Inference providers on the Hugging Face Hub is a total QoL (Quality of Life) boost
1/ After weeks of exploring model deployment options, I documented my findings in two articles. My first approach used the transformers API + FastAPI on a Hugging Face Space.
Pros: Any model, super quick deployment
Cons: Models aren’t fully optimized for production; might look into vLLM or TGI.
AI agents produce better work when you give them better context.
Snapdown captures visible text from documentation, PDFs, error messages, and tables, preserving headings, lists, code, and table structure as Markdown.
Paste it into Claude or Cursor instead of manually transcribing and reformatting it. One shortcut, entirely local.
@petergyang@karan4d@NousResearch I use Hermes, and I like it—but I wouldn’t call this self-learning. It’s more like an agent writing notes to its future self, then periodically cleaning those notes up. Useful? Absolutely. Learning? Only if those notes measurably improve what it does next.
curious about the session reuse in workflows, is that prefix caching or are you holding the whole run state in the DO?
asking bc i've been building a similar thing from the other side, self hosted instead of cloudflare native: https://t.co/76hjFvRqAg
agent is just a folder of text, one node process, any model. channels + schedules built in
Claude Opus 5 is days old. I've had agents running on it since last night — deleting instructions, not writing them.
Anthropic cut 80% of Claude Code's system prompt for this generation. Evals held.
Your CLAUDE.md is probably a rulebook.
70s breakdown:
The thing I didn't expect, watching the overnight run:
conflicting instructions cost more than missing ones.
"Leave documentation as appropriate" sitting next to "DO NOT add comments" makes the model spend its thinking resolving you instead of the problem
Everyone's racing to build the AI agent — the coding one, the sales one, the support one. Each welded to a single job.
I've been building the opposite: one engine that becomes whatever agent the person in front of it actually needs.
Every screen in that clip is the real product:
· rosa/agent.json + rosa/SOUL.md — an agent is text you can edit
· ollama:llama3.2 · keyless · $0 — runs local, no API key
· the amber card — it asks before it reads/replies, scoped to #support only
· one HTTP+SSE gateway → Slack, Telegram, WhatsApp, your site, a schedule
An AI agent is just a folder of text.
Describe it in one sentence → watch it build itself → approve anything sensitive → it's live in Slack (any messaging door).
Keyless. Local. $0. Yours to own — not renting someone else's server.
This is Ownware, open source 👇 https://t.co/76hjFvQSKI
#AIagents #opensource #buildinpublic
Build it. Own it. Ship it — into your product, to your clients, branded as yours. The one thing you can't do with a rented assistant.
Open-sourced solo. A ⭐ genuinely helps a one-person project get seen: https://t.co/poOfrXZIj8
Stop renting your AI agent.
I'm a solo dev — I just open-sourced the kit to build (and own) your own. Any model, your machine, from a folder of text.
Watch it go from text file to live in 30 seconds 👇
#buildinpublic
Ownware is the kit to build yours:
① describe it in a folder of text
② one command → a live service
③ alive in Slack, your site, your app
Support agent, shop assistant, ops bot — same kit, different text. A real engine, any model, your box.