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😱卧槽，这20 个仓库组成了一个动效工作室！？！ 给 Opus 5.2 用的 20 个工具和 Agent Skills ⬇️ 节奏、变形、绑定、物理、粒子。 01 Remotion Skills → 给 Agent 做视频的指引 02 Anime.js → 时间线和交错动画 03 Motion → 布局过渡 04 React Spring → 弹簧物理 05 Motion Canvas → 动画讲解 06 Manim → 把数学动起来 07 mo.js → 爆发和形状特效 08 Flubber → 平滑的形状变形 09 KUTE.js → SVG 补间 10 AnimXYZ → CSS 编排 11 Lottie Web → 矢量动画播放 12 Rive Runtime → 可交互的状态机 13 Inochi2D → 2D 木偶动画 14 OpenToonz → 手绘动画 15 Synfig → 矢量补间 16 Blender → 绑定和角色动画 17 Three.js → 会动的 3D 场景 18 Godot → 游戏动画系统 19 Matter.js → 2D 刚体物理 20 tsParticles → 粒子动画 想法 → 关键帧 → 运动 → 打磨 → 渲染。 去做点值得看的东西吧 ↓

🚨 HERE’S WHY BITCOIN IS NONSTOP DUMPING RIGHT NOW It's NOT because of the stock market AI bubble. It's NOT because of the rising inflation. It's NOT because of retail panic selling. Bitcoin no longer trades like a supply-and-demand asset. The crypto market has changed FOREVER. And what we're seeing right now is not ordinary price action. It’s not weak hands, changing sentiment, or retail panic. Something much bigger is happening behind the scenes, and most people haven't even realized it yet. By the time the crowd catches on, it'll already be too late. This didn't happen overnight. The structural shift has been developing for YEARS, and now we're seeing its consequences. Here's what nobody wants to acknowledge: The moment synthetic supply enters the equation, genuine scarcity loses its meaning. And once scarcity stops determining price, the market no longer discovers value through actual ownership. Price discovery moves into derivatives. That's precisely what has happened to Bitcoin. We've already witnessed this exact transformation in: → Gold → Silver → Oil → Equities Once derivatives became dominant, the underlying market structure changed permanently. Bitcoin is now facing the same reality. The entire original investment thesis was based on 2 fundamental principles: 1. A fixed maximum supply of 21 million BTC. 2. No rehypothecation. Both principles lost their effectiveness once Wall Street built an entire financial system around Bitcoin: → Cash-settled futures → Perpetual swaps → Options markets → ETFs → Prime broker lending → Wrapped BTC → Total return swaps And from that moment onward, synthetic Bitcoin exposure became effectively UNLIMITED. Not in actual blockchain supply. But in the financial markets that determine the price. Synthetic Float Ratio (SFR). This is the missing metric most people aren't aware of. When synthetic exposure exceeds the available real supply, price stops following demand. It becomes driven by positioning, hedging, and liquidation cascades. Wall Street now has the ability to manufacture Bitcoin exposure and trade against the underlying asset. Their playbook is really simple: 1. Manufacture unlimited paper BTC 2. Short aggressively into rallies 3. Trigger cascading liquidations 4. Cover positions at lower prices 5. Take profit and repeat This isn't simple speculation. It's financial inventory manufacturing. A single real BTC can simultaneously support: → An ETF share → A futures position → A perpetual swap → An options delta → A broker loan → A structured note ALL AT THE SAME TIME. 6 separate financial claims backed by one actual on-chain Bitcoin. That's NOT the decentralized free market people believe they're participating in. That's a new financial system operating under the Bitcoin name. You can ignore this reality, but you cannot escape its consequences. Quick reminder I’ve successfully called every major Bitcoin top and bottom for the past 10+ years. I publicly called the $17K BTC bottom in 2022 and the $126K top in 2025. Then, I called the $57K local bottom in 2026. Missed those calls? Don't worry, you'll get another chance soon. If you haven’t followed yet, now is the best time.

Nvidia najwyraźniej była zarządzana jak firma rodzinna. J. Huang miał 60 bezpośrednich podwładnych, więc wszyscy raportowali do niego, a nikt nie mógł mu się sprzeciwić. Jego córka trafiła do firmy jako stażystka 6 lat temu. Formalnie jest dziś dyrektorką ds. marketingu, w praktyce pełniła funkcję szefowej jego gabinetu. Stanowiska kierownicze mają też mąż córki Huanga i syn Huanga. Największym ryzykiem firmy są kontrole eksportu chipów AI, mimo to nigdy nie powołała dyrektora ds. zgodności. Tę funkcję nieformalnie pełnił główny prawnik. Ma skórzaną kurtkę, taką jak Huang. Czy takie struktury zarządzania coś wam przypominają?

AI铁王座空出来了，再聊聊老黄的辞职及影响：1、大家没注意的是老黄昨晚不仅辞掉了CEO、总裁连董事会成员的身份也辞掉，可以说退的干干净净跟英伟达彻底切割了，以后老黄只是英伟达最大的个人股东而已。一方面可能婚外情属实，另一方面也说明老黄应该也是面临了巨大的压力，不然董事会成员也不会辞掉。 2、33年以来英伟达董事会首次任命了一位董事会主席，这是跟老黄裸辞一样重要的信息，就是英伟达董事会改组。从“首席独立董事”机制改成正式董事长制，董事会的控制权也在进一步收拢。 英伟达董事会首任主席HarveyC.Jones（哈维·琼斯），1993年加入董事会，前 电子设计自动化巨头新思科技（Synopsys）的CEO和执行董事长，也是投资机构SquareWaveVentures的管理合伙人。 3、对产业的影响可能更深远 如开头所说AI铁王座空下来了，产业教主倒下了，现在看还没人能顶的上去 ：要么产业身位不够、要么资历和影响力也不够。 1）对英伟达来说，短期算是利空落地。如昨晚老黄辞职消息出来前聊到，“如果老黄离任最终消息出来，短线可以博一个大反弹毕竟核心竞争力没那么快就没了，但长期就得边走边看、看实际经营状况，看接任者是不是能真的撑得住”。老黄辞职的消息出来、昨天英伟达也确实低开高走； 2）老黄辞职，英伟达少了一个有远见、有决断力和魄力的掌舵人和领航人，需要特别注意老黄在任时几十名高管都直接向他汇报，那说明英伟达得高管没有谁能有他这样的全局视野、现在看未来是会受限的。 老黄还主导一批用英伟达的信用给数据中心建设兜底的项目，例如之前给OpenAI的合作，同时几家新云厂商特别是CRWV也都是老黄扶持起来的；影响自然也不小。 3）但有受损的就有受益的 老黄离开、英伟达的竞争对手不得笑开怀。AMD、谷歌的TPU、亚马逊的Trainium估计都看到了进一步追赶的可能性。为谷歌和亚马逊自研芯片的核心供应商博通和MRVL又能分到更多的蛋糕了。 4）对整个产业链来说 教主走了，扛大旗没有了，凭老黄一己之力扶持起来的算力产业链可能要放缓，但产业趋势已经形成，最多只是延缓并并不是停滞。 这两天在思考一个点，“slower for longer”的节奏可能更好，不需要动不动就高歌猛进、时不我待的。节奏稍稍降下来、会走的更稳更远。 4、而对整个市场来说 还是维持一周前 会到前面说的“slower for longer” ，过去几年的大干快干让硬件层分走了绝大部分利润，而慢下来，就是在模型层/应用层/平台层/硬件层之间做利润的重分配，资金的投入方喘口气，甚至于硬件层也不是通杀，流动性恐慌后，最终会回到不同环节的供需紧张程度出现定价分化。

Multi-agent workflows have a scaling problem! The agents inside the same workflow rarely need the same amount of capacity at the same time. Say you have a planner, multiple research agents, and a final synthesis agent. The planner might finish quickly while requests start piling up behind one of the research agents. Scaling the entire application would add capacity everywhere, even though only one part of the workflow actually needs it. CanyonOS is an open-source, workflow-aware platform for multi-agent Python applications and the harness between your agents. Each agent runs as a separate service with its own queue and replica limits, so the busy research agent can get additional capacity while the planner and synthesis agents stay where they are. It also handles communication between the agents and gives you visibility into what happens across the workflow after a request comes in. CanyonOS records the path a request took, including the agents it called, parallel branches, model usage, execution time, and estimated model costs. And you don't need to add tracing decorators or a separate instrumentation SDK throughout your agent code. You still integrate the workflow with CanyonOS, but tracing is handled at the platform layer. I also ran a multi-agent portfolio analysis workflow through it with separate agents working across three stocks. A single request produced 10 traceable steps across the workflow, and I could see which agents ran, where the LLM calls happened, how long each stage took, and the estimated model cost. For multi-agent applications, being able to operate each agent independently while still seeing the complete execution path becomes much more useful as the workflow grows. CanyonOS is open source. Link to the GitHub repo in the comments!

Meet Gemini 4 Hydrogen: built for rapid coding, multimodal synthesis, and everyday workflows. It finishes tasks 2.5x faster than 3.8 Flash and surpasses Argon on the @ArtificialAnlys Intelligence Index.

Today, @llama_index is an applied research lab for document OCR and document work. Our core focus is investing in building the best-in-class models+ agents at the pareto frontier of accuracy/cost/latency for document processing. To do this, we have to invest in all the different components around the research infra stack: 1. Building comprehensive and fair benchmarks. Today we have ParseBench, ExtractBench, and ExtractBench Pro. They're already the most popular benchmarks in parsing and extraction today, and we're just getting started. 2. Investing in data curation and synthetic data generation. 3. Investing in compute and our training pipeline. This has produced models like OCRia-1. 4. Investing in agent optimization - tune everything around the models to further hillclimb cost and accuracy. 5. Closing the loop based on feedback - whether from active usage of our product, implicit/explicit customer feedback, or automated verification. We are accelerating our capabilities at a crazy pace. Today, our work is documented in our research page: There is a lot more to come!

离他妈大谱了！个人独立开发者用 Claude 写了个开源 Linux 驱动，结果被起诉了！ 索赔 2.5 万美元，每拖 7 天再加 2.5 万。 按周计价。 Vino 驱动，净室逆向工程，Rust + Claude 辅助，想推进 Linux 内核。 但被 Synaptics 指控它侵犯两项英国专利英（GB2484736、GB2485613）的指控，代码仓库已被逼到私密。 Synaptics 法务的原话：“如果你想公开，我鼓励你这么做。我们欢迎一切免费宣传。” 最无语的是，Synaptics 是 OIN 成员——一个本该保护开源开发者免遭专利诉讼的组织。

We’re so impressed with the creative things builders are bringing to life with Gemini 4 Carbon. @GlitterPixely coded a meadow of 52,000 wildflowers and grass blades from scratch in CUDA and C++ with Carbon, synthesizing the wind to match every gust 🧵

We're still so small but I'm loving the team we've got at Superlogical. They're so talented and kind and love their craft. It's a different era of software development and our expectations and practices reflect that. I want to share a bit about how we work. First, everyone is trusted to push to main. We do machine-aided code review pre-push and human plus more machine-aided code review post-push. Limited to no PRs. Culturally, we've all agreed that if we ever hire someone who we DON'T trust to push directly to main, then they probably aren't the right fit for the team (so far, not an issue). Some common sense here: if someone is stepping outside their lane or comfort zone, we'll open PRs. For example, I'm pretty trash at macOS/iOS UI, so I'll open PRs for UI-affecting functionality and ask for eyeballs/help. But for business logic and internals, I push to main. Second, everyone can cut a release. Our release process is fully automated and when you start a release (for any platform) it triggers a Slack notification and a 15 minute countdown. The 15 minute warning is a final gate that allows people to review plus collaborate on a changelog. During that 15 minutes, anyone in the company can pause the release rollout. After 15 minutes, the train leaves the station. You can't override the 15 minutes (except to pause). It gives everyone a chance to step in. Third, we avoid synchronization through fast remediation rather than through planning. The server and clients on various platforms ship features as fast as they can. We use post-hoc human review and AI-assistance to identify feature mismatches across products ("Windows has support for X and Mac doesn't." or "Mac and Windows differ in their behaviors for Y in this way."). This avoids expensive "sync" meetings and coordination. We trust that producers of features are mindful of what they're producing and we trust that adjacent product lines will self-synchronize quickly. In practice, we see feature parity across platforms within 24 to 48 hours. All of this works on mutual trust. It ONLY works with mutual trust. Everyone at all times has to trust that they've got each others back: 1. Push to main only works if you fix your mess, or if you don't, that someone else will quickly but that you'd do the same (and generally in equal amounts). 2. Anyone can deploy only works if you trust everyone to cut at the right time, with the right changelog, and that people will respect the 15 minute timer. 3. Sync through fast remediation only works if you trust other teams will pick up and integrate your work quickly, which only works if you would too. It also only works if you are fairly producing mismatches (e.g. not constantly). We've got a great team. I know we're still small and that these practices will change in many ways as we scale. But this is already a markedly different practice than what I would've done 15 years ago. In general, we are building from day 1 on a foundation of mutual respect, excellence, and trust. And it's been awesome.