Enterprise AI strategy, platforms and multi-agent research
Building intelligent, private systems
Building enterprise AI platforms and researching multi-agent systems. Most of the work comes back to the same question: what can run privately, and what do you end up renting from somebody else.
Focus Areas: Enterprise AI Platforms · LLMs & GenAI · MLOps · AI Governance · Multi-Agent Systems
📦 snugZero-dependency TypeScript primitive for fitting prioritised content into a token budget. Atomic tool pairs, required items, and a validated result you can hand straight to a model. |
Phase 1 · A text-level blackboard hive with cross-inhibition and a structurally protected dissenter (Carol). Agents debate, challenge each other and converge, with one voice that cannot be silenced. Phase 2 · Agents communicate through a shared continuous-valued latent buffer rather than text, using BAPC codecs, TIES-Resolve conflict resolution and damped fixed-point iteration. Emergent collective representations confirmed at GPT-2 and 7B scale. Private for now. |
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Adapters for the snug token budget library. Drop-in fits for OpenAI, Anthropic and tiktoken. |
🔀 convergeZero-dependency TypeScript primitive for converting LLM message arrays between provider formats. OpenAI, Anthropic and Gemini, in either direction. Pure data transformation, no API calls. |
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AI colour grading for Adobe Lightroom Classic. Describe a look in plain language, hand it a reference photo, or let it choose for you. Ashla reads your image and applies the result as a develop preset. |
The governance layer for AI coding agents. Raw tickets in, reviewed pull requests out. Every step is policy-gated, human-approved and audited, with the agent as the muscle and Foundry as the brain and the seatbelt. |
I write at jeremysnr.github.io about AI infrastructure, sovereign compute and the industrial decisions Britain is making, or failing to make.
Latest: Now we have the full details, here's what happened, and here's why it terrifies me




