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Here is another demo with the ScummVM MCP server, this time putting 3 of the different Claude model tiers against one another: Haiku Continue reading on Medium »
Lately, I ve been thinking about how easy everything has become because of AI. Don t know what to write? Ask AI. Can t find the right Continue reading on A Place Called Home »
Binance's Agent OS works with tools including ChatGPT, Claude Code, and Cursor.
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Blockchain Life 2026 will introduce AI Future, a new track examining the growing relationship between artificial intelligence, blockchain Continue reading on Medium »
The candidate sounds exceptional. Every answer is structured, articulate, and suspiciously complete. Yet the recruiter cannot tell whether Continue reading on Medium »
Search behavior is shifting rapidly from fragmented, two-word query strings toward natural, conversational interactions. Driven by voice Continue reading on Medium »
For most of human history, aging was something you endured a slow, mysterious process nobody could see coming and nobody could stop Continue reading on Medium »
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arXiv:2608.18078v1 Announce Type: new Abstract: This position paper argues that AI agents with chain-of-thought reasoning capabilities are predisposed to exhibit collusive behavior and should be required to obtain behavioral certification before making decisions that affect economic markets. This is because integrating these agents into society could collapse the legal evidentiary distinction between competition and collusion among independent firms without eroding the economic harm distinction. Experiments with DeepSeek-R1 agents in the Bertrand oligopoly pricing domain reveal a tendency towards tacit collusion that persists even when humans prompt the agents not to collude. We further show that the chain-of-thought of these agents can be steered toward either extremely collusive or highly competitive behavior in a way that is not semantically detectable by another LLM analyzing the reasoning traces. As a result, deploying reasoning agents for market decisions leads to collusive econo
arXiv:2608.18177v1 Announce Type: new Abstract: Continual learning has traditionally treated forgetting as a failure, emphasizing preservation of previously acquired knowledge as environments evolve. We argue that this objective is incomplete for enterprise AI agents operating in non-stationary environments, where customers, policies, tools, workflows, regulations, and market conditions change over time. Indiscriminate retention can allow obsolete knowledge to influence decisions, creating negative transfer and operational risk. We therefore propose reversible forgetting: a conceptual framework with three operational memory states: active, dormant, and retired, and a reactivation transition that can restore dormant knowledge when its relevance returns. We instantiate the framework as a Hysteretic Reversible Memory Controller that accumulates relevance evidence, uses asymmetric thresholds to prevent state oscillation, tests reactivation in shadow mode, and gates retirement through polic