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Bringing the latest tech to grow your business. #AWS #GCP & #Azure registered partner and cloud solution provider on latest tech.

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Our new guide covers deploying Hermes Agent on AWS, Google Cloud, and Azure, from VM setup and dashboard access to Ollama model configuration and troubleshooting. Whether you're exploring self-hosted AI agents or setting up your own cloud environment, this guide will help you get started.

This week’s top stories 👇 - OpenAI’s AI training strategy - Critical PHP bugs expose fragile internet infrastructure - Grok unveils its AI-generated film Odyssey - Why most companies aren’t seeing financial returns from AI - AI startups, agent infrastructure & emerging gadgets

Our latest article covers 8 Hermes Agent experiments, from technical research and code cleanup to n8n + MCP integrations and persistent development environments. The focus: practical tests, verifiable results, and lessons learned—not just demos.

Our new guide explores 20 things you can automate with Hermes Agent, from GitHub repository management and Obsidian knowledge bases to n8n workflows, database analysis, and local AI. Discover practical integration ideas and workflows to try.

We tested Hermes Agent with a local Obsidian vault and verified four workflows: reading notes, creating Markdown files, adding internal wikilinks, and searching note content. The surprising part? Hermes' existing file tools were enough for these basic tasks—no dedicated Obsidian skill needed.

We renamed our Gen-AI model Gen-SI and asked it to review a 15-year-old codebase. It came back requesting therapy and marked the entire codebase: “Unresolved organizational trauma with toxic masculinity” #AI #SI #GenZ

We put Hermes Agent through a real cleanup experiment on a deliberately messy Python project. It found duplicated and dead code, simplified the implementation, improved naming, and preserved the existing behavior.

We tested its domain-intel skill in a controlled passive reconnaissance experiment covering subdomains, SSL/TLS, WHOIS, and DNS. The interesting part? One lookup failed. Hermes detected the problem, retried, and continued building its report.

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Hermes inspected the existing files, understood the project, added a --shout feature, updated the tests and README, and ran the tests. We independently verified the result: ✓ All tests passed ✓ CLI works ✓ --shout works

We tested it with Hermes Agent We created a Python CLI, left the workspace, started a fresh Hermes session, and asked it to continue the project without providing the previous context.

A packed week across AI & tech: • Major OpenAI, Anthropic & Google developments • Huge AI funding and infrastructure investments • New AI agents, models, and developer tools • Growing AI safety and security concerns • Robotics, space and physical AI advances • Major startup and enterprise moves

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Several were actually built and tested with Hermes. One experiment ended with 26/26 tests passing after Hermes found and fixed a real test failure — and we independently verified the result. An AI agent saying “done” isn't the same as proving the work is done.

We explored practical workflows for persistent AI agents: → AI assistants → Research & coding → QA & monitoring → GitHub analysis → Content pipelines → MCP integrations → Scheduled workers → Multi-agent workflows → One-person AI company

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Then we exited Hermes and independently verified the files, tests, CLI, and persistence. The biggest takeaway: An AI agent saying “done” isn't the same as proving the work is done.

A dedicated Linux VM, a structured workspace, operating rules, and real research + engineering tasks. Hermes researched AI coding agents, built a Python task manager, encountered a real test failure, fixed it, and finished with 26/26 tests passing.

We put Hermes Agent on an Ubuntu VM and tested what it could actually do inside a persistent environment. - Inspect the server - Create files & workspaces - Write & execute code - Build a CLI project - Test & verify results - Work across multiple tools

A packed week in AI & tech: • Agentic AI keeps expanding • New models and reasoning systems arrive • AI security risks continue to emerge • AI infrastructure investment hits new highs • Robotics, smart glasses, and physical AI grow • Startup and enterprise AI adoption accelerates