Tootfinder

🇺🇦 #NowPlaying on KEXP's #MiddayShow
Samantha Crain:
🎵 Gumshoe
#SamanthaCrain
https://samanthacrain.bandcamp.com/album/gumshoe
https://open.spotify.com/track/4ONEkpo7Yf7n6HVvav3E8A

Strongly First Order Disjunctive Embedded Dependencies in Team Semantics
Pietro Galliani
https://arxiv.org/abs/2506.15367 https://arx…

Strongly First Order Disjunctive Embedded Dependencies in Team Semantics
First Order Team Semantics is a generalization of Tarskian Semantics in which formulas are satisfied with respect to sets of assignments. In Team Semantics, it is possible to extend First Order Logic via new types of atoms that describe dependencies between variables; some of these extensions are strictly more expressive than First Order Logic, while others are reducible to it. Many of the atoms studied in Team Semantics are inspired by Database Theory and belong in particular to the class of…

LLMs the Model Context Protocol (MCP) are the Yang to the Semantic Web Project's Yin.
We now have a solution to the final hurdle—visualization.
Years of Linked Data work now come alive. I explain this, with demonstrations, in a new newsletter post.
www.linkedin.com/pulse/semant...
#MCP

DiscRec: Disentangled Semantic-Collaborative Modeling for Generative Recommendation
Chang Liu, Yimeng Bai, Xiaoyan Zhao, Yang Zhang, Fuli Feng, Wenge Rong
https://arxiv.org/abs/2506.15576

DiscRec: Disentangled Semantic-Collaborative Modeling for Generative Recommendation
Generative recommendation is emerging as a powerful paradigm that directly generates item predictions, moving beyond traditional matching-based approaches. However, current methods face two key challenges: token-item misalignment, where uniform token-level modeling ignores item-level granularity that is critical for collaborative signal learning, and semantic-collaborative signal entanglement, where collaborative and semantic signals exhibit distinct distributions yet are fused in a unified emb…

Right a few days before I'll be talking about patterns in DRAM init at #GPN23, #Binarly are posting on their type inference tooling:

Type Inference for Decompiled Code: From Hidden Semantics to Structured Insights
Learn how Binarly enhances decompiled code by recovering meaningful type info—boosting binary analysis, triage, and reverse engineering accuracy.

Revealing the Breakdown Mechanism and Heat Dissipation in Few-Layered semimetallic PtSe2
Bubunu Biswal, Abinash Tripathy, Renu Yadav, Abhishek Misra
https://arxiv.org/abs/2506.15593

Revealing the Breakdown Mechanism and Heat Dissipation in Few-Layered semimetallic PtSe2
Platinum diselenide (PtSe2) is an emerging two-dimensional (2D) transition metal dichalcogenide known for its excellent electrical and optical properties, along with remarkable air stability. For PtSe2-based electronic devices, understanding high-field breakdown and heat dissipation is crucial for designing high-performance and energy-efficient systems operating under extreme conditions. In this work, we investigate the breakdown mechanisms of semimetallic PtSe2 at both low and room temperature…

Into the Unknown: Applying Inductive Spatial-Semantic Location Embeddings for Predicting Individuals' Mobility Beyond Visited Places
Xinglei Wang, Tao Cheng, Stephen Law, Zichao Zeng, Ilya Ilyankou, Junyuan Liu, Lu Yin, Weiming Huang, Natchapon Jongwiriyanurak
https://arxiv.org/abs/2506.14070…

Into the Unknown: Applying Inductive Spatial-Semantic Location Embeddings for Predicting Individuals' Mobility Beyond Visited Places
Predicting individuals' next locations is a core task in human mobility modelling, with wide-ranging implications for urban planning, transportation, public policy and personalised mobility services. Traditional approaches largely depend on location embeddings learned from historical mobility patterns, limiting their ability to encode explicit spatial information, integrate rich urban semantic context, and accommodate previously unseen locations. To address these challenges, we explore the appl…

Announcing sff: A fast, on-the-fly SemanticFileFinder written in Rust! 🦀
It scans a directory (like your notes or a repo), finds the most semantically relevant text chunks for your query, and lets you open the file in a text editor of your choice.
No vector DBs, no GPU needed. Indexes ~2500 files with 10k chunks in 250ms on a CPU.
Perfect for searching Obsidian vaults, codebases, and more.
𝚌𝚊𝚛𝚐𝚘 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚜𝚏𝚏
𝚜𝚏𝚏 "𝚠𝚘𝚛𝚔𝚒𝚗𝚐 𝚠𝚒𝚝𝚑 𝚐𝚒𝚝"

GitHub - do-me/sff: CLI for semantic search on your computer. Searches text files and identifies the most relevant chunks to your query.
CLI for semantic search on your computer. Searches text files and identifies the most relevant chunks to your query. - GitHub - do-me/sff: CLI for semantic search on your computer. Searches text f...

I made a helper for Proximity (a word association game like #Semantle). It lets you poke around the database to see what's close to what:
https://github.com/adamhotep/userscrip

🇺🇦 #NowPlaying on KEXP's #MiddayShow
Samantha Crain:
🎵 Ridin’ Out The Storm
#SamanthaCrain
https://samanthacrain.bandcamp.com/track/ridin-out-the-storm
https://open.spotify.com/track/3in5CVZj0MDdf0Q88nZ24V