值得注意的是，這個公開調研參與者中，有超過40%來自吉林這一個省份，參與年齡也並未言明。但即便在這樣一個樣本高度單一的模糊調研裡，依然過半民眾期望「兩會」有更多討論和就業相關，能夠通過一些政策改善就業環境。還有過六成網民要求保障婚育權益，對婚戀、生育、養育家庭提出要求——而關於婚育具體要求是什麼，即便有六成網友在熱烈討論，具體的議程、建議也被一筆帶過了。
What happens when you ask a 2026 coding agent like Claude Code to build a chess engine from scratch (with no plan, no architecture document, no step-by-step guidance) in a language that was never designed for this purpose? Building a chess engine is a non-trivial software engineering challenge: it involves board representation, move generation with dozens of special rules (castling, en passant, promotion), recursive tree search with pruning, evaluation heuristics, as well as a way to assess engine correctness and performance, including Elo rating. Doing it from scratch, with minimal human guidance, is a serious test of what coding agents can do today. Doing it in LaTeX’s macro language, which has no arrays, no functions with return values, no convenient local variables or stack frames, and no built-in support for complex data structures or algorithms? More than that, as far as I can tell, it has never been done before (I could not find any existing TeX chess engine on CTAN, GitHub, or TeX.SE). Yet, the coding agent built a functional chess engine in pure TeX that runs on pdflatex and reaches around 1280 Elo (the level of a casual tournament player). This post dives deep into how this engine, called TeXCCChess, works, the TeX-specific challenges encountered during development. You can play against it in Overleaf (see demo https://youtu.be/ngHMozcyfeY) or your local TeX installation https://youtu.be/Tg4r_bu0ANY, while the source code is available on GitHub https://github.com/acherm/agentic-chessengine-latex-TeXCCChess/
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当地时间3月8日晚，伊朗总统佩泽希齐扬同阿塞拜疆总统阿利耶夫通电话，就地区最新局势发展交换意见。
，更多细节参见谷歌
mog_vm_set_global(vm);，详情可参考wps
Трамп обвинил Иран в обстреле иранской школы для девочек00:37