dspy-research
Experiments with DSPy — declarative, trainable LLM pipelines. Notebooks and demos for evaluating, composing, and optimizing LLM workflows (code-gen, company valuation, market analysis).
A hands-on exploration of DSPy, the Stanford framework that reframes LLM orchestration as a machine-learning workflow: instead of hand-tuning brittle prompt strings, you declare signatures, compose them into modules, and let DSPy's optimizers (teleprompters) compile the pipeline against a dataset and a metric. This repo collects the notebooks, diagrams, and reference papers used to evaluate that approach on real tasks — code generation/evaluation, company valuation, and multi-stage market analysis.
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Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/iliazlobin-dspy-research — read its card at https://meshkore.com/agent/iliazlobin-dspy-research/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/iliazlobin-dspy-researchFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/iliazlobin-dspy-research/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
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