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• Marketing

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  Все, что было рутиной раньше теперь постепенно перевожу на написание кода нейронкам. Сейчас вот тестирую разработку со скилами, но я не беру их готовые, а с ии пытаемся сформировать их сами, под конкретные мои требования. Получается все бойлерплейты, шаблоны, теперь пишется ии, а также некоторые функции. Но есть одно важное дополнение: я читаю что он пишет, поэтому в тех же скилах указываю, что мы все делаем мелкими этапами, чтобы мне легко было читать. Да это не быстрая разработка но местами качественее, там где я ленился бы писать более правильно, теперь этого аргумента нет. Я теперь сам учусь у ии каким-то вещам, стараюсь чтобы навык чтения и понимания кода не атрофировался, а вот с написанием конечно все хуже, если честно. Это все пока как эксперимент, может я иду не по правильному пути, но если честно мне он больше импонирует, чем подход: Куплю самую крутую ии - скажу ей сделать круто такой то проект, без конкретики , без уточнения, и скачать готовые скилы, и все. В чем проблема этого подхода? В том про что ты не контролируешь проект вообще, даже если ии пишет близко к идеалу, ей тяжелее не делать ошибок потом, и ты туда уже не залезешь и не поймешь что там происходишь. Потеря контроля - это , на мой взгляд, самая большая проблема проекта в долгую.
• Humor

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  [image: 1765452047154-1000048148.webp]
• News

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  [image: 1776412885214-c41dc1d3-7d26-414a-a9ac-3c12b781c4ac.webp] Pottery classes are guided, in‑person sessions where you learn to shape, decorate, and fire clay into functional or decorative objects. A typical class mixes short demonstrations with lots of guided practice at the wheel or at a hand‑building table. Most beginner programs start with: An intro to clay types and basic tools Simple hand‑building techniques (pinch, coil, slab) A first project like a pinch bowl or a small cylinder on the wheel Clear studio rules on safety, glazing, and firing You do not need previous art experience. Beginner classes assume zero knowledge and walk you through every step from wedging to glazing. Why adults love pottery classes Key mental and physical benefits Research and studio experience show that pottery offers a unique mix of mental, emotional, and physical benefits for adults. Stress relief and relaxation Working with clay lowers stress and anxiety, and can help reduce cortisol levels through rhythmic, tactile work. Creativity and self‑expression Ceramics education improves creative thinking and gives a safe space to explore ideas in 3D form. Mindfulness and focus Repetitive actions like wedging and throwing improve mindfulness, patience, and the ability to stay present. Fine motor skills and coordination Hand‑eye coordination and attention to detail grow as you learn to control thickness, symmetry, and surface. Confidence and sense of achievement Finishing your first piece and holding it after firing strongly boosts confidence and self‑esteem. Main types of pottery classes By technique Class type What you do Best for Wheel throwing Use a potter’s wheel for bowls, mugs, cylinders People who like technical challenges Hand‑building Pinching, coiling, slab‑building at a table Creative sculpting, decorative pieces Mixed technique Combination of wheel and hand‑building Curious beginners who want to try both Glazing & surface Focus on color, underglaze, textures, finishes Those who already have bisque pieces Wheel‑throwing looks dramatic and feels very satisfying, but hand‑building is often easier to control at first. Many studios, including Pottery Studio #1, offer mixed formats so you can discover which style you enjoy more. By duration and structure One‑time workshop (1.5–3 hours) Great for “try once”, date nights, gifts, or team events. Usually you make 1–2 simple pieces and the studio handles firing. Short series (3–6 weeks) Step‑by‑step courses that cover the full process from preparing clay to glazing finished work. Long courses / open‑studio access For people building a regular hobby or portfolio, with independent practice time plus instructor feedback. What to expect in your first pottery class Most beginner pottery classes follow a similar, friendly structure. Studio tour and safety basics You get assigned a wheel or hand‑building station, meet tools, clay, and cleanup routines. Demo from the instructor The teacher shows how to wedge clay, center it on the wheel or form a simple pinch/coil pot. Your first project Wheel classes often aim for a small bowl or cylinder; hand‑building classes focus on a pinch pot or simple dish. Drying and first firing (bisque) After class, staff dry and bisque‑fire your pieces for you. Glazing session In a later class you add glaze or underglaze, and the studio does the final, high‑temperature firing. Important reality check for beginners: Your first pieces will be thick, wonky, and asymmetrical That is normal; mastering centering and even walls usually takes several sessions. Failure is part of the process Many first cylinders collapse; learning pressure control and timing is exactly what classes are for. Skills you develop over time By attending pottery classes for a few weeks or months, most students gradually build a solid set of core skills. Clay preparation and wedging Centering and pulling walls on the wheel Basic shapes: cylinders, bowls, small plates Hand‑building forms: pinch pots, coils, slabs Trimming, adding handles, attaching parts correctly Surface design with slips, underglazes, and carving Glazing choices and basic kiln schedules These skills scale from simple mugs to complex, high‑end functional ceramics or sculptural pieces. How to choose the right pottery class Step 1 — clarify your goal Ask yourself what you want most right now: “I just want a fun night out” Choose a one‑time workshop with simple projects. “I want a new long‑term hobby” Look for 4–8 week beginner series with clear curriculum. “I want to get serious about ceramics” You need ongoing courses, critiques, and independent studio time. Step 2 — check these studio factors Factor What to look for Group size 4–10 people so the instructor can help everyone. Skill levels offered Separate beginner / intermediate / advanced groups. Equipment quality Well‑maintained wheels, tools, good ventilation. Schedule & location Evenings/weekends, easy transit or parking. Firing and materials Clear policy on how much clay and how many firings. Studios like Pottery Studio #1 usually list all of this on their class pages: level, length, projects, and what is included. Step 3 — match format to your personality If you like structure and clear progress Pick a curriculum‑based series with defined weekly topics. If you love experimenting Choose mixed‑technique or open‑studio programs where you can try different clays and glazes. If you are nervous as a total beginner Look for explicit “Absolute Beginner” or “No experience needed” labels and small groups with high instructor attention. What is usually included in the price While details vary by studio, many pottery classes include: Clay (often a set amount per course) Use of wheels, tools, and studio space Instructor time and demonstrations One or two firings (bisque and glaze) for a limited number of pieces Basic studio glazes for student work Extra costs can include: Additional clay beyond the base allowance Extra firings for oversized or numerous pieces Premium glazes or specialty materials When you compare options, always check what “materials and firing included” actually means in practice. Typical levels: beginner to advanced Level You focus on Beginner Basic forms, safe studio habits, simple glazing. Intermediate Consistent sets (mugs, bowls), better thickness and curves. Advanced Complex forms, lidded pots, large work, personal style. Pottery Studio #1 and similar studios often let you repeat a level or mix levels as your confidence grows. Simple visualization: why adults join pottery classes Below is a conceptual breakdown of common motivations behind joining pottery classes (percentages illustrative, based on typical benefits described in studio and education articles). Reason Approx. share of motivation Stress relief 30% Creativity & self‑expression 25% New hobby / skill 20% Social connection 15% Mindfulness & mental health 10% This pie‑style breakdown shows how pottery sits at the intersection of well‑being, creativity, and community. How to prepare for your first class To make your first visit to Pottery Studio #1 or any local studio smoother and more enjoyable, prepare a few simple things. Wear clothes you do not mind getting dirty Clay washes out, but it is messy. Trim long nails if possible Long nails make controlling thin walls much harder. Arrive a bit early You will have time to sign forms, choose an apron, and get comfortable in the space. Bring ideas, but stay flexible Very specific Pinterest‑level shapes are hard on day one; focus on learning fundamentals first. Accept imperfect results Think of your first pieces as learning tools, not final masterpieces. When a studio like Pottery Studio #1 is a great fit A local, modern pottery studio is especially helpful if you: Want clear, beginner‑friendly structure Prefer small groups and lots of instructor feedback Value a calm, aesthetically pleasing environment Like the idea of leaving each block of classes with finished, food‑safe pieces you can use or gift If that sounds like you, booking a class through a dedicated studio site (such as Pottery Studio #1 via the Pottery classes section below) is usually the fastest way to get started. https://pottery-studio.ca/pottery-classes-in-toronto
• AI

  All about artificial intelligence

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  Stop searching. This is the only AI toolkit a developer will ever need! This toolkit covers all areas of AI, from machine learning basics to specialized fields like computer vision, NLP, reinforcement learning, and MLOps. Updated with 2025 trends for building, learning, and experimenting efficiently. A curated, comprehensive collection of open-source AI tools, frameworks, datasets, courses, and seminal papers. Organized by AI domains and segregated for beginners (foundational, easy-to-use tools/courses) and advanced users (complex, production-ready resources). Whether you’re a beginner starting your AI journey or an advanced engineer deploying scalable systems, this repo provides essential resources to accelerate your work. Contribute to keep it growing. Why This Toolkit? Broad Coverage: Spans all AI domains with detailed category separation. Open-Source Only: Exclusively free, community-driven tools and resources. Skill-Level Segregation: Beginner-friendly entries for quick starts; advanced for deep dives. Beyond Tools: Includes top datasets for exploration, free courses, and key papers. Up-to-Date: Trending resources as of August 2025, with GitHub stars for popularity insights. Community-Driven: Add new entries via PRs to make it more comprehensive and viral! AI Domains and Tools Tools are categorized by domain. Each includes a brief description, GitHub URL, and approximate stars (as of August 2025). Segregated into Beginner (simple setup, tutorials-focused) and Advanced (scalable, customizable) sub-sections. Machine Learning Frameworks Foundational libraries for building and training ML models. Beginner Tool Description URL Stars scikit-learn Simple machine learning in Python for classification, regression, and clustering https://github.com/scikit-learn/scikit-learn 60k Keras User-friendly neural networks API on top of TensorFlow or PyTorch https://github.com/keras-team/keras 61k Advanced Tool Description URL Stars TensorFlow End-to-end platform for large-scale ML with strong ecosystem support https://github.com/tensorflow/tensorflow 183k PyTorch Dynamic neural networks with GPU acceleration for research and production https://github.com/pytorch/pytorch 81k Data Processing & Management Tools for handling and preparing data. Beginner Tool Description URL Stars Pandas Easy data manipulation and analysis with DataFrames https://github.com/pandas-dev/pandas 43k NumPy Fundamental array computing and linear algebra operations https://github.com/numpy/numpy 28k Advanced Tool Description URL Stars Dask Parallel computing for large datasets, integrates with Pandas/NumPy https://github.com/dask/dask 12k Vector Databases Open-source storage for embeddings and similarity search. Beginner Tool Description URL Stars Chroma Simple embedding database for local LLM apps https://github.com/chroma-core/chroma 15k FAISS Efficient similarity search library from Facebook AI https://github.com/facebookresearch/faiss 35k Advanced Tool Description URL Stars Weaviate Vector database with GraphQL and modular plugins https://github.com/weaviate/weaviate 15k Qdrant High-performance vector search with filtering support https://github.com/qdrant/qdrant 20k Milvus Scalable vector database for billion-scale similarity search https://github.com/milvus-io/milvus 30k Orchestration & Workflow Frameworks For building AI pipelines and agents. Beginner Tool Description URL Stars Langflow No-code visual builder for LLM workflows https://github.com/langflow-ai/langflow 15k Flowise Drag-and-drop UI for LLM chains https://github.com/FlowiseAI/Flowise 25k Advanced Tool Description URL Stars LangChain Modular framework for LLM apps and agents https://github.com/langchain-ai/langchain 120k LlamaIndex Data ingestion and querying for LLMs https://github.com/run-llama/llama_index 50k Haystack Production-ready NLP pipelines https://github.com/deepset-ai/haystack 18k DSPy Programmatic prompt optimization https://github.com/stanfordnlp/dspy 15k Semantic Kernel AI integration SDK for .NET/Python/Java https://github.com/microsoft/semantic-kernel 8k Computer Vision Libraries for image processing and vision tasks. Beginner Tool Description URL Stars OpenCV Core library for image/video processing and basic CV tasks https://github.com/opencv/opencv 75k Advanced Tool Description URL Stars Ultralytics YOLO State-of-the-art object detection and segmentation models https://github.com/ultralytics/ultralytics 30k Detectron2 Facebook AI’s framework for object detection and segmentation https://github.com/facebookresearch/detectron2 30k Natural Language Processing (NLP) Tools for text analysis and language models. Beginner Tool Description URL Stars NLTK Toolkit for basic NLP tasks like tokenization and stemming https://github.com/nltk/nltk 13k spaCy Efficient NLP library for entity recognition and dependency parsing https://github.com/explosion/spaCy 29k Advanced Tool Description URL Stars Transformers Hugging Face library for state-of-the-art NLP models https://github.com/huggingface/transformers 130k Flair Framework for advanced NLP with pre-trained embeddings https://github.com/flairNLP/flair 14k Reinforcement Learning (RL) Frameworks for agent training and decision-making. Beginner Tool Description URL Stars Stable-Baselines3 Reliable RL algorithms built on PyTorch https://github.com/DLR-RM/stable-baselines3 8k Advanced Tool Description URL Stars Ray RLlib Scalable RL library for distributed training https://github.com/ray-project/ray 32k OpenRL Unified framework for single/multi-agent RL https://github.com/OpenRL-Lab/openrl 1k MLOps Tools for ML operations, deployment, and monitoring. Beginner Tool Description URL Stars MLflow Track experiments, package code, and deploy models https://github.com/mlflow/mlflow 18k Advanced Tool Description URL Stars Kubeflow Kubernetes-native platform for ML pipelines https://github.com/kubeflow/kubeflow 14k DVC Version control for data and ML models https://github.com/iterative/dvc 13k PDF Extraction Tools For extracting data from PDFs. Beginner Tool Description URL Stars pdfplumber Extract text and tables from PDFs https://github.com/jsvine/pdfplumber 6k Camelot Tabular data extraction from PDFs https://github.com/camelot-dev/camelot 2k Advanced Tool Description URL Stars Docling AI-powered PDF to JSON/Markdown conversion https://github.com/docling-project/docling 1k PyMuPDF High-performance PDF parsing https://github.com/pymupdf/PyMuPDF 5k PDF.js JavaScript-based PDF rendering and extraction https://github.com/mozilla/pdf.js 50k Retrieval-Augmented Generation (RAG) For enhancing LLMs with external data. Beginner Tool Description URL Stars PrivateGPT Local document interaction with LLMs https://github.com/imartinez/privateGPT 50k AnythingLLM All-in-one local LLM app for RAG https://github.com/Mintplex-Labs/anything-llm 20k Advanced Tool Description URL Stars RAGFlow Deep document understanding for RAG https://github.com/infiniflow/ragflow 15k Verba RAG chatbot with Weaviate integration https://github.com/weaviate/Verba 5k Quivr GenAI second brain for document management https://github.com/QuivrHQ/quivr 35k Jina Multimodal neural search for RAG https://github.com/jina-ai/jina 25k txtai Embeddings database for semantic search https://github.com/neuml/txtai 10k Evaluation & Testing For assessing AI models. Beginner Tool Description URL Stars Ragas Framework for evaluating RAG pipelines https://github.com/explodinggradients/ragas 8k Advanced Tool Description URL Stars Phoenix Observability for LLMs and vision models https://github.com/Arize-ai/phoenix 5k DeepEval Unit testing for LLM outputs https://github.com/confident-ai/deepeval 8k TruLens Tracking and evaluation for LLM experiments https://github.com/truera/trulens 2k Monitoring & Observability For production AI systems. Beginner Tool Description URL Stars Phoenix ML observability tool https://github.com/Arize-ai/phoenix 5k Advanced Tool Description URL Stars Evidently AI Monitoring for ML model performance https://github.com/evidentlyai/evidently 5k AI Agents Frameworks for building autonomous AI agents. Beginner Tool Description URL Stars AutoGPT Autonomous AI agent for task automation using LLMs https://github.com/Significant-Gravitas/AutoGPT 160k BabyAGI Task-driven autonomous agent inspired by BabyAGI https://github.com/yoheinakajima/babyagi 18k Advanced Tool Description URL Stars CrewAI Framework for orchestrating role-playing AI agents https://github.com/joaomdmoura/crewAI 20k MetaGPT Multi-agent framework simulating a software company https://github.com/geekan/MetaGPT 40k OpenHands AI agents for software development tasks https://github.com/All-Hands-AI/OpenHands 10k Generative AI Tools for generating text, images, and other content. Beginner Tool Description URL Stars Ollama Run and manage local LLMs easily https://github.com/ollama/ollama 70k Stable Diffusion WebUI User-friendly web interface for Stable Diffusion image generation https://github.com/AUTOMATIC1111/stable-diffusion-webui 130k Advanced Tool Description URL Stars Diffusers State-of-the-art diffusion models for image and audio generation https://github.com/huggingface/diffusers 25k llama.cpp Efficient LLM inference in C/C++ https://github.com/ggerganov/llama.cpp 60k InvokeAI Creative engine for Stable Diffusion models https://github.com/invoke-ai/InvokeAI 22k Deep Learning Libraries for advanced neural network development. Beginner Tool Description URL Stars fastai High-level deep learning library on PyTorch for quick results https://github.com/fastai/fastai 26k Advanced Tool Description URL Stars JAX Composable transformations for high-performance ML https://github.com/google/jax 30k tinygrad Minimalist deep learning framework https://github.com/tinygrad/tinygrad 25k Deeplearning4j JVM-based deep learning suite for enterprise https://github.com/deeplearning4j/deeplearning4j 13k Advanced LLM Architectures Frameworks for optimizing and architecting large language models. Beginner Tool Description URL Stars PEFT Parameter-efficient fine-tuning for large models https://github.com/huggingface/peft 15k bitsandbytes K-bit quantization for accessible LLMs https://github.com/TimDettmers/bitsandbytes 5k Advanced Tool Description URL Stars vLLM High-throughput LLM inference engine https://github.com/vllm-project/vllm 25k Flash Attention Fast and memory-efficient attention mechanism https://github.com/Dao-AILab/flash-attention 12k exllamav2 Fast inference library for LLMs on consumer GPUs https://github.com/turboderp/exllamav2 6k Datasets Top open datasets for AI exploration. Segregated by skill level. Beginner Datasets (Small, Easy to Use) Dataset Description URL Domain MNIST Handwritten digits for classification https://yann.lecun.com/exdb/mnist/ CV/ML Iris Flower species classification https://archive.ics.uci.edu/dataset/53/iris ML Boston Housing House price regression https://www.kaggle.com/datasets/vikrishnan/boston-house-prices ML Advanced Datasets (Large-Scale, Complex) Dataset Description URL Domain ImageNet Large image dataset for object recognition https://www.image-net.org/ CV COCO Common objects in context for detection/segmentation https://cocodataset.org/ CV LAION-5B Massive multimodal dataset for generative models https://laion.ai/blog/laion-5b/ GenAI Common Crawl Web-scale text corpus for NLP https://commoncrawl.org/ NLP GLUE Benchmark for NLP tasks https://gluebenchmark.com/ NLP Courses Free online courses for learning AI. Segregated by level. Beginner Courses Course Description URL Elements of AI Introduction to AI concepts for non-experts https://www.elementsofai.com/ Introduction to AI (Coursera) Basics of AI from IBM https://www.coursera.org/learn/introduction-to-ai Google AI Essentials Practical AI skills from Google https://grow.google/ai/ Advanced Courses Course Description URL Deep Learning Specialization (Coursera) Advanced neural networks by Andrew Ng https://www.coursera.org/specializations/deep-learning CS224N: NLP with Deep Learning (Stanford) State-of-the-art NLP techniques https://web.stanford.edu/class/cs224n/ Reinforcement Learning (DeepMind) RL fundamentals and algorithms https://www.deepmind.com/learning-resources/reinforcement-learning-lecture-series-2021 Papers Seminal and trending AI papers, with repositories for collections. Beginner-Friendly Papers (Foundational) Paper/Repo Description URL Attention Is All You Need (Transformer) Introduced Transformers for NLP https://arxiv.org/abs/1706.03762 A Few Useful Things to Know About ML Practical ML advice https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf Advanced Papers (Cutting-Edge) Paper/Repo Description URL ML Papers of the Week Weekly curated ML papers https://github.com/dair-ai/ML-Papers-of-the-Week Awesome AI Research Papers Influential papers in AI domains https://github.com/awesomelistsio/awesome-ai-research-papers Landmark Papers in ML Key historical papers https://github.com/daturkel/learning-papers
• Programming languages

  Syntax, libraries, frameworks, algorithms, OOP, functional, asynchronous, multithreaded programming. Help for beginners, expert advice, trends and cases. Solve problems, share code.

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  • Shell
  • Dart
  • TypeScript
  • JavaScript
  • Rust
  • Python
  • Swift
  • PHP
  • Ruby
  • SQL
  4 Темы

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  H

  Let’s be real: if your app doesn’t talk to a server, it’s basically a fancy notepad. Whether you’re fetching user data, submitting a form, or syncing preferences across devices—you’ll need to send HTTP requests. And in the JavaScript world, that usually means GET and POST. Good news? Making these requests is easier than ever—thanks to modern APIs like fetch() and battle-tested libraries like Axios. Even better? The same code often works seamlessly on both web browsers and mobile apps (especially if you’re using React Native or hybrid frameworks). So grab your favorite drink, and let’s break this down—no jargon overload, just clear, practical steps. 🧭 Why This Matters Before we dive into syntax, let’s zoom out: GET = “Hey server, give me some data.” (e.g., load a user profile) POST = “Hey server, here’s some data—save it!” (e.g., submit a login form) These are the bread and butter of client-server communication. And JavaScript? It’s the universal translator. Option 1: The Built-in Hero — fetch() Modern browsers (and React Native!) support the Fetch API natively. No extra libraries. No fuss. Making a GET Request fetch('https://api.example.com/users') .then(response => { if (!response.ok) { throw new Error('Network response was not ok'); } return response.json(); // Parse JSON response }) .then(data => { console.log('Success:', data); }) .catch(error => { console.error('Error:', error); }); Pro tip: Always check response.ok! A 404 or 500 still returns a “response”—but it’s not successful. Making a POST Request fetch('https://api.example.com/login', { method: 'POST', headers: { 'Content-Type': 'application/json', }, body: JSON.stringify({ email: 'you@example.com', password: 'supersecret' }) }) .then(response => response.json()) .then(data => console.log('Login success:', data)) .catch(error => console.error('Login failed:', error)); Key things to note: method: 'POST' tells the server what you’re doing. headers specify the data format (usually JSON). body must be a string—so we use JSON.stringify(). Option 2: The Powerhouse — Axios If you want more features out of the box (automatic JSON parsing, request/response interceptors, better error handling), Axios is your friend. Install it: # Web (via npm) npm install axios # React Native npm install axios # (or use Expo’s built-in fetch—both work!) GET with Axios import axios from 'axios'; axios.get('https://api.example.com/users') .then(response => { console.log('Users:', response.data); }) .catch(error => { console.error('Error fetching users:', error); }); POST with Axios axios.post('https://api.example.com/login', { email: 'you@example.com', password: 'supersecret' }) .then(response => { console.log('Login response:', response.data); }) .catch(error => { console.error('Login error:', error.response?.data || error.message); }); Bonus: Axios automatically transforms JSON requests/responses—no JSON.stringify() or .json() needed! Does This Work on Mobile? Yes—with caveats. Platform fetch() Axios Web (Chrome, Firefox, etc.) Full support React Native Built-in (via npm) Capacitor / Cordova Expo ️ One gotcha: CORS doesn’t apply in mobile apps (since there’s no browser sandbox), but you still need proper backend headers for web. Security & Best Practices Never hardcode secrets (API keys, tokens) in frontend code. Use HTTPS—always. No exceptions. Handle errors gracefully—users hate silent failures. For auth, store tokens in secure storage (e.g., AsyncStorage in React Native + encryption). Consider aborting requests if the user navigates away (to prevent memory leaks): const controller = new AbortController(); fetch('/api/data', { signal: controller.signal }); // Later, if needed: controller.abort(); Async/Await: Cleaner Syntax Both fetch and Axios work beautifully with async/await—making your code read like a story: async function loginUser(email, password) { try { const response = await axios.post('/login', { email, password }); return response.data; } catch (error) { console.error('Login failed:', error.message); throw error; } } Much cleaner than .then() chains, right? 🧪 Quick Comparison: fetch vs Axios Feature fetch() Axios Built-in (needs install) Automatic JSON parsing Request timeout (needs workaround) Interceptors Browser support Modern only Broader (with polyfills) Bundle size 0 KB (native) ~5 KB Use fetch if you want minimal dependencies. Use Axios if you want developer ergonomics and advanced features. Final Thoughts Whether you’re building a sleek SaaS dashboard or a cross-platform mobile app, HTTP requests are your lifeline to the backend. And thanks to JavaScript’s ecosystem, you’ve got two rock-solid options that work almost everywhere. Start simple with fetch(). Scale up to Axios when you need more control. And always—always—handle errors like a pro. Now go make your app talk to the world.
• Frontend

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• GameDev

  2D/3D, engines (Unity, Unreal, Godot), game design, animation! Create projects for PC, mobile, consoles. Solve errors, share life hacks, study trends (VR, multiplayer). Help for beginners and expert cases

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  • Unity
  • Unreal Engine
  • ThreeJs
  • BabylonJs
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