A new PyTorch-based tutorial provides a from-scratch guide to building vision-based imitation learning policies for robotic manipulation, running on a single GPU and tested on the RoboSuite Lift task …
McKinsey's 2026 State of AI survey found that nearly nine in ten organisations use AI in at least one business function, yet only 37% of respondents said AI has made a positive difference to company p…
OpenAI's Codex extension for VS Code operates as an asynchronous coding agent that reads repositories, edits files on disk, and runs terminal commands, rather than the 2021 Codex autocomplete API or G…
A practitioner who rebuilt an agentic analytics platform four times concluded that reconciliation and governance, not data plumbing, are the core obstacles to making multi-source AI analytics reliable…
Data engineering is shifting from a delivery-focused discipline to what the article calls a "trust engineer" role, because AI models and agents now consume data as a runtime dependency rather than a s…
A four-time Kaggle Grandmaster at NVIDIA won the Playground Series S6E3 competition using a generative-AI-assisted workflow that generated over 600,000 lines of code and ran 850 experiments across a f…
AcruxCore published a Python tutorial showing how to build a text-to-SQL agent that separates each tool's model-facing definition from its implementation code, letting prompt engineers edit tool descr…
A modern AI inference platform is built from five layers, and its capacity is bound by the lower of two independent ceilings: how many requests fit in memory and how many the latency target allows, ac…
Agent long-term memory fails in two persistent ways — staleness, where superseded facts are still retrieved with full confidence, and poisoning, where bad facts survive across sessions — and the OWASP…
Llm-d is a Kubernetes-native distributed inference serving stack, released open source under Apache 2.0, that adds inference-aware orchestration on top of vLLM and Kubernetes without replacing either.…
Context engineering — curating the system prompt, tool definitions, message history, and accumulated tool results a model sees at inference time — is a distinct discipline from prompt engineering, and…
A security researcher tested 24 exfiltration payloads against a deny-by-default Tensorlake Sandbox configured with a single allowlisted destination, api.github.com, and none of the payloads delivered …
Prompt engineering — designing and optimizing the text inputs given to large language models — can let a less advanced model such as GPT-3.5 outperform a more sophisticated model like GPT-4 when the l…
The IEEE Global Survey found that 96% of technologists worldwide expect agentic AI innovation to keep accelerating through 2026, while Gartner predicts task-specific agent integration in enterprise ap…
The stellarshenson/claude-code-plugins marketplace released a project-management plugin for Claude Code that stores acceptance criteria and defects in two markdown files per project, docs/acc-crit-<pr…
Louis-François Bouchard, Towards AI co-founder and head of community, detailed his daily AI setup in LAI #142, running Claude Code and Codex from an always-on Mac mini while keeping reusable knowledge…
Kubernetes does not accelerate LLM inference but orchestrates GPU clusters by placing, monitoring, replacing, and scaling inference servers, according to an article on AI workload management. The piec…
Agentic data analysis tools from Anthropic (Claude Code), OpenAI (GPT Codex), and open-source alternatives (OpenCode) are stochastic, producing different code on each run, which hurts reproducibility …
LangChain has introduced Managed Deep Agents, a framework that provides a batteries-included approach to building AI agents with tool usage, context management, and subagent delegation. The article de…
GitHub's cloud agent, OpenAI's Codex, and Claude Code are shifting AI-assisted development from autocomplete suggestions to delegated outcomes, where agents research repositories, make plans, edit bra…