Ben77 (@blapta) header
Ben77 (@blapta) profile picture

Philosopher & Engineer of Money | Designing incentives, not illusions.

UTXO
Joined April 2017
Photo by Ben77 (@blapta)Photo by Ben77 (@blapta)Photo by Ben77 (@blapta)Photo by Ben77 (@blapta)Photo by Ben77 (@blapta)Photo by Ben77 (@blapta)Photo by Ben77 (@blapta)Photo by Ben77 (@blapta)Photo by Ben77 (@blapta)
Coming (very) soon.
16
57
5
449
7,571
1/ We discovered something totally unexpected about the neural structure of consciousness that overturns decades of thinking on its biological basis and brings us significantly closer to the recipe for generating conscious awareness. New preprint: biorxiv.org/content/10.64898… (led by @HesseJanis, with @FrankLanfranchi, @_conor_mcgrory, @Yuelin_Shi, and @EngelTatiana)
389
1,695
534
11,330
2,193,886
So my book now has a cover, featuring one of my favorite archival images. Forthcoming from the University of Pittsburgh Press in 2027!
64
206
14
1,586
52,374
Ben77 retweeted
Recently learned that Schmidhuber’s Formal Theory of Fun and Creativity, covering compression progress and intrinsic motivation and their relationship to beauty, curiosity, art, science, music, and jokes, was published in a Japanese scientific journal in 2009. jstage.jst.go.jp/article/sic… It is wild that this framework: compression progress as the basis for beauty, curiosity, art, science, and even jokes, was laid out in full formal detail in a Japanese journal 15 years ago.
Jürgen Schmidhuber proposed a Formal Theory of Fun and Creativity in the 1990s.🤔 people.idsia.ch/~juergen/cre…
48
122
39
1,418
120,083
Ben77 retweeted
Crypto基建可能是人类历史上前几名的资本浪费。跟之前那几次不同的是,这次连残值都没留下。 从2016年算起,整个crypto产业消耗的真实资本远超多数人的直觉。VC端累计超过1000亿美金。2017-2018年的ICO大潮吞掉了数百亿(各口径差异巨大,保守$200亿到$500亿之间,其中80%以上是骗局,存活率约10%)。比特币PoW挖矿十几年累计电费$300到$500亿之间,全部用于解一道没有任何生产性产出的数学题。DeFi hacks累计损失超过$140亿,各链基金会生态补贴又烧掉了数百亿。 合在一起,$2000亿美金级别。 钱的最大去向,年年都是"基础设施"。2024年仅infra品类就吸走55亿美金,同比增57%,创品类历史新高。L1、L2、L3、rollups、ZK层、DA层、shared sequencer、modular blockchain……以太坊白皮书发布十多年了,这些基建上面跑着的主流应用数来数去还是那几个。但新积木还在不断冒出来。 行业数据:90%到99%的Web3项目在达到长期运营之前失败。2021年以来上线的token里超过一半已经是死币。2026年上半年又有60多家crypto公司关门。 把这$2000亿放进历史坐标里: 1840年代英国铁路狂热,累计投入2.5亿英镑,接近当时GDP的一半,铁路投资占GDP的7%。股价从峰值跌了57%,三分之一经议会批准的线路从未建成。但留下了8000英里铁轨,永久性地降低了工业运输成本。 1990年代末电信光纤泡沫,五年内砸了超过5000亿美金铺设光纤网络。泡沫破裂后95%是暗光纤,仅有不到5%的已铺设容量被点亮,全球电信股蒸发超过2万亿美金市值。但那些光纤留在地下,十年后成了宽带、云计算、视频流媒体和AI数据中心的物理底座。 2000年互联网泡沫,VC投入从1995年的80亿美金飙升到2000年的超过1000亿美金,83%砸向互联网公司。NASDAQ从峰值跌了78%,5万亿美金市值蒸发。但留下了服务器集群、数据中心、光纤骨干网,最终长出了Amazon、Google和现代互联网经济。 铁路狂热留下了铁轨。光纤泡沫留下了光纤。互联网泡沫留下了数据中心。每一次大规模的资本错配,都在投资者血亏之后,为下一轮技术浪潮留下了可被复用的物理基础设施。 Crypto的两千亿美金留下了什么?几十条L1链、上百条L2/L3、无数的bridge和oracle。它们有一个致命的不同:没有物理性,没有可被后来者复用的残值。铁轨是铁轨,光纤是光纤,不管谁来用、用来做什么,物理层都在那里。但一条没有用户的区块链就是一条没有用户的区块链。AI时代不需要在ZK-rollup上面跑inference,不需要用shared sequencer来调度GPU集群。 那些被社区称为scientist的人,在一个没有真实用户需求驱动的系统里互相peer review、互相citation,形成了一套高度自洽但极度封闭的学术仿生体系。 历史上每一次基建泡沫,至少完成了一个任务:错误地、过早地、用投资者的血去铺设了未来真正需要的基础设施。Crypto连这个都没做到。它铺了一条没有目的地的高速公路,而AI时代的车跑在完全不同的路上。 两千亿美金。换算成GPU集群,够训多少个frontier model了。
50
51
19
354
71,785
Properly mastering anything means you have as good as a command over it as a professional musician has over their instrument. And you're never going to get to that level unless you engage in a massive amount of efficient practice.
3
18
278
5,852
Buku Epistemology: A Contemporary Introduction to the Theory of Knowledge, merupakan karya Robert Audi. Karya Robert Audi tersebut merupakan sebuah kajian mendalam mengenai teori pengetahuan dan pembenaran. Dalam karyanya Audi menguraikan berbagai sumber dasar pengetahuan seperti persepsi, memori, introspeksi, nalar, dan kesaksian, ia juga menjelaskan bagaimana keyakinan terbentuk melalui proses tersebut. Audi membedakan antara pembenaran situasional, yang berkaitan dengan ketersediaan bukti, dan pembenaran doksastik, yang merujuk pada keyakinan yang benar-benar dimiliki seseorang. Fokus utama Audi di awal karyanya adalah persepsi, di mana Audi menganalisis perbedaan antara sekadar melihat objek dengan memahami kebenaran proposisional tentang objek tersebut. Selain itu juga dalam karnya, Audi mengeksplorasi tantangan filosofis seperti ilusi dan halusinasi untuk membedakan antara realitas eksternal dengan pengalaman sensorik internal. Karya Audi ini membangun fondasi untuk memahami bagaimana manusia mencapai pengetahuan yang dapat dipertanggungjawabkan dalam menghadapi risiko kekeliruan atau skeptisisme. Robert Audi menjelaskan hubungan antara persepsi (perception), keyakinan (belief), dan pengetahuan (knowledge) sebagai suatu hubungan hierarkis, kausal, dan epistemis yang erat. Secara umum, persepsi berfungsi sebagai sumber dasar (basic source) yang menghasilkan keyakinan, dan keyakinan tersebut—jika dibenarkan (justified) dan bernilai benar (true)—dapat berkembang menjadi pengetahuan. Audi membagi persepsi ke dalam tiga tingkatan (hirarki) atau modus yang saling bergantung satu sama lain, yaitu: Persepsi Sederhana (Simple Perception/Perceiving of), Persepsi Objek (Objectual Perception / Perceiving to be), dan Persepsi Proposisional (Propositional Perception/Perceiving that). Secara hierarkis, persepsi proposisional bergantung pada persepsi objek, yang pada gilirannya bergantung pada persepsi sederhana sebagai fondasi paling dasar. Persepsi Menghasilkan Keyakinan (Belief), suatu keyakinan disebut sebagai keyakinan perseptual (perceptual belief) jika keyakinan tersebut berakar dan tumbuh secara kausal dari pengalaman persepsi kita, bukan sekadar karena isi atau konten keyakinannya. Hubungan ini melibatkan konsep pembenaran (justification). Meskipun keyakinan yang justified dan pengetahuan sangat berkaitan erat, keduanya memiliki perbedaan mendasar yang kurusial. Suatu keyakinan yang dibenarkan (justified belief) bisa saja salah jika kita berada di bawah kondisi yang menipu (misalnya, kita meyakini melihat seseorang secara langsung, padahal itu hanyalah foto seukuran manusia yang sangat realistis). Sebaliknya, pengetahuan menuntut kebenaran mutlak; kita tidak bisa dikatakan "mengetahui" sesuatu jika hal tersebut salah.
5
102
1
459
12,991
Ben77 retweeted
Don’t sell anything you wouldn’t want your children to buy.
823
2,970
450
31,312
2,021,938
Ben77 retweeted
Replying to @LatFilosof
just keep reading until you eventually get mad at them and think they're both idiots that is the path of the true philosopher
5
21
2
2,047
22,123
Embrace it. You'll feel better, I promise.
Me watching you realize most Bitcoiners are actually dumber than the average population:
9
5
80
3,400
Ben77 retweeted
when the dream becomes the job i used to think the dream was to make my hobby my work. to spend my life close to the things i loved: computers, tools, interfaces, systems, tiny details, the feeling of making something from nothing. the things i used to do at night, after school, after work, in the margins of life, somehow became the center of my life. and it is a dream. i don’t want to pretend otherwise. there is a very specific happiness in getting paid to care. in meeting other people who care. in building a language around your obsession. in seeing something that lived only in your head become real enough for other people to touch. but there is also a quiet pain in it. because the thing that once belonged only to you no longer belongs only to you. it becomes attached to deadlines, teams, customers, strategy, money, reputation, momentum... curiosity becomes roadmap. taste becomes decision-making. play becomes output. the thing you once used to escape the world becomes the thing through which the world reaches you. and now there is another strange layer: the machines are getting good at the work too. not just the boring parts. the parts that used to feel close to the heart. writing, coding, designing, reasoning, making taste-like decisions, turning vague ideas into something real. the things we once used as proof that we had something special inside us are becoming things a model can approximate, remix, and sometimes do faster. that can feel scary in a very specific way. not only because of jobs or competition, but because it touches the myth you built around yourself. if the thing i became good at can be done by a machine, then what part of it was really me? i think that question is painful because it arrives at the same place. the original love. before the title, before the company, before the taste became a role, before the work became useful to other people, there was a small fire inside. curiosity. obsession. the feeling that the world became more alive when you understood a little more of it, or made a tiny piece of it bend to your will. AI can make the output faster. it can even raise the floor of the craft. but it cannot want on your behalf. it cannot decide what is worth loving. it cannot protect the strange private thread that made you care in the first place. maybe that is what becomes more important now. not whether you can still produce the artifact by hand. not whether you can outperform the machine at every task. that game is already changing. the real question is whether you still have a relationship with the source. the part with no audience. no roadmap. no deadline. the part that can wander, collect strange references, make useless things. follow beauty, be wrong, be slow, be you, be true. because that part is not separate from the work. it is the source of the work. if you lose it, you can still operate. you can still manage the machine. you can still prompt, review, decide, ship, and keep things moving. but the work becomes thinner. safer. more explainable. less alive. so maybe the real discipline is not to work harder, rest more, or care less. maybe it is to keep returning to the place where the love began – before it became useful, before it became legible, before anyone needed anything from it, before even the machine could mirror it back to you. not to escape the work. to make sure the fire inside is still alive.
79
117
35
1,482
119,606
Replying to @williameijer
Truth often requires retrofitting the self around new information. That negotiation can be destabilizing, depending on what is being revealed. In many cases, denial is not merely ignorance. It is an unconscious ego-protection mechanism. Deny, deflect, accuse: these are common responses when someone cannot metabolize what is being shown to them.
5
3
1
33
1,339
Game theory explains why the hardest worker in the room is frequently the one who is being exploited the most. High output at a relatively low cost is not a virtue that any system rewards or favors. Instead, the system learns your price. When other players learn they can extract maximum value from you without granting you anything notable in return, they will do just that. Every single time. That's because you trained them to. Luckily, the solution is not complex at all: make yourself more scarce; raise your prices. Realize that hard work necessitates proportional cost. If you're truly a hard-working individual, you can't afford to be ashamed to raise those numbers.
9
117
4
845
24,310
As believers of open research, we are disappointed to see Anthropic silently degrading Fable 5 for AI development "Any topic related to building pretraining pipelines, distributed training infrastructure, or ML accelerator design... may have limited effectiveness through Claude via methods such as prompt modification, steering vectors, or parameter-efficient fine-tuning." Not only do they get to decide what you use LLMs for in research, but this also enables them to silently intervene in your research without you knowing. This sets a dangerous precedent. If a model refuses openly, users can understand the boundary. If a model falls back to another model, users can still evaluate the difference. But if a model silently modifies or weakens its own answers while still pretending to help, researchers lose the ability to know whether a failed result came from their own idea, their implementation, or an invisible intervention by the model provider. That is not safety. Safety policies should be transparent, auditable, and user-visible. On top of that, the people most harmed by this are not the largest labs with massive teams and proprietary infrastructure. It is the independent researchers, academic groups, startups, and open-source builders who rely on public tools to compete, innovate, and pioneer AI for everyone else.
156
684
115
3,795
235,771
Ben77 retweeted
A little knowledge is more dangerous than none. The total beginner knows they don't know, so they stay open. The person with some expertise is the one who nods along and misses everything new, because they've pattern-matched it to something old and filed it away. Competence can blind you in a way ignorance never could
The greatest obstacle to learning is thinking you already know.
4
17
1
138
7,149
You live in a fake world where you have to game theory, leverage, and contingency plot with rationed trust and suppressed emotions, because trusting the wrong people is lethal so you're forced to play this fake and gay game even though you just want beauty, truth and to be real.
19
155
8
1,022
25,156
Major cheat code in life: Master the graceful exit. From conversations. From parties. From opportunities. "This has been wonderful, but I need to go." No elaborate excuses. No fake emergencies. Just clear, kind departure. Most people don't know how to leave. They stay too long or leave badly. Master the exit.
59
1,008
24
7,539
198,186
Once you get a good grasp on basics, I find reading too many papers and listening too many ideas could backfire. You may be either lost in others' idea or thought process could be significantly biased. That may lead to derivative works. I find it's better to be away from your desk when the problem is not even properly granulated.
3
4
2
40
2,709
People who make non-consensus decisions don't have much time for company politics, pointless meetings, or people they don't respect.
10
20
2
194
20,035
agent时代下 每个人都有自己的终端意识 建立自己的信息网络是第一步👍
突然想聊下6551, 以及@lnkybtc 最近猛然意识到一点,这个世界剧烈变革,同时我们极度偏执,以有限印象去评估,比如认为ai的出现,将对sass 平台产生毁灭性影响, 但是最近发现 saleforce, 甲骨文,adobe 涨的不错 ai的暴富,少部分人竞争力指数级增长,但是绝大部分人依旧处于混沌状态,似乎心智未开,我们拥有世界上最强的系统,但是问不出好的问题,受制于想象力, 受制于过往习惯 所以绝大部分人,依旧需要被引导,依旧会是普通人,而传统的sass公司,😄,人家拥抱ai的进度和力度却远超普通人,比如saleforce 而这些偏见的产生和消除,应该怎么做呢,其实我们需要非常好的信息系统自动化采集,而这方面在我看来做的非常好的,就是6551,以及秋田自建的信息系统 @cryptoxiao 说起来,还是厉害,做了这么大的转型方向 其实6551,以前做那套dex, 我就基本不用,没啥兴趣,但是它转型作信息系统采集后,这就不一样 ,我也开始为之付费了,感觉他们路子终于走宽了,不再只是币圈的一个小赛道,信息优势采集做的足够好,不只是会员费的问题,自己做投资恐怕更容易是大头,核心在于,不要只盯着币圈这个地方,而是整个全球市场,这点方程式同样估计也是吧,这俩有些异曲同工之处 作为个体,我们同样需要构建良好的信息系统,感知全球市场的变化,以事实依据说话,而不是想象,破除,韭菜爱想象的幻觉, 走向更成熟的数据统计路线
1
2
30
8,544