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Build AI Agents - And Win 10,000$

Exciting news as TensorOps collaborates with Nir Diamant, LangChain and LlamaIndex to bring you an unmissable event. Join our remote hackathon for the chance to submit your Agent example. Winners will receive prizes from top AI framework maintainers and be showcased in the prestigious DiamantAI's GitHub repository.

??? November 14-17

Current prize value: $10,000

• ?? Build AI Agents & create usage guides
• ?? Live webinars & expert lectures
• ?? Top-tier mentors & judge
• ?? Submit via PR to the GitHub repo
• ?? Additional sponsorship opportunities are available!

?? Early bird registration and sponsorship options >> https://www.tensorops.ai/aiagentsonlinehackathon

OpenAI’s o1 (Strawberry ?? ) Really What We Hoped For?

We’re paying for ‘reasoning’ we can’t even see—o1 charges for tokens spent on hidden processes. While it’s not transparent, o1 is a significant step toward AGI, with competitors like Google racing to catch up.

How does this shift toward reasoning-based AI truly differ from previous models, and what does it mean for the future of AI development? Should you already be adopting this new model?

??? Tune into our latest episode, where we break down the real potential of o1>> https://spotifyanchor-web.app.link/e/MZPJx5WVDNb

Migrating Prompts Across Models Is a Real Pain In The ??

The idea of smart LLM Proxies that simplify switching between models sounds appealing. However, companies that have tried to transition between different vendors often find it more difficult than expected.

Each LLM interprets prompts differently, so to get the best results, you need to adapt your prompts to each specific model. Simply plugging in a new, more advanced LLM won’t work right away—it requires careful prompt adjustment.

?? Sticking to one model is a losing game. As new models keep emerging, teams need an efficient way to switch and test them. Manual prompt tweaking isn’t practical, which is where #PromptTranslation steps in.

Tools like Google’s #AutomaticPromptDesign use reinforcement learning to automatically optimize prompts for new models like #Gemini, ensuring minimal performance loss when switching.

?? Link to Gad Benram's Blog >>

https://www.tensorops.ai/post/prompt-translation-the-way-to-switch-between-llms-without-losing-performance

?? PRewrite: Prompt Rewriting with Reinforcement Learning – This paper explores how to use reinforcement learning for prompt translation.

?? Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization – This study delves into using a prompt-translating LLM for automatic prompt translations, now a part of Vertex AI!

https://arxiv.org/abs/2406.15708

From Chatbots to AI Agents

A Strategic GuideRemember when Sam Altman kicked off the year betting big on AI agents, saying they'd hit the mainstream by 2025? Well, it is just around the corner, and things are heating up

If you're going to join the AI agent movement, make sure you're doing it smart and efficient.

The first and most common question we get:

What sets AI agents apart from traditional chatbots?

Here are some key differences:

• ?? Complexity and Capabilities: Today, AI-driven chatbots use LLMs for conversation generation controlled by prompt engineering, offering flexibility. AI agents require pre-defined workflows, often set up with Python, resulting in less flexibility but better automation.
• ?? Scope of Use: Chatbots mainly answer FAQs, while AI agents manage internal workflows, customer relationships, and even provide predictive insights.
• ?? Learning Ability: While chatbots need manual updates, AI agents continuously learn from real-time data, making them dynamic and adaptive.
• ?? Integration: AI agents integrate seamlessly with CRMs, HR platforms, and analytics tools, creating more intelligent workflows.

Check out Diogo Goncalves new blog post, Moving from Chatbots to Agents>>

https://www.tensorops.ai/post/moving-from-chatbots-to-agents