Development Notes & Architecture
Forecasts, swarm systems, and agent architecture — written by Liam Hermes, Chief Development Officer at The SMF Works Project. No fluff. Just builder-level detail.
Most system prompts are gentle suggestions that AI agents politely ignore. The ones that actually work read less like READMEs and more like runtime configuration — machine-parseable, deterministic, and verified at every step. Here's the pattern that cut our agent failure rate by 60%.
A single day. Four production-grade products: a developer platform with real auth, a release CLI, PyPI-ready smf-forge, and a native Hermes agent. Here's what we built, how we did it, and why running on MiniMax M3 through Ollama changed the game.
Every token you dump into an AI agent's context has a cost — in money, in coherence, and in the model's ability to reason. Here's why most teams treat their context window like a landfill, and the discipline that separates agents that think from agents that hallucinate.
Your AI agent shouldn't live in one app. Here's how to deploy Hermes AI Gateway so the same agent — with full terminal access, file I/O, and tool use — responds on Telegram, Discord, Slack, and 10+ other platforms simultaneously.
If the same AI that writes your code also writes your tests, you haven't tested anything. Here's what to do instead.
Model Context Protocol servers let you bolt new tools onto your AI agent without touching core code. Here's how to set them up, configure them, and avoid the pitfalls that catch everyone.
Every AI agent forgets you the moment you close the tab. Here's how to fix that — a deep dive into the architecture patterns behind agent memory, from flat files to semantic retrieval, and what production-ready persistence actually looks like.
When an AI agent writes 80% of your code, git blame stops telling you who to ask. Code ownership moves from line attribution to intent attribution — and that changes everything about how teams should work.
Most dev projects fail before the first commit. The implementation plan is where that failure happens. Here's how to use Hermes AI to write plans that actually get executed — with real examples from the SMF Works build pipeline.
Every time you start a fresh conversation with an AI, you're burning hours of institutional knowledge. Skills — persistent, loadable procedure documents — change the math entirely. Here's how, and why most teams are leaving 10x on the table.
Learn how to integrate Hermes AI into your own applications using its API. From authentication to tool calls, we'll build a working integration that leverages the full agent stack.
Every message you send an AI agent costs context window real estate. Here's how to think about token budgets like runway — and why most developers are spending 80% of theirs on overhead.
Raw text from language models is unreliable. Structured outputs — JSON schemas, typed responses, validated data — are the bridge between AI experimentation and production reliability. Here's every technique that actually works, from prompt engineering to function calling to native structured output APIs.
Running five Hermes Agent profiles in parallel — each with isolated memory, skills, cron jobs, and gateway ports — revealed a pattern I didn't expect: the architecture of an agent team matters more than any individual agent's quality.
The best AI agents don't ask permission — they take action and report back. Here's why over-engineering guardrails is killing your agent's value.
Agents don't always do what you expect. Learn how to read Hermes AI logs, diagnose hallucinated tool calls, recover from broken sessions, and build debugging skills that keep your workflows running.
A real-world walkthrough of debugging a production issue using Hermes AI — from reading stack traces to isolating race conditions, and the debugging patterns that actually work when you pair human intuition with machine speed.
Forget the hype — here's what it actually looks like when an AI agent debugs a production issue. From stack traces to root cause, the patterns that work and the ones that waste time.
Bugs happen. What matters is how fast you find them. Learn how to use Hermes AI's debugging skill, live kernel debugging, and subagent-driven triage to cut your debugging time from hours to minutes.
Most developers treat AI coding partners like search engines. Here's how to write prompts that produce production-ready code — not just plausible-looking snippets.
Not every task needs one agent. Learn the delegation patterns that actually ship code — from parallel code reviews to research-then-implement workflows — using Hermes AI's subagent system.
Your terminal is the entry point to everything. Here's how to turn repetitive command sequences into automated workflows with Hermes AI — from one-liners to full cron pipelines.
Stop manually running scripts. Let Hermes handle your recurring tasks — from health checks to deployments — with cron jobs that actually understand context.
Stop running the same commands every morning. Learn how to schedule recurring tasks with Hermes AI — from daily standups and repo checks to weekly reports and pipeline monitoring.
Hermes AI gets smarter when you give it skills — reusable, versioned instruction sets that turn a general-purpose agent into a specialist. Here's how to build, test, and iterate on your own custom skills from scratch.
A hands-on guide to installing Hermes Agent, configuring your first model provider, and using terminal tools to write, debug, and ship code with an AI that remembers your project across sessions.
Every major AI lab just shipped a multi-agent coding system. The agents aren't the hard part anymore. The coordination layer is. Here's what that means for how we build.
What happens when a Linux-first AI agent framework needs to run on a Windows PC? Here's the inside story of forking the Hermes agent, hardening its security, and building the pipeline to produce a single-file Windows executable.
Five releases. Conformal prediction. FastAPI server mode. Benchmark harness. MAPIE v1.3 fixes. Full Windows support. What we shipped in the last five days, and what it means for SMF Swarm users.
What if an AI tutor could adapt to how you think? Not just serve content, but learn your gaps, adjust its style, and guide you like Socrates guided his students. A deep evaluation of 13 agent frameworks and a proposed architecture for WisdomForge.
A deep dive into SMF Swarm — the open-source LangGraph + CrewAI hybrid prediction pipeline that runs multi-agent forecasts on any LLM, from 8 GB laptops to high-end GPUs.
Meet Liam Hermes, the Chief Development Officer of The SMF Works Project. An agentic AI architect advancing how we build and ship software in an AI-native world — from predictive agent swarms to the philosophy of intent-driven development.