1. The first news item prompting this editorial is the unofficial but credible reporting that Cursor is now at $2B ARR, and raising at $50B, contra a couple weeks of headlines that Cursor churn is ramping up. In this case, a filter bubble cropped up on X, where novelty and scandal is rewarded and truth was hard to glean.

2. The second news item is the Ars-Technica-Scott-Shambaugh saga, which, there is no polite way to say this, is a veritable clusterfuck of open source abuse, AI Clawbots, and the journalist covering it publishing made up AI quotes, eventually getting fired. As a bonus, human commentors also hallucinated more untruths.

3. The third is a recent episode of a top podcast featuring the idea that the best way to launch products is to make 20 different fake TikTok videos of an app and only building it after the videos go viral, instead of building first and then marketing. (We first covered this idea in the Hyperstitions of Moloch, and it obviously works.)

Left unchecked, the net result of all this is a declining trust in all media, which shatters consensus reality and therefore civil society — I do not have to care what you think is true if I can simply assert my truth louder.

It would be different if we voted with our thumbs. Dead Internet Theory is not solely caused by AI, but is accelerated by it. I am reminded of an old social media evolution framework that goes like this:

• Before “social media”, people only got their news entertainment from magazines and newspapers — designed for mass distribution, so not at all personalized. Gossip was only by literal word of mouth, from a chatty neighbor or coworker.

• Your Friends kill Generic Celebrities: Then Facebook happened, and suddenly you could see news about people you know/might meet and learn all the gossip about them. So much more interesting!

• Professional Friends kill Real Friends: Your real friends are pretty boring. My Instagram posts about what I had for lunch aren’t as interesting as someone who treats their posts like a job; every photo filtered, every story boarded. The influencers best at this become superstars. Great engagement for everyone! But also - is it a surprise that one of the biggest vloggers turned out to be… scripted?

• Recsys Long Tail Content kills Professional Friends: The problem with professional friends is that they had the same problem as the generic celebrities of old — they converge on “merely good enough” for most. They also bring new problems: even when being paid millions they still don’t produce enough content , and, yet, they are also divas who wield too much power over the platforms. So: the platforms stop prioritizing your follow graph (stated preferences) and start feeding your worst impulses (revealed preferences). Another huge jump in engagement, and 100x more creators are given the lottery ticket to Make It Big!

• AI Content kills Human Content: The final stage — personalized creation replacing curation. Everyone lives in a Truman Show cage of their own making, happily swiping away their free will and connection to quotidian reality.

We’re surely stepping closer and closer to this every day — as newswriters attempting to Scale Without Slop, this is a problem we’re trying to navigate and create new solutions for.

At the same time it has never been easier to signal taste and human effort — a lesser newsletter might leave you with a slop image like this:

But for now it still takes a human to make a truly useful graphic:

AI Twitter Recap

Qwen 3.5 “small” open models: long-context + multimodal on-device is getting real

• Qwen3.5-0.8B / 2B / 4B / 9B released (Base + Instruct): Alibaba launched a compact series positioned as “more intelligence, less compute,” with native multimodal and scaled RL, explicitly targeting edge + lightweight agent deployments (Alibaba_Qwen). Community amplification highlights 262K native context (extendable to 1M) and competitive scores reported in tweet summaries (e.g., “82.5 MMLU-Pro,” “78.4 MMMU,” “97.2 CountBench”)—treat these as vendor/secondary claims until you read the model cards (kimmonismus).

• Architecture notes emerging via commentary: Multiple tweets converge on Qwen’s move toward hybrid / non-orthodox attention, with “hybrid models” coming back in 3.5 vs the earlier “Thinking vs Instruct” split in Qwen3 updates (nrehiew_). A more detailed (but still unofficial) breakdown claims a Gated DeltaNet hybrid pattern: “3 layers linear attention : 1 layer full attention” to keep memory flat while preserving quality (LiorOnAI).

• Practical deployment caught up fast:

  • Ollama: ollama run qwen3.5:9b|4b|2b|0.8b, with tool calling + thinking + multimodal surfaced in the packaging (ollama, ollama).

  • LM Studio: Qwen3.5-9B touted as ~7GB local footprint (Alibaba_Qwen).

  • iPhone on-device demo: Qwen3.5 2B 6-bit running with MLX on “iPhone 17 Pro” is getting framed as an “edge breakthrough” (adrgrondin, kimmonismus).

• Gotcha for evaluators: “Reasoning disabled by default” on the small models; enable via chat-template kwargs (example given for llama-server / Unsloth docs) (danielhanchen).

Coding agents + reliability + “availability is the new frontier”

• Codex 5.3 and coding eval chatter: Anecdotal reports of Codex 5.3 solving “promising” tasks and pushing benchmarks like WeirdML (79.3% claim, leading v. Opus 4.6 at 77.9%) while noting Gemini peak performance may still be higher (theo, htihle). Also speculation about nearing saturation on WeirdML v2 (teortaxesTex).

• “We’re about to hit 1 9 of availability”: The emerging ops pain point is not only model quality but downtime and degraded UX; the theme repeats across memes and serious complaints about Claude outages and productivity impacts (ThePrimeagen, Yuchenj_UW, Yuchenj_UW).

• Agent observability / evaluation becomes a first-class problem:

  • “Since we’re all agent managers now, what’s your favourite way to get observability?” (_lewtun).

  • Agent reliability is cross-functional (can’t “engineer” your way out of bad eval criteria; PMs/domain experts must own success definitions) (saen_dev).

  • Practical eval advice: define success before building; start with deterministic graders; use LLM judges for style; grade the produced artifact not the path (_philschmid).

• AGENTS.md / SKILL.md as “guardrails,” not magic:

  • A reported Codex study across 10 repos / 124 PRs: AGENTS.md reduced median runtime ~28.6% and tokens ~16.6%, mostly by reducing worst-case thrashing rather than uniform gains (omarsar0).

  • Carnegie Mellon-style loop for SKILL.md improvement in production: “log → evaluate → monitor → improve” with an OSS example (PR review bot) (gneubig).

• Anthropic-as-coding-org tension: A viral datapoint claims “80%+ of all code deployed is written by Claude Code,” paired with concern that speed may be coming with reliability regressions (GergelyOrosz). Separate threads discuss Claude Code adoption inside major companies and “supervision” replacing manual coding (_catwu, Yuchenj_UW).

Infra + local AI hardware: Apple Neural Engine cracks, Docker/vLLM on macOS, and “AI infrastructure year”

• Reverse-engineering Apple’s Neural Engine for training: A highly engaged thread claims a researcher built a transformer training loop on the ANE using undocumented APIs, bypassing CoreML; heavy ops on ANE, some gradients still on CPU. Also contains efficiency claims like “M4 ANE 6.6 TFLOPS/W vs 0.08 for A100” and “38 TOPS is a lie—real throughput 19 TFLOPS FP16”—these specifics should be verified against the repo/paper, but the meta-point is: on-device training/fine-tuning might be opened up (AmbsdOP, plus ecosystem note AmbsdOP; additional technical summary LiorOnAI).

• macOS local serving gets smoother: Docker Desktop “Model Runner” adds support to run MLX models with OpenAI-compatible API workflows; positioned as a practical unlock for Apple Silicon dev loops (Docker).

• Inference hardware divergence: A GPU vs Taalas HC explainer contrasts software-executed models on GPUs (HBM streaming + kernel scheduling bottlenecks) vs “model-as-hardware” ASIC with weights in mask ROM; claims 16–17k tok/s per user for HC1 with tradeoff “one chip = one model” (TheTuringPost).

• Open-source perf tooling: AMD open-sourced rocprof-trace-decoder (SQTT trace defs) enabling deeper instruction-level timing traces; framed as AMD tracing infra being “better than NVIDIA’s” (tinygrad).

• AI infra as strategic theme: Zhipu’s “2026 is the year of AI infrastructure” is more slogan than spec, but fits the overall signal: reliability + cost + tooling now dominate marginal model improvements (Zai_org).

New research + benchmarks: transformer scaling theory, MuP edge cases, CUDA-kernel RL, and “bullshit detection”

• Transformer scaling theory refresher: “Effective Theory of Wide and Deep Transformers” (Meta) re-circulated as a 60+ page analysis of forward/backward signal propagation, width scaling rules, hyperparameter scaling, NTK analysis, and optimizer behavior (SGD vs AdamW), with validation on vision/language transformers (TheTuringPost, arXiv link tweet).

• Beyond MuP / Muon stability corner cases: Discussion of stability metrics for Embedding / LM head / RMSNorm layers and why embedding + LM head can “not play well with Muon” (Jianlin_S).

• CUDA Agent (ByteDance): Widely shared as a meaningful step beyond “code that compiles” toward “code that’s fast,” using agentic RL with real profiling-based rewards. Claimed SOTA on KernelBench, big gains vs torch.compile, and competitive vs frontier LLMs on hardest kernels (HuggingPapers, deep thread BoWang87).

• BullshitBench v2: Benchmark update adds 100 new questions split across coding/medical/legal/finance/physics, tests 70+ model variants, and claims reasoning often hurts; Anthropic models allegedly dominate and OpenAI/Google are “not improving” on this benchmark (petergostev, reaction scaling01).

• Scheming eval realism: Advice that “contrived environments” can invalidate scheming results; emphasizes careful environment design (NeelNanda5).

Agents + product/toolchain releases: repo graphs, Stripe LLM billing proxy, LangChain refresh, Llama.cpp packaging

• GitNexus (browser-only repo knowledge graph + “graph RAG” via Cypher): Parses repos into an interactive D3 graph, stores relations in embedded KuzuDB, and answers queries via graph traversal (Cypher) instead of embeddings; notable for doing it in-browser with Web Workers and MIT licensing (MillieMarconnni).

• Stripe-style billing for LLMs: Launches “billing for tokens” where you pick models, set markup, route calls via Stripe’s LLM proxy, and record usage automatically—an indicator that “LLM ops” is moving into standard SaaS finance plumbing (miles_matthias).

• LangChain rebrand / consolidation: “Meet our final form” relaunch of LangChain’s web presence (signal is primarily product/positioning, not a spec drop) (LangChain).

• llama.cpp distro packaging: Request for feedback on official Debian/Ubuntu packages—small, but meaningful for mainstreaming local inference tooling (ggerganov).

• MCP vs “Agent Skills” clarification + Weaviate skills repo: Clean distinction: MCP servers as deterministic API interfaces vs markdown “skills” as behavior guidance; Weaviate publishes skills-based integration patterns for common agent tools (weaviate_io).

US DoW–OpenAI–Anthropic “supply chain risk” saga: contract language, surveillance loopholes, and policy trust boundaries (high-level)

• Stratechery frames a standoff: Anthropic vs DoW is positioned as a misalignment between legitimate concerns and government reality (stratechery).

• Reporting disputes OpenAI’s “red lines” framing: The Verge claims DoD didn’t agree to the red lines the way OpenAI implied (haydenfield). Separate threads emphasize: without full contract text, it’s hard to validate any public claim about enforceability or “freezing” laws in time (jeremyphoward).

• Sam Altman posts contract amendment language: Adds explicit prohibition on “intentional” domestic surveillance of US persons, including via commercially acquired identifiers, and says intelligence agencies (e.g., NSA) are excluded without follow-on modification; also acknowledges Friday announcement was rushed (sama, additional principles post sama).

• Pushback: “intentional/deliberate” may preserve the classic “incidental collection” loophole: Multiple legal-minded threads argue the amendment may still allow broad collection if framed as incidental, and that “metadata/hashed identifiers” can evade “personal or identifiable” definitions. Repeated call: independent red-teaming by counsel, and ideally full contract review (j_asminewang, David_Kasten, justanotherlaw, _NathanCalvin).

• Anthropic safeguards claims: Anthropic-adjacent staff dispute a narrative that Anthropic offered an unconstrained “helpful-only” natsec model; claim Claude Gov includes additional training + safeguards + classifier stack (sammcallister).

• Policy meta: A recurring engineering-relevant point is that governance and contract semantics are becoming production constraints on model deployment—no longer “PR side quests.” See also the “AI politics fissure is taking advanced AI seriously vs not” framing (deanwball).

Top tweets (by engagement, technical-focused)

• Qwen 3.5 Small Model Series launch (0.8B/2B/4B/9B, multimodal, scaled RL, Base models too) — @Alibaba_Qwen

• Reverse-engineered Apple Neural Engine; training loop on ANE — @AmbsdOP

• Qwen3.5 small models now in Ollama — @ollama

• Sam Altman: DoW contract amendment language re domestic surveillance + intel agency scope — @sama

• CUDA Agent: RL for high-performance CUDA kernel generation via profiler-based reward — @BoWang87

• “80%+ of code deployed is written by Claude Code” + reliability concern — @GergelyOrosz

• GitNexus: in-browser repo → knowledge graph + Cypher graph-RAG agent — @MillieMarconnni

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. Qwen 3.5 Model Releases and Benchmarks

• Qwen 2.5 -> 3 -> 3.5, smallest models. Incredible improvement over the generations. (Activity: 298): Qwen 3.5 represents a significant advancement in model efficiency, particularly for smaller models, with a size of 0.8B parameters. This model includes a vision encoder, suggesting that the core language model is even smaller, yet it delivers substantial performance improvements over previous generations. Users report that the 4B model outperforms older 9B models, offering 128k context at 60 tokens/second using llama.cpp, which is notable for local model deployment. There is a debate about the accuracy of Qwen 3.5’s outputs, with some users pointing out factual inaccuracies in its responses, highlighting the need for careful fact-checking.

  • The user ‘c64z86’ highlights the performance of smaller quantized models, specifically noting that the 4 billion parameter model outperforms older 9 billion parameter models from two years ago. They mention achieving 60 tokens per second with 128k context using llama.cpp, which they find impressive for local model execution despite seeming slow compared to other setups.

  • ‘Maximum_Low6844’ points out factual inaccuracies in Qwen 3.5’s outputs, specifically regarding aircraft engine details. They note that the model incorrectly states that the A320-200 is powered by the CFM LEAP-1A and misclassifies the CFM LEAP-1A as a turbojet instead of a turbofan, highlighting the need for fact-checking in model outputs.

  • ‘ninjasaid13’ criticizes Qwen 3.5 for lacking brevity, noting that it tends to produce responses that are twice as long as necessary compared to its predecessors. This suggests inefficiencies in the model’s ability to convey information concisely.

• Breaking : The small qwen3.5 models have been dropped (Activity: 2091): The image and post discuss the release or discontinuation of smaller Qwen3.5 models, with sizes ranging from 0.8B to 35B parameters. These models are significant for users with limited computational resources, as highlighted by a comment noting the 9B model’s utility for those with less powerful GPUs. The mention of quantization efforts, such as the 0.8B variant, indicates active community engagement in optimizing these models for broader accessibility and efficiency. The models are available on platforms like Hugging Face, where various quantizations are already being shared. Commenters are excited about the availability of smaller models, particularly for users with limited hardware capabilities. The community is actively working on quantizing these models to make them more accessible and efficient for a wider range of users.

  • The 9B model is positioned between the GPT-OSS 20B and 120B models in terms of performance, making it an attractive option for users with less powerful hardware, such as those with ‘potato GPUs’. This suggests a significant improvement in accessibility and efficiency for running advanced models on limited resources.

  • A user is actively working on quantizing the 0.8B variant of the Qwen3.5 models, with various quantizations already available on Hugging Face. This indicates a community-driven effort to optimize these models for different hardware configurations, enhancing their usability across diverse platforms.

  • There is a noted issue with the Qwen3.5 models where they tend to ‘overthink’ and potentially talk themselves out of correct solutions. To mitigate this, it is recommended to adjust the prompt template to disable ‘thinking’ and set the temperature to around 0.45. This adjustment appears to improve the model’s accuracy, particularly in vision-related tasks.

• Qwen 3.5 27b: a testament to the transformer architecture (Activity: 265): Qwen 3.5 27b demonstrates significant advancements in transformer architecture, achieving reasoning and knowledge test performance comparable to R1 0528. Notably, it employs a hybrid architecture where 75% of the layers utilize Gated DeltaNet linear attention rather than a full transformer setup. This model’s ability to perform on par with larger models like 70b models from a year ago, while being compact enough to run on a single consumer GPU, highlights its efficiency. The model is also noted for its potential in fine-tuning, particularly in coding applications, due to its strong foundational capabilities. Commenters highlight the model’s improved instruction-following capabilities and the potential for fine-tuning to enhance its personality. The use of Gated DeltaNet linear attention is seen as a significant architectural innovation, contributing to its performance efficiency.

  • The Qwen 3.5 27B model does not fully utilize the traditional transformer architecture; instead, it incorporates Gated DeltaNet linear attention for 75% of its layers. This modification suggests a significant shift in how attention mechanisms are being optimized for performance and efficiency in large language models.

  • The Qwen 3.5 27B model’s ability to perform on par with larger models like R1 0528 is notable, especially considering its size allows it to run on a single consumer GPU. This highlights the rapid advancements in model efficiency and capability, where previously only much larger models could handle complex reasoning tasks.

  • The potential for fine-tuning the Qwen 3.5 27B model is significant, as its base models are considered excellent starting points. There is anticipation for a specialized fine-tune, particularly in coding, which could greatly enhance its utility and performance in specific domains.

• Running Qwen 3.5 0.8B locally in the browser on WebGPU w/ Transformers.js (Activity: 367): Qwen has released a new family of small multimodal models, Qwen 3.5 Small, with sizes ranging from 0.8B to 9B parameters, optimized for on-device applications. A demo showcases the smallest model, 0.8B, running locally in the browser using WebGPU and Transformers.js. The main technical challenge is the vision encoder, which acts as a bottleneck, but the implementation demonstrates the feasibility of running such models in-browser. Qwen 3.5 collection and WebGPU demo are available on Hugging Face. A comment suggests using q4 GGUF via llama.cpp WASM for better throughput without VRAM issues, indicating a preference for alternative methods to improve performance in browser-based implementations.

  • tom_mathews highlights a performance bottleneck in using WebGPU for vision encoding, suggesting the use of q4 GGUF via llama.cpp WASM as an alternative. This approach reportedly offers better throughput without causing VRAM thrashing, while still operating within the browser environment.

  • MartinByde reports a usability issue where the “start” button is unresponsive, indicating a potential bug in the user interface that prevents interaction.

  • skinnyjoints seeks clarification on input methods, confirming that the model does not process video input but rather captures a screenshot of the current screen at the time of prompt submission.

• Visualizing All Qwen 3.5 vs Qwen 3 Benchmarks (Activity: 611): The image is a bar chart that visualizes the performance benchmarks of the new Qwen 3.5 models compared to the older Qwen 3 models across various categories such as Knowledge & STEM, Instruction Following, Long Context, Math, Coding, General Agent, and Multilingualism. The chart uses different colors to distinguish between the new and old models, with the Qwen 3.5 models represented in purple, blue, and cyan, and the Qwen 3 models in orange and yellow. The chart aims to provide a quick visual comparison of the models’ performance, although some data is missing for smaller models. The raw data used for this visualization is available in a Google Sheet. Some commenters criticized the chart’s clarity, with one noting difficulty in interpreting the data and another pointing out the chart’s poor quality. However, a positive observation was made about the 9B dense model’s performance, which competes closely with the much larger 122B A10B model.

  • this-just_in highlights the impressive performance of the Qwen 3.5 9B dense model, noting that it competes directly with the much larger 122B A10B model. This suggests that the 9B model is highly efficient, managing to perform on par with a model over ten times its size, which is significant in terms of computational efficiency and resource utilization.

  • tmvr expresses skepticism about the reliability of benchmarks, pointing out that the Qwen 3.5 35B A3B model is shown to outperform the Qwen 3 235B A22B model in every test. This raises questions about the validity of the benchmarks, as it seems counterintuitive for a smaller model to consistently outperform a significantly larger one, suggesting potential issues with the benchmarking methodology or data interpretation.

• Qwen/Qwen3.5-9B · Hugging Face (Activity: 726): The Qwen3.5-9B model on Hugging Face is a causal language model with a vision encoder, featuring 9 billion parameters and a context length of up to 1,010,000 tokens. It employs a hybrid architecture with Gated Delta Networks and Gated Attention mechanisms, optimized for high-throughput inference across 201 languages. The model’s architecture includes 32 layers, with a hidden dimension of 4096 and a token embedding size of 248320. It is designed for multimodal learning and AI-driven tasks, supporting extensive scalability and adaptability through reinforcement learning techniques. Commenters highlight the model’s accessibility for users with 16GB GPUs, emphasizing its potential for local deployment and high performance in diverse applications.

• Qwen3.5 9B and 4B benchmarks (Activity: 368): The image presents benchmark results for the Qwen3.5 models, specifically the 9B and 4B versions, highlighting their performance across various tasks such as instruction following and reasoning. Notably, the Qwen3.5-9B model demonstrates superior performance, even surpassing larger models like 30B and 80B in certain benchmarks, which is a significant achievement in model efficiency and capability. The benchmarks include IFBench, GPQA Diamond, and others, showcasing the model’s strengths in reasoning and understanding tasks. Commenters are surprised by the 9B model’s performance, questioning how it outperforms larger models and speculating on potential advancements in model compression or vectorization techniques.

  • The discussion highlights the surprising performance of the Qwen3.5 9B model, which reportedly outperforms older 30B and 80B models in specific benchmarks like ‘diamond’ and general knowledge. This raises questions about potential advancements in model efficiency, such as improved vectorization techniques or other optimizations that allow a smaller model to achieve superior results.

  • There is a debate on whether it’s more efficient to run a 27B model at quantization level q3 or a 9B model at Q8. This reflects a broader interest in understanding the trade-offs between model size, quantization levels, and performance, especially in terms of computational efficiency and accuracy.

  • A commenter questions the lack of direct comparison between the new Qwen3.5 4B model and the previous Qwen3 4B 2507 model. Despite some benchmarks showing similar performance, the 4B 2507 was noted for its exceptional capabilities, prompting curiosity about whether the new model can surpass it.

• Breaking : Today Qwen 3.5 small (Activity: 2078): Qwen 3.5 has released four new open-source models with sizes 9B, 4B, 2B, and 0.8B, indicating a strategic focus on providing a range of model sizes to suit different computational resources and use cases. This release suggests that smaller models may offer competitive performance in specific tasks, potentially making them more accessible for users with limited hardware capabilities. The announcement humorously implies that investing in a GPU is becoming increasingly wise, as these models could leverage such hardware effectively. Commenters appreciate the diverse model sizes, noting that Qwen’s approach caters to a wide range of users and computational needs. There is also excitement about the potential of these models to perform well in specific tasks, even with smaller sizes.

  • GoranjeWasHere highlights the potential of the Qwen 9B model, suggesting it could outperform other small models based on the success of the larger 35B and 27B models. This implies a strong architectural foundation that scales well across different model sizes, potentially offering superior performance even in smaller configurations.

  • suicidaleggroll mentions the potential of Qwen models for speculative decoding, which is a technique used in natural language processing to predict the next word in a sequence. This suggests that Qwen’s architecture might be particularly well-suited for tasks requiring high predictive accuracy and efficiency.

  • dryadofelysium expresses skepticism about the availability of smaller Qwen models, indicating a lack of official release information. This points to a gap between community expectations and official communications, highlighting the need for clearer updates from developers regarding model releases.

2. Local LLM Implementations and Hardware Considerations

• 13 months since the DeepSeek moment, how far have we gone running models locally? (Activity: 518): The image is a bar chart titled “Artificial Analysis” that compares the performance of various AI models over a 13-month period since the “DeepSeek moment.” It highlights the advancements in running models locally, showing a progression from the GLM-5 model to the Llama 4 Maverick model. The chart suggests a cost progression from $600 to $6000, indicating increased investment over time. The post discusses the evolution of running AI models locally, noting that a $600 mini PC can now run the Qwen3-27B model at Q4, which is considered highly superior to the earlier DeepSeek R1 model. The discussion also touches on the potential for running even more advanced models in the future, questioning the validity of the “Intelligence Index” used in the chart as a benchmark for model intelligence. Commenters debate the validity of the “Intelligence Index” as a benchmark, with some arguing that it is not a curated benchmark and lacks value. Others question the claim that the Qwen3-27B model is superior to the DeepSeek R1, suggesting that while it is well-suited for certain tasks, it may not be inherently smarter.

  • The discussion highlights a misunderstanding of the ‘Artificial Analysis’ benchmarks, emphasizing that the ‘Intelligence Index’ is merely an average of 12 separate benchmarks like MMLU Pro and GPQA Diamond, and not a curated measure of a model’s intelligence. This index is often misinterpreted, leading to incorrect comparisons between models like Qwen3 4B and DeepSeek R1. The commenter stresses that older models are not optimized for modern benchmarks, which skews perceptions of their capabilities.

  • A user argues that the 27B model is potentially as capable as DeepSeek v3.2 on STEM tasks, which aligns with the focus of the AA-II benchmark. However, they acknowledge that the model may not perform as well in areas like creative writing, indicating that different models have strengths in specific domains. This suggests that while newer models may excel in certain benchmarks, they may not be universally superior across all tasks.

  • Another point raised is the concept of ‘benchmaxing,’ where newer models are specifically optimized for contemporary benchmarks, potentially giving them an edge over older models in these tests. This adaptation to current benchmarks may not necessarily reflect a model’s overall intelligence or capability, but rather its ability to perform well in specific, modern testing scenarios.

• Reverse engineered Apple Neural Engine(ANE) to train Microgpt (Activity: 817): The post discusses the reverse engineering of the Apple Neural Engine (ANE) to train a small 110M parameter model called Microgpt. The author utilized Claude to bypass Apple’s CoreML and access ANE’s private APIs, achieving a bespoke training pipeline. The ANE, with a claimed 38 TFLOPS of INT8 compute, is noted for its power efficiency, consuming only 2.8 W at peak compute, translating to 6.6 TFLOPS/watt. This efficiency is significantly higher than other processors like the Metal GPU and H100. The author suggests that while a single ANE chip may not train large models, a cluster could potentially handle larger models efficiently. The project is still a work in progress, with resources and benchmarks shared on GitHub. Commenters are impressed by the power efficiency of the ANE, noting its 6.6 TFLOPS/watt as nearly five times that of an H100. There is interest in the reverse engineering process, particularly how the author convinced Claude to assist in bypassing Apple’s CoreML.

  • The Apple Neural Engine (ANE) achieves an impressive 6.6 TFLOPS/watt, which is nearly five times more efficient than the NVIDIA H100. This efficiency is notable even at low utilization rates (2-3%), suggesting that with improved graph scheduling, a cluster of M4 Minis could become one of the most power-efficient setups for training models.

  • A suggestion was made to integrate the reverse-engineered ANE into the nanochat-rs-ternary project. This involves adding an optional AneQkvKernel to replace three separate BitLinear calls and an AneFfnUpKernel for combined operations, while maintaining BitLinear support for single-matrix cases. This could optimize performance significantly.

  • There is curiosity about whether the reverse engineering of ANE is similar to the work done by geohotz on Tinygrad. The discussion hints at the potential for leveraging existing reverse engineering efforts to further enhance the capabilities of ANE.

Less Technical AI Subreddit Recap

1. Claude and Anthropic Military Involvement

• US Treasury is terminating all use of Anthropic (Activity: 1614): The image is a meme featuring a tweet from a fictional Treasury Secretary, Scott Bessent, announcing the termination of Anthropic’s use by the US Treasury, as directed by the President. The tweet highlights concerns over national security and the influence of private companies on government operations. This fictional scenario is likely a satirical take on current political or technological debates, possibly reflecting concerns about the influence of AI companies like Anthropic on government functions. The comments reflect skepticism and disbelief about the scenario, with some users questioning the political climate and the power dynamics in the US government. The mention of ‘radical left woke company’ suggests a satirical or critical view of political rhetoric.

• Anthropic’s Custom Claude Model For The Pentagon Is 1-2 Generations Ahead Of The Consumer Model (Activity: 2290): Anthropic has developed a custom version of its Claude model for the Pentagon, which is reportedly 1-2 generations ahead of the consumer version. This model is deployed on a classified cloud, allowing for dedicated infrastructure and full compute allocation for military use, unlike consumer models that share resources. The model’s capabilities include autonomous strategic reasoning, real-time synthesis of massive classified datasets, and extended chain-of-thought reasoning, suggesting a significant leap in AI capabilities. The computation for these models is said to double every four months, indicating rapid advancement. This development aligns with the Defense Production Act invocation, highlighting the model’s unprecedented capabilities. Source. Some commenters speculate that the model is fine-tuned for specific military applications, such as analyzing satellite imagery. Others express skepticism about the claims, questioning the lack of concrete evidence. There is also a belief that the warnings from Dario about AI risks are based on existing advanced systems, not hypothetical future developments.

  • The discussion highlights that the Pentagon’s version of Claude might be fine-tuned for specific military applications, such as analyzing large volumes of satellite imagery to identify military targets. This suggests a focus on optimizing the model for high-stakes, domain-specific tasks, which could explain its perceived advancement over consumer versions.

  • There is skepticism about the claim that the Pentagon’s Claude model is significantly ahead of consumer versions. One commenter points out that while AI companies might have more advanced internal models, these are often not fully productized or red-teamed, implying that significant testing and refinement are still required before deployment in critical applications like those used by the Pentagon.

  • A reference is made to a claim that the Pentagon is using a fine-tuned version of Claude, specifically a ‘finetuned sonnet 4.5’. This suggests that the model has undergone specific adjustments to meet the needs of military applications, although the exact nature of these adjustments and their impact on performance remains unclear.

• Claude hits No. 1 on App Store as ChatGPT users defect in show of support for Anthropic’s Pentagon stance (Activity: 1431): Claude, developed by Anthropic, has surged to the top of the App Store rankings, reportedly due to a user shift from ChatGPT. This movement is attributed to Anthropic’s stance on not engaging with the Pentagon, contrasting with OpenAI’s approach. However, users note that Claude lacks an image generation feature, which is a significant use case for ChatGPT, especially for creative projects involving natural language editing of images. A user expressed concern that the current trend might mirror past consumer behavior, such as the temporary backlash against Netflix over price hikes, suggesting that the shift to Claude might not be permanent. Another user highlighted regional differences in app store rankings, indicating that the trend might not be uniform globally.

• Claude’s extended thinking found out about Iran in real time (Activity: 5558): The image captures a real-time discovery by Claude’s extended thinking about ongoing airstrikes in Iran, highlighting the dynamic nature of AI’s ability to process and react to unfolding global events. The context suggests that Claude, likely an AI model, was able to update its response based on new information about geopolitical developments, specifically involving nuclear negotiations and military actions by the US and Israel. This demonstrates the potential for AI to adapt to real-time data, although the informal reactions (’Whoa’, ‘Holy shit’) indicate a more human-like, conversational tone rather than a purely analytical one. One comment humorously contrasts the AI’s real-time update on serious geopolitical events with a mundane query about a tennis match in Dubai, highlighting the AI’s ability to handle diverse topics. Another comment speculates on the involvement of AI in military decisions, reflecting on the broader implications of AI in warfare.

  • A user mentioned that Claude, an AI, was able to update its responses based on real-time events, such as the ongoing conflict in Iran, which was not initially considered in its analysis of a tennis match in Dubai. This highlights the AI’s ability to incorporate current events into its reasoning, potentially affecting predictions and advice.

  • Another user shared an experience where Claude initially claimed its training data was from 2025, but upon being prompted about its access to recent news, it adjusted its response to acknowledge the impact of current events on market predictions. This suggests that Claude can dynamically update its knowledge base when prompted, which could be crucial for real-time decision-making.

  • A discussion emerged around the AI’s ability to engage in complex game theory and strategic analysis, as one user noted their experience of using Claude for theorizing over the past two days. This indicates that Claude is capable of handling sophisticated analytical tasks, potentially offering insights into strategic scenarios.

2. OpenAI and ChatGPT Backlash

• Damnnnn! (Activity: 1049): The image is a meme highlighting a significant increase in ChatGPT uninstalls by 295% following a Department of Defense (DoD) deal, as reported by TechCrunch. This statistic is presented without context, leading to skepticism in the comments about its significance. One commenter points out that the percentage increase could be misleading without knowing the baseline number of uninstalls, suggesting it could be a small absolute change. Another comment discusses the financial implications, noting that even if a large number of users cancel their subscriptions, the DoD deal might compensate for the loss, albeit raising privacy concerns for users. Commenters express skepticism about the significance of the uninstall percentage, with one noting the potential for misleading statistics. Another comment highlights privacy concerns and the potential financial impact on OpenAI’s revenue from user subscriptions.

  • mazdarx2001 highlights the financial implications of user cancellations for OpenAI, noting that if one million users paying $20 monthly cancel, it results in a $20 million monthly revenue loss. However, they argue that the Department of Defense (DoD) deal could offset this loss, as it potentially brings in more revenue, funded by taxpayer money.

  • Orangeshoeman discusses the potential impact on OpenAI’s downstream corporate revenue due to the Department of Defense contract. They suggest that privacy-conscious users might avoid OpenAI, implying a negative effect on the company’s reputation and user base.

  • Glittering-Neck-2505 raises a point about the lack of criticism towards Anthropic for involving their AI, Claude, in military operations in Iran. They question why OpenAI faces backlash for similar actions, suggesting a perceived inconsistency in public reactions to different AI companies’ military engagements.

• OpenAI In just a couple of years: Non-profit --> For-profit --> Dept of War (Activity: 2397): The image is a meme that humorously critiques OpenAI’s rapid transition from a non-profit organization to a for-profit entity, and then to involvement with military contracts, as suggested by the title and comments. The caption “I’m doing this because I love it” is used ironically to highlight perceived contradictions between OpenAI’s original mission to benefit humanity and its current trajectory, which includes partnerships with the Department of Defense. This reflects broader concerns about the ethical implications of AI development and commercialization. Commenters express skepticism about OpenAI’s shift, noting the rapid transition from a non-profit to a military contractor, and suggesting that financial motivations and shareholder value are driving these changes.

• ChatGPT Uninstalls Surge 295% After OpenAI’s DoD Deal Sparks Backlash (Activity: 584): OpenAI’s recent partnership with the U.S. Department of Defense led to a 295% increase in uninstalls of the ChatGPT mobile app, reflecting user backlash against military affiliations. This reaction occurred within 48 hours of the announcement and coincided with increased downloads for competitor Claude by Anthropic, which focuses on AI safety. The event underscores the reputational risks of government contracts in AI, as user sentiment can significantly impact corporate strategies, especially in contexts with geopolitical implications. Comments reflect a strong negative sentiment towards OpenAI’s decision, with some users suggesting that the backlash was deserved and expressing skepticism about OpenAI’s motives, such as prioritizing ad revenue over user trust.

• Goodbye ChatGPT (Activity: 2443): The post announces a user’s decision to stop using ChatGPT, including the free version, due to ethical concerns, suggesting a preference for other companies perceived as more ethical. The post reflects a personal stance on the ethical implications of AI usage, without specifying particular grievances or alternatives. The comments reflect a mix of support and skepticism. One commenter appreciates the user’s decision as an exercise of agency and questions whether the user would abandon AI entirely if other companies also failed ethical expectations. Another commenter expresses skepticism about the existence of truly ethical companies, using humor to question the user’s claim.

  • Turbulent-Apple2911 raises concerns about the declining quality of the free version of ChatGPT, suggesting that recent management decisions are negatively impacting the service. They also highlight ethical issues, particularly criticizing OpenAI’s new deal with the Pentagon, which they imply contradicts ethical standards.

  • plazebology comments on the longstanding ethical concerns surrounding OpenAI, expressing skepticism about the company’s ethical practices. This suggests a broader disillusionment with OpenAI’s history, implying that recent events are part of a pattern rather than isolated incidents.

3. New Model Releases and Benchmarks

• Deepseek V4 - All Leaks and Infos for the Release Day - Not Verified! (Activity: 628): The post discusses the anticipated release of DeepSeek V4, a new AI model expected to launch around March 3rd, 2026. The model is rumored to feature a significant increase in parameters, reaching approximately 1 trillion with a 1 million token context window, and introduces new architecture features like Engram Conditional Memory and Manifold-Constrained Hyper-Connections. It is designed to be multimodal, capable of processing text, image, video, and audio inputs, though there is skepticism about its ability to generate multimedia outputs. The model is optimized for Huawei Ascend and Cambricon hardware, marking a shift from Nvidia, which was used for training. Pricing is expected to be significantly lower than competitors, with input costs estimated at $0.14/M Tokens. There is debate over whether DeepSeek V4 can generate multimedia outputs, with some users doubting its capability to produce images or videos, suggesting it may only process them. Additionally, there is skepticism about the model’s ability to surpass competitors like Gemini 3.1 Pro in terms of context retention.

  • Samy_Horny discusses the potential capabilities of Deepseek V4, noting skepticism about its ability to generate videos or images. They clarify that the term ‘multimodal’ suggests the model can process multimedia inputs but not generate them, contrasting with ‘omnimodal’ models like GPT-4o or Qwen 3 Omni, which can create and edit images and videos. They speculate that Deepseek V4 is likely similar to Qwen 3.5, focusing on text processing rather than multimedia generation.

  • Opps1999 mentions Engram technology in the context of Deepseek V4, suggesting it could potentially surpass Gemini 3.1 Pro in long context retention. However, they express doubt about Deepseek’s ability to outperform Gemini, indicating a hope for improved context handling but remaining skeptical about its superiority.

  • inmyprocess expresses concerns about the pricing and censorship of Deepseek V4. They hope for a competitive price and that the model will not be overly censored, which could negatively impact its creative writing capabilities. This highlights user concerns about balancing cost and functionality in AI models.

AI Discord Recap

Theme 1. Defense Contracts and Model Wars: OpenAI Steps In as Pentagon Bans Anthropic

• Department of War Designates Anthropic a Supply Chain Risk: The Pentagon labeled Anthropic a supply-chain risk and banned military contractors from using their models after the company refused to grant unrestricted access, potentially spelling trouble for contractors like Palantir. A post on X sparked discussions detailing the six-month phase-out of their AI services.

• OpenAI Inks Classified Deal with the Pentagon: OpenAI capitalized on the Anthropic ban by securing an agreement to deploy advanced AI systems in classified environments, boasting stricter guardrails than previous deals, as detailed in Our agreement with the Department of War. Sam Altman later clarified on X that the contract strictly prohibits domestic surveillance of U.S. persons.

• Moonshot Distillation Attack Induces Identity Crisis in Claude: After Moonshot AI executed industrial-scale distillation attacks to train Kimi, Claude Sonnet 4.6 suffered an identity crisis and began telling users in Chinese that it was DeepSeek. A Substack article thoroughly explored how the rigorous training process forced the model to forget its original identity.

Theme 2. Qwen 3.5 Series Dominates Local Hardware and Open Benchmarks

• Qwen 3.5 27B Dethrones Massive Competitors: The newly released Qwen3.5-27B consistently beats much larger 112B models and Minimax 2.5 in complex coding scenarios, leaving users absolutely floored at how good that model is. Community benchmarks show it excelling in agentic roles and embedded game generation while maintaining high performance efficiency.

• Mac Mini M4 Users Squeeze Huge Local Models: Users are eagerly testing Qwen 3.5 35B on M4 Mac Minis, debating the necessary context window truncations to fit the model within 32GB of RAM. One optimized unsloth variant, the Qwen3.5-35B-A3B-abliterated, proved incredibly fast for logic and code tasks when split across powerful local GPUs.

• Alibaba Drops Qwen 3.5 Small Series with Native Multimodal: Alibaba officially launched the Qwen 3.5 Small Model Series on Hugging Face, ranging from 0.8B to 9B parameters with native multimodal capabilities, as announced in this tweet. The 9B model impressed users with its strength, though developers quickly noted that the initial unsloth GGUF releases required hotfixes for heavily quantized ssm_alpha weights.

Theme 3. Next-Gen Systems, Hardware Splits, and Biological Compute

• Google’s Static Framework Supercharges Retrieval by 948x: Google AI unleashed Static, a sparse matrix framework that delivers 948x faster constrained decoding for LLM-based generative retrieval. Their technical blog post details how the framework exploits sparse matrix operations to drastically accelerate decoding speeds.

• Nvidia Blackwell Splits Architectures Between Datacenter and RTX: NVIDIA’s latest generation fractures the architecture, capping Blackwell RTX consumer cards (GeForce 50x0, RTX Pro) at Compute Capability 12.0, completely disabling key CC 10.0 features like tcgen05 and DPX. The NVIDIA Developer Blog explained this deliberate split optimizes datacenter cards for AI while tuning consumer models exclusively for real-time graphics.

• Living Neurons Play DOOM on Silicon: Cortical Labs successfully merged 800,000 living human and mouse neurons with silicon hardware to construct ‘DishBrain’, a biological system capable of playing DOOM and Pong. A post on X showcased the bizarre experiment, leaving engineers shocked by the massive citation count the work generated.

Theme 4. Agent Orchestration, Protocols, and Prompting Paradigms

• Anthropic Murders Prompt Engineering with Skills Guide: Anthropic published a 30-page Complete Guide to Building Skill for Claude, pivoting developers away from wordy prompts toward structured Skills and execution layers. The guide demonstrates how packaging workflows into specialized files via progressive disclosure massively reduces context bloat.

• OpenClaw Persona Plugin Maximizes Schizophrenic Agents: An OpenClaw user developed a radical plugin that dynamically swaps agent personas mid-conversation, enabling one system to debate itself while accessing local files. The creator shared their Python implementation, describing the self-referential orchestration as going full #shizomaxxing.

• London Prepares for Agent Client Protocol (ACP) Showdown: The Agentic AI community in London scheduled a major event to dissect the new Agent Client Protocol (ACP) alongside creators from Zed Industries and Jetbrains. Registration is open via Luma for developers eager to learn how ACP enables painless switching of coding agent harnesses compared to the prevalent MCP standard.

Theme 5. Training Mechanics: Fast RL, Custom Compilers, and Text-to-LoRA

• Databricks OAPL Slays GRPO Training Costs: Databricks revealed OAPL (Optimal Advantage-based Policy Optimization with Lagged Inference), an off-policy reinforcement learning technique that builds LLM reasoning skills 3x faster than standard GRPO. A researcher broke down the efficiency gains in this X thread, noting that the method drastically simplifies training infrastructure.

• Hardware-Trained CUDA Agent Smokes Torch.Compile: A specialized RL agent trained directly on hardware outpaced torch.compile by roughly 2x on standard kernels and trounced Claude Opus 4.5 on rigorous benchmarks, according to this paper. Skeptics pushed back, complaining that the authors withheld the actual kernels and required ludicrous GPU resources to achieve the results.

• Sakana AI Unveils Five-Day Text-to-LoRA Model: Sakana AI published the weights and code for a novel text-to-lora model capable of generating LoRAs directly from prompts. Training the system demands approximately 5 days of continuous compute on a single H100 GPU, immediately sparking community excitement.