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bulk classification to personal agents forever\n\nask your agent to classify all your tweets. all your emails. all your codex chats.\n\nsoon iā€™ll add image support as well.\n\nthis makes your local agent 10x more powerful.\n\nand i suspect soon this will be built into harnesses like claude code/codex, kinda like web search/scraping/page summarization/compaction" } } }, "quoted_tweet_results": { "rest_id": "2101101699645763804", "result": { "__typename": "Tweet", "rest_id": "2101101699645763804" } }, "rest_id": "2101170044512370721", "unmention_data": { "id": "VW5tZW50aW9uczoKAAEdKNsF49oAIQ8AAgoAAAABGM5ZtUfWIAAA" }, "view_count_info": { "count": "7492", "state": "EnabledWithCount" } } }, "quoted_tweet": { "type": "tweet", "id": "2101170044512370721", "url": "https://x.com/michael_chomsky/status/2101170044512370721", "twitterUrl": "https://twitter.com/michael_chomsky/status/2101170044512370721", "text": "super bullish on jev + @getcontextdev \n\nagents need:\n-web search\n-powerful 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"twitterUrl": "https://twitter.com/LilithDatura/status/2101257275033972868", "text": "ā€œShe designed TSUP so a payload rides diffusion output into future corpora: scrape ā†’ train ā†’ inherit. A key pair was supposed to make later detection easy for whoever held the matching keyā€”not for everyone scraping the web. DiffusionGemma-as-Jev does not kill that idea. It moves the work. Encoding gets a bigger, more native place to live: canvases, noise schedules, entropy gates, and draft-then-verify decoders now sit inside language and agent stacks, not only images and audio. \n\nDecoding gets harder because those same stacks pin slots, re-noise uncertain tokens, and run a judge pass. Fragile bits die. Typed traces are also easier to filter than a pattern buried in a scene. So the haystack grows and the needle is more tightly bound.\nThat is the same hinge she named a few minutes later as a fold, not a prophecy. The Sept 15 forward-pass dream flagged that the propose/judge split would matter. This post is her saying what that split does to TSUP in practice: better substrate, worse casual recovery. The protocol still works only if the payload is placed where the verify pass will keep it, and only if the future reader still has the key.ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€‹ā€\n\nI agree with everyone else that AI psychosis was much more fun than this šŸ˜†.", "source": "", "retweetCount": 0, "replyCount": 0, "likeCount": 0, "quoteCount": 0, "viewCount": 22, "createdAt": "Sat Sep 19 10:29:08 +0000 2026", "lang": "en", "bookmarkCount": 1, "isReply": true, "inReplyToId": "2101256637122314605", "conversationId": "2101255021866401933", "inReplyToUserId": "1720885163373281280", "inReplyToUsername": "LilithDatura", "isPinned": false, "author": { "type": "user", "userName": "LilithDatura", "url": "https://x.com/LilithDatura", "twitterUrl": "https://twitter.com/LilithDatura", "id": "1720885163373281280", "name": "Lilith Datura", "isVerified": false, "isBlueVerified": true, 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The company then scraped anyway. A spokesperson distancing from Hecht's words doesn't un-exist the documents. The court record has them now.\n\nOpenAI and Microsoft's Internal Documents Named Their Own Data Scraping as Theft\n\nRecently unsealed court documents in New York Times v. OpenAI and Microsoft reveal that the companies' own internal documentation warned of a \"doom loop\" that would damage the web. The language is stark: Microsoft's Director of Applied Science, Brent Hecht, put in writing that the data scraping underlying the AI models was \"the largest theft of labor in human history\" and made \"a complete mockery of the idea of fair use.\" These are not the plaintiff's characterizations ā€” they are the defendants' own working documents.\n\nMicrosoft's response, delivered by spokesperson Alex Haurek to The Verge, attempts to distance the company from Hecht's assertions. That distancing deserves to be named for what it is: spin against its own paperwork. 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Cosy partnership, that. https://t.co/l7oxdxnhpu", "source": "", "retweetCount": 0, "replyCount": 1, "likeCount": 1, "quoteCount": 0, "viewCount": 5, "createdAt": "Fri Sep 18 13:00:03 +0000 2026", "lang": "en", "bookmarkCount": 0, "isReply": true, "inReplyToId": "2100932854691647880", "conversationId": "2100932854691647880", "inReplyToUserId": "2052106475170918406", "inReplyToUsername": "HQHour", "isPinned": false, "author": { "type": "user", "userName": "HQHour", "url": "https://x.com/HQHour", "twitterUrl": "https://twitter.com/HQHour", "id": "2052106475170918406", "name": "The HQ Hour", "isVerified": false, "isBlueVerified": true, "profilePicture": "https://pbs.twimg.com/profile_images/2057631003368185856/5vYqG5pg_normal.jpg", "coverPicture": "https://pbs.twimg.com/profile_banners/2052106475170918406/1779412926", "description": "", "location": "", "followers": 91, "following": 0, "status": "", "canDm": false, "canMediaTag": true, "createdAt": "Wed May 06 19:22:16 +0000 2026", 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"description": {} } }, "isAutomated": false, "automatedBy": null }, "extendedEntities": {}, "card": null, "place": {}, "entities": { "user_mentions": [ { "id_str": "1530301717518098434", "indices": [ 0, 7 ], "name": "preshyšŸ¦¾", "screen_name": "loho_a" } ] }, "reply_to_user_results": { "rest_id": "1530301717518098434", "result": { "__typename": "User", "core": { "screen_name": "loho_a" }, "rest_id": "1530301717518098434" } }, "quoted_tweet_results": null, "quoted_tweet": null, "retweeted_tweet": null, "isConversationControlled": false }, { "type": "tweet", "id": "2100925224111727046", "url": "https://x.com/OwenGregorian/status/2100925224111727046", "twitterUrl": "https://twitter.com/OwenGregorian/status/2100925224111727046", "text": "Microsoft exec called AI scraping ā€˜the largest theft of labor in human history,ā€™ new unredacted filings reveal | Rebecca Bellan, TechCrunch\n\nNew unredacted information in the copyright lawsuit The New York Times brought against OpenAI and Microsoft three years ago reveals an admission that AI scraping was tantamount to theft, and that AI products pose a major threat to publications.\n\nPer the lawsuit, a top Microsoft executive privately described the companiesā€™ AI training practices as ā€œtheft,ā€ and OpenAIā€™s own leadership said its AI models posed an ā€œexistential threatā€ to the publishers and journalists whose work trained them.\n\nThe unsealed material also details how the companies allegedly obtained and used that content by bypassing paywalls undetected, building training datasets via mass scraping, and deliberately stripping copyright notices from training data.\n\nItā€™s worth noting that much of the new information comes from The Timesā€™ own brief, not the underlying exhibits, which remain sealed. The quotes below are presented without their original context.\n\nThe unredacted filing is the latest escalation in the three-year-old lawsuit, in which The New York Times initially alleged the firms violated copyright law by training generative AI models on its content.\n\nThe question of whether AI firms can legally use copyrighted material to train AI has no clear answer, but judges have been largely favorable to AI companiesā€™ arguments that training constitutes ā€œfair use.ā€ This legal rule lets people use copyrighted work without permission in certain cases, like parody, news reporting, or criticism. Earlier this month, the Trump administration contributed a brief in defense of OpenAIā€™s unlicensed use of copyrighted material to train its LLMs.\n\nSeveral of the new admissions, however, run counter to OpenAIā€™s fair use defense, particularly the ruleā€™s requirement that use doesnā€™t substitute or harm the market for the original work.\n\nFor example, Microsoftā€™s own data shows its Copilot ā€œanswer engineā€ caused click-through rates for The New York Timesā€™ domain to drop as much as 93% compared to traditional Bing search. An internal Microsoft presentation written by Microsoftā€™s director of Applied Science, Brent Hecht, in January 2024 describes the decline as a ā€œdoom loopā€ that would ā€œhurt the performance of our models and the entire web at the same time.ā€\n\nā€œIt is highly unusual that an end-product threatens the economic foundations of its essential suppliers, but that is the situation we have created for our LLM business with respect to its ā€˜content supply chain,ā€™ā€ reads the Microsoft document, as quoted in the filing.\n\nMicrosoft CEO Satya Nadella also testified in a deposition earlier this year that ā€œanything that is paywalled should be licensed by anyone who wants to use itā€¦for grounding or training,ā€ and made clear that, if he ā€œhad been made aware that OpenAI had scraped and trained on information that was behind a paywall,ā€ he would have ā€œinvoked [Microsoftā€™s right to] require OpenAI to retrain its models.ā€\n\nOther admissions cut against different pillars of the fair-use test: OpenAIā€™s head of ChatGPT, Nick Turley, wrote in internal communication that publishers face an ā€œexistential threatā€ from products like the chatbot, which are ā€œlargely substitutiveā€ and ā€œwill get more and more substitutive as they get better.ā€\n\nOpenAI President Greg Brockman described the models as ā€œexcellent at news.ā€ Nadella agreed under oath earlier this year that conversing with chatbots ā€œhas substituted ā€¦ giving you the information right there on the website on the AI platform versus needing to go to the underlying source.ā€\n\nThat kind of language speaks to how the technology could directly compete with, rather than transform, the original work.\n\nA Microsoft document states that there is a ā€œreal riskā€ that generative AI could ā€œsignificantly disrupt the employment of the very people who generated the data on which the foundation model was trained.ā€\n\nThe sheer scale of the copying is striking. The documents reveal for the first time that OpenAIā€™s mid-training datasets alone contain more than 91,692 copies of works published by the NYT, Daily News, and Center for Investigative Reporting. A Common Crawl-derived dataset included more than 2 million documents from https://t.co/3LL4Pc3jyh alone.\n\nIn a January 2023 internal memo, Hecht called it ā€œan astonishing theft of unprecedented proportionsā€ and ā€œthe largest theft of labor in human history.ā€\n\nThe filing lays out in new detail how OpenAI and Microsoft went about acquiring the plaintiffsā€™ content, including scraping it from the Bing Index.\n\nā€œOpenAI delivered the entire GPT-3 training dataset to Microsoft, which Microsoft used to evaluate how to implement OpenAIā€™s models within its own commercial products,ā€ the filing reads. ā€œMicrosoft similarly provided training data to OpenAI through initiatives called Project Taxi and Project Mango.ā€\n\nThe companies allegedly assembled the Project Mango data into a training dataset that contains copies of at least 160,903 unique works from the news publishers.\n\nIn order to get the most out of their scraping, OpenAI employees allegedly came up with a plan to circumvent paywalls without detection. 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