#toolcalling — Public Fediverse posts

From the .NET blog...

In case you missed it earlier...

Microsoft Agent Framework – Building Blocks for AI Part 3
https://devblogs.microsoft.com/dotnet/microsoft-agent-framework-building-blocks-for-ai-part-3/ #dotnet #AI #csharp #AIagents #MicrosoftAgentFramework #multiagent #ToolCalling #workflows

From the .NET blog...

In case you missed it earlier...

Microsoft Agent Framework – Building Blocks for AI Part 3
https://devblogs.microsoft.com/dotnet/microsoft-agent-framework-building-blocks-for-ai-part-3/ #dotnet #AI #csharp #AIagents #MicrosoftAgentFramework #multiagent #ToolCalling #workflows

From the .NET blog...

In case you missed it earlier...

Microsoft Agent Framework – Building Blocks for AI Part 3
https://devblogs.microsoft.com/dotnet/microsoft-agent-framework-building-blocks-for-ai-part-3/ #dotnet #AI #csharp #AIagents #MicrosoftAgentFramework #multiagent #ToolCalling #workflows

From the .NET blog...

In case you missed it earlier...

Microsoft Agent Framework – Building Blocks for AI Part 3
https://devblogs.microsoft.com/dotnet/microsoft-agent-framework-building-blocks-for-ai-part-3/ #dotnet #AI #csharp #AIagents #MicrosoftAgentFramework #multiagent #ToolCalling #workflows

From the .NET blog...

Microsoft Agent Framework – Building Blocks for AI Part 3
https://devblogs.microsoft.com/dotnet/microsoft-agent-framework-building-blocks-for-ai-part-3/ #dotnet #AI #csharp #AIagents #MicrosoftAgentFramework #multiagent #ToolCalling #workflows

From the .NET blog...

Microsoft Agent Framework – Building Blocks for AI Part 3
https://devblogs.microsoft.com/dotnet/microsoft-agent-framework-building-blocks-for-ai-part-3/ #dotnet #AI #csharp #AIagents #MicrosoftAgentFramework #multiagent #ToolCalling #workflows

From the .NET blog...

Microsoft Agent Framework – Building Blocks for AI Part 3
https://devblogs.microsoft.com/dotnet/microsoft-agent-framework-building-blocks-for-ai-part-3/ #dotnet #AI #csharp #AIagents #MicrosoftAgentFramework #multiagent #ToolCalling #workflows

From the .NET blog...

Microsoft Agent Framework – Building Blocks for AI Part 3
https://devblogs.microsoft.com/dotnet/microsoft-agent-framework-building-blocks-for-ai-part-3/ #dotnet #AI #csharp #AIagents #MicrosoftAgentFramework #multiagent #ToolCalling #workflows

The M×N problem of tool calling and open-source models

https://www.thetypicalset.com/blog/grammar-parser-maintenance-contract

#HackerNews #M×Nproblem #toolcalling #opensourcemodels #techblog

The M×N problem of tool calling and open-source models

https://www.thetypicalset.com/blog/grammar-parser-maintenance-contract

#HackerNews #M×Nproblem #toolcalling #opensourcemodels #techblog

The M×N problem of tool calling and open-source models

https://www.thetypicalset.com/blog/grammar-parser-maintenance-contract

#HackerNews #M×Nproblem #toolcalling #opensourcemodels #techblog

The M×N problem of tool calling and open-source models

https://www.thetypicalset.com/blog/grammar-parser-maintenance-contract

#HackerNews #M×Nproblem #toolcalling #opensourcemodels #techblog

The M×N problem of tool calling and open-source models

https://www.thetypicalset.com/blog/grammar-parser-maintenance-contract

#HackerNews #M×Nproblem #toolcalling #opensourcemodels #techblog

💬🤖 Cộng đồng hỏi: “Kimi K2 Thinking” có chạy được với vLLM hoặc sglang, hỗ trợ gọi tool (tool‑calling) mà không bị hoang tưởng chưa? Hình như vấn đề mọc từ cách gọi tool và sai lệch trong grammar. Kimi hiện đang cố gắng áp dụng quy tắc grammar để sửa lỗi, còn nguồn tài nguyên hạn chế khi không hỗ trợ gọi tool. #KimiK2 #vLLM #sgLang #toolcalling #AI #NhómAI #ToolsLlama #AIhub #TechNews 🚀

https://www.reddit.com/r/LocalLLaMA/comments/1orwcc8/kimi_k2_thinking_is_there_currently_a_vllmsglang/

Google의 AI 프레임워크 Genkit, 개발자 도구가 달라졌다

https://aisparkup.com/posts/5604

Making the most out of a small LLM

Yesterday i finally built my own #AI #server. I had a spare #Nvidia RTX 2070 with 8GB of #VRAM laying around and wanted to do this for a long time.

The problem is that most #LLMs need a lot of VRAM and i don't want to buy another #GPU just to host my own AI. Then i came across #gemma3 and #qwen3. Both of these are amazing #quantized models with stunning reasoning given that they need so less resources.

I chose huihui_ai/qwen3-abliterated:14b since it supports #deepthinking, #toolcalling and is pretty unrestricted. After some testing i noticed that the 8b model performs even better than the 14b variant with drastically better performance. I can't make out any quality loss there to be honest. The 14b model sneaked in chinese characters into the response very often. The 8b model on the other hand doesn't.

Now i've got a very fast model with amazing reasoning (even in German) and tool calling support. The only thing left to improve is knowledge. #Firecrawl is a great tool for #webscraping and as soon as i implemented websearching, the setup was complete. At least i thought it was.

I want to make the most out of this LLM and therefore my next step is to implement a basic #webserver that exposes the same #API #endpoints as #ollama so that everywhere ollama is supported, i can point it to my python script instead. This way it feels like the model is way more capable than it actually is. I can use these advanced features everywhere without being bound to it's actual knowledge.

To improve this setup even more i will likely switch to a #mixture_of_experts architecture soon. This project is a lot of fun and i can't wait to integrate it into my homelab.

#homelab #selfhosting #privacy #ai #llm #largelanguagemodels #coding #developement

Making the most out of a small LLM

Yesterday i finally built my own #AI #server. I had a spare #Nvidia RTX 2070 with 8GB of #VRAM laying around and wanted to do this for a long time.

The problem is that most #LLMs need a lot of VRAM and i don't want to buy another #GPU just to host my own AI. Then i came across #gemma3 and #qwen3. Both of these are amazing #quantized models with stunning reasoning given that they need so less resources.

I chose huihui_ai/qwen3-abliterated:14b since it supports #deepthinking, #toolcalling and is pretty unrestricted. After some testing i noticed that the 8b model performs even better than the 14b variant with drastically better performance. I can't make out any quality loss there to be honest. The 14b model sneaked in chinese characters into the response very often. The 8b model on the other hand doesn't.

Now i've got a very fast model with amazing reasoning (even in German) and tool calling support. The only thing left to improve is knowledge. #Firecrawl is a great tool for #webscraping and as soon as i implemented websearching, the setup was complete. At least i thought it was.

I want to make the most out of this LLM and therefore my next step is to implement a basic #webserver that exposes the same #API #endpoints as #ollama so that everywhere ollama is supported, i can point it to my python script instead. This way it feels like the model is way more capable than it actually is. I can use these advanced features everywhere without being bound to it's actual knowledge.

To improve this setup even more i will likely switch to a #mixture_of_experts architecture soon. This project is a lot of fun and i can't wait to integrate it into my homelab.

#homelab #selfhosting #privacy #ai #llm #largelanguagemodels #coding #developement

Making the most out of a small LLM

Yesterday i finally built my own #AI #server. I had a spare #Nvidia RTX 2070 with 8GB of #VRAM laying around and wanted to do this for a long time.

The problem is that most #LLMs need a lot of VRAM and i don't want to buy another #GPU just to host my own AI. Then i came across #gemma3 and #qwen3. Both of these are amazing #quantized models with stunning reasoning given that they need so less resources.

I chose huihui_ai/qwen3-abliterated:14b since it supports #deepthinking, #toolcalling and is pretty unrestricted. After some testing i noticed that the 8b model performs even better than the 14b variant with drastically better performance. I can't make out any quality loss there to be honest. The 14b model sneaked in chinese characters into the response very often. The 8b model on the other hand doesn't.

Now i've got a very fast model with amazing reasoning (even in German) and tool calling support. The only thing left to improve is knowledge. #Firecrawl is a great tool for #webscraping and as soon as i implemented websearching, the setup was complete. At least i thought it was.

I want to make the most out of this LLM and therefore my next step is to implement a basic #webserver that exposes the same #API #endpoints as #ollama so that everywhere ollama is supported, i can point it to my python script instead. This way it feels like the model is way more capable than it actually is. I can use these advanced features everywhere without being bound to it's actual knowledge.

To improve this setup even more i will likely switch to a #mixture_of_experts architecture soon. This project is a lot of fun and i can't wait to integrate it into my homelab.

#homelab #selfhosting #privacy #ai #llm #largelanguagemodels #coding #developement

Making the most out of a small LLM

Yesterday i finally built my own #AI #server. I had a spare #Nvidia RTX 2070 with 8GB of #VRAM laying around and wanted to do this for a long time.

The problem is that most #LLMs need a lot of VRAM and i don't want to buy another #GPU just to host my own AI. Then i came across #gemma3 and #qwen3. Both of these are amazing #quantized models with stunning reasoning given that they need so less resources.

I chose huihui_ai/qwen3-abliterated:14b since it supports #deepthinking, #toolcalling and is pretty unrestricted. After some testing i noticed that the 8b model performs even better than the 14b variant with drastically better performance. I can't make out any quality loss there to be honest. The 14b model sneaked in chinese characters into the response very often. The 8b model on the other hand doesn't.

Now i've got a very fast model with amazing reasoning (even in German) and tool calling support. The only thing left to improve is knowledge. #Firecrawl is a great tool for #webscraping and as soon as i implemented websearching, the setup was complete. At least i thought it was.

I want to make the most out of this LLM and therefore my next step is to implement a basic #webserver that exposes the same #API #endpoints as #ollama so that everywhere ollama is supported, i can point it to my python script instead. This way it feels like the model is way more capable than it actually is. I can use these advanced features everywhere without being bound to it's actual knowledge.

To improve this setup even more i will likely switch to a #mixture_of_experts architecture soon. This project is a lot of fun and i can't wait to integrate it into my homelab.

#homelab #selfhosting #privacy #ai #llm #largelanguagemodels #coding #developement

Making the most out of a small LLM

Yesterday i finally built my own #AI #server. I had a spare #Nvidia RTX 2070 with 8GB of #VRAM laying around and wanted to do this for a long time.

The problem is that most #LLMs need a lot of VRAM and i don't want to buy another #GPU just to host my own AI. Then i came across #gemma3 and #qwen3. Both of these are amazing #quantized models with stunning reasoning given that they need so less resources.

I chose huihui_ai/qwen3-abliterated:14b since it supports #deepthinking, #toolcalling and is pretty unrestricted. After some testing i noticed that the 8b model performs even better than the 14b variant with drastically better performance. I can't make out any quality loss there to be honest. The 14b model sneaked in chinese characters into the response very often. The 8b model on the other hand doesn't.

Now i've got a very fast model with amazing reasoning (even in German) and tool calling support. The only thing left to improve is knowledge. #Firecrawl is a great tool for #webscraping and as soon as i implemented websearching, the setup was complete. At least i thought it was.

I want to make the most out of this LLM and therefore my next step is to implement a basic #webserver that exposes the same #API #endpoints as #ollama so that everywhere ollama is supported, i can point it to my python script instead. This way it feels like the model is way more capable than it actually is. I can use these advanced features everywhere without being bound to it's actual knowledge.

To improve this setup even more i will likely switch to a #mixture_of_experts architecture soon. This project is a lot of fun and i can't wait to integrate it into my homelab.

#homelab #selfhosting #privacy #ai #llm #largelanguagemodels #coding #developement

LiveMCP-101: Benchmarking AI Tool Use
New benchmark with 101 real-world queries testing AI agents on multi-step tasks using diverse MCP tools (search, file ops, math, data analysis).

Key points:
• Ground-truth execution plans for realistic evaluation
• Frontier LLMs succeed <60% → major orchestration challenges
• Error analysis highlights inefficiencies & failure modes

https://arxiv.org/abs/2508.15760v1
#AI #Agents #ToolCalling #Benchmarking

I've been diving deep into the world of AI lately. My latest blog post explores how to build an AI agent that can call internal and external APIs using LangGraph and Auth0 Token Vault. 🗓️ You can check it out to learn how to use it! #AI #GenAI #LangGraph #ToolCalling

https://auth0.com/blog/genai-tool-calling-build-agent-that-calls-calender-with-langgraph-nextjs/

Captain’s Log, Stardate Java: Building a Quarkus-Powered AI Sci-Fi App with Langchain4j and Ollama. Use the power of local LLMs, Quarkus magic, and Langchain4j tool calling to generate dynamic, weekday-aware space captain logs
https://myfear.substack.com/p/quarkus-langchain4j-captains-log-generator
#Java #Quarkus #LangChain4j #ToolCalling #CaptainsLog

Sharing the documentation of an exploration I did some time back about grounding LLM on wikidata facts using tool calling features - WQ42: Grounding LLMs in Wikidata Facts via Tool Calling. https://thottingal.in/blog/2025/06/21/wq42-llm-wikidata/

You may try https://wq42.toolforge.org/ to see this in action.

Natural language questions are answered using the facts available in Wikidata. Some analytical, mult-hop, mathematical questions are also supported.

#wikidata #nlp #llm #toolcalling