Hacker News, Distilled

Original Vision vs. Current Reality

• Early hopes: bank the unbanked, cheap global payments, disintermediate PayPal/banks.
• Many posters say this largely failed: almost no everyday retail usage in Europe/US; fiat payment rails got “fast and cheap” anyway.
• Bitcoin is now framed mostly as:
  • Speculative asset / “store of value”
  • Tool for illicit use (sanctions evasion, laundering, scams, illegal markets)
  • Hedge against fiat debasement in unstable countries (with stablecoins mentioned more than BTC).

Regret, Luck, and “Mistake” Narratives

• Several personal stories of selling early or never buying; common theme: hindsight makes normal decisions look like catastrophic errors.
• Others push back: treating missed crypto gains like “not buying the winning lottery ticket” – impossible to know, and most would have sold much earlier anyway.
• Some argue that using crypto windfalls for real-life improvements (housing, debt payoff) was rational, not a mistake.

Ethics, Inequality, and Power Concentration

• Strong criticism that Bitcoin’s main “real” value is enabling crime and evasion of rules.
• Concern that wealth and control are highly concentrated:
  • Lost coins, early hoards, whales, banks, and centralized exchanges dominate supply/flow.
  • This is seen as recreating (or amplifying) existing inequality and insider advantage, not disrupting it.
• Counterview: diverting capital away from real estate and traditional assets might reduce some inequality pressures.

Store of Value vs. Risk and Energy Cost

• Supporters emphasize algorithmic scarcity and long-term “store of value” properties, comparing BTC to gold and criticizing fiat inflation.
• Skeptics highlight:
  • Extreme volatility (multi‑tens‑of‑percent drops)
  • Regulatory risk
  • Zero productive output compared to equities/bonds
• Proof-of-work’s energy use is condemned as “waste”; some wish speculation moved to non‑PoW systems.

Regulation, Politics, and Macro Context

• Debate over whether US “crypto week” and Trump-era policy are driving prices; some expect classic “sell the news.”
• Worry that regulation will be designed to favor large institutions, who will also get advance signals and exit first.
• Some see BTC as a hedge against local fiat inflation; others argue more conventional assets (foreign currency, real estate, equities, bonds, gold) are safer hedges.

Meta and Behavioral Themes

• Discussion of cognitive biases, regret, and recency bias in evaluating BTC’s rise.
• Observation that outsized crypto fortunes demoralize “rule-followers” and may incentivize riskier behavior.
• Thread also contains obvious “recovery” scam spam, ironically underscoring crypto’s fraud problem.

Support for Open Web Advocacy

• Many commenters express strong appreciation for the advocacy work and the grilling of Apple under the DMA, though some wish the questioning had been more aggressive given Apple’s polished legal deflections and “security” framing.

---

Browser Diversity vs User Choice

• One camp argues browser diversity (multiple engines) matters more than individual user choice of browser UI; without it, the web risks becoming “the Chrome protocol.”
• A counter‑camp claims Apple’s WebKit lock‑in is actually the last significant barrier preventing a Chrome/Blink monoculture and thus indirectly protects diversity.
• Others call that logic backwards: Apple isn’t “defending diversity,” it is entrenching its own engine and weakening cross‑platform alternatives.

---

EU‑Only Engines and Developer Testing

• Strong criticism that allowing non‑WebKit engines only inside the EU makes them second‑class: non‑EU devs can’t realistically test, so engines will be under‑supported.
• Workarounds like macOS VMs, remote iOS simulators, Faraday‑bag/EU Wi‑Fi spoofing, and device sharing are discussed but seen as expensive, clumsy, or inadequate for real performance/gesture testing.
• TestFlight caps and Apple licensing restrictions further limit scalable testing.

---

Apple’s Compliance Strategy and Defaults

• Many see Apple’s behavior as “malicious compliance”: implementing only what is absolutely required in the EU and adding friction via bundle‑ID rules and region locks.
• Examples are given where iOS still opens Safari or Apple Maps despite different user defaults, reinforcing the sense that defaults and “choice” are undermined.

---

Security Rationale Debate

• Apple’s position that engine bans are about security gets both support and skepticism.
• Supporters invoke scenarios of surveillance or propaganda browsers; critics say this is really about securing Apple’s control and App Store revenues against user wishes.

---

Chrome Dominance and Monoculture Fears

• Some argue lifting the engine ban would accelerate Chrome’s dominance, discouraging cross‑browser testing and threatening Firefox/WebKit.
• Others respond that Chrome is already dominant on Android and desktop; the realistic benefit of competition on iOS would be pressure on Apple to improve Safari, not instant WebKit collapse.

---

Economics of Safari and Incentives

• Safari’s Google search deal is highlighted as a huge profit center with relatively small engineering investment, seen as a core motive to preserve Safari’s privileged status.
• This is used to explain why Apple resists true engine competition instead of aggressively improving Safari across platforms.

---

Regulatory Load: DMA and CRA

• Beyond Apple’s obstacles, the EU’s Cyber Resilience Act is noted as adding heavy documentation, security, and liability requirements to browsers, with large potential fines.
• Some argue exemptions and “sandboxes” mitigate this for small players; others fear only big vendors will practically be able to ship full browsers in the EU.

---

Web Apps vs Native, and Games

• Skeptics point out that if native‑equivalent web apps were mainly being blocked by Apple, we’d already see far more serious web apps and games on Android; many don’t.
• Counter‑arguments cite missing or buggy APIs on iOS, Apple’s historic hostility to PWAs, and business incentives around in‑app purchases as jointly suppressing the web as an app platform.

---

User Experience and Dark Patterns

• Complaints extend to both Apple and Google: iOS apps and Google properties pushing their own browsers or apps via nags and dark patterns, and in‑app web views that ignore user defaults.
• These behaviors are widely seen as user‑hostile symptoms of the same underlying platform power.

Rough drafts, prototypes, and management

• Many agree with starting from a rough, end‑to‑end draft to discover requirements and “unknown unknowns” in the problem space.
• Several warn that “draft” code often gets prematurely promoted to production by managers who see a demo and declare it “done.”
• Suggested mitigations: clearly label work as mockups, deliberately leave visible rough edges, or avoid demoing too-early artifacts.

AI, bad code, and systemic dysfunction

• Consultants report repeatedly finding long-lived enterprise systems (banks, hospitals, factories) held together with hacks, TODOs, and no tests or version control.
• AI is seen as accelerating this: more code, faster, with less conceptual integrity. One example: an LLM‑like codebase in a hospital app that deleted all admin users on reboot.
• Some note this is not new; AI just speeds up an existing pattern decision-makers already don’t understand or resource properly.

Speed now vs long‑term maintainability

• Several emphasize that initial velocity must be balanced with future speed: tests, docs, decision logs, observability, and good data models pay off over time.
• Solo devs describe “lab notebooks” and decision logs as crucial for their future selves.
• There’s broad agreement that APIs, data models, and overall architecture are the hardest things to “iterate out of” later.

Data modeling, architecture, and scale

• Starting from the database schema (or core data model) is praised as making everything else simpler; getting it wrong leads to painful migrations and operational risk.
• Small teams can move fast with looser code; in large organizations, architectural mistakes and refactors become exponentially more expensive.
• Microservices are suggested as a way to keep teams small, but also criticized for adding tech‑stack sprawl and complexity.

Testing philosophy and fast feedback

• One detailed thread advocates heavy, concurrent black‑box integration tests (API + DB + dependencies), run in seconds, using randomized data and ephemeral DBs.
• Others caution against over‑optimizing for speed at the expense of realism and low‑maintenance tests; mocks and stubs are seen as both useful and fragile.
• There’s disagreement on how much unit vs integration vs “in‑between” tests are worthwhile.

“Boring tech”, frameworks, and stack choices

• A large subthread argues that mastering one “boring” stack (e.g., Django/Postgres) is a major speed advantage; frameworks like Django/Rails/Laravel are praised for rapid CRUD.
• Debate over SQLite vs Postgres: SQLite is attractive for simplicity and local/CI use, but many warn about concurrency limits and subtle production issues.
• Others counter that overuse of big frameworks or Kubernetes/Redis for simple apps adds unnecessary complexity; some prefer composable libraries (e.g., Go) despite more boilerplate.
• Frontend: many claim most apps don’t need SPAs; server‑rendered pages with small sprinkles (HTMX/Alpine, LiveView‑style) can be faster to build and maintain.

Clean code under tight deadlines

• On game jams and hacky projects, the article suggests deprioritizing code cleanliness; several commenters strongly disagree, saying good habits make them faster even under 24‑hour constraints.
• Viewpoints differ on whether you should “do it well” on the first pass or embrace messy exploration then rigorously refactor; both camps stress discipline in knowing when to clean up.

Team norms, incentives, and quality

• A recurring theme is that “good enough” is rarely explicit: ex‑big‑tech engineers and startup veterans often clash over acceptable bug levels and process rigor.
• Suggestions include team charters to define expectations around tests, refactoring, and quality.
• Some argue the real enemies of quality are incentives: consumers don’t pay for internal code quality, layoffs and rush culture punish experimentation, and vendor/AI lock‑in may worsen things.

Privacy & Local Processing

• Many see the main differentiator vs Grammarly as being fully local processing and no cloud data transfer, especially for corporate/IT environments.
• Some argue BYOK (remote models) risks diluting that advantage; others want BYOK to run heavier models on home servers.
• Several suggest the marketing should strongly emphasize privacy and show a clear comparison with Grammarly and Apple’s system tools.

Language Support & Dialects

• Underlying model (Gemma 3n) can theoretically support ~140+ languages; real-world quality beyond English is largely untested.
• Big concern that the site doesn’t clearly specify supported languages, dialects, or registers (e.g., US vs UK English, Indian English).
• Debate erupts over “practice/practise” and spellings like “colour,” with some saying correct dialect handling is a minimum requirement for a serious checker.

Quality & Behavior of Suggestions

• Users find the “fluency” mode often over-aggressive or malformed: random quotes, odd rephrasings, and occasional refusals with safety-style messages.
• Grammar checks miss some issues (verb agreement, articles) that Grammarly and LanguageTool catch.
• Others report it handles mixed-language sentences surprisingly well, and view it as a very promising first release.

Comparisons to Alternatives

• LanguageTool and Harper are frequently mentioned; both have FOSS components and can be run locally (via Java, Docker, or Flatpak).
• Several report LanguageTool with n-gram data is excellent; Harper is seen as weaker on basic errors but rapidly improving.
• Some are building similar local grammar tools atop Chrome’s built-in LLM.

Security, Trust, and Licensing

• Strong concerns about keylogging risk, especially for a tool that monitors all keyboard input.
• Critics note: app is unsandboxed, distributed outside the Mac App Store, and appears to lack a clear corporate legal entity or independent audits.
• Others respond that this risk exists for any proprietary app, but skeptics insist privacy claims need stronger technical and legal backing, or open source.

Performance, Platform & Integration

• Uses an 8B Gemma 3n model (3 GB RAM); runs on Apple Silicon and offline. Some worry about RAM overhead.
• Users report inconsistent operation in apps like Slack, VS Code, and browsers; system-wide, cross-app reliability is a key expectation.
• Praised for being a local, one-time-purchase Mac app with a free demo, but many request Windows/Linux support and editor integrations (Vim/Emacs/API).

AI Detectors & Academic Use

• One commenter worries LLM-style rewrites might trip AI-detection tools (e.g., Turnitin), making use risky for coursework.
• Others argue that light grammar correction should be distinguishable from AI-generated text, but acknowledge academic policies often ban any LLM use outright.

Car prices, regulation, and profitability

• Some argue new car prices have “doubled” in 20 years, implying automakers should be very profitable; others counter that, adjusted for inflation and hedonic quality changes, the increase is far less dramatic.
• In Europe, stricter safety, emissions, recyclability, and ADAS rules are seen as making cars materially more expensive to build.
• There’s debate over whether rising prices are mainly regulation-driven or a deliberate move upmarket (more “pseudo-luxury” trims, bigger/heavier vehicles) combined with cost-cutting in quality.

Trade policy and Chinese competition

• Commenters note the US has effectively blocked Chinese cars with very high tariffs; the EU uses more moderate, targeted tariffs to offset calculated state aid rather than ban them outright.
• Some see Chinese pricing as state-subsidized dumping to kill foreign industry; others point to teardown analyses and intense intra‑China competition as evidence of real cost advantages via automation and vertical integration.
• Chinese EVs are increasingly visible in Europe (e.g., BYD), especially where tariffs are lower or can be bypassed via local assembly.

Stellantis-specific issues

• Many see the China bankruptcy as a Stellantis management failure, not just a China problem: long-term decline in Chinese sales, weak products, and poor JV structure.
• A heavily criticized cost-cutting CEO is blamed for stripping investment, raising prices, and alienating dealers, with short-term profit followed by a sharp profit collapse. Others argue North American operations resisted necessary restructuring.
• Stellantis’ brand mix is viewed as a bundle of struggling marques; some see only a few bright spots (Peugeot/Citroën in Europe, RAM/Jeep in the US). Confusing rebrands (e.g., “Stellantis & You”, DS vs Citroën) are cited as symptomatic.

EV strategy, infrastructure, and demand

• Several commenters argue European automakers had early EV tech but shelved it to protect ICE/diesel profits, outsourcing R&D and losing competence, while Tesla and Chinese firms pushed ahead.
• Others say European consumers were slow to adopt EVs due to poor charging infrastructure, apartment living, and lower purchasing power; subsidies largely helped wealthier homeowners first.
• Stellantis is criticized for late, mediocre EVs and delayed, expensive hybrids, plus a strategic focus on higher-margin “luxury” segments.

China’s long-term planning vs Western short-termism

• Multiple comments contrast China’s long-term industrial plans (e.g., EVs, batteries, vertical integration, mega‑plants) with Western focus on quarterly results, share buybacks, and executive pay.
• There’s extended reflection on whether democracies with short electoral cycles can support similar long-horizon industrial strategies, and how low political trust and inequality feed short‑term thinking.

Why operations cost so much

• Several comments stress that the main cost is people, not hardware: hundreds of staff to plan observations, calibrate 17+ modes, maintain software, monitor health, and analyze data.
• Operating infrastructure like the Deep Space Network and contractor support (e.g., paying a prime contractor just to stay on call) adds substantial recurring expense.
• Some argue this is “incredible value for money” given the sophistication and rarity of such instruments; others see it as padded, risk‑averse bureaucracy and “cost-plus” contracting.

Underutilization vs penny‑pinching

• Many see it as irrational to spend ~$10B to launch JWST and then constrain operations to save a fraction of a percent of that per year.
• The cuts are viewed as “penny wise, pound foolish”: wasting sunk investment by throttling science output.

Private wealth, private space, and billionaires

• Multiple comments ask why ultra‑rich individuals don’t simply fund telescopes or operations “for fun.”
• Responses note: most wealth is in stock; spending 10% of net worth is still huge; some rich already fund observatories; and their priorities lean more toward launch systems, Mars/industrial visions, or profit‑linked projects than pure astronomy.
• Debate over whether extreme wealth is treated as a “high score” vs legitimately enabling philanthropy and investment.

Politics, ideology, and anti‑science sentiment

• Strong thread blaming US right‑wing / MAGA politics: hostility to government, climate science, and “globalist” benefits; desire to shrink or sabotage public institutions; “starve the beast” style budget strategy.
• Others emphasize structural government waste, perverse budget incentives, and generalized austerity rather than a targeted anti‑science plot.
• One commenter notes that Biden’s earlier projections were higher; another points out the new proposal is still ~25% below that in real terms.

International partners and alternatives

• ESA/CSA involvement raises the question of whether they could pick up operations; skepticism that Europe will or can shoulder much more, given its own missions and NASA’s history of pulling out of joint projects.
• Some suggest renting telescope time to wealthy institutions or even crowdfunding, but note that similar ideas failed to save Arecibo despite its modest needs.

Scientific return and how to measure it

• Disagreement over whether JWST advances knowledge “less per dollar” than Hubble, using paper counts vs transformative discoveries.
• Others argue raw publication counts are a poor proxy; a few unexpected results from JWST about early galaxies may be more important than sheer volume.

Stat clarification & data limits

• Commenters stress the 27% refers to homes sold in Q1, not all US homes; some find the headline misleading.
• Others note it’s a 5‑year high but still need longer historical context to judge significance.
• Several ask how “investor” is defined and question how much underlying lifestyle/intent (flipper vs second home vs rental) data the provider really has.

Who are the “investors”?

• Thread highlights that ~20% of single‑family homes are investor‑owned, and ~85% of those are “mom‑and‑pop” with 1–5 properties, only ~2.2% are large institutional (1000+ homes).
• Some argue “mom and pop” with multiple properties are effectively a rentier class, not socially benign. Others see them as middle‑class retirement strategy, especially with low‑rate mortgages locked in.

Is investor buying the core problem?

• One camp: investor activity, including small landlords and PE, crowds out owner‑occupiers, raises prices, and commodifies housing.
• Counter‑camp: investors are mostly a symptom of a constrained market; if they weren’t buying, someone else would, and the real problem is chronic undersupply.

Supply, zoning, and regulation

• Strong YIMBY current: local zoning, NIMBY opposition, height/density limits, and high compliance costs prevent supply from responding to demand.
• Others push back that post‑GFC risk, financing, and input costs also matter; not all markets are primarily regulation‑driven.
• Some propose broad “abundance” approaches: aggressively subsidize and deregulate building, including mixed‑income public housing.

Taxes, ownership caps, and other policy ideas

• Proposals include:
  • Heavy or progressive taxes on multiple homes, flips, or non‑owner‑occupied property.
  • Banning or heavily restricting corporate/PE ownership of single‑family homes.
  • Land value taxes to target unearned gains on land.
  • Ending favorable tax treatment or corporate deductions tied to housing.
• Critics warn such measures could shrink rental supply, raise rents, or amount to ineffective central planning.

Renting vs owning & impact on tenants

• Debate over whether more investor‑owned housing helps by expanding rentals or hurts by keeping would‑be buyers renting longer and pushing rents toward tenants’ maximum ability to pay.
• Some argue that with enough competition, landlords can be forced to accept lower returns; others worry about de facto cartels and price‑setting software.

Demographics and long‑term outlook

• Split views on whether population growth (“too much breeding” and immigration) vs household size and dwelling size vs policy choices drive high prices.
• A few note potential future depopulation (Japan as example) could reverse valuations, but this is speculative in the thread.

Moral, social, and class dimensions

• Many frame mass landlordism as morally dubious: turning a basic need into a speculative asset, entrenching a “new nobility” of property owners vs “peasants” locked into rent.
• Others defend small landlords as ordinary savers reacting rationally to policy‑created incentives.
• Several predict rising radicalism and policy backlash from younger generations excluded from ownership.

What Counts as “Medicine” and Pseudoscience

• Several comments stress: once a treatment is rigorously shown to work, it’s just “medicine,” regardless of origin.
• “Unproven” is distinguished from “disproven”; traditions can contain both effective and ineffective components.
• Many note it is extremely hard to know what works without expensive, large, controlled trials; intuition and anecdotes are usually misleading.

TCM Hits vs. TCM as a System

• Multiple drugs (artemisinin, arsenic trioxide, ephedrine, statins from red yeast rice, etc.) are cited as having origins in TCM or other folk practices.
• Others counter that this does not vindicate TCM’s underlying theory (qi, meridians, yin/yang) any more than willow bark vindicates medieval European medicine.
• Some frame TCM (and Ayurveda) as a massive trial-and-error reservoir: unsurprising that repeated empirical tinkering finds some real effects over millennia.

Critiques and Risks of TCM

• TCM is described as internally inconsistent: different practitioners give wildly different diagnoses and prescriptions, even for COVID.
• Concerns include: unregulated herbs, contamination, toxic plants (e.g., Aristolochia–associated renal failure), animal parts (rhino, tiger), and unknown dosages, especially for children.
• Critics see much of TCM as placebo, symptom-focused, or outright “bullshit,” with danger when it replaces effective care.

Evidence, RCTs, and Acupuncture

• Commenters debate how much RCT evidence supports TCM modalities, especially acupuncture; some claim strong nervous-system effects, others call it pseudoscience.
• The replication crisis is noted, but RCTs are still seen as better than proto-clinical “notes and anecdotes.”
• Disagreement appears on whether TCM’s individualized concepts (yin/yang body types, microbiome differences) fundamentally resist standard trial designs.

Placebo, Chronic Illness, and Patient Experience

• Many illnesses are self-limiting; placebo and time explain much “success.”
• Several anecdotes describe chronic/idiopathic issues (eczema, back pain, stress-related sickness) where conventional medicine “shrugged,” but TCM, acupuncture, chiropractic, or exclusion diets seemed to help.
• Some argue dismissing such avenues outright sacrifices potential improvements in outcomes for “intellectual purity.”

Politics, Academia, and Propaganda

• Chinese state support for TCM is linked to cost control, historical need to cover a huge population, and nationalist symbolism.
• Commenters worry about floods of low-quality pro-TCM papers from metric-driven systems (China, India, Vietnam) degrading peer review, likening it to a “51% attack.”
• Others note similar publication and incentive problems in Western academia; all funding models carry bias.

Science vs. Culture and Integration

• Several insist there is no “Chinese medicine” vs “Western medicine,” only treatments that pass or fail the same scientific tests.
• Others accuse “Western chauvinism” of assuming only Western methods can discover useful treatments.
• Broad agreement at the end: any remedy—traditional or not—should be rigorously validated, effective parts isolated and standardized, and then folded into mainstream medicine; the rest should be discarded.

Reposts and HN moderation

• Some comments questioned repeated submissions of the project; others pointed to the HN FAQ saying low-traction stories can be reposted.
• Public accusations of shilling/astroturfing were discouraged; moderators suggested emailing them instead.

What CapCut is and who this targets

• CapCut described as a very popular, low-friction editor for TikTok/Reels–style short-form creators.
• Several people doubt the overlap between that user base and GitHub users able to run a dev-heavy stack.

Installation, UX, and existing alternatives

• Many like the idea of an open-source CapCut, but say requiring Bun, Docker, Docker Compose, and Node.js will lose most casual editors.
• Suggestions: ship an AppImage/Electron-style desktop bundle instead of dev tooling.
• Existing tools (Kdenlive, Shotcut, OpenShot, Blender, DaVinci Resolve, LumaFusion) are repeatedly recommended; Kdenlive in particular is praised for stability and features, though some find its UX non-intuitive.

Legitimacy, GitHub stars, and screenshots

• Strong skepticism about the project’s legitimacy: huge star and fork counts in weeks, minimal functionality, few/no screenshots initially.
• Several commenters suspect bought stars or manipulated metrics, comparing star growth to major, battle-tested projects.
• Others note there is a live demo and screenshots (after PRs), but agree the README and homepage are misleadingly bare and waitlist-focused.

Tone, “edgy” branding, and community behavior

• The “why not CapCut” page’s aggressive, profanity-heavy copy is polarizing: some enjoy it as parody; many see it as juvenile, alienating, and possibly AI-generated.
• A linked GitHub issue thread shows a contributor behaving combatively (e.g., around a trademark complaint), seen as a red flag for project culture and code-of-conduct seriousness.
• Broader debate on OSS toxicity, generational “edgelord” style, and whether harsh tone “protects” maintainers or just attracts more toxic users.

Product shape, waitlist, and functionality

• Confusion over why an open-source project has a waitlist and in-app analytics bragging; some suspect it’s a startup “building in public” using open source mainly as marketing.
• The actual editor is accessible via /projects; basic editing (e.g., text overlays) works but many features are still TODO.
• Overall: concept widely welcomed, but current implementation, messaging, and community signals make many wary.

Alternative platforms, blocking, and self‑curation

• Experiences on Lemmy/Mastodon are mixed: some find them boring or equally toxic; others say they become usable once a handful of toxic users are blocked.
• Mastodon’s newer algorithms are seen as recreating Twitter dynamics by privileging highly followed accounts; users recommend aggressive blocking and avoiding “popular” feeds.
• Blogs and RSS are praised as calmer, more interesting spaces with less incentive to perform or provoke.

Voting systems, dogpiles, and “hivemind” effects

• Old forums are remembered as having a couple of people arguing while others observed; now downvote systems can produce dogpiles and suppress valid but unpopular opinions.
• Some participants consciously upvote greyed‑out or disagreeable but sincere comments to counteract herd punishment.
• Others report quitting Reddit after constant downvotes on technical content.

Ads, algorithms, and outrage farming

• Many tie the problem not to individuals but to ad‑driven “engagement” optimization: outrage and “ragebait” keep people commenting, resharing, and seeing more ads.
• This is framed as the internet’s “original sin”: platforms maximize quantity and emotional intensity over quality.
• Some argue that platform‑controlled feeds are inherently misaligned with user well‑being; user‑side filters/agents might help but would hurt monetization.

Is it really just “a few people”?

• Several reject the idea that a tiny group ruins everything, pointing to armies of propagandists, commercial grifters, and countless minor bad actors.
• Others invoke Zipf‑like concentration: a small minority produces a disproportionate share of toxic content, reminiscent of Usenet and shock‑jock radio.
• There’s concern that over‑focusing on “a few trolls” obscures structural incentives (ads, algorithms, propaganda industries).

Speech, anonymity, and polarization

• Some claim offline political discussion is suppressed by social and economic risk, pushing grievances into anonymous online spaces where they’re amplified.
• Others counter that “I can’t speak” often means “people dislike my behavior,” and real‑world frank speech is still possible—though some countries criminalize certain insults.
• Participants disagree whether “politically correct” or “anti‑PC” camps are more intolerant; many conclude online anonymity and scale bring out the worst in all sides.

Online vs offline reality and the study’s claims

• People debate whether online discourse is a distorted funhouse mirror or a more honest exposure of hidden divisions.
• Some note that it feels harder and harder to avoid culture‑war content despite efforts to curate.
• The cited unfollow experiment is viewed with caution: skepticism about psychology’s replication record, missing statistical details, and doubts that platforms could (or would) significantly de‑amplify outrage without gutting their business model.

How new and important is this market?

• Several commenters argue app-based restaurant delivery barely existed 15–20 years ago (outside pizza/Chinese and some B2B “corporate lunch” services), so long‑term structure is still unsettled.
• Others from Europe say delivery hasn’t exploded there in the same way, suggesting regional differences.
• Some downplay the whole topic: delivery is a tiny, luxury slice of how people get food; you can always drive or walk.

Service quality and restaurant impact

• Many report worse outcomes via apps than direct delivery: colder food, longer delays, higher prices, and smaller portions.
• Chain and local pizza places often still run their own, cheaper and more reliable delivery, seen as superior to app drivers.
• Several anecdotes describe in‑store diners being deprioritized while kitchens crank out app orders, harming the sit‑down experience.
• Restaurants face high commissions (often ~30%), integration lock‑in (POS, tablets, unified order flow), and may raise prices on app menus; some advertise 20–30% savings for ordering direct.

Drivers, labor, and social costs

• Drivers are usually contractors, bearing car, fuel, and downtime costs; critics see wages as unsustainable and heavily reliant on precarious or undocumented workers.
• Some find school/prison food bidding and ultra-cheap institutional food dystopian and exploitative.

Market concentration, monopoly, and tech middlemen

• Many see five global players dominating as a textbook oligopoly/oligopsony pattern, mirroring other industries. Others note that in any given city it’s effectively 1–3 firms.
• Debate centers on whether five big players equals “competition” or a de facto cartel with little real differentiation.
• Some argue this is just capitalism’s normal consolidation; others blame weak or captured antitrust enforcement and VC-subsidized dumping that killed local players.
• A recurring theme: tech platforms as global middlemen that undercut incumbents, gain network effects, then “enshittify” by extracting rents from both restaurants and consumers.

Alternatives, openness, and localism

• Multiple commenters wish for open-source or standardized ordering systems that restaurants could host themselves, with local delivery co‑ops or driver‑owned platforms.
• Attempts exist (independent web ordering, POS vendors’ apps, niche services), but user behavior overwhelmingly favors a single convenient aggregator app, even at 15–30% higher prices.

State of the AI talent market

• Mega-comp offers and team “blitzhires” seen as a bubble by some, a rational grab for scarce experience by others.
• Many argue offers target “experience at scale” (shipping/training models for billions), not innate “talent.”
• Deals often stock-heavy with vesting/performance clauses; signing bonuses alone can be life-changing.

Productivity and “10x/1000x” debate

• Pushback on “1000x” claims; impact is not story points. Outlier impact may come from roadmap/design leverage or broad automation.
• Skeptics see the meme as hype to justify outsized comp; defenders say rare contributors can drive disproportionate business value.

Economics, costs, and sustainability

• Doubts that hardware economics will improve: GPUs are costly; energy/bandwidth dominate; unclear profitability for frontier LLMs.
• Disagreement over money supply/wealth concentration as root cause. Some say “cash sloshing” fuels bidding; others dispute M2 narratives.
• Environmental/energy concerns: scaling LLMs may exacerbate power demand; ethical value of $100B+ AI spend is contested.

Corporate tactics, poaching, and culture

• “Blitzhire” framed as acquisition-by-speed, skirting traditional antitrust review; can damage morale and investor trust.
• Past layoffs and no-poach history cited as eroding loyalty; claims of a “social contract” dismissed by others as myth.

Market structure and capital allocation

• Fear that platform giants will hoover up apps/talent, entrenching monopolies; pessimism about application-layer opportunities.
• Critics urge broader exploration: fund many small, interdisciplinary bets vs over-indexing on a few stars; note “dark horse” breakthroughs.

AGI, hype, and returns

• First-mover-advantage assumptions questioned; unclear what durable moats exist for AGI.
• Some expect frothy valuations on “AGI announcements”; others predict volatility and pretenders.
• Comparisons to sports salaries: paying for proven performance vs hype; risk of complacency post-payday noted.

AI in games and procedural content

• Idea: local model augmentation for dynamic NPC dialogue; potential for immersion in systemic/sandbox games.
• Counterpoint: predictable, signposted dialogue has design value; “AI slop” risks confusing players; procedural content best as backdrop.

Pace, externalities, and morality

• Dispute over “faster is better”: second-order societal effects and climate costs cited.
• Analogies (printing press, oil, nukes) used on both sides; outcomes seen as path-dependent and uncertain.

Skepticism about the AI bubble and “hypercapitalism”

• Several commenters see current AI hiring and capex as classic bubble behavior: money has “nowhere else to go,” so it floods into GPUs and star researchers, not obviously into sustainable businesses.
• Some argue we may have passed “peak AI” in the current paradigm: hardware gains are flattening, serving costs are high, and most products don’t yet justify their economics.
• Others counter that if AI is truly transformative, massive spending and acceleration are justified, even if returns are uncertain and long-dated.

10x / 1000x engineer and what’s really being bought

• Many reject the literal idea of “1000x” contributors in terms of output or story points; impact is seen as mostly team-based.
• Defenders say “1000x” can make sense in terms of business value: one person’s insight or automation can displace the work of many teams or unlock huge revenue.
• A strong subthread: these mega-deals are mostly about specialized experience (training frontier-scale models, running infra at billion-user scale), not raw “talent.”

Capital allocation, morality, and inequality

• Some view $100B+ AI budgets as immoral misallocation while climate, energy transition, and inequality go underfunded; AI datacenter power demand is seen as directly worsening emissions.
• Others argue large R&D spends are better than hoarding cash, and that wasteful R&D is still R&D; the main problem is broader wealth concentration and financialization, not AI specifically.
• Long subthreads debate money supply (M2), inflation, who benefits from asset inflation, and whether “throwing ridiculous cash” into talent actually reduces or reinforces inequality.

Labor power, capitalism, and political economy

• Commenters worry AI will erode workers’ bargaining power by commoditizing expertise, shifting power further to capital owners.
• Others note AI could also lower the cost of starting firms, weakening VC leverage.
• There are broader arguments over capitalism vs “Nordic” social democracy, inheritance and copyright, and whether concentrated economic power inevitably corrupts politics.

VC strategy and AI exploration

• Some criticize current “talent wars” as over-indexed on exploitation: overpaying a narrow elite instead of funding many small, weird, exploratory efforts.
• Historical analogies (Manhattan Project, oil, nuclear weapons, the printing press) are used on both sides to argue for either aggressive acceleration or more cautious, diversified investment.

Dashboard framing and “scoreboard” concern

• Some worry the real-time counters feel like a “scoreboard” that could be read as wins rather than harms, especially by those who support crackdowns.
• Suggestions to mitigate this: invert visual language so “more is worse,” clarify normative stance in text, and avoid broker-like green “gains.”
• The creator explicitly wants to show that rising detentions and longer stays are negative, and defends using red for increases.

Data categories, labels, and interpretation

• The “criminal / other” split is widely praised as immediately revealing and contrary to a “violent gang member” narrative.
• Several ask for finer breakdowns: felony vs misdemeanor, what “other”/“other immigration violator” actually includes, and better explanation of ICE “threat levels.”
• Some object to ICE’s term “violator” as presuming guilt; alternative, more neutral labels are proposed. Others argue that changing terminology is itself political framing.
• Confusion over the pie chart’s central percentage (“Not Convicted”) leads to UX criticism.
• People want longer time series and separation of ICE interior arrests from CBP border turn-backs, noting that conflating them masks a sharper ICE increase.

Economic impact vs human costs

• There’s disagreement about emphasizing lost GDP/tax revenue:
  • Pro: may persuade “economics-only” audiences and underline hypocrisy of costly, performative cruelty.
  • Con: risks trivializing suffering, or being reframed as “jobs Americans should have,” undermining the intended message.
• Some counter that undocumented labor fills chronic shortages; others focus on remittances and wage suppression.

Legality, due process, and enforcement philosophy

• One side stresses that entering or remaining without status violates law, so deportation is legitimate; if laws are bad, change them electorally.
• The other side emphasizes:
  • visa overstay as civil, not criminal;
  • asylum and TPS as legal channels;
  • reports of citizens, legal residents, and low‑threat people detained, family separations, opaque processes, and alleged court-order violations.
• Debate arises over what “due process” requires: some say it is whatever current law and courts define; others argue constitutional protections are being eroded in practice.

Historical analogies and rhetoric

• Some compare current trends, funding levels, and dehumanizing rhetoric to early fascist dynamics, warning that large systems of detention can escalate.
• Others reject Holocaust/Nazi comparisons as offensive and a slippery slope argument.

Proposed enhancements and related tools

• Requested additions:
  • detention conditions and personal testimonies;
  • counts of children separated, citizens wrongly detained, lawsuits, and per‑detainee costs;
  • better contextual text stating the normative stance.
• One commenter references a separate police-tracking tool as a model for tying incidents to individual officials, though others worry about doxxing risks.

AI as Layoff Justification vs Genuine Transformation

• Many commenters see the memo as standard “we’re doing layoffs, here’s this year’s excuse,” not evidence of transformative AI.
• Some argue CEOs are “narrative shopping” to frame cost-cutting as innovation for investors, similar to earlier “downsizing due to AI” stories (e.g., Klarna).
• Others think leadership genuinely believes AI will enable significant headcount reductions, regardless of whether the tech ends up delivering.

Workforce, Capitalism, and Demand

• Debate over who will buy products if many white‑collar jobs disappear: concern that mass unemployment will ultimately damage demand.
• Counterpoint: no firm can rationally keep unnecessary staff just to preserve customers; that’s not how current capitalism operates.
• Broader criticism that modern capitalism prioritizes short‑term profit, regulatory capture, and externalizing costs over true efficiency.
• Some expect white‑collar workers, especially mid‑career, to be pushed toward physical or lower‑status work, mirroring earlier blue‑collar automation.

Amazon’s AI and “Agent” Hype

• Several see Amazon’s public AI stance as PR: AWS is described as a fragmented set of mediocre AI services, behind Azure/Google in coherence and monetization.
• Others argue Amazon is now reasonably positioned via its cloud footprint and investments, though execution is questioned.
• The CEO’s “agents” definition (AI systems performing tasks via tools and natural language) is noted; some call the promised “billions of agents” wishful thinking given high error rates.
• A few say anyone selling “fleets of agents” is likely pushing snake oil or management-flattering fantasies.

Reliability and Empirical Evidence

• Commenters cite recent studies (Salesforce, CMU) finding current agents far less capable and reliable than hoped, with low success on realistic office tasks.
• Skepticism that these benchmarks justify forecasts of 10–40% workforce reduction; requests for transparent, non‑marketing data.

Software Jobs, Overstaffing, and Coordination Costs

• Split views:
  • One camp: big tech is heavily overstaffed with average developers mostly doing CRUD; LLMs are already competitive with the median engineer for much of this. SWE jobs will “decline terribly.”
  • Other camp: similar “no‑code/replace programmers” hype has recurred for decades with little real displacement; demand for software is highly elastic.
• Several argue that if AI is a true force multiplier, rational strategy would be to keep or grow headcount to build more value, not shrink it—unless leadership is fixated on cost-cutting and stock price.
• Coordination costs in large firms are highlighted: more people can slow work more than they help, leading some to prefer smaller, more focused organizations.

Media Framing and Internal AI Integration

• One commenter notes CBS mischaracterized the memo: it says “fewer of some jobs, more of others,” and functions partly as AWS advertising.
• There’s unease about integrating AI deeply with internal docs/data; some warn this may accelerate being automated out of a job once pilots show even marginal savings.
• A minority suggests taxing companies that heavily replace workers with AI to fund social programs, but no detailed policy discussion emerges.

Stability, longevity, and careers

• Many commenters say Django literally started or defined their careers, from student side-projects to startups, ML labs, and long-lived companies.
• A recurring theme is stability: apps begun on very old versions (e.g., 1.4/Python 2.x) reportedly still run on Django 5 with modest migration effort.
• Django is praised as a rare web framework that’s still relevant and pleasant after 15–20 years.

Design philosophy and influences

• Django’s “batteries included” approach and strong, cohesive philosophy are repeatedly highlighted as differentiators.
• Several note it was originally shaped by PHP experience; request.GET/POST and the template system were influenced by PHP practices and Smarty, but deliberately avoid “PHP-style” arbitrary logic in templates.
• There is debate over how much it was inspired by Rails; timeline comments suggest independent origins, with mutual influence later.

ORM, admin, and “batteries included”

• The ORM is one of the most-loved features; some miss it when using other stacks and even add Django to projects just for schema/migrations.
• Others dislike the query DSL, finding it non-SQL-ish and hard to remember; some prefer SQLAlchemy’s thinner abstraction.
• The admin is seen both as a killer feature and as a trap: great for internal CRUD and fast prototypes, but hard to extend for the “last 20%” without a rewrite.

Async support and typing

• Async support is described as “clunky and incomplete”: heavy use of sync_to_async, missing transactional async DB support, and limited async support in third‑party libraries.
• A few argue that in real-world Python async projects, blocking issues are common anyway.
• Lack of first-party type hints is a noted pain point; workarounds include isolating the ORM layer and mapping to typed dataclasses/Pydantic models.

Comparisons with other frameworks

• Some have moved to FastAPI or Litestar for APIs and dependency injection, but miss Django’s ORM, integration, and tooling.
• Rails is widely respected; preferences often hinge on language taste, ecosystem size (Python wins for data/ML/GIS), and stability.
• Laravel is seen as having borrowed from Rails/Django and “moved faster” on things like job queues, websockets, modern forms, and CLI tooling.
• Flask is liked for minimalism, but several report Flask projects organically growing a “DIY Django” of bolted-on components.
• Phoenix/Elixir, Go, and others are mentioned as attractive but still lacking Django’s depth of batteries and admin.

Frontend and “best way to use Django in 2025”

• Two main camps:
  • Headless/backend-only Django with React/Vite/Next.js and OpenAPI for rich SPAs.
  • Classic Django templates plus HTMX (and sometimes Alpine.js) for moderate interactivity without heavy JS stacks.
• Some find HTMX/Alpine hard to maintain on larger UIs and prefer returning to React; others love the simplicity of server-rendered templates.

Documentation, community, and governance

• Django’s documentation is repeatedly called “gold standard” and “documentation as empathy,” including versioned docs going back many years.
• The community, local user groups, and the framework’s patient deprecation policy are seen as key to its longevity.
• Litestar is praised partly for its distributed governance, contrasted with more BDFL-driven projects; Django itself is viewed as having strong, long-term stewardship.

Critiques and differing experiences

• Some developers simply “don’t enjoy” Django, citing hidden magic, monolithic assumptions (RDB+ORM+HTML/JSON), and difficulty when using non-relational backends or non-Django ORMs.
• One commenter, despite great respect for Django’s engineering, avoids it for modern API-first work, preferring lightweight frameworks and direct SQL.
• A minority finds Django “toy-like” compared to Rails, though others point to large-scale users as counterexamples.

Funding and sustainability

• The blog’s note that the Django Software Foundation is only ~25% toward its annual funding goal sparks concern.
• Several commenters see this as emblematic of the broader problem: critical open-source infrastructure creating massive value while struggling for financial support.

Incentives & Insurance Economics

• Thread distinguishes sharply between health and life insurance: health insurers may see near‑term savings from fewer hospitalizations, but US employer‑tied coverage and short member tenure limit incentives for long‑horizon prevention.
• The ACA’s medical loss ratio (80/20 rule) and prevalence of self‑insured employers complicate intuition about insurer profits; some argue insurers actually prefer higher overall spending.
• Life insurers rely on cohort mortality tables, not “98% accurate” individual predictions; commenters say that claim misreads an actuarial report (likely a ChatGPT artifact).
• Several argue GLP‑1‑driven mortality changes are just another actuarial shock (like COVID or accelerated underwriting) that can be repriced away, not an existential threat.

GLP‑1 Costs, Access, and Generics

• US list prices ~$1,000/month are seen as the key barrier; actual out‑of‑pocket costs vary widely by insurance, coupons, compounding pharmacies, and gray‑market imports.
• Outside the US, prices are often a fraction of that; some countries’ public systems are only beginning to cover GLP‑1s, often limited to diabetes or severe obesity.
• Patents on semaglutide and others will expire over the next decade; a Canadian patent lapse and existing generic liraglutide are cited as harbingers of big price drops.
• Debate over whether governments should use tools like compulsory licensing or subsidies, versus preserving strong patent incentives for R&D.

Adherence, Regain, and Long‑Term Safety

• Many users discontinue within 1–2 years, mainly due to cost, GI side effects, or missing the pleasure of eating; refill frequency and pharmacy friction also matter.
• Studies and anecdotes suggest substantial weight regain after stopping, though not always back to baseline; some manage to maintain loss via lifestyle change, many do not.
• Long‑term (>20‑year) safety is unknown, but ~20 years of diabetes data show mostly favorable profiles with limited signals (e.g. possible small thyroid‑cancer risk).

Effects Beyond Weight & Side‑Effect Profile

• Many report dramatic appetite reduction and “food noise” relief; some also see decreases in alcohol, nicotine, and gambling behavior, improved IBS, or better financial habits.
• Others experience severe nausea, diarrhea, sulfur burps, or suspected gastroparesis; experiences range from “barely notice it” to “unlivable side effects.”
• Ongoing concern about muscle loss and “Ozempic face”; several emphasize protein intake, resistance training, and lower maintenance doses.

Moral Framing, Environment, and Life Insurance Impact

• Strong disagreement over whether post‑GLP‑1 regain is mainly “lack of discipline” versus biology and environment (ultra‑processed food, stress, ADHD, emotional eating).
• Some favor making GLP‑1s cheap and ubiquitous; others argue for tackling food systems, education, and soda/“junk food” policy first.
• For life insurers, GLP‑1s create both “mortality slippage” (temporary weight loss at underwriting) and potential long‑term mortality improvement; commenters expect models, underwriting questions (e.g. past max weight), and premium structures to adjust over time.

Visa, Red Tape & Startup Climate

• Several participants describe European work visas as overly restrictive for non‑EU tech workers: tied to a single employer, long durations, and limits on founding startups.
• Domestic entrepreneurs also report “insane” bureaucracy and red tape, especially in countries like Germany and France, making it hard to start and scale companies.
• Some note bright spots (e.g. specific talent visas, DAFT treaty, options to found companies with minimum capital), but others see these as partial fixes within an overall risk‑averse system.
• Fragmented financial and legal markets make cross‑border investment inside Europe harder than in the US, limiting scale.

Capital, Risk Appetite & Innovation

• Disagreement on whether Europe lacks capital: some say there is “eye‑watering” wealth in family offices but a culture of capital preservation; others say the problem is unwillingness to fund pre‑revenue, high‑risk tech.
• Many argue this risk aversion and slower regulatory process prevent a “European Silicon Valley” even if American Big Tech were banned.
• There’s concern that promising European firms are routinely acquired by US companies, turning a startup issue into a competition/monopoly issue.

Banning US Big Tech & Protectionism

• Pro‑ban views: US tech undermines privacy, political autonomy, and creates strategic dependence; protectionism is framed as necessary to incubate local champions (with analogies to China and historic comic‑book bans in Belgium).
• Skeptical views: a late ban without a strong domestic ecosystem would cripple European business (e.g. Microsoft dependencies), invite US retaliation, and likely just recreate homegrown abusers of users.
• Some suggest narrower tools: banning data transfers, regulating contracts, taxing devices with proprietary OSes, mandating interoperability and open source in public procurement.

Regulation vs. Competitiveness

• Critics see EU rules (GDPR, data laws, Cyber Resilience Act, e‑bike rules) as vague, punitive, and innovation‑chilling, fostering a “better safe than sorry” culture.
• Others counter that enforcement is gradual and cooperative, fines are usually modest, no one goes to jail, and regulation is essential to curb consumer‑hostile practices.
• Broader tension: should Europe prioritize quality of life and social protections, even if that means fewer hyper‑scalable tech giants?

Quality of Life, Tech & Values

• Long sub‑thread debates whether US technological dominance actually improves average Americans’ lives compared to Europeans, invoking wages, healthcare, life expectancy, and social safety nets.
• Some argue “American‑style values” (high risk, fast failure, light regulation) are required for a Silicon Valley; others explicitly reject importing that model as incompatible with European social goals.

Digital Backlogs and “Tsundoku” for Games

• Many commenters report large libraries where 50–70%+ of games are unplayed or barely started.
• Common comparison to unread books, unused Udemy courses, unplayed board games, unworn clothes: seen as a general human behavior, not unique to games.
• Some embrace the idea of an “antilibrary” or aspirational collection; others see it as spending addiction or hoarding.

Bundles, Freebies, and Deep Discounts

• Humble Bundle, Epic/Prime/GOG giveaways, and publisher/bundle deals are cited as the main drivers of unplayed games.
• People often buy a bundle for one or two titles and passively accumulate the rest.
• Heavily discounted older AAA games encourage “buy now, maybe play someday” behavior; for some, money spent on unplayed games is small relative to full-price purchases.

Why Libraries Look “Unplayed”

• Several say they pirate first, then buy to support devs but continue playing the non‑Steam copy (for convenience, DRM-free, mods, offline use), so Steam shows 0 hours.
• Some buy games solely to support small studios, Linux ports, or indie creators, without intent to play soon (or at all).
• Libraries also include keys from friends, charity bundles, family sharing, games played before Steam tracked time, and asset-only purchases for open‑source remakes.

Sales, Psychology, and Value

• Disagreement over sales:
  • One side sees sales as manipulative FOMO and a driver of wasteful collecting.
  • Others argue sales are basic price discrimination, essential for lower-income players and late adopters.
• Factorio’s “no sales” stance is praised by some as honest and long-term oriented, but others note this only works for rare “evergreen” hits.
• Several stress that time, not money, is the real constraint; they have more money than free hours, so many games inevitably go untouched.

Interpretation of the Statistics and Title

• Multiple comments criticize the HN-submitted title as misleading: data support “most games people buy aren’t played,” not “most buyers never play anything.”
• The median player reportedly hasn’t played over half their library, suggesting typical users do play some games but accumulate many more than they use.

Power impact of showing seconds

• Thread centers on a test finding ~13% less battery life on an idle laptop when taskbar seconds are enabled.
• Many note that desktop OSes heavily optimize idle power; even one extra timer firing per second can keep a CPU core and display pipeline from entering deep sleep, so a tiny periodic task can be visible in idle-only tests.
• Others find the magnitude “insane” for such a trivial feature and suspect either poor implementation or overall Windows bloat as the real culprit.

Implementation and engineering questions

• One view: the cost is fundamentally about frequent wakeups and GUI stack churn, not the tiny amount of “do the math and draw digits” work.
• Another: competent engineering (e.g., caching glyphs, minimal compositing, hardware support like panel self-refresh or hardware cursors/sprites) should make this almost free.
• Some propose OS-level APIs or hardware compositors dedicated to small static overlays (clocks, cursors) so the main GPU/CPU can stay in low-power states.

Test methodology and realism

• The test is idle-desktop-only, with sleep disabled. Several people see this as a deliberate “worst case,” good for isolating the effect but not representative of real use.
• Commenters want additional scenarios: light web browsing, video playback, or direct power-draw measurements over short intervals instead of full battery rundown.
• There’s some confusion over follow‑up tests (same laptops but with video) and how “variance” is being accounted for.

User experience and configurability

• Some actively dislike seconds (and blinking cursors) as constant attention drains and are glad it’s off by default.
• Others rely on seconds for precise timing (e.g., joining calls, casual benchmarking, personal preference) and want it as an easy option, ideally with customizable formats.
• A few suggest adaptive behavior: show seconds only on demand or stop updates after inactivity.

Broader critiques: performance, privacy, and greenwashing

• Many contrast this micro‑optimization with Windows’ heavier wastes: telemetry, “phone home” behavior, web views, ads, AI features, Edge’s cloud spellchecking, and Windows Update overhead.
• Several see Microsoft’s power‑saving nudges and “eco” badges as performative environmentalism that shifts guilt to users while the company runs huge AI/data centers and pushes hardware upgrades (e.g., for Windows 11).
• Similar issues are noted on Linux (e.g., blinking cursor power cost), but some argue alternative desktops now make it easier to avoid such trade‑offs entirely.