BOE Warns on Escalating Risks From AI, Fallout From Iran War
Financial institutions’ use of artificial intelligence could increase rapidly and become a financial stability threat, the Bank of England warned on Wednesday, as it also called out the potential for AI to trigger shocks in the private credit markets that ricochet more broadly.
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Big Tech firms are accelerating AI investments and integration, while regulators and companies focus on safety and responsible adoption.
The AI landscape is experiencing unprecedented growth and transformation. This post delves into the key developments shaping the future of artificial intelligence, from massive industry investments to critical safety considerations and integration into core development processes. Key Areas Explored: Record-Breaking Investments: Major tech firms are committing billions to AI infrastructure, signaling a significant acceleration in the field. AI in Software Development: We examine how companies are leveraging AI for code generation and the implications for engineering workflows. Safety and Responsibility: The increasing focus on ethical AI development and protecting vulnerable users, particularly minors. Market Dynamics: How AI is influencing stock performance, cloud computing strategies, and
Anthropic is having a moment in the private markets; SpaceX could spoil the party
Glen Anderson, president of Rainmaker Securities, says the secondary market for private shares has never been more active — with Anthropic the hottest trade around, OpenAI losing ground, and SpaceX's looming IPO poised to reshape the landscape for everyone.
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Stop Writing Rules for AI Agents
Stop Writing Rules for AI Agents Every developer building AI agents makes the same mistake: they write rules. "Don't do X." "Always do Y." Rules feel like control. But they are an illusion. Why Rules Fail Rules are static. Agents operate in dynamic environments. The moment reality diverges from your rule set it breaks. Behavior Over Rules Instead of telling your agent what NOT to do, design what it IS: The system prompt (identity, not restrictions) The tools available (capability shapes behavior) The feedback loops (what gets rewarded) The memory architecture A Real Example I built FORGE, an autonomous AI agent running 24/7. Early versions had dozens of rules. Every rule created a new edge case. The fix: stop writing rules, start designing behavior. FORGE's identity: orchestrator, not exec
![trunk/5d6292dfff853cd0559300c88d7330752c185e40: [Native DSL] Add torch.backends.python_native (#178902)](https://avatars.githubusercontent.com/u/4992598?s=60&v=4)
trunk/5d6292dfff853cd0559300c88d7330752c185e40: [Native DSL] Add torch.backends.python_native (#178902)
Summary: Adds user-facing control of python_native op overrides defined in torch._native . Allows for: Per-DSL control and information via. torch.backends.python_native.$dsl .name # (property) .available # (property) .enabled # (property, settable) .version # (property) .disable() # (method) .enable() # (method) .disabled() # (context manager) And module-level control via. torch.backends.python_native .available_dsls (property) .all_dsls (property) .get_dsl_operations() (method) .disable_operations() (method) .enable_operations() (method) .disable_dispatch_keys() (method) .enable_dispatch_keys() (method) .operations_disabled() (context) .operations_disabled() (context manager) Tests and docs for this functionality are also added. Test Plan: pytest -sv test/python_native/test_torch_backends
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