标签：#ens

Hi! I want to share a project that I work for a while. It started from idea to get rid off manual copying data from game design documents to game engine. Here you can define your game objects, their props, relations and everything will be stored in structural JSON format that can be read by Unity, Godot, Unreal and other engines. What we have now? construct **wiki-like documents **using a block editor and template system (markdown is supported too) design dialogues of your game in special graph editor create maps and prototype levels on canvas store and manage database of game objects use created objects inside engine directly or export data to customizable data formats (arbitrary JSON, CSV) Made it free and open source. Please try (have Windows and Mac builds) and give your feedback Source code: https://github.com/ImStocker/ims-creators Itch.io: https://nordth.itch.io/imsc-desktop

Large language models are good at sounding structured. That is not the same as being structured. Ask an AI assistant to "use first principles" and it may produce a confident answer with the phrase "first principles" near the top. Ask it to "red-team this plan" and it may list generic risks. Ask it to "apply OODA" and it may give you four headings without doing the hard part: orienting against assumptions, constraints, and evidence. That failure mode is subtle because the answer looks responsible. It has the right vocabulary. It has the right shape. But the method did not actually control the analysis. I built methodology-toolkit to target that gap. The goal is not to add more clever prompts to Claude Code. The goal is to add a small layer of discipline around non-trivial decisions: classify the problem, choose methods that fit, apply those methods explicitly, verify load-bearing claims, and stress-test plans before they harden into action. Repository: https://github.com/gagharutyunyan1993/methodology-toolkit The Problem: Methodology Theater Methodologies are useful because they constrain attention. First Principles asks you to strip assumptions and rebuild from base facts. ACH asks you to compare competing hypotheses by disconfirming evidence, not by collecting confirmations for your favorite answer. OODA asks you to separate raw observation from orientation, where bias and context do most of the work. Pre-mortem asks you to imagine the plan has already failed so optimism does not screen out obvious risks. When an AI assistant merely names those methods, you get the cost without the benefit. The answer becomes longer, more formal, and more convincing, but not necessarily more correct. That is worse than a short intuitive answer because the structure creates false confidence. methodology-toolkit treats that as the core anti-pattern: If a method is named, its steps must be walked. Not hinted at. Not summarized. Applied. Methodology theater: right vocabulary, no method

c_std: A Leak-Free, Cross-Platform Standard Library for Modern C Bringing the comfort of the C++ STL and Python's standard library to C17 — without leaving C A technical white paper. Executive summary C is still the substrate of the computing world — kernels, databases, language runtimes, embedded firmware, and the inner loops of nearly everything else. Yet the moment you step away from the kernel and try to write ordinary application code in C, you feel the gap: no growable vector, no hash map, no JSON parser, no string type that doesn't invite a buffer overflow. You either pull in a grab-bag of mismatched third-party libraries, each with its own conventions and failure modes, or you re-implement the same dynamic array for the hundredth time. c_std is an attempt to close that gap deliberately and coherently. It is a single, consistent library — written in pure C17 — that reimplements a large slice of the C++ Standard Library (containers, algorithms, smart pointers) alongside many Python-style conveniences ( json , regex , random , statistics , csv , config , even turtle graphics). It targets Windows and Linux from one source tree, compiles cleanly under -Wall -Wextra , and — this is the part I care about most — is verified leak-free under Valgrind , module by module, example by example. This paper explains the design philosophy, the architecture, and the engineering discipline that makes a library like this trustworthy enough to build on. 1. The problem: C's missing middle Every C programmer knows the two extremes. At the bottom, the language itself: pointers, malloc , memcpy , raw arrays. At the top, whatever the platform hands you — <windows.h> or POSIX, OpenSSL, a JSON library someone wrapped a decade ago. The middle — the layer the C++ STL and Python's batteries-included standard library occupy — is missing. That missing middle has a real cost. It shows up as: Re-invention. Teams write their own vector, their own string builder, their own linked list, each subt

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