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
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 has been replaced by a new reality: China is no longer just catching up, it is building an entire AI ecosystem that competes with the U.S. across performance, cost, deployment,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.
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
Lower input costs – Land, construction, equipment and labour are cheaper in China.
Model distillation – Chinese labs often train models using outputs from expensive U.S. models, reducing the compute needed.
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.
"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.)
"My gut says that a single modality trained model will always out perform a generalist one." i share that feeling
tho.. it would require more digging.
ideas:
one extra model wont hurt. (unless it drains resources from other ones)
- is this for professional stuff?? or just for "wtvr"
OSS ?
sharing/pooling dev effort can be efficient(cost saving)
YouCare – Bing/Qwant infrastructure (Bing-based in most setups)
Here’s how those engines generally compare on privacy and donation percentage, based on what they publicly state and how they’re built.
Privacy (best to weaker, roughly)
Stronger privacy (no tracking / minimal logs, clear policies):
Ekoru – Markets itself as privacy‑focused: no tracking, no personal data sold; results via Bing but with a “no profiling” stance. greenqueen.com
Ecosia – EU-based, GDPR-compliant, states it doesn’t sell personal data and anonymizes queries; uses Bing/Google/EUSP but keeps identifying data limited. support.ecosia
Lilo – French, GDPR-bound; emphasizes limited data retention and no sale of personal data; results mainly from Bing. humanrightscareers
YouCare – French engine; subject to EU privacy rules and claims not to sell personal data; still uses Bing/Qwant infra. humanrightscareers
1.org – U.S.-based; says it doesn’t sell personal information, but like most U.S. ad-funded engines it does process queries for ads and may log more than EU-centric options. ir.system1
Weaker/less clear privacy posture (still better than mainstream trackers, but more ad/affiliate oriented):
GiveWater – Nonprofit branding and mission-driven, but privacy policy is more standard ad-supported; less emphasis on “no tracking” than Ekoru/Ecosia. value-match.co
SearchScene – Uses Bing; privacy claims are relatively generic compared to Ecosia/Ekoru.
Givero – Aggregator model; privacy depends on region and partners; not known for strong “no-tracking” positioning. computer.howstuffworks
Rapusia – Ad-revenue charity model; privacy details are sparse compared to Ecosia/Ekoru. humanrightscareers
GoodSearch – Older extension-based model; privacy posture is not a primary selling point. humanrightscareers
If privacy is your top priority, the safest picks from your list are: Ekoru and Ecosia, followed by Lilo and YouCare (all EU/GDPR-leaning and explicitly anti-tracking).
Donation percentage (highest to lower, as stated)
These are the claimed shares of profits/ad revenue going to charity:
Ecosia – States it gives 100% of profits to environmental and social projects. humanrightscareers
GiveWater – Describes itself as a nonprofit that passes the money made from clicks through partner charities; effectively all net surplus goes to mission, though exact % of revenue isn’t always spelled out. value-match.co
1.org – Says it donates a portion of ad revenue to chosen 501(c)(3)s; not “all profits,” but a defined share of ad earnings. 1
Lilo – Publicly says 80% of profits go to charities/projects each month. humanrightscareers
Rapusia – States half (50%) of earnings go to social/environmental projects. humanrightscareers
GoodSearch – Typically around 50% of ad revenue to charity in its model. humanrightscareers
Ekoru – Focuses on donating ad revenue to ocean cleanup, but doesn’t prominently publish a precise % figure like “80% of profits”; it’s more “we donate our earnings” than a fixed ratio. greenqueen.com
SearchScene – Similar to Ekoru: mission-driven with donations, but no widely cited fixed percentage.
Givero – Shares ad revenue with user-selected charities; exact % varies by campaign and partner deals, not a single high-profile fixed number. computer.howstuffworks
Practical takeaways
Best privacy:
Ekoru and Ecosia (then Lilo, YouCare).
Highest stated donation share:
Ecosia (100% of profits) and GiveWater (nonprofit, effectively all surplus) are at the top;
Lilo and YouCare next (about 80% of profits);
Rapusia and GoodSearch around 50%.
If you want one engine that balances both well, Ecosia is usually the best single pick: strong privacy stance plus a clear “100% of profits to charity” model. support.ecosia
i shared my telephone number via email to another protonmail user. a day later i get an automated spam call. so maybe* they got hacked.
i was thinking this. but i dont think this anymore
i see what your saying.
i think it would be most ideal to test it.
it may be lower resource to force kill.
estimates vary.. but i average, as low as: 20x , 4x lower. (those are unverified approximations. and i really think it depends on the app)
for cache clearing.. i can see lemmy(voyager) is using 34mb(some apps like firefox use alot more).. clearing puts it down to zero.
related/relevant(for readers):https://f-droid.org/packages/com.aistra.hail via shizuku via ADB . warning: unwise use of adb can brick your system and you would have to wipe your whole phone
its highly demoralizing that AI is(and has to be) trained on reddit discussion.
but i know that reddit sucks.
the people are toxic.
the mods are toxic.
in some cases, i can verify from human experience that all 72 commenters are totally ape-shit wrong. sometimes, like 0-2% of the answers are remotely near correct(or less harmful).
good luck AI curators. i hope yall screen VERY carefully
redmagic has repairability issues. possibly overheating issues (pby durability related .. else software).
but best hardware specs and unbeatable features.
more durable than u might think. but not indestructible. its intended for gamers.. not construction workers. lol
replacement parts are pby harder to find.
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
it would help investigators if you get murdered
on the extreme opposite side of the spectrum:
https://huggingface.co/LiquidAI/LFM2.5-2.6B
or.. really extreme.. https://huggingface.co/AxiomicLabs/GPT-X2.5-135M
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.
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:
Why Chinese spending is efficient
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.
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:
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.
interesting
they provide a mesh vpn.
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.)
"My gut says that a single modality trained model will always out perform a generalist one." i share that feeling
tho.. it would require more digging.
ideas:
- is this for professional stuff?? or just for "wtvr"
extra info:
Here’s how those engines generally compare on privacy and donation percentage, based on what they publicly state and how they’re built.
Privacy (best to weaker, roughly)
Stronger privacy (no tracking / minimal logs, clear policies):
Weaker/less clear privacy posture (still better than mainstream trackers, but more ad/affiliate oriented):
If privacy is your top priority, the safest picks from your list are: Ekoru and Ecosia, followed by Lilo and YouCare (all EU/GDPR-leaning and explicitly anti-tracking).
Donation percentage (highest to lower, as stated)
These are the claimed shares of profits/ad revenue going to charity:
Practical takeaways
If you want one engine that balances both well, Ecosia is usually the best single pick: strong privacy stance plus a clear “100% of profits to charity” model. support.ecosia
glm 5.2 is already pretty good, soo...
theres also nebula and netbird
i shared my telephone number via email to another protonmail user. a day later i get an automated spam call. so maybe* they got hacked.i was thinking this. but i dont think this anymoregemini protocol
i see what your saying.
i think it would be most ideal to test it.
it may be lower resource to force kill.
estimates vary.. but i average, as low as: 20x , 4x lower. (those are unverified approximations. and i really think it depends on the app)
for cache clearing.. i can see lemmy(voyager) is using 34mb(some apps like firefox use alot more).. clearing puts it down to zero.
this app can live in my RAM all day https://f-droid.org/packages/org.billthefarmer.editor
related/relevant(for readers):https://f-droid.org/packages/com.aistra.hail via shizuku via ADB .
warning: unwise use of adb can brick your system and you would have to wipe your whole phone
its highly demoralizing that AI is(and has to be) trained on reddit discussion.
but i know that reddit sucks.
the people are toxic.
the mods are toxic.
in some cases, i can verify from human experience that all 72 commenters are totally ape-shit wrong. sometimes, like 0-2% of the answers are remotely near correct(or less harmful).
good luck AI curators. i hope yall screen VERY carefully
redmagic has repairability issues. possibly overheating issues (pby durability related .. else software).
but best hardware specs and unbeatable features.
more durable than u might think. but not indestructible. its intended for gamers.. not construction workers. lol
replacement parts are pby harder to find.