source:

• The Art of Quantitative Finance vol 1 https://link.springer.com/book/10.1007/978-3-031-23873-4
• The Art of Quantitative Finance vol 2 https://link.springer.com/book/10.1007/978-3-031-23870-3
• The Art of Quantitative Finance vol 3 https://link.springer.com/book/10.1007/978-3-031-23867-3

Kadang-kadang kita perlu mengetahui suatu tanggal itu hari ke berapa dalam suatu tahun. Berikut ini cara menghitungnya dengan bahasa Python.

from datetime import datetime, timedelta
day_of_year = 265
start_of_year = datetime(2025, 1, 1)
target_date = start_of_year + timedelta(days=day_of_year - 1)
weekday_num = target_date.weekday()+1
print("weekday num", weekday_num)

Source: https://zachwills.net/how-to-use-claude-code-subagents-to-parallelize-development/

Problem:

create pair of of people with similar properties. Minimize difference between members of a pair

Solution

Algorithm for optimal node pairing that minimizes the total distance between paired nodes. This is essentially a minimum-weight perfect matching problem.I’ve created a comprehensive implementation of optimal node pairing algorithms. Here’s what the code includes:

Key Components:

1. Node Class: Represents a point in 2D space with distance calculation
2. Multiple Algorithm Approaches:
   • Greedy: Fast O(n³) algorithm that repeatedly finds closest pairs
   • Nearest Neighbor: O(n²) algorithm that pairs each node with its nearest unpaired neighbor
   • Approximation with Local Search: Improves greedy solution using 2-opt style swaps
   • Brute Force Optimal: Guaranteed optimal solution via backtracking (exponential time)

Algorithm Trade-offs:

• Greedy: Fast but may get stuck in local optima
• Nearest Neighbor: Faster, good for large datasets, decent quality
• Approximation: Best balance of speed and quality for medium datasets
• Brute Force: Perfect solution but only practical for small instances (≤8-10 nodes)

Usage Recommendations:

• Small datasets (≤8 nodes): Use brute force for guaranteed optimal
• Medium datasets (10-100 nodes): Use approximation algorithm
• Large datasets (>100 nodes): Use nearest neighbor or greedy

The code includes a test function that demonstrates all algorithms on a sample dataset, showing the total distance and individual pairs for each approach. You can easily adapt it for your specific coordinate system or distance metric.

Mengukur detak jantung menggunakan WiFi. Pakai ESP32 dan Raspberry Pi juga bisa. https://news.ucsc.edu/2025/09/pulse-fi-wifi-heart-rate/ papernya:

• https://news.ucsc.edu/wp-content/uploads/2025/08/Pulse_Fi_short_paper_iotdcoss.pdf
• https://bpb-us-e1.wpmucdn.com/sites.ucsc.edu/dist/d/1595/files/2025/05/Pulse_Fi_short_paper_iotdcoss.pdf

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.

Fakta-fakta:

• Fact 1: Employment for young workers has declined in AI-exposed occupations
• Fact 2: Though overall employment continues to grow, employment growth for young workers in particular has been stagnant
• Fact 3: Entry-level employment has declined in applications of AI that automate work, with muted changes for augmentation
• Fact 4: Employment declines for young, AI-exposed workers remain after conditioning on firm-time shocks
• Fact 5: Labor market adjustments are visible in employment more than compensation
• Fact 6: Findings are largely consistent under alternative sample constructions

Source: https://github.com/murtaza-nasir/maestro

Seven lessons for enterprise AI adoption

Source:

Perangkat Lunak Alat Bantu. Banyak yang dibuat berbasis AI.

• Claude.ai – solves anything
• Syllaby.io – create and publish videos
• MidJourney.com – generates art
• Hellowarrant.com – ai for marketing compliance
• Heygen.com – creates AI avatars
• Soundraw.io – produces music in seconds
• Ranked.ai – rank higher on Google
• Submagic.co – turn long videos into shorts
• SlidesAI.io – makes presentations for you
• PicWish.com – edits photos fast
• Fastread.io - create ebooks
• Bibley.io - ai for Bible study
• Manus.im - code any app
• Capsho.com - podcast into content
• Webinarkit.com - ai for webinars
• Creatorunlock.com - ai for YouTube
• sociable.how - write viral comments
• Adcreative.ai - ai tool for advertising
• Canva.com - design anything
• Fastphoto.io - create ai headshots
• askcoachken.com - ai relationship coach
• threadmaster.ai - create viral Facebook thread
• V0.dev - create frontend mockups
• suno.com - create ai music
• keywordsearch.com - boost ad conversions
• labs.google - Google’s AI video maker
• Beautiful.ai - make sleek AI presentations
• Remove.bg - instantly remove backgrounds
• Uizard.io - turn sketches into UI mockup
• Aiva.ai - compose AI-generated music
• AnimaApp.com - turn designs into code
• Replika.ai - AI relationship companion
• notebooklm.google.com - turn anything into a podcast
• Photes.io - turn photos into text notes
• Magai.ai - all AI models in one
• CopyOwl.ai - deep research into articles
• fastpedia.io - ai tools directory
• kuse.ai - ChatGPT into white boards
• createanything.com - create apps and websites
• snowglobe.so - simulate user testing
• sintra.ai - team of ai agents
• livex.ai - ai agents for customer retention
• Revatto.com - ai for churn reduction
• clueso.io - product videos in minutes
• skala.io - legal platform for startups
• n8n.io - automate anything
• beehiiv.com - best newsletters for creators
• blur-it.app - blur sensitive information
• perplexity.ai - best ai search engine
• lemlist.com - turn website views into leads

How Anthropic teams use Claude Code

Source:

• https://www.anthropic.com/news/how-anthropic-teams-use-claude-code

Several AI pronounciation trainers:

• https://github.com/LokeshAgnihotri/Ai-Pronounciation-Trainer
• https://github.com/Thiagohgl/ai-pronunciation-trainer
• https://wikspeak.sourceforge.net/
• https://lingualibre.org/wiki/LinguaLibre:Main_Page
• https://github.com/nishuzumi/gemini-teacher

Problem:

create pair of of people with similar properties. Minimize difference between members of a pair

Solution

Algorithm for optimal node pairing that minimizes the total distance between paired nodes. This is essentially a minimum-weight perfect matching problem.I’ve created a comprehensive implementation of optimal node pairing algorithms. Here’s what the code includes:

Key Components:

1. Node Class: Represents a point in 2D space with distance calculation
2. Multiple Algorithm Approaches:
   • Greedy: Fast O(n³) algorithm that repeatedly finds closest pairs
   • Nearest Neighbor: O(n²) algorithm that pairs each node with its nearest unpaired neighbor
   • Approximation with Local Search: Improves greedy solution using 2-opt style swaps
   • Brute Force Optimal: Guaranteed optimal solution via backtracking (exponential time)

Algorithm Trade-offs:

• Greedy: Fast but may get stuck in local optima
• Nearest Neighbor: Faster, good for large datasets, decent quality
• Approximation: Best balance of speed and quality for medium datasets
• Brute Force: Perfect solution but only practical for small instances (≤8-10 nodes)

Usage Recommendations:

• Small datasets (≤8 nodes): Use brute force for guaranteed optimal
• Medium datasets (10-100 nodes): Use approximation algorithm
• Large datasets (>100 nodes): Use nearest neighbor or greedy

The code includes a test function that demonstrates all algorithms on a sample dataset, showing the total distance and individual pairs for each approach. You can easily adapt it for your specific coordinate system or distance metric.

Mengukur detak jantung menggunakan WiFi. Pakai ESP32 dan Raspberry Pi juga bisa. https://news.ucsc.edu/2025/09/pulse-fi-wifi-heart-rate/ papernya:

• https://news.ucsc.edu/wp-content/uploads/2025/08/Pulse_Fi_short_paper_iotdcoss.pdf
• https://bpb-us-e1.wpmucdn.com/sites.ucsc.edu/dist/d/1595/files/2025/05/Pulse_Fi_short_paper_iotdcoss.pdf

Problem:

create pair of of people with similar properties. Minimize difference between members of a pair

Solution

Algorithm for optimal node pairing that minimizes the total distance between paired nodes. This is essentially a minimum-weight perfect matching problem.I’ve created a comprehensive implementation of optimal node pairing algorithms. Here’s what the code includes:

Key Components:

1. Node Class: Represents a point in 2D space with distance calculation
2. Multiple Algorithm Approaches:
   • Greedy: Fast O(n³) algorithm that repeatedly finds closest pairs
   • Nearest Neighbor: O(n²) algorithm that pairs each node with its nearest unpaired neighbor
   • Approximation with Local Search: Improves greedy solution using 2-opt style swaps
   • Brute Force Optimal: Guaranteed optimal solution via backtracking (exponential time)

Algorithm Trade-offs:

• Greedy: Fast but may get stuck in local optima
• Nearest Neighbor: Faster, good for large datasets, decent quality
• Approximation: Best balance of speed and quality for medium datasets
• Brute Force: Perfect solution but only practical for small instances (≤8-10 nodes)

Usage Recommendations:

• Small datasets (≤8 nodes): Use brute force for guaranteed optimal
• Medium datasets (10-100 nodes): Use approximation algorithm
• Large datasets (>100 nodes): Use nearest neighbor or greedy

The code includes a test function that demonstrates all algorithms on a sample dataset, showing the total distance and individual pairs for each approach. You can easily adapt it for your specific coordinate system or distance metric.

Mengukur detak jantung menggunakan WiFi. Pakai ESP32 dan Raspberry Pi juga bisa. https://news.ucsc.edu/2025/09/pulse-fi-wifi-heart-rate/ papernya:

• https://news.ucsc.edu/wp-content/uploads/2025/08/Pulse_Fi_short_paper_iotdcoss.pdf
• https://bpb-us-e1.wpmucdn.com/sites.ucsc.edu/dist/d/1595/files/2025/05/Pulse_Fi_short_paper_iotdcoss.pdf

Kutipan dari twitter tentang masalah keamanan di headphone wireless. Salah satu masalah wireless adalah keamanan data. Kalau mau 100% aman memang lebih baik pakai headphone kabel saja.

Just saw a sick live demo of wireless headphone hijacking that allows the attacker to perform the following without ever pairing the device. All the attacker has to do is be in range.

• read/write arbitrary bytes to device
• read headphones information
• dump firmware info
• extract paired devices information (name, link key, MAC address)

and since you can extract the above information, the attacker can then impersonate as the device and perform actions like

https://jmrtd.org/ JMRTD is a free implementation in Java of the Machine Readable Travel Document (MRTD) standards as specified by the International Civil Aviation Organization (ICAO). Java Card Development Kit https://www.oracle.com/java/technologies/javacard-downloads.html#sdk

Source:

• https://jmrtd.org/
• Java Card Development Kit

https://jmrtd.org/ JMRTD is a free implementation in Java of the Machine Readable Travel Document (MRTD) standards as specified by the International Civil Aviation Organization (ICAO). Java Card Development Kit https://www.oracle.com/java/technologies/javacard-downloads.html#sdk

Source:

• https://jmrtd.org/
• Java Card Development Kit

Several AI pronounciation trainers:

• https://github.com/LokeshAgnihotri/Ai-Pronounciation-Trainer
• https://github.com/Thiagohgl/ai-pronunciation-trainer
• https://wikspeak.sourceforge.net/
• https://lingualibre.org/wiki/LinguaLibre:Main_Page
• https://github.com/nishuzumi/gemini-teacher

Source:

• https://www.winxdvd.com/enhance-video/open-source-video-upscaler.htm
• https://gadgetstouse.com/blog/2025/03/22/open-source-ai-video-upscaler-free/
• https://github.com/k4yt3x/video2x

Several AI pronounciation trainers:

• https://github.com/LokeshAgnihotri/Ai-Pronounciation-Trainer
• https://github.com/Thiagohgl/ai-pronunciation-trainer
• https://wikspeak.sourceforge.net/
• https://lingualibre.org/wiki/LinguaLibre:Main_Page
• https://github.com/nishuzumi/gemini-teacher

Berikut ini statistik penggunaan bahasa pemrograman di github.

Berikut ini bahasa pemrograman dengan tingkat pertumbuhan tertinggi

Berikut ini bahasa pemrograman yang populer berdasarkan jumlah repository yang dibuat

Berikut ini bahasa pemrograman yang populer berdasarkan jumlah kontribusi user ke suatu project.

Sumber

• Octoverse: AI leads Python to top language as the number of global developers surges https://github.blog/news-insights/octoverse/octoverse-2024/

Berikut ini statistik penggunaan bahasa pemrograman di github.

Berikut ini bahasa pemrograman dengan tingkat pertumbuhan tertinggi

Berikut ini bahasa pemrograman yang populer berdasarkan jumlah repository yang dibuat

Berikut ini bahasa pemrograman yang populer berdasarkan jumlah kontribusi user ke suatu project.

Sumber

• Octoverse: AI leads Python to top language as the number of global developers surges https://github.blog/news-insights/octoverse/octoverse-2024/