Sidekick is an AI Shopify expert (Practical AI #299)
Bibliographic record
Abstract
Today, Chris explores Shopify Magic and other AI offerings with Mike Tamir, Distinguished ML Engineer and Head of Machine Learning, and Matt Colyer, Director of Product Management for Sidekick. They talk about how Shopify uses generative AI and LLMs to enhance their products, and they take a deeper dive into Sidekick, a first-of-its-kind, AI-enabled commerce assistant that understands a merchant's business (products, orders, customers) and has been trained to know all about Shopify.Join the discussionChangelog++ members save 9 minutes on this episode because they made the ads disappear. Join today!Sponsors:Fly.io - The home of Changelog.com - Deploy your apps close to your users - global Anycast load-balancing, zero-configuration private networking, hardware isolation, and instant WireGuard VPN connections. Push-button deployments that scale to thousands of instances. Check out the speedrun to get started in minutes.Timescale - Purpose-built performance for AI Build RAG, search, and AI agents on the cloud and with PostgreSQL and purpose-built extensions for AI: pgvector, pgvectorscale, and pgai.Eight Sleep - Up to $600 off Pod 4 Ultra - Go to eightsleep.com/changelog and use the code CHANGELOG. You can try it for free for 30 days - but we're confident you will not want to return it (we love ours). Once you experience AI-optimized sleep, you'll wonder how you ever slept without it. Currently shipping to: United States, Canada, United Kingdom, Europe, and Australia.Featuring:Mike Tamir – LinkedInMatt Colyer – LinkedIn, XChris Benson – Website, GitHub, LinkedIn, XShow Notes:ShopifyShopify's Winter Edition '25Something missing or broken? PRs welcome!
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.335 | 0.064 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".