finding work as their former clients decide 261 that ChatGPT can do a good enough job. But what these companies and clients 262 fail to recognize is that ChatGPT does not write, it generates text, and anyone 263 who's spotted obviously LLM-generated content in the wild immediately knows the 264 difference. 265 266 You've gotten this far into my article, so you're recently familiar with a 267 couple dozen paragraphs of purely human writing. Contrast that with LLMs' 268 attempts, from prompts with varying degrees of detail, with my very best 269 efforts put into trying to get it to sound halfway normal: 270 271 A table with a range of LLM prompts provided to three models: ChatGPT, Claude, 272 and Gemini([50]spreadsheet) 273 274 Yikes. I particularly like how, when I ask them to try to sound like me, or to 275 at least sound less like a chatbot, they adopt a sort of "cool teacher" 276 persona, as if they're sitting backwards on a chair to have a heart-to-heart. 277 Back when I used to wait tables, the other waitresses and I would joke to each 278 other about our "waitress voice", which were the personas we all subconsciously 279 seemed to slip into when talking to customers. They varied somewhat, but they 280 were all uniformly saccharine, with slightly higher-pitched voices, and with 281 the general demeanor as though you were talking to someone you didn't think was 282 very bright. Every LLM's writing "voice" reminds me of that. 283 284 Even if the telltale tone is surmountable, LLMs are good at generating text but 285 not at generating novel ideas. This is, of course, an inherent feature of 286 technology that's designed to generate plausible mathematical approximations of 287 what you've asked it for based on its large corpus of training data; it doesn't 288 think, and so the best you're ever going to get from it is some mashup of other 289 peoples' thinking.^[51]f 290 291 LLM-generated text is good enough for some use cases, which I'll return to in a 292 moment. But I think most people, myself certainly included, would be mortified 293 to replace any of our writing with this kind of stuff.^[52]g 294 295 Furthermore, LLMs' "hallucination" problem means that everything it does must 296 be carefully combed over for errors, which can sometimes be hard to spot. 297 Because of this, while it's handy for proofreading newsletters or helping me 298 quickly add a fun feature to my website, I wouldn't trust LLMs to do anything 299 of real import. And the tendency for people to put too much trust into these 300 tools^[53]h is among their most serious problems: no amount of warning labels 301 and disclaimers seem to be sufficient to stop people from trying to use them to 302 provide legal advice or sell AI "therapy" services. 303 304 Finally, advertisements that LLMs might someday generate feature-length films 305 or replace artists seem neither feasible nor desirable. AI-generated images 306 tend to suffer from a similar bland "tone" as its writing, and their 307 proliferation only makes me desire real human artwork more. With generated 308 video, they inevitably trend towards the uncanny, and the technology's inherent 309 limitations — as a tool that is probabilistically generating "likely" images 310 rather than ones based on some kind of understanding — seem unlikely to ever 311 overcome that. And the idea that we all should be striving to "replace artists" 312 — or any kind of labor — is deeply concerning, and I think incredibly 313 illustrative of the true desires of these companies: to increase corporate 314 profits at any cost. 315 316 When LLMs are good enough 317 318 As I mentioned before, there are some circumstances in which LLMs are good 319 enough. There are some types of writing where LLMs are already being widely 320 used: for example, by businesspeople who use them to generate meeting notes, 321 fluff up their outgoing emails or summarize their incoming ones, or spit out 322 lengthy, largely identical reports that they're required to write regularly. 323 324 You can also spot LLMs in all sorts of places on the internet, where they're 325 being used to try to boost websites' search engine rankings. That weird, bubbly 326 GPT voice is well suited to marketing copy and social media posts, too. Any 327 place on the web that incentivizes high-volume, low effort text is being 328 inundated by generated text, like e-book stores, online marketplaces, and 329 practically any review or comment section. 330 331 But I find one common thread among the things AI tools are particularly suited 332 to doing: do we even want to be doing these things? If all you want out of a 333 meeting is the AI-generated summary, maybe that meeting could've been an email. 334 If you're using AI to write your emails, and your recipient is using AI to read 335 them, could you maybe cut out the whole thing entirely? If mediocre, 336 auto-generated reports are passing muster, is anyone actually reading them? Or 337 is it just middle-management busywork? 338 339 As for the AI [54]enshittification of the internet, we all seem to agree 340 already that we don't want this, and yet here it is. No one wants to open up 341 Etsy to look for a thoughtful birthday gift, only to give up after scrolling 342 through pages of low-quality print-on-demand items or resold Aliexpress items 343 that have flooded the site. 344 345 [Screenshot-2024-04-13-at-5] 346 [Screenshot-2024-04-13-at-5] 347 348 Your AI model is showing 349 350 No one wants to Google search a question only to end up on several pages of 351 keyword-spam vomit before finding an authoritative answer. 352 353 But the incentives at play on these platforms, mean that AI junk is inevitable. 354 In fact, the LLMs may be new, but the behavior is not; just like [55]keyword 355 stuffing and [56]content farms and the myriad ways people used software to 356 generate reams upon reams of low-quality text before ChatGPT ever came on the 357 scene, if the incentive is there, the behavior will follow. If the internet's 358 enshittification feels worse post-ChatGPT, it's because of the quantity and 359 speed at which this junk is being produced, not because the junk is new. 360 361 Costs and benefits 362 363 Throughout all this exploration and experimentation I've felt a lingering 364 guilt, and a question: is this even worth it? And is it ethical for me to be 365 using these tools, even just to learn more about them in hopes of later 366 criticizing them more effectively? 367 368 The costs of these AI models are huge, and not just in terms of the billions of 369 dollars of VC funds they're burning through at incredible speed. These models 370 are well known to require far more computing power (and thus electricity and 371 water) than a traditional web search or spellcheck. Although AI company 372 datacenters are not intentionally wasting electricity in the same way that 373 bitcoin miners perform millions of useless computations, I'm also not sure that 374 generating a picture of a person with twelve fingers on each hand or text that 375 reads as though written by an endlessly smiling children's television star 376 who's being held hostage is altogether that much more useful than a bitcoin. 377 378 There's a huge human cost as well. Artificial intelligence relies heavily upon 379 "[57]ghost labor": work that appears to be performed by a computer, but is 380 actually delegated to often terribly underpaid contractors, working in horrible 381 conditions, with few labor protections and no benefits. There is a huge amount 382 of work that goes into compiling and labeling data to feed into these models, 383 and each new model depends on [58]ever-greater amounts of said data — training 384 data which is well known to be scraped from just about any possible source, 385 regardless of copyright or consent. And some of these workers suffer serious 386 psychological harm as a result of exposure to deeply traumatizing material in 387 the course of sanitizing datasets or training models to perform content 388 moderation tasks. 389 390 Then there's the question of opportunity cost to those who are increasingly 391 being edged out of jobs by LLMs,^[59]i despite the fact that AI often can't 392 capably perform the work they were doing. Should I really be using AI tools to 393 proofread my newsletters when I could otherwise pay a real person to do that 394 proofreading? Even if I never intended to hire such a person? 395 396 Finally, there's the issue of how these tools are being used, and the lack of 397 effort from their creators to limit their abuse. We're seeing them used to 398 generate disinformation via increasingly convincing [60]deepfaked images, 399 audio, or video, and the [61]reckless use of them by previously reputable news 400 outlets and others who publish unedited AI content is also contributing to 401 misinformation. Even where AI isn't being directly used, it's degrading trust 402 so badly that people have to question whether the content they're seeing is 403 generated, or whether the "person" they're interacting with online might just 404 be ChatGPT. Generative AI is being used to [62]harass and [63]sexually abuse. 405 Other AI models are enabling [64]increased surveillance in the workplace and 406 for "security" purposes — where their well-known biases are worsening 407 discrimination by police who are wooed by promises of "predictive policing". 408 The [65]list goes on. 409 410 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 411 412 I'm glad that I took the time to experiment with AI tools, both because I 413 understand them better and because I have found them to be useful in my 414 day-to-day life. But even as someone who has used them and found them helpful, 415 it's remarkable to see the gap between what they can do and what their 416 promoters promise they will someday be able to do. The benefits, though extant, 417 seem to pale in comparison to the costs. 418 419 But the reality is that you can't build a hundred-billion-dollar industry 420 around a technology that's kind of useful, mostly in mundane ways, and that 421 boasts perhaps small increases in productivity if and only if the people who 422 use it fully understand its limitations. And you certainly can't justify the 423 kind of exploitation, extraction, and environmental cost that the industry has 424 been mostly getting away with, in part because people have believed their lofty 425 promises of someday changing the world. 426 427 I would love to live in a world where the technology industry widely valued 428 making incrementally useful tools to improve peoples' lives, and were honest 429 about what those tools could do, while also carefully weighing the technology's 430 costs. But that's not the world we live in. Instead, we need to push back 431 against endless tech manias and overhyped narratives, and oppose the 432 "innovation at any cost" mindset that has infected the tech sector. 433 434 Footnotes 435 436 1. When I refer to "AI" in this piece, I'm mostly referring to the much 437 narrower field of [66]generative artificial intelligence and [67]large 438 language models (LLMs), which is what people generally mean these days when 439 they say "AI". [68]↩ 440 441 2. While much fun has been made of those describing themselves as "prompt 442 engineers", I have to say I kind of get it. It takes some experience to be 443 able to open up a ChatGPT window or other LLM interface and actually 444 provide instructions that will produce useful output. I've heard this 445 compared to "google-fu" in the early days of Google, when the search engine 446 was much worse at interpreting natural language queries, and I think that's 447 rather apt. [69]↩ 448 449 3. ChatGPT was publicly released in November 2022, right as the cryptocurrency 450 industry was in peak meltdown. [70]↩ 451 452 4. Many of which are built with various other kinds of machine learning or 453 artificial intelligence, if not necessarily generative AI. [71]↩ 454 455 5. As it happens, he has also [72]written about the "AI isn't useful" 456 criticism. [73]↩ 457 458 6. Some AI boosters will argue that most or all original thought is also 459 merely a mashup of other peoples' thoughts, which I think is a rather 460 insulting minimization of human ingenuity. [74]↩ 461 462 7. Nor do I want to, by the way. I performed these tests for the purposes of 463 illustration, but I neither intend nor want to start using these tools to 464 replace my writing. I'm here to write, and you're here to read my writing, 465 and that's how it will remain. See my [75]about page. [76]↩ 466 467 8. Something that is absolutely encouraged by the tools' creators, who give 468 them chat-like interfaces, animations suggesting that the tool is "typing" 469 messages back to you, and a confident writing style that encourages people 470 to envision the software as another thinking human being. [77]↩ 471 472 9. Or, more accurately, by managers and executives who believe the marketing 473 hype out of AI companies that proclaim that their tools can replace 474 workers, without seeming to understand at all what those workers do. [78]↩ 475 476 Loved this post? Consider [79]signing up for a pay-what-you-want subscription 477 or [80]leaving a tip to support Molly White's work, which is entirely funded by 478 readers like you. 479 480 Read more 481 482 [81] A hand holds a gold "Bitcoin" coin that has been cut in half 483 484 Issue 55 – Halving a bad time 485 486 The bitcoin "halving" looms, and that may not be as good news as coiners hope. 487 Also, Terra committed fraud and Uniswap got a Wells notice. 488 489 Apr 13, 2024 490 [82] A collage of a Bored Ape, a photo of Ryder Ripps, and the BAYC logo 491 492 "The Monkey Fraud": An interview with Ryder Ripps 493 494 An interview with Ryder Ripps, a defendant in the Yuga Labs v. Ripps case about 495 Bored Ape Yacht Club trademark infringement and racism. 496 497 Apr 4, 2024 498 [83] A small fake critter with orange bristles, grey-blue skin, and large black 499 eyes, with its mouth agape 500 501 Issue 54 – Cases continue 502 503 Crypto-related litigation is in full swing, as the Terra civil fraud trial has 504 kicked off and two other cases against crypto companies have survived motions 505 to dismiss. 506 507 Apr 2, 2024 508 [84] B&W photo of Sam Bankman-Fried overlaid on a yellow-tinted photo of the 509 NYC courthouse with scrap of sentencing transcript 510 511 25 years for Sam Bankman-Fried 512 513 "The judgment has to adequately reflect the seriousness of the crime, and this 514 was a very serious crime." 515 516 Mar 28, 2024 517 518 Citation Needed features critical coverage of the cryptocurrency industry and 519 of issues in the broader technology world. 520 521 It is independently published by Molly White, and entirely supported by readers 522 like you. 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