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The Human Context Advantage: Takeaways from the Ai4 Keynote with Andrew Ng, Geoffrey Hinton, and Fei-Fei Li
From jobs and education to open models and AI infrastructure, the AI4 keynote made one thing clear: competitive advantage won't come from AI alone, but...
Your agent wants to search like a 2010 quant
The idea of empowering AI agents to retrieve information like a professional is going mainstream.
How Perplexity beat Google on AI Search with Vespa.ai
Perplexity demonstrates the quality of their search solution and show what it takes to achieve it
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Photo by
Maksym Kaharlytskyi
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Unsplash
Accelerating Map Search in Vespa: Fast-Search Map Fields
A deep dive into the map data type in Vespa, its performance pitfalls, and how the new fast-search map fields fix them.
The Future of Retail Isn't More Choice. It's Better Discovery.
Explore how AI-powered product discovery helps retailers overcome choice overload, improve relevance and deliver better shopping experiences with Vespa.ai.
Why retrieval quality is becoming the defining challenge in AI agent architecture
Why retrieval quality is the ultimate bottleneck in AI agents—and how fixing context-building solves hidden LLM generation failures.
Deploy, Discover, Inspect, Observe: A Summer Spent Making a Public Vespa MCP Server
We built a standalone Vespa Cloud MCP server as a summer interns project
Vespa Newsletter, September 2026
Advances in Vespa include time-constrained ANN search, sub-query ranking support, flexible provisioning, new rank features and telemetry export
The Human Context Advantage: Takeaways from the Ai4 Keynote with Andrew Ng, Geoffrey Hinton, and Fei-Fei Li
From jobs and education to open models and AI infrastructure, the AI4 keynote made one thing clear: competitive advantage won't come from AI alone, but from connecting it to the...
Your agent wants to search like a 2010 quant
The idea of empowering AI agents to retrieve information like a professional is going mainstream.
Re-autoresearching MSMARCO BM25, on Vespa
BM25 is having a moment. We reproduce Doug Turnbull's MSMARCO autoresearch experiment in Vespa and get a comparable MRR@10 lift from existing rank features — with twice the generalization to...
Vespa Newsletter, May 2026
Advances in Vespa include finer control over deployments, smarter ranking, richer embedding integrations, and more scalable vector search.
Photo by
Shaun Sullivan
on
Unsplash
Scaling a Vespa Application: Feeding Fast and Furiously
A tutorial on how to scale the resources in a Vespa application to increase feed throughput. Using the metrics dashboard for informed and optimised scaling.
The Vespa Cloud Metrics Dashboard
A guide to the Vespa Cloud metrics dashboard — how to move from symptom to bottleneck to action, and what's new in the latest revision.
Using Large ONNX Models with External Data in Vespa Embedders
Many ONNX models exceed the 2GB protobuf limit and store weights in external data files. Vespa now supports these models for embedders.
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