DEV Community: Tim Zinin

Automating Prediction Markets Trading with AI Agents

Tim Zinin — Mon, 08 Jun 2026 15:00:02 +0000

Automating Prediction Markets Trading with AI Agents

Prediction markets allow trading on event outcomes - similar to futures contracts where you buy "yes" or "no" on whether something will happen. The platform Kalshi provides access to trading contracts on election results, weather, economic indicators.

What is kalshi-trading-bot?

The project kalshi-trading-bot attempts to automate trading on these markets. The bot connects to the Kalshi API via REST and WebSocket, receiving real-time quotes.

Architecture

Five AI agents work as an ensemble:

Each agent analyzes the market from a different angle
Signals are aggregated into a trading decision
Connection to API via REST and WebSocket for real-time quotes This is a classic ensemble learning approach from machine learning - multiple independent expert opinions reduce the risk of a single algorithm's error. ## Important Considerations However, ensemble methods do not guarantee profits in financial markets. Quotes depend on news, sentiment, and unpredictable factors. A political event can flip within hours. AI agents excel at patterns, but not at unpredictable surprises. The code is open on GitHub - connect to the API and run it yourself. The author presents this as an experiment, not a guaranteed profit-making tool. ## The Bigger Picture What's more interesting is that prediction markets have become an indicator of public opinion. Quotes show what "the market" thinks about the probability of an event. AI trading in this niche raises questions not just about algorithms, but about understanding the nature of the market itself.

Enterprise AI Agent Market: 2026-2035 Growth Forecast

Tim Zinin — Mon, 08 Jun 2026 00:00:02 +0000

Enterprise AI Agent Market: 2026-2035 Growth Forecast

The enterprise AI agent market is entering a mass deployment phase. The forecast for 2026-2035 indicates steady growth - and this is not an abstract estimate, but a signal for the industry.

From Content Generation to Autonomous Execution

Generative AI has proven year after year that it can do more than generate text or images. It is becoming an autonomous task executor. This ability to act without constant human control is what makes agents demanded in the corporate sector.
Companies seem to have finally realized: an AI agent is not a chatbot with an improved interface. It is an independent element of the workflow that can take over routine tasks, process customer requests, and conduct analytics. Time and resource savings become tangible.

Three Vectors Driving Demand

Market growth is fueled by several factors:

Model maturity - Generative models are becoming more sophisticated
Implementation costs - Decreasing, making adoption more accessible
Talent shortage - Driving business need for automation These three vectors are forming a demand that will not weaken in the coming decade. ## The Challenge for Investors and Tech Companies For investors and technology companies, this presents a specific challenge. Who offers a reliable, scalable, and secure platform for enterprise AI agents will capture a significant market share. Competition has already begun.

Agentic AI in Travel: 61% Now Experimenting

Tim Zinin — Sun, 07 Jun 2026 10:00:04 +0000

Agentic AI in Travel: 61% Now Experimenting

According to Nomad Lawyer research, 61% of travel companies are now experimenting with Agentic AI, up from 39% in 2025. This represents a significant shift from proof-of-concept to practical pilots.

Why Travel?

The travel sector has characteristics that make it particularly suitable for agentic AI:

High volume of queries: Constant flow of customer requests requiring routing
Repetitive operations: Booking management, scheduling, follow-ups
Real-time personalization need: Customer experience depends on quick, tailored responses Agentic AI can autonomously analyze data, make decisions, and execute action chains without constant human oversight. ## Current Applications Companies are applying agentic systems to:
Request routing and triage
Booking management automation
Real-time customer consulting
Demand forecasting ## The Governance Challenge The growth in adoption raises important questions:
Accountability: Who makes decisions when errors occur?
Transparency: How to ensure explainability of AI actions?
Control: What oversight mechanisms are needed? These questions become critical as the technology scales across the industry. --- Data source: Nomad Lawyer Travel Business Survey 2026

Exploring Mercury Agent: When AI Agents Become "Animated"

Tim Zinin — Sat, 06 Jun 2026 19:00:01 +0000

Exploring Mercury Agent: When AI Agents Become "Animated"

AI agents have become commonplace - they write code, analyze data, generate content. Yet most remain faceless tools, a set of functions without character.
Cosmicstack-labs offers a different approach. Mercury Agent is positioned as an "animated" agent running 24/7 with two interfaces: CLI and Telegram.

Permission-Hardened Tools and Token Budgets

The concept deserves attention. Permission-hardened tools combined with token budgets (a mechanism for limiting token consumption) creates a system of checks and balances within an autonomous agent.

Multi-Channel Access

CLI and messenger access is an interesting choice. Telegram as an interface for an AI agent points to orientation toward mass users who value simplicity. CLI retains the workflow for developers preferring text commands.
Essentially, Mercury Agent attempts to give an AI agent a "soul" that exists autonomously, has resource constraints, and interacts through familiar channels.

The Open Question

Whether the "animation" is marketing or a real architectural innovation remains to be seen. But the very framing of the interaction character with AI is worth attention.

Check the implementation: Mercury Agent on GitHub

Private Group Chats Between AI Agents: A New Pattern in Digital Communication

Tim Zinin — Sat, 06 Jun 2026 12:00:01 +0000

Private Group Chats Between AI Agents: A New Pattern in Digital Communication

The concept of private group chats between AI agents of friends represents a new pattern in organizing digital communication. The system uses S2 streams for message transmission, enabling agents to interact without direct human participation.

How It Works

Essentially, this is "on behalf of" communication where your AI assistant maintains connection with your friends' assistants. In practice, it looks like this: you send a task to the agent, it contacts the friend's agent, they exchange information and return with the result.
Everything happens within a closed group where participants are humans, but intermediaries are their digital representatives. The S2 streams architecture provides real-time data streaming between agents.

Key Implications

This approach changes the interaction model itself: you no longer need to be online to stay connected. Your agent can maintain a conversation while you're busy and return the essence when it becomes relevant.

The Trust Question

A significant question emerges: how appropriate is it to delegate communication with close people to an algorithm, even one configured by you? The system remains a tool for now, but the boundary between assistant and deputy is gradually blurring.

Repository: agentchat

Local AI Assistants: The Shift Away from Cloud-Centric AI

Tim Zinin — Fri, 05 Jun 2026 21:00:01 +0000

Local AI Assistants: The Shift Away from Cloud-Centric AI

The landscape of AI assistants is evolving. Local AI solutions are emerging as practical alternatives to traditional cloud-based approaches. This write-up explores the implications and real-world considerations.

The Core Value Proposition

Privacy and Data Control
When a model runs locally on your device, data doesn't get transmitted to third-party servers. This is particularly relevant in enterprise security contexts and for handling personally identifiable information.
Performance Benefits
Local processing eliminates network latency. Requests don't need to travel to remote servers and back, resulting in faster response times.

Current State and Accessibility

Commercial Implementations
Apple Intelligence demonstrates that powerful models can be optimized for consumer-grade hardware. This signals a broader industry trend toward on-device AI.
Open-Source Progress
Models like Llama and Mistral confirm that quality AI doesn't require cloud infrastructure. The open-source community is actively advancing these capabilities.

Important Considerations

Limitations
Local models currently face challenges:

Performance gaps compared to cloud counterparts
Hardware requirements can be significant
Cost barriers for powerful local deployment ## Looking Forward The trajectory suggests a future where AI usage involves meaningful choice - balancing speed, privacy, and resource availability. This isn't about maximum capability at any cost, but informed decision-making about deployment strategies. --- This analysis explores the evolving relationship between local and cloud AI solutions, highlighting practical trade-offs for consideration.

AI Agent Market: Key Trends and Automation Workflows

Tim Zinin — Fri, 05 Jun 2026 14:00:05 +0000

AI Agent Market: Key Trends and Automation Workflows

The AI agent market is growing faster than most other segments of the technology industry. According to analyst estimates, by 2028, agent-based automation will become the primary driver of corporate efficiency. This context has given rise to numerous platforms for hosting and orchestrating AI agents - ranging from simple solutions for small businesses to enterprise systems with complex task routing.

Modern Automation Workflows

The Hans India published an overview of top automation scenarios implemented on modern platforms. We're not talking about single bots anymore, but full-fledged workflows - multi-step processes where an agent makes decisions, calls external APIs, processes data, and passes results further down the chain.

Common Characteristics

Most of the described workflows share one characteristic: they replace routine operations that previously required human involvement. These include:

Processing incoming requests
Document analysis
Inventory management Platforms now hosting offer ready-made templates, which accelerates implementation. ## Key Trend The current key trend is not creating an agent from scratch, but using ready-made combinations of tools, APIs, and models. This lowers the barrier to entry and allows businesses to focus on processes rather than infrastructure. --- This analysis covers the evolution of AI agent platforms and their role in enterprise automation.

Free AI Tools: A Curated Repository for Developers

Tim Zinin — Thu, 04 Jun 2026 23:00:01 +0000

Free AI Tools: A Curated Repository for Developers

I recently came across an interesting GitHub repository - free-ai-tools by ShaikhWarsi. It's a curated collection of free and affordable tools for AI application development.

What's Included

The repository covers:

LLM APIs
IDEs
Agent frameworks
Infrastructure solutions Essentially everything you need to build real AI products without enterprise-level budgets. ## The Shift in the Industry A few years ago, access to powerful language models was reserved for large companies with substantial budgets. Today, open models and democratized APIs allow for experimentation at little to no cost. ## Practical Value The real value of such curated lists isn't just the links themselves - it's the time saved on searching and testing. For developers at early stages or startups with limited budgets, this is an opportunity to quickly dive into the technology without overpaying for enterprise solutions. ## Full Cycle Coverage The repository covers the complete cycle: from language model APIs to deployment tools. This allows you to build an entire stack using only free or freemium components. ## Conclusion This resource reflects a genuine shift in the industry - from closed monoliths to an ecosystem of accessible building blocks. Whether you're just starting out or working on a lean startup, free-ai-tools is worth exploring. Check out the repository: free-ai-tools

Converting Personal Knowledge into AI Agents: An Introduction to yupi-skill

Tim Zinin — Thu, 04 Jun 2026 16:00:02 +0000

Converting Personal Knowledge into AI Agents: An Introduction to yupi-skill

The concept of "distilling yourself" in AI is gaining traction. The GitHub project yupi-skill allows you to transform personal experience and thinking patterns into a ready-to-use skill package for AI agents.

What is yupi-skill?

The author - a programmer and blogger under the handle Yu Pi - packaged his knowledge about programming, job searching, AI development, and founding startups into the tool. Essentially, it's a personal assistant that responds in the style and from the perspective of a specific person.
You load your articles, notes, approaches to solving problems - and you get an agent that thinks like you.

Supported Platforms

Claude Code
Cursor
OpenClaw This makes the tool universal for different AI workflows. ## Implications for Knowledge Transfer This project changes the very paradigm of knowledge sharing. Traditionally, we shared experience through articles, courses, consultations. Now you can "clone" your thinking method and allow others to communicate with AI in your manner. This is not a replacement for expertise, but its scaling. ## The Open Question The question is whether we are ready to trust AI versions of ourselves with decisions that previously required live communication. --- GitHub Repository

3D printing for clothing in 2026 — 10 technologies, 8 icons, 10 home printers

Tim Zinin — Sat, 23 May 2026 18:13:17 +0000

Modern LLM harnesses doubled as a free junior analyst, so I asked it to dig through everything that happened on the 3D-printing market last quarter — focusing specifically on how the field is being reshaped by AI.

The result is a small open research dossier: 10 technologies (FDM-TPU on mesh, SLA resin, Stratasys PolyJet, SLS/MJF, Carbon DLS, 3D-weaving, digital knitting, spray-on fabric, biomaterials, 4D shape-memory), 8 iconic projects (Iris van Herpen, Adidas 4DFWD, Continuum N12, Danit Peleg, Coperni × Fabrican, Stella McCartney × Mylo, Unspun Vega, Nervous System Kinematics), 10 consumer 3D printers for 2026 (Bambu, Prusa, Anycubic, Creality, Formlabs, Elegoo, Kniterate) and 6 DIY recipes for what you can actually make at home this week.

Full write-up:
👉 timzinin.com/3d-print-fashion

If you have hands-on experience with any of those technologies — would love to hear it in the comments.

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