World Wire

Nous Research has expanded the Skills System inside Hermes Agent, its open-source self-improving agent. The new addition is /learn , a command that writes a reusable skill for you . Point it at a document page, a local SDK, a past conversation, or pasted notes. The live agent gathers the material, then authors a SKILL.md on your behalf. Hermes Agent can now /learn from anything: feed it directories of any source material (code, API docs, manuals, PDFs, configs) and it distills a verifiable reusable skill pic.twitter.com/oRznwCRF3E — Nous Research (@NousResearch) June 23, 2026 Hermes Skills System Skills are on-demand knowledge documents the agent loads when needed. Each one is a folder containing a SKILL.md file with instructions. They follow a progressive disclosure pattern to keep token usage low. The format is compatible with the agentskills.io open standard. All skills live in ~/.hermes/skills/ , the single source of truth. On a fresh install, bundled sk...

Generative AI has reshaped how software gets built. What began as line-by-line autocomplete now spans full application generation, multi-agent build pipelines, and natural-language interfaces to entire codebases. Large language models trained on code can read context, follow intent, and produce working frontends, backends, and infrastructure with little manual setup. For early-level AI engineers, software engineers, and data scientists, the practical question is no longer whether these tools help, but which ones fit a given task. Some accelerate writing and reviewing code inside an existing workflow. Others remove the editor entirely and build deployable products from a prompt. Here are the top generative AI tools in code generation and coding to know in 2026 : 1. Atoms * Atoms * (10% discount coupon: MARKTECHPOST10 ) is an AI platform that turns natural-language descriptions into fully deployable applications. It marks a clear step beyond standalone code generators by...

Autoregressive large language models generate text one token at a time. Each token waits for the one before it. This serial loop leaves modern GPUs underused and keeps inference slow. The cost grows worse with long Chain-of-Thought reasoning models. Their lengthy outputs make latency the dominant part of generation. Speculative decoding is the standard fix. A small draft model proposes future tokens. The large target model verifies those tokens in parallel. Accepted tokens are kept, so the output stays lossless. But most methods, including the state-of-the-art EAGLE-3, still draft autoregressively. That serial drafting caps real-world speedups near 2–3×. DFlash , introduced by research team from UC San Diego team (z-lab), takes a different route. It is a lightweight block diffusion model built for drafting. Instead of drafting tokens one at a time, it proposes a whole block in a single forward pass. The target model then verifies that block in parallel. The research team r...

Today, Mistral AI released OCR 4 , its latest document-understanding model. This new release adds bounding boxes, block classification, and inline confidence scores alongside extracted text. It supports 170 languages across 10 language groups and runs in a single container for fully self-hosted deployments. OCR 4 also serves as an ingestion component for enterprise search, RAG, and domain-specific retrieval pipelines. TL;DR OCR 4 returns bounding boxes, typed-block labels, and per-word confidence scores, not just text. It supports 170 languages across 10 groups, with gains on rare and low-resource languages. Independent annotators preferred OCR 4 over every system tested, averaging 72% win rates. Pricing is $4 per 1,000 pages, dropping to $2 with the Batch-API discount. One endpoint serves both raw extraction and schema-driven Document AI output. Mistral OCR 4 Mistral OCR 4 extracts and structures content from a wide range of documents. Previous gene...