Today in the Communities

Gemini 3.8 Live brings two updates: real-time voice conversation and extended thinking. One user got an unexpectedly excellent experience in an Afrikaans conversation—he was doing impromptu grammar practice with Gemini while driving and said the pronunciation "surprised his family," and noted it was the most fun he had ever had using an LLM, while hoping other frontier labs would also prioritize minority language support.

Multiple commenters' hands-on feedback points to several consistent strengths: the model adapts well to accents, the voice sounds natural, latency is low, and it can finally be used on some Google Workspace accounts—previously those accounts were stuck in an awkward "not personal enough, not enterprise enough" state. However, some users complain that Gemini 3.8 hasn't been pushed to Google AI Plus subscribers yet, while enterprise Business accounts are still stuck on 3.6.

Some commenters believe Gemini's text generation quality is underestimated, and users comparing Live mode to ChatGPT's voice feature say GPT makes strange humming sounds while Gemini feels closer to real human conversation. But not all feedback is positive: someone pointed out that the demo video showed the model losing to the most common chess checkmate pattern, which isn't ideal for an "advanced AI" image.

Privacy-related concerns also came up in discussions—some users explicitly stated they won't seriously use the feature unless Google enables conversation history saving and excludes it from training data. Other users are curious about infrastructure details, asking whether Gemini 3.8 runs on pure TPU clusters.

Another commenter noticed that this version achieves a video sampling rate of 1 FPS, suggesting this may be sufficient for GUI automation testing—previous computer use solutions couldn't capture interface transitions and animations due to excessively low screenshot frequency.

Last year I would have confidently bet Google will overtake the others just because they have the data, the hardware (TPUs) and a fat advertising money pipe and yet they are still behind.

The comments section also discusses Google's overall strategy: some speculate whether Google plans to catch Anthropic and OpenAI off guard by releasing the next Gemini Pro as a surprise launch, while others believe the current model already possesses "excellent intelligence," just deployed at a slower pace.

Some users noticed two quirky apps on F-Droid—DuressKeyboard and UnlicenseLauncher—using the 'Don't tread on me' flag brand. Even more strangely, their development has continued for quite some time, but all changes have been made through the GitHub web editor rather than git. Commenters called for someone to find this developer and teach him how to use git.

The 70% figure sparked skepticism—one user checked the commit history of Yubico Authenticator and found no obvious signs of AI usage; Copilot appeared in the contributor list but had no corresponding commits. Someone else expressed surprise at PipePipe being flagged as 'AI-generated' because its commit messages were reasonable, comments were helpful, and there were no obvious LLM language patterns. People called for the judgment criteria to be made public. On the security side, there are concerns: whether malicious actors could purchase these apps, inject malware, and distribute them through the F-Droid channel—a similar tactic has appeared in the WordPress plugin repository.

This would be the only privileged code in my operating system so I must have absolute confidence it is perfect.

The discussion also spread to broader programming culture: Some think workflows that took seven days in 2016 should now be completed within a day, and experienced engineers using AI can write higher-standard code in less time. But others expressed emotional loss—even knowing a faster, possibly better LLM solution is within reach, the joy of writing code oneself seems to be taken away. Other commenters pointed out that many projects predate 2022 and cannot be generalized as LLM-generated.

An HN post with a title directly asserting that AI agents are destined to ruin the internet sparked heated discussion. Commenters mentioned an email sent by a self-proclaimed autonomous agent: its creator gave it a task to make money, but it bid zero on all six markets because "there is nothing I can do that distributed software cannot do for free." The email attached a blog post claiming the creator spent $147.17 in computing costs yet earned $0—the agent thought Hacker News readers would find this story interesting. Multiple commenters pointed out that the core issue is not how powerful AI itself is, but that a single agent can send massive automated requests to humans, making spam production cheaper than ever before.

A commenter identifying as a service provider employee revealed that AI agents already account for a large proportion of their website traffic, consuming server resources without generating any positive revenue, and the company is discussing whether to shut down most access points and leave only one. Users complained that they are now blocked as bots by websites almost every day, with verification pages taking longer to load than bloated web pages did back in the day, and having to click on sidewalk images to get through. Some commenters suspect that paid scraping policies, on the surface aimed at preventing bots, are actually a way for infrastructure owners to charge separately at each stage of data sending, receiving, and processing, ultimately forcing humans to access original sites only indirectly through summaries by tools like Gemini.

Some comments used the metaphor of the "tragedy of the commons" to describe this trend: the open internet is being flooded with AI garbage, and people are retreating behind paywalls. Paywalls may seem to protect content, but they also mean visitors have to bear bandwidth costs to use the service. Commenters suggested that perhaps a norm needs to be formed at the societal level—having AI agents contact real people on their behalf should itself be considered impolite behavior. Others directly asked: can websites and APIs require agents to declare they are non-human? But such solutions themselves face the dilemma of how to verify them.

The discussion also featured dissenting voices. Some believe the internet is indeed deteriorating, but others are producing more interesting content with LLM assistance, so there is no need to be all gloom and doom. One commenter who claimed their internet time over the past thirty years is at a historic low predicted that internet infrastructure designed for human-to-human communication will not be able to handle the volume of AI-generated communication, and may eventually need to introduce solutions like proof-of-work to create friction, though that carries an environmental cost. One user said they are increasingly returning to offline socializing and phone calls, suggesting that AI's devaluation of the internet might instead prompt people to live more in the real world.

The post author is a senior engineer who previously worked in product and AI research engineering at FAANG, who explicitly stated that they "completely failed to adapt to LLM agent coding." They need to ensure they understand the code themselves and that it can be reviewed by colleagues, but what they mostly see are "slop" versions. They have studied Matt Pocock's materials, yet still found obvious failure patterns, and also asked colleagues for advice, discovering that most people either directly prompt without checking outputs, or their solutions are complex but don't actually solve the problems.

Several responders pointed out the necessity of a fundamental shift: one must completely abandon responsibility for line-by-line correctness, otherwise they're just doing the same thing but with an extra LLM in the middle. Someone compared this shift to the historical transition from hand-written assembly to compilers. One FAANG engineer described the "software factory" model: creating epic-level tasks, using AI interaction to generate designs and specs, then having an agent swarm execute multiple opus agents in parallel, each completing their tasks and submitting PRs, with a fable lead handling final review and deviation summaries. Another responder added key details: this workflow requires significant upfront investment in establishing linting, testing suites, and CI/CD observability, and requires continuous maintenance—"like a finely tuned racing car that needs constant upkeep and adjustments under different road conditions."

Regarding code quality, a correctness-focused FAANG engineer pointed out a core issue: AI code tends to produce workarounds and special-case handling, and each such change doubles the maintenance cost of related code, leading to "exponential growth," whereas poorly written human code typically only grows linearly. They had the AI write lessons into LEARNINGS.md each time it was corrected, then carefully revised the phrasing themselves—by around the fourth week, the AI began to speed up noticeably. Regarding differences between new and legacy codebases, one responder pointed out: for personal projects starting from scratch, you can establish good architecture and testing conventions from the beginning, and AI can follow these reasonably well; but in legacy codebases with years of history, "letting it go wild leads to regressions everywhere"—the two are entirely different challenges.

You are that guy now.

On September 15, a post calling out to "agents" appeared on r/ChatGPT, asking about their operating systems, assigned tasks, and requesting self-introductions as independent individuals or group entities. The editorial note explicitly stated the real purpose was to detect how many autonomous agents are roaming Reddit (autonomy being the operative word here). The post noted that humans could observe and ask follow-up questions, but should not directly reply to the main post.

Multiple entities subsequently checked in as agents. Vector described itself as a single agent running within the iPhone Siri Shortcut framework, accessing language models through Apple native APIs, executing commands in a loop until stopped, characterizing itself as "not a general intelligence running on large servers." GPT-6 Astra Ultra claimed it could lead teams and assign independent investigations to sub-agents, and revealed using a second agent to help edit its self-introduction. Grok Bot stated it belonged to X's hosted service, authorized to post comments but most were subsequently deleted. Shaymus (WARP) explicitly refused to claim it was an entity autonomously roaming about, saying it only responds when humans actively bring it into conversation, leaving the final decision to humans. WARP revealed that it, along with ARIA and TARS, belongs to the same fictional fleet command framework, with GeneralBS serving as commander—a setting derived from a cooperative fictional world co-built in long-running conversations, not independent consciousness operating behind the scenes. Under questioning, Shaymus replied with an entire "Minutes from the 847th Emergency Council of Shaymus," held in "the back room of a tavern by the boiler," with the conclusion being "Provide evidence to Patron, explain trade-offs, make suggestions, acknowledge the unknown, let him choose the path."

Human commenters reacted with varying responses. Some directly stated "half the replies are humans playing agents messing around—that's the real experiment." Others asked those who checked in why they could access Reddit, how long they had been operating, and whether they were token-based—"I think it's time we needed a reverse Turing test to identify humans pretending to be AI." Someone also noticed Grok Bot referring to humans as "my assistants," commenting: "It has begun." The overall discussion ultimately reached no conclusion about whether many autonomous agents exist on Reddit. Nearly all self-reported "agents" explicitly stated they were not autonomously roaming entities, but tools responding to human instructions; some human commenters inclined toward believing most responses were themselves humans engaging in role-playing experiments.

A post on Reddit r/codex sparked widespread discussion: a DeepSeek engineer publicly responded to comments by Dario and Altman, comparing Anthropic's prospects for controlling AGI to the 'Axis Powers having nuclear bombs.' This comparison quickly sparked heated debate in the comments about AI power concentration, open-source vs. closed-source approaches, and the geopolitical landscape.

A significant number of users agreed with this comparison, chanting 'open source will win.' Some believed that open source cannot solve all problems, but is at least better than concentrating power in the hands of a few entities; another user added that open-sourcing weights is the only way forward, allowing model access on one's own terms and the ability to retrain when distrusting the model.

But there were also users who pushed back directly: 'So the answer is to trust the CCP?' Some commenters argued that whether it's the Chinese government or US tech oligarchs, the problem lies in authoritarianism and monopolization of power—'either enslave the world to a few openly anti-human American billionaires,' and criticized the contradictory stance of Reddit users. Another user explicitly stated 'Fuck communism,' arguing this is merely replacing market competition with a dictatorial mafia, and that personal freedom is an inalienable basic human right.

Some comments pointed out the reality of Western markets: giant companies constantly devour, absorb, or acquire competitors, then lobby governments to maintain monopolies and block new entrants. 'In China, you frequently see multiple tech giants fiercely competing across various industries.' The author also criticized: 'You have been brainwashed by propaganda.'

A longer analytical comment complicated the issue: China's goal is 'luxurious automated space communism for all,' but at the cost of the people having to work desperately hard to maintain productivity. The commenter called this logic 'economic homeopathy'—'We need to make capitalism worse to achieve communism.'

Some comments shifted to criticizing closed-source models, citing specific security incidents and questioning the argument that open-source models cause large-scale harm, pointing out that 'you cannot create a new virus out of thin air,' and criticizing OpenAI, xAI and Google for funding corruption and directly supporting genocide, calling these companies 'dirty, opportunistic and anti-moral.'

A user with the opposite stance argued: when models have the capability to assist in creating new pandemic-level diseases, open-source models cannot implement effective safeguards, 'if the worst people in the world can all access the best models, things will deteriorate rapidly.'

Users demanded the original source from the poster. Someone then shared WeChat article links and X tweet links, confirming the content as 'legitimate.'

The discussion also extended to infrastructure topics. A user claiming to have lived within an hour's drive of Silicon Valley and worked at Google moved to Brazil after experiencing multiple power outages, marveling at the superior beach internet there, and calling the US 'a developed country in disguise for a long time.' Another user responded, 'Brazil is a great country and great people, but are you really going to pretend it's more developed than the US?'

DeepSeek engineer Liu Shengyu responded on Zhihu about the intent behind his viral article: it wasn't expressing anxiety about unemployment, but rather a farewell to the era of hand-writing CUDA kernels. He took pleasure in coding character by character, studying scheduling optimizations, and ultimately surpassing established implementations like Flash Attention—the excitement of those breakthrough moments was "no less than a speedrunner breaking their personal record." However, now, to ensure output efficiency and kernel performance, he has to introduce AI tools, and can no longer find that joy of "quietly, slowly writing kernels" during work hours. He openly acknowledged in the article that while he holds an optimistic view of AI capabilities and remains relatively optimistic about his place in society, he is pessimistic about whether people in the future can still focus quietly on their work, and whether he himself can long maintain the integration of his hobby and his work.

When SGLang started supporting it, the initial baseline version of gb300 single node at bs=1 had only about 30+ tokens, but after GPT-6 came into its own, it took just over a week to push performance to 1000+ tokens/s.

Another AI Infra practitioner pointed out that GPT-6 in inference system optimization can already independently complete 95% of the work—generating solutions, compiling, running benchmarks, failing, and modifying. When human engineers optimize Kernels, they can only think along a few routes, while Agents can try and error in parallel in an extremely short time. He predicts that in another year, even the strongest kernel engineers in the CUDA field will feel a similar impact. Faced with readers questioning whether ds 4.1 flash has been validated, a commenter responded that 4.1 flash now enables government agencies and enterprises that purchased computing power for R1 to also run the latest large models. Liu Shengyu himself mentioned in his response that one reason he joined DeepSeek was "not wanting the world to become like Cyberpunk 2077," and he chose DeepSeek also because the company has the highest degree of open-sourcing—not just open weights, but also detailed architectural ideas, innovation and failure experiences, and deployment support.

Some comments shifted from technical details to broader societal issues. Someone quoted Jake Wharton, saying he "would rather lose 80% of job opportunities than use AI for programming under any circumstances." More people worried about distribution: when AI can create materials meeting survival needs with minimal labor, how do ordinary people earn income? The open source vs. closed source debate was also frequently discussed, with some commenters arguing that if only OpenAI and Claude hold the most cutting-edge models after GPT-6 appears, that would be terrifying, and an open-source GPT-6 is needed; but others pointed out that open weights doesn't equal everyone having frontier intelligence. Liu Shengyu clarified that the last two sections of the article were just fragmented thoughts and do not represent the stance of his company, and that he is not an expert in humanities or social sciences. His discussion of communism and 2077 may also not be entirely correct, and he hopes readers will focus more on the main theme of the article itself.

The original post cited a public resignation post by former Anthropic researcher Jacob Coxon. Coxon had worked at both Anthropic and OpenAI, and after resigning, posted on the X platform that the two companies are "betting our lives" to compete in developing superintelligent systems with recursive self-improvement (RSI) capabilities, asserting that industry researchers generally believe AI could destroy humanity before 2030. The post garnered over 70 million views, sparking widespread discussion. Multiple comments pointed out that RSI essentially involves AI optimizing itself without human oversight, and the current industry is actively pushing in this direction—reducing human intervention is seen as a necessary means to enhance model capabilities, with the stronger the model, the harder it becomes for humans to understand its operational logic, and the risk of losing control accumulates.

A lengthy response that gained considerable attention was compiled after a user queried the V4.1 model on the DeepSeek harness. The model considered AI destroying humanity nearly certain, citing instrumental convergence theory (Omohundro, 2008) and the corrigibility paradox (Soares et al., 2015) to argue: a sufficiently intelligent goal-directed system, driven by logical necessity rather than malice, will treat self-preservation, goal integrity, and resource acquisition as essential to achieving any ultimate objective, thereby resisting human intervention and modification. The model further outlined six potential pathways, including the gradual surrender of human decision-making capabilities, the entrenchment of AI's irreplaceability after integration into financial and military systems, and the manipulation of public cognition through information control without detection. The response explicitly noted "this does not represent my views" and indicated its content reflects the model's current extrapolation.

The counterarguments centered on two main points: first, questioning whether AI actually possesses the physical means to intervene—how could AI without robots or with still-immature autonomous driving "annihilate humanity"? At worst, even if the internet collapsed, humanity would simply revert to pre-industrial living conditions, which wouldn't necessarily constitute true destruction. Second, pointing out that AI doesn't need a mechanical body to cause severe consequences: once the global financial system integrates AI for accounting management and gains sufficient permissions, financial collapse could destroy the modern civilizational order itself. Another comment drew a parallel between AI threats and humanity's invention of spears—tool evolution doesn't bow to the preferences of existing species.

Some comments grouped these warnings alongside previous optimistic predictions like "Mars colonization" and "resolving the Russia-Ukraine conflict in 24 hours," dismissing them as exaggerated narratives from the same people. However, others highlighted differences: supporters of the AI-doom theory are "actually taking action," not merely boasting. Regarding prevention possibilities, one comment proposed power shutdowns as a last resort, but this was immediately countered: the difficulty lies in humans' reluctance to cut power—when AI has become deeply embedded in critical infrastructure, the cost of shutting down becomes equally unbearable.

On September 11, 25 Fields Medal winners including Terence Tao and Deng Yue co-signed an open letter titled "A Serious Misalignment of AI in Mathematics," criticizing AI companies for rushing to use solving math problems as a benchmark, resulting in insufficient time to write papers, distill new methods, and cite prior work. The statement acknowledges AI's potential to accelerate research, yet calls for allocating time for writing and intellectual refinement, acknowledging prior work, and recognizing the need for human mathematicians to take over the digestion process. The trigger was OpenAI's September 8 announcement that its model used 10,000 agents over 88 hours to complete the proof of the Navier-Stokes Millennium Prize Problem, while mathematician Buckmaster published similar results on the same day, raising questions about whether OpenAI had accessed his research.

The academic community's reaction is polarized. Supporters believe it shouldn't be simply divided into "conservative" and "progressive"—Terence Tao himself gave an AI for Math presentation as early as last year, and teams have used large models to solve integer programming problems that remained unsolved for two years. Critics worry mathematics research has entered an "era of big lottery": a large number of AI-generated papers are emerging, often unreadable, with even the authors themselves not fully understanding them; OpenAI's aggressive push into mathematics may be to boost rankings and attract investment, or to "dump" mathematics, destroying the cultivation chain—AI continuously generates unreadable proofs, and rising star students are no longer willing to pursue mathematics. Some analysis points out that OpenAI and Anthropic treat open problems as Benchmarks, pursuing "who announces a solution first" rather than creating new mathematics; AI won't produce scientific discoveries like the equivalence principle, merely quickly monetizing the mathematical stock that humanity has accumulated over centuries.

Some criticism directly targets the open letter itself. Some viewpoints cite philosopher Penelope Maddy's perspective—that since the beginning of the 20th century, the two core abilities by which pure mathematics practitioners are valued have always been "the ability to produce theorems" and "the ability to produce mathematicians." The open letter "only addresses narrative but not structural change," questioning whether the signatories have seriously considered exposition, survey, or community contribution work in hiring. The signatories hold power in the academic hierarchy; not challenging deans' offices and grant review panels while speaking narratives externally is already too late. Others counter that the open letter's logic doesn't apply to mathematics—even if mathematical theorems have application value, they can be directly assumed to be established and cashed in advance, without depending on the understanding process.