Evidence brief

Agent products: Fireworks joins the AI Dev Stack productionization event.

@FireworksAI_HQ announced it will join @antimetal, @braintrust, and @browserbase at The AI Dev Stack: Beyond Code. The post said that beyond coding agents, the more interesting work is the tech stack for getting them into production and keeping them reliable, and provided an RSVP link.

Published: 2026-08-12
Updated: 2026-08-13
Editorial: Frontline Lab

Source: X
Source author: @FireworksAI_HQ
Related topics: 1
Collected: 2026-08-13

Frontline Lab summary and source

Editorial summary

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Source attributionX · @FireworksAI_HQ

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Related published evidence

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X2026-08-12

Query Agent Search Mode can now configure the tradeoff between recall and precision

Query Agent Search Mode rewrites natural-language requests into one or more Weaviate queries and returns matching objects; the new filtering parameter can choose the default recall or the stricter precision.

Original source

X2026-08-12

ExtractBench finds VLMs miss rows when extracting long lists

LlamaIndex released ExtractBench, covering 370 enterprise documents and 14 systems, with a focus on testing long-list completeness; it says Frontier VLMs achieve F1 scores of 8.9–35.8% on the longest documents.

Original source

Meta Engineering Blog（RSS）2026-08-13

WhatsApp Scam Alert detects scam messages on-device

WhatsApp introduced an optional feature, Scam Alert, which uses on-device machine learning models under end-to-end encryption to identify potential scam messages; message content does not leave the device or get automatically reported.

Original source

X2026-08-12

Paper study: ExtractBench 36-page ArXiv white paper

@Llama_index reposted @jerryjliu0’s post: the team wrote a 36-page ArXiv white paper on ExtractBench, calling it an effort to create a comprehensive, schema-guided, real-world-relevant benchmark.

Original source

X2026-08-12

OlmPool shows four architecture choices weaken long context

@dair_ai said Ai2, Carnegie Mellon, and University of Washington studied normalization, GQA, pretraining context length, and sliding-window attention, finding that combining more than three of them significantly reduces long-context performance, and released OlmPool.

Original source