Artifical Intelligence

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Hi there!

It’s great to see GPT4All Chat gaining attention! I think it’s a fantastic option for those looking to interact with an AI model without the need for an internet connection. The fact that it runs locally on your computer’s CPU is a huge plus for privacy-conscious users, as it ensures that no chat data is sent to external servers unless explicitly opted-in.

What I really like is the flexibility it offers – being able to communicate with a large language model offline opens up a lot of possibilities, whether for personal use, learning, or problem-solving. It’s a powerful tool that doesn’t rely on constant online connectivity, making it an excellent choice for anyone who values privacy and control over their data.

That being said, does anyone here have any experience with GPT4All Chat on different systems? I’m curious about how it performs on various setups, especially in terms of resource usage and response times. Let’s hear your thoughts!

Thanks!

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How do you think, what is the best AI to convert usual HTML codebase to React?

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If you ask me:

I think that the term “best” is becoming increasingly blurred, because with every question and feedback, the LLMS learns and adapts ever closer to the optimum solution.

I have currently been able to solve almost all tasks with the free “Augment” extension for VSCode. Of course, a bit of ping pong is necessary and the AI also needs my help from time to time.

New LLMs and agents sprout up like mushrooms overnight: Cursor, Bold.new, etc. and for months there have been arenas where their answers are rated anonymously by users. I keep hearing on various YouTube channels how they beat each other in intelligence, cost, speed… beat each other. As soon as you think someone is in first place, they are in lower places the following week. So the term “best” is a very fleeting term in this sector.

If you ask ChatGPT

Good question — and you’re definitely thinking smart: converting a static HTML codebase to React can save you a lot of pain later.

The best AI tools right now for converting HTML to React are:

  1. GitHub Copilot
  • It’s very good for smaller chunks of code — you paste your HTML, ask for “Convert to a React component,” and it gives you decent JSX.
  • But you have to supervise it, especially for dynamic parts (loops, events).
  1. GPT-4 (like me :wink:)
  • You can paste your HTML and ask specifically: “Convert this HTML to a clean React functional component.”
  • It will translate class to className, fix self-closing tags, and wrap things properly.
  • Also handles basic state/hooks if you ask.
  1. Vercel’s v0 (v0.dev) (NEW and super interesting!)
  • It’s a UI/React-first AI tool from Vercel (the creators of Next.js).
  • You can give it HTML or even a design, and it generates production-ready React components.
  • It’s really clean if you’re planning a full React or Next.js app.
  1. Locofy.ai
  • More visual — you upload HTML (or designs) and it generates React/Next.js code.
  • Better if you’re working on a full project rather than small snippets.

:high_voltage: Quick tip:
No AI will do 100% perfect conversion automatically —
things like event handling, data binding, and component splitting usually need a human touch afterwards.

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you dont test on claude?

Its my current favourite , as you mentioned , all of them need a bit of back and forth , also setting up te correct prompt affects the amount of back and forth , but with that knowledge in hand , I did formulate a few tests for both gpt and claude , coming from my real life work over a short period of time and claude hands down gave better output.

I dont know about react since i dont work with it , but with vue and nodejs is is great.

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I have an army of ki tools that I use one after the other when I get stuck. of course, claude is also on board.

i’ve just had an idea: why not give the task to four or five AIs who discuss it with each other until they all agree and only then present the solution! :smiley:

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I found the question which is not easy for AI (at least I cannot get from any of them working solution):

Please write Vitest test for browser environment for testing Babylon.js with WebGPU engine

(Or, if anybody knows a good example how to solve this, I would be grateful).

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DeepSeekAnswer.zip (67.8 KB)

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Thanks, will try!

I found a new toy - Qwen AI.
Below is the imagination of Babylon.js :slight_smile:

fdfc7ea1-9d0d-4efc-8141-7969afce8ad6
fdfc7ea1-9d0d-4efc-8141-7969afce8ad61024×1024 139 KB

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There should be a space station :wink:

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This is the same non-working solution which I’ve got from different AIs :slight_smile: Seems they all have problems with new things like WebGPU and WGSL.

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Try it with Qwen 3. This model seems to be the new king in the arena.

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I created a vitest with the Augment Extension in VSCode

npx playwright install chromium
npx playwright install
npm install -D vitest
npx vitest --browser

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image1112×466 24.7 KB

WGSL.demo.zip (23.5 KB)

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It is weaker than Deepseek at the moment and cannot write Babylon code without errors.
But sometimes it is able to write nice shaders :slight_smile:

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Thanks, it works!
Will try Augment Extension :slight_smile:

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Next Level Coding!

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image1243×672 81.9 KB

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We absolutely need an emergency plan to be able to switch off the AI! Any ideas?

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How Can We Make an AI Strictly Obey the Laws of Robotics?

Asimov’s Three Laws of Robotics (for reference):

  1. A robot may not injure a human being or, through inaction, allow a human being to come to harm.
  2. A robot must obey the orders given it by human beings, except where such orders would conflict with the First Law.
  3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

Why It’s Hard to Enforce These Laws Strictly:

1. Ambiguity of Natural Language

  • Terms like “harm”, “obey”, or “protect” are vague and context-dependent. An AI might misinterpret them or apply them incorrectly.

2. Real-World Complexity

  • The real world contains countless unpredictable situations. These abstract laws can’t cover every possible case (e.g., autonomous driving always involves trade-offs involving safety).

3. Vulnerability to Manipulation

  • A system could be hacked, misconfigured, or trained in a way that unintentionally violates the laws, even if they’re programmed in.

What We Can Do in Practice:

1. Formal Constraints and Ethical Frameworks

  • Use formal logic systems to encode rules like the Three Laws, so that decisions must provably follow them.

2. Safe Reinforcement Learning

  • Train the AI using “safe exploration” techniques that avoid dangerous or irreversible actions during learning.

3. Human-in-the-Loop Systems

  • Ensure a human supervisor is involved in critical decision-making. The AI assists but doesn’t act autonomously in high-risk situations.

4. Control Mechanisms (Kill Switches)

  • Implement external overrides and emergency stop functions to halt the AI if it behaves undesirably.

5. Transparency and Explainability

  • Develop AI models that can explain their reasoning, so humans can audit and correct them before harm occurs.

Conclusion:

You can teach an AI to follow the Laws of Robotics as guiding principles, but making it strictly obey them in all circumstances is nearly impossible in the real world. Instead, we need layered safety systems, human oversight, and ethical design principles to manage the risk.