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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.

Source Google DeepMind
Applying technology to understand and treat disease

Oct 12

no limits stated3.0k words

What we saw

“In collaboration with scientists and research partners, Google is helping to make disease more detectable, treatable, and preve…”

They claim — @keyframes gemini-draw-border-timeline{0%{--border-draw-percent:0%;--border-mask-color:transparent;--btn-bg-color:var(--btn-bg-start);--gradient-angle:var(--start-angle,145deg)}85%{--bord…

Published 2026-10-12

Fetched 2026-10-12 23:01 UTC · 611b6aeecd885647

Torn from — the page itself

Source AWS ML Blog
Deploy decision models with llama.cpp on Amazon SageMaker AI

Oct 12

states limits2.2k words

What we saw

“Learn how to deploy decision models with llama.cpp on Amazon SageMaker AI. This technical how-to serves OpenAI-compatible chat …”

They claim — 8xlarge, 32 threads llama-cpp:server-sagemaker-cpu-v1 231/231 179/231 (77.5%) 1.715 seconds 19.291 seconds Qwen3.5-4B Q4KM, ml.

Published 2026-10-12

Fetched 2026-10-12 23:01 UTC · ae2468b8fd36a230

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

Source AWS ML Blog
Run multi-node inference workloads using Ray LLM Deep Learning Containers

Oct 12

states limits2.3k words

What we saw

“Learn how to deploy one large language model across two GPU nodes on Amazon EKS using the multi-node Ray LLM Deep Learning Cont…”

Published 2026-10-12

Fetched 2026-10-12 23:01 UTC · d2840f479e1f9b59

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

Source AWS ML Blog
ICYMI: What landed for AI builders in September 2026

Oct 9

no limits stated1.1k words

What we saw

“A monthly recap of the latest Amazon Bedrock, Amazon Bedrock AgentCore, and Strands updates from September 2026: broader model …”

They claim — Strands Decider 2B is a small, open source, 2B-parameter decision model that picks between predefined options (instead of generating text), delivering answers in ~115ms locally.

Published 2026-10-09

Fetched 2026-10-09 23:01 UTC · b8dcceb26e0c78ac

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

Source AWS ML Blog
How Postman runs Agent Mode for 40 million developers on Amazon Bedrock

Oct 9

states limits2.7k words

What we saw

“Building an AI agent that works in a demo is a different problem from running one for 40 million developers. Postman and AWS sh…”

They claim — Building an AI agent for a demo and operating one for 40 million developers are different engineering problems.

Published 2026-10-09

Fetched 2026-10-09 23:01 UTC · bb5f27d54530b3f8

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

Source Hugging Face Blog
Impactful scheduling for GPU clusters

Oct 9

states limits3.4k words

What we saw

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

They claim — Based on submitted workloads, at any moment in time we have outstanding requests for 2-3x more GPUs than are available.

Published 2026-10-09

Fetched 2026-10-09 23:01 UTC · a45cab2b414bcf3b

Torn from — the page itself

Source OpenAI
Asana cuts model costs 76x in browser tests with GPT-6.1 Sol

Oct 9

What we saw

“Using GPT-6 Astra in Codex, Asana made its browser agent 76x cheaper and 5x faster in tests to offer customers more capable mod…”

They claim — Using GPT-6 Astra in Codex, Asana made its browser agent 76x cheaper and 5x faster in tests to offer customers more capable models.

Published 2026-10-09

Fetched 2026-10-09 23:01 UTC · 0f1eab57236c05e2

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

Source OpenAI
Sophos cuts threat investigation time by 96% with OpenAI Daybreak

Oct 9

What we saw

“Discover how Sophos uses OpenAI’s Daybreak to cut cyber-threat investigation time by 96% and automate 52% of MDR cases while …”

They claim — Discover how Sophos uses OpenAI’s Daybreak to cut cyber-threat investigation time by 96% and automate 52% of MDR cases while preserving human oversight.

Published 2026-10-09

Fetched 2026-10-09 23:01 UTC · c2bd9140cf451f0f

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

Source OpenAI
LegalOn halves Codex costs while maintaining development speed

Oct 8

What we saw

“LegalOn cut estimated daily Codex costs by 65% while maintaining development speed. It matched Astra, Sol, and Luna to tasks an…”

They claim — LegalOn cut estimated daily Codex costs by 65% while maintaining development speed.

Published 2026-10-08

Fetched 2026-10-09 23:01 UTC · a13e16f89e5be705

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

Source Google AI
Introducing Playground: Create and play custom games

Oct 7

no limits stated3.0k words

What we saw

“Playground is a new experimental gaming platform that lets you create, play, and share custom games.”

They claim — @keyframes gemini-draw-border-timeline{0%{--border-draw-percent:0%;--border-mask-color:transparent;--btn-bg-color:var(--btn-bg-start);--gradient-angle:var(--start-angle,145deg)}85%{--bord…

Published 2026-10-07

Fetched 2026-10-09 23:01 UTC · 6f521205a43d3a67

Torn from — the page itself

Source OpenAI
Proaction boosts sales 60% and saves 75+ hours with Codex

Sep 25

What we saw

“With Codex, GPT-Live-1, and GPT-6 Astra, Proaction builds, operates, and sells modern fleet management faster.”

They claim — With Codex, GPT-Live-1, and GPT-6 Astra, Proaction builds, operates, and sells modern fleet management faster.

Published 2026-09-25

Fetched 2026-09-25 23:01 UTC · 794516609b5bcb0e

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

Source AWS ML Blog
Scaling MoE reinforcement learning on Amazon EKS with EFA and DeepEP with 40% more throughput

Sep 25

states limits3.7k words

What we saw

“Learn how to scale Mixture-of-Experts (MoE) reinforcement learning on Amazon EKS using Elastic Fabric Adapter (EFA) and DeepEP.…”

They claim — Performance and cost optimizations This section covers two key optimizations: using DeepEP to reduce expert-parallel communication overhead over EFA, and using Amazon Elastic Compute Clou…

Published 2026-09-25

Fetched 2026-09-25 23:01 UTC · b9cac234d7f78a36

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

Source AWS ML Blog
Accelerate multimodal RL training with SkyRL on Amazon SageMaker HyperPod

Sep 25

states limits3.7k words

What we saw

“Learn how to run SkyRL, an open-source reinforcement learning framework, on Amazon SageMaker HyperPod to post-train a Qwen3-VL-…”

Published 2026-09-25

Fetched 2026-09-25 23:01 UTC · a4bd252f1f6e25fa

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

Source AWS ML Blog
NarrateAI: production-ready LLM quality assurance on Amazon Bedrock

Sep 25

states limits4.7k words

What we saw

“NarrateAI delivers production-ready LLM quality assurance on Amazon Bedrock. This post details five techniques—adaptive pipel…”

They claim — NarrateAI transforms business intelligence for over 4,000 AWS executive leaders through a two-layer architecture built on Amazon Bedrock AgentCore, a platform to build, connect, and optim…

Published 2026-09-25

Fetched 2026-09-25 23:01 UTC · 4c2843cb765a0255

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

Source AWS ML Blog
Deploying real-time personalized speech with Qwen3-TTS on Amazon SageMaker AI

Sep 25

no limits stated2.4k words

What we saw

“Deploy the publicly available Qwen3-TTS-12Hz-1.7B-Base text-to-speech model from Amazon SageMaker JumpStart to a fully managed,…”

They claim — g6.4xlarge (1x NVIDIA L4 GPU, 24 GB), which is sufficient for the 1.7B model.

Published 2026-09-25

Fetched 2026-09-25 23:01 UTC · c28aa6c37d511074

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

Source AWS ML Blog
How Datacor built self-service rental analytics with Amazon Quick Sight

Sep 25

no limits stated3.0k words

What we saw

“Learn how Datacor built a self-service rental analytics experience for gas and welding distributors by embedding Amazon Quick S…”

They claim — An Azure Data Factory pipeline extracts source tables as Apache Parquet files and streams them into Amazon Simple Storage Service (Amazon S3).

Published 2026-09-25

Fetched 2026-09-25 23:01 UTC · 09c0269709886573

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

Source AWS ML Blog
Multi-Region training with Amazon SageMaker HyperPod and Qumulo

Sep 25

no limits stated3.6k words

What we saw

“Amazon SageMaker HyperPod and Cloud Native Qumulo let you place training compute in one AWS Region while keeping your dataset i…”

They claim — The spoke cluster runs in the US West (Oregon) Region (us-west-2) and reads the data remotely through CDF with 60 ms of network latency.

Published 2026-09-25

Fetched 2026-09-25 23:01 UTC · ad552593d4bb8e33

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

Source AWS ML Blog
Improving HCLS AI reasoning with open-source agent skills

Sep 16

states limits3.7k words

What we saw

“AI agents on foundation models often misapply healthcare and life sciences decision frameworks, citing the right guideline but …”

They claim — The collection covers 38 skills across 11 HCLS domains including genomics, drug discovery, claims operations, and medical imaging.

Published 2026-09-16

Fetched 2026-09-16 23:01 UTC · f38341d7421950db

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

Source AWS ML Blog
Fault tolerant distributed training on Amazon EKS using NVRx

Sep 16

states limits3.1k words

What we saw

“Integrate NVIDIA Resiliency Extension (NVRx) into PyTorch FSDP training on Amazon EKS to overlap checkpoint I/O with training a…”

They claim — Synchronous checkpointing adds a second source of idle time: every save blocks all ranks on I/O, which on the cluster sizes in this post consumed up to 40% of total wall time.

Published 2026-09-16

Fetched 2026-09-16 23:01 UTC · 8f850829b2016926

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

Source OpenAI
Our framework for reporting model misalignment

Sep 16

What we saw

“OpenAI shares a framework for tracking, investigating, and disclosing model misalignment, alongside six reports of unexpected o…”

Published 2026-09-16

Fetched 2026-09-16 23:01 UTC · 827a0c403a3c663f

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

Source OpenAI
Helping older adults use AI in everyday life

Sep 16

What we saw

“OpenAI and AARP are bringing free, hands-on ChatGPT workshops to 1,000 older adults across 10 U.S. cities to build practical AI…”

Published 2026-09-16

Fetched 2026-09-16 23:01 UTC · a8a40dafc20d6b2f

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

Source AWS ML Blog
Optimizing agent system prompts with Amazon Bedrock AgentCore

Sep 16

no limits stated2.0k words

What we saw

“AgentCore optimization turns production traces into proposed configuration changes, then validates them before promotion. This …”

They claim — A single reflector pass can focus on patterns in the first 5–10 traces it examines and miss failure modes that appear only in a smaller portion of the corpus.

Published 2026-09-16

Fetched 2026-09-16 23:01 UTC · 93c67c600c06037d

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

Source AWS ML Blog
Build a serverless PII redaction pipeline with Amazon Bedrock Data Automation

Sep 16

states limits2.9k words

What we saw

“Learn how to automate end-to-end PII detection and redaction from scanned documents at scale using Amazon Bedrock Data Automati…”

They claim — We tested document samples spanning six document quality levels in our use case, from clean typed forms through low-resolution 100 dots per inch (DPI) scans (Table 2).

Published 2026-09-16

Fetched 2026-09-16 23:01 UTC · 6fc8596fd9d93039

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

Source OpenAI
Reimagining advertising with AI

Sep 16

What we saw

“Explore new AI-powered advertising experiences from OpenAI, including Sponsored Agents, tools for marketers, and integrations w…”

Published 2026-09-16

Fetched 2026-09-16 23:01 UTC · 8c2a426559207a9d

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

Source OpenAI
How to connect AI usage to business value

Sep 16

What we saw

“Learn how ChatGPT Work and Codex analytics help teams understand AI usage and spend, identify training needs, and connect adopt…”

Published 2026-09-16

Fetched 2026-09-16 23:01 UTC · ec2f604ddc5e799a

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

Source AWS ML Blog
Take on your most ambitious work with GPT-6 Astra on Amazon Bedrock

Sep 8

no limits stated1.1k words

What we saw

“GPT-6 Astra from OpenAI is now generally available on Amazon Bedrock. It brings deeper reasoning and sharper judgment to your m…”

Published 2026-09-08

Fetched 2026-09-08 23:01 UTC · 61555b0baa6fc6fd

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

Source AWS ML Blog
Pathway’s brain-inspired architecture development on Amazon SageMaker HyperPod

Sep 8

states limits2.0k words

What we saw

“Pathway's Baby Dragon Hatchling (BDH) is a brain-inspired, post-transformer architecture that reasons in latent space instead o…”

They claim — A 150M-parameter model, built on Pathway’s BDH architecture, reasons recurrently in latent space, and sets a new state of the art in cost efficiency on ARC-AGI-1.

Published 2026-09-08

Fetched 2026-09-08 23:01 UTC · ac890dcdb67da5cf

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

Source AWS ML Blog
Amazon SageMaker Feature Store introduces UpdateRecord for feature-level writes

Sep 8

no limits stated2.2k words

What we saw

“Amazon SageMaker Feature Store now supports feature-level writes. With the new UpdateRecord API, you can update one or more fea…”

Published 2026-09-08

Fetched 2026-09-08 23:01 UTC · dfa6dc0f8a44644d

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

Source AWS ML Blog
Govern models with MLflow and Amazon SageMaker AI Model Registry sync: Part 2

Sep 8

states limits2.7k words

What we saw

“Governing models across accounts is the next step after automatic model registration. This post extends managed MLflow and Amaz…”

They claim — Topology 1: Hub-and-spoke central governance For larger organizations with multiple teams, it is common to have multiple separate development accounts with a central governance account.

Published 2026-09-08

Fetched 2026-09-08 23:01 UTC · 83d37ea8c0fb1187

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

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.