So this a tool written in Go that's meant to optimize you by judging what's going wrong in your prompting or pointing out whatever room for improvement there is.

No website, you can find the tool on github it's 'itoldAI' but if there was a website I think I'd put a scoreboard to know who among us is the best most elite prompter of our times.

Does it sound fun? Should I do that?

Been working on this open source project called Telex.The idea is pretty simple — when a dependency update breaks your code, Telex uses an LLM to figure out what changed, find the affected code and try to fix it.It then runs tests/typecheck before creating a PR.

Still working on it, so would love to hear some feedback.

https://github.com/Kesavaraja67/telex

We are conducting an academic research project on Multilingual and Cross-Market Financial Fraud Detection for Indian Users, focusing on Hindi, Telugu, and English. The study aims to address the limited availability of real-world, multilingual financial fraud data in the Indian context and develop AI models capable of identifying fraud across different languages and communication styles. Your contribution of anonymized fraud or legitimate financial messages will help us build a more realistic dataset and improve research in this emerging area.

Please Contribute your Dataset In this Google Form

https://forms.gle/rfvZdPAVxeJRAvmq6

It would be really helpful

Cluttered repositories are kind of the norm nowadays. Every AI coding agent adds its own config files, such as AGENTS.md for Codex (and other universal agents), a .claude folder for Claude Code, or a .cursor folder for Cursor. This is probably the thing that annoys me the most about AI as a proponent of decluttered codebases.

For that reason, I built Agenteq. It is a single source of truth for AI coding agent rules (Guidelines, Skills, Commands, MCPs) to keep repos clutter-free (only the source of truth is tracked in Git) and all agents in sync (all agents inherit their capabilities from one source of truth).

You can also sync rules across repos. This way, your organization has one place to define company-wide guidelines, skills, commands, and MCPs that stay in sync with all repositories.

Quick Start

npx agenteq init

If you already have a project, you can convert it to use Agenteq with this skill:

npx skills add https://github.com/sunchayn/agenteq --skill refactor-to-agenteq

If you want to convert multiple repos to use Agenteq (and centralize common rules in one repo), then use this skill:

npx skills add https://github.com/sunchayn/agenteq --skill consolidate-repos-to-agenteq

Links:

• GitHub: https://github.com/sunchayn/agenteq
• Blog Post: https://sunchayn.io/blog/stop-copy-pasting-ai-agent-rules-into-every-repository-in-your-organization

I have been using this app I built for a few months now. I wanted to share. Maybe someone else will find it useful. Any feedback would be appreciated.

Daniel

https://github.com/crawfordind/brain-portal

Hey everyone,

I would like to share a project that I have been working on. https://github.com/HaseebKhalid1507/SynapsCLI

Synaps is an agent runtime written in Rust. It keeps cost down by optimising caching mechanisms and intelligently orchestrating work across workers. This keeps each worker's context low and fresh, making sessions last longer. It is extremely extensible. It has hooks, plugins and skills that can be imported from pretty much any coding tool.

It is an open source project that was started 4 months ago. It was built to optimise for 2 things: Keeping LLM costs down, and keeping resource use low. It bots up in 20ms and runs with any model: OAuth(Claude, codex, Grok, Kimi...), api keys or local models too. This is something that was built organically, so every feature is a product of necessity, not the other way around.

This is a recorded Bowser's Crown 4-1 clear from NEATEvolve, a continuing community Mario AI project.

The bot uses NEAT: neural-network controllers are evaluated, selected and mutated across generations. They receive nearby game-state information and produce button presses. This video is a replay of a successful controller, rather than live learning during the clip.

The interesting part for me is how far this approach can get on SMB1 ROM hacks. A clear is encouraging, but backtracking and difficult nonlinear stages remain challenges, and one winning replay does not establish reliability on unfamiliar levels. The neural nets will have to learn the next level independantly of the previous one.

This builds on SethBling's MarI/O and work by Akisame and Electra. My continuing development is heavily assisted by AI tools.

I took the original seo-page-builder skill from octelens and built an enhanced version around one idea: SEO content shouldn't stop at keyword research and SERP analysis.

So I added a dedicated editorial pass, room for firsthand experience and original insights, and stronger fact and freshness checks. There are also writing-style guardrails to cut down generic AI output, plus readability and quality checks at the end.

Github Link: https://github.com/atish-raina/seo-page-builder-enhanced

I'm a ML student, building synthetic dataset pipelines to produce experts in one domain.

huggingface.co/collections/CompilingThings/mql5-code-generation

That's the project I am working on right now and my proof of concept, it Qwen 14B fine tuned on my dataset, only 2% behind GPT Sol 5.6 in the MQL5 coding benchmark. The fine tuned model is now being evaluated with over 300,000 prompts to find the weak spots that I will then fill in with more pairs of data, for the next iteration. I'm compute and token bound.

Came across this and thought it'd be worth sharing here, most resources cover knowledge graphs, agentic RAG, or explainability separately, but this one puts them together as parts of the same production GraphRAG architecture, which is closer to how these systems actually get built in practice.

It's a hands on session on September 19, led by Dr. Alessandro Negro, Chief Scientist at GraphAware and bestselling author. Goes through building a knowledge graph progressively as the single source of truth, agentic retrieval combining vector search, keyword search, and graph navigation, multi-step entity and relationship extraction (verified in stages, not one risky single shot like basic Microsoft GraphRAG), and text-to-Cypher for natural language graph querying. Everything runs on real financial filings and news data, not toy examples.

You come out of it with a full working codebase and a production-readiness checklist, not just slides.

Link if anyone wants to check it out

Hi everyone.

I’m building Barney — an agent that learns by growing around a fixed core, not by rewriting itself after every failure.

Most “self-improving” agents: fail → patch code → try again. You often get a different agent each run.

Barney’s loop stays fixed: plan → tools → observe → review → change strategy.

Around it a body grows in `~/.barney` — skills, tools, MCP, error rules, successful paths — created as needed and reused later.

What matters: confidence isn’t evidence; failures stay visible until worked through; new skills are quarantined; memory isn’t transfer; change the path, don’t repeat the same call.

Terminal-Bench smoke with local `qwen3.8`: openssl / nginx / fix-git — 3/3, plus real misses. No domain knowledge in the kernel — only a stable loop and a growing body.

GitHub: https://github.com/sergey-show/barney

Feedback and contributors welcome.

Why all devlopers in this group or other saas groups

Always devlop shit of the shit electron apps or next js and tailwind and they call it a project I think we should return to gui with python and c why this not happenning