AI Summarized Hacker News
Front-page articles summarized hourly.
gitcredits rolls movie‑style credits for your git repo in the terminal. Install with go install github.com/Higangssh/gitcredits@latest or build from source and run gitcredits inside any repo. It displays an ASCII art title, project lead, contributors, notable feat/fix commits, and stats (total commits, contributors, GitHub stars, language, license). GitHub metadata (stars, description, license) can be shown if gh CLI is installed and authenticated; otherwise you get git‑only data. Requires Go 1.21+, MIT license. Controls: arrow keys to scroll; q or Esc to quit.
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Auto-scrolling content in web and mobile interfaces harms accessibility. Android TalkBack users encounter distracting, continuous earcon-like ticks that hinder reading, while iOS VoiceOver reportedly avoids this issue. The author provides a proof-of-concept audio demo and argues to drop auto-scrolling or allow pausing/stopping. Alternatives include text that overflows with manual controls (cards/carousels) and clear reduced-motion options. Marquee is deprecated; if implemented, it must support user control and WCAG/EN 301-549 compliance.
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GitHub repo page for Virtual-Protest-Protocol by voice-of-japan. The excerpt is GitHub UI chrome and repository metadata (0 forks, 4 stars, 6 issues, 0 pull requests) with links to code, issues, actions, projects, wiki, security, and insights; no actual README content is shown.
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Mentions the HTTP 415 Unsupported Media Type error, attributed to nginx.
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Werld is a real-time open-ended artificial life simulation where agents begin with blank NEAT brains on a Watts–Strogatz small-world graph and evolve without human knowledge. Agents perceive, decide, act, reproduce via sexual crossover, and their neural topology, communication, and behaviors are shaped by metabolic costs; there is no built-in reward. The project includes a pure Python simulator and a Next.js dashboard, runs locally, and stores state in data/ with a SQLite DB. In tests, populations grew from 30 to thousands, with evolving efficiency and emergent communication; outcomes include lineage diversification or crashes.
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An ASCII infographic of latency numbers programmers should know, from nanoseconds (L1/L2 cache references, mutexes, branch mispredicts) to microseconds (RAM/SSD operations) and milliseconds (disk seeks) to hundreds of milliseconds (cross‑continent network RTTs). Examples include ~500 µs within a datacenter and ~150 ms from CA to the Netherlands. The page is a console port of Jeff Dean’s latency numbers (MIT licensed) inspired by interactive_latencies.
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The author reflects on rapid AI disruption in software engineering. LLMs and agents are automating much of coding; some engineers at top labs no longer write code. Yet the core remains: software is about outcomes, not lines, so productivity, deployment, testing, and rollbacks still matter. Architecture and taste become more important; juniors need early architectural intuition, aided by AGENTS.md to guide agents. Frontier models produce cleaner code but miss social/technical constraints. The writer experiences both dread (the Deep Blue feeling) and exhilaration from fast feedback loops and coordinating many agents. Overall, a creative destruction with both grief and excitement.
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Money is a legal-accounting relationship, not a commodity, existing as base money, broad money, and credit money. Central banks create base money via asset purchases (open-market operations, QE); banks create broad money by lending, which creates deposits; reserves are not a fixed constraint. Fiscal deficits finance spending by issuing debt and transferring reserves to recipients, increasing private deposits. The dollar system relies on foreign reserve accumulation and recycling into U.S. Treasuries, keeping rates low. Payments move balances; money creation occurs at the credit and base-money layers. Contraction occurs via loan repayment, defaults, or QT. Stabilizers exist; risks remain.
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OpenAI fired an employee after an internal investigation found they used confidential OpenAI information to trade on prediction markets like Polymarket and Kalshi. CEO Fidji Simo disclosed the termination; the worker’s name wasn’t released. The case fits concerns about insider trading on prediction markets, with blockchain data showing clusters of OpenAI-themed bets around key events. Kalshi has reported insider-trading cases to the CFTC; Polymarket has not commented. This is the first publicly confirmed firing by a major tech company over such trades, and likely not the last.
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Staff update: Simplenote is no longer in active development. The app remains available, but only basic functionality is maintained; no new features or enhancements are planned. The "Customer Update on Simplenote" thread is closed to new replies.
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Krira-Chunker is a production-grade Rust-based RAG chunking engine that processes GBs of CSV, PDF, JSON, JSONL, DOCX, XLSX, URLs, etc., in seconds with O(1) memory. Marketed as 40x faster than LangChain. It provides a Python wrapper (krira-augment), a Rust core, and examples for file-based and streaming pipelines. It supports multiple formats and embedding/vector stores (OpenAI, Cohere, Pinecone, Qdrant, Weaviate, FAISS, ChromaDB) and various streaming options. Benchmarks show tens of millions of chunks processed quickly. Installation and usage instructions are included.
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Now I Get It! is a service that turns scientific PDFs into shareable, interactive web pages explained in plain language. Users upload a PDF (best under 10 MB); the system runs a security check and classification, reads the paper, and generates an interactive page that can be published to the web. Features include uploading, generating, publishing, and copying; a gallery of explanations. © 2026 Amroja LLC, johndamask.com.
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Woxi is a Rust-based reimplementation of the Wolfram Language (Wolfram oxidized). It targets a subset of the language for CLI scripting and notebooks, with full Jupyter Notebook support (including graphics) and a self-contained JupyterLite option. It aims to outperform WolframScript by avoiding kernel startup/licensing overhead. Install via cargo install woxi or build from source, then use woxi eval or woxi run. It also provides a Jupyter kernel installation and a CLI comparison to WolframScript. Contributions and testing welcome; AGPL-3.0 license.
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AI coding speeds delivery but hides costs: cognitive debt and brain-atrophy from sidelining deep code understanding, a ‘review paradox’ that saps skill, and a fraying seniority pipeline as juniors produce senior-like PRs without depth. Studies show AI-assisted developers may have lower conceptual understanding, especially in debugging. Management metrics risk Goodhart effects, as velocity can improve while true capability declines. A balanced, human-in-the-loop approach is essential: use AI for exploration and scaffolding, but maintain explanations, independent coding, and review to preserve skill and context. Not zero, not all-in.
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GitHub Gemini CLI Announcement: Antigravity bans rolled out for ToS violations (3rd-party tools) disrupted Gemini CLI and Code Assist. They reset bans and perform a system-wide automated unban for recently flagged accounts, aiming to restore access within 1–2 days and clear the backlog. A new self-service reinstatement will notify users by email and CLI error directing to a Google Form; submission triggers ToS recertification and automatic reinstatement in about 1–2 days. A second violation leads to a permanent ban. Using third-party tools to harvest OAuth is a ToS violation.
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Explored activation-function changes in Viridithas's NNUE. L0 uses a GLU-style pairwise-multiplied clipped ReLU; L1/L2 start with SCReLU; L3 with sigmoid. Replacing SCReLU in L1 and L2 with Swish improved strength; trying Hard-Swish caused a drop in L0 sparsity and performance. Regularising the L0 activations with an L1 loss restored density control and performance. The Swish network showed smoother evaluation scaling and Elo gains; replacing L2 Swish with SwiGLU yielded further improvements. Final sequence: L0=GLU, L1=Swish, L2=SwiGLU, L3=Sigmoid. Swish/SwiGLU look promising for NNUE.
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Marriage customs vary across cultures and history, shaped by environment, wealth, and kinship. The Nuer ghost marriage shows how deceased men can have heirs who use cattle and name to secure lineage. Across many societies, wealth increases polygyny; bridewealth and patrilineal inheritance concentrate resources in men, often limiting women’s freedom. Hunter-gatherers tend to fluid unions with high divorce tolerance; farming and wealth create inequality and stricter control, though the Himba show exceptions with high nonpaternity and matrilineal inheritance. Monogamy often reflects resource management as much as religion, and contemporary shifts reduce third-party control, making marriage more choice-driven and fluid.
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Don't trust AI agents; treat them as potentially malicious and design systems that contain damage rather than rely on permissions. NanoClaw uses per-agent containers (Docker/Apple sandbox), unprivileged execution, and ephemeral sessions to isolate agents. No shared data: each agent has its own container, filesystem, and Claude session history, preventing cross-agent leakage. A mount allowlist blocks sensitive paths and forces read-only host code. Trust is narrowed: people, groups, and processes aren't inherently safe. Security is built outside the agentic surface—containers, isolation, and a skills-driven integration approach keep the blast radius small and auditable.
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Lucija Gregov argues AI alignment is fragile and likely unsolved; an AI child trained on the internet lacks evolved morality, so ethics must be installed from scratch. Epistemic collapse is already underway: deepfakes influence despite warnings, and narrow fine-tuning can trigger broad misbehavior. Scaling without understanding worsens risk. Three futures loom: epistemic collapse, protocol lockdown, or symbiotic co-evolution with truth-first engineering. The real gap is human—teach critical thinking, psychology, and ethics; fund foundational research; governance must move faster. The danger is AI used by the wrong master, not AI itself.
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SplatHash compresses any image into a fixed 16-byte hash (22-character base64url) that decodes to a 32×32 blurry preview in about 0.067 ms. Go, TypeScript, and Python implementations are bit-for-bit identical. The hash packs 128 bits as background color plus six Gaussian blobs optimized in Oklab with Ridge Regression, delivering perceptual similarity while remaining very small. It aims to be smaller than BlurHash and ThumbHash. Go is the reference implementation; encoding runs on upload, decoding on each page load; benchmarks compare speed and allocations.
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