VERIFIED NEWS

What has changed and why it matters.

Releases and updates linked to profiles, context and primary sources.

Serious AI incidents: how to preserve evidence, contain harm, and decide whether a failure must be reported under Article 73 of the AI Act

Serious AI incidents: how to preserve evidence, contain harm, and decide whether a failure must be reported under Article 73 of the AI Act

A model error, an operational incident, and a serious regulatory incident are not equivalent. This guide sets out a verifiable protocol for preserving evidence, containing risk, reconstructing the case, and preparing a reporting decision without prematurely reaching legal conclusions or destroying relevant information.

Private Documents with AI: How to Decide What Can Leave the Perimeter and What Requires Review

Private Documents with AI: How to Decide What Can Leave the Perimeter and What Requires Review

An operational route for deciding whether a search, summarization, extraction, classification, or drafting workflow can process private documents directly, in minimized form, in segments, or within a controlled environment. The focus is not on selecting a technology by default, but on defining data, transformations, controls, and human review before production.

Indirect instruction injection: how to stop a document, email, or web page from turning a connected assistant into a channel for data leakage or improper action

Indirect instruction injection: how to stop a document, email, or web page from turning a connected assistant into a channel for data leakage or improper action

Indirect instruction injection occurs when an assistant treats text that should be used only as data as if it were an order. This guide sets out an architecture, controls, and tests to prevent emails, documents, RAG results, or web pages from changing a task’s objective, permissions, or recipients.

CursorBench 3.2: What a Coding-Agent Benchmark Can Say—and Why It Is Not Enough to Choose a Model Outside Cursor

CursorBench 3.2: What a Coding-Agent Benchmark Can Say—and Why It Is Not Enough to Choose a Model Outside Cursor

CursorBench provides a signal about agent systems evaluated within Cursor’s harness, but a score is not a portable property of a model. The verified public documentation reviewed describes CursorBench 3.1, not 3.2; therefore, any reference to an alleged 3.2 version must be treated as unconfirmed until Cursor publishes its methodology and results.

Cost in SWE-bench: how to calculate the price per resolved issue without hiding failures, retries, or evaluation

Cost in SWE-bench: how to calculate the price per resolved issue without hiding failures, retries, or evaluation

A cost figure per resolved issue is useful only if it reveals everything that happened before the patch was obtained: failed attempts, token consumption, stopping rules, selection among runs, and evaluation resources. This analysis proposes a reproducible scorecard for reading and comparing SWE-bench results, with particular attention to SWE-bench Verified.

Recent releases

News that modifies the map of models and capabilities.

03 SEP 2026 OPENAI

GPT‑6 Astra raises the bar for capacity

The launch requires reviewing evaluation, deployment and security.

Border model ↗
JUL 24, 2026 · ANTHROPIC

Claude Opus 5 prioritizes sustained work

Code and knowledge tasks with effort checks.

Knowledge ↗
SEP 2026 · GOOGLE

Gemini 3.8 Flash reaches stable version

Multimodality, code and agents with a low latency profile.

Stable version ↗

How to track an update

A news story does not end in the headline.

01

Open the profile

Relates the ad to version, access and modalities.

Identity ↗
02

Check price

Distinguish stable, introductory rates and missing data.

Cost ↗
03

Evaluate the statement

Review conditions and comparability before accepting a benchmark.

Evidence ↗