Tuesday, 16th July 2024
OpenAI and Anthropic focused on building models and not worrying about products. For example, it took 6 months for OpenAI to bother to release a ChatGPT iOS app and 8 months for an Android app!
Google and Microsoft shoved AI into everything in a panicked race, without thinking about which products would actually benefit from AI and how they should be integrated.
Both groups of companies forgot the “make something people want” mantra. The generality of LLMs allowed developers to fool themselves into thinking that they were exempt from the need to find a product-market fit, as if prompting is a replacement for carefully designed products or features. [...]
But things are changing. OpenAI and Anthropic seem to be transitioning from research labs focused on a speculative future to something resembling regular product companies. If you take all the human-interest elements out of the OpenAI boardroom drama, it was fundamentally about the company's shift from creating gods to building products.
Codestral Mamba. New 7B parameter LLM from Mistral, released today. Codestral Mamba is "a Mamba2 language model specialised in code generation, available under an Apache 2.0 license".
This the first model from Mistral that uses the Mamba architecture, as opposed to the much more common Transformers architecture. Mistral say that Mamba can offer faster responses irrespective of input length which makes it ideal for code auto-completion, hence why they chose to specialise the model in code.
It's available to run locally with the mistral-inference GPU library, and Mistral say "For local inference, keep an eye out for support in llama.cpp" (relevant issue).
It's also available through Mistral's La Plateforme API. I just shipped llm-mistral 0.4 adding a llm -m codestral-mamba "prompt goes here" default alias for the new model.
Also released today: MathΣtral, a 7B Apache 2 licensed model "designed for math reasoning and scientific discovery", with a 32,000 context window. This one isn't available through their API yet, but the weights are available on Hugging Face.
Introducing Eureka Labs (via) Andrej Karpathy's new AI education company, exploring an AI-assisted teaching model:
The teacher still designs the course materials, but they are supported, leveraged and scaled with an AI Teaching Assistant who is optimized to help guide the students through them. This Teacher + AI symbiosis could run an entire curriculum of courses on a common platform.
On Twitter Andrej says:
@EurekaLabsAI is the culmination of my passion in both AI and education over ~2 decades. My interest in education took me from YouTube tutorials on Rubik's cubes to starting CS231n at Stanford, to my more recent Zero-to-Hero AI series. While my work in AI took me from academic research at Stanford to real-world products at Tesla and AGI research at OpenAI. All of my work combining the two so far has only been part-time, as side quests to my "real job", so I am quite excited to dive in and build something great, professionally and full-time.
The first course will be LLM101n - currently just a stub on GitHub, but with the goal to build an LLM chat interface "from scratch in Python, C and CUDA, and with minimal computer science prerequisites".
Lessons learned in 35 years of making software (via) Lots of great stuff in here from Jim Grey, with a strong focus on "soft skills" (I prefer the term "professional skills") around building relationships and making sure your contributions are visible.
This tip resonated with me in particular:
There is no substitute for working software in Production. I can’t believe now that I have been part of 18-month release projects. This was back in the bad old waterfall days, but even then it was possible to release a lot more frequently than that. The software we build is valuable. It builds the value of the company. When you hold it until it’s perfect, or everything you think it needs to be, you are holding back on building the company’s value. Find the fastest, shortest path to getting the smallest increment of the thing that will work into the customer’s hands. You can keep making it better from there.
And another tip on the subject of perfectionism:
When you deliver work you’re really proud of, you’ve almost certainly done too much and taken too long. I have a bit of a perfectionist streak. I want to do my work well and thoroughly. It took me a long time to learn that when I do that, it’s for me, not for the company. When I’ve reached 60-80% of the thing being as good as I want, I’ve probably done enough.
Mermaid Gantt diagrams are great for displaying distributed traces in Markdown.
Bryce Mecum demonstrates how Mermaid gantt diagrams can be used to render trace information, such as the traces you might get from OpenTelemetry. I tried this out in a Gist and it works really well - GitHub Flavored Markdown will turn any fenced code block tagged mermaid containing a gantt definition into a neat rendered diagram.