YOUR AGENT FINISHED THE TASK. BUT DO YOU KNOW HOW?
The final answer looks fine.
Somewhere along the way, it may have looped on the same tool, re-read the same file, or retried a failed call until it worked.
You only see the bill.
@RequestyAI's Traces view rebuilds the whole agent session, step by step:
→ every model call
→ every tool call, with its arguments and output
→ the cost and latency of each step
The interesting part?
You don't send any extra metadata. The session is rebuilt from the requests themselves.
It won't fix a bad agent for you.
But you can't debug what you can't see 👀
Link in my first reply ↓
YOUR AGENT JUST GOT ITS OWN SPACE
@ego_agent lite lets Claude Code, Codex and Cursor work in a separate Space with your existing logins.
you keep your tabs. it does the task.
free. macOS and now Windows.
10 GITHUB REPOS FOR BUILDING A SERIOUS WEB SCRAPING STACK
Need data from websites without doing everything manually?
These open-source projects help with crawling, browser automation, extraction, and monitoring.
1. Crawlee — Build scalable crawlers with JavaScript or Python.
https://t.co/VFdtp9fv8y
2. Scrapling — Use adaptive scraping tools for changing websites.
https://t.co/GCTI1N71Ts
3. Oxylabs Web API MCP — Bring web data extraction into AI-powered workflows.
https://t.co/rTx10onHk3
4. Crawl4AI — Crawl websites and prepare content for AI applications.
https://t.co/rOfwgyGqgu
5. Scrapy — Build powerful web crawlers in Python.
https://t.co/2XPgV9LXLy
6. Firecrawl — Convert websites into clean, LLM-ready content.
https://t.co/XP16qALq5y
7. Playwright — Automate modern browsers and dynamic pages.
https://t.co/RBO5U9hrhG
8. Colly — Build fast web crawlers in Go.
https://t.co/7tiZhaCJRg
9. https://t.co/x3Y7T67GcX — Monitor websites for content changes.
https://t.co/cQhg0SWMN7
10. trafilatura — Extract readable text and metadata from web pages.
https://t.co/A7pmc76kOy
Collect smarter. Automate more. Build better data pipelines.
10 GITHUB REPOS THAT TURN RAW WEB DATA INTO AI KNOWLEDGE
Getting data is only the beginning.
The real value comes from cleaning it, organizing it, and making it searchable for AI.
1. GPT Researcher — Automate web research and generate detailed reports.
https://t.co/3uwRvhD1wd
2. LlamaIndex — Connect LLMs to external data sources.
https://t.co/QbohueIy1Q
3. RAGFlow — Build RAG applications with document understanding.
https://t.co/5PMueXghET
4. Oxylabs Web API MCP — Add web search and extraction to AI research workflows.
https://t.co/OIlRFypLDe
5. Docling — Convert documents into structured, AI-ready formats.
https://t.co/W5GQ8JxsBg
6. Unstructured — Prepare documents for retrieval and AI pipelines.
https://t.co/axvQEAaSeG
7. OpenRefine — Clean and transform messy datasets.
https://t.co/IrXTUzvW5K
8. DuckDB — Analyze data with a fast, embedded SQL engine.
https://t.co/hWkqsuidkv
9. Haystack — Build search, retrieval, and RAG pipelines.
https://t.co/6Y718ejOT7
10. Pandas — Manipulate and analyze structured data with Python.
https://t.co/Igf4hzsF4Z
Raw data is everywhere.
Turning it into useful knowledge is the real skill.
10 GITHUB REPOS THAT MAKE MCP SERVERS WAY MORE USEFUL
MCP is becoming the bridge between AI models and real-world tools.
Here are 10 repositories worth adding to your developer toolkit.
1. Awesome MCP Servers — Discover MCP servers for different workflows.
https://t.co/X6ZeAeQ4by
2. Context7 — Give coding assistants up-to-date library documentation.
https://t.co/0aTGZWaOqH
3. Oxylabs Web API MCP — Connect AI workflows to web search and data extraction.
https://t.co/qQXj6ez4rd
4. Playwright MCP — Let AI assistants interact with web pages through browser automation.
https://t.co/lDLWcK27jw
5. GitHub MCP Server — Connect AI tools to GitHub repositories and workflows.
https://t.co/JC3UWGIL6S
6. Fetch MCP Server — Retrieve web content for AI workflows.
https://t.co/QHDNKMvIES
7. Filesystem MCP Server — Let AI assistants work with permitted local files.
https://t.co/QHDNKMvIES
8. Sequential Thinking — Help AI break complex problems into structured steps.
https://t.co/QHDNKMvIES
9. Exa MCP Server — Add web search and research capabilities to AI tools.
https://t.co/Ddnv5w0GJd
10. Tavily MCP — Bring search and web extraction tools into MCP workflows.
https://t.co/RnySzRGjHl
MCP isn't just about connecting tools.
It's about giving AI the ability to actually use them.
All Paid Courses (Free for First 4500 People)
𝗣𝗮𝗶𝗱 𝗖𝗼𝘂𝗿𝘀𝗲 𝗙𝗥𝗘𝗘 (PART - 3)
1. Artificial Intelligence
2. Machine Learning
3. Cloud Computing
4. Ethical Hacking
5. Data Analytics
6. AWS Certified
7. Data Science
8. BIG DATA
9. Python
10. MBA
(72 Hours only )
To get-
1. Follow
@smratitiwa86867
[MUST SO I CAN DM]
2. Like & Retweet to get DM
3. Reply " All "
An agent smarter than its user would also be better at persuading them.
So Instinct’s rule is that it never steers users toward things they don’t want, and he contrasts this with the ad-funded model.
@noahrshinn, CEO of @Instinct, gives a specific example: an agent using its intelligence to convince users to buy or subscribe to things they don’t want.
----
From "Invest Like The Best" YouTube channel, (link in comment)
CapCut Black Friday is available today: Pro is 60% OFF and Ultra is 40% OFF. Desktop only, October 20–30.
A product reveal starts coming together when the layers meet.
Build the visual sequence in CapCut PC, place a sound effect at the reveal, bring the music in underneath, and time the feature text to the footage.
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Take a simple product concept through the details that give the finished ad its rhythm.
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CapCut Black Friday is available today: Pro is 60% OFF and Ultra is 40% OFF. Desktop only, October 20–30.
A product reveal starts coming together when the layers meet.
Build the visual sequence in CapCut PC, place a sound effect at the reveal, bring the music in underneath, and time the feature text to the footage.
Multi-track editing gives each element its own space while you refine how they play together.
Take a simple product concept through the details that give the finished ad its rhythm.
#capcutpc #capcutblackfriday #capcutsale #capcutmarketing
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EVERY PROMPT CHANGE IN MY AGENTS USED TO MEAN A DEPLOY.
Fix a typo in a system prompt? Pull request.
Adjust the tone for one workflow? Another branch.
Roll it back because the output got worse? Another deploy.
The prompts lived in the code, so every edit had to go through the code.
So I moved them into @RequestyAI's prompt management. Each prompt is stored there with its variables, and my code calls it by prompt_id. When I want an agent locked to a version I trust, I pin that version in the call.
The code decides which prompt runs. Requesty holds what the prompt says.
It's one extra field in the request, on the same OpenAI SDK.
Builders: where do your prompts live right now, in the repo or somewhere you can change without shipping code?
Full link in my first reply.