Buku ini berisi referensi untuk kuliah pemrograman bahasa assembly untuk tingkat perguruan tinggi. Set instruksi yang dibahasa adalah x86-64 untuk prosesor x86-64 menggunakan sistem operasi Ubuntu 64 bit.

Kode di buku ini diuji di Ubuntu versi 22.04 LTS

Source:

• http://www.egr.unlv.edu/~ed/assembly64.pdf
• mirror

Today we’re releasing new @AnthropicAI research on how educators use AI, analyzing ~74,000 conversations from professors using @claudeai in collaboration with Northeastern University.

4 initial findings…

1. Educators are builders, not just users of AI. Faculty are creating interactive chemistry simulations, grading rubrics, and data dashboards with Claude Artifacts.
2. Educators automate the drudgery while staying in the loop for almost everything else. 77% of teaching and classroom instruction uses are collaborative, while 65% of financial/fundraising tasks are fully delegated. High-touch educational work remains human-centered.
3. Notable tension in the data: 49% of grading conversations showed automation patterns, yet faculty rated this as AI’s least effective application. This disconnect highlights ongoing debates around appropriate AI use in assessment.
4. AI is forcing pedagogical change. Professors are completely redesigning assessments—one shared: “I will never again assign a traditional research paper,” instead creating assignments requiring critical thinking even with AI assistance.

Abstrak

Abstract: We present Jet-Nemotron, a new family of hybrid-architecture language models, which matches or exceeds the accuracy of leading full-attention models while significantly improving generation throughput. Jet-Nemotron is developed using Post Neural Architecture Search (PostNAS), a novel neural architecture exploration pipeline that enables efficient model design. Unlike prior approaches, PostNAS begins with a pre-trained full-attention model and freezes its MLP weights, allowing efficient exploration of attention block designs. The pipeline includes four key components: (1) learning optimal full-attention layer placement and elimination, (2) linear attention block selection, (3) designing new attention blocks, and (4) performing hardware-aware hyperparameter search. Our Jet-Nemotron-2B model achieves comparable or superior accuracy to Qwen3, Qwen2.5, Gemma3, and Llama3.2 across a comprehensive suite of benchmarks while delivering up to 53.6× generation throughput speedup and 6.1× prefilling speedup. It also achieves higher accuracy on MMLU and MMLU-Pro than recent advanced MoE full-attention models, such as DeepSeek-V3-Small and Moonlight, despite their larger scale with 15B total and 2.2B activated parameters.