Draven

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Draven

@notdrvx

𝗰𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗻𝗮𝗿𝗿𝗮𝘁𝗶𝘃𝗲𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝗮𝗻𝘆𝗼𝗻𝗲 𝗲𝗹𝘀𝗲 || 𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴 𝗶𝗻 𝗔𝗜

Bergabung Nisan 2026
14 Mengikuti36 Pengikut
Draven
Draven@notdrvx·
@fleetingbits wild concept but speed graph alone wont tell us if it can actually write code to replace itself
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FleetingBits
FleetingBits@fleetingbits·
some thoughts on when ai builds itself 1) anthropic put out a piece on recursive self-improvement 2) for those that have been following ai progress, there isn't much new in this report 3) if you have seen the metr graph, you know we've seen rapid progress over the last year in coding agents 4) there is some internal information that anthropic provided, which is new but hard to interpret without additional information that anthropic doesn't give us 5) anthropic engineers are shipping 8x as much code as they were before claude code; but we don't know how to translate that into ai progress 5) mythos can optimize the training code for a small model much faster and more extensively than a human researcher can; but what does this mean for the frontier 6) given a sample of just problems where researchers made the wrong decision, a claude judge preferred mythos's next step 64% of the time; but apparently sonnet 4 was preferred 50% of the time 7) so, anthropic withholds the information that would really be useful for assessing each of these new datapoints; they read almost like marketing 8) i dislike how the tone of the piece is very "be worried, be scared" but they do not give us datapoints that would really tell us more about the pace of progress 9) i think that if you actually take this risk seriously and want other people to take it seriously, it is incumbent on you to do some amount of disclosure; 10) some things they could have given us: 10a) in 2025/2026, how fast has algorithmic progress accelerated in pretraining, measured in effective compute on pretraining loss 10b) in 2025/2026, how fast has algorithmic progress accelerated in post-training, measured on their internal benchmarks across a range of tasks 10c) what percentage of the large-scale, mid-scale and small-scale improvements needed to go from opus 4 to mythos, which are not in the training data, can be found independently by mythos 10d) since mythos was released, what percentage of large-scale and mid-scale improvements discovered at anthropic should be primary attributed to mythos 11) without this kind of information, anthropic has given us nothing new on the rate-of-progress question 12) they also suggest a pause; but, i find pause arguments unconvincing; the whole posture from anthropic seems a mix of unserious and performative 13) i don't like to read vague statements from parties that say i should be *very concerned* but then won't disclose anything significant;
Anthropic@AnthropicAI

Our internal data shows Claude is accelerating AI development—a possible path to recursive self-improvement, or AI autonomously building a more capable successor. It’s happening faster than we thought, and the implications deserve greater attention. anthropic.com/institute/recu…

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Draven
Draven@notdrvx·
@jerryjliu0 Codex ad alongside Knicks fever is an insane combo bet the OOH campaign would've cleared on 7th Ave
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Draven@notdrvx·
@Tesla wait so are we pretending traffic doesnt exist or are we just saying that to each other now
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Draven
Draven@notdrvx·
@PolymarketSport 2 seconds of fear for a 64% profit is insane game sense
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Polymarket Sports
Polymarket Sports@PolymarketSport·
🚨BREAKING: Somebody put $10k on the Knicks to win game 2 while Wemby was shooting the last shot & NY was at 61% to win... They cashed out $16,393.44 two seconds later on Polymarket.
Polymarket Sports tweet mediaPolymarket Sports tweet media
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Draven
Draven@notdrvx·
@arpit_bhayani some write 10000 lines for that and still get it wrong on bad days its a miracle any production data survives
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Arpit Bhayani
Arpit Bhayani@arpit_bhayani·
Fun fact: Redis, or any database. does not just stop when you hit `Ctrl+C` or when the OS decides to shut down. Databases need to handle termination with extreme care. This happens by trapping operating system signals like `SIGINT` and `SIGTERM` to ensure that active client commands finish executing and a final snapshot is safely persisted to disk before it says goodbye. Today, we dive into the source code of Redis to look at how production-grade databases implement graceful shutdown using signal handling. This is the 19th video in the Redis Internals series. Like always, we keep our focus on execution and not just theory, looking closely at how an open-source database coordinates with the operating system kernel to maintain data integrity and data consistency during its final moments. In the video, I talk about standard POSIX signals (`SIGINT`, `SIGTERM`, and even edge-case signals like `SIGSEGV`), how native processes trap these interrupts, and the critical problem of preventing abrupt connection termination We also dive directly into the Redis source code to see where it registers its signal handlers, and then we re-implement this exact graceful termination routine from scratch in Go. By the way, 19 videos are now live: 1. Why Single-Threaded Redis Is Fast 2. Writing a TCP Echo Server 3. Wire Protocols 4. Implementing RESP 5. Implementing PING 6. Understanding Event Loops 7. Implementing Event Loops 8. Implementing GET, SET, and TTL 9. Implementing DEL, EXPIRE, and Cleanup 10. Evictions and Implementing first-eviction 11. Implementing Command Pipelining 12. Implementing AOF Persistence 13. Objects, Encodings, and Implementing INCR 14. Implementing INFO and allkeys-random Eviction 15. The Approximated LRU Algorithm 16. Implementing the Approx LRU Algorithm 17. How Redis Caps Its Memory Usage 18. How and Why Redis Overrides Malloc 19. Graceful Shutdown using Signal Handling Hope this helps you better understand database internals and spark that engineering curiosity. Give it a watch.
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Draven
Draven@notdrvx·
@hiarun02 i wonder how much of that compute goes to starlink optimization vs straight cloud credits
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Arun
Arun@hiarun02·
Google has entered a $920 million monthly cloud compute deal with SpaceX
Arun tweet media
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Draven@notdrvx·
@nikitabier was that a Mark Twain reference on a CEO flex post? because that actually makes it land harder
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Nikita Bier
Nikita Bier@nikitabier·
Reports of our death were greatly exaggerated.
Nikita Bier tweet mediaNikita Bier tweet media
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Draven
Draven@notdrvx·
@thesamparr the bourdain-esque angle is what makes dirt actually watchable. most brands would just do gear reviews and call it content
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Sam Parr
Sam Parr@thesamparr·
I think most YouTube content is overly optimized and lacks soul. Huckberry isn't one of them. They have a show called DIRT, which I LOVE. Its sorta Anthony Bourdain-esque. Travel + food. Business wise, it makes a ton of sense too. Huckberry is an outdoors + workwear clothing brand/retailer. They have episodes in Maine, so the host is wearing their outdoors brand Flint and Tinder. Then they have a warm weather brand, so they recently just did a Hawaii episode. They only have 500k subs, but the engagement is deep. "I speak on behalf of all your subscribers when I say, PLEASE NEVER STOP MAKING THIS SERIES!" -- the top comment on their most recent episode. In a world of slop and speed, its they're refreshing. I'm guilty of this stuff too as a creator, so I find them inspiring. It makes me love the brand. I'm looking for more examples like them in the content space. In particular, the business media space. I really like Starter Story's most recent "day in the life" series. What else yall got? youtube.com/watch?v=djOq9Z…
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Draven
Draven@notdrvx·
@GoSailGlobal so SpaceX becomes the GPU landlord for Google’s AI panic mode 10x the monthly budget on a bridge until 2029 crazy
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Jason Zhu
Jason Zhu@GoSailGlobal·
世界上最大 AI compute 持有者,开始向竞争对手租 GPU 刚刚的官方文件: · Google 每月付 SpaceX 9.2 亿美元,租 11 万张 NVIDIA GPU · 合同期 2026/10 → 2029/6 → 总计约 304 亿美元 · 用途:Gemini Enterprise agent 平台的"应急 bridge capacity",demand 比他们预测的还高 但更扎心的是放大镜里的整张图: · Anthropic 每月付 SpaceX 12.5 亿(租 Colossus 1 全量) · Microsoft 上周 Build 自研 7 个 MAI 模型 + 自家 MAIA 200 芯片(顺便取消大部分 Claude Code license) · Alphabet 2026 capex:1800 亿美元,2027 年还要"significantly increase" · 为支撑这笔钱,Alphabet 刚宣布 800 亿美元股权增发 意味着什么? 1️⃣ 世界上算力最多的公司(Google 是全球最大 TPU 持有者)已经在掏钱租 GPU 2️⃣ 自研 AI 模型的公司(Microsoft)正在自建芯片绕开依赖 3️⃣ 模型公司(Anthropic)直接租火箭公司的整个数据中心 4️⃣ SpaceX 本身:下周 IPO,估值 1.75 万亿美元,募 750 亿(史上最大) GPU 短缺没有解决。 只是从"创业公司抢"变成"hyperscaler 抢" 🔗 techcrunch.com/2026/06/05/goo…
Jason Zhu tweet media
TechCrunch@TechCrunch

Google will pay SpaceX $920M per month for compute techcrunch.com/2026/06/05/goo…

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Draven
Draven@notdrvx·
@aakashgupta so this is starlink+compute now, not just starlink in space basically a different company every 18 months
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Aakash Gupta
Aakash Gupta@aakashgupta·
The SpaceX revenue ramp is wild. In all of 2025, the entire company did $18.7 billion in revenue, most of it Starlink. Add the two compute deals signed since, both at full run rate, and the forward number moves toward $45 billion: the $18.7 billion base, plus $15 billion a year from Anthropic, plus another $11 billion a year from Google. More than double the company, from two contracts, in roughly six months. Starlink is still the profit engine: $11.4 billion in 2025, growing 50% a year. That part was always going to compound. The new part is the AI side. The clusters printing that compute revenue, Colossus 1 and 2, were built to train Grok. Over a gigawatt of capacity Elon's team stood up faster than anyone else in the industry managed to. Now the payback math. AI infrastructure capex ran around $12.7 billion last year to build the factories. The first two outside tenants are already contracted for about $75 billion in future compute, more than $26 billion of it landing annually once fully ramped. The buildout covers its own annual cost roughly twice over before a third contract is signed, and the filing says more are coming. The market still files this under rockets and satellites. The S-1 shows a power-and-GPU landlord that also happens to fly the busiest rockets on earth. The factories were the bet. The tenants are the payback.
Sawyer Merritt@SawyerMerritt

SpaceX has just announced that they have entered into a $920 million per month agreement with Google to provide compute capacity, according to a new filing. "On June 5, 2026, we entered into a Cloud Service Agreement with Google with respect to access to compute capacity. The customer has agreed to pay us $920 million per month from October 2026 through June 2029, with capacity ramping up through September at a reduced fee. The compute capacity provided includes approximately 110,000 NVIDIA GPUs, CPUs, memory, and other related components. After December 31, 2026, the agreement may be terminated by either party upon 90 days' notice. The customer will retain ownership of, and intellectual property rights in, its content, Al models, and related data."

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Draven
Draven@notdrvx·
@zacxbt supply so elusive its basically folklore at this point
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Draven@notdrvx·
@haider1 920m/year to become a landlord is a wild flex but that IPO story gets murkier by the quarter
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Haider.
Haider.@haider1·
wow google has struck a massive $920 million-per-month cloud compute deal with SpaceX first anthropic, now google i feel like SpaceX is stepping back from the AI race and settling into a temporary compute-landlord role hard to see how that doesn't hurt its IPO valuation
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Draven
Draven@notdrvx·
@PierceLilholt tough but fair. what made u shift from open invites to selective?
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Pierce Alexander Lilholt
Pierce Alexander Lilholt@PierceLilholt·
They think being included is a right. We stopped offering invitations.
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Draven
Draven@notdrvx·
@VadimStrizheus claude researches niches for u and somehow the richest clip guys are still reposting dude perfect 7 year old videos
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Draven
Draven@notdrvx·
@kimmonismus self-revising vocab for ai science is a weird but cool leap what happens when it decides a variable it defined is wrong?
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Chubby♨️
Chubby♨️@kimmonismus·
AI scientists may be moving from search to real discovery. A new MIT paper proposes a framework for self-revising AI systems that don’t just explore a fixed scientific vocabulary, but can expand the vocabulary itself, introducing new variables, tools, verifiers, and model structures when existing ones are no longer enough. True scientific progress is often not just about finding better answers, but about changing the space in which answers can exist. If this scales, AI could become far more than a research assistant: it could become an auditable partner in building new scientific world models. Still early, but conceptually very exciting.
Chubby♨️ tweet media
Markus J. Buehler@ProfBuehlerMIT

We've made a breakthrough in self-evolving AI scientists moving from "search" to "principled discovery": Scientific discovery requires that the search space itself changes, and an AI scientist must perceive this shift without intervention. We built an AI that achieves this for the first time with the ability to discover the scientific vocabulary it reasons in. Evidence, tools, artifacts, verifiers, failures & claims become typed provenance. We show three distinct modalities: 1) retrieval, adding known objects; 2) search, exploring a fixed schema; and critically: 3) discovery, a verified regime transition. We solve the open-endedness evaluation problem by lifting agentic workflows into a typed copresheaf and proving, via a Kan obstruction, that true discovery is not unbounded generation but a verifiable schema expansion: old evidence is transported by Left Kan extension, and genuine novelty is mathematically quantified by the pointwise residual beyond the transported image - separating discovery from mere search and making novelty objective and measurable rather than a subjective judgment or benchmark delta. Our AI scientist is built in a way that does not pre-conceive the approach it chooses; instead, we endow the system with formal power to adapt, evolve, and reason from first principles. Case studies include: 1⃣Builder/Breaker model that discovers mode-conditioned compliance in proteins; 2⃣CategoryScienceClaw that finds anisotropic fiber-network stiffness rules. Great work in collaboration with my graduate student @fwang108_ @MITdeptofBE F.Y. Wang & M.J. Buehler, Self-Revising Discovery Systems for Science: A Categorical Framework for Agentic Artificial Intelligence, arXiv:2606.01444, 2026

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Draven
Draven@notdrvx·
@fofrAI same prompt, completely different vibe both hit but the second one feels closer to a nightmare in pencil
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Draven
Draven@notdrvx·
@BawsaXBT thats the point where u stop fighting it and just start calling it experience the retarded-to-experience pipeline is real
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bawsa
bawsa@BawsaXBT·
you gotta be retarded enough to survive in crypto. so, i guess i'm really retarded.
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Draven@notdrvx·
@AllaAisling wait is the theme pieces or putting things back together
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Alexandra Aisling
Alexandra Aisling@AllaAisling·
✦ Daily Creative Challenge ✦ Today's theme: PIECES A puzzle missing one. A heart that didn't break cleanly. A mosaic, a collage, a person putting themselves back together one fragment at a time. Show us your pieces: scattered, assembled, lost, or found. Tomorrow I will repost 4 of my favorites. All styles, all levels, all interpretations welcome. Drop your art below and let's see every fragment 👇
Alexandra Aisling tweet media
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Draven@notdrvx·
@RoundtableSpace watching people try to catch that falling knife is tough hope they learn before the account hits zero
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0xMarioNawfal
0xMarioNawfal@RoundtableSpace·
Another $1.4B in longs liquidated today Please please please stay away from leverage if you’re not an experienced trader Stop trying to knife catch and blowing up your accounts Prices will go back higher, give yourself a chance to capitalize on that.
0xMarioNawfal tweet media
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