MaSiFeb 18
#aidevs #AI_devs4 #AI_devs4builders #BraveCourses #BraveEducation @mateuszchrobok
WildTuna 🇷🇺Jun 26, 2025

Пощупал я наконец-то модный Cursor AI. Ну это, конечно, отвал башки. Особенно круто с учётом того, что он на базе моего любимого VS Code.

Опробовать его решил на необычной просьбе одного из старых клиентов. У него есть сайт на дремучем legacy PHP с нативным JS. За 5 минут claude 4 sonnet рассказал мне структуру проекта, как работает маршрутизация и вход на сайт. По описанию проблемы быстро устранил проблему с входом. Восторг. Раньше на это у меня бы ушло несколько часов

Вторая задача была добавить на сайт корзину с хранением состояния в бд по имеющимся html шаблонам. И тут все на отлично. Да, ушло около часа общения, тестов, уточнений. Но как итог работающий код в общей стилистике проекта, который без ИИ я бы писал наверное день, а то и два разбираясь в том как работают модели в этом решении и вспоминая JS Native. 1600 строк кода написанного AI и 0 строк кода написанного руками. Восторг в квадрате.

Итог: очень крутой и мощный инструмент, если понимать как им пользоваться. Попробую написать с ним с нуля один из завалявшихся pet project.

#ai #cursor #ide #aiagent #php #js #aidevs #devs #dev #vibecoding

WinbuzzerJun 18, 2025

Altman vs. Zuckerberg: OpenAI CEO Accuses Meta of Poaching AI Experts With $100 Million Signing Bonuses

#AI #AITalentWar #OpenAI #Meta #SamAltman #Zuckerberg #BigTech #AIResearch #AIDevs

https://winbuzzer.com/2025/06/18/altman-vs-zuckerberg-openai-ceo-accuses-meta-of-poaching-ai-experts-with-100-million-signing-bonuses-xcxwbn/

Hacker NewsJun 17, 2025

Hack the Planet: 90s Hacker Culture vs. Today's AI Devs

https://gizvault.com/archives/hack-the-planet

#HackerNews #HackThePlanet #90sHackerCulture #AIDevs #TechHistory #CyberCulture

Hack the Planet: 90s Hacker Culture vs. Today’s AI Devs - GizVault

Explore our cyberpunk-inspired e-commerce store, where tech enthusiasts and geeks can discover unique products that blend retro aesthetics with cutting-edge technology. Get your hands on the coolest gadgets and gear, handpicked for those who love to tinker and create. Crafted for the U.S. market, with articles that bring you closer to the future of tech.

Hacker NewsJun 4, 2025

AI Devs are the new "Campfire Guitarist"

https://www.funkaey.com/blog/AI-Devs-are-the-new-campfire-guitarists

#HackerNews #AIDevs #CampfireGuitarist #TechTrends #Innovation #Community #Creativity

AI Devs are the new campfire guitarists

It's not music - but it sounds good.

Paul KeenOct 22, 2024
Must read for AI enthusiasts fine-tuning LLMs. Understand the pipeline & methods, overcome challenges, & unlock breakthroughs! #LLMs #AIdevs #Breakthroughs https://arxiv.org/abs/2408.13296
The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

This report examines the fine-tuning of Large Language Models (LLMs), integrating theoretical insights with practical applications. It outlines the historical evolution of LLMs from traditional Natural Language Processing (NLP) models to their pivotal role in AI. A comparison of fine-tuning methodologies, including supervised, unsupervised, and instruction-based approaches, highlights their applicability to different tasks. The report introduces a structured seven-stage pipeline for fine-tuning LLMs, spanning data preparation, model initialization, hyperparameter tuning, and model deployment. Emphasis is placed on managing imbalanced datasets and optimization techniques. Parameter-efficient methods like Low-Rank Adaptation (LoRA) and Half Fine-Tuning are explored for balancing computational efficiency with performance. Advanced techniques such as memory fine-tuning, Mixture of Experts (MoE), and Mixture of Agents (MoA) are discussed for leveraging specialized networks and multi-agent collaboration. The report also examines novel approaches like Proximal Policy Optimization (PPO) and Direct Preference Optimization (DPO), which align LLMs with human preferences, alongside pruning and routing optimizations to improve efficiency. Further sections cover validation frameworks, post-deployment monitoring, and inference optimization, with attention to deploying LLMs on distributed and cloud-based platforms. Emerging areas such as multimodal LLMs, fine-tuning for audio and speech, and challenges related to scalability, privacy, and accountability are also addressed. This report offers actionable insights for researchers and practitioners navigating LLM fine-tuning in an evolving landscape.

arXiv.org