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好家伙！TradingView 的 Lightweight Charts 终于有人把技术指标库补齐了！ 70 多种技术指标、77 种 K 线形态，连实时计算、自动绘图和副图管理都搞定了！ tulip-rs-lwc，一套基于 Rust + WebAssembly 的 Lightweight Charts v5 技术分析插件。 以前用 Lightweight Charts 开发交易终端，K 线图虽然漂亮，但 MACD、RSI、布林带这些指标都得自己计算和绘制。 现在这套插件直接把计算和绘图封装好了，调用 API 就能使用。 核心能力： 1️⃣ 70+ 技术指标：MACD、RSI、EMA、布林带、ATR、ADX 等常用指标。 2️⃣ 77+ K 线形态：自动识别锤子线、十字星等形态，并标记在 K 线上。 3️⃣ 实时增量计算：新 K 线进来只计算新增数据，不用重复处理历史行情。 4️⃣ 自动图表布局：均线叠加主图，MACD、RSI 自动显示在独立副图。 5️⃣ Rust + WASM：通过 WebAssembly 执行指标计算，方便集成到交易终端。 6️⃣ 专业绘图：支持布林带填充、PSAR 趋势圆点和 Pivot Points 支撑压力线。 我比较喜欢它的自动布局功能，不用自己处理主图、副图和指标绘制，直接调用接口就能组合多个指标。 做 TradingView 图表开发、量化可视化或者想搭建专业 K 线交易终端的，这套源码值得收藏！ GitHub：

SpaceX has just unveiled a massive new $50 billion Supercomputing Campus that they want to build in Cochise County, Arizona. All info: • Up to 6 GW across the 9 supercomputing buildings • Construction starts 2027 • Size of each supercomputing building: ~493,000 square feet. • Total supercomputing building space: 4.44 million square feet, excluding support facilities. • SSVEC residential customers are projected to see electricity bill reductions of 10–40%. • Up to 24 GWh of grid-connected battery storage to provide backup power, stabilize the regional electric grid, support local voltage, and absorb fluctuations in electricity demand. Among the largest planned battery installations in the United States. • Additional solar generation in the region is planned to supply renewable electricity during the day and reduce demand on the local grid. • No dedicated on-site power plant or fuel storage yard is planned. • Each supercomputing building will have 10 MW of natural-gas-powered standby generation for critical equipment, operating only during utility outages and periodic testing. • No gas turbines will be installed at the site. • SpaceX plans to fund $3.6 billion in grid infrastructure upgrades, transferred to AEPCO at no cost to its members. • Up to $260 million annually in additional benefits for cooperative members at full buildout, on top of residential bill reductions. • Up to $200 million annually for cooperative members statewide, in addition to SSVEC benefits. • The supercomputers will use dry cooling with a closed-loop water circulation system rather than evaporative cooling. • SpaceX projects zero ongoing industrial water consumption. • The cooling system will be filled initially, with water reused for up to 10 years. • SpaceX estimates annual water usage equivalent to approximately 20–30 Arizona residential homes. • Projected annual water use would be less than 2.5% of a single center-pivot irrigation system's annual consumption. • Permanent jobs: Approximately 1,000. • Construction jobs: Approximately 5,500. • Annual property tax revenue: Up to $103 million in estimated new annual revenue. • Regional economic benefits: SpaceX says its investment will support public education, infrastructure, essential local services, and economic development. • Local suppliers: SpaceX is encouraging suppliers to participate in construction and development of the campus. • Noise mitigation: Equipment will be positioned away from neighboring properties where possible, with setbacks and additional screening to reduce noise. • Nighttime lighting: Outdoor lighting will remain off unless work is underway, with active lights shielded and directed downward. • Dark-sky protection: The campus will comply with Cochise County's dark-sky requirements to limit light pollution. • Nighttime construction: SpaceX says it will seek waivers when nighttime construction lighting is necessary. • Wildlife protection: The campus is being designed with consideration for the region's sandhill crane wintering habitat. This campus's purpose is to build one of the world's most advanced AI supercomputing campuses to train SpaceX's superintelligence models and support scientific and engineering breakthroughs. Learn more:

One of the most compelling parts about working at an ElonCo was the agility to pivot to whatever the most important problem was. One day I said: I wasn’t hired to go after spam, but a tsunami is coming and I’m going to drop everything to focus on that. The org is so flat and agile that your title is basically meaningless. Anyone can sound the alarm about a problem and solve it themselves—or rally the team to join them. This is how startups work but it’s very hard to make it work in a larger company.

Chelsea Gray has the clutch gene 🥶 She was pivotal in helping the @LVAces advance to the Semis with 23 PTS, 5 AST & 4 REB. She also moved into No. 9 on the postseason All-Time field goals made list passing Tamika Catchings. 2026 WNBA Playoffs presented by Google Pixel | @madebygoogle

Salesforce acquires Listen Labs for ~$2b. But who gets the 💰? My usual breakdown below 👇 The company was founded in Sep-23 and had raised less than $100m. Investors will share ~$850m of profits on $96m invested, a ~10x blended. From a huge pivot to a unicorn valuation term sheet they refused, this is truly a wild story ✍️. In the end an incredible outcome for all involved. So let's dive in! 1) The boldest move: walking away from $1.5b 🎲 Listen Labs had a signed $125m Series C term sheet from Menlo at a $1.5b valuation... and walked away from it to sell to Salesforce at ~$2b. This takes a huge amount of courage: few founders turn down a signed unicorn-plus round. @itsalfredw and @florian_jue did it 2.5 years after founding. Huge congratulations are in order for the discipline and execution of the founders here. 2) Founders and team: a life-changing outcome in under 3 years 🥳 By my best estimates, founders and team still own over half the company. At $2b, that's ~$1.1b for them to share. Specifically, assuming a 15% option pool, that is ~$300m for the team and ~$750m for the two co-founders. And, as was the case with Hugging Face, this is all from a pivot. They originally built an AI customer-interview tool to understand why their viral app BeFake was growing, then realised the tool was the business! 3) Sequoia's Bryan Schreier did it again 👑 What few people know is that Bryan was an early backer of Qualtrics... the category Listen Labs is disrupting. Now he led both the seed and the Series A here. By my estimates, those two rounds will return ~$670m combined, or ~25x on ~$27m invested, in under three years. Pattern recognition and industry knowledge have their perks, it would seem! 4) Ribbit: 4x in 8 months ⚡ Ribbit led the $69m Series B in Jan-26 at ~$500m. At $2b, that's ~4x in 8 months. Unbelievable IRR and a great return on a meaningful cheque. 5) Neo does it again, congrats @apartovi 🎯 Listen Labs went through the Neo accelerator early on, which came with a $600k SAFE. On my estimates, that cheque is worth ~$25m+ today. This comes just three months after Cursor's acquisition (a cool >1,000x for Neo). What a hit rate! This one is very straightforward: it is a massive win for everyone. And so congratulations to all involved: Sequoia, Ribbit, Conviction, Pear, Neo and the team!

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)

Day 275 of tagging @cz_binance, @Leonard_Aster, and @Aster_DEX about $ASTER. As usual, $ASTER is doing it thing. I know some people will say $BTC is dumping and so, $ASTER is dumping along it. My question is, what was $ASTER doing when $BTC, $HYPE and $LIT was pumping? I’m seeing a lot of $ASTER traders/ investors tell me they’re pivoting to other assets. Do I blame them? NO. 1 year is enough for a project to show how serious they are, if they really want to grow. Bottom line is $ASTER has been a bad investment, there’s no sugarcoating it.

Tesla accelerated the electric vehicle market by selling over 9 million battery-powered cars and trucks. Now Elon Musk’s automaker is pivoting to humanoid robots and self-driving taxis. Up next: he’s looking to build AI data centers in orbit via his rocket company SpaceX, which recently committed at least $100 billion to building a spaceport in Louisiana. His contributions added him to the list of #Forbes250# Builders: 📸: Patrick Pleul via Getty Images

经常做波段的人，可以试试这个指标。 它叫 Zig Zag，看盘时小涨小跌太多，它能按你设定的幅度过滤波动。 把主要高低点连成折线，方便看清每一轮上涨和回撤，这次低点有没有抬高，前一个高点有没有被突破。 比如一段上涨行情，中间经常夹着几根下跌的 K 线。 加上 Zig Zag 后，较小的波动会被略过，较大的上涨和回撤则用折线连接起来。 沿着折线看，就更容易比较前后两轮走势，高点是否越来越高，回撤有没有跌破上一个低点。 它有两个值得调整的参数。 第一个是 Price Deviation for Reversals，反转幅度阈值，默认是 5%。它控制价格反向变动多大，才达到识别新一段波动的幅度要求。调高数值，会过滤更多小波动，调低数值，就能保留更细的转折。 同一张图上，如果只想看较大的波段，可以把这个值调高一些。 如果觉得折线省略了太多细节，就调低一点。 不同品种的波动幅度不同，切换品种或周期后，可以重新调整。 第二个是 Pivot Legs，转折点确认所用的 K 线数量。 数值越大，通常留下的转折点越少，数值越小，就会识别更多局部高低点。它和幅度阈值一起，决定了折线有多细。 刚开始可以先在常看的周期上保留默认设置，每次只改一个参数，观察哪些转折被留下，哪些被过滤。 比如先在 4 小时图上梳理几轮较大的上涨和回撤，再切到 1 小时图看局部变化。 图上的转折价格等标签也能单独关闭。 如果主要想看高低点的排列，可以减少标签，只留下折线和 K 线，画面会更清楚。

Yep. Except: 1. This was about important internal tools. The team was stuck in some architecture nightmare of their own doing (writing it in rails but headless, with graphql api, and a SPA react app, constantly needing frontend engineers for changes). I call this kind of thing 'cosplaying an enterprise production app'. All that complexity was in the way and using straight rails was perfect in that case. 2. I make calls like this all the time. Usually someone on the team asks me to. They see what needs to happen but don’t want to be the bad guy. I’m happy to just make the call if I agree with the premise. Saves enormous amounts of meetings and change management etc. Sometimes this is jokingly referred to as Founder-mode-as-a-service here. 3. For ten years I’ve also run an internal podcast called Context, where I revisit decisions like these and explain the reasoning so everyone can learn from them. This is helpful to give people all the variables that were considered and why this was the choice made given the information available at the time. I want to teach how to make such decisions effectively without needing me. Sunk cost fallacy is a problem. 4. Any notions that Shopify is succcessful despite of me doing this, instead of because of it, will have a hard time making their argument come together I think 😄 the part of 'two weeks later tobi learns about...' is nonsese and the pivot of that project up there happens one of the more successful examples of interventions. But getting the company to work effectively with great architecture and low technical debt baggage into the right direction is literally the job, so guilty as charged I suppose. But there are always cope stories floating around like this because they are more fun, than saying 'somehow we needed tobi to stop doing silly architecture astronautics'. I can totally see that.