Breakthroughs Upon Breakthroughs Last Week On AI Agents And More

Here are the key AI-related updates and releases from last week:

📌 Gemini 2.5: Google’s most intelligent AI model yet
🔗 https://lnkd.in/gFP366rE

📌 OpenAI launches GPT-40 with native image generation

🔗 https://lnkd.in/g7ckq7KR

📌 Cisco and LangChain’s “Agntcy” Internet of Agents now on GitHub
🔗 https://lnkd.in/gQzhX5Qc

📌 Anthropic Claude can now store memories in code
🔗 https://lnkd.in/gu49kYHw

📌 Google Vertex AI Agent Engineer now generally available
🔗 https://lnkd.in/gG_KyqkQ

📌 DeepSeek AI releases V3, their new frontier after V2
🔗 https://lnkd.in/gwC6WJXV

📌 Qwen releases new VLM: Qwen 2.5-VL-32B-instruct

🔗 https://lnkd.in/ggZ_DPr6

📌 Google’s Gemini API now supports function calling

🔗 https://lnkd.in/gexXZ5JW

📌 Google slowly rolling out Project Astra

🔗 https://lnkd.in/gDPY3ehe

📌 Google deciphers language processing in the human brain via LLM representations
🔗 https://lnkd.in/gAEhjuYz

📌 Anthropic releases research on tracing thoughts in LLMs
🔗 https://lnkd.in/gFQW5SQ9

📌 Microsoft releases Copilot Research Agent and Analyst Agents
🔗 https://lnkd.in/gm9Awahi

📌 LlamaIndex releases a course on building agentic workflows
🔗 https://lnkd.in/g7Agm4h3

📌 LangChain introduces CodeAct for LangGraph

🔗 https://lnkd.in/gfWSUdbq

You Should Know:

1. Testing Gemini 2.5 API with cURL

curl -X POST https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5:generateContent \ 
-H "Authorization: Bearer $(gcloud auth print-access-token)" \ 
-H "Content-Type: application/json" \ 
-d '{"contents":[{"parts":[{"text":"Explain AI agents in simple terms"}]}]}'

2. Running OpenAI GPT-40 for Image Generation

import openai 
response = openai.Image.create( 
prompt="A futuristic AI city", 
model="gpt-40", 
n=1, 
size="1024x1024" 
) 
print(response['data'][0]['url'])

3. Deploying LangChain’s Agntcy on Linux

git clone https://github.com/langchain-ai/agntcy 
cd agntcy 
docker-compose up -d

4. Using Claude’s Memory Feature

import anthropic 
client = anthropic.Client(api_key="YOUR_API_KEY") 
response = client.memory_store( 
text="Remember my preference for cybersecurity topics", 
memory_id="user_123" 
)

5. Automating Vertex AI Agent Tasks

gcloud ai agents create --project=YOUR_PROJECT_ID \ 
--display-name="My-Agent" \ 
--region=us-central1

6. DeepSeek V3 API Example

import requests 
response = requests.post( 
"https://api.deepseek.com/v3/completions", 
headers={"Authorization": "Bearer YOUR_API_KEY"}, 
json={"prompt": "Explain multi-agent systems"} 
) 
print(response.json())

7. Microsoft Copilot Agent CLI Integration

copilot-agent --task "Analyze latest cybersecurity threats" --output report.txt

What Undercode Say:

The rapid evolution of AI agents demands hands-on experimentation. Use Linux-based deployments (docker, kubectl), automate API interactions (curl, Python), and explore cloud-based AI services (gcloud, Azure CLI). For security-focused AI testing, consider:

 Monitor AI model API calls 
tcpdump -i eth0 port 443 -w ai_traffic.pcap

Secure API keys with vault 
vault kv put secret/openai key=YOUR_API_KEY

Windows users can leverage `PowerShell` for AI automation:

Invoke-RestMethod -Uri "https://api.gemini.ai/v1/query" -Method Post -Body '{"query":"AI trends 2024"}'

For large-scale AI workflows, Kubernetes is essential:

kubectl create deployment ai-agent --image=langchain/agntcy

Expected Output:

A structured breakdown of AI advancements with executable commands for developers and cybersecurity professionals.

References:

Reported By: Rakeshgohel01 Ai – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅

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