[AINews] NVIDIA GTC : Jensen met le paquet sur OpenClaw et Vera CPU et annonce 1 000 milliards de dollars de commandes pour 2027

It is NVIDIA GTC day again, and over his signature 2hr unrehearsed keynote , Jensen gave updates on the entire NVIDIA universe and ecosystem and celebrated his InferenceMAX champions belt . As one might expect, Blackwell and Rubin are selling very very well (some accounting is necesary ), and now Vera : The final section of the keynote was focused on OpenClaw, where Jensen went extremely hard in complimenting it and then pointed out the security issues, then pitched his solution, NemoClaw : NVIDIA moves at impressive speed for a $4T company, and we had some of their next generation leaders on the pod to give more insight on how NVIDIA works this fast: AI News for 3/14/2026-3/16/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space . You can opt in/out of email frequencies! AI Twitter Recap Architecture Research: Moonshot’s Attention Residuals and the Debate Around Prior Art Moonshot’s Attention Residuals paper was the clearest technical story in the feed : @Kimi_Moonshot introduced a replacement for fixed residual accumulation with input-dependent attention over prior layers , plus Block AttnRes to keep cross-layer attention practical. Claimed results: 1.25x compute advantage , <2% inference latency overhead , validated on Kimi Linear 48B total / 3B active ; follow-up posts highlighted improved hidden-state magnitude control and more uniform gradients across depth ( paper thread , paper link ). The release triggered strong positive reactions from practitioners and researchers including @Yuchenj_UW , @elonmusk , @nathancgy4 , and multiple visual explainers such as @eliebakouch and @tokenbender . The interesting second-order discussion was whether this is new, or “new at scale” : @behrouz_ali argued the idea substantially overlaps with prior work like DeepCrossAttention , criticizing missing citations and broader ML novelty inflation; @cloneofsimo made a similar point that Google had explored related ideas earlier, while others countered that the systems work and scaling evidence matter as much as the core intuition ( context , more context ). Net: the paper mattered both as an architectural proposal and as a live example of the field’s ongoing tension between idea novelty , citation quality , and frontier-scale validation . Coding Agents, Harnesses, and Skills Infrastructure OpenAI’s Codex momentum showed up repeatedly : OpenAI Devs promoted a Codex x Notion event , while company posts and leadership commentary emphasized fast adoption. @fidjissimo said Codex is at 2M+ weekly active users , up nearly 4x YTD , with OpenAI also building a deployment arm for enterprise rollout. @sama added that “hardcore builders” are switching to Codex, and @gdb said GPT-5.4 reached 5T tokens/day within a week and a $1B annualized run-rate in net-new revenue . Product-wise, Codex also added subagents , reinforcing the shift toward multi-agent coding workflows. The infrastructure layer around coding agents is maturing fast : @AndrewYNg expanded Context Hub / chub , an open CLI for current API docs that now supports agent feedback loops on documentation. @AssemblyAI shipped a maintained skill for Claude Code, Codex, Cursor, and compatible agents so they can use current API patterns rather than stale training priors. @dair_ai highlighted a paper on automated extraction of agent skills from GitHub repos into standardized SKILL.md , with claimed 40% knowledge-transfer gains . Together these point toward a new agent tooling stack: skills files, up-to-date docs, feedback channels, and repo-mined procedural knowledge . LangChain pushed further into “agent harness engineering” : @LangChain launched LangGraph CLI for terminal-based deploy/dev flows, and the ecosystem open-sourced Deep Agents , framed by @itsafiz and @simplifyinAI as an MIT-licensed recreation of the workflow behind top coding agents: planning/todos, filesystem ops, shell access, sub-agents, and context management. Internally, @Vtrivedy10 said this is also the base for production agent work and evals. The notable pattern is that teams are no longer just shipping models; they’re shipping reference harnesses . Open-Source Agents: Hermes’ Breakout, OpenClaw Integrations, and Agent UX Hermes Agent had a strong community cycle : hackathon projects spanned home media automation ( @rodmarkun’s anime server tool ), cyber tooling ( @aylacroft ), geopolitics/OSINT forecasting ( @WeXBT ), and research visualization ( @t105add4_13 ). User sentiment was consistently that Hermes is easier to set up and more robust than OpenClaw: see @Zeneca , @fuckyourputs , @austin_hurwitz , and @0xMasonH . @Teknium also posted setup guides like enabling Honcho memory . OpenClaw still expanded its ecosystem despite the Hermes comparisons : @ollama announced Ollama as an official provider for OpenClaw; Comet launched an observability plugin for tracing calls/tools/costs; and there