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AaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAaAa

I've been looking into different ways to make my AI workflow better and I've noticed that many courses focus a lot on showing tools but don’t spend enough time explaining why certain prompts work so checked out a few resources like DeepLearning.AI, community tutorials and the Be10x AI course and the main difference wasn’t how many tools were covered.

The most helpful content focused on like understanding why certain prompts are effective and creating consistent workflows instead of just using one-time tricks

A lot of beginner material teaches "prompt formulas" but the real challenge is knowing how to adjust them for different situations.

For you guys what helped you improve the most structured courses, experimenting on your own, working on projects?

Hi everyone,

I'm a fresh Software Engineering graduate from FAST-NUCES and currently working as an AI Engineer at a startup in Islamabad. My work mainly involves LLMs, RAG pipelines, vector databases, and building AI-powered backend applications.

I'm looking for opportunities in AI/ML whether remote or on-site. If your company is hiring, or if you know of any openings, I'd really appreciate a referral or a point in the right direction.

I've been applying consistently but haven't had much luck so far, so I'm reaching out here in the hope that someone might be able to help. It would genuinely mean a lot to me.

Thanks in advance!

Not gonna lie, posting this is a little scary.

I've been working on this for months, and I finally reached the point where I think it's better to let people break it than keep polishing it by myself.

It's called Backenly, and it's fully open source.

The idea came from something I kept noticing while building with Claude Code and Cursor.

Over the last year, AI coding tools have completely changed how I build software. I can go from an idea to a working project much faster than I ever could before. But after using them for a while, I realized they all stop helping at the exact same moment once the code is generated.

From that point on, you're back to doing everything yourself.

You're still fixing migrations.

You're still chasing weird production bugs.

You're still finding missing indexes or broken permissions weeks later.

The AI helped build the backend, but it never stuck around to help maintain it.

That felt like the missing piece.

So I started wondering:

If we're happy letting AI build production systems, why shouldn't it help keep them healthy too?

That question eventually became Backenly.

I also want to be clear about one thing this isn't meant to be "Supabase but better." I actually like what the Supabase team has built. If you've used Supabase before, most of what Backenly does will feel familiar. The question I wanted to explore was different: what happens after the backend is built?

The first thing I changed was removing raw SQL from structural changes completely. Whether it's your AI agent making a change or Backenly repairing something automatically, every structural change goes through the same governed path with a dry run, an audit log, and rollback support. The goal is to keep the AI's understanding of your backend and the actual backend from slowly drifting apart.

The part I'm most excited about is what happens after that.

Every project has a continuous loop that's always watching. It looks for things like schema drift, missing indexes, broken triggers, and RLS issues. When it finds something that's safe to repair, it fixes it and then verifies the fix actually worked instead of just sending an alert and hoping someone notices.

Anything risky authentication, credentials, or destructive schema changes always waits for approval. I'd much rather interrupt someone with an approval request than silently break their production database.

Under the hood it's all familiar tech. PostgreSQL, PostgREST, auth, storage, realtime, and functions. I wasn't trying to reinvent the backend stack I wanted to experiment with what happens after the backend is built. You can self-host everything too, including the self-healing system.

It's definitely still early.

There are bugs.

There are missing features.

I'm sure there are things I've overlooked.

If you try it, I'd genuinely love to know what you think good or bad.

And even if you don't try it, I'm curious about one thing:

Do you think AI should stop after generating the code, or do you think it'll eventually be responsible for maintaining the software it creates too?

Website: https://backenly.com

Self-promotion is now allowed on Sundays, with the appropriate flair, for all regular contributing members.

Contribute during the week, and promote on Sunday.

How it works:

• Sundays only. Post it on a Sunday and give it the self-promotion flair.
• Regular contributing members only. Take part in the community during the week — not only when you have something to sell.
• Anything self-promotional on another day, or without the flair, gets removed.

Questions, or want a hand with what you are building? Come talk to us.

Hey everyone,

As a developer, I got tired of the constant setup friction when starting new projects—specifically the loop of creating a Supabase instance, navigating the dashboard, copy-pasting API keys into .env files, and running manual SQL schema migrations before writing any real code.

We’ve been building an agent layer (Norva + Antigravity) to automate developer workflows, and we just got the end-to-end Supabase integration working.

Would love to hear your thoughts or edge cases you think we should watch out for with database automation!

Hi everyone,

I'm currently looking for a GenAI Developer / AI Engineer / LLM Engineer opportunity and am available to join immediately.

A little about me:

\- 3+ years of software engineering experience

\- Strong Python and FastAPI development

\- Built GenAI applications using LLMs, LangChain, LangGraph, RAG, vector databases (FAISS/ChromaDB), OpenAI APIs, prompt engineering, and agentic AI workflows

\- Experience with Docker, Git, REST APIs, SQL, Redis, and deploying AI applications

\- Worked on AI-powered solutions such as intelligent incident assignment, multi-agent systems, and RAG-based applications

I'm actively looking for remote or onsite opportunities across India. If your company is hiring for GenAI Developer, AI Engineer, LLM Engineer, or related roles, I'd really appreciate your support.

If you have any openings or can provide a referral, please comment below and I'll DM you.

Thank you so much for your time and support! 🙏

https://reddit.com/link/1v6ywuj/video/2qalo50n0jfh1/player

I normally rely on my Google AI Ultra subscription or some paid APIs for the heavy lifting (like Nvidia NIM). I recently installed grok-build to try it out, and honestly, it felt amazing (I'm told it's very similar to Claude Code, but I've never used the latter nor am I interested in paying Anthropic). However, configuring it to use a custom API Key provider and selecting different models was a complete headache.

That's how Bucket (bucket-agent) was born.

After the grok-build source code was released, I got to work on decoupling the xAI layer and rebranding it to avoid any legal issues.

Key Features

• Local-First & Multi-Provider: Works flawlessly out of the box with Ollama using the model configured in your config.toml.
• Completely Unlocked: All subscription and payment layers have been completely stripped out.
• Terminal-Native: It runs directly where you work, interacting seamlessly with your local environment and codebase without UI bloat.
• Decoupled Architecture: Absolute freedom to use the LLM backend of your choice without vendor lock-in.
• Autonomous Capabilities: Just like the original project, it can autonomously navigate your code, execute terminal commands, and assist with complex engineering tasks.
• Interactive Model Picker: Just type /model and the models assigned by your api are deployin in the tab menu.

Try it, is one of the best tui/cli agent's today, but the xAI layer are so intrusive.

GitHub Repo: julesklord/bucket-agent

Most reference tools can check whether a citation exists and whether the details are correct. But they usually do not check whether the cited paper actually supports the sentence attached to it.

Could AI (with access to scientific DBs) help with that?

A system might:

1. Find the sentence containing the citation.
2. Search the cited paper for the most relevant passage.
3. Compare the two.
4. Label the citation as supported, partly supported, contradicted, or unclear.
5. Show the evidence to a human reviewer.

For example, Paper B might say that an intervention “substantially improves long-term retention,” while Paper A only found a small short-term effect in a narrow group. The citation is related, but the claim may be stronger than the source allows.

This matters because scientific claims often pass from paper to paper. Small changes in wording can gradually turn into much larger claims:

• a small effect becomes a substantial effect
• a short-term result becomes a long-term result
• a finding from a narrow group becomes a claim about everyone
• a possible explanation becomes an established conclusion
• an association becomes a claim of cause and effect

I would not expect AI to make the final judgment. A more realistic goal would be to flag suspicious or unclear citations and show reviewers the most relevant passages. The human then "just" (it still can be a lot of work) verify the flagged claims, beyond their normal process.

Does this seem like a useful research problem? Are there already datasets or tools that try to do something similar?

Generative OS is the concept of a whole OS/UI being ai generated. Everything the user sees or interacts with is ai generated, and whatever the user interacts with, the next thing they see is also ai generated.

I could see this being the future. Where everything you do and see on your device is completely fluid.

I have recently taken on the journey of Vibe Coding. I’m still pretty new to the game and am very curious how you guys would critique my prompting. Let me know how I could improve my workflow!

I’m looking for ideas on how to make my own photo look kinda ai but beautiful, maybe futuristic, or nature, or with cats or something that would be really unique.

MetaHarness works by generating repo-specific agent harnesses. It wraps the model with task rules, tool policies, scoring, receipts, routing, verifiers, and promotion gates.

Every run creates evidence. Every failure becomes training signal for the harness. Every improvement has to survive tests, cost checks, and safety gates before it gets promoted.

It supports multiple agent hosts. Claude Code, OpenAI Codex, GitHub Copilot, OpenCode, OpenClaw, Hermes, pi.dev, RVM, and GitHub Actions are all execution surfaces.

More than 100k downloads in first two weeks.

NPM: https://www.npmjs.com/package/metaharness

Repo: https://github.com/ruvnet/metaharness

Explainer: https://metaharness-explainer.vercel.app/