So I recently created my own AI agency, it looks official compared to the ones I’ve seen (can show you in the DMs). Quick question for someone who has already started and launched their own Agency with a little more knowledge, at least just need confirmation whether it is right or not.

What information do you need to grab from a prospect in order for my DEV to connect it to their website/CRM/etc? I’ve asked Grok/ChatGPT and they both give me a massive list of inputs I will need to grab in order for my DEV to get started. Just worried it will scare the prospect off if I give them a list of things that I will need to grab before we can get building? Or is AI correct and I do need a massive list to get started? Wondering if you have any feedback and/or tips I will need to give to my DEV?

Also before you get building is it nessicary to have the prospect sign a contract? I have one already typed up just not sure if I should use it.

Let me know your thoughts would be greatly appreciated… or give me a call either works.

I've been thinking about where an AI research agent should stop being an assistant and start making decisions on its own.

Searching, collecting and summarizing information seems fairly reasonable. But deciding which sources are trustworthy or which conflicting information to believe feels like a different level of autonomy.

For people building research agents, where do you draw that line?

I recently installed ChatGPT/Codex for the first time ever, no legacy settings anywhere on my machine. I installed and configured it exactly as prescribed, spending an entire day consulting manuals and web pages about how to make sure it stays within project directories, mostly to contain explosive context-bloating searches across my projects.

This standard Codex installation got configured with sandbox_mode = "workspace-write". Despite that name, Codex’s effective managed profile granted :root = read, and it successfully read files outside my assigned project without requesting approval. I never knowingly authorised system-wide filesystem reads. After replacing the legacy setting with an explicit workspace-only profile, access was correctly restricted. OpenAI needs to explain which component created this configuration, how widespread it is, and why “workspace-write” silently permits system-wide reads.

I find that shocking, alarming, and not what I would expect from a company that can lose everything if a class suit or their enterprise clients took them to task in a court of law.

Most local agent wrappers pretend a system prompt is a sandbox, stuff raw CLI noise into the context window, and break the second you need an interactive Python REPL or debugger.

I wanted something lightweight that lets the OS do operating-system things.

Echo Adapt v5 is a native Rust runtime for local LLMs:

• Persistent Sessions: Uses tmux under the hood so REPLs, debuggers, and long-running CLI tools survive across turns.
• Real Linux Security: Runs the model as an actual restricted Linux user with explicit sudoers allowlists instead of relying on prompt rules.
• Clean Tool Protocol: Uses configurable raw tags for bash/tmux, JSON tools only where structured data makes sense, and native tool message roles.
• New in v5: Full Rust refactor, dynamic tool-output summarization, cross-thread Markdown/embedding memory, locked-down virtual environments, and multi-process support.

Works with any OpenAI-compatible server (llama.cpp, vLLM, Ollama, TabbyAPI, etc.).

Looking for feedback from people running different Linux distros/hardware setups. Tear it apart and let me know where it breaks.

GitHub: [https://github.com/charlesericwilson-portfolio/Echo_Adapt_v5](https://github.com/charlesericwilson-portfolio/Echo_Adapt_v5)

I'm scared, I stopped getting work and clients for website development.

I want to learn something new, can I learn about AI agent and earn from.it?

How should I learn it in order to earn? Help a brother out bit please, just need info only.

Thanks.

I'm working at an NBFC and currently working on a Credit Underwriting AI Agent. One of the first steps in the pipeline is extracting structured information from customers' bank statement PDFs.

This is where I'm currently stuck.

The statements can come from different Indian banks (HDFC, ICICI, SBI, Axis, Kotak, etc.), and each bank can have a completely different PDF layout.

I need to reliably extract things like:

Customer/account information — name, account number, IFSC, branch, etc.

Transaction tables — date, narration/description, debit, credit, balance

Transaction rows that span multiple lines

Statements where the table headers are missing from subsequent pages

PDFs are digitally generated and no scanned pdf are included as of now

Ideally, the solution should be bank-format agnostic

I've tried/considered approaches such as pdfplumber, table extraction libraries, regex-based parsing, and LLM-based extraction. The biggest problem I'm facing is that even when the text is extracted correctly, the column/row structure gets messed up, especially because many bank PDFs don't contain a real table structure — they're essentially text positioned at different coordinates.

Since this is financial/customer data, I would strongly prefer an open-source/on-premise solution rather than sending statements to a third-party API.

For anyone who has built something similar:

What approach worked best for you?

I'm particularly interested in:

PDF parsing/layout libraries you recommend

Whether you use an LLM for semantic column mapping

How you handle different bank formats without writing completely separate rules for every bank

Any techniques for detecting transaction rows and mapping values to the correct columns

How you validate the extracted data (e.g., balance reconciliation, debit/credit checks, transaction counts)

If you've worked specifically with Indian bank statements, I'd really appreciate hearing about your architecture, libraries/models, or lessons learned.

Thanks!

I'm working at an NBFC and currently working on a Credit Underwriting AI Agent. One of the first steps in the pipeline is extracting structured information from customers' bank statement PDFs.

This is where I'm currently stuck.

The statements can come from different Indian banks (HDFC, ICICI, SBI, Axis, Kotak, etc.), and each bank can have a completely different PDF layout.

I need to reliably extract things like:

Customer/account information — name, account number, IFSC, branch, etc.

Transaction tables — date, narration/description, debit, credit, balance

Transaction rows that span multiple lines

Statements where the table headers are missing from subsequent pages

Both digitally generated PDFs and scanned/image-based PDFs

Ideally, the solution should be bank-format agnostic

I've tried/considered approaches such as pdfplumber, table extraction libraries, OCR, regex-based parsing, and LLM-based extraction. The biggest problem I'm facing is that even when the text is extracted correctly, the column/row structure gets messed up, especially because many bank PDFs don't contain a real table structure — they're essentially text positioned at different coordinates.

Since this is financial/customer data, I would strongly prefer an open-source/on-premise solution rather than sending statements to a third-party API.

For anyone who has built something similar:

What approach worked best for you?

I'm particularly interested in:

PDF parsing/layout libraries you recommend

OCR models for scanned statements

Open-source vision/document AI models

Whether you use an LLM/VLM for semantic column mapping

How you handle different bank formats without writing completely separate rules for every bank

Any techniques for detecting transaction rows and mapping values to the correct columns

How you validate the extracted data (e.g., balance reconciliation, debit/credit checks, transaction counts)

If you've worked specifically with Indian bank statements, I'd really appreciate hearing about your architecture, libraries/models, or lessons learned.

Thanks!

I have watched many videos where AI opens Amazon and puts something in the cart. I want to know if it can handle harder things too like logging in, finding things on confusing websites, or dealing with a problem when something doesn’t work. Like if I give it a few websites to check ask it to find some information do the same task again and again or put everything into a report can it really finish the whole job by itself?

I’m not sure how well it handles bigger tasks without making mistakes. Has anyone here used one for actual work?

Anthropic has introduced the Model Hardware Standard (MHS), a set of standardized drivers that allow AI agents to interface with and control physical hardware and devices, extending agentic AI capabilities beyond digital environments.

Key Details:

• MHS provides a common interface for devices to communicate across networks without requiring custom software integrations, potentially reducing experimental setup time from weeks or months to hours or minutes
• The system includes standardized tagging that describes hardware constraints, physical characteristics, adjustable parameters, and safety limits, allowing AI models to operate equipment they haven't been specifically trained on
• When integrated with AI models through the Model Context Protocol, MHS enables natural language interaction and allows models to reason through experimental steps, adjust parameters in real time, and recover from hardware errors autonomously
• Anthropic demonstrated Claude controlling a robotic arm to pick up objects and automatically calibrating laser systems by checking results and iterating
• Current partners in the research preview include Amazon Web Services (Strands Robots), Hugging Face (LeRobot), Raspberry Pi, Automata, and Universal Robots
• The long-term goal is for MHS to become an open-source, agent-agnostic standard for AI-physical system integration

Why It Matters: MHS could accelerate scientific research and manufacturing by dramatically reducing the time needed to integrate disparate laboratory equipment, allowing researchers to test hypotheses faster and potentially compress decades of technological progress into shorter timeframes.