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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)

C Ye @C_Ye__ & Britney’s Drama reminds me another story back in 2023. It’s kinda like Deja Vu. Britney leveraged her fame and connections to bully an unknown small-time poker player. I’ve seen many hustler players backing Britney. A lot of them were coerced into supporting her, yet none stepped up to speak up for C Ye. To me, this is grossly unfair and amounts to collective bullying. Most of them don’t even know what really happened. They may simply fear that if they fail to back her, she will get them banned from Hustler @HCLPokerShow since she is the “Queen” of hustler. XD This happened in 2023 at the Bicycle Casino in Los Angeles. Britney set up a game with Peter. She personally brought in two players, Hank and Jimmy, and asked another host, L, to bring five more. (L is just a codename — he is a very well-known Chinese host, but he prefers not to have his name mentioned here.) Most importantly, Britney bought action in all of the players — every player’s wins and losses ultimately ran through her. Charles the Prince also played in the game for about an hour and had action in Hank as well. That means both L and Charles were financially exposed to Hank’s play that night. To this day, L still believes that Hank and Britney were together at the time. The players on L’s side ended up losing. But when it came time to settle up, Britney refused to pay, claiming that someone had stolen chips from the table. The game itself balanced, though — every number was accounted for. Since Britney had action in all of the players, the “stolen chips” story looked less like an explanation and more like an excuse to avoid settling. And when people pushed back, she firmly denied owing the debt. Alan, an Australian regular who used to play at the Bike, happened to be there that night, and he took the matter into his own hands. He spent 36 hours on it. For the first 20-plus hours, he negotiated with the casino and obtained everyone’s deposit and withdrawal records along with the surveillance footage. The review showed that the person who had actually taken the chips was Hank — one of Britney’s own players, and reportedly someone very close to her at the time. Alan handed all of the evidence to Britney. She still refused to pay — and even after all of this came out, she kept up her good relationship with Hank. Only after their relationship later ended did she start distancing herself from him and publicly criticizing him, as if she had never had anything to do with him. Later, L — who was in Vegas at the time — called Britney and warned her that the police would be involved if the payment wasn’t made. Meanwhile, Alan and several other pros were right there at the Bike. Alan never left — he stayed the entire time for one purpose: to get his money. He was relentless. In the end, Britney had no choice but to pay up. Start to finish, the whole thing took 36 hours.

Nneka navigating traffic 🚦 Take a look at the Ogwumike bucket to end the first half with 12 PTS & 8 REB GSV-LAS | League Pass | 2026 Postseason Push | @DKSports Tap to watch:

Nothing but smiles in Connecticut 🤩 Mohegan Sun is electric after the @ConnecticutSun close out their final night at home with a win. 2026 Postseason Push | @DKSports | #WNBASeason30#

You already know Chiney Ogwumike rides for her sister 😎 She sounds off on her emotions towards Nneka's last game tonight! GSV-LAS | League Pass | 10pm/ET | 2026 Postseason Push | @DKSports

The sun sets one final time in Connecticut 🌅🧡 Ahead of tipoff, rewind through the dominance that defined the @ConnecticutSun and helped shape basketball across the Northeast. 2026 Postseason Push | @DKSports | #WNBASeason30#

阿里把团队内部用了两年的官方 AI Code Review Skills 开源了，采用 “确定性工程 pipeline + AI Agent” 的混合架构，专门解决通用 Agent 做代码审查时 “漏审、定位漂移、质量不稳” 的老问题。 40.5K ✨ 开源项目 OpenCodeReview： # 核心设计：确定性工程 pipeline × Agent 各司其职 确定性工程负责硬约束： · 精确文件选择：用代码决定哪些文件必须审、哪些要过滤，不依赖模型自觉； · 智能文件捆绑：把相关文件合成一个审查单元（例如 message_en.properties 和 message_zh.properties 捆绑），每个单元以上下文隔离的 sub-agent 运行，分治策略让超大变更集也稳，且天然支持并发（默认 8 个文件 worker）； · 细粒度规则匹配：内置约 54 个按语言/文件类型的规则文档（Java、Go、TS/JS、Python、Rust、SQL/XML mapper、properties 等），用模板引擎而非自然语言把规则匹配到文件特征上，从源头消除信息噪声； · 外部定位与反思模块：评论的“落点”和“内容”分别由独立的 re-location 和 reflection 模块系统性校正，这正对“位置漂移”痛点。 Agent 负责动态决策： · 深度优化的场景 prompt（内部分为 plan → grouping → main → memory_compression → re_location → review_filter 多个任务模板，可在 internal/config/template/prompts/ 看到）； · 从海量生产环境的 tool-call 轨迹（调用频率分布、单工具重复率、新工具对调用链的影响）反向蒸馏出的专用工具集，包括全文件读取、代码搜索、其他变更文件查阅等，比通用 agent 工具箱更小更稳。 # 能力面与生态集成 功能上覆盖：workspace/分支区间/单 commit 审查、断点恢复（ocr session）、全文件 scan（无 git 历史也能审计陌生代码库）、本地 Session Viewer 网页查看与回放、SARIF/JSON 输出、OpenTelemetry 可观测性、MCP Server 扩展。 作为 “Skills 生态” 级项目，它的形态相当完整：既提供 npm 全局 CLI，也提供可移植的 Agent Skill（skills/open-code-review/SKILL.md，带标准 frontmatter，可直接被兼容 skill 的 agent 加载），还有面向 Claude Code、Codex、Cursor、Kimi Code、OpenCode 等平台的插件，每种都封装成斜杠命令或可调用 skill。LLM 侧兼容 OpenAI、Anthropic、AWS Bedrock 三类协议，并可直接复用 Claude Code 的 ANTHROPIC_* 环境变量。 其中一个设计很巧妙：Delegation 模式（ocr delegate preview/rule）。此时 OCR 只做自己擅长的确定性部分（文件选择和规则解析）审查本身交给宿主 coding agent 的 LLM 执行，用户无需给 OCR 配任何 API key。这实际上是把“harness 能力”与“模型能力”彻底解耦。 # 工程质量：超出平均水准的部分 · 安全有正式的 Assurance Case（ASSURANCE_CASE.md）：完整的威胁模型、四条信任边界、T1–T7 威胁逐条给出缓解措施，并按 Saltzer & Schroeder 设计原则和 OWASP Top 10 做了映射。细节经得起推敲：所有外部进程调用只限 git 且子命令硬编码、--end-of-options 防 flag 注入；Agent 读文件路径经 pathutil.WithinBase() 在符号链接解析前后双重校验；本地 Viewer 有 Host 白名单防 DNS rebinding + 严格 CSP。这类文档在一般开源项目里非常罕见。 · 贡献规范近乎严苛（AGENTS.md）：使用 AI 必须在 issue/PR 中披露工具与模型、必须逐行理解 AI 生成的代码、禁止“AI 生成→反复修复→再修复”的循环、禁止把 commit 署名给 AI。源码强制英文（CI 有 english-check，连全角标点都查）、90% 测试覆盖率门槛、-race 与 govulncheck 每次 push 都跑、SPDX 头与 LF 行尾强制。 # Benchmark：数据情况 官方基准 AACR-Bench（已在 Hugging Face 开放）规模不小：50 个流行开源仓库、200 个真实 PR、10 种语言、80+ 资深工程师交叉验证出 1505 条标注问题。结论是同模型对比 Claude Code：Precision 和 F1 显著更高、token 消耗约为 1/9、速度更快。 需要指出两点：其一，Recall 低于通用 agent，README 自己承认这是“以精度换噪声”的刻意权衡，如果你最怕漏问题而非误报，可能不适合；其二，该基准由阿里自建，虽开放了数据集供社区复核，但独立第三方的复现结论目前还少，可以把它当作“有披露的、方向可信的参考”。

I asked @claudeai Opus 5.5 to make an animated video about a robot who realises he's stuck in an AI-generated world. I wanted to really push it: 12 worlds, 12 styles, from 8-bit to claymation to pencil sketch, you can really see how well this model has imagined all worlds. This is the best animated video you will see today made by Opus 5.5, I promise.

Dominique Malonga is HOT 🥵 She hits her first three for 19 PTS & counting! DAL-SEA | USA Network | 2026 Postseason Push | @DKSports Tap to watch:

So deserving of all the praise 🙌 New York making sure Teresa Weatherspoon feels the love ATL-NYL | USA Network | 2026 Postseason Push | @DKSports Tap to watch: