Here is your today’s AI Dev Brief from Marktechpost, covering core research, models, infrastructure tools, and applied updates for AI developers and researchers.
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Meet OpenMythos: An Open-Source PyTorch Reconstruction of Claude Mythos Where 770M Parameters Match a 1.3B Transformer
OpenMythos is an open-source PyTorch project by Kye Gomez that theoretically reconstructs the Claude Mythos architecture from first principles, hypothesizing it is a Recurrent-Depth Transformer (RDT) — a looped transformer where a fixed set of weights is applied iteratively across up to T=16 loop steps in a single forward pass, with reasoning occurring entirely in continuous latent space rather than through intermediate token emission. The architecture combines a DeepSeekMoE FFN with sparse top-K expert routing, Multi-Latent Attention for 10–20× KV memory reduction, LTI-stable recurrent injection with ρ(A) < 1 enforced by construction, Depth-Wise LoRA adapters for per-iteration expressiveness, and Adaptive Computation Time halting to prevent hidden state drift. Grounded in the Parcae scaling laws (Prairie et al., 2026), the project demonstrates that at 770M parameters an RDT matches a 1.3B standard transformer — making the case that reasoning depth is a function of inference-time compute, not stored parameter count....… Read the full analysis/article here.
Moonshot AI Releases Kimi K2.6 with Long-Horizon Coding, Agent Swarm Scaling to 300 Sub-Agents and 4,000 Coordinated Steps
Moonshot AI has open-sourced Kimi K2.6, a native multimodal Mixture-of-Experts model with 1 trillion total parameters and 32 billion activated per token, featuring a 256K context window and a MoonViT vision encoder — released under a Modified MIT License with weights on Hugging Face. The model is built around four core capabilities: long-horizon coding (it autonomously overhauled an 8-year-old financial matching engine over 13 hours, making 1,000+ tool calls and delivering a 185% medium throughput gain), an Agent Swarm that scales to 300 sub-agents executing 4,000 coordinated steps simultaneously with a Skills feature that converts documents into reusable format templates, Claw Groups as a research preview that lets humans and agents from any device running any model collaborate in a shared swarm with K2.6 as the adaptive coordinator, and proactive 24/7 autonomous agents that Moonshot's own RL team ran continuously for 5 days managing monitoring and incident response. On benchmarks, K2.6 scores 54.0 on HLE-Full with tools (leading GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro), 58.6 on SWE-Bench Pro, 89.6 on LiveCodeBench (v6), and 80.2 on SWE-Bench Verified, and is recommended to deploy via vLLM, SGLang, or KTransformers. ..… Read the full analysis/article here.
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Project Notebooks/Tutorials
▶ A Coding Implementation on Qwen 3.6-35B-A3B Covering Multimodal Inference, Thinking Control, Tool Calling, MoE Routing, RAG, and Session Persistence Codes Tutorial
▶ A Coding Implementation on Microsoft’s Phi-4-Mini for Quantized Inference Reasoning Tool Use RAG and LoRA Fine-Tuning Codes Tutorial
▶ A Coding Tutorial for Running PrismML Bonsai 1-Bit LLM on CUDA with GGUF, Benchmarking, Chat, JSON, and RAG Codes Tutorial
▶ A End-to-End Coding Guide to Running OpenAI GPT-OSS Open-Weight Models with Advanced Inference Workflows Codes Tutorial
▶ A Coding Implementation to Build Multi-Agent AI Systems with SmolAgents Using Code Execution, Tool Calling, and Dynamic Orchestration Codes Tutorial
▶ How to Build a Universal Long-Term Memory Layer for AI Agents Using Mem0 and OpenAI Codes Tutorial
▶ A Coding Implementation of Crawl4AI for Web Crawling, Markdown Generation, JavaScript Execution, and LLM-Based Structured Extraction Codes Tutorial
▶ Google ADK Multi-Agent Pipeline Tutorial: Data Loading, Statistical Testing, Visualization, and Report Generation in Python Codes Tutorial
▶ A Hands-On Coding Tutorial for Microsoft VibeVoice Covering Speaker-Aware ASR, Real-Time TTS, and Speech-to-Speech Pipelines Codes Tutorial
▶ An Implementation Guide to Building a DuckDB-Python Analytics Pipeline with SQL, DataFrames, Parquet, UDFs, and Performance Profiling Codes Tutorial