Here is your today’s AI Dev Brief from Marktechpost, covering core research, models, infrastructure tools, and applied updates for AI developers and researchers.
Meta and Harvard Researchers Introduce the Confucius Code Agent (CCA): A Software Engineering Agent that can Operate at Large-Scale Codebases
Confucius Code Agent from Meta and Harvard shows how much performance on real world software tasks comes from scaffolding rather than model size. Built on the Confucius SDK, it combines hierarchical working memory, persistent note taking, modular tools and a meta agent driven build, test, improve loop to reach 52.7 Resolve@1 on SWE Bench Pro with Claude 4.5 Sonnet, surpassing Opus based baselines...... Read the full analysis/article here.
Liquid AI Releases LFM2-2.6B-Transcript for Private On Device Meeting Transcript Summarization
Liquid AI has released LFM2-2.6B-Transcript, a post trained checkpoint derived from LFM2-2.6B and tuned specifically for long form meeting transcript summarization that runs fully on device, so the transcript does not need to leave the machine. The model targets 30 to 60 minute meetings and is positioned as delivering summary quality close to larger cloud models while staying within edge constraints, LiquidAI highlights under 3 GB RAM for long meetings and latency measured in seconds rather than minutes. It is designed to run across CPU, GPU, and NPU, and the release was developed in partnership with AMD, including evaluation work tied to Ryzen AI class hardware. Alongside the base weights on Hugging Face, LiquidAI also provides deployment friendly variants such as GGUF and ONNX for local runtimes..... Read the full X post here.
Stanford Researchers Build SleepFM Clinical: A Multimodal Sleep Foundation AI Model for 130+ Disease Prediction
SleepFM Clinical is a multimodal sleep foundation model from Stanford Medicine that learns from nearly 600,000 hours of clinical polysomnography and links a single night of sleep recordings to long term health risk, achieving strong performance on sleep staging and sleep apnea tasks and predicting 130 future diseases, including cancers, cardiovascular and mental health conditions, from 65,000 patients’ data, with open source code released as sleepfm-clinical under MIT license to reuse in clinical and research pipelines..... Read the full analysis/article here.
Project Notebooks/Tutorials
▶ [Open Source] Rogue: An Open-Source AI Agent Evaluator worth trying Codes & Examples
▶ How to Build Portable, In-Database Feature Engineering Pipelines with Ibis Using Lazy Python APIs and DuckDB Execution Codes Tutorial
▶ A Coding Implementation to Build a Unified Apache Beam Pipeline Demonstrating Batch and Stream Processing with Event-Time Windowing Using DirectRunner Codes Tutorial
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