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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)

Today, we’re committing to helping 25,000 veterans and military family members build rewarding careers by partnering with @hiringourheroes, @studentvets, and @hbibuildcareers. This commitment will make it easier for veterans and military families to access opportunities to high-growth careers that offer long-term financial stability and use skills they know best, like problem-solving under pressure and teamwork.

ByteDance dropped a banger paper on self-evolving agent harnesses! HarnessDev evaluates whether AI agents can build a runnable harness from scratch and iteratively improve it using execution feedback. Most agent benchmarks keep the harness fixed and only evaluate the model inside it. HarnessDev changes the target of evaluation itself. The agent starts from a minimal seed, builds the harness around the task, runs it, observes what worked or failed, and then modifies that harness across multiple iterations. That means the agent is not only solving the task. It is also changing the planning, memory, tool use, state management, and execution logic around itself. The paper evaluates this in two stages: • Creation: can the model build a complete runnable harness from a minimal starting point? • Evolution: can it improve that harness using feedback from previous runs? The interesting part is that runnable does not automatically mean better. Some generated memory and state mechanisms existed in the code but were barely used during execution, and improvements on visible feedback did not always transfer to held-out tasks. Only 34 of 64 harness changes moved in the same direction on both visible feedback and held-out evaluation, and only 2 of 9 final harness versions were actually the best-performing version on the held-out set. So the paper is really exposing a new challenge: Agents can already start modifying the infrastructure they run on. The harder part is making sure those changes actually generalize. I've shared the paper in the comments!

I am told the Hodge Conjecture is very close to being verified by OpenAI, and that one of OpenAI or Anthropic are also close to solving Birch-Swinnerton-Dyer. The race to be 'next' behind the scenes is unlike anything I've had described to me before If true - and it may not be, given the scale of the rumour mill right now - it could mean 3 Millennium Problems fall in the space of a month. Crazy times

Simple way to find customers using Grok Bot: 1) Use Grok Bot to search for people on X complaining about competitors or looking for alternatives (“Looking for an alternative to...”) 2) Use the @amplemarket connector to learn more about the person + company, then reach out to the poster or people commenting. 3) Turn it into a Grok Bot routine so you get these buying signals every week. The interesting part: you don’t need to guess whether someone has the problem you're solving. They just publicly told you they do. This feels like extremely low-hanging fruit for founders/sales teams.

Grok 4.6 just tied for #1# on the Artificial Analysis Agentic Index • Grok 4.6 (high) — 59 • Claude Opus 5 (max) — 59 Outperforming Claude Fable 5 and GPT-5.6 Sol We’re entering the agentic era, and this is exactly the kind of benchmark that matters so much: tool use, planning, autonomy and complex problem solving Grok 4.6 is now sitting at the very top And that matters even more as Grok powers Grok Build and Grok Bot, where the model has to go beyond answering questions and actually take actions, use tools and complete real work Grok’s agentic capabilities are getting seriously powerful

Agentic work is where @grok 4.6 lands hardest, taking the top spot on the Artificial Analysis Agentic Index at 59, tied with Claude Opus 5 Max. The index measures tool use, planning, autonomy and complex problem solving rather than single answers Grok 4.6 completes tasks in ~53 turns and ~0.5bn input tokens on average, against ~103 turns and ~2.0bn for Claude Opus 5 Max Cost of $0.84 per task, putting it on the intelligence versus cost per task Pareto frontier Enterprises buying agents pay per completed task, not per benchmark point. Turn efficiency is what determines whether a long-running workflow is affordable at volume. Two labs now sit at the top of this index with very different cost structures. Buyers get real choice on price for the first time in agentic deployment.

How does Huawei stay ahead of the curve? Meet an AI engineer helping large models think faster, work smarter, and keep pace with the world's accelerating compute demands. Quick thinking + Relentless problem-solving + Zero time for standing still.

5. Even after 9 years of growth, one of the industry’s biggest unresolved challenges is still trust at scale — how to make digital assets more secure, more intuitive, and more accessible for billions of people across very different markets and regulatory environments. That is not a challenge any one company solves overnight. But what we are doing today is continuing to invest in the fundamentals: stronger security, better compliance frameworks, more resilient infrastructure, and simpler user experiences. At the same time, we are working to help the industry mature in a way that protects users while preserving the innovation that makes this space so powerful. The opportunity ahead is enormous, but long-term leadership comes from solving hard problems responsibly. That is where our focus remains.