Hacker News, Distilled

Running R1-0528 Locally: Hardware & Performance

• Full 671B/685B model is widely seen as impractical for “average” users.
• Rough home-level setups discussed:
  • ~768 GB DDR4/DDR5 RAM dual-socket server, CPU-only or mixed CPU+GPU, achieving ~1–1.5 tokens/s on 4-bit quantizations.
  • Mac M3 Ultra with 512 GB RAM or multi-GPU rigs totaling ~500 GB VRAM for higher-speed inference.
  • Some note that with huge swap you can technically run it on almost any PC, but at “one token every 10 minutes”.
• Quantized/distilled variants (4-bit, 1.58-bit dynamic) can run on high-end consumer GPUs or large-RAM desktops, with users reporting 1–3 tokens/s but very strong reasoning.

Cloud Access, Cost, and Privacy

• Many suggest using hosted versions (OpenRouter, EC2, vast.ai, Bedrock) instead of buying $5k–$10k hardware.
• Single H100 is insufficient for full-precision R1; estimates of 6–8 GPUs or large multi-node setups.
• Debate over “free” access via OpenRouter/Bittensor:
  • One side: prompts and usage data are valuable and likely monetized or re-sold.
  • Other side: for non-sensitive tasks (e.g., summarizing public content), the tradeoff is acceptable.

Model Quality, Info, and Benchmarks

• Frustration that there’s no detailed model card, training details, or official benchmarks yet.
• Some like the low-drama “quiet drop” style; others compare it to earlier Mistral torrent-era releases.
• Early third-party signals (LiveCodeBench, Reddit tables) suggest parity with OpenAI’s o1/o4-mini–class models, but details and context are unclear.
• Broader debate about benchmarks:
  • Many think popular leaderboards are increasingly “overfitted” and unreliable.
  • Preference expressed for live, contamination-resistant, or human-arena-style evaluations, plus “vibe checks.”

Open Weights vs Open Source

• Strong argument that this is “open weights”, not open source:
  • Weights are downloadable and MIT-licensed, but training data and full pipeline are not provided.
  • Several analogies: weights as binaries, datasets/pipelines as true “source”.
• Some argue for a multi-dimensional “openness score” (code, data, weights, license, etc.) instead of a binary label.
• Training-data disclosure is seen as legally and practically difficult, especially given likely copyrighted and scraped content.

Platforms, Quantization & Ecosystem

• OpenRouter already serves R1-0528 through multiple providers; many note cost roughly half of certain OpenAI offerings for similar capability.
• Groq is discussed: extremely fast but limited model selection; hosting R1-size models would require thousands of their chips.
• Community tools:
  • Dynamic 1–1.6 bit quantizations reduce footprint from ~700 GB to ~185 GB, with tricks to offload MoE layers to CPU RAM while keeping core on <24 GB VRAM.

Motivations for Local LLMs & Use Cases

• Reasons to run locally despite pain:
  • Data privacy and regulatory needs (law, medical, finance, internal docs).
  • Very cheap high-volume or always-on workloads vs API billing.
  • Latency-sensitive coding autocomplete.
• Concrete examples shared:
  • Trading-volume signal analyzer summarizing news locally.
  • Document management (auto titling/tagging) and structured extraction.
  • Coding assistants using smaller DeepSeek/Qwen-based models for completion.

Market & Narrative

• Some speculate about timing with Nvidia earnings and hedge-fund backing; others question whether release date materially affects markets.
• Discussion that DeepSeek both relies on Nvidia hardware and may simultaneously reduce perceived need for massive, ultra-expensive GPU clusters, shifting procurement strategies and geopolitics (e.g., interest in Huawei GPUs).

Why the problems are free / goals of the project

• Creators say the main goals are:
  • Get people to read the book (large portions are free).
  • Drive usage of interviewing.io.
• They argue practice problems themselves aren’t a competitive advantage; there are already many free ones.
• They dislike paywalls and see high‑quality free content as the best marketing to engineers.

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Perceived value of the book and platform

• Several commenters praise the book for:
  • Practical guidance on resumes, outreach, and breaking into companies.
  • A more structured way of thinking about problems (e.g., “boundary thinking,” triggers).
• The AI interviewer is viewed as useful for simulating real interviews, though some users would prefer an option to just submit code and see if it passes.

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Debate: usefulness and fairness of LeetCode-style interviews

• Pro‑side:

  • Coding tests are seen as essential to filter out candidates who simply cannot code, even with strong‑looking resumes.
  • “Easy/medium” questions are defended as checking basic competence and foundational CS knowledge (arrays vs linked lists, complexity, etc.).
  • Some claim these interviews are effective in aggregate, citing the success of large tech firms and arguing they have low false‑positive rates.
  • Others say LeetCode‑type skills apply more than critics admit, especially beyond simple CRUD work.

• Critical side:

  • Many argue real jobs rarely need “fancy” algorithms; production‑quality code, design, and communication matter far more.
  • They see an arms race: as candidates train, companies raise difficulty, pushing interviews toward memorization and grind.
  • Strong concern about live‑coding anxiety and performance under observation, especially for senior/principal roles where architecture and leadership are more relevant.
  • Suggestions include simpler tasks, collaborative problem‑solving, pair programming, PR/code review, or small take‑homes, with candidates choosing the format.

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Are LeetCode-style interviews dying?

• One view: signal is degrading due to AI and cheating; these puzzles may fade.
• Counterview (including from the author): companies are conservative; DS&A interviews aren’t going away, though verbatim LeetCode questions should. The focus should shift toward teaching and evaluating how candidates think.

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Software vs other disciplines, credentials, and “grind”

• Some compare software unfavorably to other engineering fields that have licensing bodies and standardized credentials, arguing this forces companies to over‑screen.
• Others note that most engineering disciplines can also be self‑taught; what differs is tooling cost and mentoring pathways.
• There is disagreement over whether willingness to “grind” LeetCode is an important, job‑relevant signal or an arbitrary hoop.

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Interview as performance; analogies to other fields

• Commenters compare live coding to:
  • Auditions for actors, “staging” for chefs, hands‑on tests for trades, and case studies for managers/analysts.
• Others argue the best test of engineering is real work, not high‑pressure performance in an artificial setting; the NFL combine is used as an analogy for imperfect proxies.

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Meta: Show HN etiquette and tone

• A substantial subthread debates whether harsh criticism of the premise (technical interview prep itself) is appropriate in a Show HN.
• Participants cite HN guidelines: avoid fulmination, shallow dismissals, and generic tangents; be substantive, measured, and civil when critiquing someone’s work.

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Other points

• Privacy: the site uses Clearbit to enrich emails with names, but interviews are anonymous unless both sides opt in.
• A user in India hits a country restriction; the team calls this unintended and routes them to support.
• One commenter notes that “interesting, contained” problems are a finite personal resource: once you’ve seen a solution, you can’t unsee it, so you lose the chance to solve it fresh in the future.

Chickenization and Monopsony Power

• Commenters focus on the “chickenization” model: nominally independent farmers locked into a single buyer that dictates inputs, processes, and standards while unilaterally setting pay at near-subsistence levels.
• This is widely described as a monopsony, sometimes “borderline slavery,” especially because farmers invest in highly specific assets (coops, equipment) that have no alternative use.

Capitalism, Regulation, and Market Design

• Some see this as the natural outcome of under‑regulated capitalism; others refine that to “consumer‑only regulation” (strict food safety rules → few processors, but little regulation of how they treat suppliers).
• There’s a debate over whether capitalism is inherently exploitative (surplus labor theory) versus claims that surplus labor theory is “unscientific bunk.”
• Others argue these structures require regulatory capture and antitrust failure; a nationwide monopsony in such a simple product is seen by some as implausible without state-enabled barriers.

Why Farmers Don’t Just Exit or Compete

• “Why don’t they do something else?” gets answered with: lack of alternatives in rural areas, debt overhang, sunk investment, need to keep income flowing, and government program constraints.
• Suggestions like “just build your own processing and sell direct” run into food-safety regulation costs, capital requirements, distribution power, big buyers’ willingness to dump prices, and blackballing by dominant processors.
• Co-ops and artisanal/local butchery exist but only work at small, high-margin scales.

Unions, Law, and Collective Action

• Several comments argue US law structurally cripples union power (bans on sectoral bargaining, secondary boycotts/strikes, broad strike replacement, cooling-off periods).
• Others note unions historically gained rights by defying even harsher laws and repression.
• There’s disagreement over whether weakened unions are mainly due to law, to offshoring/global competition, or to past union excess.

Consumer Welfare vs Worker Welfare

• A “customers will vote with their wallets” defense of non-union models is attacked as naïve: markets get stuck in bad equilibria, and people and firms are not fully rational.
• Some argue ethics and citizenship, not just customer surplus, must shape labor rules.

AI, Gig Work, and the Future of Labor

• Many see gig platforms and algorithmic management as a direct continuation of chickenization, potentially leading to “techno-feudal” lock-in.
• Others note that in some sectors (post‑COVID restaurants, trades) labor scarcity has raised wages, suggesting dynamics are uneven.
• There’s a strong pushback on the term “unskilled labor”: physical and service work is often highly skilled but oversupplied and undervalued.

Comparisons and References

• EU is assumed to be better on farmer/animal welfare, but one comment notes EU poultry dumping has harmed African farmers.
• Marshall Brain’s novella Manna is cited as an eerily prescient portrayal of algorithmic labor control with an implausibly optimistic ending.

Motivation and Technology Choices

• Original Electron app worked but was ~1 GB and heavy to maintain/optimize.
• Rewrite uses Tauri + Rust backend with a web UI (Angular/React), chosen for:
  • Cross‑platform ambitions (Windows, later Linux) rather than Mac‑only Swift/SwiftUI.
  • Familiarity with web UI libraries.
  • Smaller bundles and better performance than Electron.
• Some commenters argue the headline overemphasizes “Rust” since UI is still HTML/JS; the main binary size win comes from using system webviews instead of bundling Chromium.

Tauri vs Electron Experiences

• Tauri praised for:
  • Much smaller binaries (tens of MB vs hundreds).
  • Good Rust integration and a pleasant backend dev experience.
• Major complaints:
  • System webviews (Safari/WKWebView, Edge/WebView2, WebKitGTK) behave inconsistently; complex apps hit serious rendering and API differences, particularly on Linux (WebKitGTK bugs, missing WebRTC, performance issues).
  • OS/browser updates can break UIs without app updates.
  • Migration from Tauri 1→2 described as “nightmare” by some: multi‑repo, Linux crashes, poor docs.
• Electron defended for:
  • Single, locked Chromium version → consistent rendering and WebGPU/Web APIs across platforms.
  • Mature ecosystem (Electron Forge, update tooling).
  • For many use cases, extra 100–200 MB disk usage is seen as acceptable; RAM usage and multiple Electron apps remain concerns.

ML Inference and Indexing Stack

• CLIP runs via ONNX Runtime using the Ort crate; main hurdle was bundling and code signing.
• Indexing speedups attributed to:
  • Rust implementation.
  • Scene detection to reduce frames.
  • ffmpeg GPU flags and batching embeddings.
• Other Rust ML stacks discussed: Candle, Burn, tch, rust‑bert; tradeoffs in performance, abstraction level, and portability.

Vector Search and Storage

• Initial choice: embedded Redis with vector search modules; gave good similarity results but caused bundling/packaging pain.
• SQLite + vector extensions (early VSS) initially produced worse results; unclear if due to configuration.
• Community recommends lancedb, usearch, simsimd, newer SQLite extensions (e.g. sqlite‑vec). OP later reports successfully replacing Redis with sqlite‑vec.

Product and UX Feedback

• No trial despite “Try” CTA leading directly to Stripe; many request a time‑limited or feature‑limited demo, not just a video.
• Price ($99, one year of updates) seen by some as studio/creator‑tier rather than mass‑consumer.
• Current version indexes/searches images and videos; text/PDF search and RAW support are planned.
• Some criticism of marketing (“Trusted by professionals”, stock‑looking testimonials), refund policy (no refunds), and VAT handling.

Why Japan Is Seen as a Good Fit

• Many commenters note that Japanese physical addressing is unusually hard for humans and software:
  • Areas (chome → block → building) numbered by build order, non-contiguous, often no street names.
  • Multiple buildings can share similar numeric sub-addresses; building names matter and are messy.
  • Historically, people relied on local knowledge, paper maps, and later GPS; delivery drivers still struggle.
• Online forms are especially painful: inconsistent fields, full‑width vs half‑width characters, kanji vs kana vs romaji, varying expectations for dashes and symbols. Browser autocomplete often fails badly.
• Comparisons: Bulgaria (block-based), Ireland’s Eircode, US ZIP+4(+2), Netherlands/UK postcodes, plus codes, what3words; Japan is described as an especially strong candidate for an abstraction layer.

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What the Digital Address Actually Is

• It’s generally interpreted as a short, stable, alphanumeric identifier that expands to a full physical address on participating websites — more like a DNS name or URL shortener than a new postal code.
• Current design:
  • Users register via Japan Post; get a 7‑character code (Latin alphanumerics).
  • Entering the code in an e‑commerce form fetches and displays the full address for confirmation.
  • The code can stay the same across moves if the user updates their address with Japan Post.
  • Codes can be deleted and reissued; system rate-limits lookups; address data is separated from other personal data.
• Commenters emphasize this mostly simplifies input and address changes, not the physical routing process (which still uses the resolved address).

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Convenience vs. Privacy and Security

• Enthusiastic views:
  • Dramatically easier address entry in Japan’s chaotic form ecosystem.
  • Single point to update after a move instead of changing dozens of merchant records.
  • Potential to reduce errors, misdeliveries, and to enable “follow-me” deliveries for long lead-time items.
• Privacy concerns:
  • A stable identifier that follows a person/household can become a stalking or tracking vector if widely recorded and resolvable.
  • Japan Post itself notes that anyone who learns a code may be able to determine the address, and random guessing might reveal some addresses.
• Some propose alternative designs:
  • True intermediary model where merchants never see the address, only the code; carriers resolve it at dispatch time.
  • OAuth-style API where users grant and revoke address access per service.
  • Throwaway or multiple codes (home, work, temporary) to combat spam and limit linkage.
• Skeptics argue the current model pushes complexity and risk onto everyone (sites must handle revocations; users must remember to rotate), while not fully solving spam or privacy.

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Relations to IDs, National Systems, and Analogies

• Several see this as akin to:
  • A “mail DNS” or URN for people/households.
  • Sweden’s SPAR (central person/address registry) or US ZIP+4+2 extended addressing.
• In Japan-specific context, commenters link it to the MyNumber digital ID ecosystem, noting plans to tie address changes across systems and raising classic “public SSN-like identifier” worries.
• Overall sentiment mixes:
  • Strong positive reactions from people dealing daily with Japanese addresses.
  • Cautious optimism from those who like the indirection pattern.
  • Ongoing skepticism about long-term privacy, enumeration risks, and cultural acceptance of another semi-permanent personal identifier.

Free vs. paid services and longevity

• Some argue free third‑party services are inherently fragile because there’s no revenue to sustain them.
• Others counter that plenty of paid services (including from large companies) are killed with short migration windows, while many free projects and open-source software (e.g., Linux) endure.
• Conclusion in thread: business model alone doesn’t predict durability; both free and paid services vanish.

Google, goo.gl, and trust

• Many see shutting down read-only goo.gl as gratuitous: redirects are simple, storage is cheap, and Google previously promised existing links would keep working.
• Speculated reasons: outdated dependencies, legal risk, internal maintenance burden, or just management wanting to reduce “distractions.”
• Google’s pattern of sunsetting products is seen as damaging to trust; some are baffled they don’t treat this reputation more seriously.

Compiler Explorer, URL shorteners, and recovery

• Using goo.gl for long stateful URLs is framed by some as “abusing” a shortener; others say shortening long URLs is the intended use.
• Critical voices say that if “links last forever” is a core principle, outsourcing to a third-party shortener was self‑defeating; others note they were also trying not to store user data themselves.
• People encourage cooperation with ArchiveTeam/Internet Archive, which have captured billions of goo.gl links.
• Discussion recognizes that even Compiler Explorer itself can’t last forever, though current funding and possible foundation plans mitigate that.

Link rot, personal archiving, and tools

• Many describe disillusionment with bookmarks as URLs rot, leading them to: save PDFs, copy text to files (Markdown/RTF), use reader views, SingleFile, WARC/WebRecorder, Zotero, Pinboard, or self‑hosted archives.
• Some automate archiving every visited page or send pages to external services for search, FTS, embeddings, or LLM tagging.
• Caveats: domains can be removed from the Internet Archive; even IA and archive.is are seen as ephemeral; self‑hosted archives lack external verifiability.
• Various timestamping and hashing schemes (including blockchain/GPG ideas) are debated, with no clear consensus on a robust, practical proof-of-authenticity system.

URLs, URIs, and content addressing

• Several explain the URL/URI/URN distinctions; others dismiss it as largely pedantic in practice.
• Content-addressed URIs (e.g., IPFS) are proposed as the only “forever” references, but critics note they don’t guarantee availability—someone must still host the content and maintain name‑to‑hash mappings.

Cool URIs, file extensions, and design

• The classic “Cool URIs don’t change” guidance is cited.
• Debate over whether URLs should include .html (clear, maps 1:1 to files) versus extensionless paths (hide implementation, allow multiple representations).
• Some advocate canonical extensionless URLs with optional format-specific variants; others see extensions as useful and human‑readable.

Ephemerality vs preservation

• Some suggest URL death may be healthy “garbage collection,” preserving only what people work to keep.
• Others emphasize historians’ desire for mundane records, warning that we can’t predict what future scholars will value.
• Multiple comments stress designing systems under the assumption that infrastructure and institutions are not permanent; nothing truly lasts forever.

LLM usage disclaimers

• Noted trend of authors disclosing that text is human-written but LLM‑assisted (links, grammar).
• Some welcome transparency, especially for “serious” writing; others see such labels as unnecessary if content quality is clear on its own.

Overall reaction to Waffle House’s response

• Many see this as a big missed marketing/PR opportunity: free goodwill, viral attention, and a chance to “lean into” the Waffle House Index mythos at essentially no cost.
• Others argue the response is exactly what a large brand will do “every single time” when someone uses their marks and appears official.
• Several commenters say this story is a reminder that Waffle House is a corporation and should be treated as such: don’t expect “cool” behavior over legal caution.

Trademark, branding, and control

• Strong consensus that using the logo, brand colors, and “Waffle House” in the domain made the site look official and is classic trademark infringement territory.
• Multiple people say US law effectively forces active enforcement or risk dilution; a C&D is seen as the standard tool, even for benign uses.
• Some push back that licensing or a “used under permission” arrangement was possible; others counter that this creates ongoing overhead and risk, so the cheapest option is to shut it down.
• Debate over how absolute the “must enforce or lose it” narrative really is; some lawyers in the thread call that an oversimplification.

Liability, disaster optics, and “disaster brand” concerns

• Several commenters note potential tort risk: if the site appears semi-official and is wrong, people might rely on it for safety decisions and sue.
• Others highlight economic risk: incorrect “closed” labels could directly cost stores revenue or create employee-management conflicts.
• Some argue Waffle House likely doesn’t want to be tightly tied to “national disasters” as a core brand message, despite the positive story of resiliency.

Scraping and data use

• People distinguish between (a) trademark issues and (b) scraping status data. Most think the C&D was about marks, but note the data source was later patched anyway.
• There’s discussion of ToS, scraping case law, and big scrapers (AI, adtech) as context; consensus is that scraping alone would have been a murkier, slower fight.

Alternatives and what could have been done

• Common suggestions:
  • Remove logo and WH-styled branding; use a generic name (“Waffle Index”, “disaster index”) and a clear “unofficial” disclaimer.
  • Aggregate multiple chains to dilute brand-specific issues.
• Some argue an individual developer is rational to comply fully: C&Ds are scary, lawyers are expensive, and low-probability IP lawsuits can still be ruinous.

Privacy, Encryption, and Data Use

• Many assume the real goal is using Telegram chats as training data for Grok; several say they will leave the platform over this.
• Long subthread on Telegram’s security: not end‑to‑end encrypted by default; messages are encrypted in transit but plaintext on Telegram’s servers. Secret chats exist but are rarely used.
• Others note E2EE doesn’t help if an AI “data harvester” is integrated into the client, or if endpoints/OS are compromised.
• Strong concern that this enables large‑scale surveillance and intelligence gathering, especially given Telegram’s heavy use in conflict zones and censored countries.
• Some argue “if it’s free, you are the product” and use implies consent; others flatly reject that as a notion of consent.

Business Logic and Comparisons

• Many compare this to Google paying Apple/Mozilla to be default search or OpenAI’s various distribution deals.
• One camp: this is normal distribution/marketing—Telegram has ~1B users, xAI is paying for default status, mindshare, and future upsell (premium AI tiers, SuperGrok, etc.).
• Another camp: if you must pay platforms and users don’t clamor for you, your product/brand is weak; this is “AI being shoved down throats” to prop up inflated valuations.
• Some see the main asset as exclusive, high‑resolution conversational data in many regions; $300M is viewed as cheap relative to AI valuations and data scarcity.
• A few note xAI’s low external traction and argue this is partly about juicing user metrics and “being in the race,” not just direct ROI.

Grok’s Bias and Telegram’s Reputation

• Multiple comments call Grok “toxic” or “racist slop,” citing its unsolicited “white genocide in South Africa” outputs as evidence of political skew/mismanagement.
• Others say Grok 3 is technically one of the better general models but acknowledge many will never touch it because of its owner’s politics.
• Telegram is described as “shady” or a hub for scams, malware, piracy, and extremist content, though some argue that’s true of any powerful communications tool.
• Concern that scammers and malware authors will integrate Grok into their Telegram‑based tooling.

User Experience, Consent, and Migration

• Unclear how intrusive the integration will be: optional bot vs woven into chats vs default assistant; several say their reaction depends entirely on that.
• Some long‑time Telegram fans (including premium users) say they’ll quit if Grok is “forced” into the app; others expect it to be mostly ignorable, like other advanced features.
• Reports that certain communities (notably furries and other privacy‑sensitive groups) are already spinning up backup rooms and accelerating moves to Signal, Matrix, or self‑hosted options.
• Others are resigned: Telegram has already “sold its soul” via ads, spam, and limited moderation; this is seen as another step in enshitification.

Power, Politics, and Surveillance

• Several see this as deepening a surveillance and influence stack: rockets, satellites, social network, and now a major chat app’s AI layer.
• Some speculate that training on Telegram’s global, often politicized content will further tilt Grok toward the owner’s worldview.
• Others link this to a broader pattern: AI integrations used as cover for data extraction and political or commercial manipulation rather than genuine user needs.

Deal Status and Trust

• Later in the thread, users note public statements that “no deal has been signed” yet, followed by clarification that an agreement in principle exists but formalities are pending.
• This mismatch in public messaging is seen by some as emblematic of a business culture where big announcements precede finalized agreements and where “total shamelessness” is an asset.

What Leta Is and How It Works

• Leta is described as a front end/proxy to Google and Brave Search APIs, not its own index.
• It strips tracking elements from results, offers no ads, and lets users pick the backend engine.
• Several commenters find it very fast, clean, and more relevant/less “crap” than direct Google, and are adopting it as their default search.
• Others are confused: the landing page doesn’t clearly explain what it is, and some only understood via the thread.

Privacy Model, Trust, and Limitations

• Core value: Google/Brave see only Mullvad’s servers, not end‑user IPs or browser fingerprints; Mullvad sees the search and the user.
• Some argue this is “trust shifting,” not true privacy, and criticize the lack of end‑to‑end client encryption as “theater.”
• Others respond that some party must see the plaintext query; the main improvement is who that is, and Mullvad’s track record and jurisdiction are seen by many as acceptable.
• Confusion over an FAQ line saying Leta is “useless” if you already perfectly block tracking; commenters interpret this as “then you gain nothing more.”

Caching, Infrastructure, and Freshness

• Leta caches search results for 30 days in an in‑memory Redis store on diskless, STBooted RAM‑only servers (same model as Mullvad’s VPN).
• This pooling of identical queries reduces API cost and arguably improves privacy by mixing users.
• Concerns:
  • Cached results may be stale; some users already see multi‑day‑old caches.
  • RAM‑only cache is lost on restarts; FAQ admits upgrades flush the cache.
  • Questions about whether caching is compatible with Google/Bing API terms.

Business Model, API Costs, and Sustainability

• Multiple commenters doubt long‑term viability: Google/Brave APIs are said to be expensive and Leta has no visible revenue.
• Hypotheses:
  • It’s a marketing/brand‑building cost center to drive VPN/browser subscriptions.
  • Prior versions were VPN‑subscriber‑only; opening it may be a growth play.
• Some see it as a “publicity stunt” that might be shut down once costs or marketing priorities change; others note it has already been running ~2 years.

Advertising, Growth, and Brand Perception

• Users report heavy Mullvad advertising (billboards, buses, subway) in London, SF, NYC, airports, etc.
• Company comments say there’s no outside investment or “lottery win”; growth over years funds these campaigns, which are cheaper than people assume.
• They prefer broad outdoor ads over tracking-heavy online ads or affiliates, to align with their privacy stance.
• Reactions are mixed:
  • Some appreciate the consistency (non‑targeted ads for a privacy product).
  • Others feel mass‑market advertising for a “privacy” brand erodes perceived ideological purity and increases fear of state pressure as they grow.

Comparison to Other Search and Tools

• Compared to Startpage/DDG: Leta is another Google proxy but not owned by an ad company; behavior is similar in concept.
• Question about “How is this different from DDG !g?” → answer: !g just redirects to Google, Leta proxies and caches.
• Some users plan to move from Startpage; others stick with DDG, Kagi, or LLMs.
• Debate on “search vs LLMs”: a few say they rarely use search now; others find LLMs unreliable or hallucinatory and still rely heavily on search and Stack Overflow.

Mullvad VPN Reputation and Ecosystem

• Mixed feedback on the VPN itself:
  • Long‑term users praise stability (especially WireGuard on mobile) and privacy ethos.
  • Others report worsening usability: CAPTCHAs everywhere, frequent disconnects, laggy DNS, blacklisting relative to smaller VPNs.
  • Some mitigations mentioned (e.g., disabling obfuscation/“quantum” tunnels).
• Mullvad staff reiterate their mission is to fight both mass surveillance and censorship, with better censorship‑circumvention tooling “on the roadmap.”
• Leta integrates into Mullvad Browser, framed as part of a broader privacy ecosystem.

Workplace Blocking and Practicalities

• Many workplaces block mullvad.net as “VPN/proxy avoidance,” making Leta unusable at work, unlike DDG.
• Discussion of coarse-grained corporate filters that block whole categories (VPN, adult themes, “AI”, some TLDs), which also hurts developers.

Naming and Positioning

• Some think “Mullvad/Leta” branding is confusing or hard to remember in English‑speaking markets.
• Others like the Swedish names (“mole” / “search”) and compare the strategy to IKEA’s non‑English naming, pushing back against Anglocentrism.

Monero vs Bitcoin: Technology, Scaling, and Market Cap

• Several commenters see Monero as technologically superior to Bitcoin (privacy, monetary design) yet with much smaller market cap, attributing BTC’s dominance to inertia, branding, and institutional access (ETFs, futures).
• Others stress that tech quality and market cap are weakly correlated; mindshare and liquidity are self-reinforcing.
• Monero’s tail emission (constant-rate issuance, uncapped supply, declining inflation rate) is praised as better money for actual use but less attractive for speculation compared to BTC’s capped supply.
• On scaling, Monero is acknowledged as heavier than BTC: larger transactions, TXO-based state, and wallet requirements to track many outputs. However, it has fewer self‑imposed on-chain limits, so in practice may handle more usage.

Privacy Models, Attacks, and Planned Upgrades

• Strong support for Monero’s default, mandatory privacy versus optional privacy coins (e.g. Zcash).
• Criticism that the article is “far from comprehensive”: missing Eve–Alice–Eve (EAE/ABA) attacks, churning weaknesses, randomness issues, flooding, and network-level spying that can link TXIDs to IPs.
• OSPEAD / “map decoder” work is cited as showing Monero’s practical privacy is substantially weaker than previously assumed, with fixes still pending and requiring a hard fork.
• Skeptics argue decoy-based privacy is inherently stochastic and systematically weaker than ZK-based designs; they note most newer privacy systems avoid decoys.
• Monero is moving toward Full Chain Membership Proofs (FCMP/FCMP++), a ZK-style scheme expected to significantly strengthen privacy, but years of work remain.

Inflation, Opaque Ledgers, and Supply Auditing

• One camp argues opaque blockchains risk undetectable inflation (e.g., breaking a discrete log could allow invisible money creation), while transparent chains can always verify supply.
• Others counter that in systems like Monero, the same cryptographic mechanisms that prevent double-spends also prevent inflation, and that Bitcoin’s scripting complexity introduces its own inflation risks.
• There is direct disagreement on whether Monero’s inflation risk is materially higher or not, with one commenter flatly labeling the “invisible inflation” concern as wrong.

Bitcoin Privacy (CoinJoin, Lightning, etc.) vs Monero

• Some ask whether BTC plus CoinJoin/Lightning makes Monero unnecessary.
• Several replies criticize Lightning as effectively a separate IOU system whose security depends on active blockchain monitoring; channel closures can still enable cheating.
• Debate over how strong Bitcoin’s double-spend protection really is in real commerce, and whether Lightning materially degrades those guarantees.
• Consensus in the thread leans toward BTC privacy tools being weaker and more complex to use than Monero’s built‑in privacy.

Evidence from Hacks, Laundering, and Court Cases

• The ByBit hack and other major thefts where BTC/USDT are quickly swapped into XMR are cited as practical evidence that Monero is hard to trace, even for Western agencies.
• Some say this “proves” XMR’s privacy; others temper that to “evidence at best.”
• One commenter notes that Monero’s use by criminals simultaneously increases liquidity and improves anonymity sets, making it more effective for all users over time.
• It’s stressed that timing and amount correlations (e.g., cashing out the exact amount received, in a single transaction) can deanonymize users regardless of cryptography.
• A critic warns that relying on public court narratives to judge privacy is dangerous because of parallel construction: authorities may secretly exploit attacks, then claim a different method in court.

Regulation, Politics, and Ethics

• Multiple comments mention de facto bans: Monero being delisted or blocked from most regulated fiat exchanges.
• Some see attempts to ban or stigmatize Monero as strong evidence the tech works; others speculate that if it weren’t already compromised, governments would have moved faster or harsher.
• There’s concern Monero could be instantly criminalized under future capital controls, effectively equated with money laundering.
• Ethical tensions: some fear private money will primarily aid ultra‑rich corruption or hostile states (e.g., North Korean operations); others point out the legacy financial system already enables elite impunity, and value Monero’s optional auditability for individuals under repression.

Alternative Privacy Coins and Project Trust

• DERO is raised as an “alternative” with fully encrypted balances, but another commenter notes a serious privacy break attributed to developer incompetence, undermining trust.
• Several participants emphasize Monero’s long track record and comparatively trustworthy, principled dev culture (e.g., ASIC resistance decisions, default full-node behavior).
• Questions arise about pseudonymous developers; the consensus is that reputation and history matter more than legal identities.

Wallets, OPSEC, and Practical Usage

• Feather Wallet (desktop) and Cake Wallet (mobile) are recommended. Feather is praised for: Tor-only connections after initial sync, onion-only peers, enforced subaddresses, and preventing address reuse.
• OPSEC advice:
  • Avoid simple exchange→exchange flows; instead withdraw to your wallet, wait days or weeks, then send out in different denominations.
  • Don’t move in/out the same amounts shortly after; timing/amount patterns can reveal you.
  • Watch out for dust attacks and avoid spending suspicious tiny outputs.
• Simple “tip jar” setup: create a wallet, back up the seed, derive a view-only wallet for monitoring, and publish a receiving address starting with “8”.

Critique of the Linked Article and Meta Issues

• Some commenters complain the article lacked a visible date, which they consider crucial for rapidly evolving topics; after this thread, the site editor adds dates and clarifies authorship.
• One commenter accuses the piece of reading like “AI slop” due to repetitive structure; the author responds angrily, stating it was manually researched and written over weeks, with a professional journalism background.
• More technically oriented critics say the article overstates the conclusion (“Monero’s privacy remains resilient”) and underplays ongoing, serious de‑anonymization research and live attacks, describing it as part of a pattern of Monero-promotional “nothing to see here” analyses.

AI, Darknet, and Future Demand for Private Payments

• A side discussion speculates that future AI regulation (especially if the US restricts model exports or mandates a “good boy list” of allowed providers) could drive demand for grey/black‑market AI services.
• One view: people will pay with crypto over Tor/onion services to access unapproved models, analogous to how darknet markets evolved.
• Another view is skeptical: if consumer hardware can run many models locally, Tor/AI marketplaces might be unnecessary, and the “darknet AI + crypto” narrative is seen as overblown.

Legal Outlook (EU and Beyond)

• The EU’s planned 2027 ban on privacy-preserving cryptocurrencies is noted; practical advice from the thread is minimal beyond a curt “do it anyway,” reflecting a belief that technical use will outlive formal legality.

Early Supermassive Black Holes & “Blowtorch” idea

• Central claim: enormous numbers of very early, long-lived SMBH jets (“blowtorches”) carved voids, seeded filaments, and magnetized the cosmic web.
• Several commenters note that the real tension is explaining how such massive black holes form so early; direct-collapse black holes are raised as a proposed mechanism but may still struggle with required numbers and growth rates.
• Some find the hypothesis appealing because JWST has found very early quasars and SMBHs, which strain standard formation-timescale arguments.

Relation to ΛCDM and Dark Matter

• Strong criticism from some that ΛCDM uses many tunable parameters and is retrofitted to anomalies (e.g., cusp/core, early structure, JWST results).
• Others push back: point out ΛCDM’s core cosmological parameter set is small and empirically constrained, and that it quantitatively explains CMB peaks, large-scale structure, and lensing.
• Disagreement over whether dark matter–based simulations are “CGI” and epicycles, or robust demonstrations that gravity + CDM naturally form the observed web.
• Some note Blowtorch Theory currently does not explain rotation curves, lensing mass, or other classic dark matter evidence.

Predictions, Falsifiability, and Math

• Supporters emphasize that the broader “three-stage cosmological natural selection” framework made qualitative predictions before JWST (early SMBHs, rapid galaxy formation, abundant early jets) which they claim were later supported.
• Critics argue these are broad, qualitative “cool story” predictions, not quantitative outputs of a model. Without equations or simulations, they see it as a narrative, not a physical theory.
• Several insist that a viable cosmological model must reproduce CMB, expansion history, large-scale structure, and be implemented mathematically; otherwise it’s not testable at the necessary level.

Cosmological Natural Selection & Universes in Black Holes

• The evolutionary, multiverse parent theory (black holes spawning new universes with slightly varied constants) is seen by many as the most speculative component.
• Some find it an elegant way to address fine-tuning and the anthropic principle; others say that without a concrete mechanism for inheritance of constants, it’s philosophy or SF, not science.

Writing Style, Communication, and Scientific Culture

• Mixed reactions to the article’s style: some praise it as engaging and accessible; others complain about meme-y headings, excess links, and perceived “dissing” of ΛCDM as making it sound crackpot.
• Debate over whether a novelist’s qualitative synthesis is a useful “ideation phase” that should later be mathematized, or just another unrigorous private cosmology.
• Meta-discussion on peer review, funding, and whether entrenched consensus in cosmology is too resistant to alternatives.

Perceived Rise in Apathy and Mediocrity

• Many describe a “who cares” culture: workers doing the bare minimum, shoddy construction, poor public services, indifferent cops, sloppy service jobs.
• Others push back: this has long been observed (Peter Principle, old bureaucracy jokes); what’s changed is scale, visibility, and tools to half‑ass.

Economic Incentives, Stagnant Futures, Late-Stage Capitalism

• Strong theme: it’s rational not to care when wages stagnate, housing is unattainable, and job security is low. Extra effort often yields “more work, same pay.”
• People cite 2008, wage stagnation, offshoring, and financialization: productivity gains and low interest rates benefited capital, not labor.
• “Act your wage” and “nothing matters” attitudes are framed as survival responses to degraded social contracts and rising inequality, not moral failure.

Phones, Social Media, and Attention Collapse

• Many blame smartphone and social media addiction for pervasive distraction at work and in life: garbage collectors, delivery workers, hospital staff, even parents at playdates glued to screens.
• Others note this predates social media (TV, mass media) but agree that constant engagement erodes attention, safety, social skills, and capacity to care.

AI, Slop Content, and Dead-Internet Vibes

• AI-written supplements and resumes are seen as the logical endpoint of ad-driven, SEO-maximized media: content optimized for clicks, not meaning.
• Some argue AI just makes existing “slop” cheaper; the real problem is a system that rewards volume over truth and depth.
• There’s anxiety about AI being used primarily to cut jobs and replace craft, further weakening incentives to care.

Work Structures, Bureaucracy, and Loss of Pride

• Large organizations, public and private, are depicted as short‑termist, metrics-obsessed, and hostile to craftsmanship (ship fast, patch later, defer real fixes).
• Bureaucratic drag (permitting, understaffed departments, union stalemates) explains slow projects as much as laziness; yet citizens experience it as “nobody gives a damn.”
• People report that loyalty and overperformance are punished or exploited, encouraging checked‑out behavior.

Counterexamples and “Bike Shop” Jobs

• Commenters note pockets where people still clearly care: trades in some countries, passionate small shops (bikes, instruments, outdoor gear), some tech niches, serious podcasts and investigative work.
• These are often passion-driven, small-scale, less financialized roles where autonomy and identity are tied to the work.

Meaning, Burnout, and Selective Caring

• Many say there’s simply “too much to care about” (news, wars, politics, climate, endless content); emotional bandwidth is finite, so apathy becomes self‑defense.
• Some advocate caring deliberately—in one’s craft, community, or relationships—as a kind of rebellion against a system that makes indifference the easier, more rational choice.

Epistemology: Truth, Facts, and “Good/Bad Ideas”

• Long subthread debates whether “good” and “bad” ideas are objective.
• One camp: truth exists independent of belief; some ideas are clearly bad (e.g., “tigers as SF pets,” Heaven’s Gate mass suicide, anti‑vax claims, jumping off a building without a parachute).
• Opposing camp: idea quality is always relative to values and point of view (what’s bad for SF residents could be good for a hostile rival city, or for an attacker exploiting GUID reuse).
• Several people distinguish consensus or “mainstream knowledge” from truth; consensus can be wrong (e.g., historical medical errors).
• Science is praised as “testimony with an invitation to reproduce,” making it more trustworthy than social consensus or anecdote.
• Some argue hard relativism is both boring and dishonest; others say even things like “white lies” are unresolvable value questions.

Human Nature, Cognition, and Blame

• Many comments argue the core problem isn’t the internet but human cognitive “zerodays” and legacy biology; tech and social media just exploit them.
• Suggested response: cultivate rationality, media literacy, and active filtering of “intellectual junk food,” akin to dieting in an obesogenic environment.
• Others are pessimistic: changing human nature is seen as nearly impossible; awareness and self‑regulation are viewed as niche, Sisyphean achievements.

Memetics, Platforms, and Incentives

• Multiple comments tie the article’s theme to engagement‑driven ad platforms: algorithmic feeds reward emotional, tribal, fast‑spreading content regardless of accuracy.
• Some see the internet as highly tribal; others argue it is historically anti‑tribal but distorted by “platform” economics.
• One line of argument: the “marketplace of ideas” has been financialized—narrative‑driven ecosystems (especially on the right) amplify convenient stories first, then hunt for supporting facts.
• Mill’s belief that truth repeatedly resurfaces is revisited; some think it still holds in the long run, others worry current information systems may permanently degrade discourse quality.

Antimemes and Good Ideas That Don’t Spread

• Commenters engage the antimeme concept: important but non‑viral ideas (e.g., extended parental leave) lack constituencies and are easily buried.
• One view: powerful interests exploit these communication asymmetries, sidelining widely supported but low‑memetic policies via corporate capture and agenda control.
• Another view: storytelling and art can convert “antimemes” into contagious memes; memetics itself isn’t inherently bad.
• A highly critical reader of the referenced book argues the author constrains “antimemes” to fit pre‑existing political positions, weakening the concept.

Trust, Parasociality, and Tribal Alignment

• Several comments describe people outsourcing judgment to favored personalities (podcasters, streamers, influencers, politicians) and defending them against contrary evidence.
• Self‑identified “rationalist” communities are cited as especially vulnerable to persuasive prose that flatters their self‑image while smuggling in dubious ideas.

Structural and Synthetic Virality Claims

• Some attribute bad‑idea virality to cheap AI bot farms, network mapping, and capture of editors and institutions; they claim “natural” virality has largely been replaced by paid campaigns.
• Others emphasize structural incentives (adtech, algorithms, media polarization) over explicit conspiracy, but all see current systems as amplifying transmissibility over truth.

Handcrafting vs. Templates

• Many commenters resonate with building fully bespoke sites: custom CSS, homegrown static site generators, shell scripts, even toy HTTP servers.
• The fun is in the making—like maintaining a classic car—not just “having” a website. Personal sites become playgrounds for experiments, Easter eggs, and odd UI (e.g., Lynx-only surprises, animated mascots).
• Others prefer off‑the‑shelf tools (Hugo, Jekyll, Eleventy, WordPress, Bear Blog) to reduce friction and get on with writing.

Writing vs. Tinkering Tradeoff

• Several people admit to spending vastly more time on backends, generators, and CSS than on content; this motivates some to return to simple SSGs or templates.
• Counterpoint: a few report that once their small custom generator stabilized, it stopped being a time sink and hasn’t blocked publishing.

Aesthetics, “Ugliness,” and Identity

• Strong disagreement on whether the featured personal site is ugly, cool, or nostalgic: some find it fun, “Geocities‑retro,” or even beautiful; others call it eye‑straining, chaotic, or nausea‑inducing.
• Some liked the earlier minimalist design better and feel less receptive to the author’s message now that the site is more jarring.
• One camp argues that “ugly but mine” is the whole point: authenticity and personal joy trump UX conventions. Critics counter that “made by me” doesn’t have to imply bad design or navigation.

Old Web Nostalgia and Non‑Conformity

• Frequent nostalgia for Geocities/Freewebs/Flash‑era individuality: unreadable text, autoplay music, spinning skeletons, and all.
• Modern template‑driven sites are seen as homogeneous, “millennial aesthetic” (grey, marble, Tailwind‑ish landing pages); personal weirdness is valued as resistance to this flattening.

Tooling, Hosting, and CSS Frustrations

• Suggestions span Neocities, GitHub Pages, S3+CloudFront, Cloudflare, and cheap VPSes; some warn about AWS egress costs.
• A long‑running meme around “centering a div” prompts discussion of CSS’s real complexity, especially vertical centering and responsive layouts.
• Static sites, tables, and minimal/no JavaScript are praised, but mobile usability can suffer if responsiveness is ignored.

Cookies, UX, and “Good Internet” Irony

• Many dislike the Good Internet Magazine page’s cookie banners and membership prompts; some find it more hostile than the “ugly” personal site.
• A few jokingly add cookie popups purely for the “modern web” aesthetic.

AI and the Joy of Coding

• Jokes about using LLMs to auto‑write posts so humans can focus on CSS; others explicitly avoid AI because writing code and handcrafting are the fun part—like hiking despite cars existing.

AI Slop, Quality, and Discoverability

• Many see “AI slop” as just faster, cheaper slop that would have existed anyway, but with much higher volume and lower barrier to entry, echoing what DAWs did to electronic music and app stores did to software discoverability.
• Some argue the absolute volume of good output has increased, but the ratio of good to bad has worsened, making quality harder to find.

Productivity, Legacy Code, and Tech Debt

• Claims that great engineers can get 4× productivity are heavily disputed. LLMs help most with loosely coupled, brownfield code; tightly coupled legacy systems remain hard for them to modify safely.
• Several posters stress that prompting often clashes with established workflows and increases context switching; for many, “AI as mandatory for everything” is experienced as a net drag.
• Others say AI is powerful for one-off utilities, CSV/ETL glue, visualization snippets, and “super-autocomplete” in typed languages, but fails on large, safety- or money-critical systems.

Cleanup Work vs Bubble Popping

• One camp expects years of high-value cleanup and redesign after AI-generated messes, analogized to the post‑outsourcing correction era; another thinks this is wishful thinking and that companies will just stack more AI on top of AI.
• A darker view: AI is a hype bubble like past fads; when it underdelivers, investment and jobs across tech will be hit, not generate a golden age of craftsman maintainers.

Concepts, Reasoning, and Complexity

• Strong disagreement over whether LLMs can “work at a conceptual level” or hold program theory.
• Critics argue LLMs are sophisticated token mimics lacking true concepts, counterfactual reasoning, or entropy-reducing design ability; any appearance of understanding is “cheating” via training data.
• Defenders point to embeddings, internal concept activations, and practical use in refactoring or simplification when explicitly asked, claiming differences are of degree, not kind.

Skill Atrophy, Education, and Work Ethic

• Multiple people report personal skill regression and “blanking out” after over-relying on AI, likening it to calculators and GPS degrading mental arithmetic and navigation.
• Academia is cited as a domain already transformed: prior assessment and remote-teaching norms are breaking under ubiquitous LLM use.
• There is concern that AI will degrade everyone’s work ethic and thinking, not just low performers, while management chases quantity over quality.

Analogies and Broader Framing

• 3D printing is a recurring analogy: genuinely useful, even transformative in niches, but nowhere near “replacing all manufacturing.” Many think LLMs will follow a similar arc.
• Several conclude AI is a powerful accelerant: it makes both good and bad engineering easier and faster, so institutional incentives and human judgment remain the real leverage points.

Historical context: why Windows is “late” here

• Several commenters note Windows has lacked a clean, unified install/update/uninstall story compared to what users perceive on macOS or Linux.
• Others push back: Windows has had Windows Installer (MSI) for ~25 years and MSIX for over a decade, plus the Microsoft Store with automatic updates; the problem is complexity, poor tooling, and inconsistent adoption.
• DOS and early Windows culture (“anything goes” directories, vendor‑supplied installers, no enforced conventions) made retrofitting a strict package framework hard, especially under strong backward‑compatibility guarantees.
• Corporate environments often rely on MSI + Group Policy or custom packaging; some say this mostly works, others still find themselves repackaging apps.

*Comparisons to macOS, Linux, and BSD

• macOS: perceived as simpler (drag‑and‑drop apps, consistent installers, Sparkle auto‑updater), though others point out many apps still ship custom installers, background updaters, and lack proper uninstallers.
• Linux/*BSD: praised for unified package managers (apt, dnf, pacman, ports) and repo-based updates; some call this “unparalleled”.
• Critiques of Linux: no clear separation of “core OS” vs add‑ons (everything is just packages in /usr), easy to break systems by removing the wrong thing, and hard to revert to a pristine baseline.
• Some note protections like “protected packages” in rpm/dnf and argue what is “core” varies per user.

Existing Windows package/update tools

• Microsoft Store provides auto-updated apps but historically came with capability and policy limitations; more recently supports classic Win32/MSIX with minimal sandboxing.
• WinGet is seen as very late, still CLI‑centric, and weaker than Linux package managers; others are happy with it and note it can use multiple sources, including the Store.
• Third‑party tools:
  • Scoop, Chocolatey: provide more uniform install locations and behaviors.
  • UniGetUI: frequently recommended GUI front‑end for WinGet/Scoop/Chocolatey and other managers; praised but flagged as a single‑maintainer risk.
• MSIX is highlighted as technically strong (delta updates, background updates, clean uninstall, AppContainer sandboxing, admin‑less installs), but tooling is awkward and Windows 10 bugs make direct use painful without intermediaries.

Pros and cons of routing third‑party updates through Windows Update

• Enthusiasm:
  • Users are tired of each app running its own updater service and welcome a central, pausable, policy‑driven system.
  • Could make auto‑updating safer and more consistent for non‑technical users; enterprises can still stage updates via domain tooling.
• Concerns:
  • Windows Update’s reputation for slowness, fragility, and surprise reboots makes some wary of it becoming a “single point of failure”.
  • Fear of dark patterns or forced feature/monetization updates (e.g., turning perpetual licenses into subscriptions).
  • Skepticism that Microsoft will adequately test or roll back third‑party updates; comparisons to CrowdStrike‑style failures arise, with no clear consensus that this approach mitigates them.
  • Some users prefer minimal OS involvement, disabling Windows Update entirely because “update” has become synonymous with disruption.

Security, platform strategy, and lock‑down debates

• Some argue Microsoft should emulate Apple: first‑party hardware, locked‑down app distribution, mandated TPM/Secure Boot to improve security.
• Others counter:
  • Windows’ appeal is partly its openness; Apple‑style lock‑down would face market and antitrust resistance and upset OEM partners.
  • Microsoft is now “services‑first”; tight vertical integration may not fit its business incentives.
• Secure Boot and TPM requirements (Windows 11) are cited as partial attempts at lock‑down that already triggered strong backlash.
• Several commenters express broad distrust of Microsoft’s motives, suspecting more control and telemetry over installed software rather than pure user benefit.

Automation goals vs human work

• Some argue that even human-level autonomous driving is desirable so people can shift from “low-complexity” driving to “higher-complexity” tasks, increasing productivity and societal wealth.
• Others strongly push back: not everyone wants or can do higher-complexity work; there is dignity in “low-skill” jobs like driving, and it’s paternalistic to tell others what work they “should” do.
• Critics question whether large numbers of realistically accessible, higher-skill jobs actually exist for millions of drivers.

Displacement, retraining, and social impact

• There is deep skepticism that “education and retraining” meaningfully scale; historical examples (farmers, factory workers, Rust Belt) are cited as producing long-term regional poverty, not smooth transitions.
• Several comments predict a cohort of permanently displaced, poorer, angrier workers; some note US welfare and retraining programs are weak, shrinking, and often designed more to push people off benefits than to help.
• A minority suggests phased change and large public investment (education-style scale) could help, but others see no sign such commitments will be made.

Regulation, safety, and risk standards

• One side warns against regulations that freeze progress or protect specific jobs (e.g., anti-automation clauses), arguing innovation benefits society and that coal-style “it’s coming back” promises are harmful.
• Others stress moral obligations not to “throw people away” and favor pacing or cushioning transitions.
• On safety, some say autonomous trucks only need to be as safe as the worst insurable human driver and may already beat the average.
• Opponents argue heavy trucks pose qualitatively bigger risks; insurance payouts can’t compensate mass casualties, and independent, stringent regulation is needed before widespread deployment.

Economics, prices, and monopolies

• Pro-automation voices expect lower logistics costs and ultimately cheaper goods; historical automation examples are invoked in support.
• Critics counter that automation in essentials (housing, healthcare, construction) has not produced lower consumer prices, largely due to regulatory capture and oligopoly; gains often accrue to shareholders.
• There is concern that autonomous freight networks will centralize into an oligopoly, with closed systems, locked-down repairs, and rent extraction that replaces, rather than eliminates, today’s labor costs.

Infrastructure and “just build lanes”

• Some propose dedicated autonomous lanes to simplify the problem.
• Others say this defeats the main economic point (reuse existing roads) and would be enormously expensive.
• Multiple commenters note that fully separated, high-throughput, steel-on-steel freight corridors are effectively “reinventing trains,” suggesting rail is the natural endpoint of that logic.

Adoption path and industry dynamics

• Many believe long-haul highway segments will be automated first; complex urban “last mile,” paperwork, specialized and delicate loads may remain human for longer.
• There’s debate whether this yields more driver jobs (focused on terminals and cities) or a large net loss.
• High attrition among new truckers is mentioned: some argue gradual automation might be absorbed by people leaving anyway, with veterans pushed into lower-paid roles; others see this as cold comfort.

Trust in specific players

• Some commenters are surprised that relatively small, lesser-known firms (like the company in the article) are leading deployments rather than perceived leaders in self-driving tech.
• The modest real-world mileage and marketing-heavy claims described in the article are viewed by some as underwhelming and possibly overhyped.

Reverse engineering, DRM, and IP expansion

• Strong support for legalizing reverse-engineering, jailbreaking, and modification of products as a way to weaken US tech monopolies and restore genuine ownership.
• Several commenters note EU countries already have limited rights to reverse engineer for interoperability, but all must also implement anti‑circumvention laws, which significantly blunt right‑to‑repair.
• Concerns over “ridiculous expansion” of IP: software patents (esp. in Europe via the Unified Patent Court) and DRM are seen as corporate overreach and even judicial capture.
• There is disagreement on Doctorow’s claim that US tariff threats explain DMCA‑style laws abroad: some say the real driver is international copyright treaties (e.g., WIPO) and domestic governments, not US pressure; others counter that duress can’t be ruled out and treaties can be changed.

Data, labor, and exploitative pricing (nursing apps)

• Many see using credit‑score/debt data to lowball nurses’ pay as clearly unethical and argue it should be illegal.
• Others frame it as “the market working” and say the real issue is cartelized platforms and artificially constrained hospital supply, not the data use itself.
• Several argue exploitation of indebted workers is a symptom worth banning directly, regardless of cartel structure.

GDPR, consent, and worker power

• Debate over whether employers could lawfully bake high‑intrusion data access into employment contracts under GDPR.
• One side claims consent-in-contract is effectively allowed and enforcement is weak, citing widespread opt‑outs from the Working Time Directive.
• Others respond that courts require “free” consent (no job‑or‑nothing tradeoff) and have struck down all‑or‑nothing models; they argue such clauses would be void.
• Discussion of uneven union strength across Europe and how stronger unions can resist such abuses, versus weaker labor regimes.

“Enshittification”: term, scope, and politics

• Large subthread on whether the term “enshittification” is politically self‑defeating:
  • Critics: it sounds juvenile, alienates academics and legislators, and blurs a specific platform‑decay pattern into a vague “everything got worse online”.
  • Supporters: it’s vivid, widely understood, has entered mainstream discussion, and elites can adopt a tamer synonym (e.g., “platform decay”) in formal contexts.
• Some see objections as tone‑policing or “pearl clutching,” arguing the real blocker is corporate influence over lawmaking, not vocabulary.

Competition, app stores, and right-to-repair

• Doctorow’s idea of alternative low‑fee app stores and open diagnostics is popular in principle.
• Skeptics note alternative app stores already exist and haven’t seen mass “flocking,” though others argue mobile platforms are still structurally hostile and recent EU actions against Apple may change dynamics.
• Broad support for killing anti‑circumvention/DRM locks on hardware (cars, tractors, games) without abolishing copyright itself.

Ethics, labor markets, and how bad systems ship

• One commenter asks how so many people agree to implement obviously exploitative systems; responses point to:
  • Economic pressure and willingness to trade morals for pay.
  • Collapse of tech‑worker scarcity after mass layoffs, reducing the ability of engineers to say “no”.
• Others link “enshittification” to broader capitalism dynamics, concentration, and the drive to extract more money once growth slows.

Old vs. new internet

• Some nostalgia for the “old good internet” where barriers to entry kept out walled gardens, with a provocative suggestion that not all technologies should be fully “democratized.”
• Counterpoint: limiting access only delays problems; it doesn’t solve structural issues of power and regulation.

Miscellaneous

• Notes about Google Translate sometimes failing on the article (possibly due to LWN blocking Google traffic) and suggestions to use Firefox’s offline translation.
• References to related talks and podcasts expanding on who “broke” the internet and how.

JavaScript Date Parsing & “Undefined” Behavior

• The core issue: new Date('2025/05/28') and new Date('2025-05-28') are specified differently.
• The spec only guarantees behavior for ISO-like strings; other formats are explicitly left to implementations, so browsers can interpret them however they like.
• Slash formats like 2025/05/28 are treated as legacy, local-time dates; dash ISO-like ones may involve time zones and are handled differently.
• Some see this as “undocumented undefined behavior”; others point out it is documented as implementation-defined, just surprising.

Legacy of Date and the Temporal Fix

• JS Date inherits design problems from Java’s java.util.Date (zero-based month, bad constructors, etc.).
• Java deprecated most Date constructors; JS can’t, due to web compatibility.
• The upcoming Temporal API is viewed as the proper fix, with explicit types:
  • Instant (timestamp), ZonedDateTime (timestamp + zone), PlainDateTime / PlainDate / PlainTime, plus Duration.
• Some compare these to PostgreSQL timestamptz, timestamp, date, time, and interval.

Why the Web Runs on This Anyway

• Historical path dependence: JS was “the toy language” shipped in browsers while heavier plugin-based stacks (Java applets, Flash, Silverlight) failed due to security, performance, and crash issues.
• Despite a weak early standard library (even Node.js started on ES3), browser ubiquity and incremental improvements made JS the de facto web language.

Browser Monoculture & Standards Politics

• Debate over whether a single dominant browser plus strong standard library would be better or worse than today’s de facto Chrome/Safari duopoly.
• Temporal’s rollout illustrates how one dominant engine can effectively gate new language features.
• The date-string behavior got locked in after complaints that spec-conforming changes in Chrome were “breaking,” eventually pushing the spec toward legacy behavior.

Dates, Time Zones, and Best Practices

• Strong consensus: date/time handling is hard everywhere; never rely on generic “auto-parse” of arbitrary date strings.
• Recurrent advice:
  • Distinguish absolute timestamps vs. calendar/clock times; store the correct concept.
  • Use explicit parsing/formatting functions and high-level APIs, not manual string hacking.
  • “Just use UTC” is not a universal solution: you may need to store original time zone (and sometimes location) for meetings, logs, or legal/UX reasons.
  • For pure calendar dates (e.g., birthdays, age checks), store date-only types; timestamps and time zones are often irrelevant or misleading.

ISO 8601, RFC3339, and Human Formats

• Many advocate ISO 8601 YYYY-MM-DD (or RFC3339) as the only sane interchange format.
• Others note ISO 8601’s permissiveness and odd variants; RFC3339 is praised for being stricter and freely accessible.
• There’s extended debate on separators (- vs /), regional formats (MDY, DMY, YMD), and how easily ambiguity creeps in.

Frustration, Humor, and War Stories

• Multiple anecdotes of bugs caused by JS silently attaching local midnight to date-only values, shifting days when converted to UTC.
• Calls for separate “Day” or local-date types and for languages to avoid forcing every date into a timestamp-plus-time-zone model.
• Links to classic “WAT” talks, xkcd, and “falsehoods programmers believe about time” underline that these problems are pervasive and longstanding, not unique to JS.

Tire Wear: Causes and Scale of the Problem

• Multiple commenters challenge or support the claim that EV tire wear is only “slightly” higher; anecdotal reports range from similar to ~30–50% worse.
• Explanations offered: extra vehicle weight, higher cornering forces, and especially high instantaneous torque plus aggressive acceleration.
• Others argue driving style dominates: light EVs with modest power can still shred front tires if driven hard.
• Some suggest software limits and better traction control could reduce unnecessary wheel slip and thus tire wear.

Brake Dust and Regenerative Braking

• Broad agreement that EVs (and hybrids/PHEVs) produce much less brake dust because regenerative braking handles most deceleration, with friction brakes mostly used below ~5 mph or when regen is limited.
• Anecdotes: very long pad life; visibly cleaner wheels vs ICE cars; some EVs lightly auto-apply brakes periodically to prevent rust.
• Question raised why BEVs beat hybrids: answer given is BEVs have much higher regen power (limited by battery size/C‑rate), while hybrids’ small batteries cap regen at low kW.

Relative Toxicity: Brake Dust vs Tire Dust

• One quoted figure: ~>40% of brake dust becomes airborne vs ~1–5% of tire wear, so lower brake dust is a big win even if tire dust rises slightly.
• Others stress tire dust is still serious: microplastics and especially 6PPD/6PPD‑quinone toxicity to some fish and possible human exposure.
• Debate over priorities: some see microplastics as minor versus climate change; others argue ocean and aquatic toxicity can’t be dismissed.

Vehicle Weight, Road Wear, and Trucks

• Concerns raised that heavier EVs may accelerate road wear and require more braking when regen is insufficient.
• Counterpoint: road damage scales steeply with axle load; heavy trucks dominate wear, passenger cars (EV or ICE) are “almost negligible.”
• Example: some modern EVs are only modestly heavier than comparable ICE models when designed as EVs from scratch.

Urban Design, Alternatives, and “Cleaning the Air”

• Several see EVs as an incremental fix; the “real” solution is less car dependence via walking, cycling, and good public transit.
• Strong back-and-forth over density, suburbs, and lifestyle: some argue dense cities are “toxic” and tech (EVs, self‑driving) will enable dispersion; others counter that human social needs and amenities inherently drive urban density.
• Some note that e‑bikes/scooters capture many EV benefits with far less weight, space, and danger.
• Others quibble with the article’s framing: EVs don’t literally “clean” air; they just pollute less.