Tech Trend Translator: The PM Brief 콘텐츠 아카이브

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  1. The agents that act before you ask
  2. AI video's Figma moment
  3. The attention bottleneck has a challenger
  4. Orchestration is the moat, not the model
  5. Voice AI drops the transcript
  6. The reasoning bill is coming due
  7. Stop paying your LangGraph bill on every request
  8. Your AI doesn't lie — it just never learned to doubt
  9. Your frozen model has headroom you haven't used
  10. MoE is the right architecture for on-device AI
  11. Vision encoders are the next pipeline to die
  12. Agent security must be baked in, not bolted on
  13. Four open-source models, one bottleneck, four different bets
  14. Your GPU dashboard is lying to you
  15. Your SKILL.md is a trainable parameter
  16. The 2× speedup your inference stack has been ignoring
  17. Microsoft built its own frontier model — and the vendor math just changed
  18. Anthropic's IPO: the $965B product playbook
  19. Nemotron 3 Ultra: speed is the new moat for agentic AI
  20. Claude is already building Claude — and the numbers are harder to dismiss than the headlines
  21. Your agent's memory system will cost more than your model calls
  22. Your reasoning API is billing you for thoughts the model could skip
  23. Agentic AI inference just got 2–7× cheaper
  24. Cosmos 3 just collapsed the physical AI pipeline
  25. The RLHF pipeline just became debuggable
  26. Text generation loses its left-to-right constraint
  27. 16x fewer input tokens, almost no accuracy loss
  28. Stop building harnesses. Build environments.
  29. Your agent isn't dumb. Your repair loop is missing.
  30. Your model isn't forgetting. It's overloaded.
  31. Your grammar constraint is a jailbreak
  32. Your benchmarks are lying to you
  33. Your multi-agent default is the bug.
  34. Frontier reasoning fits in 6.7 GB
  35. Nobel laureate joins Anthropic — read the infrastructure, not the hire
  36. Start with nothing: Hermes Agent's new setup mode signals where agent architecture is heading
  37. Sakana Fugu: a trained orchestrator bets that multi-agent coordination beats hand-rolled pipelines
  38. AI finds the bugs. Now it patches them too.
  39. Qualcomm buys the CUDA escape hatch
  40. Claude now wants your passport
  41. GPT-5.6 just got a gatekeeper
  42. Google's AI risk is execution
  43. GLM-5.2 changes the model math
  44. Local AI crossed the PM line
  45. Claude Science is the lock-in layer
  46. Copilot's Open-Weight Turn
  47. Microsoft sells the AI landing team
  48. Proof coding gets cheap
  49. Better models can break tools
  50. Agents need rails, not scale
  51. Auditable AI becomes a product feature
  52. Agents need blast doors
  53. Model routing is now risk routing
  54. The AI price war has started
  55. Run big models on spare GPUs
  56. Voice AI stops taking turns
  57. Agents need a state ledger
  58. Your LLM Needs a Language, Not Another Prompt
  59. The coding-agent shell is becoming open infrastructure
  60. GPT-Red turns prompt-injection defense into a self-play training loop
  61. Anthropic's latest agent audits show why agent safety needs a control plane
  62. Complexity-aware execution turns agent cost into a control loop
  63. Kimi K3 makes the harness part of the model
  64. Environment feedback becomes a training signal for AI agents
  65. Generative UI makes the interface part of the agent runtime
  66. Grounded code agents turn data pipelines into governed artifacts
  67. Diffusion LLMs make revision a product primitive
  68. Programmatic memory keeps the evidence an agent may need later
  69. Train agents in a world you never built
  70. Robot foundation models are learning the scaling-law playbook
  71. Agent skills are moving into the training loop
  72. Scientific coding agents are moving the bottleneck to verification
  73. Agent primitives turn multi-agent systems into reusable blocks
  74. The next agent speedup may come from calling the tool before the model finishes thinking
  75. Computer-use agents are entering the verification era: scores now need a state check
  76. Why data agents can beat coding agents before the first SQL query
  77. Qwen3.8-Max makes open-weight AI an agent-runtime decision
  78. Long-context models may need a temporary study session
  79. Your multimodal agent should compile memories before users ask
  80. Molt makes agentic RL a framework researchers can actually read
  81. WorldClaw turns text-to-3D into an editable world-building pipeline
  82. Qwen-MM-Plugins makes the agent harness multimodal, not just the model
  83. Argus makes long-running agents compound at runtime, not by changing the model
  84. AI agents can run the lab. They still cannot reliably choose the research.
  85. NVIDIA's Switchyard turns model routing into an agent runtime feature
  86. AutoDesign: The Next Agent Breakthrough May Be a Better Workshop, Not a Bigger Model
  87. The @skills Proposal: Stop Paying Prompt Space for Every Agent Skill
  88. Your AI evaluator can be right and still be persuadable
  89. Capability transfer is moving from model weights to the harness
  90. The Next Agent Security Boundary Is the Memory File
  91. RL training is becoming the product surface for agent teams
  92. StartupBench tests the gap between agent progress and usable work
  93. MidTool puts general tool use into the model's mid-training stage

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