Better engineering with SLMs with Rachel-Lee Nabors

Описание: Most teams reach for foundation models by default — even when they’re energetically wasteful and slow.

This talk shows how Small Language Models (SLMs) deliver faster response times, offline capability, local-first privacy, and dramatically lower costs using 10–30× fewer resources.

02:22 The real costs of frontier models: security, latency, business, and environmental impact
04:42 Production ≠ feature chasing: why “do everything” models aren’t always right
05:32 When to use small language models (Gemma, Qwen) vs frontier models
06:51 Model size & memory basics + quantization for deployment
08:14 A 4-step framework to choose the best model for the job
10:04 Deployment principle: keep the model near the data
11:39 Local vs remote vs hybrid: how to decide based on offline, security, frequency, battery
13:42 Three local strategies: host it, use device models, deliver it to the device
16:21 Build your own local agent: Ollama + Qwen 3 (4B) + Goose (MCP tools)
17:24 On-device in the browser: Chrome’s Prompt API + live demo

Thank you to all our partner to make this happen! A big thanks to our gold sponsors for believing in us:

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Datalinks:
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Discover how scattered data can be unified and linked to power your agents and backends, all with a single click and some prompts: https://datalinks.com/

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Opening music:
‪@NIN‬​