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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.

第二种是Episodic Pivot，也就是催化剂跳空。 他会在盘前寻找跳空超过5%、成交量明显放大的股票，再检查背后的原因，比如财报超预期，上调业绩指引，新药获批或公司业务发生重大变化。 理想形态是股票原本沉寂或长期横盘，消息出现后直接跳过原来的阻力区。 开盘后突破1分钟或5分钟高点时买入，止损放在当天最低价。如果第一次突破失败，他会等股价重新站上VWAP，在附近横盘并再次突破后重进。 走势继续转强时可以加仓，之后先分批止盈，再沿10日线或20日线退出。 生物科技股经常在消息首日冲高回落，所以他更倾向等到第二天仍然强势时再买。

The test of a thesis isn't whether it's right. It's whether you'll hold it before anyone agrees. No pivot, no refocus. Conviction usually arrives years before the market does. @gaib_ai's @konyk001 and Aptos Foundation's @aptAlix on mainstream adoption.

What do you do when stuck with a dead dribble at the elbow? Pump-fake, 360 pivot, toss it off the glass, go grab it, & pass it to the corner in midair, of course. Or at least, that's what back-to-back Kia NBA MVP @shaiglalex does 😅

Many of the great VC firms and solo GPs started out with funds of $10M or less. These micro VC firms have a risk appetite which is crucial to the industry, and play an important role in seeding risk capital where larger firms cannot. "Micro VCs play a pivotal role in democratizing access to entrepreneurial finance by allowing ventures to get funding from a new type of investors. Moreover, micro VCs encourage more kinds of limited partners to access the VC industry." - Micro VC (2022) Unfortunately, the associated regulatory costs make operating at this scale incredibly difficult in the UK. Admin and compliance often consumes about $1.4M of a $10M UK fund, versus just $450k in the US. As a result of this high cost, the median fund size in the US was $20M in 2024, in the UK (with just 3% of the volume) it was $88M. So, the UK not only has lower total venture capital volume, it also has fewer small funds — the worst of both worlds. This is a major bottleneck, but there is a fix. If you're interested in hearing more, drop me a DM. (Credit to @cupazhou and @samhuleatt for the graphic below, from The Side Letter.) Micro VC paper:

Spot Ethereum ETFs just recorded $697 million in weekly net inflows, signaling a major pivot in how Wall Street allocates to Web3 infrastructure. As traditional capital steps in to buy underlying $ETH directly off market-makers, liquidity dynamics are shifting fast. Head over to the KuCoin Blog for our full breakdown, and explore $ETH spot, futures, or yield options on KuCoin to position your portfolio. 🔗 Read more: