64.7K
STImDavis shows what Agent was getting up to in his room while everyone else was partying at the AMP Pool Party 😂👀 #agent00 #imdavisss #kaicenat #dukedennis #amp #twitch #viral #reels
@streamcreamtv
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(12)
42.8K
AGFour files you need in every agent of coding workspace. discovery.MD, research.MD , plan.MD, and progress.MD. #Claude #Vibecoding #AIAgents #AIAutomation #Coding. 
@agentic.james
45.6K
TEHow ARP works
Address Resolution Protocol (ARP) is a very fundamental protocol in computer networking. When a PC wants to send a message over the network, it has to encapsulate the data down the layers of the OSI model. At each layer, it has to fill all header information such as TCP/UDP ports in the layer 4 header, source, and destination IP addresses in the Layer 3 header, and source and destination MAC addresses in the Layer 2 header. If you think about it, all this information is available to the end client except for the destination MAC address. Address Resolution Protocol (ARP) has been introduced to resolve a MAC address based on a given IP address in a local network.
PC1 tries to ping PC3, which is in the same local area network and the same subnet 10.1.1.0/24. When the user executes the command ping 10.1.1.3, PC1 starts encapsulating an ICMP Request (ping) into an Ethernet frame before sending it over the network. Let's look at how PC constructs the protocol data unit (PDU):
At layer 4 - PC1 knows that ping works by sending ICMP Echo Request and waits for ICMP Echo Response. So it sets the protocol at layer 4 to be ICMP with Echo Request flag set. Therefore, everything needed at this layer is available.
At layer 3 - PC1 knows the destination IP address, it is explicitly mentioned by the user in the ping 10.1.1.3 command so it puts it into the destination IP field. PC1 knows its own configured IP address 10.1.1.1 and puts it in the source field. Therefore, everything needed at this layer is available.
At later 2 - PC1 knows its own configured MAC address and put in the source field. BUT there is no way for PC1 to know which end client in the LAN has 10.1.1.3 configured and what is its MAC address. Therefore destination MAC address is not available to PC1 and it has to use ARP in order to get it.
#ccna #cisco #certified #networking #data #network #internet #networkplus #networkengineer #it #datascience #security #cisconetworking #ciscosecure #ciscosecurity #ciscocertification #ciscocert #ccnacertification #networksecurity #networkadministration #ccie #networkinfrastructure #internetprotocol #binary #ip
@technical_subnets
193.8K
JGThere are lots of agent frameworks today — from visual builders to code-first SDKs.
You shouldn’t learn them all at once — pick based on your goals and the complexity you need.
⸻
1️⃣ No-code / visual platforms (best to start with)
Use these if you want to understand agent workflows without writing much code:
➤ n8n — visual workflow builder + integrations; lets agents trigger real business workflows via nodes and tools. 
➤ Microsoft Copilot Studio — low-code agent builder with drag-and-drop components and built-in logic
These are great when you’re learning or automating business processes without deep engineering.
⸻
2️⃣ Beginner-friendly low-code SDKs
Once you want custom logic, transitions to code:
➤ LangChain / LlamaIndex — modular Python / JS SDKs to build RAG, tool calling, and simple agents.
➤ These give you flexibility with minimal orchestration overhead.
Perfect for building bots, helpers, retrievers, and internal assistants.
⸻
3️⃣ Multi-agent orchestration (mid complexity)
When tasks need role separation or collaboration patterns:
➤ CrewAI — built around role-based agents, easy coordination, ergonomic configs.
This is excellent for experimenting with multi-persona workflows before committing to heavy engineering. 
⸻
4️⃣ Code-first, complex workflows
When your agent flows are long, branching, need retries, state, or deep control:
➤ Strands Agents (AWS) — an open-source, model-driven SDK where the LLM plans, reasons, and calls tools. 
➤ LangGraph / AutoGen — explicit control, state machines, and structured traces for serious orchestration and observability.
These shine when you need reliability, auditability, and complex logic.
⸻
5️⃣ How to choose what to learn/process
Here’s a practical learning path:
➤ No-code first — understand flows with n8n / Copilot Studio
➤ Low-code next — build real apps with LangChain / LlamaIndex
➤ High-code last — level up to Strands, LangGraph, or AutoGen when you need control
This progression moves from idea → prototype → robust system.
⸻
Bottom line:
There’s no one “best” agent framework.
Pick based on your task complexity, coding comfort, and system requirements — and grow your toolkit in stages, not all at once.
@jganesh.ai
12.1K
TEThis new coding AI Agent is topping SWE-Bench verified. I've tried it and it is very effective. Coupled with playwright MCP I have an awesome agent to work while i chill saving me a lot of manual testing.
Free tier includes 3k messages, or $30/mo for unlimited everything 🚀
#AugmentCode #AIprogramming #WebAutomation #SoftwareDevelopment #TechTools #CodingTips #aicoding #cursor #windsurf #cline #roocode
@techfren
231.1K
CLUse MCP and Agentic Coding without paying monthly subscription! Here’s how..
1. Install Cline addon in VS CODE
2. Cline has no monthly subscription. It provides a free tier for LLM
3. After that, you can select LLM from various sources, including locally hosted Ollama for free
4. If you select LLMs from providers like Anthropic, AWS, Google, then you just pay as you go
Follow @cloudwithraj for MCP, GEN AI, and cloud interview tips, tricks, and q/a
#ai #genai #mcp #coding #programming #tech #technology
@cloudwithraj
2.2M
MI1 Million RPS isn't about code, it's about architecture.
Here are the 8 key layers to scale your API architecture.
Unlock 200+ practical problem-solutions just like this one in the Ebook. Link in bio
1️⃣ Load Balancer
👉 Distributes traffic across multiple servers so no single machine melts.
Example: 1M req/s split across 200 servers = only 5k req/s each.
⸻
2️⃣ Horizontal Scaling
👉 Add more servers when traffic spikes instead of upgrading one big server.
Example: Black Friday? Spin up 50 more API nodes in seconds.
⸻
3️⃣ Caching Layer
👉 Serve frequent reads from Redis/Memcached to avoid DB overload.
Example: User profile cached → avoids 10M database hits/day.
⸻
4️⃣ CDN for Static Content
👉 Images and static assets load from edge servers near the user.
Example: A user in Delhi gets images from a Delhi CDN node.
⸻
5️⃣ Async Processing (Queues)
👉 Push heavy tasks to Kafka/SQS so API responds instantly.
Example: Payment API returns fast → receipt email sent in background.
⸻
6️⃣ Database Sharding
👉 Split huge datasets across multiple DB shards to scale reads/writes.
Example: Users A–M on shard 1, N–Z on shard 2.
⸻
7️⃣ Rate Limiting
👉 Block or throttle abusive clients to protect server capacity.
Example: “100 requests/sec limit” prevents bots from killing the API.
⸻
8️⃣ Lightweight Payloads
👉 Reduce JSON response size to cut latency and bandwidth.
Example: Return only “id, name, price” instead of 20 unnecessary fields.
#systemdesign #apidesign #scaling #1millionrps #distributedsystems #loadbalancing #caching #microservices #softwaredeveloper #programming #coding #devops #tech #backenddeveloper #backenddevelopment #api #interviews #database #learninganddevelopment #tech
[API Design] [System Architecture] [API Scaling] [1 Million RPS] [Distributed Systems] [Load Balancing] [Database Sharding] [High Availability]
@mission_compile
32.4K
BUYou’re overpaying for AI credits! Do this to basically have unlimited API calls:
- go to openrouter.ai
- search for :free models
- create API key
- use openai chat model
- set base url to https://openrouter.ai/api/v1
Comment “ROUTER” if you want to get the step-by-step guide
@buildusingai
136.0K
0
833.7K
SAAI agents have been promised for the past 2 years, but whenever I’ve tried them, they’ve mostly been very mediocre.
The main reasons are:
1) Hallucination
2) Smaller context window
3) Integrations
When it comes to integrations, there are two parts, (other two will get better over time).
First, using other tools and APIs.
Second, taking help from other AI agents.
As you know, the first part was solved by MCP a couple of months ago. However, it still remained unsolved to have a similar protocol for the communication of AI agents.
With the Google’s A2A protocol, agents will now be able to communicate with each other at a level that was previously impossible.
NOW your Ai agent does not have to be best at trip planning or legal. It can just take help of the best ones in the market. If you want always wanted to build some AI agents, this is the perfect time!
[google’s A2A protocol, AI agent, AI startup, Software Product]
@saasflash
21.6K
BEComment “MCP” and I’ll send you my full setup guide for free.
This is a step by step tutorial showing how I connect Claude Code + n8n MCP so I can build automations directly from the terminal.
N8N is tedious and boring to build in now
So getting lazier made me seek alternatives
This is how Claude becomes an actual automation engineer instead of a chatbot.
#ai #aiautomation #n8n #claudecode #mcp aidev automationengineering noCode aiagents
@bennettx.ai
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