I wanted to see whether a weak local model could become genuinely useful without pretending the base model was magic. LLM Foundry is the stack around the model: memory, compression, semantic retrieval, provider support, and a benchmark harness. The core idea A useful model workflow usually looks like this: read the task recover relevant memory compress the clutter ask the model check the answer use tools if needed save traces benchmark the result That is the difference between a chatbot and something you can actually trust on real work. What changed The current version now has: embedding-based semantic retrieval multi-provider support for OpenAI-compatible and Anthropic endpoints compression + memory so long tasks can be shrunk into compact context agent traces that can become training data later benchmarks and harnesses so the system is measurable The measured part The proof pack shows: Benchmark pass rate: 50% Reasoning harness: 60% Coding harness: 100% Tool-use harness: 100% Memory harness: 100% That…