❌ Operator gives up after 4 minutes.
✔️ Proxy completes it twice before Operator finishes.
✔️ Not only that, Proxy also delivers complete results.
3/
Task 2: Get the latest basketball news.
❌ Operator gets stuck on a CAPTCHA and needs human help.
✔️ Proxy bypasses it instantly with no manual input.
❌ Operator provides just a link with minimal info.
✔️ Proxy delivers full details in under a minute.
4/
Task 3: Find top-rated @Tripadvisor stays.
❌ Operator takes over 2 minutes and delivers less information.
✔️ Proxy finds 5 top-rated stays in 56 seconds with prices, ratings, and reviews.
5/
Task 4: Get the latest US economy news.
❌ Operator is slow and less informative.
✔️ Proxy provides a detailed, high-quality summary faster.
6/
What’s more, Proxy is ranked #1 globally via the WebVoyager benchmark which assesses agentic capabilities across 600 web based tasks.
7/
.... and when it comes to cost, it’s not even a question.
→ Operator costs $200.
→ Proxy is Free, with a $20/month Pro option.
→ Schedule automations to run on repeat - your own AI agent on autopilot. → Instantly share automations on X/Twitter so anyone can run them... with one click! 🤯
9/
That’s a wrap!
Proxy beats Operator where it matters most: speed, autonomy, features, and cost.
NVIDIA just removed one of the biggest friction points in Voice AI.
PersonaPlex-7B is an open-source, full-duplex conversational model.
Free, open source (MIT), with open model weights on @huggingface 🤗
Links to repo and weights in 🧵↓
The traditional ASR → LLM → TTS pipeline forces rigid turn-taking.
It’s efficient, but it never feels natural.
PersonaPlex-7B changes that.
This @nvidia model can listen and speak at the same time.
It runs directly on continuous audio tokens with a dual-stream transformer, generating text and audio in parallel instead of passing control between components.
That unlocks:
→ instant back-channel responses
→ interruptions that feel human
→ real conversational rhythm
Persona control is fully zero-shot!
If you’re building low-latency assistants or support agents, this is a big step forward 🔥
MIT and Oxford released their $2,500 agentic AI curriculum on GitHub at no cost.
15,000 people already paid for it.
Now it's on GitHub!
It covers patterns, orchestration, memory, coordination, and deployment.
A strong roadmap to production ready systems.
Repo in 🧵 ↓
10 chapters:
Part 1. What agents are and how they differ from plain generative AI.
Part 2. The four agent types and when to use each.
Part 3. How tools work and how to build them.
Part 4. RAG vs agentic RAG and key patterns.
Part 5. What MCP is and why it matters.
Part 6. How agents plan with reasoning models.
Part 7. Memory systems and architecture choices.
Part 8. Multi agent coordination and scaling.
Part 9. Real world production case studies.
Part 10. Industry trends and what is coming next.