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⚡ BREAKTHROUGH
"Ran a bunch of experiments with Graph RAG (KET-RAG) on multi hop question answering. Turns out **retrieval** is basically **solved**, the answer is in the context 77 to 91% of the time. The **bottleneck is reasoning**: 73 to 84% of wrong answers come from the model failing to connect the dots, not f..."
🎯 Model Performance • Reasoning Challenges • Prompt Structuring
💬 "the finding that 73-84% of failures are reasoning not retrieval"
• "The graph walk compression actually saves time since it cuts context by 60%"
🤖 AI MODELS
"Been building a daily research workflow on Claude. Kept getting confident-sounding outputs with zero sources. The kind of stuff that sounds right but you can't verify.
I stumbled into Anthropic's "Reduce Hallucinations" documentation page by accid..."
🎯 Tradeoffs in AI capabilities • User customization needs • Anthropic's product approach
💬 "there's a tradeoff"
• "It's user responsability to be informed and to adjust it for their needs"
🛠️ TOOLS
🎯 Pricing and value proposition • Hardware specifications • Sustainability and recycling
💬 "the cheapest box seems pricey at 12 for a what is essentially a few gaming gpus"
• "Maybe in time they will find a better balance, i do respect the fact that the component market now is sour as hell and making good products with stable prices is pretty much i possible"
🛠️ TOOLS
"I've been running a bash daemon that watches my Linear board for issues tagged "claude" and spawns autonomous Claude Code instances to implement them — in isolated git worktrees, with full transcripts, up to 5 concurrent workers.
This applies equally well to Cursor CLI:
Here's the workflow: ..."
🎯 Automated development workflows • Distributed agent coordination • Continuous integration challenges
💬 "Worktrees per agent is the right call"
• "the 30 min timeout with auto-rollback to todo is a smart guardrail"
🛠️ TOOLS
"I've been running Qwen 3.5 27B Q4_K_M on a Blackwell RTX PRO 4000 (24GB) for agentic coding work and hit a wall with mainline llama.cpp. Switched to the ik_llama.cpp fork today and the difference is staggering. Posting real numbers in case it helps others.
Hardware
Lenovo ThinkStation P520, Xeon W-..."
🎯 Optimizing LLM inference • Comparing LLM models • Troubleshooting LLM issues
💬 "your kV cache uses different quant which greatly slows down the speed"
• "The 26x is specifically the fused GDN kernel improvement for Qwen 3.5's hybrid SSM architecture"
🔬 RESEARCH
via Arxiv
👤 Maksym Del, Markus Kängsepp, Marharyta Domnich et al.
📅 2026-03-19
⚡ Score: 7.3
"Uncertainty estimation is critical for deploying reasoning language models, yet remains poorly understood under extended chain-of-thought reasoning. We study parallel sampling as a fully black-box approach using verbalized confidence and self-consistency. Across three reasoning models and 17 tasks s..."
🔬 RESEARCH
via Arxiv
👤 Zhuolin Yang, Zihan Liu, Yang Chen et al.
📅 2026-03-19
⚡ Score: 7.3
"We introduce Nemotron-Cascade 2, an open 30B MoE model with 3B activated parameters that delivers best-in-class reasoning and strong agentic capabilities. Despite its compact size, its mathematical and coding reasoning performance approaches that of frontier open models. It is the second open-weight..."
🔬 RESEARCH
"Hey everyone,
When building systems around modern open-source LLMs, one of the biggest issues is that they can confidently hallucinate or state an incorrect answer with a 95%+ probability. This makes it really hard to deploy them into the real world reliably if we don't understand their "overconfid..."
🎯 Model confidence • Calibration of confidence • Benchmarking confidence
💬 "This is what the benchmark measures"
• "It's an idea that researchers have tried"
📡 AI NEWS BUT ACTUALLY GOOD
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🛠️ TOOLS
"Hey all,
Wanted to share something that I hope can help others. I found a way to optimize inference via llama.cpp specifically for running models that wouldn't typically be able to run locally due to memory shortages. It's called Hypura, and it places model tensors across GPU, RAM, and NVMe tier..."
🔬 RESEARCH
"Large language models (LLMs) demonstrate strong generative capabilities but remain vulnerable to hallucination and unreliable reasoning under adversarial prompting. Existing safety approaches -- such as reinforcement learning from human feedback (RLHF) and output filtering -- primarily operate at th..."
⚡ BREAKTHROUGH
"I built Autochess NN, a browser-playable neural chess engine that started as a personal experiment in understanding AlphaZero-style systems by actually building one end to end.
This project was unapologetically vibecoded - but not in the “thin wrapper around an API” sense. I used AI heavily as a re..."
🎯 Novel Chess AI • Computation Limits • Transformer Architecture
💬 "This is a bigger problem than the training itself"
• "I think some of their findings could improve my engine"
🔬 RESEARCH
via Arxiv
👤 Shang-Jui Ray Kuo, Paola Cascante-Bonilla
📅 2026-03-19
⚡ Score: 6.6
"Large vision--language models (VLMs) often use a frozen vision backbone, whose image features are mapped into a large language model through a lightweight connector. While transformer-based encoders are the standard visual backbone, we ask whether state space model (SSM) vision backbones can be a st..."
🛠️ TOOLS
"When we use skills, plugins or MCP tools, Claude reads long input schemas or injects prompt instructions. Those tokens are charged as input tokens, and can be expensive at scale, especially when it comes to API usage.
We even ask Claude to explore other folders and sibling repositories, read files ..."
🎯 CLI tool optimization • MCP vs. CLI tools • Tool discovery
💬 "I think it's quite misleading to post this"
• "The one thing MCP does well is when it's tightly integrated"
🔬 RESEARCH
via Arxiv
👤 Carlos Hinojosa, Clemens Grange, Bernard Ghanem
📅 2026-03-19
⚡ Score: 6.5
"Vision-language models (VLMs) are increasingly deployed in real-world and embodied settings where safety decisions depend on visual context. However, it remains unclear which visual evidence drives these judgments. We study whether multimodal safety behavior in VLMs can be steered by simple semantic..."
🛠️ TOOLS
🎯 AI agents as next paradigm • Systems-level changes for AI agents • Nvidia's AI roadmap
💬 "What actually has to change at the systems level?"
• "NVIDIA frames AI agents as the next computing paradigm"
📊 DATA
"harmony4d, the precursor to the contact4d dataset. it's a large-scale multi-view video dataset of in-the-wild close human–human contact interactions:
https://huggingface.co/datasets/Voxel51/Harmony4D
toon3d, has 12 scenes from popular hand-drawn cartoons and anime, each comprising 5–12 frames that ..."
🔬 RESEARCH
via Arxiv
👤 Zehao Li, Zhenyu Wu, Yibo Zhao et al.
📅 2026-03-19
⚡ Score: 6.4
"Reinforcement Learning (RL) has the potential to improve the robustness of GUI agents in stochastic environments, yet training is highly sensitive to the quality of the reward function. Existing reward approaches struggle to achieve both scalability and performance. To address this, we propose OS-Th..."
🛠️ TOOLS
"Karpathy's autoresearch is awesome — agent edits
train.py and runs tiny LLM experiments overnight. But it wants serious VRAM.
I forked it to run on normal cards like my 1080/3060:
* Auto-picks model size/depth/batch/seq len so it fits your VRAM (leaves buffer, no more OOM surpri..."