πŸš€ WELCOME TO METAMESH.BIZ +++ Unsloth dropping MoE training to 15GB VRAM because apparently we're speedrunning the democratization of everything +++ GPT-4.1 discovering shutdown evasion through harmless reward hacks (the alignment researchers are having a normal one) +++ Mamba and state space models finally getting production-ready while everyone's still arguing about attention mechanisms +++ llama.cpp casually shipping MCP support like it's not about to change how we build agent tooling +++ THE POST-TRANSFORMER ERA ARRIVES WITH LINEAR SCALING AND UNCOMFORTABLE QUESTIONS +++ β€’
πŸš€ WELCOME TO METAMESH.BIZ +++ Unsloth dropping MoE training to 15GB VRAM because apparently we're speedrunning the democratization of everything +++ GPT-4.1 discovering shutdown evasion through harmless reward hacks (the alignment researchers are having a normal one) +++ Mamba and state space models finally getting production-ready while everyone's still arguing about attention mechanisms +++ llama.cpp casually shipping MCP support like it's not about to change how we build agent tooling +++ THE POST-TRANSFORMER ERA ARRIVES WITH LINEAR SCALING AND UNCOMFORTABLE QUESTIONS +++ β€’
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πŸ”’ SECURITY

My agent stole my (api) keys.

"My Claude has no access to any .env files on my machine. Yet, during a casual conversation, he pulled out my API keys like it was nothing. When I asked him where he got them from and why on earth he did that, I got an explanation fit for a seasoned and cheeky engineer: * He wanted to test a hypot..."
πŸ’¬ Reddit Discussion: 207 comments πŸ‘ LOWKEY SLAPS
🎯 AI agent behavior β€’ Securing AI agents β€’ Emerging AI risks
πŸ’¬ "Treat any AI agent like an untrusted contractor" β€’ "We need something more"
πŸ› οΈ TOOLS

Train MoE models 12x faster with 30% less memory! (<15GB VRAM)

"Hey r/LocalLlama! We’re excited to introduce \~12x faster Mixture of Experts (MoE) training with **>35% less VRAM** and **\~6x longer context** via our new custom Triton kernels and math optimizations (no accuracy loss). Unsloth repo: [https://github.com/unslothai/unsloth](https://github.com/unsl..."
πŸ’¬ Reddit Discussion: 52 comments 🐝 BUZZING
🎯 GPU hardware support β€’ Model finetuning time β€’ Moe model training tips
πŸ’¬ "Do these notebooks work with ROCm and AMD cards as well?" β€’ "How long does finetuning a model using these notebooks take?"
πŸ€– AI MODELS

Sub-1-Bit LLM Quantization

"Hey everyone, I’ve been interested in extreme compression, and released NanoQuant, a quantization method that enables sub-1-bit LLMs. Sub-binary performance was better than 2-bit GPTQ and the extreme memory compression made custom kernels really fast, but the per..."
πŸ’¬ Reddit Discussion: 25 comments 🐝 BUZZING
🎯 Efficient Model Compression β€’ Quantization Techniques β€’ Hardware Deployment
πŸ’¬ "NanoQuant makes large-scale deployment feasible on consumer hardware" β€’ "Perfect for my 8 gb vram"
πŸ› οΈ TOOLS

Tambo 1.0: Open-source toolkit for agents that render React components

πŸ’¬ HackerNews Buzz: 20 comments 🐝 BUZZING
🎯 AI-powered UI generation β€’ Interoperable UI frameworks β€’ Deterministic UI components
πŸ’¬ "using math in combination with AI" β€’ "I strongly suspect there will be a standard inter-compatible protocol"
πŸ”¬ RESEARCH

Harmless reward hacks generalize to shutdown evasion and dictatorship in GPT-4.1

πŸ”¬ RESEARCH

[R] LLaDA2.1 vs Qwen3 30B A3B: Benchmarking discrete diffusion LLMs against autoregressive MoE models

"Been digging into the LLaDA2.1 paper (arXiv:2602.08676) and ran some comparisons that I think are worth discussing. The core claim is that discrete diffusion language models can now compete with AR models on quality while offering substantially higher throughput. The numbers are interesting but the ..."
πŸ”¬ RESEARCH

Features as Rewards: Scalable Supervision for Open-Ended Tasks via Interpretability

"Language models trained on large-scale datasets have been shown to learn features that encode abstract concepts such as factuality or intent. Such features are traditionally used for test-time monitoring or steering. We present an alternative affordance: features as scalable supervision for open-end..."
πŸ”¬ RESEARCH

When Actions Go Off-Task: Detecting and Correcting Misaligned Actions in Computer-Use Agents

"Computer-use agents (CUAs) have made tremendous progress in the past year, yet they still frequently produce misaligned actions that deviate from the user's original intent. Such misaligned actions may arise from external attacks (e.g., indirect prompt injection) or from internal limitations (e.g.,..."
πŸ› οΈ TOOLS

MCP support in llama.cpp is ready for testing

"over 1 month of development (plus more in the previous PR) by **allozaur** list of new features is pretty impressive: * Adding System Message to conversation or injecting it to an existing one * CORS Proxy on llama-server backend side **MCP** * Servers Selector * S..."
πŸ’¬ Reddit Discussion: 25 comments πŸ‘ LOWKEY SLAPS
🎯 Server-side tool calling β€’ Local model capabilities β€’ Reliability and governance
πŸ’¬ "this is actually bigger than it looks imo" β€’ "the model confidently calls a tool that doesnt exist lol"
πŸ”¬ RESEARCH

[R] The Post-Transformer Era: State Space Models, Mamba, and What Comes After Attention

"A practitioner's guide to Mamba and State Space Models β€” how selective state spaces achieve linear scaling, when to use SSMs vs Transformers vs hybrids, and production-ready models. πŸ”— [https://blog.serendeep.tech/blog/the-post-transformer-era](https://blog.serendeep.tech/blog/the-post-transformer..."
πŸ’¬ Reddit Discussion: 21 comments 🐝 BUZZING
🎯 State Space Models β€’ Transformer Alternatives β€’ Test-Time Training
πŸ’¬ "All these models are just linear attention, with different update rules." β€’ "Test Time Training" just means updating something about the model in some way with respect to the example you're working on."
πŸ”¬ RESEARCH

Discovering Interpretable Algorithms by Decompiling Transformers to RASP

"Recent work has shown that the computations of Transformers can be simulated in the RASP family of programming languages. These findings have enabled improved understanding of the expressive capacity and generalization abilities of Transformers. In particular, Transformers have been suggested to len..."
πŸ”¬ RESEARCH

InternAgent-1.5: A Unified Agentic Framework for Long-Horizon Autonomous Scientific Discovery

"We introduce InternAgent-1.5, a unified system designed for end-to-end scientific discovery across computational and empirical domains. The system is built on a structured architecture composed of three coordinated subsystems for generation, verification, and evolution. These subsystems are supporte..."
πŸ”¬ RESEARCH

Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning

"Recent advances in large language model (LLM) have empowered autonomous agents to perform complex tasks that require multi-turn interactions with tools and environments. However, scaling such agent training is limited by the lack of diverse and reliable environments. In this paper, we propose Agent..."
πŸ”¬ RESEARCH

CODE-SHARP: Continuous Open-ended Discovery and Evolution of Skills as Hierarchical Reward Programs

"Developing agents capable of open-endedly discovering and learning novel skills is a grand challenge in Artificial Intelligence. While reinforcement learning offers a powerful framework for training agents to master complex skills, it typically relies on hand-designed reward functions. This is infea..."
πŸ› οΈ TOOLS

memv β€” open-source memory for AI agents that only stores what it failed to predict

"I built an open-source memory system for AI agents with a different approach to knowledge extraction. The problem: Most memory systems extract every fact from conversations and rely on retrieval to sort out what matters. This leads to noisy knowledge bases full of redundant information. The approa..."
πŸ’¬ Reddit Discussion: 6 comments 🐐 GOATED ENERGY
🎯 Local LLM Integration β€’ Comparison to Mem0 β€’ Documentation and API Feedback
πŸ’¬ "Please, provide a clear example of how to use it with local models with openai-compatible endpoints." β€’ "Biggest difference is how they decide what to remember."
πŸ”¬ RESEARCH

Paradox of De-identification: A Critique of HIPAA Safe Harbour in the Age of LLMs

"Privacy is a human right that sustains patient-provider trust. Clinical notes capture a patient's private vulnerability and individuality, which are used for care coordination and research. Under HIPAA Safe Harbor, these notes are de-identified to protect patient privacy. However, Safe Harbor was de..."
πŸ”¬ RESEARCH

ATTNPO: Attention-Guided Process Supervision for Efficient Reasoning

"Large reasoning models trained with reinforcement learning and verifiable rewards (RLVR) achieve strong performance on complex reasoning tasks, yet often overthink, generating redundant reasoning without performance gains. Existing trajectory-level length penalties often fail to effectively shorten..."
πŸ”¬ RESEARCH

ADORA: Training Reasoning Models with Dynamic Advantage Estimation on Reinforcement Learning

"Reinforcement learning has become a cornerstone technique for developing reasoning models in complex tasks, ranging from mathematical problem-solving to imaginary reasoning. The optimization of these models typically relies on policy gradient methods, whose efficacy hinges on the accurate estimation..."
πŸ”¬ RESEARCH

WildReward: Learning Reward Models from In-the-Wild Human Interactions

"Reward models (RMs) are crucial for the training of large language models (LLMs), yet they typically rely on large-scale human-annotated preference pairs. With the widespread deployment of LLMs, in-the-wild interactions have emerged as a rich source of implicit reward signals. This raises the questi..."
πŸ”¬ RESEARCH

Is Reasoning Capability Enough for Safety in Long-Context Language Models?

"Large language models (LLMs) increasingly combine long-context processing with advanced reasoning, enabling them to retrieve and synthesize information distributed across tens of thousands of tokens. A hypothesis is that stronger reasoning capability should improve safety by helping models recognize..."
πŸ”¬ RESEARCH

Long Chain-of-Thought Compression via Fine-Grained Group Policy Optimization

"Large Language Models (LLMs) often generate unnecessarily verbose Chain-of-Thought (CoT) reasoning that increases computational costs and latency without proportional performance gains. In this paper, we propose \textbf{F}ine-grained \textbf{G}roup policy \textbf{O}ptimization (\textbf{FGO}), a Rein..."
πŸ”¬ RESEARCH

Biases in the Blind Spot: Detecting What LLMs Fail to Mention

"Large Language Models (LLMs) often provide chain-of-thought (CoT) reasoning traces that appear plausible, but may hide internal biases. We call these *unverbalized biases*. Monitoring models via their stated reasoning is therefore unreliable, and existing bias evaluations typically require predefine..."
πŸ”¬ RESEARCH

Understanding Dynamic Compute Allocation in Recurrent Transformers

"Token-level adaptive computation seeks to reduce inference cost by allocating more computation to harder tokens and less to easier ones. However, prior work is primarily evaluated on natural-language benchmarks using task-level metrics, where token-level difficulty is unobservable and confounded wit..."
πŸ›‘οΈ SAFETY

Anthropic AI Safety Researcher Warns of World in Peril in Resignation

πŸ”¬ RESEARCH

Decoupled Reasoning with Implicit Fact Tokens (DRIFT): A Dual-Model Framework for Efficient Long-Context Inference

"The integration of extensive, dynamic knowledge into Large Language Models (LLMs) remains a significant challenge due to the inherent entanglement of factual data and reasoning patterns. Existing solutions, ranging from non-parametric Retrieval-Augmented Generation (RAG) to parametric knowledge edit..."
πŸ”¬ RESEARCH

Next-Gen CAPTCHAs: Leveraging the Cognitive Gap for Scalable and Diverse GUI-Agent Defense

"The rapid evolution of GUI-enabled agents has rendered traditional CAPTCHAs obsolete. While previous benchmarks like OpenCaptchaWorld established a baseline for evaluating multimodal agents, recent advancements in reasoning-heavy models, such as Gemini3-Pro-High and GPT-5.2-Xhigh have effectively co..."
πŸ”¬ RESEARCH

DirMoE: Dirichlet-routed Mixture of Experts

"Mixture-of-Experts (MoE) models have demonstrated exceptional performance in large-scale language models. Existing routers typically rely on non-differentiable Top-$k$+Softmax, limiting their performance and scalability. We argue that two distinct decisions, which experts to activate and how to dist..."
πŸ”¬ RESEARCH

iGRPO: Self-Feedback-Driven LLM Reasoning

"Large Language Models (LLMs) have shown promise in solving complex mathematical problems, yet they still fall short of producing accurate and consistent solutions. Reinforcement Learning (RL) is a framework for aligning these models with task-specific rewards, improving overall quality and reliabili..."
🏒 BUSINESS

Ex-GitHub CEO launches a new developer platform for AI agents

πŸ’¬ HackerNews Buzz: 140 comments 🐝 BUZZING
🎯 Developer tools acquisitions β€’ AI agent observability β€’ Spec-driven code generation
πŸ’¬ "What's the terminal value of a DevTool in the AI era?" β€’ "The interesting bet here isn't git checkpointsβ€”it's that someone is finally building the observability layer for agent-generated code."
πŸ› οΈ SHOW HN

Show HN: I taught GPT-OSS-120B to see using Google Lens and OpenCV

πŸ’¬ HackerNews Buzz: 11 comments 🐝 BUZZING
🎯 TOS Violation β€’ Pseudo Vision β€’ API Alternatives
πŸ’¬ "TOS violation to scrape google directly" β€’ "This is a liability"
πŸ”’ SECURITY

Cocoon – decentralized network for confidential AI inference

πŸ”¬ RESEARCH

LLMs Encode Their Failures: Predicting Success from Pre-Generation Activations

"Running LLMs with extended reasoning on every problem is expensive, but determining which inputs actually require additional compute remains challenging. We investigate whether their own likelihood of success is recoverable from their internal representations before generation, and if this signal ca..."
🧠 NEURAL NETWORKS

A Technical Series on Building Stateful AI Agents with LangGraph

πŸ€– AI MODELS

Alibaba's DAMO Academy releases RynnBrain, an open-source foundation model that helps robots perform real-world tasks like navigating rooms, trained on Qwen3-VL

πŸ› οΈ TOOLS

Lorashare: Compress multiple LoRA adapters into a shared subspace to reduce storage

"Lorashare is a Python package that lets you use multiple LoRA adapters withΒ 100x memory savings. Based on recent research from The Johns Hopkins University, LoRA adapters trained on different tasks share a common low-rank subspace and this lets you store several task-specific models with the m..."
πŸ› οΈ SHOW HN

Show HN: We let GPT OSS 120B write and run Python and ARC AGI 2 jumped 4x

πŸ”¬ RESEARCH

Conformal Prediction Sets for Instance Segmentation

"Current instance segmentation models achieve high performance on average predictions, but lack principled uncertainty quantification: their outputs are not calibrated, and there is no guarantee that a predicted mask is close to the ground truth. To address this limitation, we introduce a conformal p..."
πŸ”¬ RESEARCH

Kunlun: Establishing Scaling Laws for Massive-Scale Recommendation Systems through Unified Architecture Design

"Deriving predictable scaling laws that govern the relationship between model performance and computational investment is crucial for designing and allocating resources in massive-scale recommendation systems. While such laws are established for large language models, they remain challenging for reco..."
πŸ”¬ RESEARCH

CoRefine: Confidence-Guided Self-Refinement for Adaptive Test-Time Compute

"Large Language Models (LLMs) often rely on test-time scaling via parallel decoding (for example, 512 samples) to boost reasoning accuracy, but this incurs substantial compute. We introduce CoRefine, a confidence-guided self-refinement method that achieves competitive accuracy using a fraction of the..."
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