DIY AI Butler Is Simpler And More Useful Than Siri

[Geoffrey Litt] shows that getting an effective digital assistant that’s tailored to one’s own needs just needs a little DIY, and thanks to the kinds of tools that are available today, it doesn’t even have to be particularly complex. Meet Stevens, the AI assistant who provides the family with useful daily briefs. The back end? Little more than one SQLite table and a few cron jobs.

A sample of Stevens’ notebook entries, both events and things to simply remember.

Every day, Stevens sends a daily brief via Telegram that includes calendar events, appointments, weather notes, reminders, and even a fun fact for the day. Stevens isn’t just send-only, either. Users can add new entries or ask questions about items through Telegram.

It’s rudimentary, but [Geoffrey] already finds it far more useful than Siri. This is unsurprising, as it has been astutely observed that big tech’s digital assistants are designed to serve their makers rather than their users. Besides, it’s also fun to have the freedom to give an assistant its own personality, something existing offerings sorely lack.

Architecture-wise, the assistant has a notebook (the single SQLite table) that gets populated with entries. These entries come from things like reading family members’ Google calendars, pulling data from a public weather API, processing delivery notices from the post office, and Telegram conversations. With a notebook of such entries (along with a date the entry is expected to be relevant), generating a daily brief is simple. After all, LLMs (Large Language Models) are amazingly good at handling and formatting natural language. That’s something even a locally-installed LLM can do with ease.

[Geoffrey] says that even this simple architecture is super useful, and it’s not even a particularly complex system. He encourages anyone who’s interested to check out his project, and see for themselves how useful even a minimally-informed assistant can be when it’s designed with ones’ own needs in mind.

NVIDIA Trains Custom AI To Assist Chip Designers

AI is big news lately, but as with all new technology moves, it’s important to pierce through the hype. Recent news about NVIDIA creating a custom large language model (LLM) called ChipNeMo to assist in chip design is tailor-made for breathless hyperbole, so it’s refreshing to read exactly how such a thing is genuinely useful.

ChipNeMo is trained on the highly specific domain of semiconductor design via internal code repositories, documentation, and more. The result is a vast 43-billion parameter LLM running on a single A100 GPU that actually plays no direct role in designing chips, but focuses instead on making designers’ jobs easier.

For example, it turns out that senior designers spend a lot of time answering questions from junior designers. If a junior designer can ask ChipNeMo a question like “what does signal x from memory unit y do?” and that saves a senior designer’s time, then NVIDIA says the tool is already worth it. In addition, it turns out another big time sink for designers is dealing with bugs. Bugs are extensively documented in a variety of ways, and designers spend a lot of time reading documentation just to grasp the basics of a particular bug. Acting as a smart interface to such narrowly-focused repositories is something a tool like ChipNeMo excels at, because it can provide not just summaries but also concrete references and sources. Saving developer time in this way is a clear and easy win.

It’s an internal tool and part research project, but it’s easy to see the benefits ChipNeMo can bring. Using LLMs trained on internal information for internal use is something organizations have experimented with (for example, Mozilla did so, while explaining how to do it for yourself) but it’s interesting to see a clear roadmap to assisting developers in concrete ways.