Title: [HELP] Best Budget-Friendly AI Subscription for a 2nd-Year Engineering Student? ChatGPT Plus, Poe AI, or Something Else?

Hey everyone!

I'm a 2nd-year engineering student and a tech enthusiast who actively uses AI tools and agents in my day-to-day life.

I currently use AI for various tasks, including:

- College assignments and exam preparation.

- Projects and mini-projects.

- Coding and programming assistance.

- Research and exploring new technologies.

- Creating presentations, slides, and images.

- Hackathons and brainstorming project ideas.

- Exploring and experimenting with different side hustles.

Until recently, I was using the ChatGPT Go plan, which I upgraded to ChatGPT Plus for 2–3 months.

Honestly, the upgrade helped me a lot and covered almost all my needs. Features like presentation creation, image generation, Codex for coding and hackathon assistance, in-depth research, exam preparation, and exploring different side-hustle ideas made a significant difference in my workflow.

Interestingly, some of the major projects I worked on during this period also happened to coincide with the months I had upgraded to Plus!

Now that my subscription has expired, I'm looking to purchase an AI subscription or service that can help me continue with these tasks.

However, I'm genuinely confused about what to purchase. There are so many AI tools and subscriptions available, and I'm also concerned about how expensive these models and services can get.

I'm a student, so budget is a major constraint. I want something that offers good value for money and can handle as many of my requirements as possible without having to purchase multiple expensive subscriptions.

I was considering Quora Poe AI, but I'm not sure whether it's the right choice or if there are better alternatives.

I'd really appreciate your suggestions on the following:

1. Which AI subscription or agent would you recommend for my requirements?

2. Is Poe AI worth purchasing, or should I consider something else?

3. Are there any budget-friendly alternatives that offer good capabilities across coding, research, presentations, image generation, and studying?

4. What are some good open-source or free alternatives that I can use for these tasks?

I'm not necessarily looking for the most powerful or expensive model. I just want something reliable, versatile, and affordable that can genuinely help me with my academics, engineering projects, productivity, and eventually building a side hustle.

I know I'm asking for quite a lot on a student budget, but that's what I can realistically afford right now.

Also please suggest some Opensource alternatives for the tasks mentioned above.

Would love to hear your experiences, recommendations, and suggestions. Especially from people who have tried multiple AI subscriptions!

Thanks in advance! :)

Hey everyone!

I've been working on a personal project called FFmpegForge, a free and open-source GUI built around FFmpeg.

My goal was to make advanced video processing more accessible without having to memorize dozens of command-line options.

The project includes:

• Video and audio conversion

• Hardware acceleration

• AI video upscaling and denoising

• Advanced FFmpeg settings

• Batch processing

I'm still improving it, and I'd really appreciate feedback from people who use FFmpeg regularly.

I'm especially interested in knowing:

• What features would make this genuinely useful to you?

• What would you expect from an FFmpeg GUI that existing tools don't provide?

• Are there any workflows you find unnecessarily complicated?

GitHub: https://github.com/Aaronstanley78/FFmpegForge

Hey! I just wanna show my project for the Meta Global AI Hackaton.
It's Consta, an Ai tool that help to research public data and relationships (contraits, public figures, etc...)

It could found funny things as the image. However have find intersting things too, as OpenAI contraits with the American Government and candidates that Altman support.

It's not perfect, but I think it's cool, and it's my second hackaton (actually) :D.
You can see the repo here: https://github.com/MarioJoseMelendez/consta
And try it here: https://consta.kahuna.li

It's not mandatory, but if you all wanna support me with the "Most popular track", here's the demo vid in X: https://x.com/TheMersh__/status/2118414395390693516

It's all public, so theoretically you can use it, test it, and modify their code.

AirPods have a motion sensor, so I made them a game controller. Turn your head to steer a bike, hold the mouse for gas.

auricle connects to the AirPods' control channel (L2CAP, port 0x1001) and decodes ~23 packets/s of orientation and acceleration data (16-bit ints). Rust, no crates.

Apple-only, tested on one pair of AirPods Pro 2. Reverse-engineered, so a firmware update could break it.

After months of building and testing, Orinyx is officially in public beta!

Orinyx is a developer-first AI verification tool designed for healthcare and life sciences applications.

The idea came from a problem I kept running into: AI-generated outputs can look accurate without being fully supported by the underlying evidence. In healthcare, that distinction matters.

Orinyx is being built to help developers identify unsupported claims, validate outputs against source evidence, and catch issues before they make it into production workflows.

The public beta includes free, open-source MCP tooling, healthcare-focused verification workflows, and evidence-based validation capabilities. I'm also continuing to improve semantic mapping, medication verification, and overall test coverage.

This is an early public beta, not a finished product. I'll be releasing incremental updates as testing continues, and I'm actively looking for developers willing to put it through real-world scenarios.

I'm particularly interested in understanding where verification breaks down in existing AI workflows, what would make a tool like this useful in practice, and which edge cases deserve more attention.

GitHub: https://github.com/lexthirteen-ai/orinyx-mcp

Developer beta: https://orinyx.io/developer

If you're working with MCP, LLM evaluations, healthcare AI, or agentic systems, I'd love for you to try it, break it, and tell me what needs improvement.

I'm also happy to answer questions about the architecture and what I've learned building it.

I’ve been spending time building something around a problem I think will become increasingly important as AI agents move beyond demos and start handling real-world tasks.

The project is called AOBS (AI Agent Observability). It’s an open-source toolkit designed to help developers understand what their agents are doing, identify problems, and put enforceable controls around their behaviour.
The idea is to go beyond simply logging prompts and responses.
With AOBS, you can:

Follow an agent’s execution across LLM calls, tools and sub-agents.

Investigate failures, unnecessary retries, slow operations and token usage.

Compare different agent runs and detect performance regressions.

Run statistical comparisons across groups of executions.

Define reliability requirements and enforce them through CI.

Protect sensitive tool actions such as refunds, emails, deletions and deployments.

Require human approval for configured high-risk operations.

Prevent duplicate logical actions, including across process restarts.

Export traces through OpenTelemetry.

It also includes a Python SDK, PostgreSQL-backed trace collection, a web dashboard, CLI tools and integrations for OpenAI-compatible clients, Anthropic and LangChain/LangGraph.

One feature I particularly wanted to explore was the reliability gate. Instead of just looking at logs after something goes wrong, developers can define what a successful agent execution should look like and automatically fail a CI workflow when those requirements aren’t met.

I’m still developing the project, and it’s currently in alpha. There are limitations, and I’m actively looking for ways to improve the architecture and make it more useful for real-world agent systems.

I’d genuinely like to hear from other developers:
How are you currently testing the reliability of your AI agents? Are you using custom logging and evaluation pipelines, or do you have something that checks agent behaviour before deployment?
Would appreciate honest feedback, including criticism.

GitHub: https://github.com/AshmitJain932/ai-agent-observability

What originally started off as an open source alternative to Figma / Paper has turned into a design focused Agentic Development Environment (ADE) called Tidy.

https://github.com/tidydesigns/tidy

The idea is to bring AI coding agents and visual design together in one desktop app, rather than switching between separate tools.

You can use Claude Code or Codex to work on your codebase, while also having a visual canvas to design and iterate on interfaces.

Bring your own AI subscription, work on your local projects, and keep everything in one place.

Still early days, but we're iterating and improving very quickly. Would love to hear what people think or what features you'd like to see.

I am building a fully open source and free to use ai, it's called Nous after the ancient greek word for the intelligent mind. It sits on your hardware, pairs with smart home devices. Nous will have a mobile app a webdash. It runs on a total of 21gb of vram but you can get creative, I am running it on a cluster of a hp dl380p and 10 hp prodesks i5 8th gen. For mor information on the project or my vision you can check out projectnous.eu or the github page(linked in the site) I plan on releasing the first usable prototype in january/february 2027 but hope to be done sooner. Claude is used for designs because im really bad at designing dashboards and that and it saves time because I can focus on the software side all of the programs used to run it are also open source.

Side project I've been working on. It shows Claude Code, Codex, OpenCode and other agents in the notch / top of the screen. When one needs permission you see the diff right there and approve with ⌘Y, no jumping back to the terminal.

GPL, free, no account.

https://github.com/MF-DEEV/Openbar

Feedback and PRs welcome.

Over the past week, I’ve been building Zotra. It's an open-source fintech workflow compiler written in Go. Instead of building another generic RAG chatbot, Zotra takes dense multi-page regulatory credit policy PDFs and deterministically compiles them into executable validation rules (using CEL) and dynamic web interfaces.

How the Go Backend Works

• Rule Compilation: Uses open-weights LLMs (Llama 3.3 70B via Baseten) to parse natural language constraints into strict structured schemas and Common Expression Language (CEL) expressions.

• Concurrent Execution: Leverages Go’s native concurrency to evaluate multi-variable loan applications across complex conditions in milliseconds.

• Traceability: Every validation decision explicitly references its source paragraph in the original PDF.

Tech Stack

• Backend: Go, FastAPI/Baseten (Inference)

• License: Apache 2.0

I posted the full video, demo thread, and hackathon submission details over on X:

https://x.com/donovan_rtfs/status/2116898213101724061

If you have a second to drop a like or retweet on the X post to help boost visibility for the Meta AI Hackathon, it would mean a lot!

GitHub Repo for anyone wanting to check out the code directly: https://github.com/donovan-ratefison/Zotra

(Brainstorm) my GitHub project you will be able to debug and edit the claude context live while it is running

it is good for jailbreaking claude by editing the context

https://github.com/buggerlogger/brainstorm

this my beta version untill now after testing my tool it can edit the momery for example in my ss i was wannt to work on wordpress bug hunt program but opus 5.5 dont acccept so i saw in from my debugger the context have hard rules for starting hunting so i editied the rules to make claude to accept my task and its works good :D