A community developer, GnLOLot, has published a 1B model that runs fully on local hardware. The model is MiniCPM5-1B-Claude-Opus-Fable5-Thinking, with GGUF builds for llama.cpp-compatible runtimes. It needs no API key and makes no cloud calls. The Proposed Model The model is built on openbmb/MiniCPM5-1B. That base is a real, documented release from OpenBMB. It is a dense 1.08B-parameter model using a standard LlamaForCausalLM architecture. It has 24 layers, grouped-query attention, and a 131,072-token context length. OpenBMB reports 1B-class open-source SOTA within its own comparison set. The base already ships a native thinking template. Reasoning is toggled through enable_thinking, giving both…

MoonMath AI team has released a bf16 forward attention kernel for AMD’s MI300X GPU. It is written in HIP, not hand-written assembly. The code is open-source under the MIT license. The MoonMath.ai team reports it beats AITER v3, AMD’s own optimized kernel, on every tested shape. Bare-metal access came from HotAisle, an AMD cloud provider. Attention is the fused softmax(QKᵀ/√d)·V operation inside every transformer. The MI300X is AMD’s CDNA3 data-center GPU, with the ISA target (gfx942). This kernel runs on that hardware only. TL;DR MoonMath.ai open-sources a bf16 forward attention kernel for AMD MI300X, written in HIP, not assembly (MIT).…

The Model Context Protocol has moved from Anthropic’s internal experiment to a de facto industry standard at a speed few integration protocols have matched. Since its launch in November 2024, MCP has grown explosively: OpenAI adopted it in March 2025, Microsoft announced support in Copilot Studio in March 2025, and by late 2025 combined Python and TypeScript SDK downloads had crossed 97 million monthly. In December 2025, Anthropic donated MCP to the Agentic AI Foundation under the Linux Foundation. Gartner projects that up to 40% of enterprise applications will include integrated task-specific AI agents by the end of 2026, up…

In the world of voice AI, the difference between a helpful assistant and an awkward interaction is measured in milliseconds. While text-based Retrieval-Augmented Generation (RAG) systems can afford a few seconds of ‘thinking’ time, voice agents must respond within a 200ms budget to maintain a natural conversational flow. Standard production vector database queries typically add 50-300ms of network latency, effectively consuming the entire budget before an LLM even begins generating a response. Salesforce AI research team has released VoiceAgentRAG, an open-source dual-agent architecture designed to bypass this retrieval bottleneck by decoupling document fetching from response generation. https://arxiv.org/pdf/2603.02206 The Dual-Agent Architecture:…

Document digitization has long been a multi-stage problem: first detect the layout, then extract the text, and finally try to reconstruct the structure. For Large Vision-Language Models (LVLMs), this often leads to ‘structural hallucinations’—disordered rows, invented formulas, or unclosed syntax. The FireRedTeam has released FireRed-OCR-2B, a flagship model designed to treat document parsing as a structural engineering task rather than ‘impressionist’ text generation. Built on the Qwen3-VL-2B-Instruct architecture, this model establishes a new State-of-the-Art (SOTA) for end-to-end solutions, achieving an overall score of 92.94% on the OmniDocBench v1.5 benchmark. Shifting the Paradigm: Structural Engineering vs. Text Generation Devs often find…

NVIDIA has released Nemotron-Nano-3-30B-A3B-NVFP4, a production checkpoint that runs a 30B parameter reasoning model in 4 bit NVFP4 format while keeping accuracy close to its BF16 baseline. The model combines a hybrid Mamba2 Transformer Mixture of Experts architecture with a Quantization Aware Distillation (QAD) recipe designed specifically for NVFP4 deployment. Overall, it is an ultra-efficient NVFP4 precision version of Nemotron-3-Nano that delivers up to 4x higher throughput on Blackwell B200. https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4 What is Nemotron-Nano-3-30B-A3B-NVFP4? Nemotron-Nano-3-30B-A3B-NVFP4 is a quantized version of Nemotron-3-Nano-30B-A3B-BF16, trained from scratch by NVIDIA team as a unified reasoning and chat model. It is built as a hybrid…

def openai_chat(system: str, user: str) -> str: resp = client.chat.completions.create( model=OPENAI_MODEL, messages=[ {"role": "system", "content": system}, {"role": "user", "content": user}, ], temperature=0.3 ) return resp.choices[0].message.content def heuristic_responder(context: str, question: str) -> str: lessons = re.findall(r"Lessons=(.*)", context) avoid = re.findall(r"Avoid=(.*)", context) ltm_lines = [ln for ln in context.splitlines() if ln.startswith("[LTM:")] steps = [] if lessons: for chunk in lessons[:2]: for s in [x.strip() for x in chunk.split(";") if x.strip()]: steps.append(s) for ln in ltm_lines: if "[LTM:procedure]" in ln.lower(): proc = re.sub(r"^\[LTM:procedure\]\s*", "", ln, flags=re.I) proc = proc.split("(salience=")[0].strip() for part in [p.strip() for p in proc.split("|") if p.strip()]: steps.append(part) steps = steps[:8]…

In this tutorial, we explore how federated learning behaves when the traditional centralized aggregation server is removed and replaced with a fully decentralized, peer-to-peer gossip mechanism. We implement both centralized FedAvg and decentralized Gossip Federated Learning from scratch and introduce client-side differential privacy by injecting calibrated noise into local model updates. By running controlled experiments on non-IID MNIST data, we examine how privacy strength, as measured by different epsilon values, directly affects convergence speed, stability, and final model accuracy. Also, we study the practical trade-offs between privacy guarantees and learning efficiency in real-world decentralized learning systems. Check out the Full Codes…

Robbyant, the embodied AI unit inside Ant Group, has open sourced LingBot-World, a large scale world model that turns video generation into an interactive simulator for embodied agents, autonomous driving and games. The system is designed to render controllable environments with high visual fidelity, strong dynamics and long temporal horizons, while staying responsive enough for real time control. From text to video to text to world Most text to video models generate short clips that look realistic but behave like passive movies. They do not model how actions change the environment over time. LingBot-World is built instead as an action…

Allen Institute for AI (AI2) Researchers introduce SERA, Soft Verified Efficient Repository Agents, as a coding agent family that aims to match much larger closed systems using only supervised training and synthetic trajectories. What is SERA? SERA is the first release in AI2’s Open Coding Agents series. The flagship model, SERA-32B, is built on the Qwen 3 32B architecture and is trained as a repository level coding agent. On SWE bench Verified at 32K context, SERA-32B reaches 49.5 percent resolve rate. At 64K context it reaches 54.2 percent. These numbers place it in the same performance band as open weight…