Scored 164 articles from 151 feeds; 15 included in digest.
Run ID: run-1788767099359
Generated: September 07, 2026 at 03:48 AM ET
Summaries: gemini-flash-lite-latest; enrichment 15/15 succeeded
| Source | Type | Included | Scored | 28d Digest Rate | 28d Avg Score | 28d Hotlist Hit | 7d Article Age | 28d Confidence |
|---|---|---|---|---|---|---|---|---|
| Bogleheads forum | news | 1 | 10 | 5% | 0.05 | 0% | 1.0h | Stable |
| Guardian | news | 1 | 10 | 9% | 0.18 | 0% | 10.9h | Stable |
| Hacker News | commentary | 1 | 10 | 10% | 0.23 | 0% | 2.3d | Stable |
| Medium Artificial Intelligence (keyword) | commentary | 1 | 10 | 8% | 0.12 | 0% | 1.0h | Stable |
| MyFT | news | 1 | 10 | 6% | 0.05 | 0% | 4.1h | Stable |
| NYT front page | news | 1 | 10 | 7% | 0.07 | 0% | 8.1h | Stable |
| R/RealEstate | news | 1 | 10 | Collecting data | Collecting data | Collecting data | No recent data | Collecting |
| Reddit AntiAI | news | 1 | 10 | 6% | 0.11 | 0% | 7.5h | Stable |
| arXiv CompSci CL | research | 1 | 10 | ~6% | ~0.10 | ~0% | 4.0h | Low sample |
| arXiv CompSci ML | research | 1 | 10 | ~7% | ~0.09 | ~0% | 4.0h | Low sample |
| Medium AI (keyword) | commentary | 1 | 9 | 7% | 0.11 | 0% | 0.9h | Stable |
| Seeking Alpha News | commentary | 1 | 7 | 2% | 0.01 | 0% | 1.5h | Stable |
| FT Alphaville | news | 1 | 2 | ~8% | ~0.11 | ~0% | 4.9h | Low sample |
| Daring Fireball | commentary | 1 | 1 | ~17% | ~0.16 | ~0% | 3.6h | Low sample |
| Outside Law School Scam - Comments | commentary | 1 | 1 | ~9% | ~0.06 | ~0% | 2.4d | Low sample |
| Bloomberg Markets | news | 0 | 10 | 2% | 0.02 | 0% | 5.8h | Stable |
| Zero Hedge | commentary | 0 | 10 | 3% | 0.02 | 0% | 9.2h | Stable |
| India Times | news | 0 | 7 | 0% | 0.00 | 0% | 3.5h | Stable |
| Reddit R/FuturesTrading | news | 0 | 4 | ~1% | ~0.00 | ~0% | 9.5h | Low sample |
| WSJ US Business | news | 0 | 4 | 4% | 0.05 | 0% | 9.8h | Stable |
| TechCrunch | news | 0 | 2 | 8% | 0.11 | 0% | 7.0h | Stable |
| The Verge | news | 0 | 2 | 12% | 0.18 | 0% | 6.8h | Stable |
| Economist: Europe | news | 0 | 1 | Collecting data | Collecting data | Collecting data | 6.9h | Collecting |
| Economist: United States | news | 0 | 1 | Collecting data | Collecting data | Collecting data | 8.9h | Collecting |
| Reddit R/Ethereum | news | 0 | 1 | ~5% | ~0.05 | ~0% | 3.2h | Low sample |
| WSJ Social Economy | news | 0 | 1 | 4% | 0.01 | 0% | 5.3h | Stable |
| WSJ Tech | news | 0 | 1 | 7% | 0.08 | 0% | 7.6h | Stable |
Source: Bogleheads forum
Type: news
Included: 1
Scored: 10
28d Digest Rate: 5%
28d Avg Score: 0.05
28d Hotlist Hit: 0%
7d Article Age: 1.0h
28d Confidence: Stable
Source: Guardian
Type: news
Included: 1
Scored: 10
28d Digest Rate: 9%
28d Avg Score: 0.18
28d Hotlist Hit: 0%
7d Article Age: 10.9h
28d Confidence: Stable
Source: Hacker News
Type: commentary
Included: 1
Scored: 10
28d Digest Rate: 10%
28d Avg Score: 0.23
28d Hotlist Hit: 0%
7d Article Age: 2.3d
28d Confidence: Stable
Source: Medium Artificial Intelligence (keyword)
Type: commentary
Included: 1
Scored: 10
28d Digest Rate: 8%
28d Avg Score: 0.12
28d Hotlist Hit: 0%
7d Article Age: 1.0h
28d Confidence: Stable
Source: MyFT
Type: news
Included: 1
Scored: 10
28d Digest Rate: 6%
28d Avg Score: 0.05
28d Hotlist Hit: 0%
7d Article Age: 4.1h
28d Confidence: Stable
Source: NYT front page
Type: news
Included: 1
Scored: 10
28d Digest Rate: 7%
28d Avg Score: 0.07
28d Hotlist Hit: 0%
7d Article Age: 8.1h
28d Confidence: Stable
Source: R/RealEstate
Type: news
Included: 1
Scored: 10
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: No recent data
28d Confidence: Collecting
Source: Reddit AntiAI
Type: news
Included: 1
Scored: 10
28d Digest Rate: 6%
28d Avg Score: 0.11
28d Hotlist Hit: 0%
7d Article Age: 7.5h
28d Confidence: Stable
Source: arXiv CompSci CL
Type: research
Included: 1
Scored: 10
28d Digest Rate: ~6%
28d Avg Score: ~0.10
28d Hotlist Hit: ~0%
7d Article Age: 4.0h
28d Confidence: Low sample
Source: arXiv CompSci ML
Type: research
Included: 1
Scored: 10
28d Digest Rate: ~7%
28d Avg Score: ~0.09
28d Hotlist Hit: ~0%
7d Article Age: 4.0h
28d Confidence: Low sample
Source: Medium AI (keyword)
Type: commentary
Included: 1
Scored: 9
28d Digest Rate: 7%
28d Avg Score: 0.11
28d Hotlist Hit: 0%
7d Article Age: 0.9h
28d Confidence: Stable
Source: Seeking Alpha News
Type: commentary
Included: 1
Scored: 7
28d Digest Rate: 2%
28d Avg Score: 0.01
28d Hotlist Hit: 0%
7d Article Age: 1.5h
28d Confidence: Stable
Source: FT Alphaville
Type: news
Included: 1
Scored: 2
28d Digest Rate: ~8%
28d Avg Score: ~0.11
28d Hotlist Hit: ~0%
7d Article Age: 4.9h
28d Confidence: Low sample
Source: Daring Fireball
Type: commentary
Included: 1
Scored: 1
28d Digest Rate: ~17%
28d Avg Score: ~0.16
28d Hotlist Hit: ~0%
7d Article Age: 3.6h
28d Confidence: Low sample
Source: Outside Law School Scam - Comments
Type: commentary
Included: 1
Scored: 1
28d Digest Rate: ~9%
28d Avg Score: ~0.06
28d Hotlist Hit: ~0%
7d Article Age: 2.4d
28d Confidence: Low sample
Source: Bloomberg Markets
Type: news
Included: 0
Scored: 10
28d Digest Rate: 2%
28d Avg Score: 0.02
28d Hotlist Hit: 0%
7d Article Age: 5.8h
28d Confidence: Stable
Source: Zero Hedge
Type: commentary
Included: 0
Scored: 10
28d Digest Rate: 3%
28d Avg Score: 0.02
28d Hotlist Hit: 0%
7d Article Age: 9.2h
28d Confidence: Stable
Source: India Times
Type: news
Included: 0
Scored: 7
28d Digest Rate: 0%
28d Avg Score: 0.00
28d Hotlist Hit: 0%
7d Article Age: 3.5h
28d Confidence: Stable
Source: Reddit R/FuturesTrading
Type: news
Included: 0
Scored: 4
28d Digest Rate: ~1%
28d Avg Score: ~0.00
28d Hotlist Hit: ~0%
7d Article Age: 9.5h
28d Confidence: Low sample
Source: WSJ US Business
Type: news
Included: 0
Scored: 4
28d Digest Rate: 4%
28d Avg Score: 0.05
28d Hotlist Hit: 0%
7d Article Age: 9.8h
28d Confidence: Stable
Source: TechCrunch
Type: news
Included: 0
Scored: 2
28d Digest Rate: 8%
28d Avg Score: 0.11
28d Hotlist Hit: 0%
7d Article Age: 7.0h
28d Confidence: Stable
Source: The Verge
Type: news
Included: 0
Scored: 2
28d Digest Rate: 12%
28d Avg Score: 0.18
28d Hotlist Hit: 0%
7d Article Age: 6.8h
28d Confidence: Stable
Source: Economist: Europe
Type: news
Included: 0
Scored: 1
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 6.9h
28d Confidence: Collecting
Source: Economist: United States
Type: news
Included: 0
Scored: 1
28d Digest Rate: Collecting data
28d Avg Score: Collecting data
28d Hotlist Hit: Collecting data
7d Article Age: 8.9h
28d Confidence: Collecting
Source: Reddit R/Ethereum
Type: news
Included: 0
Scored: 1
28d Digest Rate: ~5%
28d Avg Score: ~0.05
28d Hotlist Hit: ~0%
7d Article Age: 3.2h
28d Confidence: Low sample
Source: WSJ Social Economy
Type: news
Included: 0
Scored: 1
28d Digest Rate: 4%
28d Avg Score: 0.01
28d Hotlist Hit: 0%
7d Article Age: 5.3h
28d Confidence: Stable
Source: WSJ Tech
Type: news
Included: 0
Scored: 1
28d Digest Rate: 7%
28d Avg Score: 0.08
28d Hotlist Hit: 0%
7d Article Age: 7.6h
28d Confidence: Stable
The article discusses the annual competition to find Australia's best meat pie, noting that while a hot meat pie from a roadside bakery is a staple of long Australian road trips, the criteria for winning the title are examined.
Keywords: Australia, meat pie, food competition, bakery, road trip
A Hacker News user describes using AI tools, Astra and Fable, to experiment with self-playing piano files from the PianoDisc Prodigy system. After using the tools to generate a version of Gymnopedie No 1 and analyze a purchased PianoDisc file, Fable decoded the proprietary format—which uses a 2004.5 Hz square wave on the right channel for MIDI data—and discovered obfuscation involving decoy notes. The user asks the community whether they are allowed to publish the encoder and decoder created by the AI.
Keywords: piano, hack, Fable, Ask HN, music
A Reddit user named DestroyerOfWaffles submitted a post titled "I like making things with my hands 🔨🍬" to the r/antiai community, accompanied by a link and the comment that it was a fun challenge.
Keywords: handmade, crafts, hobby, DIY
A forum poster shares their experience with a Whirlpool refrigerator purchased 33 years ago without an ice maker. To compensate for the lack of a built-in ice maker, they use a separate countertop ice maker powered by bottled water. Although the refrigerator has some broken plastic shelving, the user plans to keep using it until it stops cooling.
Keywords: refrigerator, Whirlpool, ice maker, household appliances, forum
Femi Koleoso, drummer for Ezra Collective, writes as a guest editor for the Guardian's Saturday magazine about his background as a British-Nigerian musician from north London and discusses the concept of hope. He argues that despite global challenges, hope acts as a bridge from pain to renewal and joy, citing examples of how hardship has inspired various musical genres. The article also includes cultural recommendations from Koleoso and his bandmates, featuring films, albums, and a live music venue.
Keywords: Ezra Collective, Femi Koleoso, guest editor, culture, music
A prospective homebuyer moving to a new town evaluates the local real estate market, noting that high rental costs contrast with a buyer's market for condos featuring average HOA fees of $800 per month. The author considers a condo priced at $240,000 and poses a question about proposing a non-standard purchase arrangement: putting 20% down, making no payments for 24 months, and then choosing either to pay the remaining balance or walk away from the property.
Keywords: real estate, condo, housing market, home buying, eccentric proposal
Chicago features a theatre scene with over 200 stages, ranging from Steppenwolf to intimate storefronts and improv spaces, offering productions that rival New York and London at closer range and more affordable prices.
Keywords: Chicago, theatre, Steppenwolf, improv, arts and culture
Meta has released a native 1.0 beta desktop app for Meta AI on macOS 15 and later. Built using AppKit, SwiftUI, and WebKit, the 16MB application includes features such as Quick Invoke via Option-Space, system-wide dictation, window context gathering using Screen Recording and Accessibility permissions, and a sidebar for media, artifacts, and scheduling. This release follows a series of recent AI updates from Meta, including the introduction of Muse Spark, voice and camera updates, Muse-powered image generation, and Muse Code.
Keywords: Meta AI, Mac app, macOS, Apple silicon, technology
Model upgrades can cause AI agents to lose information even when their underlying memory store remains unchanged. A controlled study examining memory portability across different architectures—including long-context raw text (LC-RAW), retrieval-augmented generation (RAG), natural-language notes (NOTES), and fixed-schema knowledge graphs (KG-fixed)—shows that fixed-schema structures transfer reliably, while compressed notes exhibit high model coupling and significant accuracy shifts based on migration direction. Additionally, partial embedding migrations in RAG systems capture only a fraction of the performance gains achieved by full re-embedding, and store-only repair of notes fails to reach performance recovery targets unless raw source histories are retained.
Keywords: AI agents, memory migration, large language models, knowledge graphs, RAG systems, arXiv
This article discusses why production AI agents require more than just a large language model, a prompt, and a few tools, exploring their architecture and core components.
Keywords: AI agent, architecture, machine learning, LLM, technology
The article presents VLA-Precision, an online reinforcement learning framework designed to improve the precision and repeatability of vision-language-action (VLA) models in real-world robotics tasks. The framework includes the Asymmetric Co-Bootstrapping (ACoB) algorithm, which addresses unreliable value signals and policy drift through timescale-based co-bootstrapping and intervention-guided behavioral learning. It also features the ACoB-Stream architecture to overcome large-VLA computational overhead via invariant-state decoupling and on-demand streaming. Evaluations across nine high-precision chemistry tasks and four robot embodiments demonstrate a 98.3% mean success rate, with significant improvements in computational efficiency and throughput.
Keywords: vision-language-action models, reinforcement learning, robotics, artificial intelligence, VLA-Precision
Digital marketing utilizes the internet, mobile devices, social media, search engines, and other digital channels to connect companies with audiences.
Keywords: digital marketing, social media, internet, business
Huawei and Xiaomi are launching high-end smartphones to target Apple ahead of the release of a foldable iPhone.
Keywords: Huawei, Xiaomi, Apple, smartphones, foldable iPhone
Paul Campos has published a field guide titled "Is Your Law School Going Broke?", concluding that all but about a dozen ABA-accredited law schools face substantial financial peril. Reviewing data from 184 schools, Campos estimates that 14 are at low risk, 29 at moderate risk, 66 at high risk, and 75 at severe risk of financial difficulties that could lead to restructuring or closure. His analysis evaluates factors such as effective tuition per faculty member—which declined by an average of 33% between 2010–11 and 2024–25—alongside endowment sizes and operational spending. The text notes that institutions at severe risk include various sub-elite and state flagship schools sustained by parent university budgets, while upcoming caps on federally guaranteed student loans will create further financial pressure.
Keywords: law school, bar exam, education, legal courses
FT Alphaville published a reading list featuring the topics curves, alien minds, benchmarks, homework, lawyers, Amazon, Doolittle, seals, and humans.
Keywords: FTAV, reading list, seals, alien minds