Stephen Smyth

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Stephen Smyth

Stephen Smyth

@StephenSmyth

Internet Marketing / Joint Ventures / The Future of Commerce is Everywhere, and That Future is Now / EX Shopify

Vancouver Katılım Mart 2008
7.5K Takip Edilen2K Takipçiler
Stephen Smyth retweetledi
Nous Research
Nous Research@NousResearch·
Shopify is the all-in-one commerce platform powering millions of businesses worldwide Thank you to the @Shopify team for building their own official Hermes Agent skill enabling your agent to manage products, orders, inventory, and fulfillments from any channel.
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Vanessa Lee
Vanessa Lee@vlaurenlee·
my typo was so bad I had to re-write. we already saved 700+ human *days* worth of time with v11 across Shopify POS checkouts. 700...days. we're on our way to making Shopify POS the fastest in-person checkout machine in the world. good work team. we're just getting started.
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Jessica Hertz
Jessica Hertz@jessicarhertz·
The execution layer gets all the attention. But the design conditions underneath it are the interesting, human part. @tobi and I get into it on the latest Context episode. We talk about my path from politics to @Shopify COO, invisible work, and why T-shaped people are becoming X-shaped. Highlights: 1:09 - A Nonstandard Career Path 5:12 - White House Transition Lessons 8:40 - T-Shaped vs X-Shaped People 10:18 - Fluid Intelligence and Agency 13:31 - Rethinking HR as Talent 15:26 - Flex Wallet and Boomerangs 17:04 - Building Systems and Teams 19:38 - AI-Assisted Small Teams 22:01 - The Social Coefficient 24:52 - Dead vs Alive Companies 27:20 - Incentives and AI Usage 29:49 - Our Relationship with Data 36:29 - Shopify's Position with AI 40:02 - Anthropomorphizing AI
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Shopify
Shopify@Shopify·
the Shopify AI Toolkit is here manage your store with your favorite agent Claude Code, Codex, Cursor, VS Code, and more
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tobi lutke
tobi lutke@tobi·
qmd 2.1 is out with all sorts of goodies. Code aware splitting, performance, official benchmarks etc etc
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Mani Fazeli
Mani Fazeli@mcfazeli·
The Shopify B2B capabilities we built for Plus merchants over the past 5 years are now available to every plan. This is HUGE! Wholesale done right, better than anyone. shopify.com/news/b2b-for-a…
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Shopify
Shopify@Shopify·
100+ AI tools. one mobile app. introducing Tinker: all the best AI tools, now in one place
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Harley Finkelstein
Harley Finkelstein@harleyf·
Brands on @Shopify are now shoppable inside @ChatGPTapp. AI shopping isn’t coming. It’s here. As always, our merchants are best positioned. Let's go.
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Felix Rieseberg
Felix Rieseberg@felixrieseberg·
By popular demand, Dispatch can now launch Claude Code sessions. Ask it to build, make, or improve something! To use it, update your Claude desktop app and make sure you have Code enabled.
Felix Rieseberg tweet media
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tobi lutke
tobi lutke@tobi·
qmd 2.0 is out. You can now use it in as a library with stable interface. Much easier to integrate. And all the other interfaces (cli, mcp, http) are now implemented in terms of that library, so that make the whole thing simpler.
tobi lutke tweet media
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tobi lutke
tobi lutke@tobi·
OK this thing is totally insane. Before going to bed I... * used try to make a new qmdresearcher directory * told my pi to read this github repo and make a version of that for the qmd query-expansion model with the goal of highest quality score and speed. Get training data from tobi/qmd github. * woke up to +19% score on a 0.8b model (higher than previous 1.6b) after 8 hours and 37 experiments. I'm not a ML researcher of course. I'm sure way more sophisticated stuff is being done by real researchers. But its mesmerizing to just read it reasoning its way through the experiments. I learned more from that than months of following ml researchers. I just asked it to also make a new reranker and its already got higher base than the previous one. Incredible.
Andrej Karpathy@karpathy

I packaged up the "autoresearch" project into a new self-contained minimal repo if people would like to play over the weekend. It's basically nanochat LLM training core stripped down to a single-GPU, one file version of ~630 lines of code, then: - the human iterates on the prompt (.md) - the AI agent iterates on the training code (.py) The goal is to engineer your agents to make the fastest research progress indefinitely and without any of your own involvement. In the image, every dot is a complete LLM training run that lasts exactly 5 minutes. The agent works in an autonomous loop on a git feature branch and accumulates git commits to the training script as it finds better settings (of lower validation loss by the end) of the neural network architecture, the optimizer, all the hyperparameters, etc. You can imagine comparing the research progress of different prompts, different agents, etc. github.com/karpathy/autor… Part code, part sci-fi, and a pinch of psychosis :)

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tobi lutke
tobi lutke@tobi·
Big qmd update. From 1.0.6 to 1.1.5 in three weeks with 20+ community PRs. Local search keeps getting better. npm i -g @tobilu/qmd
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