AI news for platform & agent engineers
Ranked signal · finite reading
One shared ranking. Scan what changed, save what matters, and stop when the finish line appears.
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For the past 3 years, AI evals have been my professional focus. 1 The most common objection I hear to evals is “our product is hard to eval”. This objection is a product smell. Artifacts that are hard for you to verif... Context & related coverage →
OpenWiki generates and maintains codebase documentation so coding agents can find the repo context they need without loading everything into one instruction file. Context & related coverage →
MiniMax-M3 : Added support for the new MiniMax-M3 model , with a fast follow-on of BF16/FP8 indexer via MSA , MXFP4 support , FP8 sparse GQA , and extensive... · DeepSeek-V4 keeps maturing : Following its debut, DeepS... Context & related coverage →
Ornith-1.0: Self-Scaffolding LLMs for Agentic Coding This is an interesting new open weights (MIT licensed) model, the first model release from DeepReinforce. [...] with variants including 9B Dense, 31B Dense, 35B MoE... Context & related coverage →
Elastic open-sourced Atlas, a system built on Elasticsearch that maintains three categories of memory for agents. Atlas integrates with agents via MCP and maintains per-user isolation of memories. When evaluated on qu... Context & related coverage →
Evaluating long-running, stateful agents needs a new kind of runner. Here's how Deep Agents, LangSmith sandboxes, and observability plug into Harbor. Context & related coverage →
Scaling inference compute, by generating many parallel attempts per problem, is a costly but reliable lever for improving language model capabilities. By default these attempts are generated independently, wasting inf... Context & related coverage →
shot-scraper video is a new command introduced in today's shot-scraper 1.10 release which accepts a storyboard.yml file defining a routine to run against a web application and uses Playwright to record a video of that... Context & related coverage →
Memory has emerged as a cornerstone of modern LLM-based agents, supporting their evolution from single-turn assistants to long-term collaborators. However, memory is not always beneficial: retrieved memories often ind... Context & related coverage →
AlloyDB is an AI-native database—it isn’t just a passive data store, it intelligently understands and processes your data. With AlloyDB, you get industry-leading vector and hybrid search, near 100% accurate natural la... Context & related coverage →
Target built a generative AI system to improve marketing campaign forecasting by retrieving and ranking similar historical campaigns. Using embeddings, vector search, and LLM ranking, it replaces rule-based workflows.... Context & related coverage →