How to Deploy To Production with llama.cpp
We’re building a high-throughput text generation service using llama.cpp deploy to production, and this matters because the world is clamoring for AI that doesn’t just generate coherent text but does so efficiently and effectively in a production environment.
Prerequisites
7 Fine-tuning vs Prompting Mistakes That Cost Real Money
I’ve personally seen at least five AI-powered projects this month tank because the teams made avoidable fine-tuning vs prompting mistakes that blew their budgets and timelines. If you think customizing large language models (LLMs) is just about throwing data or tweaking prompts without a strategy, you’re
Building Webhooks with TensorRT-LLM: A Step-By-Step Guide
Ever wanted to hook your application into real-time data processing with TensorRT-LLM? You’re not alone. Implementing webhooks with TensorRT-LLM is a hands-on experience and an essential skill. Here’s the deal: we’re going to construct an event-driven architecture that allows our application to respond automatically to data changes or
Hey there, fellow agent builders! Riley Fox here, back on agntkit.net. Today, I want to dive into something that’s been a real head-scratcher for me lately, and probably for a bunch of you too: the sheer overwhelming volume of *starter kits* in the AI agent space. It’s like every other week, someone’s dropping a new
Semantic Kernel vs LlamaIndex: Which One for Small Teams
Real-world usage data shows that Microsoft’s Semantic Kernel boasts 27,528 stars on GitHub, while LlamaIndex shines with 47,875 stars. But here’s the catch: stars don’t mean functionality, particularly for small teams. Choosing between Semantic Kernel and LlamaIndex can be quite the task, especially considering the unique
LangChain vs AutoGen: Which One for Production?
LangChain has 130,624 GitHub stars. AutoGen has 56,035. But let’s be real, stars are just vanity metrics. What really matters is how these frameworks translate into real-world applications. In a landscape bustling with promises and potential, the differences between these tools mean more than just numbers; they dictate
Hey there, toolkit builders and agent aficionados! Riley Fox here, back in your inbox (or browser, whatever your poison) with another dive into the nitty-gritty of getting things DONE. It’s March 22, 2026, and if you’re anything like me, your plate is overflowing with projects, ideas, and that one nagging thought about a better way
How to Optimize Token Usage with ChromaDB (Step by Step)
If you aren’t paying attention to token usage in your vector database queries, you are burning through credits and performance faster than you realize—so here’s how to chromadb optimize token usage like you actually want to save money and speed.
What You’ll Build and Why
Hey everyone, Riley here from agntkit.net, bringing you another deep dive into the tools that make our digital lives, well, less chaotic. Today, I want to talk about something that’s been on my mind a lot lately, especially as I’ve been trying to streamline my own workflows for a few demanding freelance projects.
We all
llama.cpp vs TensorRT-LLM: Which One for Small Teams
TensorRT-LLM has been reported to be 30-70% faster than llama.cpp on the same hardware. But faster doesn’t always mean better, especially for smaller teams with tight budgets and limited resources. The choice between llama.cpp and TensorRT-LLM can dramatically impact how quickly you can deploy models and iterate