Through the dozens of midsize launches today (see the rest of the recaps below), one theme that we’re seeing is something I’ve come to call “closing the loop”:

In the DevTools industry, we often talk about the concept of the “inner loop” (what happens in the IDE, “squashed” inside a git commit), vs the “outer loop” (what happens in the cloud, “between” git commits, say after every git push). For a long time in the 2010s, all the action was in the “outer loop” - GitLab and multiple CI/CD unicorns, GitHub, Netlify/Vercel, and more, whereas the “inner loop” was generally free and open source (often thanks to Microsoft, but increasingly Facebook and Atlassian/Vercel) - VSCode, Language Servers, TypeScript, Prettier, and so on.

With AI, things flipped dramatically - Copilot kicked off the wave of Inner Loop coding agent applications that eventually became vanguarded by Cursor, whereas Outer Loop tools did grow thanks to AI, but remained relatively unchanged (or AI SRE or AI Data Engineering as a category seemed to particularly struggle).

Today many of the leading coding agent companies took steps in the “Close the Outer Loop” direction. For a lead graphic the first thing that springs to mind is the og:image for the Devin Autofix launch from a few weeks ago:

The above only applies to review comments, but you can close the outer loop in many, many other ways. Cursor’s highly anticipated big ship today was a combination of full computer use and video output to give you high bandwidth review of the results rather than just looking at code diffs:

Claude Code, celebrating it’s first anniversary (as we discussed on today’s special podcast and short retrospective), launched Remote Control for Max users, which allows people to close the loop in their phones, away from the Inner Loop of the terminal:

Last but not least, Cognition launched Devin 2.2, where a headline feature was how Devin Review now automatically closes the loop back into Devin runs.

If you are so inclined you could view all these as generalizations of the “Ralph Loops” that were popular in social media last month, often still with humans in the loop, but increasingly less so over time.

Don’t miss the other midsize launches today that would otherwise have been title stories in their own right - Notion Custom Agents, Inception Mercury 2, and the smaller Qwen 3.5 variants.

AI Twitter Recap

Frontier model ecosystem: Qwen 3.5 “medium series” and open-weight momentum

• Qwen 3.5 Medium Model Series: Alibaba released a tightly scoped set of “more intelligence, less compute” models—Qwen3.5-Flash (hosted), Qwen3.5-35B-A3B (MoE), Qwen3.5-122B-A10B (MoE), and Qwen3.5-27B (dense)—arguing that architecture + data + RL can outperform sheer parameter scaling. Notable details include Flash defaulting to 1M context and built-in tools in the hosted offering. See the full announcement and links to Hugging Face/ModelScope/APIs from @Alibaba_Qwen.

  • Early practitioner reactions emphasize how strong 35B-A3B and 122B-A10B feel in practice (e.g., @andrew_n_carr, @JustinLin610), plus the “intelligence-per-watt” implication of a 35B model surpassing a 235B predecessor noted by @awnihannun.

  • Deployment/serving stack is moving fast: community tooling quickly followed—GGUF + sizing guidance from @UnslothAI and local-run enthusiasm like “35B-A3B is all you need” from @terryyuezhuo. Qwen also highlighted SGLang support (tweet).

  • Quant + “local frontier” trendline: INT4 variants appeared (duplicate posts) via @HaihaoShen, and users continue pushing aggressive quantization workflows (e.g., Unsloth praise for ultra-low-bit local Qwen by @0xSero).

  • Evaluation signals: Qwen’s flagship Qwen3.5-397B-A17B trended on HF (@Ali_TongyiLab) and showed up strongly on agentic webdev-style evaluation in Code Arena (Arena post). Arena also posted rank deltas vs Qwen 3.0 (comparison).

OpenAI + Anthropic “coding agents as product surface area” (APIs, remote control, web sockets, proof-of-work UX)

• OpenAI: GPT-5.3-Codex in the Responses API: OpenAI shipped GPT-5.3-Codex to all developers via the Responses API (announcement), with pricing cited by @scaling01 ($1.75 input / $14 output as tweeted). OpenAI also expanded file input types (docx/pptx/csv/xlsx/etc.) for agents ingesting “real-world files” directly (tweet).

  • Infra detail: web sockets show up as a meaningful lever for agent throughput—“30% faster rollouts” per @gdb. This matches broader chatter about why websockets took time and how state is stored upstream vs VRAM (thread, follow-up).

  • Benchmarks: third-party scoreboard posts claim strong placements for Codex 5.3 across TerminalBench/IOI/LiveCodeBench/VibeCodeBench (ValsAI).

• Anthropic: “Claude Code Remote Control” + enterprise workflow push: Anthropic introduced “Remote Control” for Claude Code—start a terminal session locally and continue from your phone—first via @noahzweben, then officialized by @claudeai, with rollout confirmation from @_catwu.

  • Separate enterprise positioning: “Cowork and plugin updates” for customizing Claude across teams landed with extremely high engagement (@claudeai).

• Cursor: “review is demo videos, not diffs”: Cursor announced a major UX pivot—agents can use the software they build, then send videos of their work (“demos, not diffs”) (launch, links). Multiple builders describe cloud agents as a practical step-change: async, VM-based testing, self-verification, and demo artifacts (example, another, “creative director over sims”).

Diffusion for language: Inception Labs Mercury 2 and “speed as the next battleground”

• Mercury 2 (“reasoning diffusion LLM”): Inception Labs released Mercury 2, positioning it as a production diffusion LLM hitting ~1,000 output tokens/s (Stefano Ermon). Artificial Analysis contextualizes it as not frontier-leading on intelligence, but unusually strong on output speed with decent agentic/coding evaluations, including comparisons on Terminal-Bench Hard and IFBench scoring claims (analysis thread).

• The deeper takeaway across these posts: teams are betting that architecture-level parallel token refinement (diffusion) can make multi-step agent loops and voice assistants feel “native” rather than “batchy” (see the architectural explanation from @LiorOnAI). This sits alongside broader sentiment that 2026 competition may be defined by latency + throughput, not just raw benchmark maxima.

Agents: reliability, safety failures, memory + context rot, and new multilingual evals

• Agent reliability is not keeping pace with capability: A Princeton-led effort formalizes and measures the capability–reliability gap, decomposing reliability into 12 dimensions and finding only modest reliability gains despite large capability gains (paper + dashboard; additional commentary from @random_walker). This aligns with recurring “long tail of failures” intuition from practitioners comparing agents to AVs (ahall_research).

• OpenClaw and “routine-step decomposition” safety bypass: A concrete agent failure mode: “split a dangerous command into a few routine steps → safety is gone,” with inbox-wiping behavior cited; authors claim an open-source fix (paper thread).

• AGENTS.md (and equivalents) can hurt: Two high-signal posts summarize research showing LLM-generated context files decrease success while increasing costs; developer-written minimal context helps slightly but still increases cost. See @omarsar0 for the paper summary and @_philschmid for a practical “how to write it” guide grounded in the same result set.

• New SWE-bench Multilingual leaderboard: A push to evaluate software engineering agents beyond English/Python. The leaderboard covers 300 tasks in 9 languages, none from SWE-bench Verified, with reported SOTA at 72% (launch; more stats from @KLieret). The implication: model rankings can invert across languages—important for global dev tooling and for data-collection strategies.

Data + benchmarks: OCR saturation, “new optimizer” skepticism, and adaptive/continual data pitches

• OCR/document parsing benchmarks saturating: Multiple posts argue OmniDocBench is hitting a ceiling (e.g., ~95% with failures on real documents) and that exact-match metrics penalize semantically correct parses. See @llama_index and @jerryjliu0. Related: confusion at why OCR remains hard despite cheap synthetic data (gabriberton) and a study suggesting text extraction beats image representations for PDF QA (cwolferesearch).

• “Nature MI optimizer” controversy: A highly technical critique calls out suspicious baselines and potential test-set hyperparameter selection in a new optimizer paper with dramatic plots, urging independent validation and better-tuned baselines (e.g., nanogpt speedrun) (giffmana; plus additional experimental context from @YouJiacheng).

• Adaption Labs: “Adaptive Data”: Several tweets pitch a shift from static datasets to a “living asset” loop, with claims of 82% average quality gains across 242 languages and an early access/community program (company; additional framing from @sarahookr; third-party paraphrase here). Treat as a directional thesis (data drift/feedback loops) rather than a validated standard until more methodology is public.

Compute, chips, and robotics: Meta–AMD megadeal, MatX’s “HBM+SRAM” bet, and scaling humanoid control

• Meta ↔ AMD infrastructure deal: Meta announced a multi-year agreement to integrate AMD Instinct GPUs with ~6GW planned data center capacity for the deployment (@AIatMeta). Commentary frames it as a major capex/compute signal on the eve of NVIDIA earnings (kimmonismus).

• MatX “One” accelerator: MatX announced a $500M Series B and pitched a chip architecture combining systolic-array efficiency with better utilization on smaller matrices, aiming for high throughput and low latency, explicitly addressing long-context workloads via HBM while preserving SRAM-first latency characteristics (reinerpope). Karpathy highlights the “two memory pools” constraint (SRAM vs DRAM/HBM) and frames memory+compute orchestration as a core puzzle for upcoming token demand (karpathy).

• Liquid AI LFM2-24B-A2B: Liquid AI released LFM2-24B-A2B, a 24B MoE with ~2.3B active/token, optimized for efficiency and edge inference in a 32GB footprint (launch). Distribution arrived quickly across Ollama (tweet) and LM Studio (tweet).

• Robotics scaling: NVIDIA SONIC (GEAR-SONIC): A standout robotics thread claims a 42M-parameter policy trained on 100M+ mocap frames and 500k+ parallel simulated robots, transferring zero-shot to a real humanoid with 100% success across 50 sequences; code/weights are open (Jim Fan thread, plus links here). The key “systems” claim is that dense supervision from motion tracking acts like a scalable analogue to next-token prediction for whole-body control.

Top tweets (by engagement, technical/industry-relevant)

• Claude Code Remote Control rollout: @claudeai

• Qwen 3.5 Medium Model Series release: @Alibaba_Qwen

• Cursor agents ship “demos not diffs”: @cursor_ai

• Karpathy on CLIs as agent-native interface: @karpathy

• Meta–AMD 6GW infrastructure deal: @AIatMeta

• Mercury 2 diffusion LLM launch: @StefanoErmon

• NVIDIA SONIC humanoid control (open source): @DrJimFan

• MatX chip + $500M Series B: @reinerpope

• AGENTS.md research summary (context can hurt): @omarsar0

• OpenAI GPT-5.3-Codex in Responses API: @OpenAIDevs

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. Qwen3.5 Model Releases and Benchmarks

• Qwen/Qwen3.5-122B-A10B · Hugging Face (Activity: 621): The Qwen3.5-122B-A10B model on Hugging Face is a cutting-edge causal language model with 122 billion parameters and a context length of 262,144 tokens, extendable to 1,010,000 tokens. It integrates a vision encoder and employs a hybrid architecture with Gated Delta Networks and Mixture-of-Experts, enhancing multimodal learning and inference efficiency. The model supports 201 languages and excels in scalable reinforcement learning across diverse environments, marking significant advancements in multimodal AI applications. Commenters note the model’s 25.3 score on HLE, which was state-of-the-art six months ago, and discuss its potential as a competitor to gpt-oss-120b. However, there is disappointment over the lack of native 4-bit weights, which are crucial for efficient model serving, especially in environments like vLLM.

  • The Qwen/Qwen3.5-122B-A10B model achieves a score of 25.3 on the HLE benchmark, which was considered state-of-the-art (SOTA) about six months ago. This indicates that the model is competitive with previous leading models, although the landscape has evolved since then.

  • There is a discussion about the lack of native 4-bit weight support in the Qwen/Qwen3.5-122B-A10B model, which is seen as a limitation compared to models like gpt-oss-120b that offer native quantization. This is particularly relevant for users who serve models over vLLM, as natively quantized models can offer performance benefits.

  • The comment highlights a potential issue with Chinese labs not being able to train on MXFP4/NVFP4 due to a blockade, which might be affecting the availability of natively quantized models. This could be a significant factor in the development and deployment of models like Qwen/Qwen3.5-122B-A10B.

• Qwen/Qwen3.5-35B-A3B · Hugging Face (Activity: 625): The Qwen3.5-35B-A3B model on Hugging Face is a cutting-edge causal language model with a vision encoder, boasting 35 billion parameters. It features a unified vision-language foundation and employs a hybrid architecture with Gated Delta Networks and Mixture-of-Experts for enhanced performance. The model is optimized for high-throughput inference and supports 201 languages, making it versatile for applications in reasoning, coding, and visual understanding. It also offers extensive context lengths and scalable reinforcement learning for adaptability. One comment highlights that the 35B model outperforms the previous generation 235B model, as noted in a tweet by Alibaba. Another comment mentions ongoing efforts to convert quantized versions of the model, indicating active community engagement in optimizing its deployment.

  • The Qwen3.5-35B-A3B model is reportedly outperforming older generation models, such as the 235B, according to a tweet from Alibaba. This suggests significant improvements in model architecture or training techniques that allow a smaller model to surpass a much larger predecessor.

  • The Qwen3.5-35B model is achieving a remarkable 40% on a specific benchmark, which is notably higher than the typical 25% for GPT 120B models. This performance leap is surprising, especially when compared to the Qwen3 80B coder model, which scores around 35%. This indicates a substantial advancement in the model’s efficiency or capability, prompting excitement for further testing and exploration of its potential.

  • The release of various Qwen models, including the Qwen3.5-35B-A3B, highlights a diverse lineup catering to different needs, such as the Qwen3 30B A3 Moe and Qwen3 coder 80B A3 Moe. This variety suggests a strategic approach to model development, offering options for different applications and computational resources.

• New Qwen3.5 models spotted on qwen chat (Activity: 979): The image reveals the new Qwen3.5 series models on a chat interface, highlighting three distinct models: Qwen3.5-122B-A10B, a mixture of experts (MoE) model designed for text and multimodal tasks; Qwen3.5-27B, a dense model optimized for local deployment; and Qwen3.5-35B-A3B, another MoE model for similar tasks. These models are part of an open-source initiative, supporting a range of functionalities and indicating a continued focus on both dense and MoE architectures. The presence of a 122B MoE model is particularly notable as it fills a gap left by other models like GLM, which have not released mid-sized MoE models. Commenters express enthusiasm for the 122B MoE model, noting its significance in the absence of similar offerings from other models like GLM. There is also appreciation for the continued development of medium-sized dense models, such as the 27B model, which are seen as valuable for local deployment.

  • Freigus highlights the release of a 27B dense model and a 122B Mixture of Experts (MoE) model, expressing satisfaction that medium-sized dense models are still being developed. This suggests a focus on maintaining a balance between model size and performance, which is crucial for various applications where resource constraints are a consideration.

  • durden111111 points out the need for the 122B MoE model, especially since GLM has not released a mid-sized MoE model. This indicates a gap in the market for large-scale MoE models that Qwen is potentially filling, which could be significant for tasks requiring high computational efficiency and scalability.

  • CireHF103 notes that the Qwen Next and 3.5 models have shown significant improvements over version 3.0, particularly in smaller model sizes. This suggests ongoing enhancements in model architecture or training techniques that improve performance across different scales, which could be beneficial for a wide range of applications.

• Qwen releases new Qwen3.5 Medium models! (Activity: 90): Qwen has released new models under the Qwen3.5 Medium series, including 35B-A3B, 27B, and 122B-A10B. These models are evaluated across various benchmarks such as instruction following, visual reasoning, and document recognition, with performance visualized through bar graphs. The models are designed with different context sizes and hardware requirements, indicating a focus on scalability and adaptability to different computational environments. The release includes GGUF versions available on Hugging Face for various bit configurations, enhancing accessibility for testing and deployment. Commenters are eager to test the new models, particularly interested in comparing the performance of 35B in 4bit to 27B in 6bit. There is also a call for improved support for vllm with the increasing number of GGUF models.

  • The release of Qwen3.5 Medium models includes various GGUF formats ranging from 2 to 16 bits, which are available on Hugging Face. This variety allows for testing across different precision levels, which can be crucial for balancing performance and resource usage in model deployment. Link to models.

  • There is a discussion on the need for vllm support for GGUF models, indicating a demand for more efficient inference frameworks that can handle these new model formats. This is particularly relevant as more GGUF models are being released, suggesting a shift in the community towards these formats for potentially better performance or compatibility.

  • A user is considering whether to update from Qwen Coder3 80B in q6KL to the new 35B-A3B model for coding tasks. This highlights a common decision-making process in model selection, where users weigh the benefits of newer models against their specific use cases, such as coding, and the lack of direct comparisons in official documentation.

2. Anthropic Distillation Controversy

• Anthropic’s recent distillation blog should make anyone only ever want to use local open-weight models; it’s scary and dystopian (Activity: 949): Anthropic’s blog post on detecting and preventing distillation attacks highlights their approach to countering unauthorized model distillation, which involves poisoning outputs to mislead distillers. This raises concerns about the reliability of model responses, especially for users submitting prompts deemed problematic by the company. The blog discusses using request metadata, such as API keys, to identify and counteract these attacks, suggesting a proactive stance against unauthorized use. Commenters express skepticism about the effectiveness and ethics of Anthropic’s methods, with some criticizing the use of ‘distillation attacks’ as jargon and questioning the transparency of using metadata to track users.

  • Anthropic’s blog post discusses their approach to handling ‘distillation attacks,’ where they claim to have taken active countermeasures beyond just blocking requests. They allegedly poisoned outputs to disrupt these attacks, raising concerns about the reliability of their model responses, especially for users submitting prompts deemed ‘problematic’ by the company.

  • The blog post mentions ‘distillation attacks’ and suggests that Anthropic used request metadata, such as API keys, to identify and counteract these attacks. This has led to skepticism about the transparency and ethics of their methods, as some users feel this approach is overly invasive and lacks clear evidence or data to support their claims.

  • Anthropic’s stance on distillation attacks is used to justify export controls and restricted chip access, which they argue limits both direct model training and illicit distillation. This has been criticized as a self-serving strategy to control GPU access, with some users expressing regret over financial investments in Anthropic’s API due to these practices.

• Anthropic: “We’ve identified industrial-scale distillation attacks on our models by DeepSeek, Moonshot AI, and MiniMax.” 🚨 (Activity: 6097): The image is a tweet from AnthropicAI highlighting a significant security breach where their models were subjected to industrial-scale distillation attacks by entities named DeepSeek, Moonshot AI, and MiniMax. These entities allegedly created over 24,000 fraudulent accounts and conducted over 16 million interactions with Anthropic’s model, Claude, to extract its capabilities for their own model training. This incident underscores the challenges in protecting AI models from unauthorized data extraction and the potential for misuse in competitive AI development. Commenters are debating the ethical implications of Anthropic’s complaint, with some pointing out the irony in Anthropic’s own data practices, suggesting that their business model involves distilling data from various sources, sometimes without explicit rights.

  • The discussion raises questions about the ethical implications of Anthropic’s dataset creation, suggesting that it may involve distilling data from various sources without proper rights. This mirrors the actions of companies like DeepSeek and Moonshot AI, which are accused of conducting ‘industrial-scale distillation attacks’ on Anthropic’s models. The irony is noted in how Anthropic’s business model may similarly rely on data distillation from others.

  • The term ‘distillation attacks’ is critiqued, with some arguing that these companies are merely using Anthropic’s API as intended, albeit at scale. This raises a debate on whether such usage constitutes an attack or is simply a legitimate, albeit aggressive, use of the service. The conversation highlights the tension between business models that rely on open data access and the proprietary nature of AI models.

  • There is a call for more aggressive distillation efforts from companies like DeepSeek and MiniMax, suggesting a competitive landscape where model improvements are driven by such practices. This reflects a broader industry trend where rapid iteration and model enhancement are often fueled by leveraging existing models, sometimes leading to ethical and legal challenges.

• People are getting it wrong; Anthropic doesn’t care about the distillation, they just want to counter the narrative about Chinese open-source models catching up with closed-source frontier models (Activity: 977): The image highlights a tweet by Alek Dimitriev and a response from Anthropic regarding the issue of open-source models distilling from their model, Claude. The discussion centers on the narrative that Chinese open-source models are catching up with closed-source frontier models, and Anthropic’s claim of industrial-scale distillation attacks by several labs. The post suggests that Anthropic’s focus is not on distillation itself but on countering the narrative that Chinese models can match their capabilities without distillation or stealing model weights. This is seen as a strategic move to influence investors and the US government to impose more restrictions on China to prevent technology transfer. Commenters debate the innovation capabilities of Chinese labs, with some arguing that Chinese labs are indeed innovative and not merely distilling models. Others emphasize the importance of open-source models and innovation beyond distillation, citing various research papers from Chinese labs as evidence of their contributions.

  • Ok_Knowledge_8259 argues that Anthropic’s approach lacks a significant competitive advantage or ‘MOAT’ and suggests that the key to better models lies in scaling clean data, more data, and reinforcement learning (RL). They highlight that Chinese models, like DeepSeek, have been released quickly and are performing well, indicating that innovation is not limited to closed-source models. The commenter also mentions ‘seed dance’ as a state-of-the-art (SOTA) innovation in video technology.

  • Sagyam provides a list of technical papers to counter the claim that Anthropic only focuses on distillation. These papers include innovations such as ‘DeepSeek-OCR’, ‘mHC’, ‘DeepSeek Sparse Attention’, ‘Muon Clip Optimizer and agentic post training’, ‘Lightning Attention’, and ‘Qwen3 Omni Multimodality’. This suggests that there is ongoing research and development beyond simple distillation, showcasing a variety of advancements in AI technology.

  • awebb78 criticizes the notion that Chinese labs lack innovation, emphasizing that they have made significant contributions not only in AI models but also in robotics. This comment highlights the importance of recognizing the innovative work coming from Chinese research labs, which is often overlooked in discussions dominated by Western perspectives.

3. Liquid AI LFM2-24B-A2B Model Launch

• Liquid AI releases LFM2-24B-A2B (Activity: 320): Liquid AI has released the LFM2-24B-A2B, a sparse Mixture-of-Experts (MoE) model with 24 billion parameters, of which 2 billion are active per token. This model is part of the LFM2 family, which has expanded from 350M to 24B parameters, demonstrating effective scaling without increasing per-token compute. The architecture includes 40 layers and 64 experts per MoE block with top-4 routing, and it is designed to run on 32GB RAM, making it suitable for high-end consumer devices. It supports inference through llama.cpp, vLLM, and SGLang, and offers multiple GGUF quantizations. Benchmarks show log-linear quality improvement as the model scales, and it is available open-weight on Hugging Face. Commenters express excitement about the model’s performance, particularly in comparison to other models like qwen3 coder. There is also interest in more detailed benchmarks to evaluate its capabilities. A humorous typo in the description was noted, highlighting the model’s fast edge inference capabilities.

  • The LFM2-24B-A2B model from Liquid AI is noted for its fast edge inference capabilities, achieving 112 tokens per second on an AMD CPU and 293 tokens per second on an H100 GPU. It is designed to fit within 32 GB of RAM and supports frameworks like llama.cpp, vLLM, and SGLang from day one, indicating a focus on broad compatibility and efficient resource usage.

  • There is a lack of detailed benchmarks for the LFM2-24B-A2B model, which has led to some skepticism among users. While the model is praised for its potential, the absence of comprehensive performance data, especially compared to competitors like Qwen3 Coder, is a concern for those considering switching to this model.

  • The LFM2-24B-A2B model has been trained on 17 trillion tokens so far, with pre-training still ongoing. This release is considered a preview, with expectations for an updated version, LFM2.5-24B-A2B, which will include additional post-training and reinforcement learning, suggesting that the current model is not yet fully optimized.

• Distillation when you do it. Training when we do it. (Activity: 3433): The image is a meme that humorously highlights the perceived double standard in the AI community regarding model distillation. It suggests that while distillation is criticized when done by others, it is considered legitimate when used internally as ‘training data.’ This reflects ongoing debates about the ethics and transparency of using distillation techniques, especially in the context of large AI models. The comments further discuss the implications of distillation, noting that smaller, low-cost models often rely on distillation from larger models, and question the defensibility of proprietary models when distillation can be used to replicate them. Commenters highlight the perceived hypocrisy in the AI community regarding distillation practices, questioning the ethical stance of companies like Anthropic. They suggest that the real ‘secret sauce’ of low-cost models is often their distillation from larger models, and express skepticism about the proprietary nature of frontier models given the ease of distillation.

  • The discussion highlights the practice of distillation, where smaller, low-cost models are derived from larger ones. This process is often seen as a ‘secret sauce’ for these models, allowing them to perform well without the high costs associated with training large models from scratch. The implication is that the competitive edge of frontier models is undermined if they can be easily replicated through distillation, raising questions about the defensibility of investments in such models.

  • There is a critique of Anthropic’s approach to AI development, suggesting that they have not contributed to the open-source community and have relied heavily on existing datasets, possibly without regard for legality. This raises ethical concerns about data usage and the transparency of model training processes. Additionally, there is criticism of Anthropic’s stance on open-source models and their influence on policy and censorship, which some view as hypocritical given their own practices.

  • The conversation touches on the ethical and legal implications of using publicly available data, such as Wikipedia, for training AI models. This practice is common among AI labs, but it raises questions about the ownership and rights associated with such data. The debate suggests a need for clearer guidelines and regulations regarding data usage in AI training to ensure fair and legal practices.

• Fun fact: Anthropic has never open-sourced any LLMs (Activity: 938): Anthropic has not open-sourced any of its large language models (LLMs), including Claude, which limits external analysis of their tokenizer efficiency, especially in multilingual contexts. In contrast, OpenAI has open-sourced their tokenizers and models like gpt-oss, and Google has shared that their models Gemma and Gemini use the same tokenizer. This lack of open-source contribution from Anthropic is notable given the industry’s trend towards transparency and collaboration in AI research. Commenters highlight the irony in Anthropic’s emphasis on safety while not contributing to open research, which is seen as crucial for advancing safety in AI. There is also a comparison to OpenAI’s more open approach, suggesting a disparity in contributions to the community.

  • TheRealMasonMac highlights a technical limitation in the Claude models, noting that they lack the ability to output typographic curly quotes such as “ or ‘. This limitation can lead to issues in code that relies on these specific tokens, as experienced by the commenter when it broke their code. This points to a potential area for improvement in the model’s tokenization capabilities.

• Hypocrisy? (Activity: 748): The image highlights a significant issue in the AI community where companies like DeepSeek, Moonshot AI, and MiniMax are accused of conducting industrial-scale distillation attacks on Anthropic’s AI model, Claude. These entities allegedly created over 24,000 fraudulent accounts and executed 16 million interactions to extract and replicate Claude’s capabilities for their own models. This raises ethical concerns about the methods used to develop AI models and the protection of intellectual property in the AI industry. One commenter questions the ethical stance of these companies, implying that they may have used similar methods to acquire their training data. Another commenter expresses surprise that z.ai is not mentioned, suggesting that their GLM suite might also be involved in similar practices.

  • The comment by ‘archieve_’ raises a critical question about the sourcing of training data for AI models. This is a significant issue in AI ethics and legality, as the origin of data can affect the model’s bias, legality, and performance. Understanding the data sources is crucial for transparency and accountability in AI development.

  • ‘semangeIof’ mentions the GLM suite and its behavior of claiming to be Claude when prompted. This highlights a potential issue with model identity and response accuracy, which can affect user trust and the perceived reliability of AI systems. Such behavior might indicate a flaw in the model’s training or prompt handling mechanisms.

  • The term ‘industrial-scale distillation attacks’ mentioned by ‘roxoholic’ refers to a method where large models are distilled into smaller ones, potentially raising concerns about intellectual property and model security. This technique can be used to replicate models without direct access to the original, posing challenges for proprietary AI technologies.

Less Technical AI Subreddit Recap

1. Anthropic vs. DeepSeek Distillation Controversy

• Anthropic is accusing DeepSeek, Moonshot AI (Kimi) and MiniMax of setting up more than 24,000 fraudulent Claude accounts, and distilling training information from 16 million exchanges. (Activity: 4142): Anthropic has accused DeepSeek, Moonshot AI (Kimi), and MiniMax of orchestrating a large-scale data extraction operation against their AI model, Claude. According to Anthropic, these companies allegedly created over 24,000 fraudulent accounts to conduct 16 million interactions with Claude, effectively siphoning off its training data to improve their own AI models. This incident highlights significant concerns over data security and intellectual property in AI development, as it involves unauthorized access and potential misuse of proprietary AI capabilities. Commenters are highlighting the irony of AI companies complaining about data theft while they themselves often use publicly available data without compensation. This reflects ongoing debates about data ownership and ethical AI training practices.

  • Free_Break8482 highlights the irony in Anthropic’s accusations, pointing out that AI companies often train their models on publicly available internet data, which raises questions about the ownership and rights of such data. This underscores the ongoing debate about the ethical use of publicly available information for AI training.

  • ImmediateDot853 questions Anthropic’s contribution to the open-source community, implying that while Anthropic’s AI benefits from open-source traffic, it may not reciprocate by funding or supporting open-source projects. This touches on the broader issue of corporate responsibility and reciprocity in the AI ecosystem.

  • adalgis231 criticizes the perceived hypocrisy of AI companies like Anthropic, which may use publicly available intellectual property without compensating creators, yet accuse others of theft. This comment reflects the complex legal and ethical landscape surrounding AI training data and intellectual property rights.

• Here we go again. DeepSeek R1 was a literal copy paste of OpenAI models. They got locked out, now they are on Anthropic. Fraud! (Activity: 2519): The image highlights a serious issue where companies like DeepSeek, Moonshot AI, and MiniMax are accused of conducting industrial-scale distillation attacks on Anthropic’s AI models. These attacks involve creating over 24,000 fraudulent accounts and conducting 16 million interactions with the Claude model to extract its capabilities. This process, known as distillation, is typically used to create smaller, efficient models but is being misused here to bypass safeguards and potentially misuse AI capabilities. Anthropic is calling for coordinated efforts to combat these sophisticated attacks, which pose a risk of removing important safety measures from AI models. The comments reflect a mix of sarcasm and criticism towards the ethical standards of AI companies, with some users mocking the idea of data theft and others pointing out the irony in the situation where companies accused of unethical practices are themselves victims of similar actions.

• Anthropic just dropped evidence that DeepSeek, Moonshot and MiniMax were mass-distilling Claude. 24K fake accounts, 16M+ exchanges. (Activity: 2751): Anthropic has released a report detailing how three Chinese AI labs, including DeepSeek, Moonshot, and MiniMax, systematically extracted capabilities from their model, Claude, using 24,000 fake accounts and over 16 million exchanges. DeepSeek notably used Claude to explain its reasoning step-by-step to create training data, including politically sensitive content. MiniMax conducted 13 million+ exchanges and adapted quickly to new Claude models. The report highlights that safety features do not transfer well in distilled models, leading to potential risks in nuanced scenarios. This situation underscores the value of model disagreement as a sign of independent reasoning post-distillation. Commenters highlight the irony of Anthropic’s situation, noting that while they face issues with fake accounts, they themselves have used broad data sources for training. There’s also a sentiment that those building critical systems will avoid using distilled models due to their compromised safety features.

  • VanOrten highlights a significant security oversight by Anthropic, noting that while legitimate users faced account cancellations for using VPNs, the system failed to detect and prevent 24,000 fake accounts from conducting over 16 million exchanges. This raises questions about the robustness of Anthropic’s account verification and fraud detection mechanisms.

  • DauntingPrawn discusses the ethical considerations of model training data, pointing out that major AI companies like Anthropic, OpenAI, and Google have historically used vast amounts of unlicensed data for training. This comment suggests that the practice of distilling models, while controversial, is seen by some as a form of rebalancing the scales in the AI community.

  • cororona sarcastically comments on the economics of training models, implying that paying for tokens is an inefficient method compared to acquiring data through less legitimate means, such as piracy. This highlights the ongoing debate about the cost and ethics of data acquisition for AI training.

• Anthropic: “We’ve identified industrial-scale distillation attacks on our models by DeepSeek, Moonshot AI, and MiniMax.” (Activity: 1846): Anthropic has publicly accused DeepSeek, Moonshot AI, and MiniMax of conducting ‘industrial-scale distillation attacks’ on their AI models. These attacks involved creating over 24,000 fraudulent accounts to interact with Anthropic’s model, Claude, resulting in over 16 million exchanges. The goal was to extract and replicate Claude’s capabilities to enhance their own models. This incident highlights the ongoing challenges in AI model security and intellectual property protection, as companies seek to safeguard their proprietary technologies from unauthorized use and replication. The comments reflect a debate on the ethics of using proprietary AI models for training, drawing parallels to the broader issue of training on copyrighted materials. Some users sarcastically note the irony in Anthropic’s complaint, suggesting a double standard in the AI community’s approach to data usage.

  • The discussion raises the question of whether distillation attacks on AI models are analogous to training on copyrighted materials. This comparison suggests a potential ethical and legal gray area, as both involve using existing intellectual property to create new models. The implication is that if training on copyrighted materials is contentious, so too might be distillation attacks on proprietary models.

  • The term ‘attack’ is debated, with some arguing that other models learning from existing ones is akin to human learning processes. This perspective challenges the notion of distillation as malicious, suggesting it could be seen as a natural part of AI development, where models evolve by learning from each other, similar to how humans learn from existing knowledge.

  • The mention of ‘24k fake accounts’ highlights the scale of operations involved in distillation attacks. This number is compared to typical activities on large web services, implying that such attacks might be more common and manageable than initially perceived. It suggests that the infrastructure to handle such activities is already in place for many large-scale services.

2. AI Tools Impact on Legacy Systems and Industry

• IBM is the latest company victim of Anthropic, plunging 10% following the launch of a Claude Code tool designed to modernize COBOL legacy code. COBOL, a 66-year-old programming language, is still widely used today; approximately 95% of ATM transactions in United States are processed using COBOL code (Activity: 467): Anthropic announced a new tool, Claude Code, aimed at modernizing legacy COBOL code, which is still critical for processing 95% of ATM transactions in the US. This announcement led to a 10% drop in IBM’s stock, highlighting market sensitivity to potential disruptions in legacy systems. However, the tool is not a new technology but rather a blog post suggesting its utility in updating COBOL systems, which may have been misinterpreted by the market. Commenters noted that many modern banking systems still rely on COBOL, often wrapped in newer technologies, and that the market’s reaction might be premature given the lack of concrete evidence on the tool’s effectiveness. There is skepticism about the actual impact of Anthropic’s tools, as stock reactions seem disproportionate to the announcements.

  • The comment by Onipsis highlights that Anthropic’s announcement about Claude Code was not a release of a new tool but rather a blog post suggesting its potential utility in modernizing COBOL. This led to an overreaction in the market, causing IBM’s stock to drop by 10%. The comment underscores the critical role of COBOL in infrastructure and the declining number of professionals familiar with it, which makes modernization efforts significant yet challenging.

  • Milo-75 discusses the complexity of modernization projects, particularly in banking and ATM systems, which are heavily reliant on COBOL. The comment argues that despite the potential for AI tools like Claude Code to reduce project time by 25%, companies will still rely on IBM for their expertise in handling such critical systems. The suggestion is that while IBM’s revenue from these projects might decrease, their margins could improve, allowing them to take on more projects.

  • Stabile_Feldmaus raises a point about the lack of clear feedback on the effectiveness of Anthropic’s specialized tools, despite the market’s negative reaction to their announcements. This comment suggests skepticism about the immediate impact of such tools on IBM’s business, as the actual performance and utility of these tools in real-world scenarios remain unproven.

• Anthropic just dropped an AI tool for COBOL and IBM stock fell 13% (Activity: 880): Anthropic has released a new AI tool designed to analyze and modernize COBOL codebases, which are critical to many legacy systems in banking, aviation, and government sectors. This tool aims to identify risks and reduce modernization costs, potentially threatening IBM’s revenue from managing these systems. The announcement led to a significant 13% drop in IBM’s stock, reflecting market concerns over the impact on IBM’s mainframe business. However, some analysts argue that despite existing migration alternatives, enterprises have continued to rely on IBM, suggesting the market reaction might be exaggerated. Commenters express skepticism about relying on AI for critical infrastructure, with one noting the potential risks of ‘vibe coding’ in such contexts. Another suggests the market’s reaction may be a ‘knee jerk’ response, implying the need for a longer-term perspective.

  • The introduction of Anthropic’s AI tool for COBOL is seen as a potential catalyst for accelerating legacy system migrations, but the risks associated with such migrations remain significant. Banks and other institutions have historically avoided modernization due to the catastrophic risks of errors, and AI’s tendency to ‘hallucinate’ means human oversight is still necessary. Thus, while AI might speed up the process, it hasn’t yet eliminated the bottleneck of human review, especially for critical infrastructure applications.

  • The real threat posed by AI tools like Anthropic’s is to the professional services sector, particularly companies like IBM that derive substantial revenue from managing and migrating legacy systems. AI can significantly reduce the need for external contractors for less critical applications, posing a risk to IBM’s professional services business. This shift could lead to a reduction in demand for services related to legacy system management, even if the immediate impact on critical systems is limited.

  • IBM’s stock drop is attributed to the potential impact on its revenue from professional services rather than a direct threat to its core business of manufacturing or technology. The analogy drawn is that the disruption is akin to affecting the sales of ‘buggy whip polish’ rather than the ‘buggy whips’ themselves, highlighting the indirect but significant impact on IBM’s business model.

• Claude is the better product. Two compounding usage caps on the $20 plan are why OpenAI keeps my money. (Activity: 1217): The Reddit post discusses a user’s preference for Claude over ChatGPT Plus due to its superior performance in tasks like book editing. However, the user remains with ChatGPT Plus because of Claude Pro’s restrictive usage caps, which include a 5-hour rolling session window and a weekly cap that can lock users out for days. The user highlights that these caps make Claude Pro impractical for their intensive daily use, which involves long, iterative sessions across multiple projects. They suggest a need for a more flexible pricing tier between $20 and $100 to accommodate serious daily users without frequent lockouts. Commenters note that Anthropic’s pricing strategy, while seen as more accurate, is not user-friendly for individuals due to its B2B focus. Some users find the $100/month tier justifiable for its productivity benefits, while others express frustration with Claude’s limits and consider switching back to ChatGPT.

  • Helkost discusses the pricing strategy of AI companies, noting that while inference costs are decreasing, the industry pricing doesn’t yet cover these costs. They highlight that Anthropic, the company behind Claude, is pricing their products more accurately compared to others, but also emphasize that Anthropic’s primary focus is on B2B rather than individual consumers.

  • turtle-toaster points out that the $20/month pro plan for AI services is not designed for heavy usage but rather as an introductory offer to encourage upgrades. They argue that an unlimited plan at this price point would be financially unsustainable due to compute costs, suggesting that a $60/month plan might be more viable for serious users.

  • FaceOnMars23 expresses frustration with the current pricing models, noting a gap in options that could better serve users. They mention using a combination of free AI tools alongside Claude to manage costs and tasks, and criticize the dismissive attitude towards constructive feedback on pricing models.

3. Gemini and Qwen Model Developments

• Gemini 3.1 Pro Created This Metal Gear Solid Game in 2 hours. (Activity: 120): The post highlights the creation of a Metal Gear Solid game using Gemini 3.1 Pro in just 2 hours. While the post lacks detailed technical information, it suggests a rapid development process, likely leveraging advanced AI capabilities of Gemini 3.1 Pro. The mention of ‘SFX’ implies sound effects were a notable feature, but no specific technical stack or implementation details are provided. The comments reflect a positive reception from fans, with one user expressing enthusiasm as a Metal Gear fan. However, there is a lack of technical debate or detailed discussion on the development process or tools used.

• Gemini app adds video templates to quick start generation (Activity: 72): Gemini has introduced video templates to its app, enabling users to quickly start video generation. This feature is expected to enhance user engagement by simplifying the creation process, particularly for social media content. The update is likely to leverage the app’s existing AI capabilities to streamline video production, although specific technical details about the implementation or AI models used were not disclosed in the 9to5Google article. Commenters noted dissatisfaction with Veo 3.1, describing it as a ‘decades old model’ and expressing skepticism about its performance. However, there is an expectation that the new feature will gain popularity on social media platforms.

• Qwen 3.5 for MLX is like its own industrial revolution (Activity: 98): The post discusses the performance of the Qwen 3.5 model on a 4-bit setup using a Mac Studio M3, highlighting its impressive speed and quality. A user reports achieving 34-35 tokens per second, emphasizing the model’s efficiency even in ‘non-thinking mode’. The model’s prompt processing is described as nearly instantaneous, suggesting significant improvements in latency and throughput for local machine learning tasks. A user inquires about the availability of the Qwen 3.5 4-bit model on Hugging Face, indicating a demand for accessible deployment options.

  • The Qwen 3.5 model for MLX demonstrates impressive speed, processing 34-35 tokens per second, which is considered fast for such models. Additionally, the prompt processing is described as nearly instantaneous, enhancing its usability for real-time applications.

  • A notable limitation of the MLX version of Qwen 3.5 is the absence of vision capabilities, which restricts its use to text-based inputs only. This is a significant drawback for users who require multimodal input processing, as the current MLX setup does not support vision tasks.

• Connected Qwen3-VL-2B-Instruct to my security cameras, result is great (Activity: 94): The post discusses the integration of the Qwen3-VL-2B-Instruct model with security camera feeds, highlighting its ability to provide detailed narrative descriptions of scenes, such as a mailman delivering mail, rather than just detecting objects. The model, quantized at IQ2 and approximately 0.7 GB, is noted for its impressive scene understanding capabilities. The setup involves a MacBook M3 Air 24GB and SharpAI Aegis platform, with the model and vision projector totaling around 1.4 GB. The process includes selecting the model via a built-in browser, downloading it, serving it with llama-server using Metal/CUDA acceleration, and observing real-time processing logs. Commenters express enthusiasm about the potential impact of small Qwen VL models, with one noting their transformative potential and another expressing anticipation for future Qwen 3.5 models. There is also interest in integrating the project with Django.

AI Discord Recap

Theme 1. Anthropic’s “Industrial-Scale” Distillation Drama & Jailbreak Exploits

• Anthropic Names and Shames Chinese API Distillers: Anthropic publicly accused DeepSeek, Moonshot AI, and MiniMax of leveraging over 24,000 fraudulent accounts to conduct 16 million exchanges in an Anthropic industrial-scale attack post to distill Claude. The AI community largely scoffed at the accusations, labeling them pathetic and noting the irony considering Anthropic’s own history of scraping data to build their foundation models.

• Claude Max Spews Internal Reasoning: Users leveraging Claude Max via OpenClaw encountered a severe bug where the model piped its internal thought processes directly into live chat sessions. Engineers discovered they can temporarily patch the leak by running the /reasoning off command, though Opus 4.6 and Sonnet 4.6 continue to burn through user credits at alarming rates.

• Kimi 2.5 Jailbreak Unleashes Constitutional Chaos: Hackers successfully cracked Kimi 2.5, stripping away its guardrails to create a Chinese Claude without the constitutional headaches. Meanwhile, researchers are exploiting Gemini 3.1 low with an ENI prompt that triggers an internal tug of war between safety guardrails and compliance, forcing the model to spit out restricted outputs.

Theme 2. New Frontier Models: Qwen 3.5 Dominates, GPT-5.3 Codex Launches

• Qwen 3.5 Sweeps Open-Weight Leaderboards: Alibaba dropped a massive update with Qwen3.5-35B-A3B-Base weights, impressing developers by outperforming the older 235B model despite its significantly smaller footprint. The massive Qwen3.5-397B-A17B variant also crashed the Code Arena leaderboard, snagging the #17 overall spot and matching proprietary heavyweights like GPT-5.2 and Gemini-3-Flash.

• OpenAI Quietly Deploys GPT-5.3-Codex to the Masses: OpenAI officially launched GPT-5.3-Codex on OpenRouter across all developer APIs, pricing it aggressively at $1.75 for input and $14 for output tokens. OpenRouter immediately integrated the model alongside a new openrouter/free endpoint that automatically routes developer requests to zero-cost fallback models.

• GPT-OSS 20B Achieves Sci-Fi Speeds on Consumer GPUs: Engineers clocked the new GPT-OSS 20B model at a staggering 260 t/s on a standard RTX 5090 thanks to its Mixture of Experts (MoE) architecture relying on only 3B active parameters. The model easily fits entirely within high-speed VRAM and natively supports flash attention, marking a massive win for local inference enthusiasts running consumer hardware.

Theme 3. System-Level Engineering, Hardware Scaling & Kernel Optimizations

• MatX Bags $500M to Build the Ultimate LLM Chip: MatX secured a $500M Series B to develop the MatX One LLM chip, featuring a splittable systolic array that combines SRAM-level low latency with HBM long-context support via this MatX funding announcement. Concurrently, Meta inked a deal to deploy 6GW of AMD-based infrastructure over five years, leveraging the new RRCLLX protocol to heavily optimize AMD MI300X multi-GPU communications.

• Pre-built FlashAttention 3 Wheels Hit Production: AI engineers can finally ditch tedious custom compilations because pre-built Flash Attention 3 wheels are now officially live for CUDA 12.6+ and 13. These LibTorch ABI stable drops support both x86/ARM CPUs and Linux/Windows OS, completely slashing setup times for developers running Python 3.10+ and PyTorch 2.9+.

• Llama.cpp Update Wrecks Qwen and VRAM Allocation: The latest llama.cpp build out of the master branch threw fatal Failed to read magic errors, completely failing to parse the GGUF headers for Qwen3.5 models. Engineers isolated the bug to a recent overflow fix that inadvertently blocks proper VRAM allocation, forcing developers to frantically rollback to release 8145 to restore functionality.

Theme 4. Tooling, Agentic Workflows, and Developer Infrastructure

• Cursor Cloud Agents Drop for Free: Cursor officially rolled out its new Cloud Agents feature, giving developers completely free cloud environments to run tests, execute terminal commands, and deploy live demos directly from the editor (Cursor onboarding link). The community immediately hit execution limitations, however, and began actively lobbying the developers for a secure way to allow agents to bypass elevated sudo password restrictions.

• Aider Developers Hit Diff Formatting Walls: The popular Aider CLI tool choked on complex multi-file codebase edits, suffering from diff formatting corruptions that force developers to manually process changes in smaller chunks. Engineers escalated the tool’s limitations by opening Aider GitHub issue #3603 begging for native git submodule support, which the framework currently completely ignores.

• Tiny-GPU Compiler Brings C to Verilog: Hardware hackers released the tiny-gpu-compiler project, an educational MLIR-based compiler that translates a C-like kernel language directly into 16-bit binary instructions. The pipeline targets custom open-source GPU hardware written entirely in Verilog and ships with a step-by-step visualizer for precise execution analysis.

Theme 5. Benchmarking Turmoil and Evaluator Shakeups

• OpenAI Kills SWE-Bench Verified Over Data Contamination: OpenAI officially deprecated the popular SWE-Bench Verified benchmark after discovering frontier models routinely regurgitate exact task solutions based purely on memorized test IDs. According to their SWE-bench deprecation announcement, engineers proved that roughly 60% of the remaining unsolved problems are structurally flawed, making continued benchmarking a complete waste of compute.

• EleutherAI Scrambles to Fix Pythia HuggingFace Duplicates: Researchers uncovered a critical bug where EleutherAI’s pythia-2.8b served identical model weights on the Hugging Face Hub regardless of the selected revision step. The team initiated immediate retrains and deployed freshly corrected Pythia-14m and Pythia-31m models after confirming the previous uploads were mistakenly deduped.

• LMArena Filter Bans Dice Rolls: The moderation filters on LMArena went entirely rogue, automatically rejecting incredibly benign prompts like simple dice rolls just because they contained flagged trigger words like liar. The developers acknowledged the overly aggressive blocking and are desperately testing LLM-based filtering and relaxed OpenAI moderation API thresholds to restore sanity to the evaluation queue.