Local models went from mostly useless to actually useful really fast. What changed?
Summary
The post notes that local AI models have become significantly more useful over the past year, moving from toys to practical tools for coding and workflows, despite still lagging behind closed models for complex tasks.
Similar Articles
Are local models becoming “good enough” faster than expected?
The article discusses the growing viability of local AI models for everyday tasks, suggesting a shift toward hybrid architectures that optimize for cost and latency rather than relying solely on frontier cloud models.
Running local models is good now
The author reports that running local AI models has become surprisingly good, with recent releases like GPT-OSS and Gemma 4 enabling agentic coding locally at about 75% accuracy of frontier models, a significant improvement from just months ago.
How I'm using local models from real-world coding
The author shares their experience using local AI models for real-world coding tasks.
Local models in mid-2026
A technical overview of the state of local AI models in mid-2026, highlighting how open-weight models have narrowed the gap to frontier models through advances in mixture-of-experts and sparse attention, enabling efficient local inference.
@vboykis: new post: how I develop recently using local models. the tooling is now good enough to do agentic workflows and everyon…
Vicki Boykis shares her experience using local AI models for development, noting that recent releases like Gemma 4 have made agentic workflows feasible locally with about 75% accuracy of frontier models.