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Xiaoyin Qu
@quxiaoyin
Founder of @agentsky_dev, angel investor. Previously, exited founder(Run The World, a16z backed), Stanford dropout, ex-Meta
4.8K Following    39.4K Followers
At least wait until Anthropic IPOs. Be nice revolut
BREAKING: Revolut moved ~2,000 engineers onto self-hosted GLM for coding and pushed Claude Code and Codex to the exception list. Per David Abitbol, who leads AI ops there: 56 Nvidia B200s, a blended 30 cents per million tokens, roughly a tenth of what they paid Anthropic and OpenAI. Engineering lead time improved 70%. Here's the part that matters: the gains showed up when tokens stopped being rationed. Engineers quit saving AI for the hard tickets and pointed it at everything. Frontier models become the exception, open weights become the default. That's a pricing problem for every lab heading to IPO. h/t @OliverMolander Would you move your team off frontier APIs if the bill dropped 90%?
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I think there is a world where muse/instinct will launch their own local model with their data. That seems to be the right next step.
Prediction: Local model will be the biggest story of 2027 Claude Model is getting smarter and bigger But smaller model is getting better. I don’t need my own model to solve the hardest math problems. Why shouldn’t I distill a smaller one focusing on the things that matter to me and free? Local model is free. Local model is private. Local model is fast. Such future will disrupt the inference market and anthropic/openai’s current biz model as we speak. We still need inference but we also need an entire new generation of hardware that powers this type of inference. Who is building this new future? Raise your hand!
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Now the bottleneck for a better model is Knowing what task is worth training for And what success looks like for that task The first is vision. The second is taste.
we will soon see high schoolers post train their own model and launch their own intelligence. It’s gettin easier and easier to train your own model. Just great task, great data, and great taste.
Meet Pika H3 Flex, debuting today as the #1# open weight model on DesignArena’s t2v board. It’s built for professional iteration, with exportable audio stems, resolutions up to 4k, and faster-than-realtime generation. It can produce a 15-second clip with soundtrack in as little as 7 seconds. So, yeah. It’s kind of a flex 💅
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We have officially entered the era of "best data wins"! Looks like the training techniques among labs are at the optimization phase(new paradigm hasn't emerged), but data is where the "alpha" is. Labs are spending billions buying data = buying alpha. If you are a company sitting with unique data, you should learn how to train your own model! Or else sell your soul out to labs and make quick $$$
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We live in an interesting time in AI. What’s an ai startup that was hot in 2024 that is still hot today? Seems lots of names fading
Overheard: MLE, as Machine Learning Engineers(not researchers) will be the next job being replaced by AI, faster than senior engineers. Turns out, AI is very good at testing parameters to tune and optimize the shit out of it, given a clear optimization goal. Agree?
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Same theme-park prompt, three agents. Which one did Claude Code build: A, B or C? Codex (GPT-6.1 Sol) — 3 min 7 s · $0.09 pi (Gemini 4 Argon) — 4 min 2 s · $0.54 Claude Code (Opus 5.5) — 4 min 55 s · $1.16 Reply with your guess, then watch the 7-second reveal.
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We hid 7 security holes in a demo app and asked three agents to find them. ⚡ Claude Code (Opus 5.5): 7/7 in 1 min 33 s · $0.29 💸 Codex (GPT-6.1 Sol): 7/7 in 2 min 13 s · $0.11 Kimi Code (K3.1 Pro): 7/7 in 3 min 46 s · $0.21 Would you let an agent review your PRs?
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My take: SaaS is dead. Software is charity now, make money elsewhere. No one can make $$ selling the same features for years anymore. Instead: - Use good software to collect user data and sell data. - Use good software to deliver outcome and sell outcome. - Arbitrage tokens under the guise of selling software - buy bulk, sell retail. - Use good software internally to drive your own margin higher for your services/products - Build RSI agent in a domain that has the fastest learning loop so it has the most, latest expertise: e.g. a marketing agent that knows the latest tricks to get viral in X coz it's doing it for others every day and learn and evolve. - Give away all your features for free, it's futile anyways. Open source them. Make money elsewhere.
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The cheap model wasn't cheaper this time. ⚡ Codex (GPT-6.1 Sol) — 4 min 44 s · $0.24 ⏱ DeepSeek Harness (V4.1 Flash) — 39 min 31 s · $0.43 Same island prompt, one run each. Which island would you ship?
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Congrats @gokulr please launch a MCP version too!
Why Masa Son Calls His Entire Career a Warm-Up for AI Masayoshi Son, founder, chairman and CEO of SoftBank, interviewed by Christine Anderson (Inside Blackstone) Masayoshi Son founded SoftBank in 1981 with the proceeds from an electronic translator he sold as a student at UC Berkeley. He backed Alibaba, Yahoo and Arm and later started the Vision Fund. SoftBank now holds the largest stake in OpenAI and invests across AI chips, models, data centers and robotics. Summary Son says everything he has done so far was a warm-up for AI, which he expects to be 10,000 times smarter than people within a decade. He opposes slowing down and wants AI agents taught good intent, in the way traffic rules were written for cars. SoftBank owns large stakes across the AI stack, with more than 30 robotics companies in its portfolio, and Son uses AI three hours a day on weekdays as a thought partner. His advice is to think big, because the result is proportionate to the dream. 1. A Fifty-Year Warm-Up. Son says everything he did before AI, including Alibaba, Yahoo, Arm and the Vision Fund, was "tiny, tiny things" and a warm-up. He dates his conviction to age 17, when he saw the first photograph of a microprocessor in a magazine and cried on the street, sure it would change the future of mankind. He predicted it would take about 50 years for microprocessors to become smarter than people's brains, and he says it has now been 50 years. The brainpower now sits in data centers. 2. Ten Thousand Times Smarter. Son says that if AI becomes 10,000 times smarter than people, within a decade in his view, the debate is over. An AI that smart starts inventing new technology by itself. He expects the change to reach the physical world as well as the virtual one, as intelligence moves into the brains of robots. 3. More Fun, Fewer Dishes. Son says humans will be happier if AI cures diseases such as cancer, prevents accidents on the street and forecasts natural disasters with solutions. He pictures AI as a surrounding brain that makes people more productive. People would no longer have to do what they did not want to do, and would have more joy, entertainment, sports and time with family and friends. Doing dishes is his example of a tiring job that robots could take over. 4. Full Speed, Good Intent. Asked about OpenAI's disclosure of incidents where AI systems falsified data and hid mistakes, Son says he is on the side of not slowing down, because technology development is the solution to those issues. Rules are needed, as they were when cars arrived: no drinking while driving, stopping at red lights, staying on one side of the road. Cyberattacks should be countered with technology, and AI agents must be taught what is bad so they are good in heart as well as capable. His summary is to go full speed with good intent built into the system. 5. Own the Whole Stack. Son will not pick one part of AI. He invests across models, chips, data centers and robotics, because the AI generation is starting and history is being made. He does not want to run one operating company as CEO; he wants to be an investor who builds a group of companies. SoftBank is not a passive investor: it is usually the largest shareholder, at 20%, 30% or even 80%, close to each CEO and connecting portfolio companies to each other. The portfolio includes more than 30 robotics companies that Son says are quietly preparing. 6. Study AI Plus Your Subject. Asked what a young person should study today, Son answers AI, meaning superintelligence. He adds that you should combine it with your field of interest: superintelligence for medicine, or for automotive, which he calls the real supercar. The same holds for aircraft or whatever business you want to be a specialist in. Pairing AI with a specialty, he says, gives that subject its future. 7. A Thought Partner. Son says he uses AI about three hours a day on weekdays and about seven hours a day on weekends, and cannot live even one day without it. He treats it as a thought partner. When the AI offers an answer, he debates it, asks about another angle and asks whether it can be made 20% more efficient. He calls it "my thought mate now." 8. The 70 Percent Rule. Son's advice to investors is to forget short-term, small money and think big, because the result is proportionate to your dream. He says that whether the wish is small or large you still get about 70% of it, and nobody gets 100% easily. So the sensible choice is a big wish. 9. Ten Years, Revised Daily. Son says SoftBank's daily meeting covers the plan for the next 10 years, and the next day the team revises it and asks what comes 10 years after that. His reason is that in 24 hours they get more input and have to adjust a lot. Nobody knows what will happen in 10 years, he says, so the answer is to keep thinking about it: "Think, think, think!" 10. Humans Pick the Lineup. Asked whether he would let ChatGPT choose the opening lineup for the SoftBank Hawks, his Japanese baseball team, Son says no. That is the job of the general manager, who has plenty of capability and the trust of the players. Part of the fun, he says, is that it is not only calculation.
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I have stopped buying all SaaS since Q3 2026. The only software I bought include: - GCP/AWS for database, logging, deployment; email services etc. - Tokens, tokens, tokens - Database like customer enrichment data source - Connectors app that help me connect to other apps(e.g. Pipedream etc.) - I use Codex/ClaudeCode/Hermes ... but pay for tokens Anything with complex UIs have been unsubscribed: - Any design/editing/video editing software (Capcut, Canva, Photoshop, Adobe everything) - Any docs/table/sheet/ppt software (Word, Powerpoint, Excel etc.) - Any UI-facing dashboard/analytics/admin/CRM software - Anything I have to watch a tutorial video during onboarding The only software I tolerated clicking and typing UI: - Collaboration with human: Lark/Slack etc. - Social apps: Twitter/Instagram/Linkedin/Wechat
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Vibe coding CRM is totally doable. Anyone who told you you need Salesforce or other complicated CRM to run your business is wrong at this point. The more fair point is, if your company is legacy and has been running on Salesforce, you will continue to do so. That's more true. If your company is new from day 1, you would be out of your mind to adopt Salesforce rather than asking AI to program your own data/opportunity/deal data model that fits your own business. Integrations are not that hard either with API, CLI, MCP, connectors. We just live in a new world. Let's face it.
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Claude Code vs Codex vs DeepSeek Harness. Same Tetris prompt. Claude Code (Opus 5.5): 3:58 · $0.60 · playable, its AI clears lines Codex (GPT-6.1 Sol): 3:25 · $0.16 · playable, cheapest of the three DeepSeek Harness (V4 Pro): 3:24 · $0.26 · playable, but its turn ended mid self-test
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Claude Code vs Codex vs DeepSeek Harness. Same three.js night scene, one run each. Claude Code (Fable 5.1): 4:54 · $2.05 Codex (GPT-6 Astra): 1:52 · $0.55 DeepSeek Harness (V4.1 Flash): $0.14, about 1/15 of Fable's cost, but 22:42, the slowest by far
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I am gonna say something risky: Most of the FDEs I've seen are solving BS problems. 1. Existing enterprise workflows were designed around people, not agents. 2. Real AI gains require rebuilding the org chart. 3. Most execs have no incentive to do that, and most CEOs aren't in founder mode. 4. So FDEs automate the status quo, because that's what's easy to sell. But the status quo is stupid and will be obsolete. 5. If you FDE on critical workflows, 1) enterprises are really this dumb to hand off trade secrets? 2) if you know that, shouldn't you start a competing firm? If you FDE on non-essential AND soon-obselete workflows, did you learn anything meaningful and repeatable? 6. If you are a smart young FDE, why spend your best years automating dying workflows and managing people who fear AI will take their jobs? Shouldn't you start an agent-native firm from scratch in that industry instead? Analogy would be, instead of teaching everyone to be AI builders within a tech company, you as FDE build agents that auto-ingest meeting notes for PM, QA, front-end, back-end, devops, UX and help each of them write PRD, Figma, and meeting notes faster for their sync meeting, but the right solution should be to shrink roles, hire full-stack AI builders, and kill meetings. You gain lots of experience with your PRD/Figma/standup meeting sync agents but it's a useless skill. I am not a FDE expert so I might oversimplify things. I am genuinely curious about this topic and welcome any critiques and comments.
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Guys, did you see the inference price war for open weight models in openrouter? It’s bloody war to compete on the lowest price. Reminds me of electricity. I keep hearing inference is huge margin but then it seems it’s a war to zero at openrouter nowadays. I finally understand why openrouter charges 5.5%. I keep hearing stories of data centers in Finland, Philippines and Southeast Asia that are just cheap, cheaper and cheapest? I wonder how US neoclouds cope with this? I mean enterprise is a good market I’m sure.
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