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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)

If CNN stopped covering Trump, they’d be doing the country a favor. Almost everything they put out on him is a lie. They’re not commentators; they’re operators in an information war aimed at the American people, and they should have been shown the door at the White House a long time ago. They talk like they might walk away. They’re still covering him, we saw it at the UN. Even they know Trump is far too consequential to ignore.

Privileged to be a major investor in @boringcompany Series D and to have helped scale the team for 5 years. Vegas Loop proved what’s possible. With the $3B raise, TBC is expanding to many more cities in the US and abroad. This is a rare moment to join the team and help bring Loop to the next 100 cities. We are inviting a select group of exceptional engineers and operators to an all expenses paid, behind the scenes tour of the Vegas Loop on Sunday, Oct 18. Years of experience is not a filter. New grads and dropouts should apply. Apply by Oct 1

🇹🇭 Your next chapter with YZi Labs could start in Thailand. Join @EASYResidency S5 and build alongside founders, investors & operators worldwide. Applications are now open until September 21, 11:59 PM GMT-7. Explore previous seasons & apply 👉

BREAKING: Starlink’s high-speed internet is now available for Hawker 700, 800 & 900 jets after AeroMech received FAA approval. Operators can get up to 1 Gbps downloads, 100 Mbps uploads and latency as low as 20 ms over land, water and remote areas.

🚨 Threat Intelligence | The StealC Info-Stealing Chain Behind the Qwen Impersonation Repository SlowMist Security Team identified a #GitHub# repository impersonating local quantized weights for Qwen 3.8 27B. A real Q4_K_M 27B package should exceed 16 GB. The asset delivered was only 487 KB — no GGUF weights, just three files: Application.cmd, a renamed LuaJIT interpreter, and an obfuscated Lua script disguised as cert.txt. The official #Qwen# project was not compromised. The repo kept the look of a normal offline model project, while the malicious ZIP sat in assets/. After deobfuscation, the script collects host data, takes a screenshot, and POSTs them to C2. When the hardcoded server fails, it reads a fallback C2 from a Polygon contract via eth_call, so operators can rotate infrastructure with a single on-chain transaction. Preserved C2 responses then delivered an inner payload we attribute to #StealC#, targeting: 🔹 Browser logins, cookies, and history — including a Chrome App-Bound Encryption bypass 🔹 Email, WinSCP, and Steam credentials 🔹 Wallet-related files and extension data, dispatched by server-side tasks MistEye reconstructed the multi-stage chain and compared 29 similar ZIPs across 23 repositories using the same Lua delivery stack. Between two collection dates, repositories, filenames, the outer PE, and the AES key had already rotated. A 27B model that downloads in 487 KB is not a model. Inspect asset size and unpack downloaded packages before running them. Read the full analysis 👇

The X Safety team conducted an investigation into suspected Chinese inauthentic accounts involved in influence operations: We identified a bot farm of approximately 200,000 accounts. Within this farm, we found 200 accounts posting in a manner that could manipulate a legitimate debate about American AI and energy policy. These posts contained claims that AI data centers are driving up household electricity prices and straining the grid. Others included AI-generated cartoons that depicted data-center operators enriching themselves at the public's expense. We remain committed to maintaining an open and authentic platform where people debate topics of public interest. We take seriously any attempts to undermine the integrity of the global town square and suspend accounts that violate our Authenticity policy.

ZERO ALPHA Research Preview | Reframing NVDA NVIDIA’s latest earnings report is the trigger event for a new round of deep research. A company already among the largest in the world just delivered 106% year-over-year revenue growth, with Data Center revenue up 117%. What is striking is not simply that NVIDIA beat expectations again, but that its core business has returned to a doubling growth rate from an already enormous base, even as AMD GPUs, hyperscaler-designed chips, and custom AI accelerators continue to enter the market. That prompted us to go back and re-examine NVIDIA’s full growth trajectory since 2023. When revenue growth, earnings growth, stock-price appreciation, and P/E are viewed together, a very different pattern begins to emerge. The first NVIDIA spring was largely top-down. The market recognized the potential of generative AI first, the stock price moved ahead, and earnings later caught up. The second spring now looks increasingly bottom-up. Revenue growth re-accelerated from: 56% → 62% → 73% → 85% → 106% while valuation multiples moved lower rather than higher. In simple terms: First Spring: P led E. Second Spring: E is beginning to lead P. This earnings report therefore may represent more than another earnings beat. It may be a signal that NVDA itself needs to be reframed. It also raises a broader question: What actually defines a true mega-cap growth stock? A high P/E alone does not define growth. The rarest structure may be a company that is already enormous, still grows its core business near 100%, generates earnings faster than its stock price rises, avoids excessive valuation expansion, and continues to create new TAM. Applying this framework to AMD, MU, SNDK, LITE, ALAB, DELL, and the hyperscalers makes the leadership hierarchy increasingly clear. Many of them have strong growth, but each still carries a weakness in valuation, cyclicality, pricing dependence, platform control, or growth durability. NVDA currently presents a more unusual combination. More importantly, at least four additional growth engines are still developing: Pricing Power Supply Efficiency Open Models Inference Specialization If these continue to develop, today’s NVIDIA may not yet represent the peak of this second growth cycle. And NVIDIA’s second spring may not belong to NVIDIA alone. Memory and storage, optical networking, and AI data-center operators could all benefit if another AI infrastructure expansion cycle is now beginning. ZERO ALPHA will therefore use this earnings report — a mega-cap company returning to 100%+ core growth — as the starting point for a six-part NVDA Research Note series: 1/6. NVDA: The Second Spring — From P Leading E to E Leading P 2/6. NVDA: What Defines a True Mega-Cap Growth Stock? 3/6. NVDA: Why It Is Still in Its Prime, Not Near the Peak 4/6. NVDA: Four New Growth Engines — How Far Can the Second Spring Go? 5/6. NVDA: Why Leaders Lose Leadership — Lessons from Intel, Tesla, and AMD 6/6. NVDA: Will the Second Spring Reignite the Entire AI Infrastructure Chain? Each note will focus on one independent question and can be read on its own. ZERO Insight The most important message from this earnings report may not be that NVIDIA beat expectations again. It may be this: When a company already this large returns to 100%+ core growth while trading at a much lower P/E than during its first AI explosion, what needs to be revalued may not be just NVDA’s stock price — but our entire understanding of mega-cap growth.

From Digital Twins to Data Engine: Cutting the Real-Data Burden with Sim-Powered Robot Learning Teaching a robot a new task could take hundreds of teleoperated demonstrations. For foundation models, adapting to an entirely new robot can cost orders of magnitude more — dedicated hardware, trained operators, months of engineering. On @boosterobotics' dual-arm robot, we studied this at two levels: ✱ Specialist: Can task-aligned simulation mixed with a small set of real demonstrations reduce the real-data burden? ✅ Yes. With only 10 real demos, the policy made no contact at all in physical rollouts (0/20). Adding 50 simulated trajectories brought contact to 17/20. ✱ Foundation: Can data accumulated across tasks build a reusable starting point (a Booster-specific model prior)? ✅ Yes. After full-parameter continued pretraining, a model adapted with just 30 demonstrations per task beat the original given twice as many: 14/16 vs 10/16 in simulated evaluation. Before any task-specific adaptation, in zero-shot simulation, it was already roughly 3× closer to the target (17.27 cm → 5.78 cm). This work runs on Axis Suite, our Physical AI solution across different robot embodiments. Distributed contributors generate task-aligned sim data on Axis Hub at scale, reducing real-data needs for specialist adaptation while powering cross-embodiment generalist training. Read the full blog: