MarktechPost·
In this tutorial, we work with NVIDIA's Open-SWE-Traces dataset to study agentic software-engineering trajectories for fine-tuning. We stream the data directly from Hugging Face, so we can process it efficiently in Google Colab without downloading everything locally. We normalize multi-turn agent conversations, parse final code patches, and build an analysis DataFrame covering trajectory length, tool usage, patch size, language distribution, and resolution outcomes. We then curate a supervised fine-tuning subset using success labels, token limits, language filters, and patch availability. The post Building Supervised Fine-Tuning Data from NVIDIA Open-SWE-Traces: Trajectory Parsing, Patch Analysis, Token Budgets, and Tool-Use Metrics appeared first on MarkTechPost.
Read full articleNvidia's investment in AI-driven drug discovery could revolutionize biotech, highlighting AI's growing role in diverse industries beyond tech. The post Nvidia invests in Generate Biomedicines, targeting $1.8T market with AI-driven drug discovery appeared first on Crypto Briefing.
Nvidia has dominated the AI chip market for years, but the era of total dependence might be ending. OpenAI just shared its plans to spice things up with Jalapeño, its custom inference chip built with Broadcom, joining Google, Apple, and SpaceX in a growing list of companies building their way out of single-supplier risk. The goal is less of a […]
Nvidia's 800VDC shift could redefine data center efficiency, impacting infrastructure investments and reshaping AI and crypto industries. The post Nvidia leads shift to 800VDC data centers as analysts flag infrastructure stocks to watch appeared first on Crypto Briefing.
NEW YORK, June 26, 2026 — Qualcomm Technologies, Inc. has announced the expansion of its strategic relationship with Hugging Face to advance open, developer-driven artificial intelligence (AI) from devices to cloud […] The post Qualcomm and Hugging Face Expand Relationship to Advance Open, Developer-Driven AI from Device to Cloud appeared first on AIwire.
Nvidia has dominated the AI chip market for years, but the era of total dependence might be ending. OpenAI just shared its plans to spice things up with Jalapeño, its custom inference chip built with Broadcom, joining Google, Apple, and SpaceX in a growing list of companies building their way out of single-supplier risk. The goal is less of a […]
BlackBerry QNX revenue rose 26% to $72.3M as NVIDIA Halos opens a robotics path and guidance climbs. What the backlog and royalties mean for the AI trade.
The industry’s next phase may depend on the companies building its infrastructure, governance, security, and real-world applications.
In this tutorial, we build a lightweight personal AI agent inspired by the architecture of nanobot, runnable entirely in Google Colab. We start from a provider abstraction, then add tool registration, session memory, lifecycle hooks, skills, and an MCP-style tool server. Rather than rely on an external framework, we recreate each building block ourselves to see how messages, tools, memory, and model responses fit together. The result is a provider-agnostic agent loop we can extend toward real LLM providers and production tools. The post Build a Nanobot-Style AI Agent in Google Colab with Tool Calling, Session Memory, Skills, and MCP Servers appeared first on MarkTechPost.
