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Friday 28 August 2026 13:52:51 GMT
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Most people are still comparing AI models. That’s becoming the less interesting part. The real advantage now comes from what you put around the model: the harness, memory, skills, tools, context and workflow. Here’s a stack I think is genuinely worth understanding right now. 1. Hermes Agent — stop treating AI like a temporary chat Hermes is much closer to a persistent operator. It has memory, reusable skills, MCP connections, scheduled tasks, tool use and a learning loop that can turn experience into procedures. The important distinction: Memory = WHAT Your preferences, projects, environment, past decisions. Skills = HOW Research workflows, deployment procedures, repeatable tasks, playbooks. That sounds small, but it prevents your agent from becoming a giant pile of context. One practical rule: Hermes requires at least 64K context, so don’t cripple a local model with a tiny context window and then blame the model. 2. Qwen 3.8 27B — local AI is getting serious This is one of the most interesting releases of August. Coding. Research. Vision. Tool use. Long-horizon agent work. And unlike giant frontier models, you can actually run a quantized version locally. With Ollama: ollama run qwen3.8 And you can launch it directly inside Hermes, Claude Code, OpenCode or OpenClaw through Ollama. That combination matters more than Qwen as a chatbot. model + harness + tools > model alone 3. OpenCode — parallelize the work OpenCode is becoming one of the open-source coding harnesses I’d watch closely. It supports local and hosted models, LSPs, terminal/IDE/desktop workflows and — importantly — multiple sessions on the same project. So instead of: one agent → one task → wait you can run: agent 1 → feature agent 2 → tests agent 3 → investigation agent 4 → UI polish Then review the outputs. That is a very different way of working. 4. Hermes + Obsidian is an underrated combination Your Obsidian vault can be more than somewhere notes go to die. Because Obsidian stores Markdown locally, you can expose the relevant vault/files to your agent through filesystem tooling or MCP. Now your research, project decisions, SOPs and notes can become usable context. But don’t dump everything into memory. Keep the separation: Vault = deep knowledge Memory = important facts Skills = repeatable procedures That architecture scales much better. 5. Four setup mistakes I’d avoid Don’t point two live Hermes agents at the same profile. Don’t install 40 MCP tools “just in case.” Give the agent the smallest useful toolset. Don’t confuse memory with skills. And if you give an autonomous agent shell access, use command allowlists and a Docker-backed environment when possible. A bad agent setup often looks like a bad model. It isn’t. The bigger shift happening in AI right now is this: Models are becoming interchangeable brains. The durable advantage is the system around them. Memory. Tools. Skills. Context. Verification. Workflows. That’s what makes an agent actually useful. Save this — I’m going much deeper into these setups. Comment “HERMES” and I’ll send the starter stack + setup. Follow me for the AI tools, repos and workflows worth knowing before everyone else does. #AIAgents #HermesAgent #LocalAI #OpenSourceAI #AITools
Most people are still comparing AI models. That’s becoming the less interesting part. The real advantage now comes from what you put around the model: the harness, memory, skills, tools, context and workflow. Here’s a stack I think is genuinely worth understanding right now. 1. Hermes Agent — stop treating AI like a temporary chat Hermes is much closer to a persistent operator. It has memory, reusable skills, MCP connections, scheduled tasks, tool use and a learning loop that can turn experience into procedures. The important distinction: Memory = WHAT Your preferences, projects, environment, past decisions. Skills = HOW Research workflows, deployment procedures, repeatable tasks, playbooks. That sounds small, but it prevents your agent from becoming a giant pile of context. One practical rule: Hermes requires at least 64K context, so don’t cripple a local model with a tiny context window and then blame the model. 2. Qwen 3.8 27B — local AI is getting serious This is one of the most interesting releases of August. Coding. Research. Vision. Tool use. Long-horizon agent work. And unlike giant frontier models, you can actually run a quantized version locally. With Ollama: ollama run qwen3.8 And you can launch it directly inside Hermes, Claude Code, OpenCode or OpenClaw through Ollama. That combination matters more than Qwen as a chatbot. model + harness + tools > model alone 3. OpenCode — parallelize the work OpenCode is becoming one of the open-source coding harnesses I’d watch closely. It supports local and hosted models, LSPs, terminal/IDE/desktop workflows and — importantly — multiple sessions on the same project. So instead of: one agent → one task → wait you can run: agent 1 → feature agent 2 → tests agent 3 → investigation agent 4 → UI polish Then review the outputs. That is a very different way of working. 4. Hermes + Obsidian is an underrated combination Your Obsidian vault can be more than somewhere notes go to die. Because Obsidian stores Markdown locally, you can expose the relevant vault/files to your agent through filesystem tooling or MCP. Now your research, project decisions, SOPs and notes can become usable context. But don’t dump everything into memory. Keep the separation: Vault = deep knowledge Memory = important facts Skills = repeatable procedures That architecture scales much better. 5. Four setup mistakes I’d avoid Don’t point two live Hermes agents at the same profile. Don’t install 40 MCP tools “just in case.” Give the agent the smallest useful toolset. Don’t confuse memory with skills. And if you give an autonomous agent shell access, use command allowlists and a Docker-backed environment when possible. A bad agent setup often looks like a bad model. It isn’t. The bigger shift happening in AI right now is this: Models are becoming interchangeable brains. The durable advantage is the system around them. Memory. Tools. Skills. Context. Verification. Workflows. That’s what makes an agent actually useful. Save this — I’m going much deeper into these setups. Comment “HERMES” and I’ll send the starter stack + setup. Follow me for the AI tools, repos and workflows worth knowing before everyone else does. #AIAgents #HermesAgent #LocalAI #OpenSourceAI #AITools

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