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🛠️ TOOLS
🎯 AI impact on SaaS businesses • Human oversight for AI-generated code • Limitations of AI-generated software
💬 "The era of bespoke consultants for SaaS product suites to handle configuration and integrations, while not gone, are certainly under threat by LLMs"
• "AI will always depend on humans to produce relevant results for humans. It's not a flaw of AI, it's more of a flaw of humans."
🛡️ SAFETY
"As agents move from chatbots to systems that execute code, and coordinate with other agents, the governance gap is real. We have alignment research for models, but almost nothing for operational controls at the instance level, you know, the runtime boundaries, kill switches, audit trails, and certif..."
🛠️ SHOW HN
🎯 AI-powered personal assistants • Local-first software architecture • Security and privacy concerns
💬 "AI really does feel like living in the future"
• "the paradigm of how we interact with our devices will fundamentally shift in the next 5-10 years"
🔬 RESEARCH
via Arxiv
👤 Jian Chen, Yesheng Liang, Zhijian Liu
📅 2026-02-05
⚡ Score: 7.3
"Autoregressive large language models (LLMs) deliver strong performance but require inherently sequential decoding, leading to high inference latency and poor GPU utilization. Speculative decoding mitigates this bottleneck by using a fast draft model whose outputs are verified in parallel by the targ..."
🛠️ TOOLS
🎯 AI Benchmark Evaluation • AI Capabilities Skepticism • Upwork Task Representation
💬 "You think AI can replace programmers, today?"
• "This post really should be edited to say 96% of tasks posted on Upwork."
🔬 RESEARCH
via Arxiv
👤 Wei Liu, Jiawei Xu, Yingru Li et al.
📅 2026-02-05
⚡ Score: 7.0
"High-quality kernel is critical for scalable AI systems, and enabling LLMs to generate such code would advance AI development. However, training LLMs for this task requires sufficient data, a robust environment, and the process is often vulnerable to reward hacking and lazy optimization. In these ca..."
📡 AI NEWS BUT ACTUALLY GOOD
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🔬 RESEARCH
via Arxiv
👤 Tiansheng Hu, Yilun Zhao, Canyu Zhang et al.
📅 2026-02-05
⚡ Score: 7.0
"Deep research agents have emerged as powerful systems for addressing complex queries. Meanwhile, LLM-based retrievers have demonstrated strong capability in following instructions or reasoning. This raises a critical question: can LLM-based retrievers effectively contribute to deep research agent wo..."
🛠️ TOOLS
"Hey r/MachineLearning,
I’ve been working on an MCP-powered “AI Research Engineer” and wanted to share it here for feedback and ideas.
GitHub:
https://github.com/prabureddy/ai-research-agent-mcp
If it looks useful, a ⭐ on the repo really help..."
🔬 RESEARCH
via Arxiv
👤 Jian Chen, Zhuoran Wang, Jiayu Qin et al.
📅 2026-02-05
⚡ Score: 6.9
"Large language models rely on kv-caches to avoid redundant computation during autoregressive decoding, but as context length grows, reading and writing the cache can quickly saturate GPU memory bandwidth. Recent work has explored KV-cache compression, yet most approaches neglect the data-dependent n..."
🤖 AI MODELS
"Friday night experiment that got out of hand. I wanted to know: how small can a model be and still reliably do tool-calling on a laptop CPU?
So I benchmarked 11 models (0.5B to 3.8B) across 12 prompts. No GPU, no cloud API. Just Ollama and bitnet.cpp.
**The models:** Qwen 2.5 (0.5B, 1.5B, 3B), LLa..."
🎯 Model Benchmarking • Tool Calling Performance • Model Tuning
💬 "Keep them coming! I'm making a list of models for round 2."
• "My feeling is that a lot of the deep reasoning is a bit blocked by relying on the ollama default settings."
🔬 RESEARCH
via Arxiv
👤 Yuxing Lu, Yucheng Hu, Xukai Zhao et al.
📅 2026-02-05
⚡ Score: 6.8
"Multi-agent systems built from prompted large language models can improve multi-round reasoning, yet most existing pipelines rely on fixed, trajectory-wide communication patterns that are poorly matched to the stage-dependent needs of iterative problem solving. We introduce DyTopo, a manager-guided..."
🔒 SECURITY
"We moved to self-hosted models specifically to avoid sending customer data to external APIs. Everything was working fine until last week when someone from QA tried injecting prompts during testing and our entire system prompt got dumped in the response.
Now I'm realizing we have zero protection aga..."
🎯 Secure AI Architecture • Prompt Injection Risks • Data Isolation Principles
💬 "Treat the LLM like a hostile user with read access to your system prompts."
• "Piracy is not a pricing problem, it's a service problem"
🔬 RESEARCH
via Arxiv
👤 Lizhuo Luo, Shenggui Li, Yonggang Wen et al.
📅 2026-02-05
⚡ Score: 6.6
"Diffusion large language models (dLLMs) have emerged as a promising alternative for text generation, distinguished by their native support for parallel decoding. In practice, block inference is crucial for avoiding order misalignment in global bidirectional decoding and improving output quality. How..."
🔬 RESEARCH
via Arxiv
👤 Xianyang Liu, Shangding Gu, Dawn Song
📅 2026-02-05
⚡ Score: 6.6
"Large language model (LLM)-based agents are increasingly expected to negotiate, coordinate, and transact autonomously, yet existing benchmarks lack principled settings for evaluating language-mediated economic interaction among multiple agents. We introduce AgenticPay, a benchmark and simulation fra..."
🔬 RESEARCH
via Arxiv
👤 Haozhen Zhang, Haodong Yue, Tao Feng et al.
📅 2026-02-05
⚡ Score: 6.5
"Memory is increasingly central to Large Language Model (LLM) agents operating beyond a single context window, yet most existing systems rely on offline, query-agnostic memory construction that can be inefficient and may discard query-critical information. Although runtime memory utilization is a nat..."
🔬 RESEARCH
via Arxiv
👤 John Kirchenbauer, Abhimanyu Hans, Brian Bartoldson et al.
📅 2026-02-05
⚡ Score: 6.4
"Existing techniques for accelerating language model inference, such as speculative decoding, require training auxiliary speculator models and building and deploying complex inference pipelines. We consider a new approach for converting a pretrained autoregressive language model from a slow single ne..."
🔬 RESEARCH
via Arxiv
👤 Shuo Nie, Hexuan Deng, Chao Wang et al.
📅 2026-02-05
⚡ Score: 6.2
"As large language models become smaller and more efficient, small reasoning models (SRMs) are crucial for enabling chain-of-thought (CoT) reasoning in resource-constrained settings. However, they are prone to faithfulness hallucinations, especially in intermediate reasoning steps. Existing mitigatio..."
🔬 RESEARCH
via Arxiv
👤 Dingwei Zhu, Zhiheng Xi, Shihan Dou et al.
📅 2026-02-05
⚡ Score: 6.1
"Training reinforcement learning (RL) systems in real-world environments remains challenging due to noisy supervision and poor out-of-domain (OOD) generalization, especially in LLM post-training. Recent distributional RL methods improve robustness by modeling values with multiple quantile points, but..."
🔬 RESEARCH
via Arxiv
👤 Junxiao Liu, Zhijun Wang, Yixiao Li et al.
📅 2026-02-05
⚡ Score: 6.1
"Long reasoning models often struggle in multilingual settings: they tend to reason in English for non-English questions; when constrained to reasoning in the question language, accuracies drop substantially. The struggle is caused by the limited abilities for both multilingual question understanding..."
🔬 RESEARCH
via Arxiv
👤 Miranda Muqing Miao, Young-Min Cho, Lyle Ungar
📅 2026-02-05
⚡ Score: 6.1
"Large language models (LLMs) exhibit persistent miscalibration, especially after instruction tuning and preference alignment. Modified training objectives can improve calibration, but retraining is expensive. Inference-time steering offers a lightweight alternative, yet most existing methods optimiz..."