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RT @yoheinakajima: had our LP summit today, sharing slides for the state of VC market section (obv not comprehensive, but what stood out t…
VC by day @untappedvc, builder by night: @babyagi_, @pippinlovesyou @pixelbeastsnft. Build-in-public log: https://t.co/UdHHGbZba5
Instead of investing in American manufacturing, Koreans should invest in cock enlargement moonshot to end the gender war. Normally that’d be politically impossible, but since they admit the issue… Eh. China will probably solve that too. Like in Sorokin’s Day of the Oprichnik.
We're in a race. It's not USA vs China but humans and AGIs vs ape power centralization. @deepseek_ai stan #1, 2023–Deep Time «C’est la guerre.» ®1
随手分享小技巧如何让你的命令行也支持指纹 Mac 很多时候我们用命令行工具执行系统动作时候，都需要手动输入开机密码，之前一直想有没有办法用指纹的方式，后面 mole 支持了这个功能，直接 mo touchid 一键设置后，后面所有的命令行需要系统权限的时候，你只需要手指按下指纹就 OK 了，非常方便，属于 mole 的一个非相关功能了。
Father of Pake • MiaoYan • Mole • XRender
RT @LakshyAAAgrawal: Why write the prompt by hand, when you can have GEPA discover a detailed spec for your task, automatically? https://t…
Asst professor @MIT EECS & CSAIL (@nlp_mit). Author of https://t.co/VgyLxl0oa1 and https://t.co/ZZaSzaRaZ7 (@DSPyOSS). Prev: CS PhD @StanfordNLP. Research @Databricks.
My top takeaways from @cohentomer (LinkedIn CPO): 1. According to LinkedIn's data, by 2030, 70% of the skills needed to do your current job will change. Whether or not you’re planning to change jobs, your job is changing, and the only question is whether you can keep up. 2. Building products isn’t complex—the process has made it complex. Product development itself is simple: research a problem, design it, code it, launch it, and iterate. But over time, companies turned each step into dozens of sub-steps requiring multiple teams, reviews, and functions. 3. LinkedIn replaced their Associate Product Manager program with an Associate "Product Builder" program, which teaches employees coding, design, and product management all together. Starting in January 2025, new hires learn all three disciplines from day one rather than specializing in one function. This represents a fundamental shift in how companies develop talent. 4. Feeding AI agents curated “gold examples” works far better than giving them access to everything. When LinkedIn tried letting AI tools access all their internal documents and data, it failed miserably—the AI couldn’t determine what was important and produced unreliable results. The key is carefully selecting the best examples and highest-quality information. Quality matters more than quantity. 5. Top performers adopt AI tools more than struggling employees do, making great people even better. Contrary to expectations that AI would help lower performers catch up, the best talent at LinkedIn uses these tools most actively. AI appears to amplify existing capabilities rather than level the playing field, with top performers having an innate drive to stay at the cutting edge of their craft. 6. The formula for measuring AI’s value is: experimentation volume × quality ÷ time to launch. This framework helps assess whether AI implementation is actually working. Success means teams can run more experiments, produce higher-quality work, and reduce the time from idea to launch—all three factors must improve together. 7. Getting AI adoption is 20% technology, 80% change management. Only about 5% of employees will naturally adopt cutting-edge tools on their own. The vast majority need active support through cultural change: updating performance reviews, celebrating wins publicly, dedicated training, and having leaders personally demonstrate tools rather than delegate. You need to give people time to invest before expecting productivity gains. 8. Teams are already saving hours per week and doing jobs they couldn’t do before. Designers are writing code and fixing bugs directly, product managers are building their own dashboards—tasks that previously required waiting for other teams. LinkedIn’s maintenance agent now automatically fixes close to 50% of failed builds without human intervention. These cross-functional capabilities are emerging as the tools mature. 9. The most important skills for builders in the AI era are the most human: vision, empathy, communication, creativity, and judgment—everything else can be automated. While AI can handle research, data analysis, coding, and design, humans still excel at five critical areas: vision (having a compelling view of the future), empathy (understanding unmet needs), communication (rallying others around ideas), creativity (finding possibilities beyond the obvious), and judgment (making quality decisions in ambiguous situations). 10. Don’t wait for permission or perfect conditions to start. Whether you’re a leader or an individual contributor, waiting for formal programs or organizational restructuring means falling behind. Start using AI tools immediately, build examples of success, and demonstrate what’s possible. The incentives align: organizations need people who can adapt quickly, and you need to stay relevant in your career.
Deeply researched product, growth, and career advice
.@meetgranola's crunched is the workplace horoscope we all need this holiday season
Partner @A16Z - started @speedrun, prev @Initialized, 2X founder - acquired into @Farmville @Zynga
