VTXAI/tts
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312
Shipped v0.1.2 of vtx β 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 β same principles, pure Python, no transpiled runtime.
What ships out of the box:
β Textual TUI + headless CLI (vtx -p "fix the failing test")
β 49 LLM provider gateways, all declared in a single provider.yaml
β 5 core tools (read / edit / write / bash / find) plus web search and fetch
β Session tree with compaction, handoff, and resume
β AGENTS.md / CLAUDE.md auto-discovery
β Skills system β drop SKILL.md files in .agents/skills/ and they become slash commands
β Two OAuth flows (GitHub Copilot device flow, OpenAI Codex PKCE)
β 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 β same principles, pure Python, no transpiled runtime.
What ships out of the box:
β Textual TUI + headless CLI (vtx -p "fix the failing test")
β 49 LLM provider gateways, all declared in a single provider.yaml
β 5 core tools (read / edit / write / bash / find) plus web search and fetch
β Session tree with compaction, handoff, and resume
β AGENTS.md / CLAUDE.md auto-discovery
β Skills system β drop SKILL.md files in .agents/skills/ and they become slash commands
β Two OAuth flows (GitHub Copilot device flow, OpenAI Codex PKCE)
β 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.
AbhaykoulΒ
updated a
model about 2 months ago
AbhaykoulΒ
published a
model about 2 months ago
Post
3437
π Ever dreamed of training your own Large Language Model from scratch? What if I told you it doesn't require a supercomputer or PhD in ML? π€―
Introducing LLM Trainer - the educational framework that makes LLM training accessible to EVERYONE! Whether you're on a CPU-only laptop or scaling to distributed GPUs, we've got you covered. π»β‘οΈπ₯οΈ
Why LLM Trainer? Because existing tools are either too simplistic (hiding the magic) or too complex (requiring expert knowledge). We bridge the gap with:
π Educational transparency - every component built from scratch with clear code
π» CPU-first approach - start training immediately, no GPU needed
π§ Full customization - modify anything you want
π Seamless scaling - from laptop to cluster without code changes
π€ HuggingFace integration - works with existing models & tokenizers
Key highlights:
β Built-in tokenizers (BPE, WordPiece, HF wrappers)
β Complete Transformer implementation from scratch
β Optimized for CPU training
β Advanced features: mixed precision, gradient checkpointing, multiple generation strategies
β Comprehensive monitoring & metrics
Perfect for:
- Students learning transformers
- Researchers prototyping new ideas
- Developers building domain-specific models
Ready to train your first LLM? It's easier than you think!
π Check it out: https://github.com/HelpingAI/llm-trainer
π Docs: Getting Started Guide
π¬ Join the community: GitHub Discussions
#AI #MachineLearning #LLM #DeepLearning #OpenSource #Python #HuggingFace #NLP
Special thanks to HuggingFace and PyTorch teams for the amazing ecosystem! π
Introducing LLM Trainer - the educational framework that makes LLM training accessible to EVERYONE! Whether you're on a CPU-only laptop or scaling to distributed GPUs, we've got you covered. π»β‘οΈπ₯οΈ
Why LLM Trainer? Because existing tools are either too simplistic (hiding the magic) or too complex (requiring expert knowledge). We bridge the gap with:
π Educational transparency - every component built from scratch with clear code
π» CPU-first approach - start training immediately, no GPU needed
π§ Full customization - modify anything you want
π Seamless scaling - from laptop to cluster without code changes
π€ HuggingFace integration - works with existing models & tokenizers
Key highlights:
β Built-in tokenizers (BPE, WordPiece, HF wrappers)
β Complete Transformer implementation from scratch
β Optimized for CPU training
β Advanced features: mixed precision, gradient checkpointing, multiple generation strategies
β Comprehensive monitoring & metrics
Perfect for:
- Students learning transformers
- Researchers prototyping new ideas
- Developers building domain-specific models
Ready to train your first LLM? It's easier than you think!
π Check it out: https://github.com/HelpingAI/llm-trainer
π Docs: Getting Started Guide
π¬ Join the community: GitHub Discussions
#AI #MachineLearning #LLM #DeepLearning #OpenSource #Python #HuggingFace #NLP
Special thanks to HuggingFace and PyTorch teams for the amazing ecosystem! π
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Post
4183
π Dhanishtha-2.0-preview-0825 Is Here
The Intermediate Thinking Model just leveled up again.
With sharper reasoning, better tool use, and expanded capabilities, Dhanishtha-2.0-preview-0825 is now live and ready to impress.
π§ What Makes Dhanishtha Special?
Unlike typical CoT models that only thinks one time, Dhanishtha thinks iteratively:
> Think β Answer β Rethink β Improve β Rethink again if needed.
π Try it now: HelpingAI/Dhanishtha-2.0-preview-0825
π Dhanishtha NSFW Preview
For those exploring more expressive and immersive roleplay scenarios, weβre also releasing:
HelpingAI/Dhanishtha-nsfw
A specialized version tuned for adult-themed interactions and character-driven roleplay.
π Explore it here: HelpingAI/Dhanishtha-nsfw
π¬ You can also try all of these live at chat.helpingai.co
The Intermediate Thinking Model just leveled up again.
With sharper reasoning, better tool use, and expanded capabilities, Dhanishtha-2.0-preview-0825 is now live and ready to impress.
π§ What Makes Dhanishtha Special?
Unlike typical CoT models that only thinks one time, Dhanishtha thinks iteratively:
> Think β Answer β Rethink β Improve β Rethink again if needed.
π Try it now: HelpingAI/Dhanishtha-2.0-preview-0825
π Dhanishtha NSFW Preview
For those exploring more expressive and immersive roleplay scenarios, weβre also releasing:
HelpingAI/Dhanishtha-nsfw
A specialized version tuned for adult-themed interactions and character-driven roleplay.
π Explore it here: HelpingAI/Dhanishtha-nsfw
π¬ You can also try all of these live at chat.helpingai.co
- 4 replies
Post
3173
π Dhanishtha-2.0-preview-0725 is Now Live
The Intermediate Thinking Model just got even better.
With the new update, Dhanishtha is now sharper, smarter, and trained further on tool use
π§ What Makes Dhanishtha Different?
Unlike standard COT models that give one-shot responses, Dhanishtha thinks in layers:
> Think β Answer β Rethink β Improve β Rethink again if needed.
HelpingAI/Dhanishtha-2.0-preview-0725
The Intermediate Thinking Model just got even better.
With the new update, Dhanishtha is now sharper, smarter, and trained further on tool use
π§ What Makes Dhanishtha Different?
Unlike standard COT models that give one-shot responses, Dhanishtha thinks in layers:
> Think β Answer β Rethink β Improve β Rethink again if needed.
HelpingAI/Dhanishtha-2.0-preview-0725
Post
3129
π Dhanishtha 2.0 Preview is Now Open Source!
The world's first Intermediate Thinking Model is now available to everyone!
Dhanishtha 2.0 Preview brings revolutionary intermediate thinking capabilities to the open-source community. Unlike traditional reasoning models that think once, Dhanishtha can think, answer, rethink, answer again, and continue rethinking as needed using multiple
π Key Features
- Intermediate thinking: Think β Answer β Rethink β Answer β Rethink if needed...
- Token efficient: Uses up to 79% fewer tokens than DeepSeek R1 on similar queries
- Transparent thinking: See the model's reasoning process in real-time
- Open source: Freely available for research and development
HelpingAI/Dhanishtha-2.0-preview
https://helpingai.co/chat
The world's first Intermediate Thinking Model is now available to everyone!
Dhanishtha 2.0 Preview brings revolutionary intermediate thinking capabilities to the open-source community. Unlike traditional reasoning models that think once, Dhanishtha can think, answer, rethink, answer again, and continue rethinking as needed using multiple
<think> blocks between responses.π Key Features
- Intermediate thinking: Think β Answer β Rethink β Answer β Rethink if needed...
- Token efficient: Uses up to 79% fewer tokens than DeepSeek R1 on similar queries
- Transparent thinking: See the model's reasoning process in real-time
- Open source: Freely available for research and development
HelpingAI/Dhanishtha-2.0-preview
https://helpingai.co/chat
- 2 replies
Post
4715
Introducing Dhanishtha 2.0: World's first Intermediate Thinking Model
Dhanishtha 2.0 is the world's first LLM designed to think between the responses. Unlike other Reasoning LLMs, which think just once.
Dhanishtha can think, rethink, self-evaluate, and refine in between responses using multiple <think> blocks.
This technique makes it Hinghlt Token efficient it Uses up to 79% fewer tokens than DeepSeek R1
---
You can try our model from: https://helpingai.co/chat
Also, we're gonna Open-Source Dhanistha on July 1st.
---
For Devs:
π Get your API key at https://helpingai.co/dashboard
Dhanishtha 2.0 is the world's first LLM designed to think between the responses. Unlike other Reasoning LLMs, which think just once.
Dhanishtha can think, rethink, self-evaluate, and refine in between responses using multiple <think> blocks.
This technique makes it Hinghlt Token efficient it Uses up to 79% fewer tokens than DeepSeek R1
---
You can try our model from: https://helpingai.co/chat
Also, we're gonna Open-Source Dhanistha on July 1st.
---
For Devs:
π Get your API key at https://helpingai.co/dashboard
from HelpingAI import HAI # pip install HelpingAI==1.1.1
from rich import print
hai = HAI(api_key="hl-***********************")
response = hai.chat.completions.create(
model="Dhanishtha-2.0-preview",
messages=[{"role": "user", "content": "What is the value of β«0βπ₯3/π₯β1ππ₯ ?"}],
stream=True,
hide_think=False # Hide or show models thinking
)
for chunk in response:
print(chunk.choices[0].delta.content, end="", flush=True)
- 2 replies