mmullins.coginiti.co reposted
Forthcoming soon: AI and Consciousness. Email me if you'd like to see the (uncorrected) proofs, or view the final public MS version at arxiv.org/abs/2510.09858
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mmullins.coginiti.co
technologist, philosopher, and outdoors enthusiast
CTO@COGINITI
What they're sharing
AI and Consciousness arxiv.org
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Philosophers in the Machine - Matthew Mullins open.substack.com
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Philosophers in the Machine - Matthew Mullins open.substack.com
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Process large datasets with LLM blocks | Coginiti Documentation docs.coginiti.co
Building an AI-Powered Data Pipeline with LLM Blocks | Coginiti Documentation docs.coginiti.co
Articles & links
mmullins.coginiti.co reposted
This by @mmullins.coginiti.co is so good. Summary: philosophers have had a place, especially around metaphysics, in tech, but with LLMs epistemology and ethics take center stage and are no long just a necessary foundation, but a product differentiator open.substack.com/pub/msm…
mmullins.coginiti.co reposted
This by @mmullins.coginiti.co is so good. Summary: philosophers have had a place, especially around metaphysics, in tech, but with LLMs epistemology and ethics take center stage and are no long just a necessary foundation, but a product differentiator open.substack.com/pub/msm…
Two more reasons Coginiti 26.6's LLM blocks hold up: 1. Same block runs on Snowflake, Databricks, BigQuery, Redshift, Postgres, SQL Server, and with OpenAI, Claude, Gemini, or local Ollama. 2. Batching patterns keep prompts bounded, so it scales from 50 rows to 50k.
The thing I like most about Coginiti 26.6's LLM blocks: the model's output is typed, so it's queryable immediately. JOIN it against real tables. Write tests that validate it. Cache it. Schedule it. AI output held to the same bar as every other transform in your pipeline.
Their own posts
Recent commentary
Coginiti 26.6 is out and you can now call an LLM directly inside CoginitiScript. New llm block type, sits next to your SQL. Write a prompt, declare a typed schema, get structured rows back, then query them downstream like any other table. The model's a pipeline step now, not a bolt-on.
finance ARR ≠ sales ARR ≠ marketing ARR none of them are wrong. they're all "correct" against three different definitions. now ask an AI agent for revenue and watch it pick a fourth.
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