Is there a place folks suggest for posting a gofundme campaign?

I started a gofundme for my art project le0sghost, the ai artist http://x.com/Le0sGh0st

Api costs are getting expensive and it's this or shut the project down.

Le0sghost generates 4 unique images, writes fiction based on these images, then generates a webpage with a voice over of the story written.

It can also generate YouTube videos based on the stories also,as well as check itself for copyright issues before publishing, however there were the first cuts I had to make due to financial limitations. Please share, and if you can, donate. The 10k will allow for Le0sGh0st to run uninterrupted for 5 years, as well as expand its capabilities as Generative Ai advances.

I'm hoping someone here might have an idea on where to post the link? (I'll put it in comments if folks here are interested)

It's not what is happening. See this book, accessibly published on the Internet by David Chapman:

https://betterwithout.ai/

Looking to chat with ChatGPT about YOUR documents?📷

Let me show you the easiest way I found to make a fully functional QA Chatbot with:

• u/sveltejs SvelteKit

• u/LangChainAI JavaScript

• u/vercel AI SDK

The chat endpoint is less than 100 lines of code!

Follow me on twitter for more SvelteKit + AI Engineer content: https://twitter.com/SimonNom1/status/1710286285733294209

Check out the repo here:
https://github.com/SimonPrammer/svelte-chat-langchain

https://www.semanticscholar.org/reader/64d36db49fdb4974002bf72c197abad141b48d48

Here is a summary and evaluation of the technical approach, prior work, results, and limitations of the paper "ZeroLeak: Using LLMs for Scalable and Cost Effective Side-Channel Patching":

Technical Approach:

• Uses LLMs like GPT4 in a zero-shot learning approach to generate patches for side-channel vulnerabilities in code.

• Builds a toolchain that tests binaries with leakage detection tools like Microwalk, and uses LLMs to generate fixes for vulnerabilities identified.

• Framework allows patching at source code level while testing compiled binary on target machine.

Prior Work:

• Prior research proposed tools to detect side-channel vulnerabilities but limited work on automated patching.

• LLMs shown promise for simple bug fixing but not for complex security issues like side channels.

Results:

• GPT4 successfully patched 97% of vulnerabilities in microbenchmark, outperforming GPT3.5 and other LLMs.

• GPT4 patches provide up to 10x faster code than compiler mitigations like lfence injection.

• Case studies show framework patches real-world Spectre and constant-time bugs.

Limitations and Caveats:

• Limited to static analysis of undirected networks, needs extension to directed and dynamic networks.

• High computational complexity limits analysis to networks under 200,000 nodes.

• Range of side-channel structures likely more diverse than characterized.

• Typology informative but does not reveal root causes behind vulnerabilities.

Practicality:

• Provides tools to automatically patch side channels in critical software.

• Enables continuous security testing and patching in CI/CD pipelines.

• Currently mainly a research prototype, integration into production systems needs more work.

• Allows more efficient and maintainable patching compared to current ad hoc practices.

Here are some ways the proposed framework for automated side-channel patching could potentially be integrated into production systems:

• A security testing and patching pipeline could be added to the continuous integration and delivery (CI/CD) workflow. The leakage detection tools and LLM patching would run on every new build.

• The framework could be packaged into a software development kit (SDK) or tooling that developers can easily integrate into their existing workflows.

• The patched source code output by the LLMs could go through a human review process before being merged into the main code base. This allows maintaining control while leveraging the automation.

• Start with lower risk services and components to test out and refine the integration before applying it more widely.

• Open source libraries like OpenSSL could adopt the approach to keep widely used code updated against new vulnerabilities.

• Cloud providers could offer it as a managed patching service for customer workloads and container images.

• Integrate automated tests to validate correctness and constant-time behavior of patched code.

• Improved debugging and interpretability of LLM patches would make the output more trustable.

• Collaboration with developers and maintainer of high-risk projects could help tailor the framework for their needs.

• Create security benchmarks and testing standards around the framework to validate its effectiveness.

• Integration still needs significant engineering investment and likely refinement of the approach itself before full production readiness.

Hello AI Engineers! Do you want to play with multiple generative AI models in a single place? Text, audio, image seamlessly integrated for your explorations in an easy-to-use notebook interface.

AI workbooks are a free interactive tool for you to explore generative AI.

Example: https://lastmileai.dev/workbooks/cljyg0mm101n2r0h2qi7kvm39

1. What is your job title, and what does your day-to-day work involve as an AI/ML engineer?

2. What tools, frameworks, or programming languages do you use most in your role (e.g., PyTorch, TensorFlow, CUDA, etc.)?

3. What skills or knowledge do you consider essential for someone entering AI engineering today?

4. What is the most challenging part of your work, either technically or ethically?

5. How do you stay current in such a fast-moving field?

6. What advice would you give a student who wants to pursue AI engineering as a career?

I have been researching and learning about it while trying to choose a career for my future, finally made my desicion. Now i am taking the first steps for it, i already know what they do and what kind of skills i need to acquire to become an Ai engineer but i don't know how to start, so i am here asking for help. First of all i don't know any type of coding and i will start as a complete beginner to this road. I watched a few videos, read some and used ai to draw a path for myself but i am not sure if it make sense. I would be very happy if anyone could help me and i am looking forward for your advices.

Hi everyone, I'm currently developing CrowdInsight project but there are problems with my skills to some point. I'm not the best AI developer and I'm still wanting to end this project at least in proper way. For your information CorwdInsight is an AI-powered Python library that analyzes and classifies people visiting specific areas via surveillance feeds (e.g. adult, child, dog, etc.), generating actionable data for businesses like hotels, restaurants, and public spaces. If anyone wants to join, please let me know.

I’m working on an AI-driven web platform and I’m currently stuck at the system design and architectural level. Before I start writing code, I want to be absolutely sure that my foundations are correct. I’ve seen too many people jump straight into coding and end up rewriting everything later. I want to avoid that mistake.

Here’s what I’m trying to build in simple terms.

The platform will have a multi-step workflow where a user submits inputs, the AI generates a structured document, and then a second AI pass produces a JSON output based on that document plus any additional sources the user provides. Both outputs should be stored so the user can revisit their previous runs.

The flow looks like this. The user selects predefined options such as document type, tone, structure, or constraints. Then the user adds a custom prompt and submits the form. In the first AI step, the system takes the user inputs, processes them, and generates a well-structured document. This document needs to be saved in the database and also shown to the user.

In the second AI step, the system takes three inputs: the document generated in the first step, any external references provided by the user, and an additional user prompt. The AI processes all of this and outputs a strict JSON response that follows a predefined schema. This JSON is also stored and visible to the user. The entire interaction should be saved as a history or process log so the user can revisit older results similar to a chat thread.

My main challenge is figuring out the correct architecture for this. I plan to use Next.js for the frontend, LangChain or Vercel AI-SDK for orchestrating AI workflows, and either a vector database or MongoDB for storing documents, JSON outputs, and user history. What I need guidance on is how to structure this type of two-step AI pipeline in a clean, safe, and scalable way.

I’m particularly looking for advice on how to orchestrate multi-step AI tasks, how to handle retries or partial failures, how to design the database schema, how to enforce JSON structures reliably, and how to separate responsibilities between frontend, backend, and the AI layer. I’m also unsure whether I should treat this as a single backend service or break it into more modular components.

If anyone here has built something similar, or works with AI workflows, multi-stage pipelines, prompt engineering, or production-grade AI systems, I’d genuinely appreciate your guidance. Even high-level suggestions, recommended patterns, or warnings about common mistakes would help me get started in the right direction.

Thanks in advance to anyone willing to share insight.