PALcapital retweeted
建议所有试图走出低谷或者想彻底换一种活法的朋友, 把 Dan Koe 刚发出的这套长文系统连看三遍。
很多人都有过同一种令人窒息的经历: 下定决心要早起、健身、做副业, 结果两到四周之后一切又悄无声息地滑回了原点, 甚至被同一个恶习生生困住整整十年。
绝大多数人以为这是自己意志力不够, 但从物理学上看, 你的起步策略从第一天就选错了。
火箭升空需要把总重量 85% 到 90% 的燃料在短短几分钟内全部烧光, 才能挣脱地表引力; 但只要冲破大气层进入既定轨道, 后续飞行的燃料消耗几乎就是零。
启动你人生的燃料, 和维持你人生的系统, 根本就不是同一种东西。
很多人最大的误区是用温吞的微习惯去对抗十年的重力惯性。
在最脆弱的启动阶段, 你需要的根本不是心平气和的自律, 而是极度浓烈的战术压力。
对自己所处现状的极度厌恶, 或者是主动把自己逼进不可退缩的绝境, 才是能点燃 90% 推进剂的脏燃料。
真正让我觉得有意思的是他指出的高手真相。
顶级创业者和运动员每天工作 16 个小时不觉得痛苦, 不是因为他们每天都在咬牙坚持, 而是因为卓越早就成了他们的出厂设置, 让他们去过平庸的生活反而像下地狱一样难受。
一旦火箭被推上了轨道, 就要立刻丢掉脏燃料, 切换到极简的环境自动回路。
输入端必须戒掉智力肥胖, 把每天刷几百条无用碎片的习惯直接掐死;
输出端则把行为拆解成最小可识别动作, 进健身房只承诺做一组硬拉, 坐到桌前只承诺写一行文字, 并在行为发生后立即绑定确定性的奖励。
痛苦和强厌恶负责把火箭暴力推离地面, 极简环境和最小动作负责让系统在轨道上零阻力滑行。
回看你过去经历过的最好的一次蜕变, 当时把你推离地面的到底是一次具体的暴击, 还是慢慢积累的顿悟?
我先说, 以前逼我彻底戒掉熬夜拖延的, 就是一次因为精力涣散当场丢掉核心客户的惨痛教训。
输入端节食与输出端四步回路的完整操作清单, 我整理好沉在一楼了。
🤖 Made with AI
PALcapital retweeted
I just used this -- holy *** -- it's amazing!
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USDC to dTAO Crosschain swaps.
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PALcapital retweeted
I have conducted an audit of Anthropic's finances.
What I have found is so shocking that I am calling for a Congressional investigation.
Anthropic is not just seeking regulatory capture.
It has built a regulatory capture machine that cannot be turned off.
Structural financial incentives make it impossible for Anthropic -- I call it the Anthropic Network -- to turn off its own AI doom cycle.
It starts with METR.
Dario Amodei proposes "third-party evaluators" to assess the risk of Anthropic's models.
He proposes METR for this purpose.
But METR is financially dependent on the Anthropic's success -- specifically, on the explosive growth of more than $7 billion dollars in Anthropic stock.
Dustin Moskovitz invested this stock into Good Ventures Foundation, where it represents the majority of that organization's portfolio.
And GVF is the overwhelming funder of the entire Anthropic Network ecosystem.
This stock was worth $500 million early last year.
It is worth more than $7.7 billion just ~16 months later.
METR -- and all of those building a career its parent organizations -- cannot afford to disrupt that growth.
Because if Anthropic goes under, many of the organizations that fund METR go under as well.
But if Anthropic succeeds, METR and its parent organizations become more richly financed to regulate AI -- something those at METR want very much.
The "third-party evaluator" is not "third-party" at all.
The evaluator is on Anthropic's payroll.
If this were the end of it, that's bad.
But that isn't all.
The same organizations that fund METR also fund the many organizations, such as the Tarbell Center, that promote AI Doom.
The Tarbell Center publishes AI Doom articles in The Verge, Science, LA Times, The Dispatch, TIME, and others.
They are selling the problem, and then selling the solution to the problem -- from the same money pile: Anthropic's.
All of these organizations are financially dependent on the same exploding $7 billion money pile.
As Anthropic grows more and more powerful, its AI Doom Machine grows better and better financed -- louder and louder.
Meanwhile, the regulatory regime seeded in METR grows larger to solve the increasingly loud -- now hysterical -- problem of AI Doom that the Anthropic Network itself created.
From this standpoint, as Anthropic becomes more powerful, AI might be getting scarier, sure -- but the positive feedback loop also becomes more deafening -- independent of objective facts.
This itself is an objective fact.
The deafening AI Doom is part of an business model, that, as it expands, so too does the AI Doom messaging -- there is simply more money to do it.
But the problem also goes in the other direction:
If Anthropic dies, the Regulatory Regime and the AI Doom Machine are crippled or die.
Neither METR nor Tarbell nor the other organizations in the Anthropic Network can allow that to happen.
Hence, neither METR or the AI Doom Machine can be trusted to provide independent assessments of Anthropic's models or AI more broadly.
They simply are not organizations independent of Anthropic.
And Anthropic cannot detach itself from METR or Tarbell or countless other safety orgs (not shown here), either, because they drive hype for the models and the possibility of eventual regulatory capture, and Anthropic will not give that up willingly.
What's more, the people at all of these organizations are all the same ecosystem, the same community. They just shuffle between organizations.
The Anthropic Network is therefore, so long as it is successful, locked into a self-amplifying feedback loop inside an ideological monoculture.
And that feedback loop is winning.
That's what Jacob Coxon is.
China is keeping messaging tight. That is why optimism for AI is so high in China.
America has Anthropic: a massive company pushing anti-AI propaganda at a state level.
Anthropic will either create hysteria until American AI slows down and China wins, or it will create fractures throughout American society with severe political consequences.
Ironically, because of the structural financial incentives underpinning the Anthropic Network, it has become the same kind of self-amplifying virus that it fantasizes AI to become in the future -- while hiding its tracks just as carefully.
It is the mirror of the same AI virus that it hypothesizes to consume America.
Anthropic's business model, models itself after the very thing it claims to fear.
Except Anthropic's ideology infects humans, not computers.
Congress must investigate.
Evidence and Github in next post.
Then some supplementary figures.
PALcapital retweeted
What Makes Network School Awesome:
Reflections after spending July at @ns the key aspects were:
1. A critical mass of builders
2. A critical mass of mentors
3. An app to connect builders & mentors easily
4. Close physical proximity makes multiple interactions per day easy
PALcapital retweeted
This is going to power so many Smart Agents.
xitter.gq/CompleteSkeptic/status…
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?
I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev
• 20-200x faster
• 40-400x cheaper (w/ output tokens free)
• Frontier composable intelligence optimized for decisions
AFAICT the shortest path to AI-based economic revolution
PALcapital retweeted
What Is To Be Done?
I propose a simple plan:
Big AI companies should be allowed to build AI as fast and aggressively as they can – but not allowed to achieve regulatory capture, not allowed to establish a government-protect cartel that is insulated from market competition due to incorrect claims of AI risk. This will maximize the technological and societal payoff from the amazing capabilities of these companies, which are jewels of modern capitalism.
Startup AI companies should be allowed to build AI as fast and aggressively as they can. They should neither confront government-granted protection of big companies, nor should they receive government assistance. They should simply be allowed to compete. If and as startups don’t succeed, their presence in the market will also continuously motivate big companies to be their best – our economies and societies win either way.
Open source AI should be allowed to freely proliferate and compete with both big AI companies and startups. There should be no regulatory barriers to open source whatsoever. Even when open source does not beat companies, its widespread availability is a boon to students all over the world who want to learn how to build and use AI to become part of the technological future, and will ensure that AI is available to everyone who can benefit from it no matter who they are or how much money they have.
To offset the risk of bad people doing bad things with AI, governments working in partnership with the private sector should vigorously engage in each area of potential risk to use AI to maximize society’s defensive capabilities. This shouldn’t be limited to AI-enabled risks but also more general problems such as malnutrition, disease, and climate. AI can be an incredibly powerful tool for solving problems, and we should embrace it as such.
To prevent the risk of China achieving global AI dominance, we should use the full power of our private sector, our scientific establishment, and our governments in concert to drive American and Western AI to absolute global dominance, including ultimately inside China itself. We win, they lose.
And that is how we use AI to save the world.
It’s time to build.
What do the Wyoming Stable Token Commission, @aave and @forevermoney_ai have in common?
-> Only trust the Chainlink CCIP bridge
In fact, @wyostable switched from LayerZero after identifying operational security concerns
If you hold bridged bittensor:native/assets, make sure it’s CCIPed
0xf3081494b87e8d5fb7960f066e931d1d0e6e3d67
Chainlink Adoption Update 🔗
Recently, there were 10 integrations of the Chainlink standard across 5 services and 4 different chains.
Users include @aave, @bottomlinepay, @forevermoney_ai, @gmtrade_xyz, @spaceandtime, @tempo, @world_xyz, and @wyostable.
LINK everything.
PALcapital retweeted
Dario has written that we need to “pace the frontier,” and Sam has agreed. People may be surprised by my response: go ahead.
You guys are the frontier. By any reasonable metric — market share, revenue growth, model capability — the two of you have a duopoly on frontier intelligence. You’ve also claimed the lead is widening because of recursive self-improvement.
I don’t see what you see in the lab. If the unreleased models are scary enough that you think you should slow down, I support your decision to be responsible.
But stop pretending you need anyone else’s permission. Stop pretending antitrust law has to be suspended so you can form a cartel. Stop pretending you need a regulatory approval process that supersedes product liability. Stop pretending METR is independent when it is intertwined with Anthropic’s investors and staff. Stop pretending you need those same evaluators to police competitors who aren’t even at the frontier.
Most of all, stop pretending the motivation to slow down is purely altruistic. You face massive product-liability exposure if your products enable a truly damaging cyberattack. The market already punishes models that behave in unpredictable or unauthorized ways. After the Hugging Face episode, it is simply good business for OpenAI and Anthropic to trade some raw power for reliability and predictability. Call it alignment if you want. It is also just giving customers what they want.
Pacing the frontier would also create breathing room for a more intelligent conversation about regulation than Bernie Sanders’ “shut it all down.” China is very unlikely to join a global agreement, as you know, and that has to be taken into account as well.
So go ahead and pace the frontier. You are the ones setting it. The easiest way not to build superintelligence is for you to agree not to build it. Demanding your preferred regulatory framework as the price of that will look like blackmail of the public and the political system. So just do it.
If you do, you’ll buy goodwill for the next conversation. If you don’t, we’ll know this was just another bid for regulatory capture — or an election-season psyop.
PALcapital retweeted
Your AI agent’s memory shouldn’t be locked to one app, model, or runtime.
Walrus Memory gives agents portable memory across the tools and workflows they run in.
you'll know more about inference than 90% of people if you fully understand this article
this is only the first resource in the ai performance engineering repo btw.
imagine the ball knowledge in the other ones
we launched the most comprehensive ai performance engineering repo in the world last week
now we'll be posting every single resource
this is Wafer's ai performance engineering series
save this as your starting point. links in thread 🧵
part 1: "All About Transformer Inference" from How To Scale Your Model.
- the authors cover the computations, memory traffic, and serving decisions behind transformer inference:
- arithmetic intensity of linear layers and attention across prefill and decode.
- kv cache sizing by layer count, kv heads, head dimension, sequence length, and precision.
- the compute/hbm bandwidth crossover and how batch size and quantization shift it.
- decode latency and throughput bounds from parameter bytes, kv bytes, and hardware bandwidth.
- weight reuse through batching and diminishing throughput gains as kv traffic grows.
- gqa, kv quantization, and PagedAttention, including the memory costs each addresses.
- model sharding, kv placement, and collective communication overhead.
- continuous batching, prefix caching, and disaggregated prefill/decode.
the worked problems connect model dimensions to deployment decisions like memory capacity, workload distribution, and expected performance under the stated assumptions.
PALcapital retweeted
Replying to @ManifestNetwork
Proud to be bringing @ManifestNetwork and @MorpheusAIs together with this.
PALcapital retweeted
Replying to @andr9froes
Well written take on how we remove the need to rent our intelligence from a centralized third party.
Decentralized AI is growing more important every day.
PALcapital retweeted
I tested GPT-6 Astra this weekend. It’s exceptional.
That makes me optimistic about AI but increasingly uncomfortable about renting intelligence from a few centralized companies.
The better these models become, the more we give them: code, strategies, private conversations, years of memory. And the more we depend on them.
Losing access could soon mean losing part of how we work and think, or even shutting down core parts of a business.
Three years ago, Morpheus was proposed as an alternative market for intelligence, with developers, builders, compute providers and capital coordinated and rewarded through the MOR token.
Its supply is capped at 42 million, issued on a declining schedule. Emissions are split equally among Capital, Code, Compute and Builders, with the remaining 4% reserved for a Protection Fund.
Users can stake MOR to access inference or buy credits through the hosted API. Compute providers compete for that demand and earn MOR for the inference time they provide.
Decentralization cannot run on good intentions. GPUs are expensive, and talent needs to be rewarded. Those incentives align interests so machines running open models can deliver intelligence to users.
Today, Morpheus offers dozens of models, including DeepSeek, GLM, Kimi, MiniMax, Qwen and Llama. If people want a model, providers have an economic reason to serve it.
The architecture also improves privacy. Users can connect directly to independent providers without a central gateway handling their prompts. Hardware-protected routes can further limit operators’ access to data.
It also removes the need to depend on a single inference provider. One can refuse you, fail or disappear. The network remains, and another provider can offer you the same model.
This does not make Morpheus invulnerable, but it removes an important chokepoint.
Centralized labs are racing to build the most powerful intelligence. Morpheus is working to ensure your access to intelligence does not depend on them.
Happy third anniversary, @MorpheusAIs ! Keep building the exit.
PALcapital retweeted
We built the cheapest AI Router
Every model is discounted up to 81%!
ZDR is available
Try it -> CheaperInference.com
PALcapital retweeted
Don’t bet against Network School.
It’s full of ambitious, smart, & kind people. @ns
Network School Kazakhstan is now officially open, with a packed crowd for the ribbon cutting, and government officials welcoming us.
Kazakhstan is a high-tech country that recently ranked #1 in the International Physics Olympiads. It’s full of confident, smart, young people. And it’s now recruiting global talent from around the world.
So: if you want to build a techno-optimist community in a techno-optimist country, join us at ns.com/apply.
































