Easy AI Daily | March 11, 2026

A digest of AI industry news covering agents, infrastructure, models, research, products, industry moves, and policy.

Agents and Tooling

Replit Agent 4: From code to "knowledge work agents"

Replit released Agent 4, evolving beyond an AI-assisted online IDE into a collaborative canvas workspace with parallel agents that build apps, websites, slides, and videos — reflecting the trend of coding agents expanding into broader office workflows.

Perplexity launches "Personal Computer", a hybrid local + cloud always-on agent

Runs on a Mac mini with access to local files, apps, and sessions, with remote control. An enterprise edition orchestrates 20 specialized models and 400+ apps, emphasizing unified agent orchestration and monitoring rather than a single chat model.

Base44 Superagents: all-in-one work agents for non-technical users

Ships with out-of-the-box integrations (Gmail, Slack, Stripe, CRM), repackaging SaaS products into schedulable agent workflows for users who don't code.

LangChain adds automatic context compression to Deep Agents

Instead of hard truncation by token count, context is summarized at task boundaries, reducing bloat in long conversations and improving stability for multi-step tool calls.

OpenAI shares technical details on agent "computer use"

Developer docs cover execution loops, filesystem context, network access, and safeguards — how to let agents act without deleting files or sending rogue requests.

Infrastructure and Hardware

NVIDIA releases Nemotron 3 Super

A 120B-parameter, ~12B-active hybrid Mamba-Transformer / SSM Latent MoE model with native 1M context, optimized for agents. Weights, data, training recipes, and infrastructure are public; NVIDIA claims up to 2.2x faster FP4 inference than GPT-OSS-120B with higher throughput and smaller KV cache.

M5 Max 128GB local LLM benchmarks

On a 14" M5 Max 128GB with mlx_lm: Qwen3.5-122B-A10B-4bit reached ~1239 t/s prompt throughput (16K context, 73.8GB peak memory); gpt-oss-120b-MXFP4-Q8 hit 2710 t/s (~64.9GB), demonstrating 100B-scale local inference is viable.

Models and Capabilities

• Qwen3.5-35B-A3B Uncensored GGUF: 35B total / ~3B active, 256 experts (8+1 active), multimodal (text/image/video), near-zero refusals — but the community questions whether capability was degraded, suggesting KL-divergence checks.
• Fish Audio S2 TTS: 80+ languages, natural-language emotion tags like [whispers sweetly], multi-speaker dialogue, ~100ms first-audio latency, claims to outperform Google and OpenAI subjectively. Code released; commercial use requires a separate license.
• Google Gemini Embedding 2: multimodal (text, image, video, audio, PDF) with Matryoshka-style dimensional reduction; community analysis finds text pricing expensive — best reserved for multimodal retrieval, with video embedding costs escalating unless frames are downsampled.
• Qwen3.5 multimodal architecture breakdown: Gated DeltaNet linear attention + full attention hybrid; 397B A17B MoE and 27B dense variants; native 262k context extendable to ~1M; multi-token prediction in training.
• Reka Edge: a vision-language model for robotics and physical AI, claiming 3x fewer input tokens and 65% higher throughput than mainstream 8B models.

Research and Methods

• PostTrainBench v1.0: benchmarks whether frontier agents can post-train language models. On GPT-5.1 Codex Max, medium reasoning beats high reasoning — extra tokens crowd out context.
• EvoSkill: an executor/proposer/skill-builder loop distills reusable skills from failures; on OfficeQA with Claude Code + Opus 4.5, exact match rose from 60.6% to 67.9%.
• AgentIR: encodes agent reasoning traces into retrieval embeddings; 68% accuracy on BrowseComp-Plus vs 52% for larger traditional embedding models and 37% for BM25.
• Karpathy's self-improving swarm: an agentic system proposed ~700 training-pipeline changes, kept 20, cutting GPT-2-level training time from 2.02h to 1.80h (~11% gain) — a concrete step toward "AI doing AI research."
• Layer-block duplication tops open leaderboard: copying a 7-layer mid-block in Qwen2-72B (no weight changes) improved all Open LLM Leaderboard metrics, topping the board on 2×4090s; scrambled layer wiring suggests transformer layers are more interchangeable than expected.
• GPT-5.4 reportedly solves an EpochAI Frontier Math open problem: described as research-relevant but not top-tier difficulty; Epoch researchers preliminarily consider the solution correct, pending setter confirmation.
• Agent reliability as a safety issue: Princeton's response to NIST argues most agent failures are reliability problems, not adversarial attacks, and need separate definitions, metrics, and mitigations.

Products and Deployment

• Google medical AI in practice: an imaging system detects ~25% of interval breast cancers missed by traditional screening; the AMIE conversational clinical reasoning system passed real-environment pilots on safety, feasibility, and patient acceptance.
• r/LocalLLaMA hits 1 million members: growth despite moderation turmoil signals local AI is moving beyond enthusiasts to a broader developer audience.

Industry and Companies

• Replit valuation hits ~$9B in 6 months, pivoting into a full productivity suite (canvas, apps, sites, slides, video), paralleling Claude Cowork and Notion's custom agents.
• Anthropic launches The Anthropic Institute, led by Jack Clark, spanning ML, economics, and social science to systematically shape public AI governance discourse.
• Yann LeCun founds AMI Labs (Advanced Machine Intelligence) with Alexandre LeBrun, raising $1.03B (NVIDIA, Samsung, Bezos among backers) to build JEPA-based world models focused on the physical world and common sense; code and papers will be open-sourced.

Policy, Governance, and Safety

• Early "recursive self-improvement" at Anthropic: per TIME, 70–90% of code for future models is written by Claude, iteration cycles shrinking from months to weeks. Claude 3.7 Sonnet was delayed 10 days for safety testing, fueling debate over slowing for safety.
• Claude Code login outage as an "intelligence blackout" rehearsal: the authentication failure idled many developers ("Silicon Valley productivity dropped 90%"); Karpathy compared it to future infrastructure-level risk when R&D depends heavily on frontier models.

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📌 Source: Easy AI Daily