My strong belief is that you should be able to ask a program how it wants to be talked to, so you can:
bsky.appยท1dยท
Discuss: Bluesky
๐ŸนGo
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Explaining Financial Models by Deleting Transactions
building.nubank.comยท14h
๐Ÿš€Performance
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Just call clone (or alias)
smallcultfollowing.comยท2dยท
๐ŸนGo
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Writing MCP servers in Clojure with Ring and Malli
latacora.comยท2d
๐ŸนGo
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<i>Colloquium</i>: Advances in automation of quantum dot devices control
link.aps.orgยท14h
๐Ÿš€Performance
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Building Versioning for AI-Generated Apps
neon.comยท14h
๐Ÿš€Performance
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Ask HN: What's your top-recommended tuts for chatbot dev with local-hosted LLMs?
news.ycombinator.comยท1dยท
Discuss: Hacker News
๐ŸŒWebAssembly
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Generative AI and the bullshit singularity
daedtech.comยท1dยท
Discuss: Hacker News
๐Ÿš€Performance
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Message length limits are infuriating โ€” any tips?
reddit.comยท10hยท
Discuss: r/ClaudeAI
๐Ÿš€Performance
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Automated Anomaly Detection & Predictive Maintenance in Borosilicate Glass Composition Analysis
dev.toยท16hยท
Discuss: DEV
๐Ÿš€Performance
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From Experience to Strategy: Empowering LLM Agents with Trainable Graph Memory
arxiv.orgยท1d
๐Ÿš€Performance
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Hybrid Autoencoders for Tabular Data: Leveraging Model-Based Augmentation in Low-Label Settings
arxiv.orgยท2d
๐Ÿš€Performance
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Automated Governance Risk Scoring & Mitigation via Multi-Modal Semantic Analysis
dev.toยท9hยท
Discuss: DEV
๐Ÿš€Performance
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Emergent Misalignment via In-Context Learning: Narrow in-context examples canproduce broadly misaligned LLMs
paperium.netยท3dยท
Discuss: DEV
๐Ÿš€Performance
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DeepPersona: A Generative Engine for Scaling Deep Synthetic Personas
arxiv.orgยท2d
๐Ÿš€Performance
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Inclusion of Role into Named Entity Recognition and Ranking
arxiv.orgยท2d
๐Ÿš€Performance
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First Rust Program, Hack Assembler from Nand2Tetris
reddit.comยท2dยท
Discuss: r/rust
๐Ÿ“C Programming
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Expand Your SCOPE: Semantic Cognition over Potential-Based Exploration for Embodied Visual Navigation
arxiv.orgยท2h
๐Ÿš€Performance
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Correcting False Alarms from Unseen: Adapting Graph Anomaly Detectors at Test Time
arxiv.orgยท2d
๐Ÿš€Performance
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Pipelex: Building Reliable AI Workflows with Business Logic, Not API Calls
pub.towardsai.netยท1d
๐ŸนGo
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