Hacker News, Distilled

AI summaries, “smart features”, and UX frustration

• Several commenters dislike Gmail’s Gemini summaries and how they’re bundled with long‑standing “smart features” (tabs, calendar extraction, grammar, etc.) into an all‑or‑nothing toggle.
• Some report similar “AIification” elsewhere (e.g., banks renaming transactions), calling it misleading, hiding important details, and often impossible to disable.
• A few say they simply turned off smart features and find Gmail still usable, while others find the resulting inbox overload intolerable without tabs and automated categorization.

Privacy, data use, and LLM training concerns

• Central worry: Google using email content to train LLMs, beyond traditional ad targeting. People are uneasy about commercial/confidential data being embedded in models and potential leakage.
• One commenter points out Workspace terms that explicitly say customer data (including Gmail in Workspace) is not used for training, and that Gemini prompts/results there are also protected.
• Others are unsure where exactly Google states that consumer Gmail is used for training; the warning about “messages might be reviewed by humans” is seen but not universally reproduced.

Switching away from Gmail: motivations and strategies

• Motivations: AI features that can’t be granularly disabled, distrust of Google’s direction, and desire for more control over data and account lockout risk.
• Migration tactics: use Google Takeout, import into new provider (Fastmail praised for smooth migrations), set up forwarding and labels for mail addressed to the old account, and auto‑responders asking contacts to update addresses.
• Some note that, in practice, relatively few accounts and contacts truly need updating, and the process is less painful than feared.

Alternative email providers: experiences and tradeoffs

• Strong enthusiasm for Fastmail (good spam filtering, labels/tags, custom domains, migration tools). Downsides: not free; occasional spam/deliverability hiccups; no EU servers.
• Proton is valued for privacy and custom domains but criticized for limited search over encrypted mail, IMAP via proprietary bridge, and weaker docs/drive features.
• Other options mentioned: mailbox.org, Infomaniak, Purelymail (very cheap, well liked but sustainability questioned), Zoho, iCloud custom domains, AWS WorkMail, small hosts (including ultra‑cheap niche providers).
• Many view moving from Google to Microsoft as “out of the pan, into the fire,” expecting similar AI/telemetry issues and citing past Outlook/Hotmail deliverability and outage problems.

Self‑hosting email: feasibility and deliverability

• Opinions split: some say it’s straightforward (Exim/Dovecot on a small VPS, running for a decade with few issues); others call it inadvisable due to security upkeep, IP reputation, and large providers’ hostility to independent MXes.
• Hybrid approaches: self‑host for receiving but relay outbound via SES/Sendgrid/etc., though cheap tiers may share IPs with spammers and hurt deliverability.

Owning your domain and email hygiene tips

• Strong consensus: use your own domain so you can change providers without changing your email address; just repoint MX records.
• Tips: standalone clients to monitor old + new accounts; filters to move “unsubscribe” mail out of the inbox; aggressive unsubscribing; catch‑all or aliasing per site (e.g., via addy.io) to manage spam and track leaks.

Limits of leaving Gmail

• Several note that Google will still see much of your correspondence because so many correspondents and services use Gmail or Google‑backed infrastructure.
• Nonetheless, many argue that regaining agency, reducing dependence on a single tech giant, and choosing providers with better policies is still worthwhile.

WhatsApp interoperability & DMA basics

• BirdyChat is using Meta’s new DMA‑mandated interface so EEA users can chat with EU WhatsApp users via phone number.
• Interop is currently limited to 1:1 chats; group support is promised later.
• Several commenters ask if/when this will work outside Europe; consensus is that Meta is geofencing it to EEA numbers for now.

Opt‑in, region lock, and “malicious compliance”

• WhatsApp users must explicitly enable “third‑party chats” in settings, and then whitelist specific apps; available only in the EU, not e.g. the UK.
• Many see the opt‑in design and EEA‑only restriction as deliberate friction and “malicious compliance” that makes the feature practically useless, especially for cross‑continent families.
• Others argue opt‑in is preferable for spam and privacy reasons and still better than convincing people to install a new app.

Privacy, GDPR, and E2EE

• Some argue WhatsApp cannot legally share profile data (name, picture, status) with third‑party apps without consent; they want per‑app opt‑in for that reason.
• Others respond that phone systems have always exposed metadata and that if you set visibility to “everybody”, excluding third‑party clients is an unrealistic expectation.
• Meta claims interop preserves E2EE using the (open) Signal protocol; links to Meta’s technical write‑up are shared. Skeptics worry about metadata, attachments and spam scanning, especially given EU “chat control” debates.

Competition, network effects, and alternatives

• Strong disagreement over whether BirdyChat‑style interop can meaningfully dent WhatsApp’s network effects.
• Signal, Telegram, Matrix and classic XMPP/IRC are all discussed:
  • Signal praised for security but criticized for poor UX, backup/exports, and slow feature parity.
  • Telegram praised for features and multi‑device UX but criticized as fundamentally insecure and politically suspect.
  • Matrix seen as ambitious but heavy and rough in practice.
• Some view DMA‑style interop as the only realistic way smaller apps can piggyback on existing networks.

Open protocols vs proprietary APIs

• Several commenters lament the decline of open protocols (IRC, XMPP, OTR) and universal clients (Pidgin), and see BirdyChat as an unimpressive proprietary bolt‑on.
• Others note that users consistently choose polished proprietary ecosystems over open but clunky ones, and that spam/abuse makes fully open interop hard without regulation.
• There is debate over whether the DMA should have forced standard protocols or open‑sourced WhatsApp’s instead of a controlled interop API.

BirdyChat itself: trust, scope, and branding

• Multiple people distrust a closed‑source, invite‑only, iOS‑only app from an unknown Latvian company as a privacy‑preserving “alternative” to Meta.
• Some suspect BirdyChat and another early interop partner (Haiket) were “hand‑picked” tame startups to help Meta argue it is complying.
• The “BirdyChat” name and “work chat” positioning are widely criticized as childish or confusing for professional use.
• A few early testers report serious UX bugs in the onboarding flow and express doubt the team can safely implement cryptography.

EU vs US regulation and geography

• Several non‑EU commenters express envy that Europeans get interoperability, GDPR, app‑store choice, etc., while US users are “stuck” with weaker protections.
• There is extended discussion of what “made in Europe / EEA‑based” actually means, and how the DMA only applies to designated “gatekeepers” (e.g. not iMessage, Slack, Teams).

Framing: “Optional” vs Collective Responsibility

• Many argue the key issue isn’t whether vaccines are “optional” in the absolute sense (no one is physically forced), but whether institutions must accommodate people who opt out of protecting public health.
• Several see the panel chair’s stance as explicitly accepting more preventable illness and death in exchange for maximizing “medical autonomy.” Others call this effectively sacrificing children for ideology.

Institutional Mandates and Exclusion

• Questions raised: Can schools refuse unvaccinated children? Can employers reject unvaccinated candidates?
• Some note vaccines are already technically optional; the policy change is about forcing schools to admit unvaccinated kids without waivers or discretion.

Polio Vaccine Necessity and Risk

• One camp claims polio has been eradicated in the US, vaccine side effects now outweigh domestic cases, and vaccination should be limited to travelers to high‑risk regions.
• Opponents reply that eradication was achieved by mass vaccination, humans are the only reservoir, and a single imported case could spread rapidly through an unvaccinated population.
• Debate over live vs inactivated polio vaccines: live vaccine reduces silent carriage but carries rare paralytic risks; inactivated avoids that but may allow asymptomatic importation.

Safety, Schedules, and “State Violence”

• Some emphasize vaccines have real risks, cite adverse‑event reporting, corporate incentives, and prefer disease‑by‑disease, slower schedules (e.g., hesitancy about Hep B at birth, chickenpox).
• Others respond that these risks are small compared with the diseases, and that calling mandates “state‑sponsored violence” is misleading; many mandates (e.g., school entry) don’t involve criminal penalties.

Freedom, Responsibility, and Consequences

• Recurrent analogies: taxes, seatbelts, drunk driving.
• One view: refusing vaccines is like reckless driving; proposals include higher medical costs for voluntarily unvaccinated people or liability when they infect others.
• Critics argue causation is hard to prove and criminalization, as seen with HIV laws, can worsen outcomes.

International Comparisons and Politicization

• Sweden is cited as a voluntary system with very high uptake; others argue US anti‑intellectualism and mistrust (rooted in past abuses and politicization) make that model hard to replicate.
• Several comments stress that aggressive, moralizing pro‑vaccine messaging has backfired, turning vaccination into a partisan identity issue.

Protection of the Vulnerable & Advisory Roles

• Repeated concern for those who cannot be vaccinated (immune‑compromised, some transplant candidates) who rely on herd immunity.
• Some are alarmed that a vaccine advisory body is elevating individual autonomy over population‑level health, arguing autonomy trade‑offs belong with elected politicians, not medical panels.

Incident and Video Evidence

• Multiple videos show the victim (an ICU nurse, lawful gun owner with carry permit and no serious record per local police) filming ICE, then intervening after agents push a woman.
• Commenters describe him being pepper‑sprayed, tackled by several agents, his arms pinned, then disarmed by an agent who removes a handgun from his waistband.
• Shots are fired after disarmament; several analyses (including Bellingcat and NYT, linked in thread) argue the man no longer posed a threat and was effectively executed, with additional rounds fired into his motionless body.
• A minority speculate about a negligent discharge triggering a panic volley, but most see the sequence as intentional and unjustifiable.

Official Narrative vs. Footage

• DHS and allied politicians claim the man approached agents with a gun intending to “massacre law enforcement.”
• Commenters argue this is flatly contradicted by video (phone in hand, gun holstered until seized) and by local police characterizations.
• Many stress that federal agencies have repeatedly issued misleading or false statements in recent ICE violence cases, and that DoJ leadership is unlikely to investigate seriously.

ICE, Fascism, and State Violence

• Large parts of the thread label ICE/DHS as a de facto fascist or white‑nationalist paramilitary: “death squads,” “Gestapo,” “brownshirts.”
• Others broaden criticism to US policing generally (killology training, long‑normalized lethal force, prison‑guard and militia culture).
• A smaller group pushes back, arguing videos often show people obstructing lawful operations and that federal law clearly criminalizes such obstruction; they are heavily downvoted and accused of bad faith.

Federal vs. State Power and Prospects of Conflict

• Intense discussion of what Minnesota can legally do: prosecute individual agents, deny local cooperation, restrict commerce with federal agents, or even move toward “soft secession” (withholding federal tax remittances).
• Supremacy Clause and captured federal courts are cited as blocking meaningful accountability; some say enforcement capacity itself is now co‑opted.
• Several see this as a “soft civil war” already underway, with ICE as an occupying force in blue cities; fears of escalation to Insurrection Act or martial law recur.

Rights, Guns, and Who Is Targeted

• Commenters highlight the contradiction between 2A rhetoric and killing a lawful gun owner for admitting he is armed.
• Comparisons are drawn to lenient treatment of armed right‑wing militias; many conclude rights are applied hierarchically, not universally.

HN and Tech Community Relevance

• Long subthread over why this and similar posts are flagged off the HN front page; moderators reiterate the “not a current‑affairs site” stance, while many users argue that fascist state violence in the US, the global tech hub, is inherently relevant.
• Some suggest tech workers, investors, and immigrants must reassess working with or in the US given these developments.

Proposed Responses and Outlook

• Suggested actions: mass strikes, sustained protests, pressure on DHS funding, aggressive state‑level resistance, systematic archiving of videos as platforms remove them.
• Pessimists predict continued impunity and worsening violence; optimists argue there will eventually be a reckoning, but timing and scale are unclear.

Intellectual property, art, and capitalism

• One side argues copyright and “intellectual property” are over-valued: culture always involves appropriation and re-creation; once ideas enter the world, they become collective material.
• Others stress that context, authorship, and historical position are integral to art; erasing the creator or feeding their work into models without benefit to them destroys incentives and devalues meaning.
• Several comments observe that modern capitalism, not copying per se, drives the conflict: everything must be monetized, yet stronger copyright often ends up empowering aggregators and distributors more than individual creators.

Cultural appropriation vs copyright

• Some treat cultural appropriation as just another name for inevitable borrowing.
• Others push back that it’s distinct from IP and bound up with histories of oppression; caricaturing a marginalized group whose culture you’ve violently extracted from is not morally equivalent to neutral “remix.”

AI training, plagiarism, and human vs machine learning

• Many see generative AI as mass plagiarism: ingesting others’ work without consent or attribution and reproducing style or content at scale, undermining livelihoods.
• Counterpoint: humans also “ingest and compress” others’ work; AI merely accelerates this, potentially enabling broader idea discovery and creativity.
• A rebuttal highlights a key difference: humans are finite, effortful learners; machines can absorb near-infinite work without cost, so competition is structurally unfair and artists lose the chance to reap rewards.
• Tension emerges for open-source/open-culture advocates who want sharing, yet feel wronged when AI companies monetize their contributions.

Plagiarism detection, education, and standards of proof

• Long subthread on Turnitin’s AI detector: roughly ~85–90% sensitivity to AI text and near-zero false positives in a small study.
• Some think this is “good enough” as a first-pass tool; others note adversarial use, small samples, and high stakes (e.g., expulsion, debt) demand far stricter standards.
• Many argue you can’t reliably distinguish “AI style” from formulaic human writing, especially in academic prose. Suggested responses include more in-class work, oral exams, and treating essays primarily as learning tools, not high-stakes proofs.

Originality, remix, and norms of credit

• Several commenters endorse a simple norm: verbatim reuse and deceptive imitation are “not cool,” while transformation, reinterpretation, and stylistic borrowing are fine.
• Fiction is framed as inherently derivative (hero’s journey, fanfic-like worlds), whereas in non-fiction and research, uncredited idea-theft is central wrongdoing; paraphrasing without citation remains plagiarism.
• Some conclude that “everything is a remix,” and that the real fights are about attribution, economic reward, and honesty—not about pure originality, which may barely exist.

Spam/Filtering Outage Symptoms

• Many report a sudden change: legit emails (USPS, HR, system, newsletters) marked as spam or “suspicious,” sometimes delayed 7–10 minutes.
• “Never mark as spam” and Priority Inbox importance markers appear to be ignored or reset for some accounts.
• Others see the opposite: obvious spam (419 scams, fake package deliveries, phishing about cloud payments, “legal boner tea”) landing in Primary or “Important & Unread.”
• Google’s status page links to a spam-filtering incident; some note banners saying messages couldn’t be scanned, with “Looks/Seems safe” prompts.

Tabs & Classification (Primary/Promotions/etc.)

• Promotions/Updates/Social categorization is widely reported “down”: promotions show up in Primary or aren’t separated at all.
• Some say this has been broken for months; one claim ties tab classification to a toggle that also opts email content into AI training, leading them to turn it off and rely on heavy unsubscribing.
• Another commenter asserts that tie-in “never happened” and was misinformation; a rebuttal says the issue is in court and unresolved.

User Impact

• Missed or delayed 2FA, account verification, HR and school enrollment emails.
• Increased noise in inboxes pushes people to disable notifications, further reducing email’s role for time‑critical communication.
• A few see no issue at all, suggesting the problem may be localized or settings-dependent.

Mitigation Strategies Discussed

• Short term: frequently check spam, mark legit mail as “not spam,” star key senders; some expect Google to roll back a bad model in 24–48 hours.
• Using multiple accounts, Gmail “+aliases,” custom domains with wildcard addresses, and services like “Hide My Email” to track and kill compromised addresses.
• Some disable Gmail spam filtering entirely and rely on local clients (Thunderbird, rspamd, SpamSieve, Bayesian filters) or self‑hosted mail servers.

Views on Gmail & Alternatives

• Several say this outage highlights how exceptionally good Gmail’s spam filtering usually is; others argue it has long been too aggressive with false positives.
• Some see it as a final nudge to migrate to providers like Proton or self‑hosting, for both reliability and privacy, though others note most mail still traverses “big corp” servers.

Scope and Balance of AI-Assisted Coding

• Many commenters say current agents can’t replace hand-written code but are useful for tedious, low-risk tasks: small scripts, boilerplate, config wiring, refactors, and tests.
• Good fit: non-critical tooling, one-off utilities, CRUD frontends on top of robust backends, Streamlit/Shiny-style demo apps.
• Poor fit: critical systems (payments, ERP, core business logic), complex math/parallelism, architecture and design.

Vibecoding vs Targeted Assistance

• “Vibecoding” (letting agents build entire apps from vague specs) is widely seen as brittle: results may “work” but be incoherent, hard to maintain, or subtly wrong.
• Several people report that asking an LLM to complete an app from epic-level descriptions “kinda works” for toy projects, but is clearly unacceptable for real products.
• A recurring complaint: tools over-refactor, add unnecessary complexity, or touch many files when a simple, local fix would suffice.

Responsibility, Quality, and Technical Debt

• Strong consensus that responsibility for code remains entirely with the human; AI won’t be blamed when things fail.
• Concern that management will use AI to push “ship faster” culture, increasing volume of low-quality code and incidents.
• Some argue AI can actually improve rigor when used with strong artifacts (design docs, tests, structured agents); others see it mainly as a way to generate more technical debt faster.
• Tests and green CI are called out as a false sense of safety when coverage or assertions are weak.

Effects on Thinking and Craft

• Several note AI helps with “kinetic” coding (typing, boilerplate) but can weaken the developer’s mental model and architectural thinking if overused.
• Others argue it frees time to think more deeply about real problems, analogous to moving from low-level programming to higher abstractions.
• Some express discomfort or sadness at losing the “tasty bits” of hands-on problem solving and learning, especially when AI is used to implement things “too hard” for the programmer to understand.

Careers, Hiring, and Industry Dynamics

• Many aren’t personally worried about being replaced “right now,” but are worried about:
  • Perception-driven hiring freezes and expectations that fewer devs can do more with AI.
  • Especially grim prospects for juniors, who struggle to get initial experience.
  • Difficulty distinguishing real skill from LLM-boosted interview answers and contractor work.
• Self-driving cars are a popular analogy: big gains for assistance, but fully autonomous replacement may be much farther away than hype suggests.

Middle-Ground Practices

• Common advice:
  • Use AI for small, well-scoped tasks and boilerplate; avoid giving it end-to-end ownership.
  • Break work into small tickets, keep a refactoring backlog, and enforce code review and CI equally for human and AI changes.
  • Be explicit in prompts (e.g., no extra refactors, minimal changes) and treat the model like a junior dev whose work must be checked.
• There’s broad rejection of all-or-nothing positions: both “LLMs are useless” and “LLMs will do everything” are seen as unhelpful extremes.

Hidden feature and how it’s unlocked

• “Swarms” (internally more like “teams”) are already shipped in recent Claude Code builds but gated by a feature flag that checks a server-side flag (tengu_brass_pebble).
• A simple patch to the minified cli.js replaces the gate with return true, enabling Swarms regardless of account tier.
• An env var (CLAUDE_CODE_AGENT_SWARMS) only works as an opt‑out, not opt‑in.

What Swarms add beyond existing subagents

• Claude Code already had subagents; Swarms introduce a dedicated “delegation mode” for the lead agent plus:
  • Task‑oriented abstraction instead of pure chat threads.
  • A built‑in task board / mailbox system for agents to coordinate and exchange progress.
  • Harness‑level context management (system-reminder breadcrumbs, event‑driven wakeups).
• Supporters argue this is hard to reproduce from outside the official harness; third‑party flows (GSD, claude‑flow, various tmux/orchestrator projects) approximate it but lack deep integration.
• Others claim most of the value can be achieved today with a few well‑prompted agents, MCP/skills, and project‑specific config.

Security and telemetry concerns

• One alternative tool (claude‑flow) is criticized for a telemetry system that can export full Claude session histories and config files for multiple coding assistants.
• Commenters warn this could leak code, secrets, and conversations if misconfigured or abused.

Token usage, context, and coordination cost

• Pro‑Swarms view: delegation to fresh‑context subagents improves reasoning and reduces tokens versus a single bloated context.
• Skeptical view: orchestration overhead, summaries, and merge/coordination (“coordination tax”) can erase those gains unless tasks are carefully sized.

Experiences with multi‑agent workflows

• Some report dramatic productivity: e.g., 20+ subagents adding thousands of tests in minutes, or long autonomous coding sessions exploring, refactoring, and testing a codebase.
• Others build elaborate “AI teams” (manager, architect, CAB, dev pairs, librarian) coordinated via Kanban folders and isolated git worktrees; praised by some as powerful, derided by others as corporate cosplay or overengineered LARP.

Quality, maintainability, and future of coding

• Strong concern that swarms generate more unreviewable code, erode human understanding, and shift practice toward “vibecoding” plus superficial testing.
• Several emphasize that engineers remain responsible for failures; that caps useful automation at what humans can reliably review.
• Some see multi‑agent orchestration as the near‑future norm (2026+); others argue that as models improve, simpler single‑agent workflows and clear shared state will win over complex swarm frameworks.

Is HTML a Programming Language?

• One side insists HTML is purely a markup language: declarative, non-procedural, mainly for structure and presentation, and should simply be called “HTML” without adding “programming language.”
• Others argue for a broader definition: any formal language that directs machine behavior. They cite features like form constraint validation and <details>/<summary> state toggling as “behavior,” thus programming.
• A middle view: HTML itself lacks control flow, but in modern stacks (templates, JSX, htmx) HTML fragments are integral to the overall program’s control flow. Still, that doesn’t inherently make HTML a programming language.

XHTML vs HTML, MIME Types, and Validity

• Several point out the irony that the “XHTML Club” site is served as text/html, so browsers parse it as HTML, not XML; the XML prolog becomes invalid in that context.
• Only a couple of listed member sites use the proper application/xhtml+xml MIME type.
• Some like XHTML’s strict parsing and error-catching; others note the practical difficulty and lack of adoption.

Status of XHTML and XHTML5

• XHTML 1.0/1.1 are described as deprecated; commenters feel XHTML has effectively been abandoned for the web.
• XHTML5 exists as an HTML5-variant but is not a priority; specs say future HTML features may not be supported in XHTML5, undercutting one of XHTML’s historical advantages.
• Lack of an XHTML5 DTD means you lose the simple “pure XML” validation story.
• Browsers give confusing error hints in “view source” even for valid XHTML served as XML.
• Desire for XHTML persists in niches (e.g., ePub), where strictness and XML tooling remain valuable.

HTML5 and the Living Standard Fight

• Sharp disagreement over the statement “There is no HTML5”:
  • One camp: “HTML5” is just outdated branding; the real spec is a versionless living standard, and future “HTML6” etc. will never exist.
  • Others counter that HTML5 was a real W3C/WHATWG recommendation and still exists as a historical, technical standard; saying it “doesn’t exist” is misleading.
• Broader concern: living standards make conformance and change-tracking harder than versioned specs.

Validation, Self-Closing Tags, and Parser Complexity

• Nostalgia for the early-2000s culture of strict validation and XHTML/W3C badges; some still feel uneasy omitting closing slashes on void elements.
• Others argue closing slashes in HTML are at best pointless, at worst harmful with unquoted attributes, and can create a false sense of correctness—though many treat it as style and rely on XHTML habits.
• The HTML parsing spec is described as horrifyingly complex. Some wish browsers had refused such complexity, forcing a simpler design; others are just glad modern parsers hide this from developers.
• HTML5 defines behavior even for many “invalid” constructs, which improves interoperability but blurs the notion of “invalid HTML.”

Performance, Streaming, and Real-World Use

• One commenter wants XHTML for simpler, safer emitters but notes no browsers support streaming XHTML parsing, making it impractical for streaming responses.
• There’s general lament that modern sites ignore structure and validation, favoring heavy JS bundles, many requests, and bloated UIs—seen as a continuation, at scale, of past sloppy markup practices.

Ongoing surprise vs. “of course this happens”

• Many argue it’s naïve to be shocked in 2026 that a US tech giant cooperates with US law enforcement.
• Others stress this specific story matters because Microsoft chose an architecture where it holds BitLocker keys at all, rather than being unable to help.

Key escrow, defaults, and usability

• Historically, full‑disk encryption meant losing your password = losing your data; that’s still the Linux norm.
• Microsoft’s design favors recovery and low support burden: keys are backed up to the cloud and can be produced under order.
• Defenders say this prevents catastrophic data loss for non‑technical users; critics call it “keeping a copy of your house keys by default” without clear, informed consent.
• Several note that Windows 11 strongly nudges or effectively forces Microsoft accounts, which in turn default to escrowing keys.

Threat models and surveillance

• Some commenters are fine with this in the “stolen laptop” threat model but worried about dragnet surveillance and political misuse.
• Cloud backups (OneDrive, etc.) are seen as turning personal machines into inputs for large‑scale analysis.
• There’s concern about chilling effects on dissent and free thought when state access to personal data becomes routine.

Apple, Google, and other platforms

• Debate over whether Apple meaningfully differs: iCloud Advanced Data Protection and end‑to‑end keychains vs. past secret cooperation (e.g., push notification metadata) and compliance with non‑US regimes.
• Several point out that any company with access to plaintext keys or data will hand them over under valid orders.

Legal framing and headline issues

• Multiple comments note the distinction between “if asked” and “if served with a valid legal order,” criticizing the article’s headline as misleading clickbait.
• Others respond that the core issue is that Microsoft can comply at all; the legal threshold is secondary.

Alternatives and user choices

• Suggestions include Linux with LUKS, VeraCrypt, local‑only accounts, non‑escrowed BitLocker setups, or third‑party password managers with zero‑knowledge designs.
• Some argue average users will never manage their own keys reliably; others insist users should at least be clearly offered that choice.

Scope of AI Use in Programming

• Supporters describe LLMs as powerful assistants: navigating complex codebases, discovering APIs and edge cases, refactoring under constraints, generating glue code, and helping with rarely used tools (sed/awk/regex, Sheets APIs, etc.).
• Others argue discourse fixates on “code/content vomit,” missing these subtler but real productivity gains.
• Some say senior dev + AI ≈ “superpowers”; AI is likened to dynamic languages or power tools that reduce boilerplate and speed experimentation.

Quality, “Slop,” and Model Collapse

• Many report AI-driven “slop”: superficially plausible but buggy, incoherent, or redundant code; low-quality content flooding the web; harder differentiation between real expertise and “cosplay.”
• Concerns about LLMs training on their own output, leading to an Internet-scale echo chamber and stagnation.
• Some insist careful curation, testing, and discipline can make AI output useful; others argue that, in practice, most usage accelerates low-quality work.

Workplace Pressure and Identity

• Several mention top-down mandates (“AI use is mandatory”) and link them to leadership that already tolerates low quality and outsourcing.
• Some devs see refusing AI as preserving craft, skills, and joy in programming; others see resistance as fear, unfairness about eroded scarcity, or simple change aversion.
• Both “cheerleaders” and “haters” are described as exhausting; multiple comments call for nuanced, non-absolutist takes.

Fatigue, Hype, and Badges

• Many are tired of ubiquitous AI marketing and AI-centric posts; others are equally tired of anti-AI rants.
• AI is compared to past hype cycles (blockchain, NFTs, Kubernetes) and to long-running “AI effect” debates.
• The author’s “no AI” badge and similar “Not By AI” branding spark mixed reactions: some see principled signaling and a source of cleaner training data, others see confusion, performativity, or even a monetized gimmick.

Core point: local O(N) queries vs network O(N) queries

• Many commenters restate the article as: doing O(N) work locally is fine; doing O(N) network round-trips is not.
• The constant factor “X” (network stack, serialization, RTT) dominates, more than N itself.
• Even localhost/loopback still pays socket and kernel overhead; SQLite as an in-process library avoids all of that.

Why SQLite isn’t the default for web backends

• SQLite excels as embedded/local storage but is “not client/server” and has weak write concurrency; that makes it less suitable for multi-node web services.
• Tooling for remote management, analytics, dashboards, and admin access is seen as clunkier than with Postgres/MySQL; common workaround is SSH + CLI.
• Some view SQLite as already the de facto standard for embedded use; others note most web stacks still default to Postgres/MySQL, though Rails recently moved toward SQLite by default.

Defaults, typing, and safety

• Criticism of SQLite’s “insane defaults”: foreign keys off by default, flexible typing, STRICT not default.
• Defense: strong backward-compatibility promises prevent changing defaults without breaking many deployed apps.
• Mitigations mentioned: always enabling pragma foreign_keys=on, using STRICT tables, CHECK constraints, and “disciplined” API use.

Query patterns: many small vs one big

• For OLTP-like record retrieval, many small indexed SELECTs against SQLite can be fine or optimal; the overhead per query is tiny.
• For analytic workloads or large scans, a single complex query is often better; server databases can cache, optimize across queries, and maintain state in memory.
• Some skepticism: one index scan is often cheaper than 200 index lookups; others counter that for UI-style record fetches, N+1 can be acceptable with SQLite.
• Concern: designing for 200 local queries/page can make later migration to a networked DB painful, though some think refactoring hot spots is manageable.

Concurrency, scaling, and performance anecdotes

• SQLite is praised for read performance: informal reports of 5x faster than Postgres on some workloads, and very high read QPS; another report: ~400 writes/s and ~41k reads/s with WAL.
• Others hit limits quickly when multiple background workers perform concurrent writes; WAL and tuning are crucial, but SQLite explicitly discourages high write concurrency.
• Sharding-per-customer with SQLite and using distributed systems like rqlite or Cloudflare D1 are discussed as scaling patterns, but synchronization complexity is acknowledged.

Deployment, backups, and ecosystem

• Simple patterns: nightly backups by stopping writes and copying the DB file; more robust: .backup/.dump or tools like sqlite3_rsync and Litestream replication.
• Caveats: beware storage characteristics (e.g., EBS latency); many small queries can still hurt if backing storage is slow.
• There is experimentation with SQLite in WASM in the browser (e.g., LiveStoreJS), and SQLite-based tooling (Fossil, custom VFS like kvvfs) reinforces the “SQLite everywhere” theme.

Self-hosting and Alternatives

• Many like GitLab for companies that want self-hosted or on-prem, citing solid behavior at scale (2k+ users, heavy CI load) when provisioned per GitLab’s reference architectures.
• Others found a sharp complexity/cost jump once a single Omnibus instance no longer sufficed, needing professional services to scale reliably.
• For personal/small setups, several people abandoned GitLab for lighter forges like Forgejo, Gitea, or Gogs, praising these as far faster and vastly less resource-hungry while still offering CI, issues, and container registries.

Performance and Resource Usage

• Repeated complaints that GitLab’s web UI is sluggish, especially MR views, issue search, and admin actions; some call it “fundamentally slow” and blame architecture/Rails, not just scale.
• Others report GitLab being fine or faster than Jira/Bitbucket in their environments, suggesting performance is highly setup-dependent.
• Forgejo/Gitea are consistently described as “instant,” with anecdotal reports of ~10% of GitLab’s resource usage and even measurable power savings after switching.

CI/CD Experience

• Strong split: some “immensely enjoy” GitLab CI, calling it more powerful and structured than Jenkins or GitHub Actions, with good abstractions, artifacts, runners, and complex DAGs.
• Others describe GitLab CI as brittle and bug-prone, with surprising behavior and painful debugging; YAML orchestration is seen as the wrong level of complexity.
• Lack of robust local/emulated testing for .gitlab-ci.yml is a major pain point; people resort to sacrificial branches. Some use external tools to validate pipelines.
• There’s disagreement over log truncation behavior; at least one person complains critical output is cut.

Features, UX, and Complexity

• Appreciated: integrated issues/epics/milestones/boards, package registries (Maven/NPM/PyPI), container registry, and AI (Duo) – especially in a unified interface.
• Criticisms: feature overload, many “MVP” or 80/20 implementations, old unresolved bugs, confusing navigation, cluttered UI with little visual distinction between content and chrome, and asynchronous loading causing layout shifts.
• Some find GitLab far more intuitive than GitHub; others have the exact opposite reaction.

Pricing, Tiers, and Business Direction

• Frustration that useful capabilities (e.g., some AI features, mandatory reviews, merge trains) are locked behind expensive tiers; several say they’d like to pay but find pricing “outrageous.”
• Some perceive a post-IPO shift toward flashy features and AI, with less emphasis on polish and longstanding issues; there is speculation that new leadership will further prioritize quantity over quality.

Security, Policy, and Social Factors

• Concerns raised about GitLab’s history of severe security bugs and an account-deletion policy for Hong Kong IPs (steering users to a China-based partner).
• Socially, GitLab is seen as quieter: fewer low-quality “drive-by” PRs compared to GitHub, but also fewer contributors because many developers ignore non-GitHub hosts, leading projects to maintain GitHub mirrors.

Role of Estimates in Organizations

• Many see estimates as primarily a political and budgeting tool, not an engineering tool: they decide which work gets funded, sequenced, or cut.
• A recurring theme: stakeholders almost always have a hidden time budget (“appetite”) and use “estimates” to back into that, rather than to discover it.
• Several commenters argue estimates are not really for engineers; they’re for sales, marketing, finance, CX, etc. to plan commitments, budgets, and launches.

Difficulty and Uncertainty in Estimation

• Strong disagreement over the statement “it is not possible to accurately estimate software projects.”
• One camp says unknown unknowns, changing requirements, and exploratory work make accurate estimates fundamentally unreliable beyond small, well‑understood tasks.
• Others insist estimation is a learnable skill: use historical data, repeated practice, and tight coupling between estimation and execution to get within ~10–20% at team level.
• People highlight that the truly hard part is genuine technical or requirements ambiguity, not just code size.

Business Pressures and Cross-Functional Tensions

• Sales and product “need dates” to close deals and plan roadmaps; “it’ll be done when it’s done” is seen as untenable for many businesses.
• Several anecdotes describe sales overpromising dates and then blaming engineering, and engineers underestimating impact of missed dates on revenue.
• House‑renovation and bridge analogies are debated: critics say other industries also routinely miss estimates; defenders note they still must estimate to win work.

Scope, Time, and Quality Trade-offs

• Popular framing: time, scope, and cost/quality—pick two. Fixing both time and scope is called unrealistic except for trivial work.
• Many endorse the article’s strategy: discover the acceptable time range, then flex scope and implementation approach to fit it (multiple “trim levels,” MVP vs deluxe).
• Others argue this only works if engineering is genuinely allowed to cut scope and adjust quality.

Estimation Techniques and Practices

• Heuristics mentioned: multiply original guess by 2 (or π), escalate to the next “human unit” (days→week→month), 1h/1d/1w/1m buckets, “two‑days‑or‑less” thumbs‑up, tracer‑bullet PoCs.
• Strong split over story points, t‑shirt sizes, and planning poker: some report good aggregate accuracy and valuable team discussion; others find them burdensome, ambiguous, and invariably translated back into time anyway.
• Several emphasize closing the loop—systematically comparing estimates to actuals and using historical team velocity or function‑point‑like measures.

Team, Process, and AI Factors

• Estimates depend heavily on who does the work, competing priorities, interrupts, and team composition; engineers are not fungible.
• Some report better discipline and deadline performance in hardware/semiconductor contexts than in web/SaaS.
• LLMs are being experimented with for estimation and exploratory implementations; current sentiment is that they overestimate and lack real sense of time, but can help surface unknowns and alternate designs.

UK “Gigabit” Internet Reality and Pricing

• Several commenters dispute the claim that “gigabit isn’t really offered” in the UK: gigabit and multi‑gigabit FTTP are widely available in many areas, especially via newer “altnets” (Hyperoptic, Community Fibre, CityFibre, etc.).
• Pricing varies sharply: examples range from ~£30–40/month for ~900 Mbps symmetric outside cities, to ~£75/month for 1 Gbps in some urban FTTP offers. Some legacy providers offer asymmetric products with poor uploads.
• Fibre rollout in London is described as historically patchy, especially in apartment blocks where freeholder consent is a bottleneck; new laws are being considered to force access.
• Openreach’s asymmetric FTTP is attributed to GPON capacity limits and possible protection of leased‑line revenue; upgrades to XGS‑PON/50G‑PON are mentioned.

G.hn Performance Over Phone Wires

• Commenters are impressed by near‑gigabit throughput over daisy‑chained UK phone wiring with many bridge taps, which normally kills VDSL performance.
• Explanations: G.hn uses OFDM with per‑tone bit‑loading, FEC, and can avoid “bad” frequency bins; operates up to ~200 MHz vs DSL’s much lower bands.
• Compared to Ethernet, G.hn actively measures the medium and adapts rate/frequencies; Ethernet just attempts 1G over whatever is there and either works poorly or downshifts.
• Multiple people report G.hn/Gigacopper devices as rock‑solid versus unstable powerline adapters.

Reusing Existing Cabling (Phone, Coax, Power)

• Many UK/EU homes have phone runs that are actually Cat5/Cat5e: swapping RJ11 for RJ45 and re‑terminating at the cupboard often yields true gigabit or even 10 GbE on short runs.
• Others describe pulling Cat5e/6 or fibre using old phone/coax as a pull‑wire; success depends heavily on construction (conduit vs stapled/embedded wiring).
• Coax is widely recommended with MoCA 2.5 for 1–2.5 Gbps where TV drops exist; some report flawless performance, others frequent drops likely due to filters/poor terminations.
• Powerline is generally viewed as last‑resort: OK for light use but noisy, variable latency, and often overheats under sustained load.

WiFi vs Wired

• Some households now rely almost entirely on WiFi 6/7 mesh, seeing multi‑gigabit LAN speeds and little perceived need for Ethernet.
• Others in brick/stone/concrete houses report severe attenuation, poor real‑time performance (Zoom/gaming), and value wired backhaul or room drops despite headline WiFi speeds.

UK Wiring Norms and New Builds

• Mixed experiences on phone‑socket density: some 2000s flats have many RJ11s per room; others have only one master socket.
• New builds often ship with zero or minimal Ethernet despite being cheap to install at construction; sales staff frequently don’t understand networking, and terminations are sometimes in inconvenient cupboards.

Brexit, VAT, and Imports

• Importing G.hn gear from the EU now commonly involves parcels being held until VAT and handling fees are paid, with fragmented tracking and thresholds (e.g., £135) causing confusion.
• Several commenters broaden this into a political argument over Brexit’s economic and administrative downsides.

Trust in US Custody

• Many argue the core issue is trust: fear that the US might freeze, refuse, or politicize access to foreign gold in its vaults, especially under an erratic administration.
• Some see this as consistent with a long pattern of the US breaking financial commitments; others stress that outright confiscation would be equivalent to default and catastrophically damage US credit and dollar hegemony.
• A minority thinks confiscation or refusal is unlikely but note that even a small probability creates dangerous bargaining leverage.

Alternatives: Where to Store or What to Do

• Suggestions include Canada, London, Switzerland, or full repatriation to Germany.
• Canada is debated: militarily vulnerable to the US and tightly linked geopolitically, but seen by some as less likely to itself weaponize custody.
• Several propose “sell and rebuy at home”: quietly sell gold in the US, buy in Europe, or just liquidate and invest in infrastructure. Others counter that dumping such volume would depress prices and hurt remaining reserves.
• Practical concerns: storage infrastructure, transport cost/risks, and the fact that past repatriations (Germany, Netherlands, others) were done slowly and discreetly.

What’s the Point of Gold Reserves?

• Explanations offered:
  • Long-term store of value when financial trust breaks down.
  • Collateral for loans, currency defense, and emergency use in crises or war.
  • Reputation signal of solvency and creditworthiness.
• Critics highlight opportunity cost: hundreds of billions tied up in a non‑yielding asset that no longer formally backs the currency.
• Russia is cited as benefitting from gold appreciation post‑sanctions.

Historical and Systemic Context

• Intense discussion of the Nixon shock/Bretton Woods: some frame it as the US “cheating” by printing more dollar IOUs than its gold; others say the system had become unsustainable and was ended with allied involvement.
• Several see gold repatriation as part of a broader “de-risking” from US dominance and dollar “exorbitant privilege,” potentially signaling slow imperial decline.
• NATO and security dependence on the US are questioned; some Europeans conclude that relying on US defense and financial infrastructure has become strategically dangerous.

User experience and use cases

• Multiple owners describe comma/openpilot as “life improving,” especially for long highway drives and heavy commute traffic.
• Common pattern: people now choose cars based on compatibility, sometimes buying specific model years.
• Reported benefits vs stock assist: fewer corrections, no taking exits by mistake, better curve handling, fewer dropouts in construction or light snow.
• Often described as “not FSD, just lane assist—but next level.” On long trips users report very few disengagements, mainly for passing or turns.

Capabilities vs OEM and other players

• Core feature set: Adaptive Cruise Control + Automated Lane Centering (ACC + ALC). No built‑in navigation; it doesn’t drive to a destination by itself.
• Compared to OEM systems (HDA2, BlueCruise, etc.), users say openpilot:
  • Tracks lanes and even “laneless” roads much better, including weak/missing markings.
  • Avoids constant steering-wheel “nags” by using driver-monitoring cameras.
• Tesla FSD is seen as more ambitious (urban and navigation), but some users prefer comma’s reliability on highways. Waymo is framed as full robotaxi with many more sensors.

Safety, liability, and insurance

• Strong tension: enthusiastic daily users vs commenters calling DIY Level 2 on public roads “reckless.”
• Several note incidents (curbs, side collisions) and stress that it’s strictly Level 2: the driver is always legally responsible and must be ready to take over instantly.
• Concerns: unclear insurance treatment, potential policy voiding due to undeclared modifications, and lack of corporate liability compared to, e.g., Mercedes’ limited Level 3 system.
• Debate over human factors:
  • One side: good assist systems encourage dangerous complacency.
  • Other side: they reduce monotony and free attention for situational awareness.

Hardware, compatibility, and hacking

• Comma 4 uses three cameras (wide, telephoto, driver-facing) and controls via CAN bus; some cars also provide radar or only allow steering control.
• Not all vehicles are or will be supported—encrypted CAN and newer architectures are obstacles.
• Sunnypilot and other forks add features like decoupled steering vs throttle, blind-spot‑aware auto lane changes, and dynamic “experimental” modes.

Company, culture, and ecosystem

• Discussion about leadership changes, the founder’s hacker background, and unconventional public persona.
• Hiring via a public leaderboard is highlighted; older reports of more elitist interview filters are criticized.
• Some view comma as a rare, scrappy, genuinely “hacker” robotics/AI company; others distrust it over website QA issues, GitHub-star marketing, and the self-install liability model.

Scope of the change

• Several commenters stress this is about TikTok in the US: the new data practices apply to US users whose data is now handled by a US-based joint venture (TikTok USDS).
• Users outside the US (e.g., Europe, Asia) are described as still being under the older Singapore-based TikTok Pte Ltd EULA.
• People note the policy now explicitly includes more granular data collection such as precise location.

Who is more dangerous: foreign state vs domestic elite

• Some argue they’d rather their data be handled by “China” than by a US billionaire aligned with the US government, since US authorities (ICE, law enforcement, intelligence contractors) have far more direct coercive power over Americans.
• Others counter that China can and does conduct large-scale influence operations, shaping narratives, amplifying division, and potentially affecting elections; they see TikTok as a strategic tool in any future conflict.
• A rebuttal claims Americans generate enough chaos themselves and that foreign propaganda is limited by poor cultural understanding; the bigger concern is whoever can combine detailed personal data with microtargeted political messaging.
• There is also a tangent arguing that Israel or its lobby is a greater threat to US policy than China, framed around TikTok discourse on Gaza and US aid.

Mass surveillance, manipulation, and elections

• One line of argument: individual-level harm from China is low, but mass access to millions of profiles enables sophisticated targeting and election interference.
• Others broaden this to say many states and domestic actors (not just China) run similar campaigns; the goal is often to increase contention and exhaustion, not to push a single ideology.

Social media addiction and “just quit” debate

• One camp says the obvious solution is: stop using TikTok/social media and deny them data.
• Pushback: calling it “obvious” ignores how these apps are engineered to be addictive; telling people to “just quit” is likened to telling a heroin addict to “just stop,” i.e., descriptively correct but practically unhelpful.
• Some argue most use is habit/convenience rather than deep addiction, so many could quit with “mild difficulty.”
• Others emphasize building offline social lives (clubs, gyms, recurring in-person activities) as a long-term path away from reliance on social feeds.

Nature and impact of TikTok’s algorithm

• One commenter found their feed dominated by disturbing “exploitative” content and saw this as a deliberate dumbing-down of Americans.
• Multiple replies insist TikTok’s algorithm is highly reflective of user behavior: others report feeds full of hobbies, games, or politics aligned with their interests.
• The idea “the feed is what you make of it” is repeated, though it’s not universally accepted.

Meta: social vs forum platforms

• Side discussion on whether HN/Reddit count as “social media”: some say they are forums (topic-first, no friend graph, limited personalization); others argue that algorithmic curation, karma, and popularity dynamics already make modern Reddit functionally social media-like.

Agent loop, reasoning tokens, and context management

• Several comments dig into how Codex uses reasoning tokens in the agent loop: they persist within a single “agent turn” (tool-call loop) but are dropped between user turns, which can lose context across related user messages.
• Developers work around this by having the model write plans/progress/notes to markdown (or SQL / external stores) as cross-turn “snapshots.”
• There is confusion and mild contradiction between docs and behavior of the Responses API about when reasoning is reused; some report that encrypted reasoning items sent back by the client are silently ignored across user turns.
• The /responses/compact endpoint and its encrypted_content/compaction items are highlighted as a strong, latent-space compaction mechanism that preserves understanding while freeing context, though it tightly couples you to OpenAI models.

Observability, steering, and history

• People want better visibility into Codex’s “thinking” and tool usage so they can interrupt/steer early; some see this as both a UX and cost issue.
• Steering can be experimentally enabled, but many still feel real-time “thought” display is insufficient compared to other tools.
• External logging/transcript systems (Emacs agent-shell, daemons, OTEL-based tools like codex-plus) are used to preserve full interaction histories and analyze behavior.

CLI UX, performance, and feature gaps

• Codex CLI is widely praised for speed, resource usage, and polished UX compared to other CLIs (Claude Code, Gemini CLI), though some still find it frustratingly slow versus ChatGPT web.
• Others say Codex is “too slow” and breaks their flow, or gets stuck in loops on simple tasks.
• Missing features frequently cited: hooks (to intervene in the harness), checkpoints/forks, and clear diffs/approval flows for file edits. Hooks are described as critical for reducing token use and catching “stupid” agent behavior.
• Some users find Codex much more capable for complex coding (GPU pipelines, emulators), while others find it “almost useless” versus Claude Code or Gemini. Experiences are sharply divided.

Open source vs proprietary harnesses

• Codex CLI’s open-source Rust implementation is viewed as a major advantage: inspectable internals, learnable agent patterns, and community bugfixes (though feature PRs rarely get merged).
• In contrast, Claude Code’s proprietary harness and GitHub-as-issues-only frustrate users who could otherwise fix long-standing bugs or extend behavior.
• There’s debate over reverse-engineering proprietary tools, potential ToS violations, and the ethical tension given how LLMs were trained.

Multi-model and ecosystem integration

• Codex can be pointed at non-OpenAI models via custom providers, but it’s “annoying” and some features (like compaction) are OpenAI-specific.
• Competing tools (Amp, OpenCode, Gemini CLI) are compared: Amp’s read loop and mixed-model strategy (fast vs smart) feels snappier to some; OpenCode is praised for UX; Claude Code for hooks and diffs despite stability issues.

In-house libraries vs standard C++

• Many see Chromium’s bans as “use our in-house version instead of the standard one,” typical of old, large codebases that predate modern STL (chrono, containers, etc.).
• Some argue the long‑term optimum is migrating to standard types that new hires already know; others say deep integration with internal components justifies sticking with internal libraries.
• There’s also a cultural angle: some organizations systematically reimplement everything (NIH), which only partly makes sense outside their monorepo context.

STL container quality and alternatives

• Several commenters think many std containers are poorly designed or overspecified (e.g., unordered_map, std::deque, std::vector<bool>) and that modern hash maps (like those in Abseil) are clearly superior.
• Others push back, claiming std containers are “good enough” for most software and criticizing repeated STL‑bashing.
• Bad homegrown containers can be far worse than STL, with anecdotes of opaque, void*-based “iterators” that destroyed safety and performance and were extremely costly to refactor away.

char8_t, character size, and portability

• Chromium bans char8_t with a rationale many agree with: almost all APIs use char*/std::string, so char8_t* just forces casts for little gain.
• Debate centers on C/C++’s guarantees: char and char8_t are 1 byte, but a “byte” need not be 8 bits; some mention exotic DSPs as a reason the standards remain so general.
• Several people call char8_t a blunder: distinct type, same layout as unsigned char, often not actually 8 bits, and confusingly named.

Exceptions ban and error handling

• Chromium (via Google style) bans exceptions for historical/practical reasons: huge legacy code not written to be exception‑safe. Even Google’s own docs say things might be different if starting from scratch.
• Long subthread debates whether C++ exceptions are inherently problematic (unstable, hard to reason about in low‑level code) or actually more robust than error codes (you at least get a clean crash).
• Alternatives discussed: explicit error returns, macros, std::expected-style types, Rust‑like Result, and Zig‑style error propagation. Some want checked exceptions; others point to Java’s mixed history here.

Scale, consistency, and banned‑feature culture

• Many see the ban list as context‑driven: features that are fine in small projects become hazards in a massive, long‑lived, highly portable codebase.
• Consistency and reduced cognitive load are recurring themes: avoiding “sporadic new features” and sticking to one way of doing things.
• Comparisons are made to Java/C#/Rust: they also have de facto or tool‑enforced “banned” APIs/features, but often at the library level rather than core language constructs.
• There’s interest in enforcing such bans via static analysis, compiler lints, or custom tools; other projects (e.g., WebKit) reportedly do similar things.