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Vilhelm von Ehrenheim
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Vilhelm von Ehrenheim

@while
Co-Founder & Chief AI Officer at QA.tech πŸš€ Building the verification layer for AI SDLC Based in Stockholm | Join us: qa.tech
Stockholm
qa.tech
Joined July 2007
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    Vilhelm von Ehrenheim
    @while
    May 13
    Super proud to have been selected as the winner for the Vercel Day competition on @ProductHunt!! πŸš€
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    Product Hunt 😸
    @ProductHunt
    May 12
    we ran our first @vercel Day competition and the results are in winner getting a pitch to Vercel Ventures: @getqatech runners up getting $30k in Vercel perks: @specstoryai @calendarpipe @tal_elor @Helloaria_AI @arkyhq congrats to everyone who launched πŸŽ‰
  • user avatar
    Vilhelm von Ehrenheim
    @while
    Apr 26
    Reading the decision stack by @bfgmartin! This book really resonates w my experience in pretty much all organisations I have worked in. Big or small. The book draws on analogies w physics - Speed, Velocity and Momentum. Very relevant in engineering today where speed is easier
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    Vilhelm von Ehrenheim
    @while
    Apr 23
    Building out better AX @getqatech! πŸš€ Verification from inside the cc sessions to sanity check my crazy new features is soo nice!
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    Vilhelm von Ehrenheim
    @while
    Apr 19
    Everyone shipping with Cursor or Claude Code today, what’s your actual verification workflow? Reading every diff? Running tests? Trusting it? Genuinely curious what people actually do, not what they say they do. @ericzakariasson @bcherny @antonosika what are you seeing?
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    Vilhelm von Ehrenheim
    @while
    Apr 11
    Honestly feel like this is the same as we have done in ML for ages. Understand the problem you are trying to solve and set up good evals. With solid train and test splits. That way you can improve your model and reason about performance. There are no shortcuts. Build solid
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    Viv
    LangChain
    @Vtrivedy10
    Apr 8
    Article cover image
    Article
    Better Harness: A Recipe for Harness Hill-Climbing with Evals
    TL;DR: We can build better agents by building better harnesses. But to autonomously build a β€œbetter” harness, we need a strong learning signal to β€œhill-climb” on. We share how we use evals as that...

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