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These are earlier cards, kept as text. Pictures and short summaries stay only while a card is on the first page; the link goes to the original either way.

Terms on this pageLarge language modelZero Data RetentionDistillationQuantizationCheckpointInferenceBenchmarkAgentAll terms →
Source AWS ML Blog
Govern models with MLflow and Amazon SageMaker AI Model Registry sync: Part 1

Sep 8

no limits stated2.7k words

What we saw

“Managed MLflow on Amazon SageMaker AI now syncs richer model metadata (training metrics, evaluation results, inference specs, a…”

They claim — In Part 2, we extend the same building blocks to cross-account governance topologies for larger and regulated organizations.

Published 2026-09-08

Fetched 2026-09-08 23:01 UTC · 698907095c767428

Torn from — the listing only (the original would not open)

Source OpenAI
How GPT-5.6 Sol helps run quantum computing experiments

Sep 8

What we saw

“See how an MIT researcher uses GPT-5.6 Sol with Codex to autonomously run quantum computing experiments, analyze results, and c…”

Published 2026-09-08

Fetched 2026-09-08 23:01 UTC · 3436bbf0a4d0cef3

Torn from — the listing only (the original would not open)

Source AWS ML Blog
Automated agent evaluation with Amazon Bedrock AgentCore and GitHub Actions

Sep 8

states limits3.9k words

What we saw

“Wire Amazon Bedrock AgentCore Evaluations into a GitHub Actions pipeline: deploy an AI agent and an OAuth-protected MCP server …”

Published 2026-09-08

Fetched 2026-09-08 23:01 UTC · 2f38597109a0e9d9

Torn from — the listing only (the original would not open)

Source AWS ML Blog
Benchmarking small LLM inference on SageMaker AI: G7 vs G5 and G6

Sep 8

no limits stated3.1k words

What we saw

“Benchmark two 30B Mixture-of-Experts models, Qwen3-Coder-30B and NVIDIA Nemotron-3-Nano-30B, across G5, G6, G6e, and G7 GPU ins…”

They claim — The G5 and G6 configurations each use four GPUs with 96 GB of aggregate GPU memory, while G7 uses two GPUs with 64 GB.

Published 2026-09-08

Fetched 2026-09-08 23:01 UTC · 9e16169b26831c0c

Torn from — the listing only (the original would not open)

Source Hugging Face Blog
Wire It, Run It, Deploy It: AI Workflows in Gradio

Aug 25

no limits stated1.1k words

What we saw

This source gives a title and a link, nothing to quote.

Published 2026-08-25

Fetched 2026-08-25 10:14 UTC · b7e36ffcde895599

Torn from — the page itself

Source OpenAI
Disrupting a new covert influence campaign from Russia

Aug 25

What we saw

“OpenAI banned Russia-origin accounts using AI to promote a fake Israel-based think tank and a “sovereignty” index praising …”

Published 2026-08-25

Fetched 2026-08-25 10:14 UTC · 3fd3c5c15867e636

Torn from — the listing only (the original would not open)

Source OpenAI
Advancing price-performance for developers with GPT‑5.6 in Kiro

Aug 24

What we saw

“GPT‑5.6 is now available in Kiro, helping developers plan, build, review, and test software with better price-performance.”

Published 2026-08-24

Fetched 2026-08-25 10:14 UTC · da36a5582dc91b20

Torn from — the listing only (the original would not open)

Source Google DeepMind
What does “full-stack” AI actually mean?

Aug 21

no limits stated197 words

What we saw

“A Google DeepMind engineer breaks full-stack development into five simple layers and explains how it affects everyday users.”

Published 2026-08-21

Fetched 2026-08-25 10:14 UTC · 52877114a19c0bfb

Torn from — the page itself

Source Hugging Face Blog
Measuring benchmark optimization in speech recognition

Aug 21

states limits2.5k words

What we saw

This source gives a title and a link, nothing to quote.

They claim — We evaluated 11 widely used open-source ASR models and found that several of the highest-scoring systems reproduced benchmark transcripts from the VoxPopuli English and LibriSpeech (clean…

Published 2026-08-21

Fetched 2026-08-25 10:14 UTC · 8de96fd631a45d0c

Torn from — the page itself

Source Hugging Face Blog
Up to 3.2x Faster Inference with LFM2.5-DSpark

Aug 20

no limits stated1.3k words

What we saw

This source gives a title and a link, nothing to quote.

They claim — Faster inference : up to 3.18 throughput improvement on a GPU and up to 2.87x on-device.

Published 2026-08-20

Fetched 2026-08-25 10:14 UTC · b41fa9c74f86007c

Torn from — the page itself

Source OpenAI
Introducing AI Futures

Aug 20

What we saw

“Introducing AI Futures, a new OpenAI blog exploring how transformative AI could reshape power, governance, the economy, and ind…”

Published 2026-08-20

Fetched 2026-08-25 10:14 UTC · ec2332dc6523ff28

Torn from — the listing only (the original would not open)

Source OpenAI
Stampli cuts launch hours by 68% using ChatGPT Work

Aug 20

What we saw

“With a fixed deadline and design resources committed elsewhere, Stampli used Codex and ChatGPT Work to compress weeks of launch…”

Published 2026-08-20

Fetched 2026-08-25 10:14 UTC · 1476f459afd654a9

Torn from — the listing only (the original would not open)

Source Google AI
5 new ways to level up your learning with Search

Aug 19

states limits1.1k words

What we saw

“Here’s how you can use Google Search tools to stu…”

Published 2026-08-19

Fetched 2026-08-20 07:11 UTC · 1b0691e082f0e950

Torn from — the page itself

Source OpenAI
Offering Zero Data Retention for frontier models

Aug 19

What we saw

“OpenAI reaffirms Zero Data Retention for eligible…”

Published 2026-08-19

Fetched 2026-08-20 07:11 UTC · 971da21a3ac32a67

Torn from — the listing only (the original would not open)

Source OpenAI
ChatGPT Ads expands across Europe

Aug 18

What we saw

“ChatGPT Ads is expanding to 31 European markets. …”

Published 2026-08-18

Fetched 2026-08-20 07:11 UTC · 18da8bfc1e0decb7

Torn from — the listing only (the original would not open)

Source Hugging Face Blog
How Much Memory Does Your Agent Actually Need?

Aug 18

states limits1.9k words

What we saw

This source gives a title and a link, nothing to quote.

Published 2026-08-18

Fetched 2026-08-20 07:11 UTC · f2683b1da6428494

Torn from — the page itself

Papers, repos, models, blogs, Reddit and podcasts — once a day, by machine. Everything collected gets taken apart once — the README, the abstract, the model card — and what that turns up is kept for good, whether or not it becomes a card. Titles and links go to the original; nothing is reproduced here.

The grey line on each card is what the teardown found, not what the source advertises: the licence, the last commit, the model it was built on, whether the paper states its own limits. Open “What we saw” to check what was read to get it. Where it says listing only, the original could not be read — some sites refuse us, and we do not pretend otherwise.

A line in quotation marks is a sentence lifted from the source itself — their number, not a measurement of ours. We copy it and leave it unchecked; that is the point of quoting it.

Topics are assigned by matching words in the original title, quote and tags against a fixed list — no model reads the card. Some cards match nothing and carry no topic at all; pick a topic and those drop out. Source and topic narrow together.

Each card keeps the sentence we actually read at the moment we read it, plus a fingerprint of the response we received. That sentence — not the whole page — is what we can still stand behind if the original later changes or disappears.