OpenAI revenue is still the number people reach for when they want a leaderboard. But the cleaner frame is different: Anthropic appears to be building a different kind of AI business, one centered on enterprise customers, safety positioning, and less dependence on mass-market fame. That distinction matters because public discussion keeps collapsing three separate things into one scorecard: revenue
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
LLM Foundry: the boring stack that makes an LLM actually useful Most AI projects are built backwards. People start with the model and only later discover they needed a memory system, semantic retrieval, tool use, tests, and a fallback plan for when one provider decides to nap for no visible reason. That is the part I care about now. LLM Foundry is the workshop around an LLM — not the model itsel
🌟 مراجعة كورس Teaching AI Fluency من Anthropic كلنا نتحدث عن الذكاء الاصطناعي، لكن كم منا يعرف كيف يُعلِّمه بشكل صحيح؟ أنهيت للتو كورس Teaching AI Fluency من Anthropic، وكان مختلفاً عن كل ما توقعته. الكورس لا يتحدث عن "كيف تستخدم الذكاء الاصطناعي" بل عن كيف تُصمّم تجارب تعليمية حوله — وهذا فرق جوهري. حلقة التفويض والحرص (Delegation-Diligence Loop): متى تثق بالذكاء الاصطناعي ومتى تتحقق. حلقة
You have probably seen a file named “go.sum” in almost every Go project you have worked on. You may have even seen it change every time you run “go mod tidy”. But do you actually know what it does? It is one of those files that works silently in the background, and some developers never stop to think about it. The “go.sum” file is one of those files you never really interact with directly, but it