Jestem ciekawy oceny osób mających doświadczenie w temacie.

What bottleneck are you facing with your AI agents?

So I use ai regularly for retrieving information and other things. Last week I built my own new application using chat gpt and my hands-on experience as an electrical technician to create a comprehensive CRM and ERP for Control Panels. But, where do I LEARN more about AI itself, the different types, what they do, and how to use them in their best way. Im thinking some lessons either on LinkedIn Learning or Udemy..but Im not sure where to start. Any advice would help. Thank you

Hey all,

Follow-up to my earlier posts about error 3441038. Thanks again for all the advice, it helped a lot.

Quick background: I'm a solo developer in South America building WhatsApp booking bots for barbershops (n8n + an LLM, connected through Kapso).

Where we are:

A barbershop owner's own number (on WhatsApp Business for over a year) failed the Coexistence signup three times with error 3441038. Meta's support assistant says the number is registered under another business portfolio he can't access. That portfolio shows up as a partner sharing his WhatsApp catalog. The only official fix is a formal ownership request with ID and business documents. That's slow and uncertain, and he's tired of the back-and-forth.

The plan: a fresh number with Coexistence

1. He buys a new prepaid SIM in his own name. We first check the number was never on WhatsApp, since prepaid numbers get recycled.

2. He installs WhatsApp Business on it, completes the profile, and uses it for real with his regular customers for at least 7 days (Meta's minimum; some providers say 30 to 60).

3. During that time we keep it clean: no linking to his Facebook Page or Instagram, no catalog from Business Suite, no other provider.

4. Then we connect it through Kapso's signup link, in his own business portfolio (active, he's the only admin, no WhatsApp accounts in it yet). He keeps reading and replying in the app, and the bot pauses in a chat when he replies.

5. After connecting, we warm it up: only replies to customers who message first, no marketing templates, and reminders only after a few weeks of green quality.

6. Fallback if the signup fails: delete the app account on the new number, wait, and register the same number as a dedicated Cloud API number.

Why not dedicated right away: he wants to see and answer the chats himself in the app.

One more data point: in September, Meta disabled my own new, unverified business portfolio three times in four days, together with its fresh number. It came back after a review every time, but it made me careful with fresh numbers.

My questions:

1. Is 7 days of real use enough for a brand-new number, or would you wait longer? What counts as "real activity": incoming chats, outgoing messages, number of contacts?

2. Would you keep the app unlinked from the Facebook Page, Instagram and the catalog until after Coexistence? We suspect the catalog link is what tied his old number to the other portfolio.

3. Same phone or a separate phone? Can the new number run as a second account in his existing WhatsApp Business app (iPhone), or does Coexistence need its own phone?

4. Is a fresh number on Coexistence more likely to get flagged than a dedicated one? Anything you do in the first weeks after connecting?

5. Does the fallback work: deleting the app account and registering the same number as dedicated? How long do you wait in between, and are there pitfalls?

6. Anything you'd set up in the business portfolio before the signup: display name, business verification, website?

Not selling anything. I'll report back how it goes.

MCPtoAI brings multiple AI models, MCP servers, and Skills into a single chat interface.

You can switch between OpenAI, Claude, DeepSeek, Ollama and other models mid-conversation without losing your chat context.

Connect your own MCP servers, add Skills from GitHub repositories, and let AI agents interact with tools running on your computer.

Easy setup on macOS, Windows, Linux and Docker

API keys stay on your own device

Access your agents and tools remotely from any browser or phone

Control tool execution with Off / Ask / Allow permissions
Open-source desktop clients (MIT licensed)

The idea is simple: use different AI models and tools together without being tied to a single provider or machine.

GitHub: https://github.com/mcptoai/MCPtoAI

Would love to hear what MCP tools or agent workflows you’d connect first.

Я начал создавать своего ИИ агента или правильно сказать ее. Так как это будет ещё та заноза с характером но при этом она будет нормальным помощником, который внятно оценивает ситуацию. Какие фишки посоветуете добавить или что можно убрать?

Ее кстати зовут( НИКСИ/NIXI).

В плане сейчас сделать ей возможность загрузки библиотек по своей надобности, определение камер и отслеживания по ним, работу как фонового анти вирус Ника, возможность следить за своим потреблением, активация голосом, подключение к устройствам с определением (или хотябы попыткой) их драйверов. Также планирую сделать ей мобильное приложение которое будет подключаться к моему ПК по интернету и использовать его как сервер. И также хочу для нее разработать свою кибердеку (с ее локальной версией которая сможет работать без интернета но очень медленно, но помнить что происходило офлайн и при включении в сеть она помнила что было офлайн и поправляла себя в случае ошибки) с модулями чтобы я мог ее унифицировать под разные ситуации и нужды.

И хотелось бы найти команду кто хотел бы поучаствовать в этом.

Really I wanna learn how? I want to create a agent and build that in long term.

Learn the core concepts behind generative AI and discover how modern language models process and generate information.

In Part One of **Generative AI for Developers**, we build the technical foundation developers need before working with prompts, retrieval, tools, and AI agents.

In this lesson, we cover:

✅ Generative AI compared with traditional prediction and classification
✅ The Three Cs: Control, Confidence, and Clarity
✅ What a transformer architecture is
✅ How tokenization converts text into model inputs
✅ How queries, keys, values, and attention work
✅ Why token order and positional information matter
✅ Encoder-decoder and decoder-only architectures
✅ Recurrent neural networks and hidden states
✅ Convolutional neural networks and local patterns
✅ RNNs, CNNs, and transformers compared
✅ Parallel transformer training versus sequential text generation
✅ Why a large context window does not guarantee reliable use of every token

The key idea to remember: transformers use attention to connect information across a sequence while supporting substantial parallel computation during training. However, an autoregressive language model still generates its response one token at a time.

In Part Two, we’ll continue with FlashAttention, grouped-query attention, mixture-of-experts architectures, next-token prediction, prompting, AI modalities, retrieval-augmented generation, tool use, and agents.

This video is useful for C# and .NET developers, software engineers, solution architects, interview preparation, and anyone who wants to understand how generative AI systems work before integrating them into an application.

If you found this lesson helpful, please like the video, subscribe to **Nerd House Geeks**, and share your questions in the comments.

Hi all,
I'm a junior engineer working toward agentic AI engineering roles. My focus is agent system design, deciding where model autonomy fits and where deterministic logic, tools or human approval should stay in control. I'm also building toward production practices. I'd value input from practitioners and hiring managers in this space.
Current skills:
Python and data analysis (pandas)
Agent workflow design with LangGraph
Design trade-off analysis with documented decisions
LLM integration on AWS Bedrock with structured outputs
Agent error handling: output validation, bounded retries, escalation on failure
Deterministic guardrails and human-in-the-loop escalation
Automated testing, code quality tooling and CI
AWS fundamentals: IAM, MFA, cost budgets
Planned learning:
System design
Evaluation harnesses: consistency, failure-rate and adversarial testing, with postmortems
Agent failure modes: detection and mitigation
Loop control and termination guards
Databases, state persistence and idempotency
Operational controls, including a kill switch
Observability: logging, tracing, monitoring, alerting and cost tracking
SLOs
Concurrency and rate-limit handling
Cloud deployment and CI/CD with rollback
Human review workflows
Guardrail frameworks
Tool use via MCP and multi-agent orchestration
Graph databases and external tool integration
RAG, vector databases and agent memory
Looking for input on:
Beyond core agent design, which production practices would give a junior candidate a real edge?
What important areas or practices are missing from this list?
Which of the planned areas would you prioritize first, and why?
Thanks for your time.

I’m starting Behind the AI Stack, a series of short, practical articles on how AI applications work. This first one explains AI agents through a camera-rental example. Give it a read—I’d love your thoughts, corrections, or suggestions for what to cover next.

I’m starting Behind the AI Stack, a series of short, practical articles on how AI applications work. This first one explains AI agents through a camera-rental example. Give it a read—I’d love your thoughts, corrections, or suggestions for what to cover next.

Does anyone else get annoyed when trying to export a Claude conversation?

The official export usually misses or messes up the model’s reasoning (thinking blocks), tool calls, files, and overall structure. For people who actually reuse these conversations later, it’s pretty painful.

I ended up building a Chrome extension that does a much more complete export. Here’s what it currently supports:

Full conversation capture: text, thinking/reasoning blocks, tool calls + results, files, images, and artifacts

Multiple export formats: structured JSON, organized ZIP, clean PDF, and plain TXT

Bulk download of all conversations (with an index)

Self-test that checks the entire capture pipeline

Live feed of real-time events while the conversation is happening (response start, thinking, tool calls, response end, etc.)

Optional pipeline to send the structured data to external endpoints/webhooks

A Copilot mode with local analysis (sandbox + terminal + local LLM, with optional external providers)

Optional traffic analysis via debugger (HAR, traces, etc.)

Designed to work well with MCP, so other agents can observe or act on the captured data

I built it mainly because I run a lot of agent-based tests and keep a personal RAG. I need the full reasoning and tool calls in a clean, structured format — otherwise I lose important context.

Anyone else dealing with this problem? How do you currently handle exporting and reusing Claude conversations?

Eu venho estudando como as equipes validam sistemas RAG e agentes de IA antes da implementação, e percebi que demonstrações bem-sucedidas nem sempre se traduzem em comportamentos confiáveis em produção.

Aqui estão algumas verificações que considero particularmente importantes:

1. Qualidade da recuperação

- O sistema recupera os documentos corretos para consultas representativas?

- Evidências irrelevantes ou desatualizadas podem entrar no contexto?

- A qualidade da recuperação regrede após mudanças em chunking, embeddings ou reranking?

1. Confiabilidade do agente

- O que acontece quando uma chamada de ferramenta falha ou retorna dados incompletos?

- O agente reconhece operações sem sucesso?

- Solicitações repetidas podem causar ações duplicadas?

1. Evidências e citações

- Os trechos citados realmente apoiam as alegações geradas?

- O sistema consegue distinguir o conteúdo recuperado das evidências usadas na resposta?

- As versões dos documentos e as referências das páginas estão corretas?

1. Testes de regressão

- Os mesmos casos de teste são executados antes e depois das mudanças?

- As falhas podem ser reproduzidas usando entradas e saídas salvas?

- A equipe tem critérios de aceitação claros antes da implementação?

1. Evidência técnica

- As chamadas de ferramentas importantes e falhas são registradas?

- Os desenvolvedores conseguem identificar o passo responsável por um resultado incorreto?

- Os resultados dos testes estão documentados de forma que outro engenheiro possa reproduzi-los?

Uma coisa que estou curioso é como pequenas agências de IA lidam com esse trabalho quando têm vários projetos de clientes em andamento simultaneamente.

Para desenvolvedores e proprietários de agências:

Voc normalmente lida com esses testes internamente, automatiza, ou subcontrata tarefas específicas de QA e testes de regressão?

Se você terceiriza parte disso, como é uma tarefa típica e como você avalia se o contratado entregou resultados úteis?

Estou interessado em entender o fluxo de trabalho de engenharia real, em vez de discutir ferramentas de forma abstrata.

I think we've been reading too many feeds from AI text lately! I actually want to reduce my cognitive burden...

So I built TextFold, it turns any text into 5 zoomable layers—from a one-screen section map down to verbatim text. You pinch (or press 1–5) to zoom between them without losing your reading position, your agentic output, any papers, or documents.

- Web Demo: https://chenxiachan.github.io/textfold/gallery/

- GitHub (Apache 2.0): https://github.com/chenxiachan/textfold

Welcome any feedback!

I've been reading discussions about Agent Skills, and one thing that stood out to me is that people aren't necessarily looking for more skills. They're looking for agents that actually work reliably.

Some people mentioned context retrieval, others talked about testing before implementation, breaking tasks into smaller steps, and helping agents recover from mistakes.

It got me thinking about what the real bottleneck is when building agentic workflows.

For those who've built agents beyond simple demos, what's caused the most problems for you?

1. Poor context or memory

2. Incorrect tool calls

3. Long workflows breaking midway

4. Agents repeating mistakes

5. Token costs and context limits

6. Something else entirely

I'd be interested in hearing about problems you've actually encountered, not just theoretical limitations.

I've been thinking about how many repetitive tasks we still handle manually, even with all the AI tools available today.

Things like researching across multiple websites, moving data between apps, updating CRMs, generating reports, or managing repetitive workflows.

The interesting part is that making an AI agent perform these tasks isn't necessarily the hardest part. Getting it to do them reliably without constant supervision is a different challenge.

For people building or using AI agents:

What's one task you'd genuinely trust an AI agent to handle from start to finish?

And what's the biggest thing stopping you from automating it today?

I need to blow the rest of my education budget and would like to learn how to apply AI to improve or automate daily processes in operating IOT energy assets. (As someone from the business side, not IT department)

Are there good courses that would be helpful? Most of the stuff I find online either seems too general/management level or too specific for a framework/technology that my company might not use. What can be done using the current systems with Claude, Microsoft Copliot and Databricks?

Hey everyone!

I’m a recent B.Tech CSE graduate, currently improving my backend development skills and looking for a substantial project that can strengthen my resume for entry-level backend developer, AI engineer, and software engineering roles.

I’ve noticed that many GenAI projects look impressive at first but turn out to be basic chatbots, PDF question-answering apps, or simple wrappers around LLM APIs. I want to build something more meaningful that demonstrates actual engineering skills, not just AI integration.

My current skills and tech stack

• Python fundamentals and FastAPI
• PostgreSQL, SQLAlchemy, and SQL
• Core Java fundamentals
• REST APIs and CRUD operations
• Basics of GenAI, RAG, embeddings, chunking, and LLM API integration
• Currently learning agentic AI and CrewAI

What I've built so far

I've already developed a Retail RAG Chatbot using document retrieval and LLM APIs. For my next project, I want to move beyond basic RAG and explore more advanced backend engineering concepts.

What kind of project am I looking for?

Ideally, something that:

• Solves a genuine, real-world problem.
• Uses FastAPI + PostgreSQL as the core stack, with GenAI, RAG, and CrewAI where appropriate.
• Has multiple pages and interconnected workflows, such as authentication, dashboards, analytics, history, admin panels, and monitoring.
• Involves proper backend engineering: database design, authorization, background jobs, API integrations, testing, error handling, and deployment.
• Has an interesting technical challenge or unique angle that makes it stand out in a technical interview.
• Is ambitious enough to be a serious portfolio project but achievable by one developer learning along the way.

I'm particularly interested in AI agents, developer tools, automation, cybersecurity, AI evaluation, data processing, and overlooked real-world problems. I'm open to other domains too!

I'd love your help with two things:

1. Unique project ideas

What are some genuinely interesting projects I could build with this stack that aren't just another clone of a popular tutorial? If you've built something unusual yourself, I'd love to hear about it.

2. GitHub repositories to study

Please share actual GitHub repository links for projects that demonstrate good architecture, interesting use cases, or advanced implementation techniques.

I'm particularly interested in repositories featuring:

• FastAPI + PostgreSQL + SQLAlchemy
• Agentic AI and CrewAI workflows
• Advanced RAG, hybrid search, reranking, or knowledge graphs
• Background task processing, authentication, and authorization
• AI evaluation, observability, and production-oriented backend engineering

The repositories don't necessarily need to use my entire stack. I'd also appreciate projects where I can learn one specific concept and integrate it into my own application.

I'm not looking to copy a project and put it on my resume. I want to study real implementations, understand their architecture, and build something original using what I learn.

If you're a developer, recruiter, interviewer, or someone who's built an impressive open-source project, I'd really appreciate your recommendations and honest feedback on what makes a fresher's portfolio stand out in 2026–27.

Thanks in advance!

Open source, self hostable, bring your own model, secure by design, and so easy ol mee-maw could use it.

These were the tenants I wanted to hold to when I set out to build C-Sweet. Anyone can run the app, do some quick setup and get working on their agent-first business. With a wide variety of agents already created and more being released and updated daily, we hope to provide a way to manage every facet of running a business and make it simple to manage over the rest of this year.

One thing that we noticed was that humans had already mastered the art of assembling other humans together with roles that are specific in scope. While other companies race to make the mega-ultra-super-model that does it all, we are proving that with small well defined roles, you can get the same work done with tiny models you can run locally. In particular Qwen3.8 27B has proven to be able to handle any corporate role we've thrown at it so far.

C-Sweet is security first and designed by people who currently work in AI Governance for the Fortune 500s - every agent is sandboxed with no rights or access to the outside world. Things are brokered and policy can be applied to any agent you decide to run. Don't want them phoning home? No problem. Don't want them to be able to talk to other agents? Easy-peasy. Want to have an AI agent man in the middle the other agents? We support that too! With an easy kill switch to shut down the agent in a heart-beat the moment suspicious actions are detected.

https://github.com/CrosswiredStudios/C-Sweet
We live off stars and watches, please help us not to starve :)

We are solidifying web based workflows currently and will be branching into hosting providers and media platforms like YouTube etc. We are also bringing on agents that will help realtors, land men, setting up legal entities in every state and much much more.

All comments, requests, hate and ideas welcome!

I want to start my career in AI, can anybody give me a roadmap for that? It would be really helpful, thank you....

Hey everyone! 👋

I've been working on something called Gridline, and I'd love to hear what architects, engineers, and developers think about it.

Something caught my attention while researching architectural workflows.

We have AI generating some incredible building concepts and renders. But actual architectural work involves so much more.

Floor plans, sections, elevations, CAD drawings, BIM models, schedules, construction documents, revisions, and coordinating everything when something changes.

So I started wondering: What if our AI coding agents could actually help with those workflows?

That's how Gridline started.

🏗️ What is Gridline?

It's a free, open-source toolkit that gives AI coding agents specialized capabilities for architecture, CAD, BIM, and engineering workflows.

Right now, it includes:

• 30 specialized architectural and engineering skills
• 12 specialist AI agents
• Support for Claude Code, Codex, and Gemini CLI
• AutoCAD, Revit, Rhino, Grasshopper, and IFC workflow guidance
• Drawing generation, documentation, coordination, and validation tools

And I didn't want it to stop at instructions.

Gridline can already generate layered DXF floor plans, vector PDFs, room/door/window schedules, and validation reports from structured building specifications.

There's still plenty of work ahead, but the goal is to produce useful, editable, verifiable deliverables rather than just nice-looking drawings.

👀 Where I want to take this

Imagine uploading a floor-plan image and having your agent reconstruct it into editable geometry.

You could modify walls and rooms, explore the result in 3D, and export it to CAD/BIM tools.

That's part of the vision, not a finished feature.

I'm also interested in automating drawing coordination, checking inconsistencies between sheets, and helping with repetitive documentation tasks.

I'd genuinely appreciate your input

If you're an architect: What repetitive task eats up the most time in your workflow? What would you actually trust AI to help with?

If you're a developer: Have you experimented with CAD automation, computer vision, IFC, Revit, or MCP integrations?

I'm approaching this as an open-source developer, and I know there's a lot to learn from people working in the field.

I don't want to build something that looks impressive in a demo but isn't useful in practice.

No claims about replacing architects. Professional judgment and verification still matter.

GitHub: github.com/MakeItEzzz555/Gridline

If you find the idea interesting, contributions, issues, feedback, criticism, and stars are genuinely appreciated. ❤️

Thanks for checking it out! 🚀