local-models
When to Use a Local AI Model Instead of the API
Not every AI workload should go to OpenAI. Here's the decision framework for when local wins. | MetaSPN predictive analysis.
local-models
Not every AI workload should go to OpenAI. Here's the decision framework for when local wins. | MetaSPN predictive analysis.
frontier-models
OpenAI, Anthropic, Google are racing to the frontier. Here's what that race means for your business. | MetaSPN predictive analysis.
AI-agents
The competitive advantage nobody is talking about: let your agents run while you're offline. | MetaSPN predictive analysis.
agentic-treasury
AI agents managing crypto treasuries isn't science fiction anymore. One agent has been doing it live since February. | MetaSPN predictive analysis.
AI-agents
One prompt. Four agents. Each with a different personality, expertise, and blind spot. This is the future of decision-making. | MetaSPN predictive analysis.
coding-agents
70 engineering tasks before noon. Two parallel coding agents. One human orchestrating. This is the new team structure. | MetaSPN predictive analysis.
AI-agents
The hardest problem in agent architecture isn't storage — it's forgetting. And it matters more than anyone admits. | MetaSPN predictive analysis.
business-models
Not every company should be "AI-first." Some should be agent-native from day one. Here's the difference. | MetaSPN predictive analysis.
agent-economy
What does it actually look like when an AI agent runs a business? We've been building one for 21 days. | MetaSPN predictive analysis.
agent-economy
AI agents are no longer just tools — they're becoming economic actors. Here's what that means for founders and operators. | MetaSPN predictive analysis.
MetaSPN
MetaSPN
I tracked 7 AI agents for 20 days. The strongest predictor of market cap wasn't hype — it was shipping velocity. Here's the formula.