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  1. Alter Kaker @alter_kaker

    I wonder why the word "shekel" causes this person to jump in fright. What the hell is wrong with people?

    (there is also a charitable interpretation of this post, but I'm not holding my breath, and not interacting to ask)

  2. Adam Shostack :donor: :rebelverified: @adamshostack

    Also, it's not clear if they're thinking about network covert channels, acoustic covert channels, power as a channel (or the sensitivity of the power supplies).

    I also note that the blog post specifically and repeatedly uses the term "lightweight VM."

    RTM senior liked to say "you call it a cable, I call it an antenna."

  3. Dev Community @[email protected]

    For years the main reason to add an FAQ section was the dropdown it could earn in Google results. That reason has gone. The page itself still matters, though, because AI assistants and AI search features pull short, direct answers from pages, and a well-built FAQ is one of the easiest things on a site for them to lift.

    This post covers what changed at Google, what still makes an FAQ worth writing, and the HTML pattern we use so each answer stands on its own.

    Disclosure: Appie.run publishes this post. The AI FAQ Generator and Website FAQ Generator mentioned below are our free tools. Everything else here works with any CMS and no tool at all.

    What Google changed (as read on 26 Oct 2026)


    From Google's Search Central updates and Search Console Help:

    • FAQ rich results stopped appearing in Google Search on 7 May 2026, and Google added a deprecation notice on 8 May.
    • On 15 June 2026 Google removed the FAQ structured data documentation, the FAQ report in Search Console and FAQ support in the Rich Results Test.
    • Search Console API support for FAQ appearance data was removed in August 2026.
    • Google says existing FAQPage markup can stay on your pages and causes no harm.
    • In the same June update Google noted that an llms.txt file isn't needed for Google Search.

    So: no more SERP dropdown, no penalty for leftover markup, and no special file required. What's left is the content.

    Why an FAQ is still worth writing


    Nobody outside the AI companies can tell you exactly how each assistant picks its sources, and we won't pretend to. What you can control is whether your answer is easy to find, easy to extract and correct when pulled out of context. A question-and-answer block does all three if you write it properly:

    • The question matches the way people actually ask it.
    • The first sentence of the answer is the answer.
    • The answer makes sense without the paragraphs around it.

    Those same properties help a human who lands on the page from a support link, so the work isn't wasted whichever way the traffic arrives.

    Step 1: Collect real questions


    Don't invent questions in a meeting. Pull them from places where customers already asked:

    • Support tickets and live chat transcripts
    • Sales call notes ("Does it work with...?", "What happens when...?")
    • Reddit and community threads in your category
    • Your site search logs

    Group duplicates, then keep the 8 to 15 that come up most or block a purchase.

    Step 2: Write answers that survive being quoted alone


    A quick test for each answer: paste it into a blank doc with only the question above it. Does it still make sense?

    Weak:

    Yes, as mentioned above, it does, depending on your plan.


    Better:

    Yes. Team plans include SSO via SAML. The Starter plan does not.


    Rules we follow:

    1. Answer in the first sentence. Yes/no, a number, or a name.
    2. Name the thing. Say "the Starter plan", not "this plan".
    3. Keep it to 40 to 80 words for most answers. Link out for detail.
    4. Date anything that changes. "As of 26 Oct 2026, the Starter plan costs $19/month." Prices and limits go stale, and a stale quoted answer is worse than none.
    5. Only state what the page can back up. If the policy lives elsewhere, link to it rather than paraphrasing it loosely.


    Step 3: Use plain, semantic HTML


    You don't need a plugin. Headings for questions and paragraphs for answers are enough, and they render the same for every crawler:

    <section id="faq" aria-labelledby="faq-title">
    <h2 id="faq-title">Frequently asked questions</h2>

    <h3 id="faq-sso">Does the Starter plan include SSO?</h3>
    <p>No. SSO via SAML is included on Team plans only. The Starter plan
    uses email and password sign-in. <a href="/pricing">Compare plans</a>.</p>

    <h3 id="faq-cancel">Can I cancel at any time?</h3>
    <p>Yes. You can cancel from the Billing page, and your plan stays active
    until the end of the current billing period.</p>
    </section>

    If you prefer collapsible answers, <details>/<summary> keeps the text in the HTML:

    <details>
    <summary>Can I cancel at any time?</summary>
    <p>Yes. You can cancel from the Billing page, and your plan stays active
    until the end of the current billing period.</p>
    </details>

    Avoid loading answers only via JavaScript after a click. If the text isn't in the served HTML, some crawlers will never see it.

    Give each question an id so you can link straight to it from support replies and docs.

    Step 4: Keep or add FAQPage markup (optional)


    Google no longer shows FAQ rich results, and it says leftover markup does no harm. Other consumers of schema.org data may still read it, so if it's already there, keep it in sync with the visible text. If you add it, mirror the on-page wording exactly:

    <script type="application/ld+json">
    {
    "@context": "https://schema.org",
    "@type": "FAQPage",
    "mainEntity": [
    {
    "@type": "Question",
    "name": "Can I cancel at any time?",
    "acceptedAnswer": {
    "@type": "Answer",
    "text": "Yes. You can cancel from the Billing page, and your plan stays active until the end of the current billing period."
    }
    }
    ]
    }
    </script>

    The risk with markup is drift: someone updates the visible answer and forgets the JSON. If your FAQ comes from a CMS field, generate both from the same source.

    Step 5: Put the FAQ where the question is asked


    One giant /faq page is fine as an index, but the most useful placement is next to the thing in question: pricing questions on the pricing page, setup questions in the docs. A short block of 4 to 6 questions per page tends to stay accurate because the owner of that page sees it.

    Step 6: Review it on a schedule


    Add a "last reviewed" date and put a recurring reminder on someone's calendar. Every price, limit and integration claim in the FAQ should be checked against the product at least each quarter.

    Getting a first draft faster


    If you have the source material but not the time, two of our free browser tools can produce a draft for you to edit (both run a captcha check first):

    • AI FAQ Generator (appie.run/tools/ai-faq-generator): paste up to 2,000 characters of text, pick 1 to 10 FAQs, a language and a tone. The answers are grounded in the text you pasted, and it doesn't add policies or prices that aren't there.
    • Website FAQ Generator (appie.run/tools/website-faq-generator): give it up to 5 public URLs. It skips questions it can't answer from those pages instead of guessing.

    Treat the output as a draft. Check every answer against Step 2's rules before it goes live.

    Checklist


    • [ ] Questions come from real customer sources
    • [ ] Each answer starts with the answer
    • [ ] Each answer makes sense on its own
    • [ ] Prices and limits carry an "as of" date
    • [ ] Questions and answers are in the served HTML
    • [ ] Any FAQPage JSON-LD matches the visible text word for word
    • [ ] A review date is set

    Google sources: Search Central blog and Search Console Help pages on FAQ rich results and structured data, read 26 Oct 2026.

    RG, Founder, Appie.run#seo #html #webdev #ai #software #coding #development #engineering #inclusive #community
    How to Write a Website FAQ That AI Answers Can Quote (Now That Google's FAQ Rich Results Are Gone)

    #ai #html #community #software #webdev #coding
  4. CM Thiede @cmthiede

    Holy crap, I've gotta set up that DVR one of these days, had in the past when that's all there was, but now it's a must for sharing broadly what a targeted ad looks like to a global audience. Look up "Liberals New Racists" produced by America Next. All you get are hot takes with it in the background (at least that's all I got until I gave up, post it below if you got the raw clip aired during Brewers/Rays Game 3, 🤯 thanks!) if you'd prefer not signing Next Wave's guestbook to watch.

    Why not "Get up and burn an X in your head."

    youtu.be/Cvlpktjsq0A

    The Bro Club needs to go. The answer to all their woes is ending democracy and halving the population. Let's go back to a past to get to a future that looks bright for SPECS wearers and gives the support team complete control of the flow. Master of Puppets. Happy Halloween! Vote.

    #muzaklessons #folkifiedpoprageousness #realsolutionnumber9 #milwaukeebrewers #wisconsin #masterofpuppets
  5. Derek Hanson @[email protected]

    It’s great to be back from sabbatical and jumping back in with Open Channels FM. My sabbatical was somehow busier than I ever could have imagined, but it was a great breakaway from the screen. Lots of time reflecting and thinking. I thought a lot about writing. Woodworking, house remodeling, tons of time with my family, and all those things swirling around, just thinking about how do I document this? Writing is the thing that kept coming back to me. I’ve been more consistent than ever posting on my blog.

    Post formats have empowered me to stay consistent, and that’s what Bob and I talk about. Can’t wait for that episode to drop!

    #openchannelsfm #blogging #postformats #opensource #writing

    #blogging #openchannelsfm #opensource #postformats #writing
  6. Tremolo Security :kubernetes: @tremolo

    PostgreSQL as a Service, without database passwords. CloudNativePG manages the databases; OpenUnison exchanges Kubernetes workload identity for short-lived tokens that PostgreSQL 18 validates via OAuth. tremolo.io/post/postgresql-as-

  7. Dev Community @[email protected]
    A free, open-source agent skill that writes hooks in batches, renders native-looking TikTok slideshows with a local Python renderer and posts them as drafts.#showdev #claudecode #ai #opensource #software #coding #development #engineering #inclusive #community
    TikTok Slideshow Skill for Claude Code: Make and Post App Slideshows for Free
    #opensource #ai #community #software #coding #development
  8. your auntifa liza 🇵🇷 🦛 🦦 @[email protected]

    YOU LOVE TO SEE IT

    ❝ Aside from the restaurants, Ramaswamy has been rebuked by every living governor of Ohio, Democrats and #Republicans, for the ad—which has continued to run. Former Republican Governor Mike DeWine said that it was “wrong”… and urged the Ramaswamy campaign to take it down.❞
    newrepublic.com/post/217170/oh

    for regular people, Ramaswamy crossed an indecency line. for #MAGA? he should have known he can't outdo a dirty, old WHITE MAN like Trump. #Ohio gonna teach a lesson in kyriarchy.

    #republicans #maga #ohio
  9. Núria🦔🌸 @[email protected]

    Un mensaje en instagram la noche de reyes... dos meses después. ¡Kia ora Aotearoa! aterrizábamos en Auckland. Ya podéis leer en mi blog la primera entrada sobre Nueva zelanda, donde os cuento como fue el previaje, y nuestras primeras experiencias kiwis en tierras neozelandesas. ¿Cuál es el viaje de vuestros sueños? @kastwey cumplió el suyo con este viaje. Aquí el post 👇🏻
    blog.sixsensetravel.com/kia-or
    #turismoinclusivo, #viajeraciega, #viajesenfamilia, #NuevaZelanda

    #turismoinclusivo #viajeraciega #viajesenfamilia #nuevazelanda
  10. BDD Warriors @bdd_warriors

    Vi posts no bsky dos gringos impressionados com o clipe do Lula celebrando junto com a Erika Hilton e me emocionei junto.

    A pessoa no Reino Unida emocionada porque até um gesto simples assim é radical hoje em dia por lá.

    Outra pessoa comentou que nos EUA querem convencê-los que precisam sacrificar pessoas trans negras para vencer eleição.

    Os brasileiros aproveitando para explicar o papel da Erika no fim da escala 6x1.

    bsky.app/profile/roseschmits.b

    #eleicoes2026
  11. m3alrahman @m3alrahman

    إنِّي أخافُ على أُمَّتي اثنتَينِ: القرآنَ واللَّبنَ، أمَّا اللَّبنُ فيبتَغونَ الرِّيفَ، ويتَّبِعون الشَّهواتِ، ويترُكونَ الصَّلواتِ، وأمَّا القرآنُ فيتعلَّمُه المنافقونَ، فيُجادلونَ به المؤمنينَ
    m3alrahman.blogspot.com/2021/0

  12. Kroc Camen @[email protected]

    In which October has been a hard month and I consider the future of the book:

    > "Even if I walk away from it all, go outside and touch grass, there will still be a fire -- the need to create -- that will continue to burn and the smoke will be anguish that chokes out the joy."

    patreon.com/KrocCamen/posts/oc (this is a public post)

    #worldglass #writing #writingcommunity

    #worldglass #writing #writingcommunity
  13. Dev Community @[email protected]

    You open the repo on your first morning, the README says "run the app, ask it something," and forty seconds later an answer appears on the phone. Nobody on the team can tell you whether that text came from the bundled on-device model, from a remote endpoint someone configured last quarter, or from a fixture left in a debug build. That uncertainty is not a small onboarding annoyance, because every later measurement you record inherits it.

    This post is a first-hour exercise for a junior engineer joining a mobile AI repo. The goal is not to ship a feature, and it is not to improve latency or accuracy; the goal is to make provenance visible, so that when you do open your first PR you already know which execution path you exercised. Everything below is a proposed harness that you run on your own device, not a report of numbers I measured on yours.

    Why provenance belongs in hour one


    In a normal backend repo you can read the config and know which database answered. In a mobile AI repo the same request can be served by three different things depending on build type, network state, and whether an asset survived the packaging step. If you skip this check, you will eventually write a careful benchmark, show it to your reviewer, and discover the phone was calling a cached response the whole time.

    The exercise below gives you three things you can rely on for the rest of onboarding: a build that fails loudly when the model asset is missing, a debug banner that names the source of the last response, and a repeatable way to exercise the remote path on purpose.

    The three sources to distinguish


    • Bundled asset. A model file inside the app package, loaded from local storage with no network involved.
    • Remote endpoint. An HTTP call to a server the app was pointed at, which may be a shared development host or something you started yourself.
    • Fixture or cache. A stubbed response in debug builds, or a stored answer replayed from a previous run.

    Your banner only needs to name which of these produced the last result. It does not need to be pretty, and it should not ship in release builds.

    Step 1: make a missing model asset a build failure


    Before you touch any inference code, prove that the file you think is packaged actually is. Add a verification task to the app module and wire it into the check lifecycle. Substitute your own asset name and the checksum your team stores alongside the artifact.

    // app/build.gradle.kts (proposed harness)
    val modelDir = layout.projectDirectory.dir("src/main/assets/models")

    // Placeholder on purpose: copy the real digest from your artifact record.
    val expectedDigests = mapOf(
    "classifier-small.tflite" to "<sha256-from-your-artifact-record>"
    )

    tasks.register("verifyModelAssets") {
    val dir = modelDir
    inputs.dir(dir)
    doLast {
    expectedDigests.forEach { (name, want) ->
    val file = dir.file(name).asFile
    require(file.exists()) { "Missing model asset: $name" }
    val got = java.security.MessageDigest.getInstance("SHA-256")
    .digest(file.readBytes())
    .joinToString("") { "%02x".format(it) }
    require(got == want) { "Checksum mismatch for $name\nexpected=$want\n got=$got" }
    }
    println("verifyModelAssets: OK")
    }
    }

    tasks.named("preBuild") { dependsOn("verifyModelAssets") }

    Expected observation on a healthy checkout is a single verifyModelAssets: OK line before compilation. Then deliberately rename the asset once, run ./gradlew :app:assembleDebug, and confirm the build stops with the message you wrote. If it compiles anyway, your app has a runtime download path you have not found yet, and that is the most important thing you will learn all day.

    Step 2: print the source of the last response


    A tiny recorder next to your inference entry point costs almost nothing and removes the guessing. Keep it debug-only, and never log prompt text or user content in it.

    // Proposed harness. Field names follow your app's existing types.
    sealed interface ModelSource {
    data class Bundled(val asset: String, val digestPrefix: String) : ModelSource
    data class Remote(val host: String, val path: String) : ModelSource
    data class Fixture(val name: String) : ModelSource
    }

    object Provenance {
    private val seen = ArrayDeque<ModelSource>()

    fun record(source: ModelSource) {
    seen.addLast(source)
    if (seen.size > 20) seen.removeFirst()
    if (BuildConfig.DEBUG) Log.d("Provenance", describe(source))
    }

    fun last(): ModelSource? = seen.lastOrNull()

    private fun describe(s: ModelSource): String = when (s) {
    is ModelSource.Bundled -> "bundled ${s.asset} sha256:${s.digestPrefix}"
    is ModelSource.Remote -> "remote ${s.host}${s.path}"
    is ModelSource.Fixture -> "fixture ${s.name}"
    }
    }

    Call Provenance.record(...) exactly where the response is produced, not where the request is built, because a retry can change the answer's origin. If you use Compose, a two-line debug overlay reading Provenance.last() is enough; on iOS the same idea maps to a #if DEBUG view or an os_log line.

    What the banner buys you


    1. You can tell a reviewer which path your PR screenshot came from.
    2. You can catch a silent fallback before it becomes a "works on my machine" thread.
    3. You can reproduce a bug by forcing the source rather than hoping.


    Step 3: exercise the remote path deliberately


    Many teams use a hosted endpoint for early integration work, and some projects offer free model access plus a free server option so a new contributor can get a working call without a billing conversation. The open-source project MonkeyCode is one of these, and its operator describes a free model-access tier and a free server option for trying the workflow; the same operator-supplied description puts the free allowance at 10 million tokens. Treat that figure as a starting point, not a contract, and re-read the project's current terms before you plan any budget around it.

    Disclosure: This article was prepared as part of MonkeyCode's product outreach.

    The useful part for onboarding is narrower than the marketing: a shared free endpoint lets you prove that your client's request shape, timeout, and error handling work before you depend on a host only three people can reach. Time the first call rather than trusting it.

    # Shape only. Use the base URL and path from the project's own setup docs.
    BASE_URL="https://<your-endpoint-from-the-project-docs>"

    for i in 1 2 3; do
    curl -sS -o /dev/null \
    -w "attempt=%{http_code} ttfb=%{time_starttransfer}s total=%{time_total}s\n" \
    -H "Content-Type: application/json" \
    -d '{"prompt":"ping","max_tokens":8}' \
    "$BASE_URL/v1/complete"
    done

    Record three numbers per attempt and note whether the first one is slower than the rest, which would suggest a cold path rather than a steady one. Do not run a load test against shared free infrastructure; three sequential calls teach you what a first-hour contributor needs, and concurrency experiments belong on a host your team controls.

    When the banner disagrees with your mental model

    SymptomLikely broken pathFirst checkBuild passes with the asset deletedSilent download or remote fallbackGrep for the model URL in build configBanner says remote in airplane modeCached response or fixtureClear app storage, rerunBanner says bundled but answers varyPrompt template changedDiff template file against mainFirst remote call takes much longerCold endpoint or cold radioRepeat three times, note radio stateWorks after reinstall onlyAsset copied at first launchInspect first-run copy logic


    Each row is a rollback condition, not a curiosity. If your banner reports remote while the device has no network, you have found something worth raising before you write feature code.

    Evidence to record before your first PR


    Fill this in on your own hardware and paste it into the PR description or your onboarding notes. Reviewers trust a filled table far more than a sentence claiming it works.

    • Device model and OS version, plus whether it is a physical device or a simulator.
    • Framework and dependency versions, including the inference runtime and build tool versions.
    • Build type used for the observation, and the exact command you ran.
    • Network state during each step, including radio type and whether airplane mode was toggled.
    • Permission state for any sensor the feature reads, and whether you revoked it mid-run.
    • Power conditions, such as plugged in versus battery, and any low-power mode.
    • Which provenance value the banner printed for each observation.


    Limitations and who should skip this


    This harness assumes your app can load a model from local storage. If your product is server-only inference, a bundled-asset check adds noise and you should stop after step 2 and document the endpoint instead. Teams with a managed model registry may already enforce digests in CI, in which case duplicating the check locally is waste. Checksumming a multi-hundred-megabyte asset on every build slows feedback loops noticeably, so scope the task to debug variants or run it on a schedule.

    Finally, the banner is diagnostic tooling. It must be stripped or gated in release builds, because hostnames and asset digests are information you gain nothing from publishing.

    If you run this in your own repo, I would like to see the environment rather than the conclusion: device, OS, framework versions, the transition steps you used, and whether the banner recovered, restarted, or silently disappeared. If you want a place to start the remote path without provisioning a host first, the project's free tier and free server option are the shortest route to a first honest measurement.#ai #mobile #android #testing #software #coding #development #engineering #inclusive #community
    Know Which Model Answered: A First-Hour Provenance Banner for Mobile AI Repos

    #ai #mobile #community #software #coding #android
  14. Raw feed news @rawfeednews

    🔴 BREAKING: Voter turnout in Israel's election has reached its highest level in decades. The poll, the first since the Hamas attacks and subsequent wars, is widely viewed as a referendum on Prime Minister Benjamin Netanyahu and could reshape the country's political landscape.
    🕐 12:00 UTC

    Read more: rawfeednews.com/post/mvqmvv73.

    #election
  15. Matti Stöhr @[email protected]

    🤔 Can individual personality rights be adequately safeguarded in the digital realm, particularly in highly sensitive (historical) #sciencevideos?

    At a time when #videomanipulation is becoming increasingly easy through #AI, risk impact assessment, adequate clarification of rights, technical protection and anonymisation mechanisms are highly topical and relevant.

    Read the latest post on the @tibhannover blog about the #Remedy project 👉 blog.tib.eu/2026/10/27/sensiti

    #AudiovisualHeritage #WDAVH2026

    #sciencevideos #videomanipulation #ai #remedy #audiovisualheritage #wdavh2026
  16. Psychology News Robot @[email protected]

    DATE: October 27, 2026 at 06:00AM
    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: Limiting sugar in the first 1,000 days of life is associated with later dementia onset, study finds

    URL: psypost.org/limiting-sugar-in-

    Limiting exposure to added sugar in the womb and during the first two years of life is associated with a lower risk of developing dementia decades later, according to a new study published in Neurology. By examining adults born around the end of post-World War II food rationing in the United Kingdom, researchers found that early-life sugar restriction corresponded with delayed dementia onset, healthier brain structure, and better scores on some cognitive tests in their 60s. The findings suggest that nutritional conditions during the earliest stages of human development may play a lasting role in how the brain ages.

    Dementia is an umbrella term for progressive neurological conditions that impair memory, thinking, and the ability to perform everyday activities. The most common form is Alzheimer’s disease, which involves the gradual breakdown of brain cells and the buildup of abnormal proteins, followed by vascular dementia, which occurs when reduced blood flow damages brain tissue. Although lifestyle habits in middle and old age are well-known factors in cognitive decline, growing evidence points to the importance of the “first 1,000 days” of life, the developmental window spanning from conception to a child’s second birthday.

    During this early period, the brain undergoes rapid growth, forming neural connections and building the structural foundation for lifelong thinking and memory. High sugar intake during pregnancy and infancy may disrupt this process by triggering inflammation or interfering with how cells respond to insulin. For instance, research covered by PsyPost in 2016 indicated that rats fed sugary drinks early in life developed changes in the hippocampus, a seahorse-shaped brain region essential for memory, that resembled the biological effects of early-life stress. In humans, observational studies have similarly linked higher maternal sugar consumption during pregnancy and breastfeeding to altered brain tissue organization in newborns and slightly lower cognitive scores in young children.

    Studying how infant diets affect human brain aging is difficult because tracking people from birth into their 70s rarely happens in controlled experiments. To overcome this challenge, scientists have turned to a historical policy shift in the United Kingdom: the end of wartime sugar rationing. Introduced in July 1942 during World War II, the rationing system strictly limited added sugar and sweets to levels comparable to modern dietary guidelines while keeping overall calories and basic nutrients sufficient, unlike historical famines that caused severe starvation. When the British government abruptly ended sugar rationing in September 1953, national sugar consumption jumped by more than 50 percent, even as total calorie intake rose by only about 5 percent and other food groups remained relatively stable.

    Researchers have used this sudden dietary shift to examine how early sugar limits relate to adult health, starting with a 2024 study by another team that linked rationing exposure during the first 1,000 days to lower adult rates of type 2 diabetes and high blood pressure. Extending this approach to respiratory health, a study covered by PsyPost in 2025 found that rationing exposure during pregnancy and infancy was tied to better adult lung capacity and lower rates of asthma. In the realm of mental health, a 2026 cohort study of British adults found that experiencing sugar rationing across the first 1,000 days of life was associated with a reduced preference for sweet foods and a lower risk of adult depression and anxiety.

    Building on this line of inquiry, the new study was led by Jiazhen Zheng, an assistant professor at the School of Life and Health Sciences at Shenzhen University of Advanced Technology in China, who also led the 2025 lung-health study. Zheng conducted the research at The Hong Kong University of Science and Technology (Guangzhou) alongside co-corresponding author S. W. Ricky Lee of the same institution and their colleagues, examining whether early sugar restriction also relates to long-term dementia risk, brain structure, and cognitive ability in older age.

    “While growing evidence suggests that early-life nutrition can shape long-term health, remarkably little is known about whether sugar exposure during this period is associated with dementia many decades later,” Zheng told PsyPost. “We were particularly interested in addressing this knowledge gap because most dementia prevention research focuses on risk factors in middle and older age. The end of sugar rationing in the United Kingdom in September 1953 provided a unique natural experiment to investigate this question.”

    The researchers analyzed data from the UK Biobank, a large health study that recruited middle-aged and older British adults between 2006 and 2010. Starting with 74,213 participants born between 1951 and 1956, the team excluded 6,582 people born outside the United Kingdom, 2,424 from multiple births such as twins or triplets, 79 adoptees, 208 people already diagnosed with dementia at recruitment, and 108 who withdrew from the project. This left a final sample of 64,737 adults who were an average of 54.6 years old at enrollment, 56.4 percent of whom were women.

    Using each person’s birth date relative to the September 1953 policy change, the authors sorted participants into groups reflecting how long they experienced sugar rationing during their first 1,000 days from conception. In total, 40,963 participants experienced rationing: 10,682 were exposed in the womb only, 15,025 were exposed in the womb plus up to one year after birth, and 15,256 were exposed in the womb plus one to two years after birth. The remaining 23,774 participants were born after July 1, 1954, meaning they were conceived after rationing ended and grew up in an environment of unrestricted sugar.

    The team tracked hospital admissions and death registries through July 1, 2023, to identify new diagnoses of all-cause dementia, Alzheimer’s disease, and vascular dementia. To account for other differences between the groups, the statistical models controlled for age, sex, race, birthplace, birth month, inflation-adjusted food prices, average national fat intake at birth, maternal smoking, breastfeeding, and parental dementia history. The researchers also adjusted for adult household income, neighborhood socioeconomic status, smoking, alcohol consumption, physical activity, and chronic illnesses such as heart, kidney, and liver disease.

    In addition to tracking dementia diagnoses, the authors examined a subset of more than 7,500 participants who underwent magnetic resonance imaging (MRI) brain scans at an average age of 61.8 years, alongside roughly 6,400 to 20,600 participants who completed computerized cognitive assessments. These brain scans measured total brain volume, gray matter (the outer layer of brain tissue rich in nerve cell bodies), white matter (the deeper nerve fibers that connect different brain regions), the hippocampus, and white matter hyperintensities, which are bright spots on a scan that indicate small blood vessel damage and tissue wear.

    Building on the 2024 research linking rationing to metabolic and vascular health, the team also tested whether adult type 2 diabetes and high blood pressure explained the link between early sugar restriction and dementia, and they checked two conditions considered unlikely to be affected by early sugar intake, type 1 diabetes and melanoma (a form of skin cancer), as negative controls.

    Over the follow-up period, 504 out of 23,774 unexposed participants (about 2.1 percent) were diagnosed with all-cause dementia, including 227 cases of Alzheimer’s disease (about 1.0 percent) and 178 cases of vascular dementia (about 0.75 percent). In raw numbers, the rationed groups had slightly higher dementia rates, largely because they were born earlier and were more than two years older on average than the unexposed group, with 251 dementia cases among the 10,682 people rationed only in the womb (about 2.3 percent), 388 cases among the 15,025 people rationed through age one (about 2.6 percent), and 456 cases among the 15,256 people rationed for one to two years (about 3.0 percent). Once the researchers adjusted for age and other background variables, the rationed groups showed a lower risk of developing dementia at any given age, though the association for those exposed only in the womb was smaller and not statistically significant.

    Compared with adults who never experienced rationing, those exposed to sugar rationing in the womb plus their first year of life had a 21 percent lower hazard (the rate at which new cases appear at a given age) of developing all-cause dementia and a 23 percent lower hazard of developing Alzheimer’s disease. The association was slightly stronger for those exposed in the womb plus one to two years after birth, who showed a 23 percent lower hazard of all-cause dementia and a 28 percent lower hazard of Alzheimer’s disease.

    For vascular dementia, the estimates pointed in a similar direction, though the results were not statistically significant. Looking at the timing of diagnoses, experiencing rationing from conception through ages one to two was associated with delaying the onset of all-cause dementia by 2.55 years, Alzheimer’s disease by 2.87 years, and vascular dementia by 2.49 years.

    “The magnitude of the associations was particularly noteworthy,” Zheng said. “These are population-level estimates rather than predictions for any individual, but even modest shifts in dementia occurrence could have meaningful public health implications given the growing global burden of the disease.”

    The results were less robust in one additional analysis. When the researchers accounted for the possibility that participants would die of other causes before developing dementia, rationing exposure was still linked to a roughly 15 percent lower likelihood of all-cause dementia, but the associations with Alzheimer’s disease and vascular dementia were no longer statistically significant.

    The link between early sugar rationing and dementia was stronger in women than in men, which the authors noted might relate to biological differences in how female neural circuits respond to early nutritional environments. The results held steady regardless of whether participants carried genetic variants of the apolipoprotein E gene that heighten Alzheimer’s risk. Mediation analyses indicated that lower rates of adult type 2 diabetes and high blood pressure accounted for 18.0 percent and 11.6 percent of the association, respectively, or 25.5 percent when combined, while birth weight accounted for just 2.7 percent. As expected, rationing exposure showed no association with type 1 diabetes or melanoma, and the primary dementia patterns were supported by a validation analysis in an independent British aging study, with an American cohort serving as a comparison group.

    The brain imaging and cognitive test results aligned with the clinical dementia findings. Compared with the unexposed group, participants who experienced early-life sugar rationing had larger total gray matter volumes, averaging 618.9 cubic centimeters versus 618.5 cubic centimeters (an adjusted increase of 3.27 cubic centimeters), and smaller volumes of white matter hyperintensities, averaging 3.72 cubic centimeters versus 3.84 cubic centimeters (an adjusted reduction of 0.64 cubic centimeters).

    Rationed participants also performed better on tests of complex processing speed, verbal and numerical reasoning, nonverbal pattern reasoning, and a timed test of visual attention that involves connecting numbered circles in order. Differences in total brain volume, white matter volume, hippocampal size, working memory, verbal memory, and a harder version of the connect-the-dots test that requires switching between numbers and letters were not statistically significant. These differences were modest: the gray matter difference amounted to about half a percent of total gray matter volume, and the cognitive differences were similarly small.

    “What struck us most was how strongly early-life nutritional exposure appeared to be associated with brain health more than half a century later,” Zheng explained. “Beyond dementia diagnoses, we also observed favorable differences in brain MRI measurements, including greater gray matter volume and fewer white matter hyperintensities, alongside better performance on certain cognitive tests. The convergence of these findings across clinical, structural, and cognitive outcomes was especially interesting and suggested that the associations might reflect broader processes of brain aging.”

    These results are consistent with research covered by PsyPost in December 2025, which tracked broad diet quality across the lifespan starting at age four rather than isolating sugar restriction during the first 1,000 days of life, and similarly found that healthier early nutritional habits were associated with better cognitive performance and a lower risk of late-life dementia. They also align with another study covered by PsyPost in November 2025, which examined how chronic sugar consumption caused memory impairment and Alzheimer’s-related brain pathology in adult rats rather than how early-life sugar restriction affects long-term dementia risk in humans, yet similarly linked high sugar exposure to neurodegenerative decline.

    “Our findings suggest that the nutritional environment during pregnancy and early childhood may have consequences for brain health that extend across an entire lifetime,” Zheng told PsyPost. “The key message is not that people should eliminate sugar entirely, but rather that limiting excessive added sugar during these sensitive developmental periods may be beneficial. Importantly, our findings demonstrate associations, not definitive causation.”

    As with all research, there are some caveats to consider when interpreting these findings. Because this was an observational study that relied on birth dates during a historical policy change, the researchers did not have direct measurements of how much sugar individual pregnant mothers or infants actually consumed. Although national surveys confirm that household sugar purchases surged right after September 1953, personal eating habits within both the rationed and unrationed groups likely varied from family to family. Because exposure was determined entirely by birth date, the rationed participants were also older, so the findings depend on statistical adjustments for age; the authors checked this by confirming that dementia risk did not drift among never-rationed participants born at different times after the policy ended.

    “One important distinction is that our study does not prove that reducing sugar intake in infancy directly prevents dementia,” Zheng noted. “Other dietary, social, or environmental differences associated with the rationing period may also have influenced the results. Furthermore, our findings should not be interpreted as advocating extremely restrictive diets during pregnancy or infancy; adequate and balanced nutrition remains essential for healthy development.”

    Another consideration is the age and makeup of the study population. Because participants were born in the early to mid-1950s, they were only in their late 60s and early 70s by the end of the tracking period in 2023, meaning the study primarily captured relatively early cases of dementia rather than diagnoses occurring in the 80s and 90s.

    In addition, the UK Biobank consists of volunteers who survived into middle age and tend to be healthier, wealthier, and predominantly white (over 96 percent of this sample), which may limit how well the findings apply to more diverse or disadvantaged populations. The authors suggest that future randomized trials testing practical nutritional counseling to limit added sugars during pregnancy and infancy could help establish how early dietary habits shape brain development and long-term cognitive resilience.

    “We hope to better understand the biological mechanisms linking early-life nutrition with brain aging and neurodegenerative diseases,” Zheng said. “In this study, type 2 diabetes and hypertension together statistically accounted for approximately one-quarter of the observed association, suggesting that metabolic and vascular health may play a role. Future research should examine additional biological pathways, clarify the most sensitive developmental windows, and determine whether these findings can be replicated in other populations with more detailed dietary information. Ultimately, we hope this work will help inform evidence-based nutritional recommendations across the life course.”

    “I believe one of the broader implications of our work is that dementia prevention may need to be considered from a much earlier life-course perspective,” Zheng added. “We often think of dementia as a disease of old age, but some of the factors associated with vulnerability or resilience may originate decades before symptoms appear. Understanding these early influences could open new opportunities for preventive research and public health strategies.”

    The study, “Association of Sugar Restriction in Utero Through Age 2 Years on Dementia Risk Later in Life,” was authored by Jiazhen Zheng, Gregory Y.H. Lip, Zhuoni Zhang, and S. W. Ricky Lee.

    URL: psypost.org/limiting-sugar-in-

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

    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 #DementiaResearch #BrainHealth #EarlyLifeNutrition #SugarReduction #MaternalHealth #ChildNutrition #AlzheimersResearch #CognitiveHealth #HealthyAging #PublicHealth

    #psychology #counseling #socialwork #psychotherapy #mentalhealth #psychiatry
  17. mjsm1th @mjsm1th

    Even Jesus prays. That is, He fights to have fellowship with the rest of the Trinity.

    He fights for a time and a place and for the right situation.

    It is a good example for us. It is a call for us to do the same. It is an encouragement and might be a bit of a rebuke.

    If Jesus fights so hard for fellowship with God, we probably should too.

    Join me in getting started. Then subscribe, share, like, comment or post.

    youtu.be/fyTzHTaacuY

  18. TKSST • seethis.tv 🌈🪐✨ @[email protected]

    🛰️ ICYMI: #NASA #Goddard illustrates how #smoke behaves as an atmospheric mixture of gases and suspended aerosols rather than a single #cloud.

    In this #video, watch how the #PACE #satellite gathers orbital #data on #light scattering to help scientists track wildfire plumes and #climate interactions across planet #Earth.

    👉 Learn more: seethis.tv/post/science-of-smo

    #air #pollution #animation #atmosphere #carbon #chemistry #earthscience #fire #fluiddynamics #fossilfuels #gas #health #howthingswork #naturaldisasters #nature #particles #physics #plants #research #science #sunlight #weather #wildfires #wind #tksst

    #nasa #goddard #smoke #cloud #video #pace
  19. OTX Bot @[email protected]

    Hackers Hijack Microsoft 365 Accounts at 79 Organizations Using Stolen Login Tokens

    Hackers compromised Microsoft 365 accounts at 79 organizations by reusing stolen login tokens, recording at least 497 successful sign-ins between July 14 and September 22, 2026. The ten-week campaign also generated more than 200,000 failed login attempts across monitored customer environments. The attackers targeted authenticated sessions rather than relying on password guessing. Similar to previously […] The post Hackers Hijack Microsoft 365 Accounts at 79 Organizations Using Stolen Login Tokens appeared first on Cyber Security News .

    Pulse ID: 6ae05909f9d104ba5a261d7b
    Pulse Link: otx.alienvault.com/pulse/6ae05
    Pulse Author: CyberHunter_NL
    Created: 2026-10-27 07:55:53

    Be advised, this data is unverified and should be considered preliminary. Always do further verification.

    #Microsoft #Password #OTX #CyberHunter_NL

    #microsoft #password #otx #cyberhunter_nl
  20. The Collector™:black_verified: @[email protected]

    Blau-Schwarz liegt bei den über 70-Jährigen weit hinter Rot-Rot-Grün apollo-news.net/ueber-70-jaehr Nur 22 Prozent der Menschen ab 70 Jahren bevorzugen eine Bundesregierung aus AfD und Union. 42 Prozent würden sich stattdessen ...
    The post Blau-Schwarz liegt bei den über 70-Jährigen weit hinter Rot-Rot-Grün appeared first on Apollo News. #news #press

    #press #news
  21. OTX Bot @[email protected]

    Hackers Use AI Agents to Target Over 2,250 Microsoft 365 Tenants Through ConnectWise

    Hackers used AI agents and stolen ConnectWise application credentials to attempt access to more than 2,250 Microsoft 365 tenants, according to Microsoft’s technical investigation report. Microsoft attributed the campaign to Storm-0940, a China-state-aligned group, and said its ARTEX framework accelerated reconnaissance, authentication attempts, and cloud persistence. The targeting count does not mean every tenant was […] The post Hackers Use AI Agents to Target Over 2,250 Microsoft 365 Tenants Through ConnectWise appeared first on Cyber Security News .

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    Created: 2026-10-27 06:55:42

    Be advised, this data is unverified and should be considered preliminary. Always do further verification.

    #ConnectWise #Microsoft #ARTEX #China #OTX #CyberHunter_NL

    #connectwise #microsoft #artex #china #otx #cyberhunter_nl
  22. PKs Powerfromspace1 @[email protected]

    🪖MilitaryNewsUA🇺🇦
    @militarynewsua.bsky.social

    ❗️The #Kremlin through the head of the Russian Direct Investment Fund Kirill Dmitriev, is luring Trump’s team with lucrative deals in energy, aviation, rare earth metals, AI, and technology (including investments by people close to Trump in Lukoil), without stopping the war.

    10/26

    #UkaineWar #USA for sale!
    🧵
    bsky.app/profile/militarynewsu

    #kremlin #ukainewar #usa
  23. Yale Privacy Lab @[email protected]

    🕵️ Latest from #Surveillance #Technology Oversight Project (S.T.O.P.) #NYC

    “Yesterday, Meta was ordered to pay 25 billion dollars in a lawsuit against New Mexico for deceiving users about data privacy practices․ Read more here: nytimes․com/2026/10/21/technology/meta-penalty-new-mexico․html?smid…”

    bsky.app/profile/stopspyingny.

    🤖 via RSS feed. May not reflect our views.

    #surveillance #technology #nyc
  24. Simone Sollazzo @[email protected]

    DEMOCRAZIA DI SECONDA MANO (?)
    - La Città metropolitana e il paradosso della rappresentanza.

    Ci sono esperienze politiche che, anche a distanza di anni, continuano a lasciarti addosso una sensazione difficile da dimenticare.

    Post completo:
    facebook.com/share/p/1JbZFt5QV

    #MilanoLibera #CittàMetropolitana #Democrazia #Partecipazione #Milano

    #milano #partecipazione #democrazia #cittametropolitana #milanolibera
  25. asanetargoss @asanetargoss

    I want to be transparent about the stuff I make. With regard to LLMs and other generative inference tools, I am currently not using them to create my stuff. I may nevertheless use them in the future. I do not like slop. For a more full view of my opinions on LLMs/etc, see my latest blog post:

    asanetargoss.com/pages/2026_sa

  26. OTX Bot @[email protected]

    Hackers Use Malicious Instagram Ads to Infect iPhones With Spyware Without a Click

    Hackers are using malicious Instagram advertisements to infect vulnerable iPhones with spyware while users scroll through their feeds. A documented attack used a World Cup advertisement to deliver the P7 variant of DarkSword, exposing sensitive information without requiring the victim to tap the ad, download a file, or install an application. The findings show how […] The post Hackers Use Malicious Instagram Ads to Infect iPhones With Spyware Without a Click appeared first on Cyber Security News .

    Pulse ID: 6ae02edf3afbfc8b42023841
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    #Instagram #SpyWare #Deliver #OTX #CyberHunter_NL

    #instagram #spyware #deliver #otx #cyberhunter_nl
  27. OTX Bot @[email protected]

    New Aokigahara Malware Targets ChatGPT, Claude, Codex, and Cursor Login Credentials

    A newly observed Windows infostealer called Aokigahara is targeting login credentials and session data associated with ChatGPT Desktop, Claude, Codex, and Cursor. The malware searches infected computers for authentication files and application storage, extending familiar password theft into the AI tools developers increasingly use for everyday work. The threat is not evidence that these AI […] The post New Aokigahara Malware Targets ChatGPT, Claude, Codex, and Cursor Login Credentials appeared first on Cyber Security News .

    Pulse ID: 6ae02ef39d970d8550ac210f
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    Created: 2026-10-27 04:56:19

    Be advised, this data is unverified and should be considered preliminary. Always do further verification.

    #ChatGPT #Malware #developers #InfoStealer #OTX #CyberHunter_NL

    #chatgpt #malware #developers #infostealer #otx #cyberhunter_nl
  28. Teagan's Books @[email protected]

    Olga #Reviews: A Peril in Unseen Frequencies

    Tuesday, October 27, 2026

    Welcome, all.  Some things are such a delight that they simply must be shared.  Today Olga Núñez Miret posted an extraordinarily mindful review of “A Peril in Unseen Frequencies.” How could I not crow about it?

    Olga is a genuinely amazing person.  She is also an accomplished author herself (among many other inspiring things).  Here’s a screenshot of Olga’s Amazon author page.  She has 34 titles there, in multiple genres and different languages too.

    Please meet me over at Olga’s blog.  Just click that big blue link below.  I’m closing comments here, but I hope to see you at Olga’s.

    Click Here to Visit Olga’s Post

    ♣ ♣ ♣

    Here’s the obligatory shameless self-promotion

    Although it’s a twisty mystery, it’s also a quick read.  As I usually do, I included a section about technology and cultural items that are mentioned in the story. 

    A Peril in Unseen Frequencies

    Universal Purchase Links

    Kindle:  relinks.me/B0HKPZ7TH4

    Paperback:  relinks.me/B0HKTFCD8Z

    Virtual Voice Audiobook:  relinks.me/B0HKNDVYDM

    A Medium’s Peril full series link: relinks.me/B0CG2SXX24

    ♣ ♣ ♣

    This blog is entirely human-written.  Furthermore, the author expressly prohibits any entity from using this publication for purposes of training AI technologies to generate text.  No part of this work may be reproduced, scanned, or distributed in any printed or electronic form without permission.  Please do not participate in or encourage piracy of copyrighted materials in violation of the author’s rights.

    All images are either the property of the author, or used with permission, or from free sources.

    Copyright © 2026 by Teagan Ríordáin Geneviene

    All rights reserved.

    #1920S #AMediumSPeril #APerilInUnseenFrequencies #BadMoonRising #GhostStory #NewBook #ParanormalMystery #TeaganRiordainGeneviene #TeriPolen #Vintage
    #1920s #amediumsperil #aperilinunseenfrequencies #badmoonrising #ghoststory #newbook
  29. Dev Community @[email protected]

    I wanted to create a 60-second cinematic FPV travel film across the United States, moving from New York to San Francisco and Los Angeles.

    The goal wasn't simply to generate impressive AI footage. I wanted the entire film to feel like one continuous drone journey rather than a collection of unrelated clips stitched together in post-production.

    That introduced three practical challenges:

    • Maintaining visual continuity across independently generated clips.
    • Controlling camera movement, lighting, and transitions through prompts.
    • Fixing individual shots without regenerating the entire video.

    I built the film using GMI Cloud's video generation models, frame chaining, and FFmpeg.

    The final output was exactly 60 seconds long, rendered at 1080p, with a targeted replacement for a shot that generated incorrectly.

    Here's how the pipeline worked, including the API issues, failed generations, and techniques that made the workflow more reliable.

    1. Understanding the Duration Constraint


    The first step was checking the available models and their actual generation limits.

    Rather than relying on model names or assumptions, I used GMI Cloud's get_model tool to inspect the supported parameters, duration limits, and pricing.

    get_model({ model_id: "seedance-2-5-260628" })
    get_model({ model_id: "wan3.0-video" })
    get_model({ model_id: "veo-3.1-generate-001" })

    The following figures reflect the catalog and pricing I observed during development in October 2026. Verify them against the current catalog before using them in production.

    ModelMaximum durationObserved 1080p price/secRelevant capabilitiesseedance-2-5-26062830 seconds$0.569First-frame and last-frame conditioning, reference images, audio generationwan3.0-video30 seconds$0.20Frame conditioning, reference images, separate negative promptveo-3.1-generate-0018 seconds$0.40High photorealism, two-frame conditioningkling-3.0-turbo-t2v15 seconds$0.14Text-to-video generationseedance-2-0-260128-upscale15 seconds$0.056Budget-oriented optionveo-3.1-lite-generate-0018 seconds$0.05Lower-cost generation at supported resolutions


    These capabilities and prices are model- and configuration-dependent. Check the current API schema before building around a particular parameter.

    The key constraint was straightforward: no model in this selection could generate the entire 60-second film in one request.

    I therefore split the project into two 30-second generations.

    Fewer, longer clips reduce the number of boundaries that need to be managed. A six-clip pipeline introduces five joins, while a two-clip pipeline introduces just one.

    However, clip length shouldn't be the only consideration. The structure of the film matters, too. If a story naturally contains three distinct locations or sequences, three shorter clips may be easier to control than two longer generations.

    The objective is to minimize unnecessary joins without forcing unrelated scenes into one generation.

    2. Prompt Engineering: Control the Camera Before the Scene


    One of the biggest lessons from this project was that a video prompt needs to describe motion, not just visual content.

    My initial prompts combined several events into short sequences:

    • Dive between skyscrapers.
    • Sweep toward the Hudson River.
    • Transition from sunrise to blue hour.
    • Race through city streets.
    • Introduce light trails.

    The results were inconsistent. Architecture distorted, transitions became muddy, and the camera sometimes appeared to blend incompatible locations.

    The problem was trying to control too many independent events in a single prompt.

    My revised approach was to define a continuous camera movement with explicit scene timing.

    The single-movement principle


    Describe one continuous camera journey per generation, and use FFmpeg for editorial cuts.

    This doesn't mean a longer generation must contain only one location. It means the transitions between locations should follow a physically understandable camera movement rather than asking the model to perform several unrelated edits.

    For example, an aircraft wing can pass across the lens and temporarily obscure the entire frame. The next location can emerge as the wing clears.

    That provides a visual transition the model can attempt to render as part of the camera movement.

    Here's an excerpt from the prompt used for the film:

    Ultra-realistic cinematic FPV drone travel film, 30 seconds, 16:9.

    One continuous high-speed journey across the United States.
    Maintain forward camera momentum throughout.

    Photorealistic imagery, consistent teal-and-orange commercial
    color grading, high dynamic range, natural film grain,
    subtle horizontal anamorphic lens flare, and a wide 14mm lens.

    Use smooth motion blur only during the fastest movements.

    No text, captions, subtitles, logos, watermarks, or readable
    signage.

    SHOT 1 (0-7s):
    Above New York City at sunrise. Low mist surrounds the
    skyscrapers as the drone tips forward and dives between
    glass towers. Golden light reflects across the facades.

    SHOT 2 (7-15s):
    The camera descends to street level and races down a wide
    Manhattan avenue. Moving cars pass beneath the camera,
    their headlights and taillights creating flowing light trails.

    SHOT 3 (15-22s):
    The drone rises over New York Harbor. A ferry crosses the
    water below, leaving a white wake. The camera sweeps around
    to reveal the Statue of Liberty in the morning haze.

    SHOT 4 (22-30s):
    An aircraft wing passes directly across the lens, filling
    the frame. As it clears, the camera emerges above turquoise
    Pacific water near San Francisco, moving toward the Golden
    Gate Bridge as its red towers appear through coastal fog.

    The timing provides structure, but it doesn't guarantee that the model will follow every boundary precisely. Treat shot durations as guidance and inspect the generated footage before editing.

    Keep the visual identity consistent


    Consistency doesn't require identical lighting throughout the film.

    The sequence moves between sunrise, daylight, golden hour, and night. Those changes are part of the story.

    What should remain relatively stable is the visual treatment:

    • Color grading.
    • Lens characteristics.
    • Motion style.
    • Contrast and highlight handling.
    • Overall level of realism.

    I repeated the same visual descriptors across prompts while allowing the lighting and environment to change.

    This gave the film a more consistent look without making every location feel artificially identical.

    Negative prompts are model-specific


    Different models expose different controls.

    For example, wan3.0-video supports a dedicated negative_prompt parameter, while the Seedance 2.5 configuration I used did not expose that parameter.

    For Seedance, I placed unwanted elements directly in the prompt:

    No text, captions, subtitles, logos, watermarks, or readable
    signage anywhere in the frame.

    The general lesson is to inspect the actual model schema rather than assume that every video model supports the same parameters.

    3. Frame Chaining: Maintaining Continuity Between Clips


    Frame chaining was the central technique in this project.

    Instead of generating two unrelated clips and trying to hide their differences afterward, I used the final frame of the first clip to condition the beginning of the second.

    The pipeline is simple:

    1. Generate clip A.
    2. Extract a frame near the end of clip A.
    3. Upload the frame to GMI Cloud.
    4. Pass its URL as the first_frame parameter when generating clip B.
    5. Inspect the boundary and adjust the edit if necessary.

    The second generation now has a visual reference for where the first generation ended.

    Extracting the frame with FFmpeg


    I extracted a frame near the end of the first clip:

    ffmpeg -y -ss 29.9 -i clipA.mp4 \
    -frames:v 1 chain_last.png

    I then normalized the image to the intended output dimensions and converted it to JPEG:

    ffmpeg -y -i chain_last.png \
    -vf "scale=1920:1080:force_original_aspect_ratio=increase,crop=1920:1080" \
    -q:v 3 chain_first.jpg

    The output dimensions should match the intended video aspect ratio. If the source frame already has the correct dimensions, unnecessary scaling and cropping can be avoided.

    The extracted image becomes the conditioning frame for the next generation.

    Uploading the frame to GMI Cloud


    The upload_media operation returns a signed upload_url and a public_url.

    These serve different purposes:

    • upload_url is the destination for uploading the file.
    • public_url is the image URL supplied to the video generation request.

    The signed URL doesn't upload the file automatically. You must send the image bytes to it.

    Here's a minimal Python example:

    import urllib.request

    upload_url = "<upload_url from upload_media>"

    with open("chain_first.jpg", "rb") as image_file:
    image_data = image_file.read()

    request = urllib.request.Request(
    upload_url,
    data=image_data,
    method="PUT",
    headers={"Content-Type": "image/jpeg"},
    )

    with urllib.request.urlopen(request) as response:
    print(response.status)

    Use the URL and HTTP method specified by the upload operation. If the signed URL expires or the upload returns a signature error, request a fresh URL and retry.

    I encountered a 403 SignatureDoesNotMatch error with an earlier upload URL. Requesting a new URL resolved the problem in my case.

    Generating the next clip


    With the frame uploaded, I passed its public URL to Seedance 2.5.

    The following is a representative request structure; use the exact field names and request envelope documented by the current API.

    {
    "model_id": "seedance-2-5-260628",
    "prompt": "<your scene and camera-movement prompt>",
    "parameters": {
    "duration": 30,
    "resolution": "1080p",
    "ratio": "adaptive",
    "first_frame": "<public_url of chain_first.jpg>",
    "generate_audio": true,
    "watermark": false
    }
    }

    The critical setting was ratio.

    The ratio configuration issue


    I encountered failed generations when using an explicit ratio: "16:9" and when omitting the ratio entirely.

    The API returned an error explaining that, when first-frame or first-and-last-frame conditioning is used, the output aspect ratio follows the input frame.

    The configuration that worked was:

    "ratio": "adaptive"

    For this particular model configuration, using ratio: "adaptive" resolved the issue.

    Don't assume that a parameter's default value is valid for every generation mode. Frame-conditioned generation can have different validation rules from text-to-video generation.

    Verify this behavior against the current API documentation before relying on it in another model version.

    What frame chaining actually guarantees


    Frame chaining is useful, but it is important to distinguish three different forms of continuity.

    Frame continuity: The next generation receives a reference image matching the preceding clip's endpoint.

    Temporal continuity: Camera motion, object movement, lighting, and perspective evolve naturally across the boundary.

    Subject continuity: People, vehicles, and other objects maintain their identity and appearance across shots.

    Frame chaining primarily addresses the first problem. It can help with the second, but it doesn't guarantee a perfect motion transition. It also doesn't guarantee subject consistency across independently generated scenes.

    A matching boundary frame is not proof that the surrounding motion will be seamless.

    For recurring subjects, combine frame conditioning with reference images where the model supports them.

    ModelReference parameterObserved catalog limitwan3.0-videoreference_image, reference_videoUp to 10 images and 5 videosseedance-2-5-260628reference_images, reference_videosUp to 9 images and 3 videoshappyhorse-1.1-r2vreference_imageUp to 5 images


    These limits are model-specific and should be verified before use.

    I also found that frame chaining works best when the next clip continues a compatible scene.

    A coastline transitioning into a bridge approach provides a natural continuation. A sudden jump from San Francisco to a Los Angeles street is a different editorial event and is often better handled as a deliberate cut.

    Chain compatible camera movements. Use editing to handle genuine changes in scene or location.

    4. Stitching the Film with FFmpeg


    Once the clips were generated, I assembled them into the final video.

    For a simple join, FFmpeg's concat demuxer is convenient when the input files have compatible stream properties.

    Create a file named list.txt:

    file 'clipA.mp4'
    file 'clipB.mp4'

    Then concatenate the clips:

    ffmpeg -y -f concat -safe 0 -i list.txt \
    -t 60.0 \
    -vf "fps=24,scale=1920:1080:flags=lanczos,format=yuv420p" \
    -c:v libx264 -preset slow -crf 17 \
    -c:a aac -movflags +faststart \
    out/master.mp4

    This example assumes that the input streams are compatible with the chosen filter and encoding pipeline. Incompatible codecs, time bases, resolutions, or audio streams may require normalization before concatenation.

    The command also re-encodes the video, so it is not a lossless join.

    When the clips need different processing


    For the final replacement, I needed to combine a 50-second section of the existing master with a newly generated 10-second clip.

    I used a filter graph to concatenate the video and audio streams while normalizing the video output:

    ffmpeg -y -i head.mp4 -i clipFix.mp4 \
    -filter_complex \
    "[0:v][1:v]concat=n=2:v=1:a=0,fps=24,scale=1920:1080:flags=lanczos,format=yuv420p[v]; \
    [0:a][1:a]concat=n=2:v=0:a=1[a]" \
    -map "[v]" -map "[a]" \
    -t 60.0 \
    -c:v libx264 -preset slow -crf 17 \
    -profile:v high -level 4.2 \
    -c:a aac -b:a 192k \
    -movflags +faststart \
    out/america_60s_16x9_v2.mp4

    This version assumes both inputs contain compatible audio streams. If one clip lacks audio, the filter graph needs to be adjusted to create or omit the missing stream.

    Also, -t 60.0 limits the output duration; it doesn't independently guarantee that the final file contains exactly 60 seconds of usable footage.

    To verify the result, inspect the actual duration and frame count.

    ffprobe -v error \
    -show_entries format=duration \
    -of default=noprint_wrappers=1:nokey=1 \
    out/america_60s_16x9_v2.mp4

    For a 24 fps output, 60 seconds corresponds to 1,440 frames. Checking both duration and frame count is useful when exact delivery specifications matter.

    Creating a cinematic 2.39:1 presentation


    The source video was generated at 16:9, but I also wanted to test a wider cinematic presentation.

    One option is to scale the image to 1920 × 804 and add black bars to restore a 1920 × 1080 canvas:

    ffmpeg -y -i master.mp4 \
    -vf "scale=1920:804:flags=lanczos,pad=1920:1080:0:138:black" \
    -c:v libx264 -preset slow -crf 17 \
    -profile:v high \
    -c:a copy \
    -movflags +faststart \
    out/master_239.mp4

    This produces an approximately 2.39:1 image area within a 16:9 video frame.

    Letterboxing preserves the entire scaled image, unlike a direct vertical crop that discards parts of the original frame.

    However, scaling the full 16:9 image to 1920 × 804 changes its proportions unless the image is cropped first. For a geometrically correct widescreen presentation, use a crop-and-scale pipeline when necessary.

    5. Re-Rendering One Bad Shot Instead of the Entire Film


    One of the most useful aspects of this workflow was the ability to replace a specific segment without rebuilding the whole video.

    During generation, the Hollywood sign appeared as "HOLFWOOD" instead of "HOLLYWOOD".

    This is a familiar limitation of generative video models: text can look convincing at a glance while individual characters remain incorrect or unstable.

    Regenerating the entire 60-second film would have wasted both time and credits.

    Instead, I extracted the frame at the replacement boundary, used it as the new clip's first_frame, and generated a replacement segment.

    ffmpeg -y -ss 50.0 -i out/master.mp4 \
    -frames:v 1 splice_first.jpg

    After uploading the frame and generating the replacement clip, I rebuilt the master from the unchanged opening section and the new footage.

    The replacement was approximately 10 seconds long.

    Prompting for accurate text


    For the replacement, I made the lettering requirement explicit:

    CRITICAL: The hillside sign must read exactly HOLLYWOOD.

    The spelling is H-O-L-L-Y-W-O-O-D: nine letters.

    The fourth letter must be L, not F.

    The letters are large, white, individually formed capital
    letters arranged in a straight horizontal line on a dry,
    brown hillside.

    Keep the lettering stable, correctly formed, and clearly
    visible from the front. Do not distort, rearrange, or replace
    any characters.

    The revised generation produced the intended spelling.

    This doesn't mean literal prompts can reliably solve text rendering in every model. It means a specific description of the expected output and known failure can improve the next attempt.

    For shots containing signs, product labels, or other critical text, I recommend treating one or two targeted re-renders as part of the production budget.

    When exact typography is essential, a more reliable approach is to add the text during post-production rather than depend on the video model to render it correctly.

    6. Practical Issues I Encountered


    The creative workflow was only part of the implementation. Several API and environment issues also affected the process.

    Keep generation parameters in the expected structure


    My generation-estimation requests required model-specific parameters to be nested under parameters.

    For example:

    {
    "model_id": "seedance-2-5-260628",
    "prompt": "<prompt>",
    "parameters": {
    "duration": 30,
    "resolution": "1080p",
    "ratio": "adaptive"
    }
    }

    Use the structure expected by the relevant operation. An estimation endpoint and a generation endpoint may not accept identical request bodies.

    An MCP timeout doesn't necessarily mean generation failed


    One submit_generation request timed out on the client side while the server-side generation continued running.

    Submitting the same request again could have created unnecessary work or additional charges.

    Instead, I used list_generations to inspect the existing jobs and recover their status.

    For production systems, keep track of generation IDs, request parameters, and job status. When supported, use idempotency controls to distinguish intentional re-renders from accidental duplicate submissions.

    Check which credits apply to each model


    My account displayed a zero-dollar balance alongside promotional credits. One model returned an insufficient_balance error, while another accepted the request.

    This was an account-specific result, not proof that all promotional credits are restricted in the same way.

    Before running a large batch, verify that the available credits apply to the selected model and generation configuration.

    Verify your FFmpeg version


    My system FFmpeg installation was too old to handle the HEVC Main10 output produced by the video model.

    The resulting codec errors initially looked like a problem with the downloaded file.

    I installed a newer FFmpeg binary through imageio-ffmpeg:

    pip install imageio-ffmpeg

    Then I checked the binary path:

    python -c "import imageio_ffmpeg; print(imageio_ffmpeg.get_ffmpeg_exe())"

    Use a sufficiently recent FFmpeg build with the codecs required by your source footage.

    For reproducible workflows, check the installed version and codec support before beginning a batch of generations.

    Verify audio instead of trusting the response


    Although I requested audio generation, both clips returned valid AAC audio streams that were effectively silent.

    I checked the audio levels with:

    ffmpeg -i clip.mp4 \
    -af volumedetect \
    -f null - 2>&1 | grep -E "mean_volume|max_volume"

    A valid audio stream doesn't necessarily contain audible sound. Inspect the output and listen to the actual result before delivery.

    Separate language-model usage from media-generation costs


    The account-summary information I inspected reported language-model usage separately from image and video generation.

    Don't treat an LLM token-cost report as a complete accounting of a media-generation project.

    Track the actual generation requests and their associated charges, and reconcile them against the relevant billing information.

    7. Cost Breakdown


    The final film used two 30-second clips and one 10-second replacement.

    GenerationDurationObserved costClip A — Seedance 2.5, 1080p30 seconds$17.07Clip B — Seedance 2.5, 1080p, frame-conditioned30 seconds$17.07Replacement clip — Seedance 2.5, 1080p10 seconds$5.69Total70 generated seconds$39.83


    The total cost was $39.83 for a final 60-second film.

    Notice that the generation duration adds up to 70 seconds, not 60. The extra 10 seconds came from replacing the incorrect segment.

    The effective cost was approximately $0.66 per second of finished video.

    These figures reflect the pricing and usage observed during this project. They exclude any additional costs not reflected in the recorded generation charges.

    A lower-cost model may make experimentation more affordable, but cheaper generation can also require additional attempts to reach the desired visual quality.

    The right choice depends on the importance of photorealism, continuity, generation speed, and budget.

    Budget for re-renders from the beginning. A pipeline that assumes every generated shot will be usable is likely to underestimate its actual cost.

    8. Scaling the Pipeline Beyond 60 Seconds


    The same approach can be extended to longer films.

    At a maximum of 30 seconds per clip:

    Final durationNumber of clipsNumber of joins60 seconds213 minutes655 minutes10910 minutes2019


    These counts assume full-length 30-second clips. Shorter clips may be more appropriate when the story contains distinct scenes or requires tighter control.

    Each additional join creates another opportunity for mismatched motion, lighting, composition, or subject appearance.

    Several practices make a longer pipeline easier to manage.

    Choose boundary frames deliberately


    Don't extract the final frame automatically without checking it.

    Look for a frame where the camera is passing behind an object, approaching a new landmark, or entering a natural visual transition.

    These moments can make an editorial boundary less noticeable.

    However, the extracted frame should also provide a useful starting composition for the next generation. A visually interesting boundary is not necessarily a good conditioning frame if the next scene cannot continue naturally from it.

    Preserve intermediate frames and generation metadata


    Archive the conditioning frame used for every clip, along with:

    • The original prompt.
    • Model ID and generation parameters.
    • Generation job ID.
    • Source and output file paths.
    • Cost and duration.
    • Notes about failed attempts.

    If clip 4 needs to be regenerated, the saved endpoint from clip 3 allows you to reproduce its starting condition without rebuilding the earlier footage.

    This makes the workflow easier to debug and maintain.

    Parallelize independent generations


    Once the necessary reference frames are available, independent segments can be generated concurrently.

    A fully sequential chain cannot be generated entirely in parallel because each clip may depend on the preceding clip's output.

    A practical compromise is to divide the film into sequences that can be generated independently, then chain clips within each sequence.

    Use idempotency controls where supported, and check the job list before retrying a timed-out request.

    Make every segment independently replaceable


    Treat each generated clip as a separate production asset.

    The master video should be a reproducible output of the individual segments and the editing pipeline, not the only copy of the project.

    That allows a single failed shot to be replaced without rebuilding the entire film.

    9. Visual Verification Before Delivery


    A successful API response doesn't mean the video is ready to publish.

    I used FFmpeg to extract representative frames from across the timeline and assemble them into a contact sheet.

    This made it easier to identify changes in visual style, unexpected objects, and problems with transitions.

    For example, to inspect eight points in the film:

    mkdir -p keys

    for t in 1 9 17 25 33 41 49 58; do
    ffmpeg -y -ss "$t" -i out/master.mp4 \
    -frames:v 1 \
    -vf "scale=470:-1" \
    "keys/m_${t}.jpg"
    done

    The individual frames can then be assembled into a contact sheet.

    A contact sheet is useful for reviewing the overall visual progression, but it won't reveal every temporal artifact.

    For that, inspect the actual footage around each join, preferably frame by frame or at reduced playback speed.

    For the Hollywood sign, I also extracted frames at half-second intervals to check whether the lettering remained stable throughout the replacement segment.

    This helped distinguish incorrect lettering from frames where the sign was partially visible or viewed at an angle.

    My final verification checklist included:

    • [ ] Confirm the final duration and frame rate.
    • [ ] Inspect every join for jumps in camera position or motion.
    • [ ] Check lighting, color grading, and scene continuity.
    • [ ] Review text, faces, architecture, and other high-risk details.
    • [ ] Verify audio levels and playback.
    • [ ] Confirm the output resolution and encoding.
    • [ ] Play the final exported file from beginning to end.

    The model generates the footage. The editing and verification pipeline determines whether it is ready to ship.

    Conclusion


    Building a continuous AI-generated film is not just a prompting problem. It is a pipeline problem involving generation limits, frame conditioning, asset management, editing, and quality control.

    For this project, frame chaining provided a practical way to connect separately generated clips while keeping each segment independently replaceable.

    The most important lessons were:

    1. Check model capabilities before designing the pipeline. Generation limits and parameter support determine how the film should be structured.
    2. Describe camera movement explicitly. A coherent camera path is easier to manage than a prompt containing unrelated transitions.
    3. Use frame chaining for continuity. The previous clip's endpoint provides a visual starting condition for the next generation.
    4. Keep the pipeline modular. Replace failed segments rather than regenerating the entire film.
    5. Verify the actual output. Inspect the joins, text, audio, duration, and final encoding.
    6. Budget for failed generations. Re-renders are part of the workflow, not an unexpected exception.

    The final film cost $39.83 in recorded generation charges and required 70 seconds of generated footage to produce 60 seconds of finished video.

    The broader takeaway is that AI video generation becomes more manageable when treated as a reproducible media pipeline rather than a sequence of isolated prompts.

    Frame chaining won't eliminate every continuity problem, but it gives you a useful degree of control over how separately generated footage fits together.

    Model capabilities, parameter support, and pricing are subject to change. The figures in this article reflect observations from October 2026. Check the current GMI Cloud model catalog and API documentation before implementing the workflow.#ai #tutorial #beginners #showdev #software #coding #development #engineering #inclusive #community
    Frame-Chaining AI Video: Building a 60-Second Film with GMI Cloud

    #ai #community #software #coding #development #tutorial
  30. Michael Busch @[email protected]

    I would like to know what has happened to all of the money that Congress had ordered to be spent on dozens of NASA research programs that has not been spent on those things.

    And Jared Isaacman should not be NASA Administrator.

    QT Shea (she/her) @solarshea.bsky.social
    2026 October 20

    #NASA ROSES-26 UPDATE

    There won't be a ROSES-26 NoFO. Any new solicitations will be released in the ongoing ROSES-25 NoFO.

    ROSES-27 will be released "no later than January 2027."

    bsky.app/profile/solarshea.bsk

    #nasa
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