What does the ideal information environment look like?
defenderofthebasic.substack.com·1d·
Discuss: Substack
🔲Cellular Automata
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Naïve Shuffle Algorithm (2007)
blog.codinghorror.com·1d·
Discuss: Hacker News
λFunctional Programming
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My first fifteen compilers (2019)
blog.sigplan.org·4d·
Discuss: Hacker News
⚙️Compiler Design
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Decoding Kiroween
dev.to·16h·
Discuss: DEV
⚙️Compiler Design
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Natural Building Blocks for Structured World Models: Theory, Evidence, and Scaling
arxiv.org·1h
🔲Cellular Automata
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Demo: Statistically Significant Results On Biases and Errors of LLMs Do Not Guarantee Generalizable Results
arxiv.org·1h
🛡️seL4
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Story and essential meaning dynamics in Bangladesh's July 2024 Student-People's Uprising
arxiv.org·1h
🕸️Graph Theory
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I'm working on a project I've been dreaming about for months and it feels good
news.ycombinator.com·7h·
Discuss: Hacker News
🧩Parser Combinators
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Design-Based Supply Chain Operations Research Model: Fostering Resilience And Sustainability In Modern Supply Chains
arxiv.org·1h
🕸️Graph Theory
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Imperfect Language, Artificial Intelligence, and the Human Mind: An Interdisciplinary Approach to Linguistic Errors in Native Spanish Speakers
arxiv.org·1d
⚙️Compiler Design
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3 Experiments That Reveal the Shocking Inner Life of AI Introduction: Is Anybody Home?
hackernoon.com·1d
🔬Unikernels
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Automatic Minds: Cognitive Parallels Between Hypnotic States and Large Language Model Processing
arxiv.org·1d
📚Automata Learning
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OpenSIR: Open-Ended Self-Improving Reasoner
arxiv.org·1d
✓Automated Theorem Proving
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Writing an LLM from scratch, part 26 – evaluating the fine-tuned model
gilesthomas.com·1d·
Discuss: Hacker News
📚Automata Learning
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LLM-Driven Cost-Effective Requirements Change Impact Analysis
arxiv.org·1d
📦Opam
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Parleying with the Principled
lesswrong.com·1d
📐Hoare Logic
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Leaving Open Philanthropy, going to Anthropic
lesswrong.com·1d
🔲Cellular Automata
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GrowthHacker: Automated Off-Policy Evaluation Optimization Using Code-Modifying LLM Agents
arxiv.org·1d
📦OPAM
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A Systematic Literature Review of Code Hallucinations in LLMs: Characterization, Mitigation Methods, Challenges, and Future Directions for Reliable AI
arxiv.org·1d
⚙️Compiler Design
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Energy Loss Functions for Physical Systems
arxiv.org·1h
📐Linear Algebra
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