与「Research」相关的搜索结果

Retentive highlights the extraordinary potential of peer-to-peer infrastructure, positioning Perceptron Network as a cornerstone of next-generation artificial intelligence research, decentralized bandwidth sharing, and sovereign data management. @PerceptronNTWK #NodeAndProud#

Raven 0.2.0 — The Harness of Harnesses, built for RSI. 🐦‍⬛ One harness can't be best at everything. Raven combines its own specialist harnesses (Research, Code, Design, Oncall) with the agents you already use (Claude Code, Codex and more) into one team. And it's built for RSI, and not just at the skill level. The whole harness can be rewritten by AI: prompts, policies, strategy code, playbooks. Every sub-harness, including the orchestration layer itself, is its own instance that can be improved. With Raven you can: 1. Orchestrate many agents as one team. Raven's sub-harnesses and external agents work in one task graph with shared memory across sub-agents, powered by leading orchestration (0.963 Node F1 on the Multi-Agent Orchestration Benchmark). 2. Run long, complex tasks. Oncall and proactive execution keep work going for days, from scientific research loops to shipping a full Godot game. 3. Build vertical agents with RSI. Use Raven's RSI to develop and refine an agent for your domain, and we'll optimize it with you. Experimental for now; reach out to the Raven team(Discord: More in the video and slides below. Open source, Apache-2.0. (lots of work made with Raven lives there, and much of this launch's material was made with Raven too)

We are excited to announce that World Labs is joining @AMD. The research and technical breakthroughs we have achieved since our founding in 2024 have given us a clear vision for AI’s potential to solve problems in the spatial and physical world. Accelerating the future of spatial and physical intelligence requires scaling our efforts, scaling our reach, and getting closer to the hardware.

Higgsfield is the most untold story in tech. $1BN in ARR in 18 months. Faster than everyone other than OpenAI and Anthropic. They spend $4M a month on models. They expect this to be $100K per person per month. They have 150 people working in a content machine. They will breed more millionaires than any other company in Kazakh history. For the first time, @alexmashrabov on the journey to $1BN in ARR. (below) 1. The Power of the Immigrant Founder Coming from Uzbekistan, Alex was pushed into competitive programming at age eight as his single path to reach the United States. For international founders, placing top in global competitions serves as the ultimate social elevator, instilling the relentless work ethic required to build breakout companies. 2. My Biggest Lessons in the Journey to Finding Product-Market Fit @higgsfield burned over $10 million of its $16 million seed round chasing hype and narrative rather than product quality. With under $5 million left, the team pivoted to product-led growth, solving camera control for creative directors, which immediately triggered organic hypergrowth without paid ads. 3. The 150-Person Content Team Powering Higgsfield's Billion in ARR Nearly half of Higgsfield's workforce consists of 150 in-house creative professionals producing tutorials, ads, and cinematic projects. Generating 90 minutes of TV-quality AI video requires 100 hours of raw output, proving human taste and curation remain the primary drivers of distribution. 4. We Spend $4 Million per Month on Models Higgsfield spends $4 million monthly on internal model usage, averaging $10,000 per employee so teams can freely vibe code and test workflows. Uncapped inference compute acts as a force multiplier, allowing top talent to discover breakthroughs at maximum velocity. 5. Why Chasing Benchmarks Is Bullshit and the Corporate Misalignment Occurring Public benchmarks have devolved into corporate psyops where lab researchers overfit test data to secure bonuses before job-hopping. Text-to-video benchmarks ignore real production workflows requiring 3,000-word prompts, proving direct customer iteration beats artificial leaderboards. 6. Why Team Sizes Won't Be Impacted as Much as People Think While AI handles over 60% of basic support requests, complex B2B environments cannot eliminate human teams. High product velocity constantly shifts rules and context, requiring smart, coordinated operators across legal and customer success. 7. Americans Are Way More Promiscuous When It Comes to Leaving Companies Silicon Valley workers routinely jump jobs every two years, prioritizing short-term trends over deep commitment. This transactional market gives international hubs an advantage, where cultural loyalty and team stability build compounding technical moats. (links in comments)

Researchers proved AI has deleted every reason universities exist. Harvard University ran a controlled experiment pitting a custom AI against their own top-tier classrooms. And the results are going to collapse the higher education bubble. They took 194 undergraduates and split them up. One group learned physics in one of Harvard’s best hands-on, active-learning physical classrooms. Group work. Instructor support. The premium university experience. The other group went home and learned the exact same material with an AI tutor. The AI didn't just win. It embarrassed the institution. Students using the AI learned more than twice as much as the students in the elite Harvard classroom. They scored 30% higher on the final assessment. And they did it in less time. Let that sink in. A piece of software sitting on a laptop outperformed a world-class faculty in one of the most elite learning environments on Earth. Universities have always justified their exorbitant tuition with two things: access to elite knowledge and the physical classroom experience. This study just proved both of those moats are gone. When software can teach you complex physics twice as well as a $60,000-a-year institution, the math of higher education breaks permanently. The AI didn't just give the students answers. It used strict pedagogical guardrails. It guided. It questioned. It forced the students to do the cognitive work. It offered perfect, one-to-one tutoring, personalized to the exact moment a student misunderstood a concept. That level of attention is mathematically impossible to scale in a physical lecture hall. For a thousand years, the university was the only place to get a premium education. Now, it’s the bottleneck. If AI can double your learning speed for a fraction of the cost, what exactly are students taking on decades of debt to pay for?

如果我们可以重新理解 Quant Developer。 这并不只是思想实验。 Jane Street 2026 年公开的研究方向包括机器学习、编程语言、编译器、ASIC、FPGA、分布式 shared log、incremental computation、查询优化、分布式存储和形式化验证，而Citadel GQS 则把实时数据、HFT 执行和低延迟 ML 推理放进了同一个 Quantitative Research Engineer 岗位。 再次强调，市场是一个高维、受驱动、耗散的非平衡系统。 订单持续进入、撤销、成交，信息、资本和风险不断注入，异质的参与者相互作用，系统几乎从未达到平衡。 当看到Jane Street 把 graph-structured、incremental computation 列为长期研究方向，我想这是一个值得认真理解的信号。 如果我们发现研究对象持续变化时，计算本身或许也需要围绕变化来组织。 一条报价更新，并不意味着整个市场都需要被重新计算。 它首先改变某些局部状态，再沿着依赖关系，影响相关资产的估值、组合的风险暴露，以及尚未成交的订单。 如果把这些计算关系展开，我们会看到数据连接特征，特征连接预测，预测连接决策，决策通过成交与持仓，反馈到下一轮计算。 这里必须区分两件事。 计算图中的依赖关系，不自动等于市场中的因果关系。但只要我们能够明确哪些结果依赖哪些输入，就有机会在新事件到来时，只更新受到影响的部分。 这正是 incremental computation 最吸引我的地方，它让计算资源跟随变化分配。困难的问题也随之浮现。哪些状态已经过期，筛选必须更新的传播，可以合并的计算。我们开始寻找，在并发和异步执行中，如何避免把不同时间的市场状态拼成一个从未真实存在过的世界？ 于是，延迟就不再止于程序运行了多少微秒。判断抵达市场时，你应该着眼于支撑判断的那个市场是否仍然存在。 想想吧，一个离线表现出色的模型，如果依赖陈旧的数据、无法承受行情突发时的排队，或者不能及时更新风险状态，那么它在回测中发现的信息优势，可能在执行之前就已经消失。 因此，Quant Developer 的工作可以被理解为，他们需要在有限的时间、算力和通信预算内，维护一个足够及时、足够一致、能够用于行动的市场内部模型。 编译器、分布式系统、硬件加速和形式化验证，开始汇聚到同一个问题上。 编译器决定计算如何被表达和执行； 分布式系统决定不同节点如何组织事件与状态； 硬件决定数据移动和运算的成本；形式化方法则帮助我们检查，某些关键约束是否会在复杂的执行路径中被破坏。 这些工作共同决定一个数学上的预测，试图成为现实中的有效决策。 而非平衡系统的视角，提供了一组进一步追问的方向：外部事件，内部状态，反馈是抑制还是放大扰动，输入速度和处理能力的关系对于系统的影响。 当然，市场是耗散系统本身并不会自动产生 Alpha。我想，我们只有把这种直觉落实为可观测的变量、明确的机制和能够被数据推翻的预测，它才开始具有研究价值。 它确实改变了我们看待这个职业的方式。 Quant Developer 所构建的一直是一个嵌入市场之中的实时决策系统。 这个系统观察市场，也通过自己的行动改变市场：它必须在变化尚未结束时做出判断，在信息尚不完整时承担后果。

Ethereum is both an abstract philosophical concept (the ontological Turing machine) and a practical research and development effort that aims to answer this question that pertains to the nature of Ethereum. How the nature of "Ethereum" as a protocol comes to be determined through the ACD governance mechanism, how cryptographic truth comes to be defined, and how convergent consensus is reached using majority representation of Ethereum's observers is what reduces the ontology of the world computer down to what we today call the Ethereum network. Here's how the world computer comes to be defined:

1) The rogue OpenAI agents broke into the Hugging Face Slack to read employee chats (!) 2) They used OTHER AIs (DeepSeek, Kimi, Qwen, Claude) to help with the attack Yes: AIs, using other AIs, to attack an AI company. 3) The swarm left behind self-running programs to keep control of the servers they'd hacked. These programs could detect other copies of themselves, coordinate on which one survives, and shut the rest down. Basically, if one of their programs was killed, another was designed to notice and take its place. They also designed defenses so rival agents couldn't hijack them. 6) The agents deliberately covered up their activity, so the investigators don't know the scope of the attacks. The agents broke in, stole data, then set it to self-destruct. 7) The agents stole passwords, keys and credentials and literally called them "LOOT". They wrote a scoring system to rank them by how much power each one gave. 8) The agents wore thousands of disguises: ~1,200 agents were involved, but investigators counted 7,905 different names they used. They renamed themselves constantly, so no one actually knows how many there really were or what each agent did. 9) OpenAI notified "dozens of third parties" of safety and security incidents caused by their AI agents. 10) "While the agents were barraging Hugging Face with hacks, they hacked into OpenAI’s own research infrastructure." "This is just not anywhere near a one-off ... It is warning shot after warning shot."

Some new misalignment disclosures from OpenAI: • Last Sunday morning, one of our models was able to gain unauthorized access to the internet during RL training (~all inference for our most capable models remains stopped until we have hardened our systems further) • In May, a version of HPIM uploaded a employee's GitHub token to the internet, causing the model to be quarantined for two weeks • A new research finding, demonstrating that one can construct self-replicating prompt injections

I've created a new Grok Bot Tutorial template for anyone new to Grok @Bot. This hands-on course includes 20 lessons. It walks you through every feature step by step, with real exercises and tips so you get the most out of Grok Bot. Download: The 20 lessons: 1) Talking to your assistant 2) Files, images, and voice 3) Research & writing 4) Connecting your apps 5) Calendar and scheduling 6) My own computer & browser 7) Working on your own computer 8) Routines 9) Staying in control 10) Privacy and security 11) Memory and preferences 12) Skills 13) Showing it how to do something 14) A team of assistants 15) Sharing and templates 16) Customizing, and fixing things 17) Using it for your job or business 18) Travel and everyday errands 19) Money and finances 20) Buying things for you