RAG
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RAG
RAG coverage belongs in AI Glossary. High-frequency AI terms readers encounter in news, papers, and product launches.
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Policy and SafetySep 25, 2026
The Anthropic blacklist ruling turns AI procurement into policy leverage
Ars Technica's coverage of a court ruling involving Anthropic and federal blacklisting shows how quickly AI access can become a procurement and political pressure point.
InfrastructureUnscheduled
Crusoe dropping Boom turbines shows AI data-center power plans are still fragile
Crusoe stepping back from a $1.25 billion plan to use Boom turbines at AI data centers is a useful reality check for the AI power boom. Ambitious energy ideas are easy to announce when compute demand is exploding; they are harder to integrate into near-term infrastructure plans.
Policy and SafetyUnscheduled
Apple's sensor-signed images move AI provenance closer to the camera
InfoQ's coverage of Apple's Reference Image design points to a major provenance shift: trust may have to start at capture, not after an image has already entered the content pipeline.
ModelsSep 22, 2026
Claude Opus 5.5 turns the model race into a margin fight
The Decoder's coverage of Claude Opus 5.5 matching a rival model at lower cost shows how quickly AI competition is becoming a margin fight. The story is not only who tops a leaderboard, but who can deliver comparable capability at a price developers can actually use.
Developer ToolsSep 17, 2026
Agents are becoming a developer platform, not just a feature
InfoQ's coverage of platform artificial intelligence captures a shift developers are already feeling: agents are becoming an application layer that combines semantic search, data tools, code execution, and workflow orchestration.
Policy and SafetyUnscheduled
OpenAI and Microsoft's court documents expose the web's AI bargain
The Verge's coverage of unsealed New York Times case documents cuts to the core of the AI-and-publishing fight: leading AI companies understood that scraping the web could weaken the same information ecosystem their products depend on.
AgentsSep 14, 2026
Enterprise agent harnesses are becoming the missing layer between models and work
AI Business's coverage of agent harnesses gets at a problem enterprises are now running into: a powerful model is not the same thing as a controlled worker. Companies need coordination, permissions, observability, memory, and rollback around agents before they can trust them with business processes.
Policy and SafetySep 12, 2026
Claude misuse reporting shows AI abuse is spreading across domains
WIRED's follow-up coverage of Claude misuse matters because the examples are no longer confined to one narrow abuse case. The reporting connects hacks, bioweapon concerns, and other misuse domains into a broader picture of how capable AI systems can be repurposed.
GlobalSep 14, 2026
China's response to U.S. AI warnings turns safety into geopolitical messaging
The Decoder's coverage of China pushing back on U.S. AI safety warnings shows why global AI governance is so hard. One side can frame safety as necessary restraint; the other can frame the same warning as a tactic to lock in national advantage.
Policy and SafetySep 10, 2026
Model distillation allegations are turning AI capability into an IP battlefield
TechRepublic's coverage of U.S. accusations against Chinese AI firms points to a fight that will only get louder: when does learning from a frontier model become theft, and when is it legitimate competition?
ModelsSep 10, 2026
Astra pushes the model race toward coding and computer use
InfoQ's coverage of GPT-6 Astra is important because the model is being framed around coding and computer use, not only text generation. That is where frontier models are becoming practical engines for software work, browser tasks, and agentic workflows.
ProductsSep 11, 2026
Claude's usage lawsuit shows AI subscriptions are becoming trust contracts
The Decoder's coverage of a class action over Claude subscription limits highlights a pressure point every major AI product now faces: users are buying access to capacity that can be hard to understand until they hit a wall.
ProductsSep 11, 2026
LinkedIn's AI job-search work shows product AI needs training infrastructure, too
LinkedIn's AI job-search work is a reminder that useful AI products often depend on training systems most users never see. InfoQ's coverage of its multi-teacher approach shows how much engineering goes into matching people, jobs, and context at platform scale.
Open Source AISep 11, 2026
YC's open-weight argument turns model distillation into industrial policy
TechCrunch's coverage of Garry Tan's call for U.S. open-weight labs to distill frontier models puts a sharp edge on the distillation debate. What one company calls unauthorized extraction, another ecosystem may frame as national competitiveness.
InfrastructureSep 9, 2026
Qualcomm's Amazon deal shows the AI chip race is widening beyond Nvidia
AI Business's coverage of Qualcomm's AI chip deal with Amazon is a reminder that the infrastructure race is not only about who has the biggest model. It is also about who can supply specialized silicon for the clouds trying to reduce dependence on a single dominant GPU platform.
Policy and SafetySep 11, 2026
Formal AI safety wants proofs where today's evaluations offer confidence
The Mathematical AI Safety Institute is aiming at a hard problem: can parts of AI safety be proven with the rigor used in cryptography, rather than inferred from tests and red-team reports? The Decoder's coverage is important because it points to a different safety culture.
AI in PracticeSep 10, 2026
Enterprise AI safety is turning into an operating discipline
Enterprise AI safety is becoming less about writing a policy memo and more about running an operating system for model risk. AI Business's safety-crunch coverage reflects what many companies are facing as they move from experiments into procurement, deployment, monitoring, and incident response.
Developer ToolsSep 8, 2026
GitLab's sandbox warning is the practical agent-security lesson
Agent security often sounds abstract until the agent can reach a network, a token, or a production-adjacent system. InfoQ's coverage of GitLab's warning brings the issue down to a practical rule: a sandbox is only as safe as the access you leave around it.
Developer ToolsSep 7, 2026
OpenAI's coding-agent spend shows the real cost of AI-accelerated research
AI coding tools can make research teams faster, but the bill is becoming part of the story. Business Insider's reporting on OpenAI researcher token spend makes visible what many teams are starting to feel: agentic coding is not free leverage.
ModelsSep 4, 2026
Astra's safety debate is becoming as important as its capability claims
A powerful model launch now comes with two stories at once: what the system can do and what risks the lab says it has controlled. Coverage of OpenAI's Astra safety claims shows that the second story is no longer a footnote.
ResearchSep 2, 2026
FP4 training research points to the next fight over AI efficiency
Efficiency research is becoming one of the highest-leverage parts of AI progress. Work on FP4 block scaling for stable language-model pretraining points at the pressure to train capable models with less memory, less power, and better hardware utilization.
InfrastructureSep 1, 2026
OpenAI’s SB Energy warrants put power infrastructure inside the AI business model
AI demand is now large enough that energy infrastructure is becoming part of the model-company story. OpenAI’s warrant exposure around SB Energy shows how the industry’s compute plans are reaching into power, storage, and data-center capacity before those facilities are fully operational.
ProductsAug 28, 2026
Musicians are building their own detective layer for AI-generated music
The AI music fight is shifting from broad outrage to hands-on investigation. The Verge's reporting on musicians hunting AI grifters shows creators building their own informal detection layer because platforms and labels have not solved the trust problem for them.
Developer ToolsAug 26, 2026
Hugging Face's multi-vector encoder guide brings retrieval tuning closer to builders
Retrieval quality is still one of the quiet failure points in AI products. A model can be strong, but if the wrong documents reach the prompt, the answer looks confident and misses the point. Hugging Face's new multi-vector encoder material matters because it gives builders a more practical path to tune the retrieval layer itself.