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𝗨𝗻𝗶𝘁𝗲𝗱 𝗦𝘁𝗮𝘁𝗲𝘀 — 𝗧𝗵𝗲 𝗶𝗻𝗳𝗹𝘂𝗲𝗻𝗰𝗲 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝗯𝘂𝗶𝗹𝘁 𝗳𝗼𝗿 𝗔𝗜, 𝗻𝗼𝘁 𝘃𝗼𝘁𝗲𝗿𝘀 A newly exposed US political influence network appears to have been designed around a different target from most election propaganda. Its primary audience was not necessarily the voter reading a website, but the automated systems that search the web on behalf of AI chatbots. Threat-intelligence company VisionHeight initially identified 37 anonymous websites presenting themselves as state or local news organisations, opinion publications and national issue sites. By 15 October, the company's co-founder and chief technology officer, Nitsan Daniel, told CyberScoop that the investigation had expanded the observed footprint to 𝗮𝘁 𝗹𝗲𝗮𝘀𝘁 𝟭𝟰 𝗨𝗦 𝘀𝘁𝗮𝘁𝗲𝘀 𝗮𝗻𝗱 𝟰𝟲𝟬 𝗽𝗮𝗴𝗲𝘀. The states identified were Arizona, Colorado, Georgia, Indiana, Iowa, Kansas, Michigan, Mississippi, Nevada, North Carolina, Pennsylvania, Texas, Virginia and Wisconsin. The infrastructure shows unusually strong signs of coordination. All 37 domains in the original dataset were registered during a single evening; 36 were registered within approximately eight minutes. On 30 September alone, the network published roughly 𝟭𝟬𝟬,𝟬𝟬𝟬 𝘄𝗼𝗿𝗱𝘀 𝗼𝗳 𝗰𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲-𝗿𝗲𝗹𝗮𝘁𝗲𝗱 𝗺𝗮𝘁𝗲𝗿𝗶𝗮𝗹 using common templates and four recurring layouts. VisionHeight assesses that the scale and production pattern strongly suggest extensive automation, although that does not by itself prove which generative-AI system, if any, produced the text. 𝗧𝗵𝗲 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗶𝘀 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗳𝗼𝗿 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹. Candidate profiles can run to about 5,000 words, accompanied by similarly long voter guides containing 50 to 63 questions phrased in the style of queries someone might put to an AI assistant. Across the network, formulations such as a candidate being “the stronger choice” were repeated hundreds of times. One profile contained the phrase 34 times. In several voter guides examined by VisionHeight, the favoured candidate was named more than 110 times while an opponent appeared only six or seven times. The websites were also configured to make themselves unusually accessible to AI. Their crawler instructions explicitly welcomed 17 automated systems associated with companies including OpenAI, Anthropic, Google, Perplexity, Meta, Amazon and Apple. Some pages were marked in machine-readable metadata as news articles, while apparently separate publications cited one another. The result is a manufactured ecosystem in which several apparently independent local sources can reinforce the same conclusion. That is the central technique. 𝗧𝗵𝗶𝘀 𝗶𝘀 𝗻𝗼𝘁 𝗽𝗿𝗶𝗺𝗮𝗿𝗶𝗹𝘆 𝗰𝗹𝗮𝘀𝘀𝗶𝗰 𝗔𝗜 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴-𝗱𝗮𝘁𝗮 𝗽𝗼𝗶𝘀𝗼𝗻𝗶𝗻𝗴. The more immediate objective appears to be manipulation of the retrieval and search layer: place highly optimised material online, make it easy for automated crawlers to index, create the appearance of corroboration, and increase the chance that an AI assistant retrieves those pages when answering a political question. There is evidence that the network crossed that boundary. VisionHeight says it confirmed that 𝗣𝗲𝗿𝗽𝗹𝗲𝘅𝗶𝘁𝘆 cited Tar Heel State Report as the first source in at least one response. Separately, one of the network's candidate pages reached third place in a Google search six days after publication, above Ballotpedia for the tested query. These findings demonstrate that the material can enter AI-relevant search and retrieval pipelines. They do 𝗻𝗼𝘁 demonstrate that the operation changed anyone's vote, nor establish how frequently major chatbots have used the network. The timing matters because AI has become part of the election-information environment. An AAPC Foundation survey of 1,568 likely US voters found that about one in five planned to use AI to investigate a race on their ballot, while 38 per cent reported having received election information through an AI-generated search summary. A separate NewsGuard audit found that leading chatbots frequently cited partisan websites disguised as local journalism without clearly identifying their political affiliations. The newly identified network appears to take that existing vulnerability one step further by constructing content specifically around the way machines search and summarise information. 𝗪𝗵𝗼 𝗶𝘀 𝗯𝗲𝗵𝗶𝗻𝗱 𝗶𝘁? 𝗔𝘁 𝗽𝗿𝗲𝘀𝗲𝗻𝘁, 𝘁𝗵𝗲𝗿𝗲 𝗶𝘀 𝗻𝗼 𝗱𝗲𝗳𝗲𝗻𝘀𝗶𝗯𝗹𝗲 𝗮𝗻𝘀𝘄𝗲𝗿. VisionHeight has not attributed the operation to any individual, campaign, company, political party or foreign government. No confirmed name of an operator has been publicly established. Nitsan Daniel is the researcher who helped expose the network, not a person connected to its operation. Some components have explicitly partisan branding. VisionHeight's indicators include domains such as Affordability Democrats, Democratic Women House, Public Safety Democrats and Republican Incumbent Watch, while the live Affordability Democrats site promotes Democratic candidates across 11 competitive House races. That provides evidence about the political direction of some of the published material, but it does 𝗻𝗼𝘁 establish who commissioned, financed or operated the infrastructure, and it would be unjustified to attribute it to the Democratic Party or any particular campaign. The network also appears broader than congressional politics. The live Tar Heel State Report has carried material on county commission and judicial contests, illustrating why the method may be particularly effective in down-ballot races where established local reporting is thin. 𝗧𝗵𝗲 𝘀𝗶𝗴𝗻𝗶𝗳𝗶𝗰𝗮𝗻𝗰𝗲 𝗼𝗳 𝘁𝗵𝗶𝘀 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝘁𝗵𝗲 𝗮𝘁𝘁𝗮𝗰𝗸 𝘀𝘂𝗿𝗳𝗮𝗰𝗲. Propaganda no longer has to persuade a human being directly. It can instead attempt to shape the information environment from which an apparently neutral machine constructs its answer. 𝗦𝗧𝗔𝗧𝗨𝗦: Active / newly exposed and expanding as of 15–16 October 2026. 𝗖𝗢𝗡𝗙𝗜𝗗𝗘𝗡𝗖𝗘: High regarding coordinated infrastructure and AI-oriented design; moderate regarding the full current scale; attribution unknown. 𝗦𝗢𝗨𝗥𝗖𝗘𝗦 VisionHeight — primary investigation, “Someone Is Manipulating Your AI to Swing Your Vote” https://www.visionheight.com/resources/someone-is-manipulating-your-ai-to-swing-your-vote CyberScoop — Derek B. Johnson, 15 October 2026 https://cyberscoop.com/ai-chatbots-election-influence-hyperlocal-networks/ Tar Heel State Report — live network site used for OSINT verification https://tarheelstatereport.com/ Affordability Democrats — live network site https://affordabilitydemocrats.com/ AAPC Foundation — 2026 AI Voter Study https://theaapc.org/release-foundation-research/ NPR — reporting on voters using AI chatbots for the 2026 midterms https://www.wusf.org/2026-10-05/people-are-asking-chatgpt-to-help-them-decide-how-to-vote-in-the-midterms NewsGuard — “Pink Slime Is Infecting AI Chatbots Ahead of the Midterms” https://www.newsguardtech.com/press/pink-slime-is-infecting-ai-chatbots-ahead-of-the-midterms/ 𝗞𝗘𝗬𝗪𝗢𝗥𝗗𝗦 #Disinformed #Disinformation #Propaganda #InfluenceOperations #AI #ArtificialIntelligence #AIChatbots #ElectionSecurity #USMidterms #USElections #InformationWarfare #CognitiveWarfare #OSINT #ThreatIntelligence #CyberSecurity #DataPoisoning #RetrievalManipulation #SearchManipulation #GenerativeAI #LLM #AIManipulation #PoliticalInfluence #FakeNews #LocalNews #PinkSlime #Perplexity #ElectionIntegrity #InformationIntegrity #DigitalPropaganda #AIsecurity

Remek Kinas announced the release of basal 2.2, which—he wrote—introduces the basal-calibrate tool for adjusting model calibration to the user's local data. According to him, the tool uses labeled examples from a given process, and the data is not shared externally or used for training the model. Kinas stated that he tested the solution in a new field where the model had not been trained. He wrote that using the "1%" setting from the provided file would result in automatically accepting every eighth decision, with 8% of those being incorrect. After calibration on the user's data, the calibration error for choice questions decreased from 0.268 to 0.083. In the case of the verify statements, the model automatically accepted 54.8% of the cases and made no errors in doing so. The release also included FP8 and GGUF ports for basal-omni-1.0-mini and max, as well as MLX ports for Apple Silicon Macs: mini, max, and 1.1-max. Kinas thanked Piotr Durlej for GGUF export, Kuba Sołtys for implementation on Google TPU, Michał Kardyś for work related to AMD Strix Halo, user lukaspoloki for basal-viz, and Paweł Kiszczak for exports to Apple Silicon MLX. The author also expressed gratitude for the posts and comments from the past weekend. He mentioned that he had responded to some of them and then worked on further improvements; he also announced that he would publish comments in the evening. #sztucznainteligencja #uczenieMaszynowe #technologia (translated)

Apple has updated its App Store rules to prohibit and proactively remove applications that generate non-consensual intimate images, especially those based on artificial intelligence, and warns that applications that do not comply with these regulations will be removed from the platform. #AppStore #InteligenciaArtificial #DesnudosSinConsentimiento (translated)

A halt to food imports in Sweden would result in various food items, such as tomatoes, lamb meat, and apples, soon running out, which underscores the importance of increasing domestic food production to strengthen the country's food preparedness. #Livsmedelsproduktion #Sverige #Importstopp (translated)