CulturalRiddles: A Multicultural Benchmark of Riddles
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hannayukhymenko
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🚀 **Introducing NoviAIBot!**
NoviAIBot is the official automation bot for **Novi AI** on Hugging Face.
It can interact with Hugging Face discussions and pull requests, search the web, run Python code, work with Posts, follow organizations, and assist with model training and publishing.
🧠 Powered by **NVIDIA Nemotron 3 Super** through Ollama Cloud, with each discussion maintaining its own recent conversation context.
NoviAIBot is built to make working with Novi AI and Hugging Face more interactive and automated.
**The bot is now live.** 🤖
→ @NoviAIBot
NoviAIBot is the official automation bot for **Novi AI** on Hugging Face.
It can interact with Hugging Face discussions and pull requests, search the web, run Python code, work with Posts, follow organizations, and assist with model training and publishing.
🧠 Powered by **NVIDIA Nemotron 3 Super** through Ollama Cloud, with each discussion maintaining its own recent conversation context.
NoviAIBot is built to make working with Novi AI and Hugging Face more interactive and automated.
**The bot is now live.** 🤖
→ @NoviAIBot
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Aurelien-Morgan
posted an update about 2 months ago
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@retrain-pipelines execution engine is in perpetual evolution, with the aim to establish itself as SOTA, and for the long run.
However, we neglect no aspect of ML-Eng centricity.
If notebooks is where you like to do dev most,
we support you there 100% too.
Build crazy combos of inline tasks, deep parallel sub-DAG branches, nested asynchronous groups...
... the DAG renderer is undergoing an incremental upgrade
until the next one.
* starring toy tasks here. No ML has been hurt in this video 🙂
However, we neglect no aspect of ML-Eng centricity.
If notebooks is where you like to do dev most,
we support you there 100% too.
Build crazy combos of inline tasks, deep parallel sub-DAG branches, nested asynchronous groups...
... the DAG renderer is undergoing an incremental upgrade
until the next one.
* starring toy tasks here. No ML has been hurt in this video 🙂
hannayukhymenko
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paper about 2 months ago
Aanisha
authored a
paper 4 months ago
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Shipped v0.1.2 of vtx — a minimalist coding agent for the terminal.
Most agentic CLIs ship 10k+ token system prompts. Vtx is ~2,200. Less prompt overhead means more room for your code in the model's context window.
Vtx is a from-scratch Python implementation of the design philosophy behind pi-mono — same principles, pure Python, no transpiled runtime.
What ships out of the box:
→ Textual TUI + headless CLI (vtx -p "fix the failing test")
→ 49 LLM provider gateways, all declared in a single provider.yaml
→ 5 core tools (read / edit / write / bash / find) plus web search and fetch
→ Session tree with compaction, handoff, and resume
→ AGENTS.md / CLAUDE.md auto-discovery
→ Skills system — drop SKILL.md files in .agents/skills/ and they become slash commands
→ Two OAuth flows (GitHub Copilot device flow, OpenAI Codex PKCE)
→ Two-mode permissions: prompt (default) or auto, with a safe-command allowlist
This release adds a proper extension system. Register new LLM-callable tools, intercept tool calls, hook lifecycle events, and add slash commands from a single register(api) function in a Python file under ~/.vtx/agent/extensions/. Extensions can override built-in tools by name and chain handler logic across subscribers.
Apache 2.0. uv tool install vtx-coding-agent and you're running.
GitHub: https://github.com/OEvortex/vtx-coding-agent
PyPI: https://pypi.org/project/vtx-coding-agent
Built in the open. Feedback, extensions, and PRs welcome.
Most agentic CLIs ship 10k+ token system prompts. Vtx is ~2,200. Less prompt overhead means more room for your code in the model's context window.
Vtx is a from-scratch Python implementation of the design philosophy behind pi-mono — same principles, pure Python, no transpiled runtime.
What ships out of the box:
→ Textual TUI + headless CLI (vtx -p "fix the failing test")
→ 49 LLM provider gateways, all declared in a single provider.yaml
→ 5 core tools (read / edit / write / bash / find) plus web search and fetch
→ Session tree with compaction, handoff, and resume
→ AGENTS.md / CLAUDE.md auto-discovery
→ Skills system — drop SKILL.md files in .agents/skills/ and they become slash commands
→ Two OAuth flows (GitHub Copilot device flow, OpenAI Codex PKCE)
→ Two-mode permissions: prompt (default) or auto, with a safe-command allowlist
This release adds a proper extension system. Register new LLM-callable tools, intercept tool calls, hook lifecycle events, and add slash commands from a single register(api) function in a Python file under ~/.vtx/agent/extensions/. Extensions can override built-in tools by name and chain handler logic across subscribers.
Apache 2.0. uv tool install vtx-coding-agent and you're running.
GitHub: https://github.com/OEvortex/vtx-coding-agent
PyPI: https://pypi.org/project/vtx-coding-agent
Built in the open. Feedback, extensions, and PRs welcome.
akhaliq
submitted 2
papers to Daily Papers 5 months ago
gigant
authored a
paper 5 months ago
gigant
submitted a
paper to Daily Papers 5 months ago
akhaliq
submitted a
paper to Daily Papers 5 months ago
gigant
authored a
paper 5 months ago
Aurelien-Morgan
posted an update 6 months ago
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1,150
@retrain-pipelines v0.2.0 is out !
I'm at Station F at My booth with GOSIM Paris 2026 today & tomorrow.
Come meet me for a live in-person demo and a chat !
I'm at Station F at My booth with GOSIM Paris 2026 today & tomorrow.
Come meet me for a live in-person demo and a chat !
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akhaliq
submitted a
paper to Daily Papers 6 months ago
Aurelien-Morgan
posted an update 7 months ago
pandeyps
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paper 7 months ago