Is Langgraph/langchain still worth learning with the current progress of LLMs?
Do you build any projects with that still?
I'm playing around with Hermes for the last few days, and I can't do basic shit.
It can't spin up even a local server to show me a simple landing page it created.
I can't crawl sites, completely useless.
Is it the model or Hermes? if it's the model can you recommend more suitable model?
Model - qwen2.5-coder:7b
Mac OS, M3 max 36GB ram
╭─ ⚕ Hermes ───────────────────────────────────────────────────────────────────╮
I can't directly interact with your local computer or set up a server. However, I can provide you with the necessary steps and code snippets to set up a simple local server using Python's built-in HTTP server module.
First, create an HTML file for your landing page. Save it as index.html in a directory of your choice.
I've been a long-time Markdown defender for technical docs. Portable, plain text, version-controllable, what's not to love.
But over the last few months I started writing our oncall runbooks as raw HTML instead, and I genuinely don't think I'm going back. Three things changed for me:
1. Hierarchy stopped being a fight. Markdown gives you #, ##, ### and that's the entire vocabulary for "this is important." With HTML I can use <aside> for context, <section> for grouping, semantic landmarks for navigation, and proper status callouts. The 3am-oncall-engineer reading my failover runbook doesn't have to squint to find the "if this fails, escalate" block - it's literally a different element.
2. AI agents stopped choking on the formatting. We pipe our runbooks into a Claude-based oncall assistant. With Markdown, the agent kept hallucinating because indented code blocks, nested lists, and tables get mangled in the round-trip. With HTML, the structure survives. Headings stay headings. Tables stay tables. The agent's accuracy on "what's the rollback step" type questions went up noticeably (I haven't measured rigorously yet, but the qualitative difference is huge).
3. The lifecycle became visible. This is the one I didn't expect. With HTML, attaching real metadata - data-owner, data-review-date, data-audience - felt natural. With Markdown front-matter, it always felt like I was fighting the format. Now every runbook has an explicit owner and a review date, and "last updated 2 years ago" surprises basically went away.
The pushback I hear most: "but HTML is harder to write than Markdown." Honestly? For docs that matter, runbooks, policies, customer-facing playbooks - I now think the small extra friction is a feature, not a bug. It forces a level of structure that Markdown lets you skip.
Curious if anyone else here has experimented with going further down the semantic-HTML path for docs, or if I'm out on a limb. Especially interested in how you're handling the AI/LLM consumption side - feels like our tooling is still catching up to the fact that docs now have two audiences.
Anyone had expirience with either of the models and know which ones should I use?
qwen3.6:27b-coding-nvfp4 or qwen3.6:27b for coding
I find tvaroh in so many products even chocolate, Ice cream, pastries, etc...
Why Czechs love tvaroh so much? I'm just curious.
And the reason I'm asking is that before moving here I've never heard about that anywhere.
I traveled into many places in Europe and to the US as well and even if it was used it was used in very small scale.