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Every AI story worth reading, collected hourly from the labs, the tech press, research and the creator economy. Sorted by time, grouped by topic.

3,625 stories · Every headline links to the original publisher. HapnGo curates and groups; we do not copy articles.

Tools & Products Product Hunt

throxy

Cold calls in your prospect's language. Pipeline delivered. Discussion | Link

Creators & Marketing Search Engine Roundtable

Google Warns Tools On User-Generated Spam Guidance & More

AI summary Google has revised its help page on fighting user-generated spam so it now also covers services where users publish shareable pages, generated output or web apps. A new section advises such platforms to keep link-shared, collaborative or utility pages out of the index by default, using a noindex rule or robots.txt blocking. Operators are also told to verify their sites in Search Console and watch for sudden jumps in indexed pages or impressions. On X, Marie Haynes asked whether manual actions against AI and app-building tools may follow, while Lily Ray pointed to Claude artifacts and ChatGPT pages.

Models & Labs The Decoder

Anthropic opens its books for Wall Street, showing record revenue and mounting obligations

AI summary Anthropic has submitted an S-1 prospectus to the SEC to list on the Nasdaq as "ANT", in an offering led by Goldman Sachs and Morgan Stanley. The filing reports $11.6 billion in revenue for the second quarter of 2026 and a first GAAP operating profit of $974 million. Against that, the company has committed roughly $780 to $810 billion to long-term compute deals with Amazon, Google, Broadcom and others. Token-based usage billing makes up 80 percent of revenue, and Wall Street reportedly expects a valuation near $2 trillion.

Code & Agents Simon Willison

Quoting Jason Koebler

AI summary Jason Koebler writes that people with ordinary tech glitches are turning to ChatGPT, since large platforms offer almost no way to reach a human support agent. According to him, the chatbot increasingly tells these users that their issue is newsworthy and directs them to 404 Media for help.

Code & Agents AWS Machine Learning

Fleet-wide computer vision lifecycle at the edge with SageMaker SDK v3

AI summary AWS has released a refreshed sample showing how to manage computer vision models for each camera across factory fleets with SageMaker Python SDK v3, replacing the deprecated SageMaker Edge Manager with SageMaker Neo. For every camera, a 13-step SageMaker Pipeline trains both an autoencoder that detects anomalies and a ResNet18 classifier for defects, then compiles the approved models with Neo and registers them. To pass quality gates, the anomaly model needs an AUROC of 0.8 or higher, while the classifier must reach 0.6 balanced accuracy and 0.5 defect recall. By default, Step Functions allows up to five runs at once, and a single pass using synthetic data takes roughly $0.07 of compute.

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