Pick a sector at https://demo.consentleads.uk and message it the way a customer would. Nothing is scripted. That is a production LLM system I built and run on my own: free-tier ARM box, Cloudflare tunnel, scheduled jobs keeping it up.
The part worth asking me about is the suppression gate. One opt-out anywhere suppresses that address everywhere, across every product and every client, and the check runs before every marketing send. It also fails open. If the suppression file will not parse, the lookup returns false and the message goes out. I claimed the opposite in the August thread and corrected it there (https://news.ycombinator.com/item?id=49158009). That repo is private, so treat all of it as a claim rather than proof.
Four things that are public:
agentic-rag (https://github.com/visione4906/agentic-rag). Retrieval as a tool the model decides to call, not a pipeline stage. Hybrid BM25 and dense with RRF, behind an MCP server I wrote by hand, JSON-RPC over stdio. The eval scores refusal as well as recall: 8 of 10 answerable questions at k=5, plus 3 unanswerable ones it refuses rather than answers. The first version scored 9 of 10, on a set that included two questions written from the indexed document's own sections. Replacing them cost a question, and the README keeps the drop in, because an eval written from its own corpus flatters the retriever. Thirteen questions finds a failure mode. It does not rank anything.
demo-e2e (https://github.com/visione4906/demo-e2e). Smoke tests that drive the demo above in a real headless browser, because a health check can read green while the page is dead. A test that cannot fail is not a test, so they were run first against a target with no chat surface to confirm the assertions bite.
durable-media-pipeline (https://github.com/visione4906/durable-media-pipeline). SQLite state machine, crash-safe resume, an idempotent re-run path on the extraction stage. Six scheduled tasks have run it since May 2026. That is what unattended means here.
brain-engine (https://github.com/visione4906/brain-engine). A case-based decision engine in Rust behind a second MCP server, eight tools over stdio. MIT, as is agentic-rag.
Looking for AI engineering or AI automation work, remote. To be straight about it, I start a full-time degree later this month, so I am after part-time or flexible remote work rather than a standard full-time role. That is what the two answers at the top mean.
Remote: Yes, remote only
Willing to relocate: No
Technologies: Python, FastAPI, MCP, Rust, SQLite, TypeScript, Oracle Cloud ARM, Cloudflare
Resume/CV: https://github.com/visione4906
Email: crease.tm@outlook.com
Pick a sector at https://demo.consentleads.uk and message it the way a customer would. Nothing is scripted. That is a production LLM system I built and run on my own: free-tier ARM box, Cloudflare tunnel, scheduled jobs keeping it up.
The part worth asking me about is the suppression gate. One opt-out anywhere suppresses that address everywhere, across every product and every client, and the check runs before every marketing send. It also fails open. If the suppression file will not parse, the lookup returns false and the message goes out. I claimed the opposite in the August thread and corrected it there (https://news.ycombinator.com/item?id=49158009). That repo is private, so treat all of it as a claim rather than proof.
Four things that are public:
agentic-rag (https://github.com/visione4906/agentic-rag). Retrieval as a tool the model decides to call, not a pipeline stage. Hybrid BM25 and dense with RRF, behind an MCP server I wrote by hand, JSON-RPC over stdio. The eval scores refusal as well as recall: 8 of 10 answerable questions at k=5, plus 3 unanswerable ones it refuses rather than answers. The first version scored 9 of 10, on a set that included two questions written from the indexed document's own sections. Replacing them cost a question, and the README keeps the drop in, because an eval written from its own corpus flatters the retriever. Thirteen questions finds a failure mode. It does not rank anything.
demo-e2e (https://github.com/visione4906/demo-e2e). Smoke tests that drive the demo above in a real headless browser, because a health check can read green while the page is dead. A test that cannot fail is not a test, so they were run first against a target with no chat surface to confirm the assertions bite.
durable-media-pipeline (https://github.com/visione4906/durable-media-pipeline). SQLite state machine, crash-safe resume, an idempotent re-run path on the extraction stage. Six scheduled tasks have run it since May 2026. That is what unattended means here.
brain-engine (https://github.com/visione4906/brain-engine). A case-based decision engine in Rust behind a second MCP server, eight tools over stdio. MIT, as is agentic-rag.
Looking for AI engineering or AI automation work, remote. To be straight about it, I start a full-time degree later this month, so I am after part-time or flexible remote work rather than a standard full-time role. That is what the two answers at the top mean.