Agents just forget.
That’s been the frustrating reality of every AI agent framework out there. LangChain, AutoGen, CrewAI – they can orchestrate complex tasks, use tools, and even parallelize operations. But shut down the session, and poof. All that learned nuance about your specific data structures, all that painstakingly acquired context, evaporates. You’re back to square one, every single time.
We’ve been so fixated on what AI agents can do, we’ve completely neglected to ask what they can keep. This is the question Hermes Agent is finally addressing. And after a week of relentless daily testing, I can tell you this: the gap between its performance on Day 1 and Day 7 isn’t just noticeable. It’s the difference between a clumsy intern and a seasoned pro.
The Humble Setup
My task: curate the daily firehose of AI and developer news. Think new models, framework updates, open-source releases. Every morning, I spent a good 30-40 minutes manually sifting through HackerNews, arXiv, and GitHub. It’s tedious, error-prone, and frankly, I kept missing gems because my human bandwidth is finite.
This is precisely the kind of repetitive, context-dependent job an agent should excel at. So, I set up Hermes with the same task, day after day, to see if it could actually improve. If Day 7 would genuinely outperform Day 1.
My hardware is decidedly mid-range: a Windows 11 machine with a GTX 1650 (4GB VRAM) and 16GB RAM. Nothing fancy.
The setup itself was suspiciously simple.
# Install (Linux/macOS/WSL2 — I used WSL2)
curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash
# Launch
hermes
No cryptic YAML files. No shouting about environment variables. No dependency nightmares. The installer nudged me for a model provider, and I pointed it to OpenRouter with a Nous Hermes model. The first prompt? Back in under 10 seconds. Astonishingly painless.
The Daily Grind
Here’s the exact instruction I fed it:
Every morning at 8AM, find the 3 most relevant AI and developer
news items from the past 24 hours. I care about open-source models,
agent frameworks, and local inference. Skip anything that's just hype
with no technical substance. Post the results to my Telegram.
One simple directive. Then, I let it run.
Day 1: A Messy First Draft
The initial output was… rough. Six items, two of which were pure TechCrunch fluff – the kind of breathless “AI is changing everything” drivel devoid of any actual technical insight. One GitHub release was three weeks old. But amidst the dross, there was a glimmer: a new quantization method for running LLMs on consumer hardware. Progress, of a sort.
The Telegram message itself was a rambling mess. Unformatted, no clear structure, just raw text. The summaries were glorified restatements of headlines, not the analysis I craved.
Here’s the nascent skill file after that first day:
# skill: daily_ai_digest
version: 1.0
created: 2026-05-09
## task
Search for AI and developer news. Summarize and post to Telegram.
## steps
1. Search "AI news today"
2. Search "developer tools news"
3. Collect top results
4. Write summary
5. Post to Telegram
## tools_used
- web_search
- telegram_send
## notes
First run. Results were broad. User wants 3 items.
Twelve lines. A rudimentary blueprint. But it existed. And that’s the crucial distinction for Hermes: it starts with something, and it builds upon it.
Day 2: The First Hint of Acumen
I repeated the process. No tweaks, no manual intervention.
Day 2 yielded five items. The fluff pieces? Gone. Hermes had started intelligently pulling from Hacker News and GitHub Releases – demonstrably better sources. One item still slipped through – a VentureBeat funding round that merely mentioned AI – but the other four were genuinely valuable. The summaries were longer, providing context and nuance beyond the superficial. One even flagged a library update as a breaking change, information buried deep within release notes, not the headline. Hermes was digging deeper.
The Telegram output also saw improvement. A clean, numbered list. Each entry boasted a title, a concise summary, and a direct link.
The skill file reflected this nascent learning:
# skill: daily_ai_digest
version: 1.2
created: 2026-05-09
last_improved: 2026-05-10
## task
Find and deliver 3 relevant AI/dev news items.
User wants technical depth, not hype.
## search_strategy
queries:
- "AI <a href="/tag/developer-tools/">developer tools</a> release site:github.com"
- "open source LLM 2026"
- "AI news site:news.ycombinator.com"
source_deprioritize: [techcrun
