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🔗 Parser Combinators

Monadic Parsing, Recursive Descent, Grammar Composition, Error Handling

'If Bayrou fell, it is because he refused the golden rule: first build a majority'
lemonde.fr·16h
📝ABNF Parsing
norns shield | monome/docs
monome.org·1d
🔌Single Board PC
The growing power of artificial intelligence was a deciding factor in a court's decision not to break up Google.
dw.com·1d
🤖AI Curation
Warming Seas Threaten Key Phytoplankton Species That Fuels the Food Web
news.slashdot.org·20h
🧊Arctic Storage
New emojis will include Bigfoot, orca whale, treasure chest and more
techcrunch.com·8h
📡Bluetooth Archaeology
Show HN: BuildKit 2.0 – the shadcn for AI integrations
buildkit.picaos.com·5d·
Discuss: Hacker News
🌀Brotli Internals
The largest supply-chain attack ever…
youtube.com·6h
🔓Open Source Software
The Grounded Turing Test
whattotelltherobot.com·5d·
Discuss: Hacker News
🔲Cellular Automata
Show HN: Local, extensible and fast macOS transcription app
github.com·1d·
Discuss: Hacker News
🎙️Whisper
How Do Committees Invent? – Melvin E. Conway
melconway.com·3d·
Discuss: Hacker News
✨Effect Systems
Beyond Vibe Coding: A practical guide to AI-assisted development
beyond.addy.ie·3d·
Discuss: Hacker News
⚡Proof Automation
MVRS: The Multimodal Virtual Reality Stimuli-based Emotion Recognition Dataset
arxiv.org·20h
🌀Differential Geometry
Why 'Clean Code' Is Harder Than It Sounds
dev.to·5d·
Discuss: DEV
📏Code Metrics
Do LLM Modules Generalize? A Study on Motion Generation for Autonomous Driving
arxiv.org·5d
🎙️Whisper
Learning Multidimensional Urban Poverty Representation with Satellite Imagery
arxiv.org·1d
🧠Machine Learning
Turning Documentation into Intelligence: RAG with Just SQL + LLM
dev.to·3d·
Discuss: DEV
📡RSS Automation
🤯NativeMind: Local AI Inside Your Browser
dev.to·1d·
Discuss: DEV
🌀Brotli Internals
Human Preference-Aligned Concept Customization Benchmark via Decomposed Evaluation
arxiv.org·5d
📊Learned Metrics
Automated Thread Fatigue Life Prediction via Digital Twin and Bayesian Optimization
dev.to·2d·
Discuss: DEV
⚙️Modern Assembly
Wild Refitting for Model-Free Excess Risk Evaluation of Opaque ML/AI Models under Bregman Loss
arxiv.org·6d
🧠Machine Learning
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