11,000-Year-Old Dinner Party: Why Neolithic People Hauled Wild Boars Across Mountains
scitechdaily.com·14h
🌍Cultural Computing
I built a finance aggregator to manage wealth across banking and crypto
vin.gs·1d·
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🔗Data Provenance
Farewell to my Dad
blog.mattsbit.co.uk·2d·
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🏠Personal Archives
Improved homomorphic evaluation for hash function based on TFHE (2024)
cybersecurity.springeropen.com·3d·
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🔐Homomorphic Encryption
Show HN: I built Nexty.dev – 90 days since launch, 70 paid customers
nexty.dev·14h·
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🔄RSS Infrastructure
7 Must-Know Machine Learning Algorithms Explained in 10 Minutes
kdnuggets.com·19h
🧠Machine Learning
50x rendering speed improvements in Hologram (Elixir web framework)
hologram.page·3d·
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🎬WebCodecs
Max MRR: Your growth ceiling
longform.asmartbear.com·1d·
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🧬Bitstream Evolution
Secure Overseas E-Commerce Review Accounts, Avoid Detection by Platforms
news.ycombinator.com·21h·
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🔍BitFunnel
The Visual Studio Skeptic
brethorsting.com·1d·
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🔄Language Evolution
Be thoughtful when retiring old domain names
news.ycombinator.com·10h·
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📡DNS Archaeology
How misinformation spreaders reframe news from reputable sources to support false claims
phys.org·15h
📰Content Curation
Vibration Anomaly Detection using XIAO ESP32-S3
hackster.io·4h
👁️System Observability
Back-end Design Patterns in Rails Eliminate Bloated Code!
blog.codeminer42.com·6d
📦CBOR Extensions
Clean numerical simulation (CNS) of three-dimensional turbulent Kolmogorov flow
arxiv.org·3h
🔗Tailscale
Boardcon Compact3576 – A feature-rich Rockchip RK3576 SBC with Android 14 and Debian 12 support
cnx-software.com·14h
🔌Operating system internals
Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data
arxiv.org·3h
📊Learned Metrics
Show HN: CCTray – macOS menu bar app to keep an eye on your Claude Code metrics
github.com·2d·
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🪟Awesome windows command-line
Show HN: O3 beats Sonnet 4 at coding (in our codebase, wrt our preferences)
news.ycombinator.com·3d·
Discuss: Hacker News
📏Code Metrics
Aligning Large Language Model Agents with Rational and Moral Preferences: A Supervised Fine-Tuning Approach
arxiv.org·3h
💻Local LLMs