Tencent Cloud Open-Sources TencentDB Agent Memory v2.0: A Team-Level Memory Hub for AI Coding Agents
Tencent Cloud has open-sourced TencentDB Agent Memory v2.0, a team-level memory hub that turns conversations, documents and code into four governed, reusable assets — Chat Memory, Skill, LLM-Wiki and Code-Graph. It is MIT-licensed, self-hosted via Docker, and integrates with Claude Code, OpenClaw, H...
Building a Multimodal RAG Pipeline with NVIDIA NeMo Retriever, Hosted NIMs, LanceDB, Reranking, and Grounded Generation
In this tutorial, we build an advanced multimodal retrieval-augmented generation pipeline with NVIDIA NeMo Retriever. We begin by configuring a Python 3.12 environment, installing the required packages, and performing offline PDF text extraction without relying on a GPU or external API key. We then ...
NVIDIA AI Releases NOOA: An Object-Oriented Python Framework That Turns an AI Agent Into a Single Python Class
NVIDIA Labs has open-sourced NOOA (NVIDIA Object-Oriented Agents), a model-agnostic Python framework for building AI agents. Agent development today is split across prompt templates, tool schemas, callback code, and workflow graphs. NOOA collapses all of it into one Python class. Methods are the act...
Cloudflare launches Kitesurf, a browser built for AI agents
Cloudflare has introduced Kitesurf, a cloud-hosted browser designed for AI agents instead of people. The company says the browser uses less computing power than Chromium for common automation tasks, helping developers build browser-based AI agents more efficiently.
Small Language Models with Hugging Face transformers Library + smolLM3
Running a 70B model in production is expensive, and for many tasks, unnecessary. If you're building a focused pipeline, a well-trained 3B model will match or beat the 70B on your specific task at a fraction of the cost.
Learn how to use generative AI at work, build RAG and agentic apps, fine-tune models, work with the Hugging Face ecosystem, and prototype AI products with hands-on resources.
As soon as I received my first Raspberry Pi, I knew that it would be a wonderful platform to bring AI into the physical world. Since the initial hardware didn’t have good CPU support for fast arithmetic, I ended up writing code that ran on the GPU so I could get the speed I needed […]
My Fall-Detection Model Scored 94%, and It Was Lying to Me
How a single evaluation choice inflated my results by 25 points, and what rebuilding honestly taught me about ML systems people might depend on
The post My Fall-Detection Model Scored 94%, and It Was Lying to Me appeared first on Towards Data Science.
How HSP GRUPPE builds AI capabilities for tax advisory
Discover how HSP GRUPPE uses ChatGPT Enterprise to boost productivity, improve work quality, and create more capacity for tax advisory and client service.
Microsoft Open Sources code-testing-generator: a Polyglot Unit-Test Agent That Hits 92.1% Task Completion Versus 78.9% for Stock Copilot
Microsoft has open sourced code-testing-generator, a polyglot unit-test agent shipping in the MIT-licensed dotnet/skills repository. It reads a repository before writing anything — detecting the language, test framework, existing conventions, and the real build and test commands — then plans, writes...
MS-MLB: An Open Machine Learning Benchmark for Blood-Based MS Classification
arXiv:2608.05196v1 Announce Type: new
Abstract: Multiple sclerosis (MS) is diagnosed through clinical assessment, magnetic resonance imaging, laboratory evidence when appropriate, and exclusion of better explanations. Blood RNA expression data may contain disease associated immune signal, but a blo...
When Do Corrective Features Help? An Agent for Corrective Feature Discovery on Black-Box Forecasters
arXiv:2608.05207v1 Announce Type: new
Abstract: Frozen pretrained forecasters often fail in structured, recurring ways that are costly to repair through fine-tuning. We study corrective feature discovery: mining interpretable features of a frozen forecaster's residual to drive a lightweight post-ho...
arXiv:2608.05234v1 Announce Type: new
Abstract: Building reliable applications that leverage large language models (LLMs) remains a significant challenge. While LLMs offer impressive capabilities across diverse tasks, their outputs often lack accuracy and provide no clear measure of confidence. Thi...
Decoupling Perception from Description: Computation-Grounded Representation Alignment between Multivariate Time Series and Language
arXiv:2608.05238v1 Announce Type: new
Abstract: Training multimodal models to align time series with language runs into a self-supervision trap. The usual recipe asks an LLM to read a series and write a description, so label quality is capped by the perceptual skill the model is supposed to learn. ...
arXiv:2608.05242v1 Announce Type: new
Abstract: In this work, we explore an alternative paradigm for spatial reasoning by explicitly disentangling 3D perception from reasoning, rather than jointly acquiring implicit 3D perception and reasoning through large-scale training. Our key observation is th...
Agentic Nesting: A New Methodology for Existing Enterprise Application Integration and Services
arXiv:2608.05159v1 Announce Type: new
Abstract: Enterprise operations extensively rely on multiple heterogeneous business systems and information applications, which also result in severe data silos and process fragmentation. Enterprises have invested considerable financial and material resources i...
The Ignition Index: Measuring Global Workspace Dynamics in Language Models
arXiv:2608.05160v1 Announce Type: new
Abstract: We introduce the Ignition Index (I), a validated scalar metric that operationalizes Global Workspace Theory's (GWT) all-or-none ignition prediction in transformer language models. The metric fits a four-parameter sigmoid to per-layer linear probe accu...
Woodpecker Distillation: Weak Models Diagnose Reasoning Bugs in Strong Models
arXiv:2608.05168v1 Announce Type: new
Abstract: Large language models often fail on reasoning tasks despite possessing the capability to solve them. We argue that many such failures arise from localized reasoning bugs in intermediate steps rather than from global incompetence. We show that these bu...
SkillTrace: Multi-Trace Provenance Auditing for LLM-Agent Skill Reuse
arXiv:2608.05204v1 Announce Type: new
Abstract: LLM-agent ecosystems are rapidly growing around reusable skills: mixed-modality packages of metadata, natural-language instructions, code, tools, references, and operational workflows. As skills become marketplace artifacts, auditing their reuse is no...
Liquid AI Releases LFM2.5-2.6B: An On-Device Agentic Model With 128K Context, Tool Calling, And Open Weights
Liquid AI released LFM2.5-2.6B, an agentic model that plans, calls tools, and completes multi-step tasks entirely on-device. The 2.69B parameter model pairs 22 double-gated short convolution blocks with 8 GQA blocks across 30 layers, handles 131,072 tokens of context, and decodes at 220 tokens/s on ...
Continuous diffusion and flow matching models could represent a powerful alternative to autoregressive approaches for language modelling (LM), as they unlock a host of advantages currently reserved for continuous modalities, including accelerated sampling and tilting. Recently, several works have de...