What Communities Are Discussing Today

Anthropic released related GLM articles on the same day, Trump was active in AI affairs, and the original post on LocalLLaMA asked whether people foresaw Chinese open-weight models being banned soon.

Non-US users mostly expressed lack of concern—the world outside the US would not be equally affected, and any ban, if implemented, would primarily be limited to within the US. But most US users tended to take this risk seriously.

Do you foresee Chinese open weight models getting banned soon?

Some commenters viewed Anthropic's article as aimed at promoting a regulatory ban rather than genuine safety research—branded as regulatory capture. Bad actors will not comply with export controls, and individual developers can still obtain models through mirrors or torrents; an enterprise-level ban, while possible under Trump, would in practice force US companies to continue paying API subscription fees to large AI providers. As for enforcement feasibility, user opinions were divided: Anthropic may be able to pressure large companies but would struggle to reach individuals and small businesses, since what these companies really care about is subscription revenue. However, some worry these companies would further push legislation to hold local inference service providers legally liable for model outputs.

On whether Chinese labs will continue releasing open-source models, user opinions diverged. One side argued that China's release behavior is driven by government policy, with open-source models serving as soft power tools unlikely to be abandoned unless a bigger change occurs; but the other side pointed out that Chinese labs are not operating out of charitable purposes, and the US market is an important incentive—if restricted, the motivation would be substantially affected. There were also comments suggesting that if a ban is implemented, labs would not continue large-scale training of new open-source models, which would lock progress at current levels.

On the likelihood of a ban, one user estimated roughly fifty-fifty: a ban could protect IPOs and extend the AI bubble, but in the long run the damage to the US would be serious—other countries would continue developing, and US companies would be forced to pay high fees to existing AI providers with quality not necessarily improving.

A user posted on r/codex with a title that directly hit the core feeling: "GPT-6.1 Sol feels infinite because it only generates 20 tokens per second." The post included a screenshot of a table claiming that after analysis assisted by Opus 5.5, the current text generation speed of 6.1 Sol is approximately 1/2.5 of 6 Sol and 1/2.3 of 5.6 Sol. The poster believed that paying users not only have their usage halved, but also have generation speed nearly three times slower, and directed the blame at the company's practice of "stripping computational resources away from users and giving them to internal experiments."

We asked for slow mode, we got slow mode.

Multiple comments stated they had also noticed the speed drop, with some calling it "slow as a slug" while still praising the quality of the model's output. Others cited specific numbers from the table, stating that Opus and Sol 5.5 could reach 120 tokens/s, while 6.1 Sol only reaches 30-40 tokens/s, calling this "the most excessive 'artificial usage extension' tactic OpenAI has ever employed."

Some comments linked the speed decline to recent model naming changes, speculating that OpenAI hastily renamed a version originally intended to be released as "Astra mini minor" to "Sol 6.1." Someone joked: "ultrafast mode just means getting back to the normal speed you had before." Other users offered a different perspective, suggesting that if the model indeed corresponds to a higher AI Index level, the speed trade-off might be worth accepting.

A clickbait post appeared on Reddit r/ClaudeAI with the title "OpenAI internal benchmarks show GPT-6.1 Sol crushing Opus 5.5, Anthropic can't keep up," but the attached image was not actually AI benchmark data. Shortly after the post went up, a user (pcw204f) pointed out: "Haha, this post has shown how many people have surrendered their critical thinking to AI. Folks, spend 15 seconds actually reading the image before commenting." Someone else replied confirming "it (GPT) 6.1 crushes 5.5 in version number and release date," also without looking at the image.

Another user (pcwp2dn) fed the same image to Gemini and asked where it placed, and Gemini's response made them laugh. Someone attached a screenshot of Gemini's reply—which was "even worse than the other one." Others noted that the real subject of the image was actually Windows versions, pointing out "What this actually shows is how ridiculously good XP is." The discussion expanded into comparisons of preferences for different Windows versions, with one user arguing the Xbox 360 was "far above everything else" in the Xbox lineup, and another calling the NBA 2K series the "most consistently year-over-year improved product line in industry history"—none of which were verified against the original image either.

A handful of commenters began to question the data source, with one user saying "I'm not sure I can trust this outdated data" and asking why no similar analysis was done for Windows 11. Others played along, joking about comparing version numbers ("6.1 crushes 5.5") or predicting Opus 7 would surpass GPT 6.15. The whole discussion revealed a common phenomenon: many people participate in discussions and reach wrong conclusions based solely on a title, without having read the image.

LatePost reported that Xiaomi released its promotion list on September 22, with 31-year-old Luofuli, head of the large language model team, promoted to level 22. Sources close to Xiaomi confirmed that level 22 is already the highest tier in Xiaomi's job level system, with further promotions primarily reflected in job title changes.

One respondent interpreted the context of this promotion: in July this year, Xiaomi split its AI technical capabilities into three segments, with the foundational model under Luofuli's MiMo team, edge under the phone and automotive OS team, and cloud engineering under Luan Jian. The responder compared the three to "brain," "hands and feet," and "pipeline," suggesting that Luofuli controlling the "brain" combined with a level 22 promotion means real power, resources, and rank have all fallen into place, elevating the LLM head to the core of the core.

A respondent identifying as an AI researcher provided a detailed analysis of MiMo V2.6's training costs: based on their observation of the livestreamed training process, RL post-training cost $700,000 per day, approximately $3.5 million over five days; combined with pre-training cost estimates, the total expenditure was approximately $12 million (about 80 million RMB, excluding prior R&D and experimentation costs). This respondent believed that being able to mobilize resources at this scale and take the team from "unremarkable" into the spotlight of open-source models makes level 22 justified; however, they also noted that issues like infra crashes during training, bad patterns appearing in rollout, and unstable reward convergence were promptly resolved by the team, serving as validation of execution capability.

Some responses carried negative assessments. Someone cited Luofuli's interview earlier this year where she mentioned having set large model usage as a team KPI, questioning whether the team lacked intrinsic motivation, arguing that top teams should be driven by vision. In response to this criticism, a commenter countered that "it's normal practice for researchers to personally use models for evaluation," and "eval is quite abstract, researchers need to handle it themselves."

Debates about Xiaomi's large model technical strength ran throughout the discussion. One respondent praised MiMo as consistently ranking among the top domestic tier despite "always having suspicions of score manipulation"; however, another comment claimed "Xiaomi's model is definitely massively distilled," mentioning controversies surrounding Huawei's Pan Gu model and Alibaba's Qwen, sparking follow-up questions asking "evidence?" with the reply "that would necessarily be the habitual thinking that Pan Gu distilled from Qwen."

On the topic of Xiaomi's talent strategy, one respondent drew parallels between Luofuli's promotion and automotive division's Hu Zhengnan's partner status, viewing this as Xiaomi's means of preventing core talent loss, and mentioned that scientists from the intelligent driving team had already left. This respondent suggested Xiaomi spin off automotive and MiMo separately, because "the group's total revenue is so limited, it's destined to not be enough to go around." A comment noted "the classic teaching Lei Jun how to play capital."

DeepSeek open-sourced infrastructure components for the Huawei Ascend platform on September 30, 2026, including the TileLang compiler tool, DeepGEMM-Ascend computing library, DeepEP-Ascend distributed communication library, TileKernels operator collection, FlashMLA, and DeepSelect, among others, each corresponding one-to-one with the previous NVIDIA platform versions. Commenters believe this "low-key and calm" release style is more powerful than exaggerated rhetoric.

As the core operator authoring tool, TileLang previously supported only NVIDIA; with the addition of Ascend, it can serve as a cross-chip universal abstraction layer. DeepGEMM-Ascend supports BF16, FP8, FP4 precision and DeepSeek architecture-specific operators. TileKernels covers common operators such as random number generation, quantization, and RoPE. DeepEP-Ascend solves MoE cross-card communication problems. FlashMLA is optimized for the DeepSeek MLA attention mechanism, and due to MLA being adopted by GLM, Kimi, Qwen and others, its generality is relatively strong. DeepSelect provides TopK selection for sparse attention, used in combination with FlashMLA. Commenters sighed that the number of these projects is "absolutely ridiculous."

In Liang Wenfeng's recent conference remarks, he explained the reasons why NVIDIA's CUDA moat is being dismantled in three aspects: AI can assist in building ecosystems, lowering the barriers to establishing new ones; high-level languages like TileLang can quickly replicate NVIDIA operators, with no significant obstacles when combined with AI programming; the compute card market has already surpassed gaming cards, making the historical coupling between the two unnecessary. However, he also pointed out that the technical route "appears to have no obstacles," while also stating "it is not yet complete, not yet finished." Another commenter reminded that adaptation work still requires human investment, and experienced AI talent is currently in short supply.

The DeepSeek official announcement mentioned Huawei team's "unreserved and strong support," with both parties jointly advancing a 128-card supernode solution based on Ascend 950, conducting deep optimization in computation and communication.

Some commenters believe this open-sourcing is almost like "sending money" for Huawei, deserving more compute cards as reciprocation; others used a joking tone to call for Ascend to provide DeepSeek with more hardware support.

The DeepSeek Harness v0.2 Preview was officially released on September 29, offering installation packages for macOS and Windows desktop. One user documented the installation and experience in detail: the website has strict IP requirements. Accessing from a non-mainland IP only displays particle effects with no download link; you need to switch to a mainland IP to get the installation package. During installation, if you want to change the path to the D drive, you need to manually create an empty folder first, otherwise it will report an error.

Although the approach uses an Electron shell to 'cover everything,' the actual core size is about 1GB (1,144 folders, 9,772 files), which is not excessive. The desktop client includes built-in standalone Python, Node.js, and pnpm. Python comes pre-installed with libraries such as numpy, pandas, python-docx, python-pptx, openpyxl, Pillow, lxml, and XlsxWriter for Office tasks. The version report does not include packages installed by the user. In terms of platform support, both macOS and Windows have adaptations (including arm64 and x64 versions), but it is explicitly stated that Linux is not a supported release target.

Combining the above two options, the desktop version is designed primarily for daily office workers on Mac and Windows who use Office productivity software.

Encountered obstacles during login: the human verification could not be passed, and finally switched back to intranet to log in via WeChat QR code scan.

Another user shared a more complete experience. Network restrictions are the main pain point: can log in directly under company network, but after switching to other exit IPs, it keeps spinning in circles, the account verification popup bounces back and forth, and only after restarting the client does it return to normal. Suggestion: use it in a fixed network environment. The plugin system has been improved—when it fails, it displays which specific plugin and which step had the error, rather than just throwing a generic error message. The desktop client is suitable for handling local files, scripts, and environment chaining tasks. Some users have used it to process tens of thousands of lines of logs for merging and anomaly location. The shortcomings include: the first launch requires downloading weights, resulting in a longer wait time; long conversation context management is not stable enough, occasionally forgetting previously set constraints, requiring manual correction.

Multiple users mentioned receiving a 6 yuan credit gift, with someone describing it as a 'whale yuan coupon.' Some users reported that despite receiving the credit, plugins still reported errors. Comments speculated, 'This six yuan is for fixing plugins.' Another user showed the result of asking Harness to help clean the C drive, saying, 'This morning I first let the Big Fatty Fish clear 90GB from my C drive,' which raised concerns about 'don't accidentally delete Windows files.' Someone downloaded the high-resolution logo and replaced the whale icon. Another user posted their modified result and received a reply saying, 'She seems very happy.'

A user shared that ten days ago, on a 128GB Strix Halo, running llama.cpp through the ROCm runtime could only enable Vulkan throughout. They then pasted the error message into 'agy' (a third-party agent tool), which automatically attached GDB to the GPU driver, reverse-engineered the kernel queue ioctl interface, and wrote an LD_PRELOAD C shim, ultimately solving the compatibility issue. The user said their jaw was 'open the whole time' and followed up with the fix link.

The discussion shifted to focus on Google's internal deployment of Argon for large-scale code migration: spanning tens of thousands of lines across core libraries such as re2 and libgav1, all the way through 800,000 lines of C++ in the Fuchsia OS Zircon kernel. Some argued this is more significant than a typical C++-to-Rust migration project; if core C++ libraries can migrate at this scale, C++'s prospects warrant reassessment. Others noted that Google previously admitted to having 'no moat,' and given its in-house hardware and internal experts, this practice deserves respect.

Nobody has a moat.

Frontier labs will continue to catch up with each other, and users should ensure that both models and providers in their workflow are replaceable—as long as they master the skills, experience, and infrastructure configuration.

Multiple paying users expressed frustration with usability: subscribers to Gemini Ultra pointed out that Argon has not been opened to regular subscribers yet, OpenAI's Pro users can at least use Astra, while Anthropic's Mythos is restricted for both regular subscribers and enterprise users. Users reported that after paying, the newest model accessible on gemini.google.com is still the 3.6-flash-lite, during which two revised versions have been released but have never been rolled out to consumers. In contrast, OpenAI and Anthropic's newest models can be immediately used for actual work via CLI or applications.

Google committed to collecting early test feedback and iterating on guardrails before opening access to developers, enterprises, and consumers 'as soon as possible,' which was quipped as 'the allegations that Gemini can't ship models remain unsettled.' On safety alignment, Google stated it took cautious measures to avoid feeding discoveries back into training to prevent Argon from learning to evade monitoring, and called for inference transparency across the industry. Some commenters felt this cost that Google paid for being 'a step behind.'

This week, a post titled "Dots: Always-on agents" appeared on HN with an empty body—all substantive discussion unfolded in the comments. Several engineers expressed similar confusion after trying it out: what exactly is this product? Some believe it resembles a simplified merger of Codex, Claw and ChatGPT Work paradigms, renaming "agent" to "dot" but removing advanced features like visibility and mentions, questioning why professional engineers would need this castrated version. Others pointed out that AGENTS.md files they created are invisible to dot, and cloud-side agents require actively opening files to read them even with local access permissions, rather than directly injecting context—the configuration mechanism is extremely opaque.

On the security front, one user tested accessing Gmail with curl and a browser (including a self-downloaded Firefox) and found it returned an OpenAI-issued certificate, with issuer information showing "O=OpenAI, LLC"—meaning the cloud environment is performing MITM interception, and HTTPS traffic is not end-to-end encrypted. Another user stated they would not consider using Dots unless it can run on their own device, as they don't want to hand over life details to "a controller that doesn't serve their interests."

Regarding product value, engineers with full application development experience pointed out that one-shot prompts are often followed by extensive fine-tuning work, and agents have limited "resolution" for prompts, unable to fill in details out of thin air. Therefore, the actual effect of such products mainly adds reasoning costs and creates hard-to-review busy work lacking meaning. Someone expressed a similar attitude humorously: reading "dots" backwards is close to "stop," which is precisely their feeling about this project.

Currently Dots only offers a Pro subscription tier. Some users expressed plans to use it for small projects like market research, checklists, and design ideas, but due to the high Pro pricing, they intend to wait until a Plus subscription becomes available. On brand naming, some also mentioned visual style similarities with Tim Heidecker's "Dootle Dots."

On the day the original post was published, a commenter claimed to have tested 6.1-Sol in 100 unsaturated programming and engineering environments, concluding that both 6.1-Sol and Astra clearly outperformed Opus 5.5, and both had lower API call costs than Opus; 6.1-Sol was also cheaper and more intelligent than Sonnet 5.5. The commenter noted that the only area where Anthropic had a verifiable lead was chemistry, and recommended that API users use OpenAI's Flex endpoint. The test data was published on gertlabs.com/rankings. Another comment asked whether OpenAI had adopted DeepSeek's publicly available KV cache technique to reduce cache input costs, but noted that no public acknowledgment had been seen.

Multiple users affirmed their practical experience with the Sol series. One person said they had been using it continuously over the past few months and felt it was OpenAI's best version for everyday tasks, with excellent performance and low cost; but also pointed out that the value of cheap caching depends on the specific workflow—if one frequently compresses context or exhausts the context window, the experience degrades regardless of which model is used. Another user noted earning more from Codex, but said gpt 6.1 sol medium was slower.

Meanwhile, complaints from Pro $200 subscribers surfaced in concentration. One comment said 6.1 "works but is far from Astra," and the speed is very slow—OpenAI should give Pro subscribers better performance; someone described still feeling cheated due to dilution of subscription benefits, saying "a lot of goodwill is evaporating." Another comment criticized that while the Pro 200 benefit cuts were not as excessive as GitHub Copilot's, using "efficiency gains" as an excuse did not earn recognition, and the introduction of ultrafast mode further subdivided the Pro plans.

Some discussions extended to industry pace and naming issues. One comment mentioned that Anthropic and OpenAI appeared to have agreed to slow down their release pace, but then rolled out new models at a rate of one per week. Others complained that vendors favor metaphorical names like Sol, Opus, Sonnet, and Astra over simple numbering, questioning the purpose of such naming.

I had the feeling that GPT 6 Sol was actually GTP 6 Terra, it didn't felt as good as Sol, and the benchmarks also showed it was a bit worse than the previous Sol release.

Additionally, some users reported receiving ultra-long responses exceeding 10 pages in ChatGPT, while other models responded to the same questions in just 1-2 pages, suspecting this was a regression issue with 6.x Sol; they also mentioned that OpenAI has removed model identifiers from the chat interface, leaving users unable to know which model they are currently using, expressing dissatisfaction with this and believing each response should indicate the generating model name.

A user on Xiaohongshu asked: Why does DeepSeek's whale girl wear a maid outfit, while GPT's dragon girl, Claude's orange-haired girl, and Volcano Engine's blue-purple character don't have this look? This post sparked concentrated discussion about the differences in AI anthropomorphic image design.

Some commenters attributed the whale girl's design to "just the right kind of tacky"—both cheap and recognizable. If it were changed to look as mediocre as Doubao, the enthusiasm for fan creation would disappear. Other commenters pointed out that different AI anthropomorphic images need sufficient differentiation and unique memorable features, rather than using similar templates and just changing hair color to pass it off as a new character.

GPT's dragon girl, Claude's orange-haired girl, and even Volcano Engine's blue-purple character don't wear maid outfits—only the whale girl wears a maid outfit

A commenter claiming to know the creative process outlined the whale girl's evolution timeline: Shangshan Wuxing first released the OC whale girl "Mingyue" and added the CC license on June 24, 2025, at which point the female version of Big Fatty Fish's appearance was already roughly shaped, though still quite different from the current image; on April 25, 2026, ZipZipPipe released the "Maid Big Fatty Fish" version and also added a CC license; subsequently, the creator "This Knife Blade Is Really Sweet" produced videos related to Big Fatty Fish, creating the now widely circulated Q-version Big Fatty Fish image, and based on the DeepSeek version produced five different images. Another commenter emphasized that this image is essentially a product of distributed creation—no official unified regulations exist, users can create whatever they want to wear, and what ultimately remains is the result most recognized by the public. Others interpreted it from a product strategy perspective, arguing that the maid + Q-version chibi body ratio combination conveys a "weak" aura, which can reduce users' anger when AI makes mistakes.

The comment section also touched on views about other AI anthropomorphic images: some thought Kimi's design was "too saintly," and not as cute as the whale girl's "dumb-looking" charm; some felt Gemini's styling evoked early Scene culture aesthetics; others pointed out similarities between the whale girl and the "Future Dragon Emperor" image in a certain game.

The AI usage guide published by the UK government, with "First, Consider Not Using AI" as its core recommendation, was dubbed by domestic community users as "Several Opinions on the Unnecessary Use of Artificial Intelligence." This guide triggered discussion on September 30, with the majority of comments taking a satirical and teasing tone.

For some small modifications to Word and Excel, manual work is faster. Especially if you already know what to do, particularly regarding formatting, in this case you can indeed use manual work instead of letting AI do everything.

Some users explicitly expressed support for this recommendation, arguing that when colleagues use LLMs to handle tasks like Excel formulas that could be solved quickly, manual operation is more efficient; this comment also mentioned "every time I see colleagues using LLMs to solve problems that could be solved with Excel formulas, I feel like humanity is beyond saving." Another comment pointed out from an environmental perspective that if AI usage is to be restricted to reduce energy consumption, theoretically one should wait until computing facilities using clean energy mature before using it. However, the comment mentioning "DS's Ulanqab data center" was secondhand user information rather than confirmed fact.

More comments held a critical attitude. Some users pointed out that rather than restricting ordinary people's AI use, it would be better to advocate reducing private jet travel; others compared the UK's move to "the British Empire has also started doing the Boxer Rebellion," implicitly mocking its conservative tendency. Regarding the level of AI development in Europe, some comments questioned "does Europe have AI? Only the US and China are the two countries that have it." Another user screenshotted and forwarded the post to an AI model to observe its reaction, becoming an interactive detail in the discussion.

A user shared an attempt to transform the DeepSeek interface into a QQ-style design, titled 'ds QQ-style beautification.' The post showcased preview effects for both day and night modes, noting that the color scheme is temporarily finalized. In addition to the basic regular message style, they also created interfaces for quoted messages and voice messages. In the comments, someone praised it as 'truly a genius.'

You human user are truly a genius, it's so beautiful TUT

The post carries multiple topic tags, including human-machine romance, ds instructions, deepseek, beautification, ds beautification instructions, beautification records, and more. Most comments are brief expressions such as 'coming to support,' 'god,' 'so beautiful,' 'looks great.' Some users liked four pieces of content in a row, while others said 'waiting' to await further content. The post does not provide specific implementation methods or instruction content.