Sunday, 26 April 2026

 And so, in this moment, when the night presses close, we must decide: do we let them dim the lights on our golden age, or do we stand, one last time, against the dying of our shared dream?

#KeepTheLightsOn #NotOneStepBack #CanadaRises #VoteForTomorrow #BuildDontStrip #AusterityKills #MadeInCanada #HistoryEchoes #elbowsup -April 2nd 2025

 


 Citizen children removed with parents (2025–2026)

There are multiple reports of U.S.-citizen children leaving the U.S. with deported parents. That is not the same as deporting an adult citizen directly, but it does involve citizens being taken out of the country.

  • PBS/AP reported three U.S.-citizen children removed with their mothers in 2025.
  • Federal judge ordered return of citizen twin girls sent to Guatemala in 2026.



Brian José Morales García

Reported by The Texas Tribune as a 25-year-old man who says he was born in Denver and was deported to Mexico after a traffic stop in Texas. He says he had a U.S. birth certificate; DHS disputed his citizenship claim. So this is alleged / contested, not fully settled yet. 

Amazon Photo Scams

 Amazon Photo Scams



There was a time when ownership implied possession. You bought a camera, you took photographs, you placed them in a drawer or an album, and there they remained: dusty perhaps, but indisputably yours. Now ownership has been replaced by custodianship. Your memories are no longer kept by you so much as warehoused on your behalf by smiling corporations whose chief innovation has been to charge rent on sentimentality.

The trick begins with convenience, that most seductive word in the modern lexicon. “Back up everything,” the interface whispers, with the false innocence of a pickpocket asking for the time. No tedious decisions, no folders, no need to distinguish between the wedding portrait and the accidental ten-minute video of the inside of your pocket. Photos, videos, duplicates, blurry mistakes, thirty-seven versions of the same cat—send them all into the cloud. Why burden the user with choice when passivity can be so elegantly monetized? What is sold as simplicity is often merely the removal of agency.

Then comes the second act, in which abundance becomes congestion. Videos are the real contraband here: bulky, silent gluttons consuming storage while masquerading as memories. The user discovers, usually too late, that the system is excellent at ingesting files and oddly coy about explaining where the space has gone. File sizes are obscured, sorting tools are primitive, controls are hidden like state secrets. You may browse your life in glossy little thumbnails, but should you wish to administer it, the platform suddenly develops all the transparency of a Soviet archive. This is not incompetence so much as incentive. A maze with no exit is still a functioning maze.

And then, as predictably as rain after thunder, arrives the subscription offer. A modest monthly fee, less than the price of coffee, less than the cost of your own frustration. Why spend an afternoon untangling years of digital clutter when you can simply click “upgrade”? This is the genius of the arrangement: to make disorder profitable and discipline inconvenient. One does not need a conspiracy when one has a pricing model. The consumer is not extorted in any theatrical sense; he is merely nudged, softened, and patiently cornered until payment resembles relief.

The user, of course, is not helpless. One may still keep local archives, external drives, properly named folders, and the old unfashionable habit of deciding what is worth saving before saving it. But such behavior now feels almost rebellious, like baking one’s own bread or repairing one’s own shoes. We have been trained to confuse management with drudgery and dependence with ease.

So the cloud persists as many modern systems do: useful, impressive, and faintly contemptuous of the people who rely on it. It offers limitless memory at the small cost of forgetting how to manage one’s own life. And if, in the end, you pay monthly for the privilege of storing seventeen accidental videos of the floorboards, you may at least admire the elegance of the trap.

Friday, 24 April 2026

 


I. The First Mistake Was Thinking Language Would Hold Everything Still

I didn’t arrive in Nagasaki and Sasebo expecting confusion. I arrived expecting eventual clarity, the kind you get when you assume that unfamiliar things are only temporarily unlabelled. Food is usually like that in daily life. You don’t always know a new dish immediately, but you assume it can be named, filed, and retrieved later. That assumption is so normal it feels invisible until it stops working.

What I encountered instead was a steady refusal of the world to stay inside the level of naming I was operating at. I could see the food, I could taste it, I could remember it, but I could not reliably anchor it in language in a way that allowed return. The words I was given were often too broad to be useful for repetition. “Fish.” “Vegetables.” “Seasonal dish.” These were not wrong answers. They were just insufficient ones for the task I was trying to perform, which was not understanding, but retrieval.

At the time, I didn’t recognize that distinction. I thought I was missing vocabulary. I thought the solution was simply to learn more names.

So I escalated the problem to something I assumed would be more precise: scientific naming.

That didn’t solve it either.

Because scientific classification didn’t behave the way I expected it to behave. It didn’t separate everything I felt was different. Carrots—Western, Japanese, heirloom, red, thin, dense—collapsed into Daucus carota subsp. sativus. Sweet potatoes, regardless of their texture or culinary identity, remained Ipomoea batatas. The system underneath language was not multiplying distinctions in the way my experience suggested it should.

That was the first rupture. Not confusion, but compression.


II. Cultivars, Categories, and the Collapse of Everyday Precision

The second stage of learning came when I realized I was looking for the wrong level of difference. I was searching at the species level when the meaningful variation was happening at the level of cultivation, selection, and cultural usage. That is where the concept of the cultivar became unavoidable, because it explained why something could feel completely different while still being biologically the same.

A carrot is not a single object in the way I had been treating it. It is a branching outcome of human selection over time. The kintoki carrot, for example, used in Japanese cuisine, especially in Kyoto traditions, is still part of the same species as the Western orange supermarket carrot, yet it behaves differently in texture, color, and culinary role. The difference is not biological separation; it is agricultural intention layered over time.

Once that idea settled, the memory of the meals in Kyushu began to reorganize itself. What I had experienced as “unknown vegetables” started to resolve into structured variation. Not mystery, but multiplicity. Not absence of knowledge, but presence of a system I had not been trained to read at that resolution.

And that’s when something else became visible: English does not consistently preserve cultivar-level detail unless there is cultural pressure to do so. Some terms survive—daikon, kabocha, shiitake—because they enter culinary circulation as distinct objects. Others collapse into general categories like “vegetables,” “greens,” or “root vegetables,” not because the distinction does not exist, but because it is not required for everyday communication.

What I had experienced as loss of information was actually a selective compression of information based on assumed need.


III. Sansai, Season, and the Second Layer of Disappearing Detail

The deeper I reconstructed the experience, the more I realized that a significant portion of what I had eaten was not even part of the cultivated agricultural layer I was initially focusing on. In rural Kyushu, especially in regions like Nagasaki and Sasebo, food systems include a strong presence of sansai, or mountain vegetables. These are not industrial crops in the conventional sense. They are seasonal, partially wild, and closely tied to local ecological cycles.

Plants like warabi (Pteridium aquilinum), fuki (Petasites japonicus), yomogi (Artemisia princeps), nanohana (Brassica rapa subsp. oleifera), and takenoko (bamboo shoots, typically Phyllostachys spp.) do not behave like standardized supermarket vegetables. They emerge according to season, geography, and environmental conditions, and they are often embedded in cultural practices that assume familiarity rather than explanation.

What made this difficult at the time was not the absence of these names in Japanese. It was the way translation often collapses them into broader categories when moving into English. “Seasonal vegetables” becomes a catch-all phrase that hides the internal structure entirely. That phrase is not incorrect, but it is structurally incomplete for anyone trying to reconstruct a specific eating experience later.

So I was not dealing with unknown food. I was dealing with known food that had been compressed for communication.


IV. The Real Problem Was Not Naming, but Retrieval

The turning point in understanding came much later, when I realized my original intention was not linguistic. I was not asking for names in the abstract. I was trying to solve a very practical problem: how do I get this again?

That shifts everything. Because naming is not just classification at that point. It becomes a retrieval system. A label is only useful if it survives time, context, and translation in a way that allows the same object or experience to be re-accessed.

So when I was told “fish,” I was not being given a useful retrieval key. Inside the kitchen, there was almost certainly a specific species—salmon (Oncorhynchus spp.), mackerel (Scomber japonicus), sea bream (Pagrus major), or something local and seasonal—but what reached me was a compressed category that functioned for immediate communication, not future reconstruction.

The same applied to vegetables. “Daikon” was already one of the few terms that survived that compression intact. But beyond that, I was often given labels that were not designed to function as precise re-ordering tools. They were designed to function as descriptions of availability, not catalogs of identity.

And that is where the frustration lived. Not in not knowing what I ate, but in not being able to return to it.


Appendix: The Border Problem Between Language, Biology, and Everyday Use

What I eventually had to understand is that this is not a failure of Japanese, or English, or scientific naming. It is a structural mismatch between three systems that operate at different levels of resolution.

Biology operates at a classification level that is stable but not aligned with lived culinary distinction. Species like Daucus carota or Ipomoea batatas do not reflect the sensory and cultural variation that food experience actually depends on. Biology is precise, but it is not oriented around human repetition of meals.

Cultural naming systems operate at a different level entirely. In Japan, terms like daikon, kintoki ninjin, shungiku, or warabi preserve distinctions that matter within that culinary tradition. English does the same in its own way, but not always at the same granularity, especially outside specialized or imported food contexts. Both systems compress and expand depending on need.

Then there is translation, which sits at the boundary between these systems. Translation does not aim for maximal precision. It aims for functional equivalence. That means it often collapses multiple distinct items into a single communicable category when the receiver is not expected to require fine-grained differentiation for action. “Fish” is sufficient if the goal is to serve fish. “Vegetables” is sufficient if the goal is to describe a side dish category.

The problem arises when a person is operating at a different intention than the system assumes. I was not trying to consume and move on. I was trying to build a path back. That requires stable, specific, cross-context identifiers. And those are not always provided at the point of service, even when they exist upstream in the kitchen or in biological classification.

So what I experienced was not missing knowledge. It was a gap between levels of description: biological, culinary, and communicative. The food existed at full resolution. The language I received did not always carry that resolution forward in a reusable form.

And that is why it took so long to reconstruct what I had already eaten. Not because it was unknown. But because it was never fully encoded in a way that survived the journey into memory as something I could reliably return to.

 



#japan #江戸門戸

読む (yomu) = to read
本を読みます。
Hon o yomimasu.
= I read a book.

読みたい (yomitai) = want to read
読んだ (yonda) = read (past)
読むつもり (yomu tsumori) = plan to read

#Japanese #LearnJapanese #JapaneseWords #ScholzNotes


見る (miru) = to look / to see
映画を見ます。
Eiga o mimasu.
= I watch a movie.

見たい (mitai) = want to see
見た (mita) = saw
見るつもり (miru tsumori) = plan to see

#Japanese #LearnJapanese #JapaneseWords #ScholzNotes


調べる (shiraberu) = to look up / to investigate
単語を調べます。
Tango o shirabemasu.
= I look up a word.

調べたい (shirabetai) = want to look up
調べた (shirabeta) = looked up
調べるつもり (shiraberu tsumori) = plan to look up

#Japanese #LearnJapanese #JapaneseWords #ScholzNotes


勉強する (benkyou suru) = to study
日本語を勉強します。
Nihongo o benkyou shimasu.
= I study Japanese.

勉強したい (benkyou shitai) = want to study
勉強した (benkyou shita) = studied
勉強するつもり (benkyou suru tsumori) = plan to study

#Japanese #LearnJapanese #JapaneseWords #ScholzNotes


忘れる (wasureru) = to forget
名前を忘れました。
Namae o wasuremashita.
= I forgot the name.

忘れたい (wasuretai) = want to forget
忘れた (wasureta) = forgot
忘れるつもり (wasureru tsumori) = plan to forget

#Japanese #LearnJapanese #JapaneseWords #ScholzNotes


練習する (renshuu suru) = to practice
毎日練習します。
Mainichi renshuu shimasu.
= I practice every day.

練習したい (renshuu shitai) = want to practice
練習した (renshuu shita) = practiced
練習するつもり (renshuu suru tsumori) = plan to practice

#Japanese #LearnJapanese #JapaneseWords #ScholzNotes

Monday, 20 April 2026

 




 $900M per day sounds abstract until you translate it into actual military “things.” Here’s what that level of spending typically means in real hardware and operations.


💰 What $900M/day actually buys in war terms

Think of it as being split into 5 big buckets:


1) 🚀 High-end missiles (the biggest money sink)

These are the expensive precision weapons used in early strikes and defense.

Typical unit costs:

  • Tomahawk cruise missile: ~$2 million each

  • Patriot interceptor: ~$3–4 million each

  • THAAD interceptor: ~$10–15 million each

So $900M could equal roughly:

  • ~300–400 Patriot missiles, OR

  • ~400 Tomahawks, OR

  • ~60–80 THAAD interceptors, OR

  • a mix of all three in smaller numbers

👉 In a real war, you burn through these fast, especially for missile defense.


2) ✈️ Air operations (sorties + aircraft wear)

Every flight costs fuel, maintenance, and aircraft time.

Typical costs:

  • F-35 flight hour: ~$30,000–$40,000

  • F-15/F-16: ~$25,000–$30,000 per hour

  • B-2 stealth bomber: ~$150,000–$200,000+ per hour

So $900M/day can fund roughly:

  • 10,000–20,000 fighter flight hours/day, OR

  • 400–600 stealth bomber hours/day

That translates into:

  • Hundreds of strike sorties daily

  • Constant surveillance + refueling missions


3) 🧨 Precision bombs & munitions

Cheaper than missiles, but used in huge volume.

Typical costs:

  • JDAM guided bomb: ~$20k–$30k

  • Hellfire missile: ~$100k+

So $900M/day could buy:

  • 30,000–40,000 guided bombs, OR

  • 9,000+ Hellfire missiles

👉 These are what actually destroy infrastructure, buildings, etc.


4) 🚢 Naval + carrier operations

If carriers are involved:

  • Aircraft carrier strike group: ~$6M–$10M per day to operate
    (includes ships, jets, crew, fuel, escorts)

So:

  • $900M/day = ~90–150 carrier-strike-group days of operations per day of war
    (obviously not literally that many groups—just cost equivalence)


5) 🛰️ Logistics, intelligence, fuel, support

Often overlooked but massive:

  • Satellite surveillance

  • Drone operations

  • Tanker aircraft (refueling jets mid-air)

  • Ammo transport + repair + spare parts

  • Cyber operations

This can easily be:

  • $100M–$300M/day alone in high-intensity war


🧠 The key insight

$900M/day doesn’t mean “buying stuff once.”

It mostly means:

  • burning through stockpiles

  • replacing expensive precision weapons

  • keeping aircraft constantly in the air

  • running a global-scale logistics machine


⚡ What this level of war actually feels like (militarily)

A ~$900M/day campaign typically looks like:

  • Hundreds of air sorties daily

  • Dozens to hundreds of missile intercepts

  • Constant satellite/drone surveillance

  • Repeated precision strikes on infrastructure

  • Fast depletion of high-end missile stockpiles


📉 The real constraint (more important than money)

The bigger issue isn’t the cash.

It’s:

  • how fast you can manufacture replacement missiles

  • how long interceptor stockpiles last

  • whether industry can scale in real time



Tuesday, 14 April 2026

full map of the AI landscape across domains, of AI in Western markets, layers of intelligence infrastructure.

full map of the AI landscape across domainsof AI in Western markets,  layers of intelligence infrastructure.









🌐 FULL AI LANDSCAPE (Western Market) — by domain

1) 🧠 General Intelligence / “Thinking Partner”

These are the closest things to a universal assistant.

AICompanyStrength
ChatGPTOpenAIBest all-purpose reasoning, writing, coding, multimodal work
ClaudeAnthropicBest structured long-form thinking, safety, coherence over long documents
GeminiGoogleStrong integration with search + Google ecosystem

👉 Role: “external mind” for thinking, writing, decisions


2) 🔍 Knowledge Discovery / Real-Time Research Layer

This replaces “Googling + reading 10 tabs.”

AICompanyStrength
Perplexity AIPerplexity AIFast cited answers, best AI search engine
Google Search + AI OverviewsGoogleLargest index of live web knowledge
You.comYou.comSearch + apps + customizable AI answers

👉 Role: “external reality scanner”






3) 📝 Writing / Publishing / Language Craft

These tools specialize in expression, clarity, tone.

AICompanyStrength
GrammarlyGrammarlyPolishing tone, grammar, professional clarity
ClaudeAnthropicDeep essays, argument structure, narrative flow
ChatGPTOpenAIFlexible style shifting (emails → essays → scripts)

👉 Role: “language engine”


4) 💼 Work / Office Productivity Layer

AI embedded into tools people already use daily.

AICompanyStrength
Microsoft CopilotMicrosoftBest for Word, Excel, Outlook automation
Google Workspace AIGoogleDocs, Gmail, Sheets intelligence
Notion AINotionKnowledge base + structured thinking + notes

👉 Role: “AI inside your job environment”





5) 🎓 Academic / Research Intelligence Layer

Designed for papers, citations, evidence-based thinking.

AICompanyStrength
ElicitElicitFinds + summarizes academic papers
ConsensusConsensusExtracts conclusions from scientific literature
Perplexity AIPerplexity AIReal-time research with citations

👉 Role: “compressed academia interface”


6) 🧑‍💻 Coding / Software Engineering Layer

This is where AI becomes a technical collaborator.

AICompanyStrength
GitHub CopilotGitHubReal-time code completion in IDE
ChatGPTOpenAIDebugging, architecture, explanation
ClaudeAnthropicLarge codebase reasoning + refactoring

👉 Role: “pair programmer + system designer”


7) 🎙️ Meetings / Voice / Conversation Capture

AI that “attends” your life.

AICompanyStrength
Otter.aiOtter.aiTranscribes meetings + summaries
Fireflies.aiFireflies.aiMeeting memory + CRM integration
Zoom AI CompanionZoomBuilt into video calls

👉 Role: “external memory for conversations”


8) 🎨 Creative / Media Generation Layer

Where AI becomes production engine.

AICompanyStrength
MidjourneyMidjourneyHigh-quality artistic images
RunwayRunwayVideo generation + editing
Adobe FireflyAdobeCommercial-safe design generation

👉 Role: “visual imagination engine”


9) 🧩 Automation / Agents / Task Execution Layer

These don’t just talk — they do work.

AICompanyStrength
Zapier AIZapierConnects apps + automates workflows
Make.com AIMakeComplex automation chains
OpenAI Agents / tools ecosystemOpenAITool-using reasoning agents

👉 Role: “digital labor automation layer”


🧠 Big picture (important insight)

What you’re actually looking at is not “competing chatbots.”

It’s this:

AI is splitting into a full cognitive stack:

  • Thinking (ChatGPT, Claude)

  • Searching (Perplexity)

  • Writing (Grammarly, Claude)

  • Working (Copilot, Google AI)

  • Researching (Elicit, Consensus)

  • Coding (Copilot, ChatGPT)

  • Remembering (Otter, Fireflies)

  • Creating (Midjourney, Runway)

  • Acting (Zapier, agents)


If you want to go deeper (this is where it gets interesting)

  • 🧭 “Which AI replaces which job function”

  • 🧠 “Which AI is closest to being a full personal operator”

  • 💼 “Best AI stack for a student / entrepreneur / journalist”

  • 📉 “Which tools are hype vs structurally important”


https://honorificabilitudinitatibus1.blogspot.com/2026/04/full-map-of-ai-landscape-across-domains.html


ZENO,unpublished,Psychohistory,influencer,Caligula,computers,culture,EDUCATION,