Hacker News, Distilled

Skepticism about “AI will create jobs”

• Many argue the slogan sounds like generic political sales talk (“protect kids / create jobs”) rather than a serious claim, and note fear-based messaging in the linked video.
• Several see a looming demand problem: firms cut headcount; laid‑off workers buy less; money circulates mainly among AI vendors and big customers, shrinking the consumer base.

Historical automation vs this AI wave

• One camp: past tech (tractors, dump trucks, industrialization, agricultural advances) massively reduced labor in one sector but freed people to do other, often better‑paid, work; overall employment remained robust.
• Counter‑camp: that story hides winners/losers (Detroit, Ruhrgebiet, offshoring) and ignores today’s bifurcated incomes and precarious, low‑rights jobs.
• A key worry: earlier waves automated physical tasks and created intellectual/service work; if AI can handle most intellectual work, what’s left for average humans to transition into?

What jobs might appear or change?

• Concrete suggestions: more trades (plumbers, carpenters, electricians), testers/QA for AI‑generated “slop,” AI content verification teams, data‑center and infra roles (power, chips, construction, security), AI consulting/SaaS, delivery gigs.
• Critics say this is mostly job reallocation and some is “bad” job creation (cleaning up AI messes), not net new opportunity.
• Some expect expansion of face‑to‑face human services (care, therapy, social/experiential work) because authentic interaction can’t be perfectly replicated.

Economic theories and “this time is different”

• Pro‑market view: for ~250 years tech killed jobs but unemployment rates stayed stable; unemployed people are a resource and markets reliably find new uses.
• Skeptics push back: economists’ track record is poor, and “past performance is no guarantee of future results,” especially if AI+robots can both think and act at or above median human ability.
• Dystopian strand: either super‑effective AI triggers a consumer‑demand death spiral under current profit incentives, or AI underdelivers after huge capital misallocation—both ending badly without structural change (e.g., UBI or new welfare metrics beyond GDP).

Views on the ex‑Google executive and AI hype

• Multiple commenters question his credibility and motives, noting he runs an AI startup (emma.love) and making fun of claims like needing “350 developers” for that app.
• His prior public statements (AGI by 2026, LLMs as “conscious,” AI‑run utopia in 15 years) are widely characterized as delusional or marketing, not grounded analysis.

Status of Quantum Computing and Motivation for PQC

• The referenced “dog factoring RSA” paper is widely seen as satire targeting hype, not a serious argument against quantum computing or PQC.
• Participants are split on timelines: some argue quantum computers have been “5–10 years away” for decades and may never scale; others point to clear theoretical and engineering progress (better factoring estimates, fault-tolerant codes).
• Intelligence agencies’ guidance (e.g., protect against “store now, decrypt later” attacks by ~2030) is cited as a strong reason to start deploying PQC now for data that must remain secret for decades.
• Skeptics stress that zero real-world progress has been made on actually breaking deployed crypto; proponents counter that low migration cost justifies hedging.

Overhead and Practicality in SSH and Other Protocols

• OpenSSH’s PQC work is limited to key agreement (KEX), not bulk encryption, so overhead is per-connection, not per-packet.
• Many commenters emphasize that asymmetric crypto has always been expensive relative to symmetric; PQ KEMs mainly affect the handshake.
• ML‑KEM is said to be very fast, faster than classic DH at equivalent security and close to X25519; its public keys (~1 kB) are larger but “lost in the noise” for typical SSH sessions.
• There is some concern that at very high connection rates (e.g., DNS, TLS with many short sessions) larger handshakes and signatures matter more, especially in TLS certificate chains.

Hybrid Schemes and Security Tradeoffs

• OpenSSH uses hybrids (e.g., mlkem768x25519‑sha256) that combine a classical and a PQ algorithm, so security is at least as good as the classical part if PQ is later broken.
• Hybrids improve robustness against algorithmic flaws but increase code surface, potentially raising implementation/side‑channel/DoS risk; others argue modern verified implementations mitigate this.
• Some discussion explores whether “encrypting twice” can ever weaken security; consensus is that with independent keys and proper combiners, it should not.

Algorithm Choices and Ecosystem Direction

• Question of “better” KEX: ML‑KEM‑based hybrids have better performance/smaller keys; NTRU Prime (sntrup) has different security assumptions and a strong pedigree.
• NTRU Prime’s presence in SSH is partly historical; broader standards (TLS, IPsec, NIST PQC) are converging on ML‑KEM, suggesting it will be the dominant choice.
• NSA’s CNSA 2.0 set is purely PQ without requiring hybrids, but hybrids remain allowed in practice and widely used during transition.

Who Still Used AOL Dial‑Up (and Why)

• Main guesses: very rural users with no broadband or cellular coverage; elderly users reluctant to change; people maintaining long‑standing @aol.com addresses for business, trust, or fear of losing access.
• Some were knowingly paying for dial‑up they no longer used, treating the bill as “insurance” that their AOL email would remain active.
• Others appear to be “zombie” subscriptions: auto‑pay accounts where the actual dial‑up access isn’t used at all.

Rural Connectivity and Alternatives

• Starlink, fixed wireless, and cellular “home internet” are discussed as replacements, but:
  • Starlink is seen as good but expensive, especially for low‑income rural users.
  • Many rural or mountainous areas still have weak or nonexistent cell coverage or only 2G/3G.
  • Old DSL services can be oversubscribed, slow, and overpriced; some telcos are actively backing away from landlines.
• Some commenters note the paradox that dial‑up is nearly dead not just from demand, but because POTS lines and modem‑compatible voice paths are disappearing or going VoIP.

AOL’s Business and Shutdown Logic

• Several recall AOL dial‑up as an incredibly high‑margin profit center well into the 2010s, effectively subsidizing AOL’s other money‑losing ventures.
• There’s debate on why to shut it down:
  • One side: even a six‑figure user base at $10–$20/month could be run cheaply with modern soft‑modem infrastructure.
  • The other: customer numbers are likely shrinking rapidly (aging, deaths, churn), making it a dying line with regulatory and infrastructure entanglements, especially as telcos try to retire copper.

Dial‑Up vs the Modern Web

• Multiple accounts say dial‑up became functionally unusable as sites bloated: HTTPS overhead, huge JS bundles, and timeouts make even 128 kbps mobile throttling feel worse than old 30–56 kbps dial‑up once did.
• Some note a few “light” holdouts (Hacker News, Craigslist, text‑mode or basic HTML email) remain viable, but mainstream sites don’t.

Nostalgia and Technical Memories

• Extensive reminiscence about AOL’s ubiquity (CDs/floppies everywhere, “You’ve got mail!”, keywords, chat rooms) and early modem eras (300 baud up through “56k”, ISDN, campus T1s).
• Several correct misconceptions: 56k was kilobits, rarely achieved in practice, with typical speeds much lower and high latency.
• Many share stories of long downloads, BBSes, AOL CDs as coasters/floppy stock, and how lightweight clients once delivered chat and email over tiny bandwidth compared to today’s Slack/Teams.

Theft vs Copying Terminology

• Large subthread disputes calling copyright infringement “theft.”
• Many argue: copying doesn’t deprive the owner of the original, so it’s not theft but infringement; “theft” is emotional rhetoric used by rightsholders.
• Others counter: what’s “stolen” is income, opportunity, or ownership claim over IP; morally it feels like theft even if legally distinct.
• “Identity theft” is criticized as misdirection from authentication failures; language can shift blame to victims.

Piracy, Harm, and Artists’ Livelihoods

• One side: piracy and AI training undermine creators’ ability to get paid, pushing art toward being a hobby for the wealthy.
• The opposing view: no one has a right to income in a specific field; open source and “sharing” show alternatives.
• There’s disagreement whether saying “piracy isn’t theft” is nitpicking or essential to keep moral categories clear.

AI Training, Fair Use, and the Law

• Dispute over whether training on copyrighted data without consent is fair use or mass infringement.
• Some argue law targets distribution of copies, not “using” them to learn; training is analogous to research.
• Others cite “harm to the market” and “amount taken” tests (e.g., UK fair dealing) and claim commercial AI training clearly fails them.
• A recurring theme: if OpenAI’s scraping is illegal, it should be fined; if not, piracy is effectively decriminalized, exposing a corporatocracy.

Human Learning vs AI Learning

• Pro‑AI commenters analogize models to humans reading books, learning styles, then creating new works; if that’s legal for a person, why not for a machine?
• Critics respond that scale and commercialization matter: one trained model serves millions, unlike individually trained humans, and the product wouldn’t exist without others’ works.

Scale, Power, and Corporate Control

• Concern that tech giants “scalp” culture, build monopolistic moats, and starve original sources (e.g., news sites) by summarizing content.
• Some see two legal regimes emerging: permissive for corporations, strict for individuals.

Cultural Impact of AI Models

• Worry that models encode median, mass‑market culture, weakening incentive and visibility for fringe/experimental art.
• Others think fringe work will still exist for “committed freaks” and can be surfaced with the right prompts.

Debate over the Example Image

• Several question the article’s image evidence: without showing prompts, similarity may just reflect requested composition/styles.
• Many argue the AI output is not a literal copy; overall composition similarity alone is likely non‑infringing.

AI access & the digital divide

• Several commenters see the article as highlighting a real “AI divide”: expensive frontier models risk widening productivity gaps between rich and poor countries.
• Others argue the bigger barriers are still basics: internet, devices, language, and digital literacy; many low‑income users only have feature phones or no phone at all.
• Some note smartphone penetration is high overall but still leaves billions without capable devices or affordable data.

Is ChatGPT Pro a necessity or a luxury?

• Many call Pro a luxury product, not comparable to water or food, and see little justification for outrage that it’s unaffordable in poor countries.
• Others push back: even if not essential, AI may become key infrastructure (like the internet once was), affecting competitiveness and job prospects.

Pricing, costs & subsidies

• Repeated point: LLM inference has real marginal compute cost, unlike traditional software/IP, so deep global discounts aren’t free for providers.
• Some say corporations aren’t charities; they don’t see a business reason to sell Pro at a loss in low‑income countries.
• A moral argument is made that rich countries or firms should subsidize AI access to reduce inequality; critics counter that scarce money would do more good if spent on basic needs or direct cash to the poor.

Comparisons: education, wages & labor

• A side debate compares the cost of ChatGPT Pro to CS degrees. Some argue AI still narrows gaps relative to extremely expensive foreign degrees; others say this is a false equivalence (degrees are one‑time, skills persist, AI is a subscription).
• Multiple comments challenge the article’s use of GDP per capita; they argue salaries (especially of likely AI users, e.g., white‑collar workers) are more relevant and can make Pro economically rational, even in poor countries.

Capabilities & practical impact

• Some argue access to AI meaningfully boosts productivity (e.g., for developers), creating a competitive edge over those without it.
• Others say current models are not “Ferraris” and cannot replace skilled workers; they require “babysitting,” and the hype may lead to disillusionment (or an AI winter).
• One commenter flags that the advertised 128k‑token context in Pro is effectively lower in practice, suggesting marketing overstates capability.

Politics, fairness & broader concerns

• Thread drifts into taxation, immigration, and whether “morally right” policies must also be economically painless.
• Some worry broad global access to powerful AI has under‑discussed second‑order risks (safety, misuse) and question pushing frontier models everywhere in the name of egalitarianism.

Developing exploit-finding skills

• Suggested path: heavy practice in reverse engineering, debugging, and reading past exploit write‑ups to learn common patterns and “code smells.”
• Emphasis on perseverance and passion for understanding other people’s complex code, not just building new things.
• Recommendations include browser exploit blogs, formal trainings, and classic exploitation books; some point to CTF-style resources like pwn.college.
• Key skill is narrowing focus to security‑relevant boundaries (e.g., renderer ↔ broker IPC) rather than “the whole codebase.”

Bugs in large projects & sandbox escapes

• Some argue large, complex projects are easier to mine for serious bugs because of many interacting components and rich attack surfaces.
• Others note that in mature targets like Chrome, years of fuzzing and prior research make new high‑impact bugs harder to find.
• Explanation of the bug: typically a two‑stage chain—first compromise the renderer, then use this logic/timing bug to escape the sandbox via mishandled Windows handles and thread control.

Money: is $250k “life-changing”?

• Strong disagreement: some say $250k (pre‑tax) is clearly life‑changing, especially for down payments, debt payoff, or in cheaper regions.
• Others in high‑cost cities say it doesn’t materially change daily life or enable retirement, framing “life‑changing” as “can stop working or radically change path.”
• Debate over how much location, existing income level, and housing markets affect this perception.

Bug bounty size, corporate wealth, and comparisons

• Some note $250k is a microscopic fraction of Alphabet’s income; others call that comparison meaningless, arguing payouts should track researcher incentives and black/grey‑market value, not company profit.
• Comparison to Mozilla: Chrome pays an order of magnitude more for similar bugs; some say that shows Google is more serious about browser security, others counter Mozilla’s much smaller revenue and different context.
• Discussion on whether bounties should approach grey‑market prices to keep exploits out of offensive use.

Black/grey markets vs. disclosure ethics

• Many comments dissect how grey‑market exploit brokers, intel/LEO customers, and tranch-based payments work, and note those can reach high six or seven figures for full chains.
• Significant ethical thread: selling to criminals or states vs. reporting to vendors; some argue “being a decent human” should outweigh higher grey‑market payouts.
• Practical obstacles to “double-dipping” (sell then report): trust, OPSEC, detection in the wild, and loss of future employability.

Languages, memory safety, and browsers

• Long side discussion on C’s null‑terminated strings: seen as a major source of bugs and a historical design mistake; others argue abstractions or safer languages are the real solution.
• Counterpoint: this specific Chrome bug is a logic/timing error, not memory corruption; using Rust or another memory‑safe language wouldn’t have prevented it.
• Mention of emerging memory‑safe browser efforts (e.g., Servo) and separate concerns around JIT engines as “inner platforms” that remain risky regardless of implementation language.

Bug bounties as a career

• Yes, some people live off bug bounties, often in low‑cost regions or by focusing on volume of smaller server‑side bugs.
• For high‑end client‑side chains like this, realistic cadence is a small number of big payouts per year; risk and income variability are compared to sales/commission‑based work.

Site & Guide Format

• Many noted the site was down from traffic (“HN hug of death”).
• Several found the guide shallow and chopped into tiny sections full of links and teasers, with little dense content.
• The style felt condescending or stressful to some, especially the heavy cross-linking and “next we’ll learn…” structure.
• Others pointed out it’s very US‑centric and part of an American “self‑improvement” culture, less common in parts of Europe.

Funerals, Grief, and Social Obligations

• A large subthread debated whether you should attend funerals, especially for friends or for someone close to a friend.
• One side: you should go even if it’s painful; funerals are for the living, presence matters more than words, and avoiding them is selfish, immature “comfort worship.” Attendance is framed as a core social obligation and a way to support community and show respect.
• Opposing side: it’s acceptable, even necessary, to skip if it’s overwhelming or harmful to mental health; “self‑preservation” and boundaries are valid. Some reject any expectation to care about the deceased’s family or join culturally/religiously loaded rituals.
• Middle ground: funerals are one option among many; if you can’t go, you can still support mourners in other ways, but avoid giving blanket “just don’t go” advice to socially struggling people.

Neurodivergence, Masking, and Work

• Experiences with autistic and “neurodiverse” coworkers were mixed: some valued their straightforwardness; others described disruptive behavior and stressed that workplaces aren’t therapy.
• A side discussion argued about “masking”: one view sees constant self‑editing as fake and exhausting; others say everyone filters themselves somewhat, and neurodivergent people often must mask more to function socially.
• Several distinguished between considerate self‑control and manipulative “selling yourself”; the same skills can be used either way.

Cultural and Topic Differences

• Multiple comments stressed that “basic social skills” are culture‑specific; norms differ across US, Europe, and Asia (e.g., small talk expectations, funeral etiquette, hunting/fishing as topics).
• Popular small‑talk topics with men in some places: sports, cars, hunting/fishing, handyman work; elsewhere, food, travel, music, politics, or hobbies.
• A recurring tip: you don’t need domain knowledge; asking curious questions and listening well often suffices.

Alternative Resources and Deeper Issues

• Several recommended other resources (e.g., succeedsocially.com, classic negotiation and relationship books) as more thorough.
• Some doubted whether text alone can teach social skills without practice.
• One thread highlighted that social difficulties often stem from trauma or chronic childhood neglect, not just missing “tips,” and that autism and other neurodivergence complicate the picture.

Overall Morale and Emotional Climate

• Morale ranges from energized and “loving coding again” to exhausted, angry, or deeply demotivated.
• Some feel like they’ve gained a “superpower”; others feel like “cavemen” wasting time trying to make tools useful.
• A number report psychological damage: loss of motivation to learn, blog, or publish OSS because it feels like “why bother, AI will do it / train on it.”

Perceived Productivity Impact

• Claims split sharply:
  • Enthusiasts report 30–95% of their code or workflow now aided or written by AI, enabling solo devs to ship work that once needed teams.
  • Many others say AI does 0–10% of their work, or even slows them down due to bad suggestions and extra verification.
• One linked study (of experienced OSS devs) is cited as showing ~19% productivity decline when using LLMs, reinforcing skepticism for seniors.

Hiring, Job Security, and Offshoring

• Most say hiring hasn’t stopped; some orgs are still expanding engineering teams, especially seniors.
• Others report slowed hiring and salary pressure, but attribute more to offshoring and cost-cutting than AI directly (though AI is sometimes used to justify offshoring).
• A few are actively planning to leave tech, assuming AI will erode remote/outsourced opportunities first.

Management Expectations and Pressure

• Common complaint: leadership believes (or pretends) AI can do “30–50% of the work,” cutting headcount while workload stays the same.
• Some managers wield AI as a cost‑cutting pretext, or mandate AI usage and measure people on “shipping with AI.”
• Devs are frustrated by PMs waving oversimplified LLM outputs as proof tasks are “trivial” and should be done “by end of day.”

Where AI Helps vs. Where It Fails

• Helpful niches repeatedly mentioned:
  • Boilerplate code, simple CRUD, tests, small scripts, config scaffolding.
  • Documentation stubs, meeting transcription, rewriting tickets/notes, summarizing large codebases or papers.
  • Debugging when you can paste large logs + code, or exploratory work in unfamiliar libraries.
• Weaknesses:
  • Complex, domain-heavy, legacy, safety-critical, or hardware/embedded code.
  • Existing large codebases with messy history and undocumented domain rules.
  • Hallucinated APIs, deprecated patterns, brittle tests, and “slop” PRs that OSS maintainers often reject.
  • Code agents that frequently go off the rails, require micromanagement, and still can’t complete end‑to‑end tasks.

Junior vs. Senior Engineers

• Many observe juniors or “struggling” devs benefit more: help with syntax, patterns, and basic scaffolding.
• Several seniors say their own net productivity gain is tiny or negative; they spend more time reviewing, correcting, and ensuring quality.
• Some argue that if you see 50%+ gains, it may reflect prior low productivity or reliance on shallow work; others strongly disagree and point to solo‑consultant success stories.

Impact on Knowledge Ecosystem and the Web

• Multiple comments worry that LLMs are killing Stack Overflow and niche info sites by diverting traffic while being trained on their content.
• Maintainers and site owners report:
  • Floods of low‑quality AI PRs in OSS.
  • Traffic drops due to AI summaries, forcing them to divert months to “damage control.”
• Concern that future LLMs will have worse training data as today’s Q&A and documentation ecosystems degrade.

Societal / Ethical Concerns and Personal Futures

• Some see AI as primarily a “ruling class” profit play, with workers and independent publishers paying the price.
• Worries about younger devs depending on AI, never learning fundamentals, and long‑term software quality collapsing.
• A few in precarious situations (e.g., in war zones) feel AI specifically undermines one of the few portable, secure careers they had.

Views on the Hype Cycle and the Future

• Many compare the current hype to crypto, Agile/Scrum, or “year of Linux on the desktop”: real utility, but wildly oversold.
• Some are “biding time” for the bubble to burst; others think we’re at an early stage of a real shift where devs become more like architects/AI‑orchestrators.
• Consensus points: writing code was never the main bottleneck; communication, requirements, domain knowledge, and organizational drag still dominate, and AI hasn’t fixed that.

Anxiety about “computers on wheels”

• Many commenters fear the day their simple ICE car dies and they’re “forced” into software-heavy, cloud‑connected vehicles with short support lifetimes.
• There’s nostalgia for pre‑telematics cars that can be kept running indefinitely with mechanical skill and a machine shop.
• Several people now explicitly avoid buying new cars due to embedded connectivity and opaque software control.

Smartphone integration vs OEM infotainment

• Some see Android Auto / CarPlay as the least‑bad option: move complexity to a replaceable phone, keep the car as a “dumb display + buttons.”
• Others warn this is changing: CarPlay Ultra and deep OEM integrations blur lines, potentially centralizing even the instrument cluster in the phone ecosystem.
• Debate over whether CarPlay Ultra renders on the phone or in-car, and how safety regulators would view phone‑rendered critical displays.
• GM’s removal of CarPlay/AA (at least in US EVs) and Lexus forcing users into their app are cited as anti‑user moves.

Connectivity, surveillance, and security

• Strong concern about constant tracking: cellular modems, OEM apps, data sales to insurers and others.
• Some argue that if a car is offline and static, lack of updates is fine and safer than network exposure.
• Others note security‑critical systems (immobilizers, keyless entry) may need patches, but some say the only real fix is to remove such features.

DIY disabling of telematics and attack surface

• The Hyundai teardown is praised as a model: physically removing the modem is seen as vastly reducing attack surface.
• Suggestions include cutting or loading antennas, but some note hidden backup antennas and complex RF paths.
• There are reports of cars that won’t start or misbehave if the telematics unit is removed, making such hacks risky.

Legal, regulatory, and eCall tensions

• In the EU, mandatory eCall complicates disabling microphones and modems; some argue it’s a safety feature, others see it as unwanted surveillance.
• Frustration with post‑purchase “click‑to-accept” T&Cs pushed via OTA updates; several question their legality and call for regulators or courts to invalidate such one‑sided changes.
• Ideas floated: huge government bug bounties with punitive fines, stronger right‑to‑repair and privacy rules, and clearer pre‑sale disclosure.

Alternatives and workarounds

• Strategies include: buying older, simpler cars; favoring models with minimal telematics; maintaining “fleets of antiques”; converting ICE to EV with open hardware; or shifting to low‑tech bikes and e‑bikes (though even e‑bikes are starting to get “smart”).

Ambiguity: Satire vs. Sincere Optimization

• Many readers aren’t sure if the post is satire, “token‑in‑cheek,” or dead serious; several explicitly invoke Poe’s Law.
• Some conclude it’s at least partly real (author says they actually did it), but framed humorously and optimized for HN visibility.

Health, Sleep, and Addiction Concerns

• Multiple commenters are disturbed by reorganizing sleep around a paid AI service, seeing it as a sign of unhealthy dependency or addiction.
• Strong warnings: never trade health for money or productivity; sleep deprivation and burnout can have long-term costs.
• Others push back on absolutes: people have always traded health/sleep for goals; short intense periods can enable later freedom, and circumstances differ.
• A few urge the author to seek professional help to assess whether this is compulsive behavior.

Alternatives to Contorting Life Around Claude Limits

• Many suggest simpler options: pay for Claude Max, use the API, get another account, run local models on a GPU, or switch to tools with looser limits.
• There’s debate about ToS: multiple personal accounts and scripted “24/7 usage maximizing” are described as disallowed, while business plans are a possible workaround.
• Others script a single early-morning request or use cron/CLI to align buckets more gently, rather than disrupting sleep.

Polyphasic Sleep & Solo Sailing

• Several share experiences with polyphasic or 28‑hour cycles: interesting but brittle, easily derailed by social life or missed naps.
• Concern that such fragile schedules are dangerous at sea, where unexpected multi‑hour tasks (weather, equipment failures, other vessels) are common.
• Sailors describe both the “joys” of offshore life and the very real risks of fatigue, including anecdotes about boats sunk when someone fell asleep.

Work, Burnout, and Productivity

• Long debate on whether intense “crunch” periods are ever worth it.
• Some say overwork in early years enabled later low‑hour weeks; others argue burnout is never worth it and often accompanied by survivor bias.
• There’s tension between “love of coding/startups” and concerns about normalized self‑exploitation, especially for B2B SaaS.

AI Coding, Quality, and Culture

• Mixed views: some see this as “unhinged productivity hacking” and emblematic of AI‑driven workaholism.
• Others joke that AI power users are just spending 10x the time, or liken AI’s intermittent rewards to an addictive slot machine.
• Concerns about an era of terrible, AI‑generated code surface, countered by notes that humans already produce plenty of bad code.
• A subset expresses fatigue with near‑constant AI content on HN and suggests filtering it out, even via AI itself.

Initial Reaction and Free Speech Concerns

• Many commenters initially took the scenario at face value: Tesla remotely disabling a Cybertruck over a rap song using its branding.
• This was framed as a free speech issue and a sign that “ownership” of modern connected cars is illusory if the manufacturer can brick them over terms-of-use disputes.
• Some argued such behavior, if true, should carry serious civil and even criminal liability, especially if a car is disabled in live traffic.

Skepticism, Verification, and Forensics

• Others were immediately skeptical, noting the sole source was a social media video by the rapper, with no independent reporting.
• Several technical red flags were identified:
  • The on-screen message appeared as a video in the car’s media player (visible UI elements, recreation by another owner in ~15 minutes).
  • The “update failed, return to dealer” text doesn’t match Tesla’s usual behavior or terminology (Tesla has no “dealers”).
  • The VIN in the letter failed the official check-digit algorithm and wasn’t found in Tesla’s recall database.
  • The legal title and reused signature on the purported cease-and-desist letter didn’t match the lawyer’s current role and appeared copied from an older letter.
• Commenters later linked external coverage and Tesla’s own statement calling it a hoax, leading many to conclude the incident was fabricated for clout/marketing.

Broader Concerns About Remote Control and Ownership

• Even assuming the event was fake, many focused on the capability: connected cars already allow remote control for repossession, theft tracking, rentals, etc.
• Some argued that if a manufacturer can disable a product you bought, you don’t truly own it; this was tied to a wider trend of “hardware as a service” and post-sale control.
• Others pointed to historical analogs (OnStar shutdowns, rental fleets remotely disabling cars) and argued for laws restricting remote deactivation except under narrow, judicially supervised circumstances.

Reflection on Bias and Media Literacy

• Late in the thread, several commenters criticized how quickly people believed the story, noting it “felt plausible” mainly because of Tesla’s and its CEO’s reputation.
• There was a call for higher standards of evidence for viral outrage claims, especially ones emerging solely from influencer-style social posts.

Soviet Edition & Art Style

• Many find the 1976 Soviet illustrations “amazing,” nostalgic, and distinctive; others call them naive or childish.
• Defenders note that tone fits The Hobbit as a children’s book.
• The style is compared to Rocky & Bullwinkle villains and Samurai Jack; some speculate the latter may have been influenced by Soviet-era visuals.

Bilbo’s Model & Soviet Pop Culture Links

• The illustrator based Bilbo’s look on a well-known Soviet actor, confirmed by the actor himself in an interview where he’s gifted the book.
• The actor was also the voice of Soviet Winnie-the-Pooh, strengthening the cultural resonance.
• Some see resemblance to Western comedians, which amuses readers.

Global Tolkien Illustrations

• Commenters share other beloved editions: East German (Klaus Ensikat), Romanian, Bulgarian comic adaptation, and the Swedish Tove Jansson Hobbit.
• Jansson’s art is widely praised; some say the Soviet edition owes it a stylistic debt but is “more conventional and stiff.”
• Another notable 1970s illustrator (a European monarch under pseudonym) is cited as a favorite of Tolkien.

Gollum: Size, Look, and Retcons

• Long thread on Gollum’s size: Jansson drew him huge; people debate whether early texts were ambiguous.
• Passages about Bilbo jumping over Gollum and the ring fitting both characters are used to argue he must be small and roughly hobbit-sized.
• Others note descriptions of Gollum as “black” and orc-like in LOTR, contrasting with pale film versions.
• The Soviet “spaghetti Gollum” gets particular affection.

Trolls, Orcs, and Folklore

• One reader thinks the Soviet trolls and battle scenes misread the book (trolls as giants, goblins too human).
• Others argue big, drunken, humanlike trolls are consistent with The Hobbit and Scandinavian folklore, and that D&D and films have since shifted expectations.

Books as Objects: Value and Loss

• The Jansson-illustrated Swedish Hobbit commands high prices; most other books don’t.
• Debate over “worthless” books: abundance vs lack of demand vs forgotten but beautiful editions.
• Reports from Sweden of hardcovers being refused by charities and libraries culling low-circulation titles, raising fears of many non-digitized books being effectively lost.

Soviet/Russian Re-readings of Tolkien

• The Last Ringbearer is recommended as a serious, sympathetic retelling from Mordor’s side, motivated by worldbuilding and economics.
• A satirical communist reading of LOTR (Mordor as USSR, orcs as workers, hobbits as kulaks) is recounted; some note that “Mordor revisionism” remains popular.
• Others argue historically that Tolkien’s anti-industrial, Catholic, anti-communist stance made him genuinely ideologically opposed to the Soviet project.

Translation & Initials

• The “D-zh. R. R. Tolkin” cover sparks a detailed explanation of Russian practice: initials aim to match original sounds, so English J → “Дж.”
• Examples with English/French “Charles,” choices for representing W, and special cases of Russian authors’ own stylized initials are discussed.

Personal Memories & Pre-Jackson Imagination

• Several recall this Soviet Hobbit as their first “grown-up” book or first Tolkien encounter; its art still overrides the Peter Jackson films in their mind’s eye.
• Others reminisce about different pre-film illustrators shaping how they see hobbits, dwarves, trolls, and Gollum.

How People Are Actually Using AI to Code

• Several modes described: “vibe coding” whole features from short prompts, using AI as super‑autocomplete, rubber‑ducking / explanation, or doing long, structured back‑and‑forth design sessions.
• Many say AI works best on small, well‑scoped tasks (100–200 LOC, boilerplate, glue code, simple Rust/React bits) or for reading unfamiliar code and summarizing docs.
• Some senior devs claim they now “only program using LLMs” but always review, refactor, and keep architecture decisions human‑driven.

Perceived Benefits

• Faster first drafts, fewer keystrokes, and removal of tedious work (CRUD, React boilerplate, parsing helpers, UI they’d never have built otherwise).
• Helps juniors or non‑experts tackle languages and projects they previously found too intimidating.
• Frees some to think more about architecture and higher‑level design instead of low‑level details.

Risks: Laziness, Lost Understanding, and “Slop”

• Many echo the article: letting AI design and write large chunks leads to poor “bird’s‑eye” understanding and painful maintenance.
• Reports of convoluted codebases, stylistic mess, security issues, and “mountains of barely working slop,” especially in open source contributions.
• Some reviewers notice teammates firing off AI‑generated PRs, relying on others to catch obvious bugs, optimizing for perceived velocity.

Prompt Engineering and “You’re Holding It Wrong”

• One camp insists skillful prompting/context is essential; AI should be treated like an overeager junior whose work must be guided and checked.
• Critics push back that “you prompted it wrong” is unfalsifiable and shifts blame from real model limitations; deterministic, predictable behavior is still lacking.

Productivity Studies and Experience Gaps

• A frequently cited study on experienced OSS devs reported a 19% productivity decline with LLMs.
• Others cite different studies showing 25–55% gains, especially for junior developers.
• Several suggest: AI seems more beneficial for less experienced devs; for seniors the picture is mixed and highly workflow‑dependent.

Proposed Middle Ground / Best Practices

• Use AI as a force multiplier, not an architect; never ship code you don’t understand.
• Keep your own checklist of pitfalls (security, correctness) and fold recurring issues back into prompts or project docs.
• Restrict AI to well‑defined tasks, write design docs first, review every diff, and sometimes deliberately code without AI to avoid skill atrophy.

Cost, Debt, and ROI Calculus

• The $130k price tag for a US state-school CS degree is widely seen as “huge” and potentially unjustifiable, especially given today’s weak junior job market.
• Several note that a large chunk of that cost is living expenses, which exist regardless, but tuition has still become “absurdly expensive” even at non-prestigious state schools.
• Some argue six-figure debt is an unnecessary burden for a career path where skills can be self-taught and entry-level roles are shrinking.

Hiring, Credentials, and Visas

• Many report that in practice almost all candidates they see in big tech have degrees; some teams explicitly filter out non-grads.
• Others say the degree is rarely discussed in interviews and mainly serves as an HR gatekeeper.
• A degree is often required for work visas and for some government roles, regardless of field.
• There’s disagreement on the value of open-source portfolios: some say almost no one looks, others say they do and have hired from them.

Learning, Fundamentals, and Soft Skills

• Pro-degree voices emphasize structured exposure to algorithms, OS, compilers, graphics, etc., plus discipline, teamwork, and communication skills gained in college.
• Several say the non-CS courses and “growing up” aspects of university (liberal arts, social life, soft skills) were more valuable than the coding itself.

Alternatives and Cost-Reduction Strategies

• Popular suggestions:
  • Start at community college, use AP/CLEP to skip gen eds, then transfer.
  • Choose math or another “hard” discipline and learn programming independently; or major in something else and minor in CS.
  • Foreign universities (e.g., Germany, Finland, Australia) or online programs (e.g., University of London via Coursera) as cheaper, legitimate degrees.
  • Co-op/internship-heavy schools to graduate with experience and minimal debt.
  • Freelancing/small-business work as an alternative “internship pipeline.”

AI, Job Market, and Future Uncertainty

• Several are pessimistic: junior roles are scarce, competition (including H‑1B/master’s grads) is intense, and AI reduces the need for entry-level “grunt work.”
• Others argue CS will matter more as automation grows; AI is complex, math-heavy, and good practitioners will remain in demand.
• Some believe AI abstractions will commoditize much work; others say real competence will still require deep understanding.

Advice for a Highly Advanced Teen Programmer

• Many note he’s atypical and already beyond intro CS; strong recommendation to test out/skip lower-level classes if he goes.
• Some suggest leveraging his CS strengths and using college to gain complementary skills (business, finance, other engineering fields) rather than paying to relearn what he knows.
• A minority view: if he can’t access a top recruiting school and must pay full freight, a CS degree may not be the best use of $130k.

Early Industrial Tech and the Wright Brothers

• Several comments dig into how “bicycle mechanics” were actually working in the high tech of the time: precision bicycle manufacturing, wind tunnels, propeller theory, wing-warping patents, etc.
• This is used to reframe them as serious technologists, not lucky tinkerers, and to show how the bicycle boom fed directly into aviation.

Cultural Shocks, Art, and Myths

• The Rite of Spring riot prompts discussion of similar culture clashes (Astor Place Riot, Schoenberg’s Skandalkonzert).
• Some argue the famous “all hell broke loose” accounts were heavily mythologized; later performances went smoothly and the music is now seen as accessible and exhilarating.
• Classical music and opera then were mass culture, not just for elites.

Time, Speed, and Loss of Freedom

• Ancient complaints about sundials are compared to modern scheduling and time discipline: “what becomes measurable becomes controllable.”
• Some see clocks and per‑hour scheduling as eroding organic rhythms of life (work until done, then rest), a theme extended to cars, trains, and “diseases of speed.”
• Pascal’s line about being unable to sit quietly is revisited in a world full of digital distraction and loneliness.

Noise, Cities, and Somatic Anxiety

• Historical “neurasthenia” is linked by some to brutal city conditions: overcrowding, constant traffic noise, smoke, lack of sewers, thin social ties for new migrants.
• Others discuss modern analogs: low‑frequency industrial noise, trains, planes, and “The Hum”; sometimes complaints are psychosomatic, sometimes there’s a real but hard‑to-diagnose physical source.

Drugs, Radioactivity, and Being ‘Coked Out’

• Several note widespread early-20th‑century use of cocaine, morphine, and later amphetamines, suggesting this likely amplified anxiety and volatility.
• Others highlight naive enthusiasm for radioactivity (radium products, shoe-store X‑rays), later replaced by fear after accidents, as an example of oscillating public sentiment toward new tech.

Communication Revolutions and Continuity

• Books like The Victorian Internet are cited to show the telegraph created many “modern” phenomena: low‑latency markets, online‑style chatter, long-distance romance, legal debates about remote contracts.
• Commenters argue today’s internet/AI wave fits into a long chain of 2%/year growth “revolutions” rather than a singular break with history.

Cars, Suburbs, and Modernity’s Trade‑offs

• Multiple threads lament car‑driven urban form, sprawl (e.g., New Jersey), and the externalization of costs (noise, pollution, climate).
• Some contrast tribal or premodern lifeways with industrial modernity, arguing our value system is self‑justifying; others stress the undeniable gains in medicine and comfort.
• The article’s “we lost our minds” framing is questioned: were we actually losing humanity, or just struggling through a massive material and social transition whose benefits and harms we still haven’t equilibrated?

Program strategy & XB‑1 demonstrator

• Some see XB‑1 as classic investor-pleasing vaporware: a small, non-representative prototype flown a few times, retired quickly, then used to claim near-term readiness for commercial supersonic.
• Others with aerospace experience say this looks like normal spiral development: a focused test platform sharing only selected subsystems with the final aircraft, retired once required data is collected.
• There’s debate over whether retiring XB‑1 so soon is prudent risk management or a sign that the company is afraid of a crash harming fundraising.
• Comparisons are made to historic Concorde-related demonstrators and earlier privately funded supersonic efforts, with some calling Boom’s “first independently developed” phrasing mostly PR.

Engines as the critical bottleneck

• Multiple comments emphasize that the custom “Symphony” engine is on the critical path; without it, the airframe is just a glider.
• Rolls‑Royce’s withdrawal after years of study is read as a strong negative signal on technical or commercial feasibility.
• Developing a new airframe and a new powerplant simultaneously is widely described as a known “don’t do this” in aviation due to compounded risk and schedule.
• Commenters question claims that a large range loss from fuselage changes was fully recovered via rapid engine redesign.

Market, economics, and cabin design

• Some argue the real bottleneck for travelers is ground time (security, boarding, gates), so speed gains don’t matter much, especially on shorter routes.
• Others note that for long transoceanic or inter-hemispheric flights, hours saved in the air are significant, especially for business travelers.
• There’s extended discussion of premium cabins: recent trends toward more high-yield seats, all‑business configurations, and whether airlines would trade fuel efficiency for a better cabin.
• Many doubt the “airliner” positioning, predicting a niche product for ultra-wealthy or all‑business operations, if it flies at all.

Software, simulation, and “AI”

• The described mkBoom parametric design/simulation system impresses some as powerful design automation and integrated analysis.
• Others say this is just standard multidisciplinary design optimization with scripting around existing CFD/FEA/CAD tools, rebranded as “AI” and “proprietary” for investors.
• There is concern about management expecting every engineer to “leverage AI,” with worries about safety-critical design using opaque tools.
• Questions are raised about how full-aircraft simulations are actually done, with informed speculation about a stitched-together stack plus empirical models.

Timelines, certification, and PR tone

• FAA certification alone is cited as 5–9 years for a new aircraft, leading several commenters to view a 2029 entry-into-service target as unrealistic.
• Some see the article as aimed at tech-style “move fast” investors rather than reflecting the slower, heavily regulated reality of aerospace.
• A few even wonder if repetitive phrasing in the article suggests AI-assisted marketing copy.

Scale and Meaning of South Korea’s Population Decline

• Some see South Korea and Japan as having “won” economically but now facing demographic self‑erasure.
• Others argue “collapse” is overstated: current populations are far larger than mid‑20th‑century levels, and some shrinkage might be environmentally beneficial.
• Counterpoint: a 0.7 fertility rate implies extreme aging and >75% population loss over a century, which many consider a genuine systemic threat.

Debate on Long‑Term Projections

• One side: century‑scale extrapolations are “just math” and vital for planning (e.g., inverted age pyramids).
• Other side: assuming current trends’ second derivative is constant is seen as naive; historical fertility has shifted unpredictably.

Economic, Welfare, and Military Implications

• Concern that too few workers will support many retirees, collapsing pension systems and constraining problem‑solving capacity.
• Fears of retirees voting themselves unsustainable benefits.
• Military manpower shrinkage is framed as an early, measurable symptom.

Causes: Culture, Urbanization, and Work–Life Balance

• Many comments blame hyper‑capitalist, overworked cultures (SK, JP, increasingly India, US) that make family life unattractive or unaffordable.
• Others emphasize changing preferences: people choosing lifestyle, media, and careers over children, even with subsidies.
• Women’s empowerment and urbanization are both cited as major drivers of low fertility.

Effectiveness of Pronatalist Policies

• Examples from Nordic countries, France, Eastern Europe, Russia, South Korea, and Hungary: incentives raise fertility slightly or temporarily but don’t reach replacement.
• Some argue current programs are too weak; “real” pronatalism would mean free childcare, healthcare, transport, large housing and cost subsidies, and long, well‑paid parental leave.
• Others think even very generous schemes can’t overcome the perceived effort and opportunity cost of multiple children.

Immigration, Identity, and Alternative Paths

• Europe and the UK are described as offsetting decline with immigration, raising debates about integration, culture, and “replacement” narratives.
• Some see immigration as the only realistic lever; others call it zero‑sum or politically destabilizing.
• A minority argues illiberal/authoritarian measures (tax penalties, compulsory childbearing, bans on “distractions”) might be tried, but most note such paths would be dystopian and untested.

Persistent Push for Mass Surveillance

• Many see Chat Control as the latest in a long line of near-identical surveillance proposals (compared to the Data Retention Directive, PRISM, Patriot Act/FREEDOM Act), repeatedly reintroduced until one finally passes.
• Commenters argue the political incentive is asymmetric: authorities only need to “win once”, while civil society must block each iteration.
• Some suggest structural fixes: supermajority requirements for reintroducing failed laws, or constitutional-level privacy rights that are extremely hard to amend.

EU Institutions, Power, and Website Accuracy

• There is extensive clarification of how EU lawmaking works: the Commission proposes, the Council (member states’ governments) and Parliament (MEPs) must both approve.
• The current initiative is being driven at Council level, with Denmark’s presidency pushing for a quick vote; Parliament is seen as more skeptical.
• The site fightchatcontrol.eu is criticized for initially marking many MEPs as “supporting” by default based on their government’s position; this was later adjusted to show “unknown” with a country-colored border.
• Some worry that once such a law passes, Parliament cannot unilaterally repeal it; only the Commission can initiate repeal.

Exemptions for Politicians and Elitism

• A central outrage point is that EU politicians (and possibly law enforcement) would be exempt under “professional secrecy,” which is taken as implicit admission the system is insecure and dangerous.
• This is framed as “rules for thee, not for me,” reinforcing perceptions of an emerging “feudal” or elitist order, with ordinary citizens treated as serfs.

Technical Nature and Limits of Chat Control

• The core threat is described not as “breaking encryption” mathematically but as mandating client-side scanning before encryption, destroying the end-to-end trust model.
• Anticipated enforcement mechanisms: compelling major apps to integrate scanners, banning non-compliant services from app stores, and later extending to OS- or hardware-level controls (often linked in discussion to a separate “ProtectEU” roadmap).
• Several note that serious criminals will just layer their own encryption or steganography over any mandated channels, so the system mainly affects ordinary users.

Age Verification, Porn, and Scope Creep

• Chat Control is discussed together with EU moves toward mandatory age verification (including a little-noticed amendment threatening prison time for operators that don’t implement “robust and effective” verification for porn).
• Some support restricting minors’ access to porn; others argue it’s unenforceable, pushes users to riskier sites, and normalizes global ID checks.
• A separate EU “Digital Identity Wallet”–based age-verification scheme is mentioned as more privacy-preserving in design, but many distrust any centralized infrastructure.

Politics, Blame, and Public Attitudes

• Debate over whether this is a left/right issue: some blame “liberals” or “the left,” others point out that civil-liberties-oriented left (Greens, some left groups, Pirates) generally oppose Chat Control, while many establishment parties of both sides support it.
• Widespread cynicism about democracy’s effectiveness: some see EU bodies as remote, technocratic, and shielded from consequences; others insist democratic pressure has delivered past victories and must be maintained.
• COVID-era emergency measures are cited by some as proof the public will accept far-reaching controls; others reply those measures were temporary and saved lives.

What Individuals Can Do and “Plan B”

• Common concrete actions: email or call national ministers and MEPs, support digital rights groups (e.g., EDRi, national NGOs), donate to privacy projects, and raise awareness of misrepresentations.
• Some discuss technical “plan B” options if the law passes (self-built apps, decentralized messaging, Tor, LoRa/mesh, local pre-encryption), but many stress that technical workarounds alone cannot solve a fundamentally legal–political problem.

Parsing quirks and “noisy” syntax

• Some find Zig’s parser too strict: e.g. needing a space before orelse and unclear errors when structs aren’t wrapped in .{}; this is especially painful for people with RSI.
• Several commenters describe Zig as visually noisy, disliking @TypeOf, .{} literals, and sigils like @ and leading .. Others say this “brutalist” style improves predictability and removes surprises.

Struct literals, .{}, and type inference

• Defenders of .{} / .x = argue it avoids redundant type names in nested initializations and is less noisy than Rust’s explicit Type { field: ... }.
• Critics note a planned removal of explicit T{} weakens the “dot means inferred type” story and makes code harder to navigate and click-through.
• Debate over whether the leading . in .{} is worth the parsing convenience; some would prefer dropping it.
• There’s comparison with C99’s designated initializers as a “gold standard” that Zig and Rust still don’t fully match.

Lambdas, closures, and runtime polymorphism

• Zig’s lack of lambdas/closures surprises developers coming from C++/Rust. Common workaround is named functions plus an explicit “context” parameter or manually defined vtables.
• Supporters say this keeps allocation and control flow explicit and avoids hidden heap use; critics counter that capturing needn’t imply heap allocation (citing C++/Rust), and manual vtables are verbose.
• Function syntax (fn foo(a: i32) i32) is seen by some as blocking elegant lambda/arrow forms; others note Go manages anonymous functions with a similar declaration style.

Multiline strings and line comments

• Zig’s multiline raw strings using \\ prefixes spark strong reactions.
• Proponents argue it’s a clean, indentation-safe solution that keeps newlines unambiguous, and becomes comfortable with tooling and highlighting.
• Opponents find it visually “insane”, confusing next to // comments, and prefer traditional """/backtick/“dedent” schemes used in Kotlin, Go, Java, C#, etc.

Type placement, let-style binds, and tooling

• Ongoing type-syntax debate: some prefer name: Type (Rust/Zig/Pascal style) for parsing simplicity; others want Type name for faster visual type lookup.
• There’s tension between designing for greppability vs assuming advanced IDEs; some insist CLI grep/ripgrep is still crucial on large codebases, others argue IDE search is strictly superior.
• Concerns are raised that heavy comptime and inferred types may be unfriendly to LSP/intellisense.

Other syntactic/design flashpoints

• No operator overloading is seen as blocking “lovely” vector/matrix syntax; suggestion of explicit overloaded operators (like #+) meets pushback over symbol soup.
• Some praise Zig’s try/catch and loop-as-expression model; others miss Rust-like implicit block returns, optional-chaining (a?.b?.c), and monadic-style APIs.
• Meta-thread: many argue syntax does matter as the primary interface to a language, but preferences are highly subjective; several compare Zig unfavorably to Kotlin, Go, Ruby/Crystal, or D, while others emphasize Zig is “fun to write” and more readable in large, industrial code.

Overall reception and value

• Many commenters like the core idea: a centralized search across high‑quality engineering blogs, helpful when learning new technologies or seeking deep dives.
• Some say they’d use it weekly if performance and UX improve.
• A few question the premise that the “best insights” come from big-company blogs; they find that claim overstated.

Performance, UX, and Cloudflare issues

• Multiple reports of very slow search and filtering, especially on mobile; these were later partially addressed.
• Suggestions to debounce search input to avoid firing too many requests, and to improve filter responsiveness.
• Complaints about Cloudflare challenges: captchas, CPU‑intensive checks that can lock up browsers or drain batteries, described as effectively “DoS-ing” the client.
• Dislikes for infinite scroll and lack of a visible scrollbar.

Feature requests & roadmap ideas

• Strong demand for RSS: both a master feed and possibly user‑defined feeds; also date filtering.
• Requests for filters by topic (including excluding AI/LLM content), language tags (e.g., C#, ASP.NET), relevancy/“hottest” sorting, and ordering by latest.
• Ideas for user accounts to curate personal lists, upvotes, and a weekly automated newsletter of top articles.
• Suggestions for Fediverse/ActivityPub integration.

Content scope, sources, and curation

• Many requests to add specific company blogs (Netflix, Fly.io, Ramp, ClickHouse, JetBrains, etc.) and to expand far beyond the initial ~16 companies.
• References to large public lists of engineering blogs and OPML blogrolls as seeding sources.
• Debate over breadth: some argue for a tightly curated 10–20 best blogs; the author leans toward hundreds plus user‑level curation.
• Discussion of using RSS as a standard vs. its incompleteness for full historical indexing; question raised about using AI for custom parsing.

Alternative tools & related projects

• Several related projects mentioned: personal-blog search engines, more social link aggregators, topic-focused “lenses” on other search engines, and general developer news aggregators.
• Feedback on these tools includes parsing quality, summaries vs originals, randomness vs daily “front pages,” and trust concerns around browser extensions.

Meta discussion: engineering and RSS culture

• Side thread on “engineering” being de facto narrowed to software/AI and frustration about title dilution vs licensed professions; counterpoint that licensed titles (e.g., P.Eng.) still carry meaning.
• Nostalgia for the “RSS era” and arguments that RSS is still alive and useful for discovering and following blogs.