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🛡️ SAFETY
🎯 AI Ethics • Fallibility of AI Design • Importance of EQ in AI
💬 "This is why, imo, it's so important that EQ is prioritized."
• "Relational intelligence is the key and way forward."
🤖 AI MODELS
"Hey everyone, we just open-sourced KaniTTS2 - a text-to-speech model designed for real-time conversational use cases.
\## Models:
Multilingual (English, Spanish), and English-specific with local accents. Language support is actively expanding - more languages coming in future updates
\## Specs
\..."
🎯 Open source model • Voice quality comparison • Limitations of Hugging Face
💬 "Open source = you have the resources used to train the model"
• "That's why the first guy is cute"
🛠️ SHOW HN
🎯 Mobile AI capabilities • Privacy benefits of local AI • Challenges of running large models on phones
💬 "This feels like a bunch of empty promises; yes, technically it can run some models, but how useful is it actually?"
• "The privacy angle is the real killer feature here IMO. There are so many use cases (journaling, health tracking, sensitive work notes) where people self-censor because they know it's going to a server somewhere."
🛠️ TOOLS
"Hey everyone,
I’m a backend developer with a background in fintech. Lately, I’ve been experimenting with multi-agent systems, and one major issue I kept running into was **collision**.
When you have multiple agents (or even one agent doing complex tasks) accessing the same files, APIs, or context,..."
🎯 File locking • Stale state • Audit trail
💬 "Systems blow up when one agent holds a lock but the context changes"
• "Keep the logs and lock metadata together too"
🧠 NEURAL NETWORKS
"We've been building an open-source memory system for Claude Code and wanted to know: how well does agent memory actually hold up over months of real use?
Existing benchmarks like LongMemEval test \~40 sessions. That's a weekend of heavy use. So we built MemoryStress: 583 facts, 1,000 sessions, 300 ..."
🎯 Verbose Feedback • GitHub Integration • Product Architecture
💬 "at least be verbose in your complaints"
• "This is great thank you"
🔬 RESEARCH
via Arxiv
👤 Kaitlyn Zhou, Martijn Bartelds, Federico Bianchi et al.
📅 2026-02-12
⚡ Score: 6.9
"Despite speech recognition systems achieving low word error rates on standard benchmarks, they often fail on short, high-stakes utterances in real-world deployments. Here, we study this failure mode in a high-stakes task: the transcription of U.S. street names as spoken by U.S. participants. We eval..."
🔬 RESEARCH
via Arxiv
👤 Nicholas Lee, Lutfi Eren Erdogan, Chris Joseph John et al.
📅 2026-02-12
⚡ Score: 6.9
"Test-time scaling has become a standard way to improve performance and boost reliability of neural network models. However, its behavior on agentic, multi-step tasks remains less well-understood: small per-step errors can compound over long horizons; and we find that naive policies that uniformly in..."
🔬 RESEARCH
via Arxiv
👤 Jianke Yang, Ohm Venkatachalam, Mohammad Kianezhad et al.
📅 2026-02-12
⚡ Score: 6.9
"Explaining observed phenomena through symbolic, interpretable formulas is a fundamental goal of science. Recently, large language models (LLMs) have emerged as promising tools for symbolic equation discovery, owing to their broad domain knowledge and strong reasoning capabilities. However, most exis..."
📡 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
👤 Krish Agarwal, Zhuoming Chen, Cheng Luo et al.
📅 2026-02-12
⚡ Score: 6.9
"Real-time video generation with Diffusion Transformers is bottlenecked by the quadratic cost of 3D self-attention, especially in real-time regimes that are both few-step and autoregressive, where errors compound across time and each denoising step must carry substantially more information. In this s..."
🔬 RESEARCH
via Arxiv
👤 Zhen Zhang, Kaiqiang Song, Xun Wang et al.
📅 2026-02-12
⚡ Score: 6.8
"AI agents are increasingly used to solve real-world tasks by reasoning over multi-turn user interactions and invoking external tools. However, applying reinforcement learning to such settings remains difficult: realistic objectives often lack verifiable rewards and instead emphasize open-ended behav..."
🔬 RESEARCH
via Arxiv
👤 David Jiahao Fu, Lam Thanh Do, Jiayu Li et al.
📅 2026-02-12
⚡ Score: 6.7
"Retrieval augmented generation (RAG) has been widely adopted to help Large Language Models (LLMs) to process tasks involving long documents. However, existing retrieval models are not designed for long document retrieval and fail to address several key challenges of long document retrieval, includin..."
🔬 RESEARCH
via Arxiv
👤 Tunyu Zhang, Xinxi Zhang, Ligong Han et al.
📅 2026-02-12
⚡ Score: 6.6
"Diffusion large language models (DLLMs) have the potential to enable fast text generation by decoding multiple tokens in parallel. However, in practice, their inference efficiency is constrained by the need for many refinement steps, while aggressively reducing the number of steps leads to a substan..."
🔬 RESEARCH
via Arxiv
👤 Nick Ferguson, Josh Pennington, Narek Beghian et al.
📅 2026-02-12
⚡ Score: 6.6
"Unstructured documents like PDFs contain valuable structured information, but downstream systems require this data in reliable, standardized formats. LLMs are increasingly deployed to automate this extraction, making accuracy and reliability paramount. However, progress is bottlenecked by two gaps...."
🔬 RESEARCH
via Arxiv
👤 Leon Liangyu Chen, Haoyu Ma, Zhipeng Fan et al.
📅 2026-02-12
⚡ Score: 6.6
"Unified models can handle both multimodal understanding and generation within a single architecture, yet they typically operate in a single pass without iteratively refining their outputs. Many multimodal tasks, especially those involving complex spatial compositions, multiple interacting objects, o..."
🧠 NEURAL NETWORKS
"Hey folks, I have been working on **AdaLLM** (repo:
https://github.com/BenChaliah/NVFP4-on-4090-vLLM) to make NVFP4 weights actually usable on Ada Lovelace GPUs (sm\_89). The focus is a pure NVFP4 fast path: FP8 KV cache, custom FP8 decode kernel, ..."
🎯 GPU compatibility • Quantization techniques • Model conversion
💬 "8GB vram is less than the peak VRAM in my benchmarks"
• "The real win is quality retention at low bitwidths"
🔬 RESEARCH
via Arxiv
👤 Manjunath Kudlur, Evan King, James Wang et al.
📅 2026-02-12
⚡ Score: 6.5
"Latency-critical speech applications (e.g., live transcription, voice commands, and real-time translation) demand low time-to-first-token (TTFT) and high transcription accuracy, particularly on resource-constrained edge devices. Full-attention Transformer encoders remain a strong accuracy baseline f..."