Tag: AI
Oregon AI Accelerator reveals inaugural cohort
I’ve been nudging folks to see if anyone had any details about who was selected for the new Oregon AI Accelerator. And all of that nudging finally paid off. Curious — like I was — about who they selected…? Here’s the list of the 20 companies they picked.
Bringing “tech’s biggest All Stars” to Portland
One gripe I often hear about Portland folks is that we don’t tend to think big enough. It comes from a good place. Recognizing that we’ve got a ton of creativity, talent, and potential. We just don’t always have the risk tolerance. But that may be changing if AJ Green of the AI Collective has anything to say about it.
Portland startup AheadComputing raises $30 million
I’ve been talking about the hardware renaissance that’s beginning to take shape around here. You see, as AI computing resources become more and more scarce, I hypothesize that we’re going to see people working to advance the technology to make things more efficient and effective. And a local startup helmed by Intel alums is definitely running that playbook. AheadComputing just raised $30 million bringing it to $53 million raised in total.
Beyond Context Graphs: Why 2026 Must Be the Year of Agentic Memory, Causality, and Explainability
The context graph debate is important, but it’s just the beginning. The real revolution comes when agents can not only access past decisions, but truly learn from them — building causal models, explaining their reasoning, and reflecting on their own performance. That’s when we’ll move from decision traces to genuine artificial intelligence.
Lex: A Formal Ontology Language for Property Graphs
Property graphs have become the dominant paradigm for modeling connected data, yet they lack the formal semantic rigor that RDF/OWL brought to knowledge representation. While RDF graphs are constrained by their triple-based structure, property graphs offer richer modeling capabilities through typed edges with properties. However, this expressiveness comes at a cost: the absence of standardized formal semantics and constraint languages that can reason about graph structure and properties simultaneously.