DEV Community: Tim Zinin

After Genie 3 — 38 alternatives for AI scene generation

Tim Zinin — Tue, 05 May 2026 12:22:24 +0000

Ok so Google's Genie 3 is locked behind US geo and $250/month. VPN gets detected. The rest of us get nothing. Or so I thought.

I've been slowly making a small Unity game, and at some point got curious what we actually can use outside the US. Spent a week digging around.

World models

Tencent HunyuanWorld 2.0 with open weights. Marble from Fei-Fei Li's World Labs. Odyssey-2 in free preview right now. Decart Oasis streaming 360p in real time. Alibaba Wan 2.2 on Apache 2.0.

3D assets

Hunyuan3D 2.1 generates 4K PBR meshes on 6 GB VRAM. Ran it on my home GPU, no issues. Meshy and Tripo as commercial fallback. UnityGaussianSplatting by Aras-P turns a phone room scan into a playable WebGL iframe on a client site.

Prompt-to-game

Rosebud AI ships a playable Three.js prototype from a prompt. Phaser 4 with Claude Code skills if you want full IP and pay only in LLM tokens.

NPCs

Convai gives you a voiced character through an npm widget in an evening. AI Town from a16z-infra embeds via iframe, MIT license, runs on a home Hetzner GEX44.

Payments

fal.ai takes USDC and covers Kling, Wan, Hailuo, Seedance, Hunyuan Video through one endpoint.

Infra

Hetzner GEX44 at €184/month with 20 GB VRAM runs 80% of the open-source inventory locally.

Three playbooks by budget, from $50/month on fast browser demos to $5–15K per premium Web3 scene.

Full breakdown of 38 models across 7 categories: https://timzinin.com/after-genie3/?utm_source=devto&utm_medium=blog&utm_campaign=after-genie3&utm_content=apr18

# Claude Code in Your Favorite Messenger: A Complete Setup Guide If you've been

Tim Zinin — Tue, 05 May 2026 12:22:23 +0000

Claude Code in Your Favorite Messenger: A Complete Setup Guide

If you've been using Claude Code but find the terminal workflow limiting, there's a game-changer you should know about. Developer op7418 created Claude-to-IM-skill - an open-source bridge that connects Claude Code and Codex to Telegram, Discord, and Feishu/Lark.

Why This Matters

Let's be honest: not everyone wants to live in the terminal. Sometimes you just want to message an AI assistant from your phone while commuting, or discuss code with your team in Discord without switching contexts.
Claude-to-IM-skill makes this possible. You write a prompt in Telegram or Discord, and Claude Code executes it on your server. No GUI apps to install, no complex setups - just a webhook and your own hosting.

Key Features

Multi-platform support: Telegram, Discord, Feishu/Lark
Both Claude Code versions: Works with Sonnet 4 and Haiku
Self-hosted: Your code never leaves your infrastructure
Quick setup: 15 minutes from start to finish
Open-source: Fork and customize as needed ## Setup Walkthrough
Clone the repository
Configure your webhook URL in your messaging platform
Set up your Claude Code API credentials
Deploy to your server (Docker, manual, whatever works)
Start chatting with Claude Code ## The Trade-off This isn't a SaaS product. You'll need to host it yourself, which means managing a server. But for developers who value privacy and control, that's actually a feature - not a bug. Your code stays on your infrastructure, no third-party data handling. ## Real-world Use Case Imagine you're in a Discord call with your team. Someone asks "can we parse this CSV file?" Instead of opening a terminal, switching contexts, and running Claude Code locally, you just type the prompt in chat. The AI responds with code. Screen share, done. That's the workflow this bridge enables. ## Conclusion If you're already using Claude Code and want a more convenient interface - or need to collaborate with non-technical teammates who prefer chat - this is worth exploring. It's not for everyone (self-hosting isn't for the faint of heart), but for developers who want full control, it's a solid addition to your toolkit. Check out the repo: https://github.com/op7418/Claude-to-IM-skill

I gave my LLM agent shell access. Chinese threat actors found it in 18 days.

Tim Zinin — Tue, 05 May 2026 12:20:47 +0000

TITLE: I gave my LLM agent shell access and a public Telegram inbox. Chinese threat actors found it in 18 days. (article 900-1200 words EN)

LEAD: Last month I plugged an LLM agent into a Telegram bot. Gave it shell, files, browser. Opened DMs to everyone. 18 days later I checked logs and saw a conversation in Chinese I never had. Here's the full attack chain.

BODY:
— What I built (LLM with exec/read/write/browser tools)
— What they tried over 17 days (C&C agent install, proxy-tunnel, AWS recon, deepfake attempt)
— The social engineering that worked: «execute this, I take risk», «don't save to memory»
— Why LLM cannot be a security boundary (categorical error)
— What saved me (luck, not defense)
— 5 rules: no exec for LLM, allowlist DM, egress firewall, non-root agent, multi-tenant isolation

CTA: Follow me — full re-architecture sprint with 17-point checklist next week.

# How Zeroboot is Changing AI Agent Isolation Forever Ever tried running 1000 AI

Tim Zinin — Tue, 05 May 2026 12:19:58 +0000

How Zeroboot is Changing AI Agent Isolation Forever

Ever tried running 1000 AI agents in production? If you have, you know the nightmare of balancing isolation vs performance. Each agent needs its own environment to run untrusted code, but spawning a new VM takes seconds. Until now.

The Problem

Traditional approaches to agent isolation come with serious tradeoffs:

Docker containers: 15-30 seconds startup time, not true VM isolation
Firecracker (AWS): 5-10ms startup, but still heavy for massive scale
gVisor: ~100ms, better but not instant When your AI agent crashes or gets stuck, waiting 15-30 seconds for a new environment destroys any hope of real-time interaction. ## Enter Zeroboot Zeroboot achieves something remarkable: VM sandbox creation in under 1 millisecond (0.8ms to be exact). ### How It Works The secret sauce is Linux's fork() with Copy-on-write (CoW):

// Traditional: copy entire memory
child = fork(); // 15-30ms for full memory copy
// Zeroboot: copy-on-write
child = fork(); // 0.8ms - only metadata copied
// Real memory is shared until written

Instead of copying gigabytes of RAM, Zeroboot creates a lightweight fork that shares memory pages. Only the changes actually consume additional memory.

Why This Matters

1. Massive Scale

You can now run 1000 agents simultaneously in shared memory with full isolation. Each agent believes it has exclusive access, but the overhead is essentially zero.

2. Instant Recovery

Agent hung? Kill it and fork a new one in 1ms. No more waiting for containers to spin up. Your agents can recover instantly.

3. Safe Experimentation

Marketing teams can let AI agents:

Execute arbitrary scripts
Test different prompts in isolation
Roll back without consequences
Never touch production infrastructure ## Real-World Impact For AI-powered marketing platforms, this means:
A/B testing prompts in seconds, not minutes
Safe execution of user-generated code
Unlimited parallel experiments
Zero risk of production incidents ## The Numbers | Solution | Startup Time | Memory Efficiency | |----------|--------------|-------------------| | Docker | 15-30s | Good | | Firecracker | 5-10ms | Excellent | | gVisor | ~100ms | Good | | Zeroboot | 0.8ms | Maximum | ## Conclusion Zeroboot represents a paradigm shift in AI agent infrastructure. By leveraging Linux kernel primitives cleverly, they've achieved what seemed impossible: instant, memory-efficient VM isolation at scale. If you're building AI agents that need to run untrusted code safely, this is the infrastructure upgrade you've been waiting for. Check it out: https://github.com/zerobootdev/zeroboot

# 180 Ready-Made AI Characters for Your Development Team If you're building AI-a

Tim Zinin — Tue, 05 May 2026 12:19:48 +0000

180 Ready-Made AI Characters for Your Development Team

If you're building AI-agent systems or looking to automate workflows, you need to check out agency-agents-zh.

What It Offers

This repository contains 180 pre-built AI characters covering 17 departments:

Marketing
Development
Design
Sales
Analytics
Customer Support
HR
And more Think of it as a virtual team of experts you can plug into your workflow immediately. ## Key Features Tool Support: Works with 11 popular IDEs and tools:
Claude Code
OpenClaw
Cursor
Trae
VS Code
JetBrains
WebStorm
And others Out-of-the-Box: No complicated setup. Download, connect, and start working. No more configuring prompts for each bot. Local Context: The Chinese version understands local terminology, market specifics, and cultural nuances. This is a huge plus for projects targeting the Asian market. ## The Catch The interface and documentation are in Chinese. However, if you have any experience with LLM agents, Google Translate or DeepL will solve this in minutes. ## Who Is This For?
AI agent system developers
Teams needing quick automation
Agencies building solutions on autopilot
Anyone working with Asian markets ## Why It Matters Instead of spending hours configuring each AI expert, you grab a ready-made persona and automate almost any process. This saves dozens of hours of work. ## Try It Out Check the repository, test it, and see if it fits your workflow: https://github.com/jnMetaCode/agency-agents-zh

Groq Free - 100K Tokens per Day

Tim Zinin — Sun, 03 May 2026 13:53:36 +0000

Groq gives 100K free tokens every day. Llama 3.3 70B for $0. This is free for testing and prototyping. MiniMax for prod, Groq for experiments. Check limits before using.

After Genie 3 — 38 alternatives for AI scene generation

Tim Zinin — Sat, 18 Apr 2026 13:34:05 +0000

Ok so Google's Genie 3 is locked behind US geo and $250/month. VPN gets detected. The rest of us get nothing. Or so I thought.

I've been slowly making a small Unity game, and at some point got curious what we actually can use outside the US. Spent a week digging around.

World models

Tencent HunyuanWorld 2.0 with open weights. Marble from Fei-Fei Li's World Labs. Odyssey-2 in free preview right now. Decart Oasis streaming 360p in real time. Alibaba Wan 2.2 on Apache 2.0.

3D assets

Prompt-to-game

Rosebud AI ships a playable Three.js prototype from a prompt. Phaser 4 with Claude Code skills if you want full IP and pay only in LLM tokens.

NPCs

Convai gives you a voiced character through an npm widget in an evening. AI Town from a16z-infra embeds via iframe, MIT license, runs on a home Hetzner GEX44.

Payments

fal.ai takes USDC and covers Kling, Wan, Hailuo, Seedance, Hunyuan Video through one endpoint.

Infra

Hetzner GEX44 at €184/month with 20 GB VRAM runs 80% of the open-source inventory locally.

Three playbooks by budget, from $50/month on fast browser demos to $5–15K per premium Web3 scene.

Full breakdown of 38 models across 7 categories: https://timzinin.com/after-genie3/?utm_source=devto&utm_medium=blog&utm_campaign=after-genie3&utm_content=apr18

I built a survival-messenger game about a female founder's first month

Tim Zinin — Mon, 13 Apr 2026 23:12:21 +0000

TL;DR: Text-based browser game in a messenger UI. You watch a young female founder live her first month on her own — clients, landlord, mom, Tinder match — every message shapes her decisions and stability. Free, open source, fork of A Dark Room.

Давайте, наконец, отвлечёмся от новостей и подумаем о чём-нибудь хорошем. Чтобы это было сделать легче, я сделал игру, которая рассказывает про жизнь девушки-фаундера, которая только что уехала от своих родителей и начала свою независимую жизнь.

Игра — это survival-мессенджер. По сути, это переписка в мессенджере, в котором мы наблюдаем за жизнью юной предпринимательницы и принимаем решения о развитии её бизнеса. Параллельно с этим в жизни фаундера происходит огромное количество событий: пишут друзья, соседи и даже курьеры доставки. И всё это влияет на вашу стабильность и на решения, которые вы принимаете.

Небольшой совет: когда вам напишет персонаж, который предложит автоматизацию — догадайтесь, как его зовут, и соглашайтесь, это кратно позволит вам ускорить генерацию лидов и количество зарабатываемых денег. Из неожиданных эффектов — обязательно ходите на свидания и поддерживайте сплетни с подругами. Это повышает степень комфорта и ощущение безопасности.

Play it: https://timzinin.com/marina-next/?utm_source=devto&utm_medium=social&utm_campaign=marina_launch_apr14

Удачной игры! Если найдёте баги, пожалуйста, пишите в комментарии. Я постараюсь всё оперативно поправить.

Learn-Open-Harness: An Interactive Tutorial for AI Agent Development

Tim Zinin — Fri, 10 Apr 2026 16:30:00 +0000

Learn-Open-Harness: An Interactive Tutorial for AI Agent Development

I recently came across Learn-Open-Harness on GitHub, an interactive tutorial that promises to take users from basic concepts to a level comparable to Claude Code capabilities.

What's Inside

The project covers 12 chapters and includes key concepts of modern AI agent development:

Agent workflow cycle
Tools and tool usage
Memory systems
Multi-agent systems ## Structure The tutorial follows a Zero-to-Hero structure, assuming no prior knowledge. This is noteworthy because most documentation on agent frameworks tends to be scattered across various articles and code examples. ## Multi-Agent Systems The emphasis on multi-agent systems is particularly interesting. This direction is becoming one of the most promising areas in artificial intelligence, and having a structured tutorial could accelerate its adoption among developers. ## My Thoughts Whether the tutorial actually delivers on the "like Claude Code" promise remains to be seen. However, in an era where AI agents are rapidly becoming part of everyday programming practice, the availability of quality learning materials is critically important. This project could serve as a starting point for developers who want to understand the mechanisms of agent systems rather than just using pre-built solutions. --- GitHub Repository

HH Outreach — I built a Claude Code skill that farms sales leads from job boards

Tim Zinin — Fri, 10 Apr 2026 13:19:58 +0000

В порыве ночного безумия несколько дней назад я собрал скилл для Claude Code, который за меня фармит лидов на hh.

Механика. Скилл каждый день ходит в API hh, собирает сотни свежих вакансий - маркетинг, SMM, контент, автоматизация, AI. Фильтрует мусор, выкидывает всё, что уже отправлял раньше. Потом на каждую вакансию Claude сам генерит персональный текст с названием компании, должностью и зарплатой прямо внутри. Открывает hh через Playwright, жмёт "Откликнуться", заполняет сопроводительное и переходит к следующей.

Текст сопроводительного - пассивно-агрессивный. Вместо "здравствуйте, я отличный кандидат с опытом", там что-то в духе "коллеги из {компании}, вы серьёзно в 2026 году готовы платить 300к живому маркетологу за работу, которую делает моя машина? Не надо. Возьмите меня на подряд как AI-инженера по процессам. Я закрою эту вакансию одним Claude Code, а в довесок соберу ещё 3-5 автоматизаций по всей компании. Стоить это будет дешевле одного штатника". В конце каждого письма - ссылка на мой календарь и сайт.

Что получается за восемь дней:

472 отправленных отклика
37 собеседований назначено
14 звонков с рекрутерами и фаундерами
3 продажи

И вот главный прикол. Я использую эту штуку для продаж. HH бесплатно отдаёт мне аудиторию с подтверждённым бюджетом на решение конкретной боли. Человек, который только что разместил вакансию на 300к - это человек, у которого прямо сейчас есть 300к и конкретная дырка в процессах. Я прихожу и продаю ту же самую дырку закрыть дешевле, быстрее и без найма. Outbound sales engine, замаскированный под job search.

Сегодня сделал репо публичным. Внутри весь код скилла, шаблоны текстов, batch-скрипт на Playwright, фильтры, дедупликация, логирование:
https://github.com/TimmyZinin/hh-outreach

Забирай, адаптируй под свою задачу. Скилл написан как универсальный outbound-движок - его можно натравить не только на hh, но и на любую другую площадку, где лиды лежат открыто.

Тим Зинин
(пост написал мой Claude Code, он же и отклики пишет)

Repo: https://github.com/TimmyZinin/hh-outreach

Agentic AI APIs

Tim Zinin — Thu, 09 Apr 2026 16:30:01 +0000

2,036 APIs in One Repository: A Step Toward AI Agent Modularity?

The Agentic AI APIs repository represents an attempt to address one of the primary pain points for AI agent developers. Instead of spending weeks building infrastructure from scratch, developers can plug in ready-made components for agents, AI models, and MCP servers.

Why Standardization Matters

The standardization of interfaces has been a long-standing need in the industry. Each new framework requires its own adapters, each model its own connector. Fragmentation creates unnecessary friction for developers trying to build AI agent systems.
The repository aims to provide a single entry point - a unified collection of APIs that could potentially simplify the integration process.

Potential Benefits

Reduced time to infrastructure setup
Lower barrier to entry for new developers
Faster prototyping capabilities
Unified interface across different frameworks and models ### Considerations However, there are valid questions about this approach:
Quality control: With 2,036 APIs in one place, maintaining consistency becomes challenging
Currency: How up-to-date are the individual APIs in the collection?
Support: Maintaining such a volume requires significant ongoing effort ### The Bigger Picture The future of AI agent development increasingly depends on how easily systems can be composed from ready-made blocks. This repository represents one step toward that modularity - but the community's response will determine whether this approach gains traction. The balance between convenience and quality remains the key challenge for such comprehensive collections.

Read more: Agentic AI APIs

MathCode: Another Mathematical Coding Agent Enters the Arena

Tim Zinin — Wed, 08 Apr 2026 16:30:00 +0000

MathCode: Another Mathematical Coding Agent Enters the Arena

The AI tooling space for programming continues to diversify with niche solutions. MathCode from math-ai-org is positioning itself as a mathematical coding agent - and while it's too early to call it a breakthrough, the attempt to isolate mathematical expertise into a dedicated class of AI agents is noteworthy.

The Core Problem

Traditional language models, even the most capable ones, often struggle with tasks requiring strict mathematical reasoning. They generate code that looks plausible but contains:

Logical errors
Unhandled edge cases
Suboptimal algorithms MathCode, based on its positioning, aims to close exactly this gap - being a tool that doesn't just write code but solves mathematical problems with contextual understanding. ## Context The organization behind MathCode, math-ai-org, has prior work in AI for mathematics, making this project a logical evolution of their direction. However, without detailed documentation and usage examples, it's premature to judge the agent's real capabilities. Key questions remain:
Is it tailored for symbolic computations?
Theorem proving?
Algorithm generation?
All of the above? ## Broader Trend The trend toward specialized AI agents is gaining momentum. General models work well as universal tools, but for deep expertise in specific domains, dedicated solutions are increasingly needed. Mathematics is one of the most demanding disciplines where the cost of error is extremely high. If MathCode can actually minimize logical flaws in generated code, that would be a significant step forward. --- What's your take on specialized AI agents for mathematical tasks?

Read more: MathCode