Build Your Own Road Map for Igbo Language Digital Activism
diff.wikimedia.org·18h
🌍Cultural Computing
Divine Provenance
asimov.blog·10h·
Discuss: Hacker News
🧲Magnetic Philosophy
What LLMs Know About Their Users
schneier.com·19h·
Discuss: Hacker News
💻Local LLMs
Trump’s War On Knowledge Requires Re-inventing Academic Publishing
techdirt.com·3h
🏛Digital humanities
40,000-year-old mammoth tusk boomerang is oldest in Europe — and possibly the world
livescience.com·12h·
Discuss: Hacker News
🦴Database Paleontology
Unveiling Insights: 18 Essential Resources for Exploratory Data Analysis (EDA)
dev.to·23h·
Discuss: DEV
⛏️Archive Mining
Things stech writers shouldn't care about yet
passo.uno·2d·
Discuss: Hacker News
📚MARC Evolution
OpenAI's Codex Aims to Ease Programmers' Headaches
hackernoon.com·1d
🤖Archive Automation
Could Open Table Formats End the Reign of Snowflake and Databricks?
prequel.co·12h·
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📚MARC Evolution
Deep Dive into Databend UDF, implementing your data solutions with Python, WASM
databend.com·15h·
Discuss: Hacker News
📋DFDL
Evaluating the Use of LLMs for Documentation to Code Traceability
arxiv.org·3d
🔄Reproducible Builds
The Guide to the Foundation Models Framework
azamsharp.com·16h·
Discuss: Hacker News
λLambda Formalization
Call for Participants: Regional Training Programme “Emergency Response for Documentary Heritage”
ica.org·1d
💾Data Preservation
Why Dyad?: A Perspective for Modelica Users
juliahub.com·15h·
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💧Liquid Types
The collective waste caused by poor documentation
shanrauf.com·1d·
Discuss: Hacker News
📦Deflate
SACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization
arxiv.org·2h
🔍BitFunnel
Seeing is Believing? Mitigating OCR Hallucinations in Multimodal Large Language Models
arxiv.org·2h
🤖Advanced OCR
Show HN: TableSprint- Supabase alternative with vibe coding features
tablesprint.com·1d·
Discuss: Hacker News
📲Digitization
Agentic AI: Implementing Long-Term Memory
towardsdatascience.com·1d
💾Persistence Strategies
Biomed-Enriched: A Biomedical Dataset Enriched with LLMs for Pretraining and Extracting Rare and Hidden Content
arxiv.org·2h
🔍Information Retrieval