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I dramatically underestimated the value of Grok Bot in my Tesla. Can you technically do everything you’re able to do with Grok Bot in the Tesla just by launching the app on your phone and going voice mode? Sure. But the magic isn’t in the raw capability itself, it’s in the combination of raw capability and total seamlessness. I often have fleeting thoughts while driving around. Some of these things aren’t significant enough to make me fumble around with my phone while supervising FSD, but when I literally don’t have to lift a finger and can just “Hey Grok” anything computer related into reality, I find myself taking advantage daily. And the little things add up. I’m on my way to work and remembered that I have a few documents on my home computer that I am going to need for work. So I just asked “Hey Grok, can you grab those 4 pdf files from my Mac downloads folder and move them into X folder on my one drive?” Now they’ll be where I need them to be when I get to my desk, and I won’t have to re-download them on my work machine. I have to work on a presentation today for a talk in a few weeks. “Hey grok, go into my sales enablement folder and have Claude Code build me a deck focused on X product for Y industry.” Claude already has my full job context, styling preferences etc and can build a nearly finished deck with a prompt. Now when I get to my desk, instead of building from scratch, I can review and prompt revisions before beginning to dig through my email backlog. Feels insane.

📢 Claude Sonnet 5.5 Is Now Live on As @AnthropicAI’s latest Sonnet model, Claude Sonnet 5.5 is the faster, more cost efficient complement to Claude Opus 5.5, delivering 30%+ faster output and up to 30% lower cost per task. Built for software engineering, Agent workflows, and professional documents, with image/PDF understanding, report, presentation, spreadsheet & UI generation, 5 reasoning levels, and a 1M-token context window, with no extra charge for long-context usage. Now available on both API and Web Chat! 👉 Try now: 🔗 Learn more:

2️⃣ Visual-first conversational search Ask any question about arts and culture and receive a visually rich, context-aware answer grounded in authoritative partner archives—pairing insightful explanations with high-resolution artworks, curated stories, and events.

We recently had a user whose account was compromised. Our customer service team identified the situation quickly and froze the account. We then worked with the user to restore their original email and authenticator, helping secure the account and prevent further risk. Unfortunately, an API key that remained on the account allowed the attacker to transfer the funds before the issue could be fully contained. We are still investigating exactly what happened. But one thing is clear to us: we do not believe the user should have to bear the consequences of this incident. We immediately put together a dedicated team to handle the case, and we have fully compensated the user. We have always believed that being user-first means more than just saying it. When our users need us to step up, we will. Thank you to everyone for your trust, and especially to the user involved for giving our team the time today to work through this properly. We’ll keep learning, keep improving, and keep moving forward.

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)

我从2025年就一直反复强调。 现在所有大学本科生最重要的第一节课，就是买一个最大的coding plan，用上claude code或者codex， 第二节课是自己做一个最最最小版本的coding agent，可以对比codex或者claode code的基本功能，只要能输入一个基本功能，iteratively让agent完成写代码、编译、测试、 运行的功能即可，一切在terminal里，先把terminal和tool calling功能做好， 第三节课是认真观察codex和claude code的基本功能，把里面的memory、skills、multi agent/subagent、background tasks、session管理、context compression、TUI/GUI设计、如何可视化diff、如何管理好额外的btw等等类似的功能、如何把goal的功能放进去、如何实现scheduled tasks、如何实现权限管理等等，一步步一点点摸索实现出来。 我反复讲，一个计算机本科生能看完立党AI研究学习教程，把上面这三节课做完，就已经吊打清华计算机80%以上的本科生了。