What turned up in the last three days — collected by machine, published by hand.
What gets picked here ends up in Teardowns →
Collected 2026-09-08 23:01 UTC · 46 kept from 280
Sep 8
no limits stated1.1k words
“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)
Sep 8
states limits2.0k words
“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)
Sep 8
no limits stated2.2k words
“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)
Sep 8
states limits2.7k words
“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)
Sep 8
no limits stated2.7k words
“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)
Sep 8
“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)
Sep 8
states limits3.9k words
“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)
Sep 8
no limits stated3.1k words
“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)
Nothing on this page matches what you picked.
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.