BettaFish/MiroFish and OpenClaw push agents against two trust boundaries: how much we can trust what AI says, and how much we let AI do inside our environments.
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Claude Code or Codex: How Coding Model Differences Become Product Experience
A developer-facing review of Claude and GPT coding differences, and how those differences show up in agent runtimes, task boundaries, context organization, and workflow experience.
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Spec Is Not the New Paradigm: Vibe Coding, SDD, and Software Engineering in the AI Era
AI-era software engineering is not moving toward spec-first. As code generation costs collapse, it shifts toward feedback-first through prototypes, integration feedback, and living constraints.
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What Is Harness? From Model Plus Harness to Engineering, Product, and User-Friendly Shells
LangChain’s agent equals model plus harness framing is useful only at a coarse level. In real engineering, harness is better understood through engineering, product, and task interfaces.
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Three Short Sci-Fi Stories
Three short science-fiction pieces inspired by an Anthropic account ban, AI-managed society, and a new class divide beyond wealth. The outlines and prose were assisted by LLMs.
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Generative AI Will Not End Work Directly; It Rearranges Labor and Expands Demand First
Generative AI is more likely to compress old tasks, rearrange labor, and create new services and demand than directly eliminate work.
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From Memory Formation to Governance: A Panorama of Agent Memory
Memory is not an external database but the lifecycle management of long-term cognitive state—its formation, activation, update, and governance. This piece maps the panorama of agent memory: five memory types, the activation/write ends, how the field evolved, and why evaluation still lags behind methods, with hard problems in write correctness, temporal validity, conflict resolution, forgetting, and system-level evaluation.
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Putting Deterministic Guardrails Around LLMs: Agent Harness Engineering from Claude Code
What makes agents deliverable is not only the core loop, but the surrounding engineering that turns language requests into tool contracts, routing, validation, isolation, recovery, and governance.
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From LSH to K-Center Greedy: Semantic Embeddings for Deduplication, Cleaning, and Sample Selection
Semantic embeddings are not only for retrieval. LSH filtering, Faiss deduplication, and K-center greedy sampling all use the same representation space for redundancy and coverage.
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The Evolution of Reward Design: From RLHF to RLVR
A survey of how rewards evolve from preference pairs, verifiable outcomes, process supervision, rubrics, and open-agent ranking, gradually redefining what a good answer means.