At a time when the words “AI” and “hype” have become almost synonymous, it’s crucial to be smart about choosing your sources of information. There is far too much information noise out there, and sifting through the sea of articles from various AI evangelists and generated garbage to find something truly worthwhile is incredibly difficult. In this post, I’ll share the materials I read to stay up-to-date on the latest developments. ...

It’s summer. Time to plan a vacation getaway. You open ChatGPT, select the increasingly popular “Travel Advisor” GPT, and start discussing options. The advisor gives excellent suggestions, offers fascinating details about local attractions, generates pretty good itineraries, and generally leaves a great impression. Sure, some oddities pop up here and there, but you dismiss them as harmless hallucinations. You settle on Barcelona. Excellent choice. In the same chat, you switch to another familiar and popular GPT, “Booking Agent,” which has never let you down, and book your accommodations. ...

In the previous post, I broke down the basic concepts of the Griptape AI framework, and now it’s time to put them into practice. We’ll try to use them to develop a small application that helps run a link-blog on Telegram. The application will receive a URL, download its content, run it through an LLM to generate a summary, translate that summary into a couple of other languages, combine everything, and publish it to Telegram via a bot. The general flow can be seen in the diagram below: ...

What on Earth is Codex? Good question, right? The thing is, until recently, OpenAI had a model called Codex, which was used as the foundation for autocompletion in GitHub Copilot. Then, OpenAI released a console agent for development, which they named, so no one would get confused, Codex. 1 Everyone had a laugh at OpenAI’s naming skills 2, and life went on. Until the fateful day when a tweet like this appeared from Sam Altman: ...

Today we will look at Griptape, a framework for building AI applications, which offers a clean Pythonic API for those tired of LangChain’s abstraction layers. It provides primitives for building assistants, RAG systems, and integrating with external tools. Honestly, in my experience, most people tired of LangChain switch to custom-written wrappers around lower-level libraries like OpenAI or LiteLLM. But who knows, maybe they’re missing out. Let’s dive in. A Bit of History Personally, I’ve been hearing about Griptape for about a year and a half. As far as I remember, It started as a sort of LangChain competitor with quite similar primitives, but their paths gradually diverged. As of the time of the writing, it has 2.3k stars on GitHub, which is somewhat less than LangChain’s 109k, but still enough to consider the project quite mature. Besides the open-source framework, it has also developed its own cloud where you can run your applications, ETLs, and RAGs, and a visual builder, Griptape Nodes, allowing non-professionals to click together applications in minutes. 1 ...

Alright, Here We Go I got my hands on the Re:Camera from Seeed. Essentially, it’s a small box (a cube about 4 cm per side), wrapped in a heatsink. Inside, there’s a dual-core RISC-V based MPU (updated: only one core is visible to the system, the second one is apparently reserved for special operations), an ancient 8051 microcontroller, an OmniVision camera sensor, LEDs for illumination, Wi-Fi, BT, and, you know, all sorts of peripherals. RAM is a bit scarce, only 256 megabytes, so getting Greengrass on it will be problematic. You can connect Ethernet via a special dongle-adapter that barely stays put, but for development, there’s no point, because the camera shares its network over USB type C, and it’s easier to work that way. If you’re short on storage (and the device comes in 8 GB and 64 GB built-in storage options), you can stick in a MicroSD card. You can also stick the box to something metallic, as it has magnets on one side. ...