i think a key consideration is wether there are alot of developer for the lang.
a foss project will grow faster if its written in rust,js,cpp,python

my last 5 uploads averaged 10kilobyte per img (webp).
i don’t disagree to say that we shouldn’t use the best technology available.
but relevant to note: proper optimization of the image goes a loooong way.(often more effective than just relying on a file format with theoretical-max efficient compression.
i love this subject… because i hate reddit where ppl upload a screenshot of plain-text from twitter and it costs me 1MB of scarce mobile data, to see some garbage post. fuck that

“the internet” is a super broad term.
i had cancelled my home ISP to save cash. really a smart move IMO.
i think everyone(or technically, most people)… need some basic internet for some specific uses.
a mobile data plan should be the only thing you really need.
you could (ideally) talk to your neighbors and negotiate to share their wifi.

https://vger.to/lemmy.ml/post/50758732

create a sub. write a sidebar with definition, resources, and guidelines. then moderate it for the greater good

low-level compilers can output very ugly-looking assembly. he probably did this and then used LLM to super-optimize it. may be perfomant, but id guess that theres a risk that its unsafe.

kagi has api pricing $12/1000 .
its quality. but there are competitors who charge 30% of that.
quality? idk. yes…no… maybe sometimes.
but its alot cheaper if you dont need the highest quality service.
** im not talking about LLM… but llm also price per 1K req… and are a competitive alternative… despite risk of hallucination

personally, i would be cautious with discord too

ai summary

Summary

The United States’ former focus on “can we stay ahead of China in AI?” has been replaced by a new reality: China is no longer just catching up, it is building an entire AI ecosystem that competes with the U.S. across performance, cost, deployment, financing, standards, developer adoption and global reach.

Key points

• China’s AI surge is ecosystem‑wide. Companies such as DeepSeek, Moonshot AI, Alibaba, Tencent, Zhipu AI and MiniMax are not isolated successes; together they show a coordinated, repeatable ability to produce world‑class models.

• Washington’s response is lagging. U.S. policymakers continue to treat each Chinese breakthrough as a discrete event, while China pursues a long‑term, systematic “ecosystem statecraft” strategy that integrates industrial policy, finance, standards, education, diplomacy and commercial expansion.

• Ecosystem statecraft vs. company‑by‑company competition. The U.S. still relies on frontier innovation and export controls, but China is reshaping the whole technology stack—making AI easier to deploy, customize and integrate, and encouraging worldwide developer adoption.

• Strategic intent. President Xi’s calls for AI cooperation, open‑source development and involvement of developing nations signal Beijing’s aim to become the architect of a global AI ecosystem, protecting core capabilities at home while exporting its stack abroad.

• Policy implications for the U.S.

  • The U.S. must move from a company‑centric debate to a national strategy that builds a competing ecosystem—combining research, standards‑setting, talent pipelines, financing, trusted alliances and diplomatic credibility.
  • America still holds major strengths: world‑class universities, a vibrant venture‑capital market, a dominant semiconductor industry and frontier research labs. Yet, historical precedent shows that lasting leadership depends more on who creates the adoptable ecosystem than who invents the first model.

• Global adoption dynamics. Nations are now weighing security, cost, financing and long‑term reliability rather than merely choosing between U.S. and Chinese hardware. Trust, developer communities and standards have become decisive competitive advantages.

• Conclusion. The decisive question for the United States is not whether its firms can keep building the most capable models, but whether it can marshal a coherent, resilient national strategy that yields an AI ecosystem that the world chooses to trust and build upon.

i distilled this article

Summary of the article “How China gets better bang for its buck than America in AI” (Aug 3 2026)

• U.S. AI spending is massive – Bloomberg Intelligence estimates U.S. data‑centre capital outlays could exceed $740 billion in 2026, with Nvidia alone negotiating a $250 billion financing deal for a $500 billion data‑centre run by OpenAI. Alphabet announced a $205 billion AI budget.

• China spends far less – Chinese tech firms are projected to invest less than one‑tenth of the U.S. amount in data centres. Yet their models perform only slightly behind U.S. equivalents. For example:

  • K3 (Moonshot AI) scores ≈ 95 % of Anthropic’s Fable 5 on common benchmarks while being 70 % cheaper to run.
  • Alibaba’s newly released model ranks among the world’s best on certain metrics.

• Why Chinese spending is efficient

  1. Lower input costs – Land, construction, equipment and labour are cheaper in China.
  2. Model distillation – Chinese labs often train models using outputs from expensive U.S. models, reducing the compute needed.
  3. Hidden spending – Some expenditures on high‑end chips are masked as “cost‑saving” techniques that make inferior hardware achieve higher performance (e.g., DeepSeek’s efficiency tricks).

• Export restrictions limit Chinese capital use – U.S. bans on advanced AI chips (Nvidia designs, TSMC manufacturing) prevent China from buying the most powerful hardware.

  • Chinese firms are pushed toward domestic alternatives (Huawei, SMIC).
  • Sanctions also block access to cutting‑edge chip‑making equipment, forcing costly work‑arounds and capping production capacity.

• Domestic demand constraints – Chinese enterprises spend < 10 % of what U.S. firms spend on IT, despite China’s GDP being two‑thirds of the U.S. (or a third larger in PPP terms). This throttles revenue prospects for AI providers, curbing their willingness to invest heavily.

• Strategic focus differs – The Chinese Communist Party emphasizes diffusing AI across the economy, not pursuing a race toward artificial general intelligence (AGI). Fewer than ten Chinese firms target AGI, compared with dozens of U.S. players.

• Investor attitudes – Chinese investors have historically punished over‑spending on AI, whereas U.S. investors once rewarded aggressive budgeting. This cultural difference keeps Chinese AI budgets modest.

• Potential bottlenecks for China – Despite restraint, China may face compute shortages:

  • ByteDance experiences ten‑hour processing times for some videos.
  • Alibaba Cloud, Zhipu AI, and Moonshot’s K3 have long waiting lists or quickly sell out capacity.
  • Over‑restriction could stifle growth if AI services cannot meet user demand.

Overall takeaway: China achieves comparable AI performance to the U.S. while spending a fraction of the capital by leveraging cheaper resources, model‑distillation techniques, and a strategic focus on wide‑scale diffusion rather than raw computational power. However, export bans, limited domestic chip capacity, modest corporate demand, and cautious investors together create both an efficiency advantage and a risk of under‑provisioned infrastructure.

wrong thread.

“Or is it really just keeping your mouth shut if you aren’t knowledgeable about something.”
i often abreviate by saying “i know some stuff about X topic…” just leave it at that.
if they really want to listen then they will request my expertise. im not going to beg to abused while im trying to do someone a favor.
but first, i just dont conversate deeply with shitty people.
if someone has a good heart, then maybe they might not be the best conversation partner. thats ok. as long as they deliver on all the essential value, that i need from them.
complaining to the city clerk about various issues with the city. i think thats challenging and requires alot of carefully scripted+rehearsed fomation&responses.
i dont have a good answer. but maybe act kinda like a carefully controlled, unemotional, plan-adhering robot. (add back in some scripted emotion so that you dont sound like a robot.)

deleted by creator

theres also nebula and netbird

gemini protocol

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goddamnit. all i wanted was a muffin !
why are you counting my pockets so aggressively ?

guarana tea

oh shizz
really? i was just kidding. aah ITS EVERYWHERE

Analogy

Imagine a restaurant where diners place orders through a discreet slot on the kitchen door.

• The web user is the diner who slips a note into the slot asking for a specific dish.
• The web‑service backend is the chef who receives the note, prepares the meal, and slides it back out the slot for the diner to collect.

Now, the diner adds a bizarre request: “Please serve the food, but I never want you to know you ever cooked it.” In practice, the chef can’t fulfill this—once he’s touched the ingredients and used the stove, his hands are inevitably stained, and the kitchen’s heat tells the story. Likewise, when a web user asks a server to fetch data, the server must process the request, so it inevitably knows it handled that request, even if it can’t tie it back to the individual’s identity.

Ad‑blockers, cookies, IP addresses, user‑agents, and browser signatures are like the various “fingerprints” a diner might leave on the restaurant’s doorstep:

• Cookies are tiny crumbs the diner drops on the floor while walking to the slot. The chef (or later staff) can later sweep them up and infer that the same person visited before.
• IP address is the diner’s shoeprints on the hallway carpet leading to the kitchen—an easy way to trace where the diner entered from.
• User‑agent / browser signature is the unique style of the diner’s napkin (its color, fold, and logo). Even if the napkin is discarded, the pattern tells the chef which brand of napkin was used.
• Ad‑blockers are like the diner wearing a cloak that blocks the kitchen staff from seeing the crumbs he drops, trying to keep the chef from noticing the usual “tipping” (ads) that would normally be left behind.

Just as no chef can truly be blind to the fact that he cooked a dish, a web service inevitably knows it processed a request, and the “fingerprints” left behind (cookies, IP, user‑agent, etc.) let it—or any intermediary—recognize or track the diner unless the diner takes strong steps (like using a cloak or wiping the floor) to hide those traces.

https://f-droid.org/packages/com.fauxx.full

if theft is a concern. record IMEI number and they(carriers etc) will probably blacklist the phone from mobile networks and maybe aid the tracking(investigation…if any). which, afterwards, lowers resale value