In this video, I share my 6 secret tips on how to get an ML job in 2025 because doing so feels almost impossible... At least, if you don't know what options ...
I follow the journey that led to the explosion of Large Language Models. From Jordan's pioneering work in 1986 to today's GPT-4, this documentary traces how ...
Let's build GPT: from scratch, in code, spelled out.
We build a Generatively Pretrained Transformer (GPT), following the paper "Attention is All You Need" and OpenAI's GPT-2 / GPT-3. We talk about connections t...
PGVector offers infrastructure simplicity at the cost of missing some key features desireable in search solutions. We explain what those are in this blog.
New research reveals how ChatGPT, Claude, and other AI crawlers process web content, including JavaScript rendering, assets, and other behavior and patterns—with recommendations for site owners, devs, and AI users.
What It Actually Takes to Deploy GenAI Applications to Enterprises: Arjun Bansal and Trey Doig
Join Trey Doig and Arjun Bansal as they recount Echo AI’s journey rolling out its conversational intelligence platform to billion-dollar retail brands. They’...
Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity | Lex Fridman Podcast #452
Dario Amodei is the CEO of Anthropic, the company that created Claude. Amanda Askell is an AI researcher working on Claude's character and personality. Chris...
Making it easier to build human-in-the-loop agents with interrupt
While agents can be powerful, they are not perfect. This often makes it important to keep the human “in the loop” when building agents. For example, in our fireside chat we did with Michele Catasta (President of Replit) on their Replit Agent, he speaks several times about the human-in-the-loop component
Everything we learned, and everything we think you need to know, from technical details on 24khz/G.711 audio, RTMP, HLS, WebRTC, to Interruption/VAD, to Cost, Latency, Tool Calls, and Context Mgmt
Reducing hallucinations in large language models with custom intervention using Amazon Bedrock Agents
This post demonstrates how to use Amazon Bedrock Agents, Amazon Knowledge Bases, and the RAGAS evaluation metrics to build a custom hallucination detector and remediate it by using human-in-the-loop. The agentic workflow can be extended to custom use cases through different hallucination remediation techniques and offers the flexibility to detect and mitigate hallucinations using custom actions.
Supercharging LLM Application Development with LLM-Kit
Discover how Grab's LLM-Kit enhances AI app development by addressing scalability, security, and integration challenges. This article discusses the challenges faced in LLM app building, the solution, the architecture of the LLM-Kit as well as the future plans of the LLM-Kit.
A guide to Amazon Bedrock Model Distillation (preview)
This post introduces the workflow of Amazon Bedrock Model Distillation. We first introduce the general concept of model distillation in Amazon Bedrock, and then focus on the important steps in model distillation, including setting up permissions, selecting the models, providing input dataset, commencing the model distillation jobs, and conducting evaluation and deployment of the student models after model distillation.
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