Developer Builds Python Bot to Automate Blackjack on Discord's OwO Bot

·1 views

A developer has created GhoSty OwO BlackJack Farm, a Python-based Discord self-bot that automates Blackjack sessions on the popular OwO Bot economy platform. The project, now at version 2.1, uses classic basic blackjack strategy for hit/stand decisions and conservative bet sizing rather than random or aggressive wagering. To mimic human behavior and avoid detection, the bot employs randomized delays between actions and alternates between timed play sessions and rest cycles. It relies on the legacy discord.py version 1.7.3, since the self-bot functionality it depends on was removed in later releases. The developer emphasizes the project is strictly educational, warns that self-bots can violate Discord's Terms of Service, and explicitly states the tool offers no guarantee of winning outcomes.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Developer fixes three open source bugs in one day, each backed by a failing test

A developer participating in DEV's Summer Bug Smash challenge set a personal rule to fix only real bugs and accompany each fix with a test that fails before the change and passes after it. Over a single day, they submitted pull requests to three separate open source projects. One notable fix targeted OpenClaw, a TypeScript-based AI assistant, where cancelling a settings change was incorrectly triggering a gateway restart and dropping active sessions. The root cause was a planning baseline that updated prematurely, causing the system to detect a mismatch even after the user reverted their changes. The fix pins planning to the last accepted configuration and advances the runtime baseline only when a reload is genuinely committed, while carefully preserving restarts required by plugin or runtime changes.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Ryker Chrome Extension Lets Users Visually Edit HTML and Markdown in Browser

A new Chrome extension called Ryker has been launched to simplify the editing of HTML and Markdown documents directly within the browser. Built for users who work with AI-generated HTML, Ryker allows edits to be made visually rather than through source code. As users proofread or rearrange content, the extension records all changes as machine-readable, prompt-ready change requests. These exported change logs can then be handed back to an AI agent to handle the actual implementation. Ryker supports both live webpage editing and loading local HTML or Markdown files for inline review.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Engineer shares open-source scaffold to bring AI agents safely into production

A software engineer with experience running a 25-agent production platform at Microsoft has published a lightweight open-source framework designed to make AI agents enterprise-ready. The scaffold, available on GitHub under an MIT licence, centres on three core mechanisms: a quality gate that scores agent outputs before any action is taken, a human approval gate that requires sign-off for high-stakes operations, and a provider abstraction layer that prevents vendor lock-in across models such as OpenAI, DeepSeek and Qwen. A state machine component explicitly models allowed workflow transitions, ensuring agents can only propose actions that a defined ruleset permits. The author argues that without such guardrails — covering output grading, auditable human oversight and model-agnostic design — AI agent projects remain demos rather than systems businesses can trust in production.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer Releases Open-Source Scaffold to Add Safety Guardrails to AI Agents

A developer who previously ran a 25-agent production platform has published an open-source framework called ai-agent-scaffold on GitHub, designed to help teams safely deploy AI agents beyond the demo stage. The lightweight harness centers on three core mechanisms: a quality gate that blocks substandard outputs before execution, an approval gate that routes sensitive actions to a human reviewer, and a model-agnostic provider layer that prevents vendor lock-in. A state machine component further restricts agents to only legally defined workflow transitions, reducing the risk of uncontrolled actions. Every blocked attempt and human decision is logged to an audit trail, addressing compliance requirements that enterprises typically raise before approving AI deployments. The project aims to close the gap between a functional AI prototype and a production-ready system with accountability built in.

0 comments Read more at DEV Community