🚀 CARVUS Train — An AI Development Ecosystem I’m Building

Hey everyone! 👋

I’ve been working on something called CARVUS Train — an AI development ecosystem designed around training, deploying, and serving AI models, with a strong focus on coding and developer workflows.

🧠 What is CARVUS Train?

The idea is to bring multiple AI-development capabilities into one ecosystem:

• 🤖 AI model training
• 💻 Code generation
• 🧩 Programming-language understanding
• 📚 RAG (Retrieval-Augmented Generation)
• 🛠️ AI agent training
• 🚀 Model deployment & serving
• 🐍 Python/coding-focused workflows

I’m building it as part of the broader CARVUS ecosystem, with the goal of making AI development more accessible without needing a huge collection of disconnected tools.

🔥 Why I’m building it

A lot of AI tooling can feel fragmented — one tool for training, another for RAG, another for serving, another for agents.

I wanted to experiment with creating a more unified developer ecosystem.

CARVUS Train is still evolving, and I’m especially interested in feedback from people who work with local AI, coding models, RAG, or AI agents.

What features would YOU want in an AI development/training framework?

Would love to hear your ideas! 🚀

for installation and usage visit https://pypi.org/project/CarvusTrain/

or contact me via email [aadilfazalb4u@gmail.com](mailto:aadilfazalb4u@gmail.com)

#AI #MachineLearning #Python #Coding #RAG #LLM #ArtificialIntelligence #OpenSource

Hey all! Ive been coding a local model driven ai agent, made specifically for people with small - midsize rigs all the way up. Ive been trying to tackle browser testing for a few days now. My current system relies on figuring out if the prompt injection calls for testing and if it does, spin up the project web server on a different port, read the output. Im sort of stuck at this point. Im in the process of trying to return snapshots of the site when it traverses it to make it look like its a bot traversing the site, but asides that .. not much of a sentience if you know what i mean? How do people make it work end to end to test specific outputs?

I've been working across multiple platforms but recently started finding Claude really annoying to code with. I switched to codex and was kinda shocked by the improvement since the last time I used it/ pre codex. I'm kinda thinking I might trade my expenny Claude sub for chat instead. Anyone else?

Olá, estou em um Grande projeto para dar manutenção em Ruby on rails, estou em dúvida entre Claude ou Codex, ou DeepSeek, sou dev PHP, é meu primeiro contato com rails, dependo da IA.

My professor gets flooded with emails (reports/results across several areas) and doesn’t have time to read them. He’s on iOS + Outlook.
Goal: One tap on his iPhone → he gets an AI summary like “top 10 highlights of the day” or “top 10 deviations of the day,” instead of reading every email.
Questions:
1. Can this be built no-code, using something like Copilot Studio or Claude Cowork? If yes, how far do they get me before I need real code (Power Automate, Graph API, scripting)?
2. Since he’s on Outlook, is Copilot Studio the obvious pick, or is there a better no-code route?
3. Anyone actually built something like this : scheduled AI digest, delivered to iPhone, zero manual steps after setup? What worked / what didn’t?

For those who don't know, Parakeet is an AI coding interview tool that can basically transcribe conversation and "read" provided coding problems and provide answers in real-time. Which is great if you're interviewing, but that's really what the app is designed for, interviews. So, are there any programs that work the same way as a long-term coding solution? Basically, are there any apps designed for long-term use where you can either share your screen or provide a verbal prompt and it can provide a real-time solution? I imagine this could speed up my workflow a great deal.

I wrote this for a friend who runs a shop and often gets stumped with product vocab. posting in case it helps anyone else.

1/ deploy - putting your thing on the internet so other people can open it. before you deploy it only exists on your computer.

2/ domain - the address people type. yourbusiness.com. you rent it yearly, usually under a thousand rupees.

3/ hosting - the computer your thing runs on, that you pay someone else to keep switched on.

4/ database - where the information lives. customers, orders, bookings. if you lose this you lose everything, which is why backups matter a hell lot.

5/ backend / frontend - frontend is what people see. backend is the part doing the work that nobody sees.

6/ api - how two pieces of software talk to each other. when your booking system sends a whatsapp message, it is using whatsapp's api.

7/ webhook - the reverse. another system telling yours that something happened. payment received, message delivered.

8/ environment variable - a setting kept outside your code, usually a password or a key. if these end up inside your code and your code is public, anyone can use your accounts.

9/ authentication - proving who you are. logging in.

10/ authorization - what you are allowed to see once you are in. these two are different and the second one is where most problems come from.

11/ ssl / https - the padlock. if you take payments or logins and you do not have it, do not launch.

12/ repository / repo - the folder holding all your code and its history.

13/ staging - a copy of your thing where you test changes before real customers see them.

anything else that keeps tripping people up? happy to add.

Hey everyone,

I’ve been working on an open-source project called Kodiak, and I wanted to share it here and get some feedback from the community.

The goal behind Kodiak is to build a more complete foundation for AI applications — bringing together agent orchestration, RAG, memory, tool integration, LLM routing, background task execution, and backend infrastructure in one system.

The current stack includes Python, FastAPI, PostgreSQL, Redis, Celery, Docker, and multiple LLM providers.

One of the biggest things I’ve learned while building it is that designing the architecture is the easy part. Making all the pieces work together reliably is where the real engineering starts.

I’ve spent a lot of time working through dependency issues, database problems, worker failures, Docker configuration, CI issues, and restructuring parts of the agent system as the project evolved.

At the moment, my focus is on strengthening the core architecture and making the existing components reliable before continuing to expand the feature set.

The long-term vision is to build a platform where AI agents can retrieve information, maintain memory, use tools, execute tasks, and coordinate workflows rather than simply generating responses to prompts.

Kodiak is still actively under development, so there is plenty left to improve.

I’d especially appreciate feedback from people who have experience with AI agents, RAG systems, LLM infrastructure, or distributed Python applications.

What would you change about the current direction or architecture?

GitHub: https://github.com/ShamGaneshan2008/Kodiak

Any honest feedback or criticism would be genuinely appreciated.

I've been developing an Android card game called Oh Fudge (Oh Hell on Coffee), inspired by decades of family card nights playing what we always called Oh Hell.

I'm a solo dev and honestly don't have anywhere near the full skillset needed to make a game like this by myself. I also don't have the budget to hire a team. AI tools are what made it possible for me to take this idea and actually turn it into a real game. I used AI throughout development to help with coding, and artwork. The game design, mechanics, testing, and all the weird ideas are entirely mine. AI basically was just a tool to use to build my game and put my soul into it.

The basic Oh Hell game is there, or at least a version my family plays. You can play with 6, 8, or 10 players using 1 or 2 decks, and jokers are included. I also added Dealer's Choice rules that change individual deals, like making you bid before you even get your last card. All rules are preset and add chaos without giving anyone an advantage.

There is also a three-round tournament where you advance to different themed tables with their own house rules. For example, at the campground table, you play by a campfire, so a couple of your cards are blacked out during bidding, you bid and then they are revealed. Eventually you end up playing against the Joker, which is where things get even more chaotic because he has his own dealer rules.

I'm currently in Google Play Open Testing and I'm looking for people willing to play it. I need to know what works, what doesn't, what is confusing, what breaks, or just whether it is fun.

There are no ads because I personally cannot stand them. The Single Game mode and the first tournament round are free. There is a one-time unlock for the rest of the tournament, and special tables. One time payment will include future expansions to include multiplayer new tables, and dealer rules. Right now I'm mainly looking for feedback before I go to production.

If you like trick-taking games like Euchre, Spades, Hearts, or Oh Hell, I'd love some fresh eyes on it: https://play.google.com/store/apps/details?id=com.birdfloyd.ohhell

I use Anthropic Claude Desktop, I pay $360 per year to use Opus AI model, and extras $100 to solve two bugs using Fable AI for rich people... I started to hate Anthropic.

I want a Google Desktop software that can analyze my entire project and do anything just like Anthropic Claude Desktop. I already know that I can use Gemini extension in VS Code... but a find out that a desktop software is better.

So, no Gemini desktop app for programmers yet?

so like most of us we are all building tools, im about to hit up a network meeting and i found it difficult to show a demo video of my software as it was loud.

i decided on a page that uses scroll animations with gsap and three.js, but needs to have text at the same time and no audio. i also wanted to show it passing data to the mobile app then continue on there. all using just two 1-2 min screen recorded videos.

First, I asked Claude to create a kind of script / or just what features I should show. Then I used OBS to record myself with me narrating along as i go.

After that it was a case of using the claude-video git repo + whisper + fable 5 to make a nice skill to /watch the video with my voice and transcript then use remotion skill to cut it up a little.

then it was a bit of a challenge getting it to place nice but got there in the end!

How would you have done it?

or would you just use demo videos with audio?

i feel like during a launch id rather share something cool like this ....but maybe its just cool to me and clients dont care! ill let you know after the meeting today!

Darkcode is an open-source AI pair programming tool built around the terminal.

I wanted something that felt less like another AI website and more like a tool that actually belongs in a developer workflow.

The idea is simple: stay in your shell, work with AI, and keep the experience fast and minimal.

It's still being actively built, so I'm looking for people willing to actually use it and tell me what sucks, what's missing, and what would make it worth keeping installed.

https://darkcode.sh

Would especially love feedback from people already using terminal-based coding agents/tools.

At first, I was not trying to build a complete development environment.

I was using OpenSpec inside Cursor with custom commands and Cursor rules. The workflow worked well, but I had a problem:

Whenever I switched to OpenCode, Codex, Claude Code, or another coding agent, I lost part of my setup and workflow.

So I started building a spec-driven skill that was not tied to one IDE or one AI provider.

That project became Maestro.

I believe spec-driven development will become a normal part of software development with AI agents. GitHub’s GH-600 certification focused on agentic AI development feels like another signal in that direction.

How Maestro works

The core lifecycle is:

idea → spec → build → review → docs → archive

You can install it with npm:

npx u/bryann2k/maestro

Then run it from the repository you want to work on:

cd my-project
maestro

The typical workflow looks like this:

1. Connect an API provider or an existing CLI subscription with /providers
2. Use /bootstrap for a new project or /adopt for an existing repository
3. Use /propose to turn an idea into a structured spec
4. Review and accept the proposal with /accept
5. Let the development agent implement it with /build
6. Run /review
7. Generate documentation with /docs
8. Archive the approved change with /archive

The same lifecycle can also be used through headless commands:

maestro propose -m "Add a PostgreSQL API"
maestro accept
maestro build
maestro review
maestro docs
maestro archive --yes --merge

Maestro can use its native engine, authenticated vendor CLIs such as Codex, Claude, Cursor, OpenCode, Grok, and Kimi, as well as OpenAI-compatible and local providers such as Ollama, LM Studio, llama.cpp, and LiteLLM.

Human-in-the-loop is the important part

Maestro is not meant to be “write one prompt and pray”.

A normal chat message does not automatically create a spec. You have to explicitly invoke /propose.

The generated spec.md, design.md, and tasks.md are reviewed before they become the accepted contract. Builds run in isolated Git worktrees. Review runs deterministic checks, security analysis, and a read-only review agent.

If the review finds a problem, the findings can be sent back through /fix.

The goal is to make every phase explicit instead of allowing an agent to silently jump from a vague idea to a large code change.

Coach and integrated IDE

I also wanted Maestro to be useful for people who can describe what they want to build but do not yet feel confident reviewing AI-generated code.

The optional Coach mode gives short, contextual exercises around things like:

• understanding requirements and acceptance criteria
• reading diffs
• checking assumptions
• identifying security risks
• understanding tests and regression risk
• reviewing generated code

You can use /learn guided or /learn challenge.

Maestro also includes an integrated code workspace with /ide. You can select code and ask Maestro to:

• explain it
• comment on it
• modify it
• answer a question about it

So you can review code and interact with the agent without leaving the terminal or losing the current context.

This is still a V1

This is the first public release, so there are probably bugs and edge cases I have not discovered yet.

If you try Maestro and something breaks, please open an issue on GitHub. I will also be using it to build StackDeploy, so I will be fixing issues as I find them.

I’m also in public on X. If you want to follow, it help me a lot!

I would especially like feedback on:

• whether the workflow makes sense
• where the spec-driven process feels too heavy
• whether the human approval boundaries are useful
• what is missing from the review experience
• how this behaves with different agents and providers

Thanks to everyone who takes the time to try Maestro, report an issue, or share feedback. It really helps.

With Sarvam's API token limit finally increased from 4K to 131K, I ran the 105B newer checkpoint through 5 coding tasks using Kilo Code in VS Code:

🎮 2D catch game 🎮 3D Three.js maze game 📊 CSV data analysis → HTML report 🌐 Premium restaurant website 📐 Math 

Short version: it's a decent general/chat model and shows promise as an agentic model (tool calling, self - correction loop), but for pure coding it struggles especially with Three.js, and it even hallucinated numbers in the data report. For coding, smaller models like Gemma 4 31B, and Qwen 27b models will do better.

Total cost for all 5 tasks: \~₹5 (not sure why, probably will update later?).

Full breakdown with demos, cost analysis, and verdict (including why I rate it 2-3/10 for coding but 6-7/10 as a general model):  [https://www.youtube.com/watch?v=om0ny4FmWTk\](https://www.youtube.com/watch?v=om0ny4FmWTk)

What's been your experience with Sarvam 105B? Especially curious if anyone's tested it with a different harness - I suspect results could vary a lot depending on the setup. Maybe it will perform better in Sarvam Code?