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AI Submissions for Mon Dec 15 2025

It seems that OpenAI is scraping [certificate transparency] logs

Submission URL | 210 points | by pavel_lishin | 105 comments

• A user minted a fresh TLS certificate and, seconds later, saw a GET /robots.txt hit on the new subdomain from “OAI-SearchBot/1.3” (OpenAI’s crawler UA).
• Suggests OpenAI is monitoring Certificate Transparency (CT) logs to discover new hostnames—something many bots have done for years.
• Proposed “hash-the-domain” CT designs were pushed back on: CT’s purpose is public, third‑party auditability of issuance; hiding names would weaken that.
• Consensus: domain names aren’t secrets. If hostname secrecy matters, don’t rely on it—use wildcards, private PKI, or keep sensitive services off publicly trusted certs.
• Side thread: skepticism about DNSSEC/NSEC3 for typical web use, and the perennial tension between transparency and domain enumeration.
• Takeaway: Treat CT as a public broadcast. New certs can trigger near‑instant discovery and crawling; expect your robots.txt to be fetched immediately.

The discussion focused on the fact that Certificate Transparency (CT) log monitoring is a standard, widely known practice rather than a novel discovery. Commenters noted that a diverse range of actors—from Google and the Internet Archive to security firms and "script kiddies"—have tracked these logs for years to index the web or find vulnerabilities.

Key points include:

• Expected Behavior: Ideally, CT logs are intended to be consumed; users argued that specialized crawlers (like OpenAI's) using them to bootstrap discovery is a logical use case, not necessarily a security overstep.
• Tooling Issues: A significant portion of the thread complained about the unreliability of crt.sh (a popular CT search tool), leading to suggestions for alternatives like Merklemap and Sunlight for faster, more stable queries.
• Privacy & Architecture: The community reiterated that "public means public." If distinct internal subdomains are sensitive, users should utilize wildcard certificates or private Certificate Authorities (CAs) rather than relying on obscure hostnames with public certs.
• Knowledge Gaps: While some dismissed the post as "boring" (or "yawn"-worthy), others defended it as a necessary realization for developers encountering the "lucky 10,000" phenomenon—learning for the first time how tightly coupled HTTPS security is to public domain announcement.

If AI replaces workers, should it also pay taxes?

Submission URL | 570 points | by PaulHoule | 935 comments

AI’s profit boom revives the “robot tax” debate: if machines replace workers, who pays?

• Big Tech is pouring record profits into AI while announcing layoffs (Amazon, Meta, UPS). With most public revenue tied to labor taxes, policymakers worry automation could shrink the tax base.
• Proposals resurface: Edmund Phelps and Bill Gates once floated a “robot tax.” But many economists warn it’s hard to define what counts as a robot/AI and could distort innovation.
• Brookings’ Sanjay Patnaik: don’t tax AI directly; instead, raise capital gains taxes to address risks and revenue loss.
• IMF: also advises against AI-specific taxes, but suggests rebalancing toward capital—higher capital taxes, possible excess-profits levies, and revisiting innovation incentives that may displace workers.
• Labor-market outlook is mixed. Goldman Sachs sees AI lifting global GDP by ~7% over a decade; the IMF sees up to 0.8 pp annual growth through 2030. The ILO says 1 in 4 workers are exposed, with most jobs transformed rather than destroyed.
• Oxford’s Carl Frey: tax systems have shifted toward labor and away from capital, nudging firms toward automation; rebalancing is key to support job-creating tech.
• Stockholm’s Daniel Waldenström: no clear rise in unemployment; keep taxing labor, consumption, and capital—no special AI tax.
• Context: OECD corporate tax rates fell from 33% (2000) to ~25% today, while workers’ tax wedges barely budged. Amazon exemplifies the tension: +38% profits and major AI spend alongside 14,000 layoffs.
• Robotics industry (IFR) rejects ad hoc taxes, arguing automation boosts productivity and jobs; warns against taxing “production tools” instead of profits.

Why it matters: Governments must shore up revenues without smothering productivity. The emerging consensus: skip a robot tax, but rebalance away from labor and toward capital to align incentives with broad-based job growth. Open question: how to do that without chilling innovation.

Here is the summary of the discussion based on the provided text:

Summary of Discussion:

The discussion centers on the tension between funding societal infrastructure and the economic mechanics of automation, with a general consensus that while the current tax system fails to capture value from AI and capital accumulation, a specific "robot tax" may be the wrong solution.

Capital, Labor, and Tax Strategy There is broad agreement that capital owners currently avoid funding the state’s social systems (safety, infrastructure, research) compared to wage earners. However, commenters argue that implementing a specific "robot tax" is a knee-jerk reaction or a "sci-fi distraction."

• Alternative Proposals: Instead of taxing "robots," participants suggest taxing capital gains and wealth more effectively. One line of reasoning argues that the distinction between "robots" and standard capital equipment is arbitrary; therefore, general capital taxation is more appropriate.
• Corporate vs. Individual Tax: A debate emerged regarding corporate tax rates. Some users argued for eliminating corporate income tax entirely—viewing it as "deadweight loss"—and instead taxing distributions (dividends/income) to individuals at higher rates.
• The "Corporate Shell" Loophole: Critics of eliminating corporate tax pointed out that business owners would simply hoard wealth within the corporation, claiming personal expenses (cars, rent) as business costs or taking loans against corporate assets to avoid realizing taxable income.

Friction vs. Redistribution A philosophical debate arose regarding the economic impact of taxation:

• The Innovation Argument: Some argued that taxes create "friction" that slows automation. Since automation theoretically lowers costs and increases living standards, slowing it down contradicts the goal of societal improvement.
• The "Trickle-Down" Critique: Counter-arguments noted that productivity gains have not historically trickled down to real wages since the 1970s. From this view, "friction" (taxation) represents necessary societal consent and cost internalization to prevent monopolies and rent-seeking behavior.

Compensation and Healthcare The conversation diverted into why real wages appear stagnant. Some commenters claimed that total compensation has risen if employer-sponsored healthcare premiums are included. Others rebutted this, arguing that the US healthcare system is inefficient and inflated; therefore, rising premiums represent a transfer of wealth to the medical industry rather than increased real value or purchasing power for the worker. Several users suggested decoupling healthcare from employment entirely to improve labor mobility.

Microsoft Copilot AI Comes to LG TVs, and Can't Be Deleted

Submission URL | 297 points | by akyuu | 301 comments

A Reddit user says a recent LG webOS update added Microsoft’s Copilot app to their TV with no uninstall option. What the app actually does on a TV isn’t clear, but it mirrors Microsoft’s broader push to put Copilot everywhere, moving beyond PCs and into living rooms. Rollout appears to vary by region/model: some users say they’ve had it for months; others in the EU report not seeing it.

Why it matters

• Forced bundling on “smart” TVs revives long‑running bloatware and privacy concerns, especially when apps can’t be deleted.
• LG’s “Live Plus” feature (Automatic Content Recognition) can scan what’s on screen to personalize recommendations and ads. LG says you can disable it in Settings > All Settings > General > Additional Settings (wording varies by model).
• Commenters predict regulatory scrutiny in the EU and vent broader frustration with TV OSes that are slow, ad‑laden, and hard to control.

What you can do

• Disable Live Plus/ACR, voice assistants, and ad personalization in settings.
• Keep the TV off the internet or put it on a restricted VLAN/guest network.
• Use an external streaming box you control; consider DNS/ad‑blocking.
• If shopping, look for models with “dumb” modes or better app control.

HN discussion: 117 comments, with themes of privacy, unwanted AI bundling, and whether TVs should be treated more like neutral displays than ad platforms.

Here is a summary of the Hacker News discussion:

Isolate the TV, Upgrade the Box The overwhelming consensus among commenters is to treat modern smart TVs solely as "dumb" monitors. The most common advice is to never connect the TV panel to the internet to prevent data collection and forced updates (like the Copilot one). Instead, users recommend relying entirely on external hardware for streaming.

The Hardware Debate

• Apple TV: Highly recommended as the "mainstream" option; users argue that the higher hardware cost pays for a premium, ad-free experience, as opposed to the data-subsidized business models of Roku, Fire TV, and Chromecast.
• Nvidia Shield: Remains a favorite among enthusiasts for its performance and codec support (like 4K/AI upscaling), though many expressed frustration that the hardware hasn't been refreshed since 2019.
• Custom Launchers: For those stuck with Android/Google TV based devices, commenters suggested replacing the default ad-laden interface with third-party launchers like Projectivity Launcher or Flauncher to strip away bloatware and tracking.

Paranoia and Philosophy A thread of discussion formed around the fear that keeping a TV "offline" might not be enough, with some speculating that manufacturers might eventually embed cellular modems or use open Wi-Fi networks to exfiltrate data (though others dismissed this as currently cost-prohibitive). Finally, a philosophical debate broke out regarding the decline of TV as a "shared cultural experience," arguing that algorithmic recommendations have isolated viewers into niches, destroying the communal aspect of broadcast television.

Nvidia Nemotron 3 Family of Models

Submission URL | 55 points | by ewt-nv | 8 comments

NVIDIA debuts Nemotron 3: an “open” family of efficient agentic AI models with 1M-token context — Nano ships today

What’s new

• Three-tier lineup: Nano (small/efficient), Super (for collaborative agents, IT ops), Ultra (SOTA reasoning). Nano is available now; Super and Ultra arrive in coming months.
• Architecture: Hybrid Mamba-Transformer Mixture-of-Experts for high throughput; larger models add LatentMoE and Multi-Token Prediction; trained with NVFP4; long-context up to 1M tokens; RL-based post-training across diverse environments; inference-time “reasoning budget” control.

Nano highlights

• Size: 3.2B active (3.6B with embeddings), 31.6B total params (MoE), tuned for low-cost inference.
• Performance: Claims higher accuracy than GPT-OSS-20B and Qwen3-30B-A3B on popular benchmarks; 1M-token context and stronger RULER scores across lengths.
• Throughput: On a single H200 (8K in / 16K out), 3.3× Qwen3-30B-A3B and 2.2× GPT-OSS-20B.

Open release today

• Weights: FP8 quantized and BF16 post-trained Nano; BF16 base model; plus the GenRM used for RLHF.
• Data: Massive new corpora including 2.5T English tokens from curated Common Crawl (with synthetic rephrasing/translation), 428B CC-derived code tokens (equation/code preserving, LaTeX-standardized), refreshed curated GitHub code, specialized STEM/scientific coding sets, and new SFT/RL datasets.
• Recipes: Full training/post-training pipelines via the NVIDIA Nemotron developer repo; white paper and Nano technical report.

Why it matters Nemotron 3 targets agentic/reasoning-heavy workloads with MoE efficiency (small active params, big total capacity), ultra-long context, and tunable inference costs—aiming to beat mid-size baselines while being cheaper to run. Independent validations and license specifics will be key to watch as Super/Ultra roll out.

Here is a summary of the discussion:

Real-World Performance and Use Cases Users engaged in high-volume data processing (ETL) and analysis for venture capital workflows reported that NVIDIA’s smaller models are outperforming competitors. One user noted that for tasks requiring strict compliance, tool calling, and low hallucinations, these models beat Llama 3 and other open-weight models under 125B parameters. Several participants highlighted the cost-effectiveness of the release, noting that the models are currently accessible for free with generous limits via OpenRouter.

Technical Architecture and Release Quality The discussion praised NVIDIA’s "Day 0" availability of training recipes and datasets, though one user noted a broken link for the SFT data. Technical conversation focused on the Hybrid MoE architecture and 1M token context; however, a clarification was made regarding quantization: while the upcoming Super and Ultra models are native FP4, the currently released Nano model was not pretrained in FP4.

Licensing and Hardware Comparisons Users expressed relief regarding the licensing terms, pivoting to Nemotron now that it appears viable for commercial settings compared to previous restricted releases. Comparisons were drawn to high-speed inference providers like Cerebras and Groq, with some debate over whether Cerebras would eventually support this architecture given their current limited model list. A dissenting voice offered skepticism, dismissing the announcement as "misleading benchmarks."

AI Submissions for Sun Dec 14 2025

AI agents are starting to eat SaaS

Submission URL | 285 points | by jnord | 285 comments

• Thesis: AI coding agents are shifting the build-vs-buy calculus. For many “simple” tools, it’s now faster and cheaper to build exactly-what-you-need than to license SaaS—threatening demand, especially at the low and mid tiers.
• Signals: Engineers are quietly replacing services with agent-built one-offs:
  • Internal dashboards instead of Retool-like products
  • Local video pipelines via ffmpeg wrappers instead of external APIs
  • Fast UI/UX wireframes with Gemini 3
  • Nicely designed slide PDFs generated from Markdown via Claude Code
• Enterprise ripple: Teams are starting to question automatic SaaS renewal hikes. What was once a non-starter—“we can’t maintain that”—is becoming a real option worth scoping.
• Why maintenance may not block this:
  • Agents lower upkeep (library migrations, refactors, typed ecosystems) and don’t “leave the company”; an AGENTS.md can preserve context.
  • Security posture can improve by keeping data behind existing VPNs and reducing third‑party exposure.
  • SaaS has maintenance risk too (e.g., breaking API deprecations).
• Fit: This won’t flip non-technical SMEs overnight. But orgs with decent engineering capability will scrutinize procurement, trim over-featured SaaS, and prefer bespoke/internal tools where needs are narrow and well-understood.
• The SaaS economics hit:
  • Slower new logo growth as “good enough to build” expands.
  • More worrisome: NRR compression as customers downsize usage, avoid seat expansion, or churn to internal builds—undermining the core assumptions behind premium SaaS valuations.

Bottom line: Agents won’t kill all SaaS, but they’re poised to deflate broad, feature-heavy segments and force vendors to justify price with defensibility (deep moats, compliance, data gravity, collaboration/network effects, or truly hard problems).

Here is a summary of the discussion:

Skepticism from the SaaS Front Lines While the article suggests a shift away from SaaS, several industry insiders pushed back on the feasibility of customers building their own tools.

• The Maintenance Barrier: User bnzbl, a CTO of a vertical SaaS company, argued that the "threat model" assumes customers want to build tools, but most lack the desire or capacity to maintain them. They noted zero churn to internal alternatives so far, suggesting that while AI increases velocity for dev teams, it cannot replicate the tight feedback loops and domain expertise of a dedicated SaaS vendor.
• The "Bus Factor" Risk: SkyPuncher and cdrth warned that tools built by non-technical teams (like Sales or HR) using AI wrappers inevitably become unmaintainable technical debt once the "random dev" or "gritty guy" leaves the company. Corporations pay for SaaS specifically for SLAs, support, and continuity.

The "Interface Layer" Shift A significant portion of the debate focused not on replacing SaaS entirely, but on how AI changes the user experience.

• SaaS as a "Dumb Pipe": TeMPOraL and jswn theorized that users don't want software; they want results. The real disruption might be AI agents acting as "personal secretaries" that navigate complex SaaS UIs on behalf of the user.
• Commoditization: If AI agents handle the interface, SaaS products could be reduced to commoditized back-end APIs. mmbs noted this creates a dangerous disconnect for vendors: if an AI operates the software, vendors lose the ability to influence users via ads, recommendations, or sticky UI features.

The Rise of the "CEO Builder" Commenters shared anecdotes suggesting the "build" trend is already happening in specific pockets, often driven by impatience rather than cost.

• Shadow IT 2.0: drnd shared a story of a CEO using "Lovable AI" to code his own dashboards because the engineering team was too busy. While William_BB critiqued this as creating technical debt, hrmfx countered that it eliminates the "lost in translation" phase between requirements and implementation.
• Internal Replacement: rpnd (a self-described "grumpy senior") and CyanLite2 mentioned they are actively using AI to replace "crappy third-party APIs" and GRC tools with internal code to save money and reduce dependencies.

Enterprise Reality Check

• Organizational Moats: Crowberry and gwp pointed out that for large enterprises, the bottleneck isn't code generation—it's permission management. Internal agents struggle to navigate the complex web of SSO, ERP access, and security policies that established SaaS vendors have already solved.

AI and the ironies of automation – Part 2

Submission URL | 243 points | by BinaryIgor | 111 comments

AI and the Ironies of Automation, Part 2 revisits Bainbridge’s 1983 insights through the lens of today’s LLM “agent” stacks. The author argues that even in white‑collar settings, oversight often demands fast decisions under pressure; if companies expect superhuman productivity, humans must be able to comprehend AI output at near‑superhuman speed or any gains vanish. Stress further narrows cognitive bandwidth, so UIs must either reduce the need for deep analysis or actively support it under duress. Channeling Bainbridge, the piece calls for “artificial assistance”—up to and including “alarms on alarms”—to surface rare-but-critical anomalies and combat monitoring fatigue. By contrast, many current agent setups (a supervisor plus generic or specialist workers) effectively give one human the worst possible UI: thin visibility, weak alerting, and high cognitive load. The takeaway: design AI agent oversight like an industrial control room—clear displays, prioritized alerts, and rapid error detection—or risk repeating the classic automation failures Bainbridge warned about.

The discussion threads explore the long-term consequences of replacing manual expertise with AI oversight, debating whether efficient automation inevitably erodes the skills necessary to manage it.

• The Paradox of Skill Erosion: Users highlighted a core insight from Bainbridge’s 1983 paper: while current system operators possess manual skills from the pre-automation era, future generations will lack this foundational experience. Some suggested that if programmers become mere "operators" of AI, they may need to dedicate 10–20% of their time to manual side projects just to maintain the expertise required to debug or validate AI output.
• The "Ecological" Collapse of Data: Several commenters argued that AI outputs are "polluting the commons" of training data. As AI generates more low-cost content and displaces human creators, the pool of "fresh" human culture shrinks, potentially causing models to drift or degrade—a scenario likened to ecological collapse or the destruction of a genetic library.
• Commercial vs. Fine Art: There was a debate regarding the "Artpocalypse." While high-end speculative art (e.g., Banksy) relies on human narrative and may survive, "working artists" in advertising and media face displacement. Counter-arguments noted that businesses might hesitate to fully adopt AI art due to the inability to copyright the output and potential legal liabilities surrounding the training data.
• Practical Utility in Coding: Skepticism arose regarding the actual efficiency gains of current AI agents. One user cited an internal survey at Anthropic suggesting that even AI researchers often find the overhead of prompting and debugging code agents greater than the effort of writing the code manually, particularly for one-off tasks or datasets.

Kimi K2 1T model runs on 2 512GB M3 Ultras

Submission URL | 226 points | by jeudesprits | 114 comments

I’m ready to write the digest, but I’ll need the submission details. Please share one of the following:

• The Hacker News thread URL
• The article URL (and, if possible, the HN title/points/comments count)
• The article text or a screenshot

Preferences (optional):

• Length: quick TL;DR (2–3 sentences) or fuller summary (150–250 words)?
• Extras: key takeaways, why it matters, notable HN comments, caveats?
• Audience: general or technical tone?

Drop the link(s) and I’ll get started.

Article: Kimi k1.5 is an entry-level multimodal model (Inferred context based on "Kimi K2" discussion)

Summary of Discussion The discussion centers on the performance and "personality" of the Kimi K2 model, with users praising it as a refreshing alternative to major US-based models:

• Distinct Personality: Users describe Kimi K2 as having high emotional intelligence (EQ) and a distinct writing style—it is "direct," "blunt," and less prone to the excessive politeness or sycophancy found in RLHF-heavy models like Claude or GPT. One commenter notes it is "extremely good" at calling out mistakes and "nonsense" in user queries.
• Benchmarking Debate: A sub-thread debates the validity of benchmarks like EQ-Bench (where users claim Kimi ranks #1). Skeptics argue that "LLMs grading LLMs" is unreliable because models "memorize" rather than "reason," while others counter that human judges are statistically less consistent than model-based grading.
• Prompt Engineering: An advanced discussion on linguistics and prompting emerges, where a user explains how to make other models mimic Kimi's directness by using system prompts that suppress "Face Threatening Acts" (FTAs)—instructing the AI to ignore social politeness buffers and maximize direct, epistemic correction.

Show HN: Open-source customizable AI voice dictation built on Pipecat

Submission URL | 21 points | by kstonekuan | 10 comments

Tambourine: an open-source, universal voice-to-text interface that types wherever your cursor is. Think “push-to-talk dictation for any app,” with AI that cleans up your speech as you go—removing filler, adding punctuation, and honoring a personal dictionary. It’s positioned as an open alternative to Wispr Flow and Superwhisper.

Highlights

• Works anywhere: email, docs, IDEs, terminals—no copy/paste or app switching. Press a hotkey, speak, and text appears at the cursor.
• Fast and personalized: real-time STT plus an LLM pass to format and de-um your text; supports custom prompts and a personal dictionary.
• Pluggable stack: mix-and-match STT (e.g., Cartesia, Deepgram, AssemblyAI/Groq, or local Whisper) and LLMs (Cerebras, OpenAI, Anthropic, or local via Ollama).
• Quality-of-life features: push-to-talk (Ctrl+Alt+`) or toggle (Ctrl+Alt+Space), overlay indicator, system tray, transcription history, “paste last” (Ctrl+Alt+.), auto-mute system audio (Win/macOS), device selection, in-app provider switching.
• Cross-platform: Windows and macOS supported; Linux is partial; mobile not supported.
• Under the hood: a Tauri desktop app (Rust backend + React UI) talks to a Python server using Pipecat SmallWebRTC; FastAPI endpoints manage config/provider switching. Licensed AGPL-3.0.
• Roadmap: context-aware formatting per app (email vs. chat vs. code), voice-driven edits (“make this more formal”), voice shortcuts, auto-learning dictionary, metrics/observability, and an optional hosted backend.

Caveat: “Build in progress”—core works today, but expect breaking changes as the architecture evolves.

Tambourine: An open-source, universal voice-to-text interface

Tambourine is a cross-platform (Windows/macOS) desktop utility that brings push-to-talk dictation to any application. Built on Tauri, it combines real-time speech-to-text with an LLM to clean up grammar and remove filler words before typing at your cursor. The stack is pluggable, supporting various cloud providers (OpenAI, Anthropic, Deepgram) as well as an "open alternative" route using local models via Ollama.

Discussion Highlights

• Cloud vs. Local dependencies: Several users questioned the "open source alternative" framing, noting that if the tool requires proprietary API keys (like OpenAI) to function, it is merely a shim. The author clarified that while defaults may use robust cloud APIs, the architecture is built on Pipecat and fully supports swapping in local LLMs and STT.
• Offline capabilities: Following the critique on cloud dependencies, the author confirmed that users can run the tool without internet access by configuring OLLAMA_BASE_URL for local inference and using a local Whisper instance for transcription.
• Documentation updates: Users suggested that local inference capabilities should be front-and-center in the documentation to validate the "open alternative" claim; the author updated the README during the discussion to reflect this.
• Platform support: The developer confirmed the app is built with Tauri and has been personally tested on both macOS and Windows.

I wrote JustHTML using coding agents

Submission URL | 18 points | by simonw | 15 comments

JustHTML: a zero-dependency Python HTML5 parser built with coding agents

• What’s new: JustHTML is a pure-Python HTML5 parser that passes 100% of the html5lib test suite, ships with a CSS selector query API, and aims to handle messy real-world HTML (including misnested formatting) — the author even claims it outperforms html5lib on those tricky cases.

• Why it matters: HTML5 parsing is defined by a notoriously complex algorithm (notably the “adoption agency algorithm” with its “Noah’s Ark” clause). Hitting full spec tests in pure Python, without deps, is rare — and this project doubles as a case study in using coding agents effectively.

• How it was built:

  • Leaned on the exhaustive html5lib-tests so agents could iterate autonomously against a clear goal.
  • Started with a handler-based architecture per tag; iterated to full test pass.
  • Benchmarked and profiled extensively; briefly swapped in a Rust tokenizer that edged past html5lib speed.
  • After an existential detour (why not just use html5ever?), pivoted back to pure Python for zero binaries.
  • Optimization phase used a custom profiler, a 100k-page real-world corpus, and iterative agent-driven tuning; Gemini 3 Pro was the only model that moved the perf needle.
  • Coverage-driven code deletion removed “untested” paths, shrinking treebuilder from 786 to 453 lines and boosting speed.
  • Added a custom fuzzer to stress unknown corners.

• Tools and agents: VS Code + GitHub Copilot Agent (auto-approve with a manual blacklist), later Claude Sonnet 3.7 for big leaps, and Gemini 3 Pro for performance work.

• Takeaway: Projects with rich, authoritative test suites make ideal targets for autonomous coding agents — they provide objective progress signals, enable safe refactors, and can even guide aggressive cleanup and performance wins.

Discussion Summary:

The discussion focuses on the architectural decisions behind JustHTML, the efficacy of coding agents, and comparisons to existing tools.

• Architecture & Optimization: The author (EmilStenstrom) clarified that JustHTML is not a direct translation of the Rust library html5ever, but rather a scratch-build that eventually adopted html5ever's logical structure. The initial "handler-based" Python approach hit a performance ceiling due to object lookup overhead; guiding agents to rewrite the architecture to match the "closer to the metal" style of the Rust library resulted in the parser becoming ~60% faster than html5lib.
• Agents & Complexity: Simon Willison (smnw) and the author discussed why parsers are good targets for AI: existing test suites provide objective "right/wrong" feedback loops. However, the author noted that the "Adoption Agency Algorithm" (handling misnested tags) remained notoriously difficult to convince agents to implement correctly, requiring significant human steering.
• Comparisons: Users asked how this compares to Beautiful Soup (bs4). The author noted that bs4 defaults to Python’s standard library parser (which fails on invalid HTML), whereas JustHTML implements full HTML5 compliance for handling real-world messiness.
• Code & Content Critique: A user questioned the claimed "3,000 lines of code," finding nearly 9,500 lines in the source directory. Another user criticized the accompanying blog post for having an "LLM-generated" feel with excessive numbered headers, which the author admitted were generated while the text was manual.

If a Meta AI model can read a brain-wide signal, why wouldn't the brain?

Submission URL | 134 points | by rdgthree | 90 comments

Magnetoreception, biomagnetism, and a wild “what if” about the brain

• The post rockets through evidence that many organisms sense Earth’s magnetic field (magnetotactic bacteria, plants, insects, fish, turtles, birds, mammals). For humans, it flags a 2019 Caltech study where rotating Earth-strength fields triggered orientation-specific changes in alpha-band EEG—suggesting an unconscious magnetic sense.

• Then it flips the lens: living tissue also emits magnetic fields. Magnetoencephalography (MEG) measures the brain’s femtotesla-scale fields to map neural activity in real time.

• The author highlights 2023 Meta/academic work training models on public MEG datasets to decode aspects of what people see/hear/read with millisecond precision—casting it as “we read minds,” i.e., extracting image/word-level representations from noninvasive brain magnetism.

• Provocative leap: if brains both detect magnetic fields and broadcast rich, information-bearing magnetic signals, could the brain “read its own” magnetic field as part of its computation or state monitoring? Could subtle geomagnetic or lunar-modulated effects nudge mood/behavior?

Why it’s interesting

• Reframes magnetoreception as widespread and potentially relevant to humans.
• Positions MEG + ML as a fast, noninvasive route to decoding dynamic brain representations.
• Floats an audacious hypothesis about self-sensing via magnetism.

Reality check

• Human magnetoreception remains debated; effects are small and context-dependent.
• Current MEG decoders infer coarse categories/semantic features, not arbitrary thoughts.
• Self-magnetic feedback is likely far weaker than established electrical/ephaptic coupling in cortex.

Here is a summary of the discussion:

Skepticism and Experimental Flaws The discussion opened with skepticism regarding the cited EEG studies. Users suggested the reported "brain waves" might simply be the EEG equipment acting as an antenna picking up environmental electromagnetic fluctuations, rather than the brain responding. One commenter proposed using pneumatic (air-tube) headphones to isolate the subject from magnetic interference to establish a proper control group.

The "Binaural Beats" Tangent A significant portion of the conversation pivoted to binaural beats. A user recalled a study where the cognitive effects of binaural beats disappeared when subjects used non-magnetic (pneumatic) headphones, implying the mechanism might be electromagnetic interference rather than audio frequencies.

• Anecdotes: Users debated efficacy, with reports of binaural beats aiding focus, creativity, and deadline crunching (even if just a placebo). One user claimed a specific video cured migraines, while another urged caution regarding medical symptoms.
• Consensus: Links provided suggest the science is mixed or unproven, though some subjective benefits remain.

fMRI and TMS Reality Checks Commenters questioned the hypothesis by pointing to strong magnetic fields used in medical imaging:

• fMRI: If the brain uses delicate magnetic fields for state-monitoring, why don't the massive fields in fMRI machines cause loss of consciousness or extreme hallucinations? Users noted that strong fields do cause visual artifacts (magnetophosphenes), but not total system failure.
• The Dead Salmon: The famous "dead salmon" fMRI study was brought up (and clarified) as a lesson in statistical noise ("hallucinations" in data) rather than biological reaction.
• TMS: While Transcranial Magnetic Stimulation (TMS) definitely alters brain activity, users argued this is due to standard electromagnetic induction of electrical currents, not a specialized "magnetoreception" sense.

Theoretical Critiques

• FPGA Analogy: One user compared the hypothesis to Dr. Adrian Thompson’s 1990s research, where evolutionary algorithms programmed FPGAs to utilize physical electromagnetic phenomena in the silicon substrate to function—suggesting "wetware" might do the same.
• "False North" Logic: A critic described the article as "conspiracy theory logic": taking a proven small effect (weak sensing) and a proven technology (MEG) to bridge a gap to a grand, unsupported philosophical conclusion about consciousness.
• The Mirror Problem: A user metaphorically argued against self-sensing: "Cameras can't see their own eyes," to which another replied, "Mirror sold separately."

AI Submissions for Sat Dec 13 2025

RemoveWindowsAI

Submission URL | 62 points | by hansmayer | 56 comments

RemoveWindowsAI is a popular PowerShell script (MIT-licensed, ~3.9k stars) that strips Windows 11 (25H2 and beyond) of Microsoft’s expanding AI stack—aimed at users worried about privacy, performance, or bloat. It targets Copilot, Recall, AI features in Paint and Notepad (Rewrite), Edge integrations, Input Insights/typing telemetry, Voice/Voice Access, “AI Fabric” services, AI Actions, and related search/UI hooks.

Highlights

• Deep removal, not just toggles: disables registry/policies, deletes Appx (including “nonremovable” and WindowsWorkload), removes hidden CBS packages and files, and force-deletes Recall tasks.
• Blocks reinstalls: installs a custom Windows Update package to prevent AI components from reappearing via CBS.
• UI or headless: offers an interactive launcher plus non-interactive flags like -nonInteractive, -AllOptions, and per-feature options.
• Safety net: optional backup mode enables full reversion; a revert mode is provided.
• Scope: tracks the latest stable Windows builds (not Insider) and invites issues for new AI features/keys.
• Caveats: must run as admin, on Windows PowerShell 5.1 (not PowerShell 7); AV tools may flag it (false positives per author). As it modifies CBS and core packages, test in a VM and review the script before use—future updates or features may break or be removed.

User Ownership and Microsoft’s Intent The discussion highlights a pervading sense of disenfranchisement, with users arguing that the operating system no longer serves the owner but rather functions as a data-harvesting platform for Microsoft. Commenters describe the effort required to permanently remove these features—and Microsoft's persistence in reinstalling them—as evidence of "user-hostile" mechanics. This sparked nostalgia for older, quieter OS versions (like Windows 9x/NT) and comparisons to Microsoft's 1990s "Embrace, Extend, Extinguish" culture, which many feel is still embedded in the company's DNA.

Technical Implementation and Alternatives

• LTSC as a solution: Several users suggest that rather than scrubbing consumer Windows, it is easier and cleaner to install Windows LTSC (Long-Term Servicing Channel), usually via massgrave methods, to avoid bloat by default.
• Security habits: The script's installation method (irm ... | iex) drew comparisons to Linux's curl | bash convention; while common for tools like Windows Activation Scripts (MAS), users debated the security implications of remote string execution.
• PowerShell Versions: There was specific technical commentary regarding the script's reliance on Legacy PowerShell 5.1, highlighting surprise that the newer PowerShell 7 lacks the backward compatibility to handle these specific core OS manipulations.

AI Utility vs. "Wall Street Posturing" Skepticism surrounds the AI features themselves. Users argued that the rapid integration of Copilot and Recall is "signaling to Wall Street" to boost stock prices rather than addressing user needs, often citing that basic features (like text selection in IDEs) remain buggy while AI is forced in. While some users see potential value in local LLMs/NPUs, the consensus leans toward viewing cloud-tethered OS features as intrusion or "trespassing" on personal devices.