r/coding

Hey everyone

I just finished building a small RPG game in Python and wanted to share it here for feedback.

It’s a turn-based combat game where you fight different enemies like Goblins, Skeletons, Dark Mages, and a final boss (Ancient Dragon).

⚔️ Features:

- Turn-based combat system

- Leveling + XP progression

- Weapons & armor equipment system

- Inventory system (potions, items, etc.)

- Quests with rewards (gold + XP)

- Different enemy scaling based on level

- Boss fight with multiple phases

I built it mainly to practice OOP and game logic in Python, and I learned a lot about structuring systems like combat, inventory, and progression.

It’s still not fully balanced and I know there are things I can improve, especially:

- Damage balancing

- XP progression

- UI/UX improvements

Would really appreciate any feedback or suggestions 🙏

GitHub:

https://github.com/Ezz08/DragonFall-GameProject

Over the past few months, my co-founder and I have been building an AI-powered code review platform.

During development, we kept running into the same question:

Why are we trusting a single LLM to approve code that will eventually run in production?

Today, most AI code review tools rely on one model to evaluate everything:

• Security
• Performance
• Architecture
• Code quality
• Maintainability
• Best practices

That feels increasingly risky.

Human teams don't work like that. We rely on specialists, peer reviews, and different perspectives before merging critical code.

So we started experimenting with a different approach.

Instead of asking one AI to review a Pull Request, we orchestrate multiple specialized AI reviewers, each responsible for a different domain, and then aggregate their findings into a single review.

The hypothesis is simple:

>

We're not trying to build another coding assistant.

We're trying to answer a different question:

How do we know AI-generated code is actually safe to merge?

I'd genuinely love feedback from experienced engineers.

Some questions I'm curious about:

• Do you trust AI reviews enough to merge without reading the code?
• Have you seen AI reviewers miss obvious issues?
• Would multiple specialized reviewers provide more confidence, or just more noise?
• Where do you think AI code review is heading over the next few years?

If anyone is interested, we've documented our thinking and the architecture behind what we're building here:

https://docs.acrity.io

I'd really appreciate honest criticism. If we're wrong, I'd rather learn now than six months from now.

apple's safari mcp server only exposes 17 tools and runs inside an isolated webdriver session, while the community safari-mcp implementation has around 96 tools and can work with existing browser sessions.

the difference is pretty interesting. apple seems to be treating mcp as a clean-room debugging environment rather than giving agents access to your actual browser state.

there's also the bigger issue that most browser automation tooling is still heavily chromium-first.

this comparison goes deeper into both approaches:

https://rune.codes/hub/tech-trends/the-safari-mcp-server-could-change-how-developers-debug-websites

do you think browser mcp tools should be isolated by default, or is access to real browser sessions more useful?

https://leaddev.com/ai/engineering-managers-ditch-cloud-ai-for-local-llms

CTOs, engineering managers, and staff engineers are rushing to deploy autonomous AI agents across their businesses – either through their own volition or because of the clamor of demand from rank-and-file workers. However, they should think twice, a new study shows.

Enterprise large language model (LLM) agents are likely leaking company secrets, and throwing more compute at the problem is only making it worse, the study finds.

In part, that’s because of the AI’s ability to retrieve and synthesize vast amounts of internal data, from Slack messages to board transcripts, to automate tasks. By gathering that information, they also create issues with contextual integrity.

When retrieving dense corporate data, these agents routinely fail to disentangle essential task data from sensitive, contextually inappropriate information. Higher task completion rates often directly correlate with increased privacy violations.

Read the full story: https://leaddev.com/ai/frontier-ai-models-haemorrhage-sensitive-data