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🔒 SECURITY
"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..."
🎯 AI agent behavior • Securing AI agents • Emerging AI risks
💬 "Treat any AI agent like an untrusted contractor"
• "We need something more"
🛠️ TOOLS
"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..."
🎯 GPU hardware support • Model finetuning time • Moe model training tips
💬 "Do these notebooks work with ROCm and AMD cards as well?"
• "How long does finetuning a model using these notebooks take?"
🤖 AI MODELS
"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..."
🎯 Efficient Model Compression • Quantization Techniques • Hardware Deployment
💬 "NanoQuant makes large-scale deployment feasible on consumer hardware"
• "Perfect for my 8 gb vram"
🛠️ TOOLS
🎯 AI-powered UI generation • Interoperable UI frameworks • Deterministic UI components
💬 "using math in combination with AI"
• "I strongly suspect there will be a standard inter-compatible protocol"
🔬 RESEARCH
"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 ..."
🔬 RESEARCH
via Arxiv
👤 Aaditya Vikram Prasad, Connor Watts, Jack Merullo et al.
📅 2026-02-10
⚡ Score: 7.3
"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..."
🔬 RESEARCH
via Arxiv
👤 Yuting Ning, Jaylen Jones, Zhehao Zhang et al.
📅 2026-02-09
⚡ Score: 7.1
"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.,..."
🛠️ TOOLS
"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..."
🎯 Server-side tool calling • Local model capabilities • Reliability and governance
💬 "this is actually bigger than it looks imo"
• "the model confidently calls a tool that doesnt exist lol"
🔬 RESEARCH
"A practitioner's guide to Mamba and State Space Models — how selective state spaces achieve linear scaling, when to use SSMs vs Transformers vs hybrids, and production-ready models.
🔗 [
https://blog.serendeep.tech/blog/the-post-transformer-era](
https://blog.serendeep.tech/blog/the-post-transformer..."
🎯 State Space Models • Transformer Alternatives • Test-Time Training
💬 "All these models are just linear attention, with different update rules."
• "Test Time Training" just means updating something about the model in some way with respect to the example you're working on."
🔬 RESEARCH
via Arxiv
👤 Xinting Huang, Aleksandra Bakalova, Satwik Bhattamishra et al.
📅 2026-02-09
⚡ Score: 6.9
"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..."
📡 AI NEWS BUT ACTUALLY GOOD
The revolution will not be televised, but Claude will email you once we hit the singularity.
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🔬 RESEARCH
via Arxiv
👤 Shiyang Feng, Runmin Ma, Xiangchao Yan et al.
📅 2026-02-09
⚡ Score: 6.9
"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..."
🔬 RESEARCH
via Arxiv
👤 Zhaoyang Wang, Canwen Xu, Boyi Liu et al.
📅 2026-02-10
⚡ Score: 6.8
"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..."
🔬 RESEARCH
via Arxiv
👤 Richard Bornemann, Pierluigi Vito Amadori, Antoine Cully
📅 2026-02-10
⚡ Score: 6.8
"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..."
🛠️ TOOLS
"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..."
🎯 Local LLM Integration • Comparison to Mem0 • Documentation and API Feedback
💬 "Please, provide a clear example of how to use it with local models with openai-compatible endpoints."
• "Biggest difference is how they decide what to remember."
🔬 RESEARCH
via Arxiv
👤 Lavender Y. Jiang, Xujin Chris Liu, Kyunghyun Cho et al.
📅 2026-02-09
⚡ Score: 6.8
"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..."
🔬 RESEARCH
via Arxiv
👤 Shuaiyi Nie, Siyu Ding, Wenyuan Zhang et al.
📅 2026-02-10
⚡ Score: 6.7
"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..."
🔬 RESEARCH
via Arxiv
👤 Qingnan Ren, Shiting Huang, Zhen Fang et al.
📅 2026-02-10
⚡ Score: 6.7
"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..."
🔬 RESEARCH
via Arxiv
👤 Hao Peng, Yunjia Qi, Xiaozhi Wang et al.
📅 2026-02-09
⚡ Score: 6.7
"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..."
🔬 RESEARCH
via Arxiv
👤 Yu Fu, Haz Sameen Shahgir, Huanli Gong et al.
📅 2026-02-09
⚡ Score: 6.7
"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..."
🔬 RESEARCH
via Arxiv
👤 Xinchen Han, Hossam Afifi, Michel Marot et al.
📅 2026-02-10
⚡ Score: 6.6
"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..."
🔬 RESEARCH
via Arxiv
👤 Iván Arcuschin, David Chanin, Adrià Garriga-Alonso et al.
📅 2026-02-10
⚡ Score: 6.6
"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..."
🔬 RESEARCH
via Arxiv
👤 Ibraheem Muhammad Moosa, Suhas Lohit, Ye Wang et al.
📅 2026-02-09
⚡ Score: 6.6
"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..."
🔬 RESEARCH
via Arxiv
👤 Wenxuan Xie, Yujia Wang, Xin Tan et al.
📅 2026-02-10
⚡ Score: 6.5
"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..."
🔬 RESEARCH
via Arxiv
👤 Jiacheng Liu, Yaxin Luo, Jiacheng Cui et al.
📅 2026-02-09
⚡ Score: 6.5
"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..."
🔬 RESEARCH
via Arxiv
👤 Amirhossein Vahidi, Hesam Asadollahzadeh, Navid Akhavan Attar et al.
📅 2026-02-09
⚡ Score: 6.5
"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..."
🔬 RESEARCH
via Arxiv
👤 Ali Hatamizadeh, Shrimai Prabhumoye, Igor Gitman et al.
📅 2026-02-09
⚡ Score: 6.5
"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
🎯 Developer tools acquisitions • AI agent observability • Spec-driven code generation
💬 "What's the terminal value of a DevTool in the AI era?"
• "The interesting bet here isn't git checkpoints—it's that someone is finally building the observability layer for agent-generated code."
🛠️ SHOW HN
🎯 TOS Violation • Pseudo Vision • API Alternatives
💬 "TOS violation to scrape google directly"
• "This is a liability"
🔬 RESEARCH
via Arxiv
👤 William Lugoloobi, Thomas Foster, William Bankes et al.
📅 2026-02-10
⚡ Score: 6.3
"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..."
🛠️ TOOLS
"Lorashare is a Python package that lets you use multiple LoRA adapters with 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..."
🔬 RESEARCH
via Arxiv
👤 Kerri Lu, Dan M. Kluger, Stephen Bates et al.
📅 2026-02-10
⚡ Score: 6.1
"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..."
🔬 RESEARCH
via Arxiv
👤 Bojian Hou, Xiaolong Liu, Xiaoyi Liu et al.
📅 2026-02-10
⚡ Score: 6.1
"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..."
🔬 RESEARCH
via Arxiv
👤 Chen Jin, Ryutaro Tanno, Tom Diethe et al.
📅 2026-02-09
⚡ Score: 6.1
"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..."