Instead of relying solely on external dev tooling, embed tool-calling capabilities directly within the base AI model so it can act as an "intellectual grunt" able to invoke developer-built tools in context.
We’re running out of tokens. We need to figure out a way to generate synthetic data that’s effective for pushing the frontier of intelligence in these models out.
It’s a recent innovation that models are trained specifically to excel at tool calling, reducing the need for selective design around tool capabilities.
A pipeline gathering real developer MCP examples to generate vast synthetic tool-calling data, judged by an LLM rubric and refined via reinforcement learning to optimize agentic tool use.
Models at Google DeepMind generate their own synthetic data via reinforcement learning to extend token limits and advance capabilities without external datasets.
Gemini and GPT-4.1 provide usable context windows while Llama 4’s advertised 10 million-token window remains non-functional in practice for developers.
Conflicting reports of a 2 million token context window versus 160 K highlight the importance of validating LLM context length with official API specs before relying on extended-context features.
Open-source coding LLMs like Moonshot’s Kimik K2 instruct can match or outperform closed models on SWEBench benchmarks, suggesting an opportunity to embed high-performance open models in IDEs and dev tools.
Unsloth’s open-source fine-tune optimization project provides a local version of the model, requiring about 245 GB of model data and roughly 1 TB of disk space for on-prem experimentation.
Moonshot’s trillion-parameter model uses a mixture-of-experts sparse attention design that activates only 32 billion parameters at once, demonstrating how sparse MoE can deliver large model capacity with reduced compute.
Downloading and running a 350GB AI model like Kimi requires serious on-premises hardware, so deployment planning must account for large resource needs.
Build a SaaS offering that provides real-time re-ranking of vector search results on the fly, reducing compute costs by avoiding full embedding recalculation.
Super Linked markets itself as a "vector computer" that enables rapid on-the-fly re-ranking of search query results without full recalculation of vector embeddings.
