home.social

Search

991 results for “data_from_space”

  1. Initial draft of part 3 in the recommendation series: We've gone from 'characterise' → 'recommend' → (now) 'predict’.

    SHOW ME WHAT YOU GOT (Part 3): Predict

    mostlylucid.net/blog/show-me-w

    This shows how to use ML-adjacent techniques to predict destinations based on current behaviour and changes in behaviour. This allows you to begin to predict what a user may wind up doing, a system is starting to break, or tone in a conversation is drifting into dangerous territory.

    I also show what *prediction* means in ML/adaptive systems – and that it's only ever as good as the data you collect.

    Part 4 is 'intervention’, where I cover experimentation in recommendation systems, allowing automated A/B 'product improvement' suggestions, etc.

    As ever, open to work – see the site! mostlylucid.net/resumes

  2. 🦅 NEW: A team of 68 scientists combined 17 years of #GPS and accelerometer records from about 850 #birds and #bats across 38 species with radar #data, then matched the flights to wind and rising air.

    Across #vultures covering more than 4.3 million km, they spent as little as 0.1% of flight time flapping, and some #species traveled more efficiently than #swimming #animals.

    👉 zmescience.com/ecology/animals

    #condors #thermals #gliding #flight #physics #energy #migration #biomechanics #ornithology #science #nature

  3. Canadians assured of safety put at risk because we outsourced data management to the US.

    Enough. Bring our data home and keep it here.

    “Up until the investigation was published, the Canadian Crime Stoppers Association (CCSA) and individual chapters had not spoken out about the extent of the breach, only saying they awaited a detailed report from Navigate360, the U.S.-based third-party company that manages its data through platform P3.”

    cbc.ca/news/investigates/canad



  4. ✈️ Flights: every civil aircraft in the air over Europe right now, from public ADS-B data. Airliners, private planes and business jets on one live map. Zoom anywhere in the world to see what's overhead.
    remoteview.online/app?layers=f
    #OSINT #maps

  5. Guy who thinks he has discovered the Unified Field Theory and Fine Stricture Constant: I have equations that describe known quantities from first principles!
    Me, with a BS in something other than physics: that’s just fitting the existing data. Do you have a testable hypothesis for where your theory predicts something other than the standard model?
    Guy: You’re moving the goalposts!!! You’ll never accept proof!

  6. "British MPs who criticised Palantir in parliament received “intimidating” letters from the US spytech firm, Novara Media can reveal. The MPs say it won’t stop them from continuing to hold Palantir to account.

    Former Labour leader Jeremy Corbyn and Labour and Cooperative MP Rachael Maskell both received letters from Palantir’s UK CEO Louis Mosley in June, detailing the MPs’ anti-Palantir contributions in the House of Commons, and offering to meet.

    Corbyn slammed the letters from Mosley as intimidation. “I am in no doubt as to why Palantir is writing these letters to MPs: to intimidate its critics into silence,” he told Novara Media. “Their tactics of intimidation will not work and they will not shut me up. I will never be deterred from speaking out against Palantir, which should have no place in our public services whatsoever.”

    Palantir, co-founded by billionaires Alex Karp and Peter Theil, has close ties to the Trump administration. Its increasing entrenchment in the British state via contracts with the NHS, Ministry of Defence and various police forces has sparked significant national security and data privacy concerns."

    novaramedia.com/2026/10/22/pal

    #UK #Palantir #BigTech #Lobbying

  7. Wolves roam into Finnish villages after Google destroys 1,300 acres of forest for AI data centers — forest where wolf packs used to live is now an expanse of mud tomshardware.com/tech-industry
    This is thankfully a less common but still very serious side effect of an
    ---
    257 uses from 257 accounts

  8. In today's email:

    Google uses my SIM card to get my phone number and adds it to the data profile they've created on me.

    Another reminder that #Google doesn't get #consent.

    If you do not want Google to "obtain your number from your carrier" then follow these steps:

    1: Stop the monitoring service by following the listed steps.

    2: Remove the phone number(s) they have already retrieved.

    3: Never trust Google to do what is right.

    #tech #privacy

  9. Google corta 250 mil árvores na Finlândia para um data center: impactos ambientais, riscos ESG e o que você pode fazer em 2024

    Introdução


    Em menos de 48 horas a manchete “Google derruba 250 mil árvores na Finlândia para um novo data center” dominou o Twitter, o Reddit e as colunas de tecnologia. O caso não é só mais um episódio de desmatamento; ele reúne sustentabilidade, conformidade regulatória e pressão ESG – três vetores que definem a agenda corporativa em 2024.

    Neste artigo você vai encontrar:

    • Dados reais sobre a área afetada e a pegada de carbono.
    • Como a legislação da UE e da Finlândia se aplica ao caso.
    • Ferramentas práticas (scripts, APIs e planilhas) para monitorar impactos ambientais e métricas ESG.
    • Passos concretos que investidores, profissionais de TI e cidadãos podem adotar agora.


    1. O que realmente aconteceu?

    ItemDados oficiaisFonteÁrvores derrubadas≈ 250 000Metsähoito (consultoria florestal)Área desmatada≈ 1 200 ha de floresta mistaFinnish Environment Institute (SYKE)Compensação propostaPlantio em áreas já degradadas (≈ 300 ha)Licença ambiental finlandesaEmissões previstas do data center (20 anos)12,5 Mt CO₂eRelatório de Sustentabilidade Google 2023

    Por que a compensação não basta?


    A Diretiva‑UE Habitat (92/43/EEC) exige que a compensação mantenha ou melhore a biodiversidade local. Plantar árvores em áreas já degradadas não garante a presença das espécies de fauna e flora que habitavam a floresta original, gerando risco de não conformidade.

    2. Implicações ESG em 2024


    • Investidores institucionais (BlackRock, Norges Bank) já incorporam métricas de biodiversidade nos seus engagements.
    • Na votação de acionistas de 2024, a Google recebeu 12 votos negativos por práticas de desmatamento.
    • O Corporate Sustainability Reporting Directive (CSRD) da UE exige relato detalhado de impactos sobre biodiversidade a partir de 2025.


    Checklist rápido para analistas ESG


    - [ ] Verificar licenças ambientais (SYKE, EU Habitat Directive)
    - [ ] Confirmar se a compensação inclui áreas de alta biodiversidade
    - [ ] Avaliar a % de energia renovável usada no data center
    - [ ] Mapear emissões de construção vs. operação (Scope 1‑3)
    - [ ] Checar histórico de votos de acionistas e engajamento de investidores

    3. Como medir e monitorar o impacto na prática

    3.1. Usando a API do Global Forest Watch (GFW)


    # Instale o cliente Python
    pip install gfw-api-client

    # Script simples para obter a área desmatada em 2023‑2024
    import gfw_api_client as gfw
    from datetime import date

    # Coordenadas aproximadas do data center (lat, lon)
    lat, lon = 61.5, 24.0

    # Consulta de perda de cobertura florestal nos últimos 12 meses
    data = gfw.get_deforestation(
    lat=lat,
    lon=lon,
    start_date=date(2023, 1, 1),
    end_date=date(2024, 12, 31)
    )

    print(f"Áreas desmatadas: {data['area_ha']} ha")

    O script devolve a área em hectares, permitindo comparar o número oficial (1 200 ha) com medições independentes.

    3 .2. Planilha de cálculo de emissões (Scope 1‑3)

    FonteEmissão (t CO₂e/ano)FórmulaEnergia elétrica5 800Consumo (kWh) × Fator de emissão (0,2 t/kWh)Refrigeração (fluido)1 200Volume (L) × GWP (1 800)Construção (materiais)5 500Massa (ton) × Emissão por tonelada (1 t CO₂e)
    Dica prática: importe a tabela acima para o Google Sheets e use a função =SUM(B2:B4) para obter a emissão total anual. Atualize os valores conforme relatórios de consumo.


    4. O que empresas de tecnologia podem fazer agora

    AçãoComo implementarBenefício ESGReavaliar locais de expansãoUse o Google Earth Engine para mapear áreas de alta biodiversidade antes de comprar terrenos.Reduz risco de multas e votos negativos.Compensação baseada em biodiversidadeInvista em projetos de restauração que incluam espécies nativas e corredores ecológicos.Alinha-se ao critério Habitat da UE.Relatórios de carbono em tempo realIntegre sensores de energia IoT com a plataforma CarbonWatch (API).Transparência para investidores e compliance CSRD.Engajamento de stakeholdersCrie um forum trimestral com ONGs locais, investidores e comunidades.Fortalece a licença social para operar.

    Exemplo de integração IoT (Node‑RED)


    [
    {
    "id":"1",
    "type":"mqtt in",
    "topic":"datacenter/energy",
    "broker":"mqtt://broker.mycompany.com",
    "name":"Consumo de energia"
    },
    {
    "id":"2",
    "type":"function",
    "func":"msg.payload = msg.payload * 0.2; // fator de emissão tCO2e/kWh\nreturn msg;",
    "name":"Calcular emissões"
    },
    {
    "id":"3",
    "type":"http request",
    "url":"https://api.carbonwatch.io/v1/ingest",
    "method":"POST",
    "name":"Enviar para CarbonWatch"
    },
    {"id":"1","wires":[["2"]]},
    {"id":"2","wires":[["3"]]}
    ]

    Esse fluxo captura o consumo de energia em kWh, converte para toneladas de CO₂e e envia para a plataforma de monitoramento.

    5. Guia rápido para investidores e cidadãos


    1. Verifique o relatório ESG da empresa (CSRD 2025).
    2. Use a ferramenta “Carbon Disclosure Project (CDP)” para comparar a pegada de carbono do data center com a média do setor.
    3. Acompanhe a votação de acionistas via ProxyVote ou eVestment.
    4. Pressione por auditorias independentes: solicite que a Google contrate uma auditoria de biodiversidade reconhecida (ex.: WWF ou IUCN).
    5. Participe de campanhas locais: a ONG SuomiLuonto aceita doações para projetos de reflorestamento em áreas críticas da Finlândia.


    Conclusão


    O caso Google‑Finlândia ilustra como a sustentabilidade, a regulação e a pressão ESG se entrelaçam na prática corporativa. As métricas e ferramentas apresentadas aqui permitem que profissionais de TI, analistas de investimento e cidadãos acompanhem de forma objetiva os impactos ambientais e exijam ações corretivas. Em 2024, a transparência não é mais opcional; é o termômetro que determina o futuro das grandes empresas de tecnologia.

    Ação recomendada: implemente, ainda este mês, um dos scripts ou planilhas acima e compartilhe os resultados com sua comunidade ou com o comitê de sustentabilidade da sua empresa. Cada dado concreto fortalece o diálogo entre negócios e meio ambiente.


    #google #desmatamento #finlândia #portugues #software #coding #development #engineering #inclusive #community
    Google derruba 250 mil árvores na Finlândia: riscos ESG e ação em 2024

  10. ----------------

    🛠️ Tool: PacketSmith v5 Integrates Yara-X into Network Traffic Analysis
    ===================

    PacketSmith from Netomize shipped version 5, codenamed Pinus strobus, and the headline feature is an integrated Yara-X engine. The vendor describes the release as the result of extended R&D for network detection engineers, SOC analysts, and malware and vulnerability researchers, adding "unique, unparalleled" capabilities. Marketing aside, the integration itself looks substantive: Yara-X is not only pointed at payloads, it is wired into a structure of protocol attributes that the compiler can reference.

    Key features
    • Scoping across data categories: raw frames, individual TCP/UDP packets, reassembled TCP/UDP streams, and ICMP (v4/v6) payloads.
    • Deep protocol attributes provided as custom Pattern Identifiers (PaIDs), nested identifiers that let rules target network metadata instead of raw bytes only.
    • Cross-layer correlation: a single YARA condition can reference link-layer, network-layer, and transport-layer dissected data simultaneously.
    • Results as high-detail console output, JSON, or XML Workbook, with the option of logging matched content.

    Technical implementation

    The dns case study is the most concrete part of the announcement. It targets the Denis backdoor, publicly documented by Kaspersky on Securelist under "Use of DNS tunneling for C&C communications".

    Sample indicators:

    MD5: 1a4d58e281103fea2a4ccbfab93f74d2
    SHA-1: 1a2cd9b94a70440a962d9ad78e5e46d7d22070d0
    SHA-256: f5872f49943c39b73026fc3982b85330953a138cc27c23487a28103337bfdbb5
    Win32 EXE, Microsoft Visual C++, 179,202 bytes

    Detected behavior: the malware sends check-in DNS requests to Google's public resolver 8.8.8.8 on port 53, base64-encoded with the custom alphabet [A-Za-z0-9_-], and the initial check-in consists of one tunneling label at a fixed 32-byte length. The vendor ships a reference capture (denis_bkdr_dns, 2,292 bytes) together with a sample rule combining ip4 and dns PaIDs. The rule condition shown:

    dns.is_set and not dns.over_tcp and not dns.flag.response
    and dns.flag.opcode == 0
    // optional check:
    ip4.is_set and ip.dst.addr == "8.8.8.8"

    Use cases

    1. Detection engineering against known malware captures, where rules are validated directly on pcaps.
    2. Tunneling signatures built on protocol structure, which holds when payload content is encrypted, since the pattern lives in field shape, resolver choice, and flag combinations.
    3. Offline pcap triage. The vendor's own analogy: PacketSmith plus Yara-X is to a pcap what an IDS/IPS is to live traffic.

    Limitations and open questions
    • The claim that no other tool on the market has this capability is vendor-provided and unverified.
    • The post demonstrates only the ip4 and dns PaIDs. The announced protocol list at other layers is larger, but per-protocol identifiers require the official documentation.
    • The sample rule is tailored to this sample's fixed 32-byte label, so expect tuning for other tunneling families and false-positive pressure from legitimate long DNS labels.
    • The release material is framed around pcap analysis; nothing in the post describes high-volume live traffic handling.

    References
    • PacketSmith official documentation, with technical specifications and advanced use cases for identifying malware C&C traffic and network exploitation attempts.
    • Kaspersky Securelist: Use of DNS tunneling for C&C communications.

    🔹 tool #PacketSmith #YaraX #NetworkDetection #DNSTunneling

    🔗 Source: packetsmith.ca/yara-x-packetsm

  11. London’s suicide death rate drops for the first time in three years, ONS data reveals

    Reported suicide deaths in London have fallen from 8.3 to 7.3 deaths per 100, 000, according to data…
    #London #UnitedKingdom #UK #GB #England #Headlines #News #Europe #EU #Britain #Death #GreatBritain #london #Sucide
    europesays.com/uk/1269990/

  12. query: hypothetically, if I were to take the box of our old Pokémon cards home from my parents' house later today, is there any easy way* to, like, assess if there's anything of value in there

    *for a definition of easy that includes me being perfectly happy to do a bunch of manual data entry

    #pokemon #pokemontcg

  13. Alibaba's personal agent Qwen Go! can now build working apps from a plain description, no coding needed. Built on Alibaba Cloud's Meoo platform, which writes the code, sets up the database and deploys the result, the apps keep updating their data and can be shared. Invite-only beta for now. pandaily.com/qwen-go-my-apps-n #China #Tech #AI

  14. 🔥 Hiring Alert! | PhysioNet Competitor (Biomedical Signal Processing & ML) (Biotechnology)

    Contractor work at $60–120/hr, remote and flexible

    ✅ Building problems from physiological signals and time-series data
    ✅ Writing reference solutions in Python or MATLAB
    What signal dataset have you found hardest to clean?

    👉 Apply here: nearskill.in/jobs/physionet-co

    #RemoteJobs #Hiring #Biotechnology #AIJobs

  15. I finally went cold turkey with Facebook back in 2022, after realizing that all my efforts to warn people about Covid had the sole effect of alienating me from everyone who interacted with me there. And that most of the thousand FB “friends” were just digital data points with names attached that had no actual relationships with me. And then Zuckerberg wanted me to give him all my data for AI training and I finally said I’m done.

    Downloaded years of posts and comments representing untold wasted hours (which I have not even looked at since) and nuked my account. Felt great.

    I haven’t used Twitter since the world’s second-biggest asshole bought the place. There was no coming back from that.

    Reddit is a shell of its former self, and the news is awful anyhow. I use it to gauge how much #Covid is happening. (Right now, quite a bit.)

    This is it, little old Mastodon on a good instance. Even here, I have to limit how much I’ll scroll because within minutes everything is terrible. Not nasty, not manipulative, just heart-rending screams into the void.

  16. The group chat said hallucinate. My brain said help.

    hallucinate: hallucinate: generate information not backed by its training data.

    ✈️ HIT SHARE → Copy Link, or you’re legally banned from using “hallucinate” in the group chat for 7 years 💀

    slanglane.com/terms/hallucinat

    #Slang #English #LearnEnglish #Vocabulary #GenZ

  17. 🚨🇺🇸 University Hospitals disconnects systems after detecting unusual server activity

    University Hospitals in Ohio disconnected systems from the internet after detecting unusual activity on October 22.
    ⠀
    The precautionary shutdown has affected its public websites, MyChart patient portal and other digital services. Disruptions may continue for several days while the health system investigates with outside experts.
    ⠀
    University Hospitals says patient care has not been affected.

    Patients needing information about appointments or test results should contact their provider directly. The health system has not confirmed that the incident was a cyberattack or disclosed whether any data was accessed.

    Sources: cleveland19.com/2026/10/23/uni

  18. #Gukhanmun 0.3.0 is out with Han character variants, reading selection from following particles, punctuation profiles, and a bundled loanword dictionary.

    沖縄県 (shinjitai) now finds 沖繩縣 (Kanxi form) in the dictionary and reads 오키나와현, while --hanja-variant-set re-emits hanja in shinjitai or Kanxi form. 一切 defaults to 일절, but a following particle such as 一切를 selects 일체, and --punctuation modern turns 하나、둘。 into 하나, 둘.. The National Institute of Korean Language's loanword spelling examples are bundled into the ko-kr preset as well, which is why 香港 now reads 홍콩.

    Upgrading from v0.2.x changes ko-kr output and moves the bundled dictionaries to format v2, so upgrade the bindings and data packages together.

    https://github.com/dahlia/gukhanmun/discussions/32

    #hanja #hangul #korean #國漢文 #國漢文混用 #國漢文混用體 #국한문 #국한문혼용 #국한문혼용체

  19. Identity theft is more prevalent today than it was a decade ago. Troves of data breaches and immature identity verification processes allow criminals to scoop up people’s information and cause them undue harm…

    I fell victim to a VRBO (an Expedia company) scam back in ‘21. It happened in Berkeley, California. When I rented the VBRO the host seemed legitimate. Unfortunately when we returned to the property from an event, we discovered the criminals had stolen all of our group’s belongings and the host’s things were intact.

    These companies need to be held at a much higher standard. SSN and birthdays are some of the many identifiers that are available in data breaches.

    #vrbo #cashapp #identitytheft #expedia #airbnb #ssn #databreach

    404media.co/amanda-smith-opene

  20. Bobinet: text-to-RDF knowledge graph generator following the recipes of Anthropic's graph cookbook, adapted for linked data.
    – a named graph and a checked quote per statement
    – terms from published ontologies
    – links to Wikidata and DBpedia if descriptions agree

    github.com/kvistgaard/bobinet

  21. DATE: October 23, 2026 at 09:00AM
    SOURCE: PSYPOST.ORG

    ** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
    -------------------------------------------------

    TITLE: People who believe conspiracy theories overestimate how many others agree, new research finds

    URL: psypost.org/people-who-believe

    Reading a conspiracy theory does more than just introduce a suspicious narrative; it also inflates a person’s estimate of how many other people believe the claim is true. According to new research published in the European Journal of Social Psychology, this exaggerated sense of public agreement may help explain why people come to accept conspiracy theories after encountering them. In one of the experiments, showing people concrete statistical data about how few others actually endorsed these claims reduced their own belief in them.

    Conspiracy theories are explanations for events that assume multiple actors are secretly working together to achieve a harmful or unlawful goal. When people feel uncertain about how to interpret events, they often look to others for cues on what to think or do. Psychologists refer to these cues as descriptive norms, which are perceptions of how common a particular belief or behavior is within a group. Relying on descriptive norms is a form of social proof, where people assume that if many others hold a view, that view is likely valid.

    Although conspiracy theories frequently draw massive attention online, actual believers usually represent a minority of the broader public. Still, those who endorse these narratives often assume their views are widely shared. For example, a 2021 study of British participants found that people consistently overestimated how much their peers believed in conspiracy theories, and that thinking peers endorsed these ideas strongly predicted a person’s own belief. Similarly, a 2023 study in Turkey indicated that people who believed local conspiracy theories overestimated how many ordinary citizens and experts agreed with them.

    Correcting these misperceptions can alter how people think. In a 2021 experiment with British parents, showing participants that most other parents rejected anti-vaccine conspiracy theories reduced their own endorsement of those claims. Building on this foundation, the new research was led by Jan-Willem van Prooijen, head of the Social Psychology section at Vrije Universiteit Amsterdam, senior researcher at the Netherlands Institute for the Study of Crime and Law Enforcement, and endowed professor of radicalization, extremism, and conspiracy thinking at Maastricht University.

    He and his colleagues wanted to test whether simply encountering a conspiracy theory inflates perceived descriptive norms, and whether providing hard data on public skepticism can reduce belief in a wide variety of general conspiracy theories.

    “People often encounter conspiracy theories, for instance online,” van Prooijen told PsyPost. “One problem is that extreme voices that spread conspiracy theories (e.g., online influencers) sometimes get excessive exposure. That might raise the impression that these conspiracy theories are more widely believed than is actually the case. We wanted to examine these dynamics in our research: Do conspiracy beliefs depend on how normative people think they are, and can we use these insights to develop an intervention?”

    To investigate these questions, the researchers conducted six online studies totaling 2,906 participants from the United States and the United Kingdom. All six studies were preregistered, meaning the researchers publicly recorded their hypotheses and analysis plans before collecting data.

    In the first pair of studies, the researchers examined the relationship between personal conspiracy beliefs and perceived norms using 300 American participants in Study 1a and 299 British participants in Study 1b. Participants rated how plausible they found 10 popular conspiracy claims, such as the idea that the 9/11 terrorist attacks were an inside job by the U.S. government, on a scale from 0 to 100, and then used the same scale to estimate the average plausibility rating given by the other participants in the study.

    In both countries, the actual average belief in these 10 theories was relatively low, sitting at 33.09 out of 100 in the American sample and 34.16 in the British sample. However, participants estimated the group average to be higher, guessing 39.85 in the United States and 42.30 in the United Kingdom. Those who strongly endorsed the conspiracy theories themselves were much more likely to think other participants believed them too, with strong correlations of 0.65 in the U.S. sample and 0.71 in the U.K. sample, and their estimates of the group average were less accurate than the estimates of skeptics.

    The researchers also tested whether conspiracy believers saw themselves as standing out from the crowd, given that past research has linked conspiracy belief to a desire to feel unique. By comparing each person’s own rating to their estimate of the group rating, the team found that stronger believers saw the gap between their own views and the group’s as no larger than skeptics did.

    These initial findings align with research covered by PsyPost in 2025, which found that individuals who are overconfident in their thinking abilities tend to hold stronger conspiracy beliefs and vastly overestimate how many others agree with them.

    Because the first two studies only looked at correlations, the researchers next tested whether reading a conspiracy theory directly causes people to think the idea is popular. They recruited 505 American participants for Study 2a and 501 British participants for Study 2b. In each study, half of the participants were randomly assigned to read an anonymous blog post claiming that pharmaceutical companies secretly suppress medical cures to boost their profits, while the other half read a blog post arguing that pharmaceutical companies do not hide cures.

    After reading and summarizing the text, participants rated their own belief in four statements about pharmaceutical conspiracies on a 0 to 100 scale, and estimated how the other participants in the study rated them. In the American sample, reading the conspiratorial blog post raised personal belief to an average of 61.78, compared to 49.46 among those who read the non-conspiratorial text. It also pushed estimates of other people’s belief up to 62.64, compared to 51.00 in the control group.

    The British sample in Study 2b showed the same pattern. Personal belief averaged 59.78 after reading the conspiracy post versus 41.99 in the control group, while estimates of others’ beliefs rose to 60.28 compared to 46.41 in the control condition. Statistical mediation analyses were consistent with the idea that the increase in personal belief ran through this heightened sense that others believed the claim. But the reverse pattern, in which people’s newly strengthened beliefs shaped their guesses about others, also fit the data, so these analyses cannot establish which came first.

    “One reason why people believe conspiracy theories is because they assume many others believe them as well,” van Prooijen explained. “Conspiracy beliefs thus spread via social influence processes. Understanding these processes can help design effective interventions to reduce conspiracy beliefs.”

    To test whether changing perceived norms can directly alter conspiracy beliefs, the researchers manipulated the norm information given to participants in Studies 3 and 4. In Study 3, 501 American participants were presented with six popular conspiracy theories, such as the claim that the government uses subliminal messages on television. Half were told that “especially many” previous participants found the statements plausible, while the other half were told that “only a few” found them plausible.

    This vague wording did not produce a statistically significant difference between the two groups. Participants told that many others believed the claims scored an average of 44.48 out of 100 on personal belief, compared to 41.65 among those told that few others believed them. The authors noted that words like “many” and “few” are open to personal interpretation; for instance, learning that even 10 percent of the public believes an extreme claim might still register as a large number to some people. Because this study did not measure whether participants’ sense of the norm actually shifted, the authors acknowledge another possibility: that people do not take their cues from norm information at all, and that the link seen in earlier studies reflects people projecting their own beliefs onto others.

    In Study 4, the team tested whether providing specific, factual statistics would be more effective. They recruited 800 American participants and showed them eight conspiracy theories from the first study. Half of the participants saw a bar chart for each theory displaying the actual distribution of ratings from Study 1a, along with the median score and a note explaining that most participants did not find the statement plausible at all. The other half saw the conspiracy statements without any statistical data.

    Providing concrete data shifted how participants viewed both the public consensus and the theories themselves. Those who saw the charts estimated the previous group’s average belief at 20.95 out of 100, compared to 38.34 among those who received no data. In turn, personal endorsement of the conspiracy theories dropped to an average of 24.06 in the group that saw the factual charts, compared to 33.70 in the control group.

    “We have done two studies testing the intervention, but it only worked if we gave people factual statistical information of how few people believed a conspiracy theory,” van Prooijen noted. “If we provided more abstract information, only stating that ‘few people’ believe the theory without providing concrete numbers, the intervention did not work. It remains speculative why that is the case, but it is nevertheless informative for future interventions based on this research.”

    “The practical significance is that these insights quite directly suggest an intervention to reduce conspiracy beliefs,” van Prooijen said. “We have tested the effects of exposing people to factual statistical information of how widespread belief in a particular conspiracy theory is. Such information often clarifies to people that a conspiracy theory actually is not widely endorsed at all, which decreases the likelihood that people will believe the theory themselves.”

    As with all research, there are some caveats to consider when interpreting these results. Participants in brief online experiments may partly respond the way they think researchers expect, which the authors note could mean these effects are smaller in everyday life, and the “others” in these studies were fellow online participants rather than the broader population. In addition, social media algorithms and online communities can surround users with like-minded believers, creating environments where conspiracy theories genuinely are the local norm.

    “It is important to keep in mind that in everyday life, these processes are likely to be more complicated,” van Prooijen said. “For instance, we examined the effects of exposing people to conspiracy theories on their perceptions of how normative belief in these theories is. But in real life, people to some extent also choose what information they wish to be exposed to. People may for instance actively search the Internet looking for conspiracy theories, and be motivated to think belief in these theories is widely shared.”

    The authors also point out that factual norm interventions could backfire if applied to the rare conspiracy theories that actually are endorsed by a majority of the public, such as theories surrounding the assassination of John F. Kennedy. Future studies could examine how these social processes operate in real-world online networks and in more collectivistic cultures, where pressure to conform to group norms may be even stronger.

    “An important caveat is that the intervention that we tested might only work in communities where a particular conspiracy theory is indeed not so widespread,” van Prooijen added. “Some conspiracy theories ARE widespread, for instance in the general population (e.g., about the Kennedy assassination), or in specific groups (e.g., online anti-vaccination groups). These are complications that need to be investigated further, and are important to take into account before putting these insights into practice.”

    “We were excited to see that our findings almost naturally suggested an intervention, which indeed had the anticipated effects,” he continued. “At the same time, the intervention was successful in only one experiment. We need more testing to assess the effectiveness of this approach. One possible goal is to examine these issues in a longitudinal design: What is the long-term effectiveness of an intervention based on information about descriptive norms?”

    The study, “Everyone Knows It’s a Hoax: The Role of Perceived Descriptive Norms in Conspiracy Beliefs,” was authored by Jan-Willem van Prooijen, Paul K. Bergmann, Vladimira Čavojová, and Jakub Šrol.

    URL: psypost.org/people-who-believe

    -------------------------------------------------

    Private, vetted email list for mental health professionals: clinicians-exchange.org

    Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot

    -------------------------------------------------

    #psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #ConspiracyTheories #Psychology #SocialPsychology #NewResearch #Misinformation #CriticalThinking #SocialInfluence #BehavioralScience #MediaLiteracy #ScienceNews

  22. You review a 30-line template diff. Production sends 40,000 tokens assembled at request time from six sources, four of which changed this week and none of which appear in the pull request. The diff you approved is not the change you shipped.

    This is the part of LLMOps that keeps biting teams after they have evals, tracing, and a gateway in place. The template is versioned in git. The context is assembled by code paths nobody treats as release artifacts.

    The prompt is a build output


    List what actually goes into a request for a typical RAG-plus-tools system:

    • The system template (in git, reviewed).
    • Top-k retrieved chunks from an index that gets rebuilt on its own schedule.
    • Tool JSON schemas, whose descriptions are prompt text written by whoever last touched the tool.
    • A conversation summary produced by another model call.
    • A user profile or tenant config blob.
    • Few-shot examples chosen at runtime by a selector.

    Each is a dependency with its own version and its own deploy cadence. The retrieved chunks change when someone reindexes. The tool schema changes when a backend engineer renames a parameter and rewrites the description. Neither triggers a review of the prompt.

    So when quality drops on Tuesday and no code shipped, there is no diff to look at. That is the failure this whole post is about.

    Fingerprint everything the model sees


    Compute a content hash over the inputs that can change model behaviour, and log it with every trace:

    • model id and version
    • template version
    • retrieval snapshot id (the index build, not the query)
    • the fully rendered prompt
    • decoding parameters that matter

    Store fingerprints always. Store full rendered prompts sampled or redacted, depending on your data rules — full-text retention is a compliance decision, not an observability one. The fingerprint is small, cheap, and answers "what changed" during an incident in one query.

    The trade-off: a fingerprint that includes a timestamp, a request id, or a per-user greeting has near-zero collision rate and zero diagnostic value. Hash the parts that affect generation, not the metadata around it. I have watched a team spend a day on "why is our prompt hash changing every request" when the answer was a locale string.

    A small assembler with provenance and eviction


    You do not need a framework for this. You need three properties: every section carries a trust level, every section carries an eviction priority, and over-budget on a pinned section is a hard error rather than a silent trim.

    from dataclasses import dataclass, field
    from hashlib import sha256
    import re

    @dataclass
    class Section:
    name: str
    text: str
    trusted: bool # False for retrieval, tool output, user uploads
    priority: int # higher survives truncation
    pinned: bool = False # never evict; over budget means fail loudly

    INSTRUCTION_LIKE = re.compile(
    r"(ignore (all )?(previous|prior) instructions|you are now|system prompt)",
    re.IGNORECASE,
    )

    def screen(text: str) -> str:
    # Cheap heuristic, not a guarantee. Log hits and route to a stricter path.
    return INSTRUCTION_LIKE.sub("[filtered]", text)

    def count_tokens(s: str) -> int:
    # Swap in tiktoken/the provider counter. One counter per model family.
    return len(s) // 4

    @dataclass
    class Assembled:
    prompt: str
    fingerprint: str
    dropped: list[str] = field(default_factory=list)

    def assemble(sections, budget, model_id, template_version, snapshot_id):
    ordered = sorted(sections, key=lambda s: (s.pinned, s.priority), reverse=True)
    kept, dropped, used = [], [], 0
    for s in ordered:
    body = f"<{s.name} trust={'high' if s.trusted else 'low'}>\n"
    body += (s.text if s.trusted else screen(s.text)) + f"\n</{s.name}>"
    cost = count_tokens(body)
    if used + cost > budget:
    if s.pinned:
    raise ValueError(f"pinned section {s.name} exceeds budget")
    dropped.append(s.name)
    continue
    kept.append((s, body))
    used += cost
    # Render in original order so layout is stable across requests.
    pos = {id(s): i for i, s in enumerate(sections)}
    kept.sort(key=lambda pair: pos[id(pair[0])])
    prompt = "\n\n".join(body for _, body in kept)
    h = sha256()
    for part in (model_id, template_version, snapshot_id, prompt):
    h.update(part.encode())
    return Assembled(prompt, h.hexdigest()[:16], dropped)

    # Usage: build once per request, log the fingerprint beside the trace id.
    ctx = assemble(
    sections=[
    Section("question", "why was my claim denied?", trusted=True, priority=10, pinned=True),
    Section("policy_chunk", retrieved_text, trusted=False, priority=5),
    Section("profile", tenant_blob, trusted=True, priority=1),
    ],
    budget=12_000,
    model_id="claude-sonnet-4",
    template_version="support-v14",
    snapshot_id=index_build_id,
    )

    Two details in there are load-bearing. Sections render in original insertion order rather than priority order, because reordering the same content changes model behaviour and makes your eval deltas unattributable. And pinned turns budget pressure into an exception instead of a degraded#ai #llm #devops #python #software #coding #development #engineering #inclusive #community
    Context Assembly Is a Build Step: Versioning the Prompt You Actually Send

  23. A wave of AI-powered wearable devices is entering the market, designed to integrate artificial intelligence into daily routines as productivity tools or personal companions.

    Devices range from the $49.99 audio-recording Bee pendant and Amazon's recent personal AI hardware to continuous listeners like Friend and meeting transcribers like Limitless, raising ongoing questions about daily personal data recording and har…

    en.hacks.gr/ta-nea-forita-gkat

    #ArtificialIntelligence #Wearables #Hardware #Amazon

  24. I run the WooCommerce store behind a small online clothing shop. Most of the orders are made to order and ship from print partners in several countries, so the delivery promise differs by destination. Google's merchant listings can show that promise, and the return window, right in search results, but only if the product markup says so. WooCommerce's built-in Product schema does not.

    This post walks through adding hasMerchantReturnPolicy and shippingDetails to the offer that WooCommerce already prints, without a plugin.

    What WooCommerce outputs by default


    WooCommerce prints JSON-LD for each product through the WC_Structured_Data class. The offer part comes from generate_product_data(), and before it is added to the markup it passes through a filter:

    apply_filters( 'woocommerce_structured_data_product_offer', $markup_offer, $product );

    That filter is the cleanest place to hook in. It receives one offer array and the WC_Product, and whatever you return becomes the offer in the final JSON-LD.

    The return policy


    Schema.org models returns as a MerchantReturnPolicy. For a fixed return window, Google wants the country it applies to, the category of the policy and the number of days. The shop in this example accepts returns for 30 days.

    function shop_return_policy() {
    return array(
    '@type' => 'MerchantReturnPolicy',
    'applicableCountry' => 'US',
    'returnPolicyCategory' => 'https://schema.org/MerchantReturnFiniteReturnWindow',
    'merchantReturnDays' => 30,
    'returnMethod' => 'https://schema.org/ReturnByMail',
    );
    }

    Add returnFees only if you can state it truthfully for every product. Leaving out a property is better than guessing.

    Shipping details per destination


    Delivery time is split into handling time (the days before the parcel leaves) and transit time. Here processing takes 1 to 5 business days, and transit depends on the region, so the code builds one OfferShippingDetails entry per country:

    function shop_shipping_details() {
    $transit = array(
    'US' => array( 8, 15 ),
    'CA' => array( 8, 15 ),
    'GB' => array( 8, 15 ),
    'AU' => array( 10, 20 ),
    );

    $details = array();
    foreach ( $transit as $country => $days ) {
    $details[] = array(
    '@type' => 'OfferShippingDetails',
    'shippingDestination' => array(
    '@type' => 'DefinedRegion',
    'addressCountry' => $country,
    ),
    'shippingRate' => array(
    '@type' => 'MonetaryAmount',
    'value' => SHOP_SHIPPING_RATE, // your real rate for this destination
    'currency' => 'USD',
    ),
    'deliveryTime' => array(
    '@type' => 'ShippingDeliveryTime',
    'handlingTime' => array(
    '@type' => 'QuantitativeValue',
    'minValue' => 1,
    'maxValue' => 5,
    'unitCode' => 'DAY',
    ),
    'transitTime' => array(
    '@type' => 'QuantitativeValue',
    'minValue' => $days[0],
    'maxValue' => $days[1],
    'unitCode' => 'DAY',
    ),
    ),
    );
    }
    return $details;
    }

    The rate is a constant here on purpose. If the rate differs by country or by cart total, read it from your shipping zones instead of hard-coding one number for everything.

    Hooking it in


    add_filter( 'woocommerce_structured_data_product_offer', function ( $offer, $product ) {
    if ( ! $product instanceof WC_Product || ! $product->is_purchasable() ) {
    return $offer;
    }
    $offer['hasMerchantReturnPolicy'] = shop_return_policy();
    $offer['shippingDetails'] = shop_shipping_details();
    return $offer;
    }, 10, 2 );

    Put this in a small site plugin or in the child theme's functions.php. A site plugin survives a theme switch, which is why I prefer it.

    Checking the result


    View the page source of a product and search for application/ld+json. WooCommerce prints the block near the end of the page. Then paste the product URL into Google's Rich Results Test and into the Schema Markup Validator at validator.schema.org. The first tells you whether the page is eligible for merchant listing features, and the second catches typos in types and property names.

    After a few days, the Merchant listings report in Search Console shows whether Google has picked the fields up across the catalog, along with any products that are missing a required property.#woocommerce #wordpress #php #seo #software #coding #development #engineering #inclusive #community
    Adding return and shipping details to WooCommerce product schema

  25. DATE: October 24, 2026 at 10:00AM
    SOURCE: PSYPOST.ORG

    ** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
    -------------------------------------------------

    TITLE: The psychology behind Taylor Swift’s billion-dollar media empire

    URL: psypost.org/the-psychology-beh

    The intense, one-sided connections fans feel toward pop star Taylor Swift and their fear of missing out on shared cultural moments strongly predict whether they will attend her concerts and watch her films. These predictive links highlight how the psychological need for social belonging drives large-scale consumer behavior, according to a recent study published in Psychology of Popular Media.

    A parasocial relationship occurs when an audience member develops a one-sided bond with a media figure, such as a musician, actor, or internet personality. While the celebrity is unaware of the individual fan, the fan experiences genuine emotions and reactions that mimic real-life friendships.

    Taylor Swift has cultivated an exceptionally robust parasocial dynamic with her audience over her career. By interacting with followers online and weaving personal details into her music, she has built an environment where fans feel they truly know her. When her Eras Tour launched, it shattered records, selling over two million tickets in a single day. The researchers noted that Swift used the resulting ticketing challenges to align herself with her fans, strengthening their perceived bond.

    Such pervasive cultural conversation often triggers a phenomenon known as the fear of missing out, commonly referred to as FoMO. Researchers divide this concept into two distinct categories. Trait fear of missing out is a long-standing personality characteristic where a person generally worries that others are having rewarding experiences without them. State fear of missing out is a temporary, situation-specific anxiety in response to a particular event.

    The researchers anchored their investigation in Self-Determination Theory. This psychological framework suggests that human beings are motivated by a fundamental need for relatedness, or the desire to feel connected to others. In the context of entertainment, people might consume specific media to satisfy this need, either by bonding with a celebrity or by joining a community of fellow fans.

    Lindsey Conlin Maxwell and Caroline Neese of the University of Southern Mississippi, along with Alec C. Tefertiller of Baylor University, designed the study to see how these psychological elements influenced audience behavior. They used Swift and her wildly popular Eras Tour as an ideal testing ground. The team aimed to determine if long-term personality traits could predict parasocial bonds and if those bonds predicted purchasing tickets for concerts and movies.

    To capture these emotions at their peak, the researchers conducted a national survey during the opening weekend of the concert film, “Taylor Swift: The Eras Tour,” in October 2023. They recruited 303 adults in the United States using an online task platform. The respondents answered questions designed to measure their baseline personality traits, their current feelings about the movie release, and their perceived relationship with the pop star.

    The survey asked participants to rate their agreement with statements about missing out on rewarding experiences in general, capturing their trait-level anxieties. It also asked specific questions about whether they feared others would enjoy watching the concert film more than them, measuring their temporary state of anxiety. Additionally, the questionnaire evaluated the depth of their parasocial relationship with Swift and recorded their actual behaviors regarding concert and movie attendance.

    The data revealed that an individual’s general fear of missing out was a strong predictor of their parasocial relationship with Swift. The researchers noted that this long-standing personality trait explained roughly one-third of the variations in how strongly people felt connected to the singer. People who routinely worry about being left out of social experiences appear highly motivated to build imaginary bonds with media figures to alleviate that apprehension.

    Both the personality trait and the parasocial relationship predicted concert-related behaviors. As scores on the parasocial relationship scale increased, respondents were nearly twice as likely to have attempted to buy Eras Tour tickets. They were also substantially more likely to have actually attended one of the live stadium shows.

    Similar patterns emerged for the concert film. High levels of baseline anxiety about missing out, as well as a strong perceived relationship with Swift, predicted whether someone had already seen the movie during its opening weekend. These factors also predicted whether a person intended to see the film in theaters later or watch it once it became available on streaming platforms.

    One of the most notable results involved temporary, event-specific anxiety. The researchers tested whether attending the live concert satisfied fans to the point of reducing their anxiety about the movie release. The data showed the exact opposite pattern.

    People who had already attended the live Eras Tour experienced higher levels of temporary anxiety about missing out on the movie than those who had not attended the concert. Attending the concert did not quench the desire to engage with the media event. Going to the live show actually increased the likelihood that someone wanted to see the theatrical release.

    The researchers suggest that a parasocial relationship can become so potent that individuals seek repeated positive experiences with the celebrity to maintain the bond. Because fans experience a strong sense of relatedness when participating in these events, the desire to rejoin the community and relive the excitement remains high. The shared euphoria among thousands of fans, known as collective effervescence, likely fuels this ongoing demand.

    The attempt to replicate the concert experience inside movie theaters capitalized on this collective effervescence. By trading friendship bracelets and singing together in theaters, fans who could not attend the live shows found a way to participate in the cultural phenomenon. This shared environment allowed audiences to maintain their parasocial relationship with the singer while simultaneously relieving their anxieties about missing out.

    While the study provides a detailed look at consumer motivations, it carries a few limitations. The participants were drawn from a convenience sample, and they were allowed to self-select into a survey they knew was about Taylor Swift. As a result, roughly two-thirds of the respondents identified as fans, and the demographic makeup was predominantly female and white, which could affect how well the results apply to the broader population.

    The research relied on self-reported survey data, which is useful for identifying predictive links but cannot definitively prove cause and effect. It remains possible that other unmeasured variables influence both the psychological traits and the purchasing habits of the respondents.

    Future investigations could explore how fans develop interpersonal relationships with one another based on their mutual parasocial connections to a single celebrity. The researchers also recommend looking closer at how social media amplifies the fear of missing out during major entertainment events. Measuring the direct impact of shared euphoric experiences could further explain why people are so driven to participate in massive cultural moments.

    The study, ““It’s You and Me, There’s Nothing Like This”: Parasocial Relationships, Fear of Missing Out, and How Fans Consume the Products in Taylor Swift’s Media Empire,” was authored by Lindsey Conlin Maxwell, Alec C. Tefertiller, and Caroline Neese.

    URL: psypost.org/the-psychology-beh

    -------------------------------------------------

    Private, vetted email list for mental health professionals: clinicians-exchange.org

    Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot

    -------------------------------------------------

    #psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #TaylorSwift #TaylorSwiftErasTour #Swifties #Psychology #ParasocialRelationships #FearOfMissingOut #FOMO #FanCulture #PopCulture #EntertainmentIndustry

  26. Personal inconvenience aside, this just isn't a good way to run an archive. They are a single datacenter fire, server compromise, lawsuit or organizational fuckup away from loosing everything.

    Making it impossible for anyone to mirror their most useful data is terrible for long term preservation. Yes, I know you can bulk download collections, but I don't want a terabyte of pirated movies: the wayback machine is far more valuable and is not something that exists anywhere else.
  27. Common Crawl: October 2026 Crawl Archive Now Available. “The crawl archive for October 2026 is now available. The data was crawled between October 5th and October 18th, and contains 2.16 billion web pages (or 371.19 TiB of uncompressed content). Page captures are from 41.4 million hosts or 34.3 million registered domains and include 830.1 million new URLs, not visited in any of our prior […]

    https://rbfirehose.com/2026/10/23/common-crawl-october-2026-crawl-archive-now-available/
  28. Can AI help fix website accessibility issues? Yes. But human expertise still matters.

    Steve shares how the team is experimenting with connecting Accessibility Checker to AI through an MCP server, helping AI access issue data and support fixes.

    Where would AI help most in your accessibility workflow? Tell us in the comments!

    From Accessibility Craft, the web accessibility podcast.

    #AccessibilityCraft #WebAccessibility #AI

Share on Mastodon

Enter the server where you have an account.