AI Hustle: News on Open AI, ChatGPT, Midjourney, NVIDIA, Anthropic, Open Source LLMs: TabbyML: $3.2M Funding to Challenge GitHub Copilot

Welcome to the OpenAI podcast, the podcast that opens up the world of AI in a quick and

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Tune in daily to hear the latest news and breakthroughs in the rapidly evolving world

of artificial intelligence.

If you've been following the podcast for a while, you'll know that over the last six

months I've been working on a stealth AI startup.

Of the hundreds of projects I've covered, this is the one that I believe has the greatest

potential, so today I'm excited to announce AIBOX.

AIBOX is a no-code AI app building platform paired with the App Store for AI that lets

you monetize your AI tools.

The platform lets you build apps by linking together AI models like chatGPT, mid-journey,

and 11Labs.

Eventually, we'll integrate with software like Gmail, Trello, and Salesforce so you

can use AI to automate every function in your organization.

To get notified when we launch and be one of the first to build on the platform, you

can join the waitlist at AIBOX.AI, the link is in the show notes.

We are currently raising a seed round of funding.

If you're an investor that is focused on disruptive tech, I'd love to tell you more

about the platform.

You can reach out to me at jaden at AIBOX.AI, I'll leave that email in the show notes.

The AI-assisted code generation sphere is experiencing a burst of competition and startup

TabiML, which is the brainchild of former Google employees, is making waves.

Recently, the startup secured, as I mentioned, over $3 million, $3.2 million in seed funding

to further enhance its open-source code generator.

Differentiating from GitHub's co-pilot, TabiML offers a unique proposition as a self-hosted

coding assistant, so Menzang, one of the co-founders, emphasized the tool's robust customization

capabilities, saying, quote, we foresee a landscape where the customization and software

development will be a predominant need for all businesses.

I think while proprietary software solutions may have matured, when juxtaposed with GitHub's

open AI-fueled tool, open-source solutions like TabiML kind of stand out.

So according to Zang, he believes that they stand out a lot.

This is particularly evident in larger enterprise settings.

Lucy Gao, Zang's co-founder, pointed out how engineers in these environments often

utilize proprietary code, and this makes tools like co-pilot less effective, whereas open-source

solutions like TabiML can actually kind of thrive.

So Gao illustrates this with a bit of a straightforward example, saying, quote, if an employee pens

a code line, I can seamlessly pull it using TabiML.

However, as with many AI innovations, co-generators can have their shortcomings.

So sometimes, delivering buggy outputs, Gao, however, sees this challenge as fairly manageable

for self-hosted platforms like theirs, as users make alterations to TabiML's auto suggestions

or dismiss them the tool learns and actually improve.

So if it's saying, like, hey, here's a great bit of code for what you're working on, and

you say, no, that's not relevant, it's actually going to learn from that and get better, which

I think is very interesting.

So the primary objective of these AI-driven co-generators isn't to necessarily oust human

programmers, but to kind of complement their efforts.

For now, I'm sure in the future, these things will be able to just completely automate a

lot of these tasks and oust them.

It's kind of funny, a lot of people talk about the fact that AI is not going to replace everyone's

jobs.

It's just going to help people that augment everything you're doing.

Well, yeah, but also I feel like it's going to replace a lot of jobs.

And if you're not really aware of that, or if you're not willing to admit that, then

it's going to just be more disruptive when it actually happens.

So I think in any case, a recent survey by GitHub highlighted that co-pilots recommendations

were accepted by users at a 30% rate.

So further is then kind of spotlighted an intriguing statistic from a Google developer

gathering nearly a quarter of the tech giant software engineers and encountered over five

assistive instances daily via its AI enhanced internal code editor, which is called CIDR.

So launched just a few years ago, Tabi ML has already gained, I think some solid attention.

They have over 11,000 GitHub stars.

And one of the partners and ZooCap have been identified as the primary investors in this

funding round.

Actually, I think that was young key partners and ZooCap.

And I think addressing this kind of looming competition with the behemoth co-pilot, of

course, where the big showdown is going to happen, Yang speculates that OpenAI's advantage

might start to slip as AI models evolve and the expenses and all the expenses kind of associated

with computing power decline.

So currently GitHub and OpenAI's edge kind of comes from their ability to roll out AI

models hosting tens of billions of parameters via cloud networks, although there are costs

to deploying such large models, co-pilot has ingeniously balanced this out to some extent

by, you know, batching requests.

So you know, regardless of that, I think the tactic has its limitations.

Microsoft reportedly bled over $20 per GitHub co-pilot user monthly in the initial months

of this year as disclosed by the Wall Street Journal.

So definitely not profitable and costing them a ton of money.

I think in contrast to that, Tabby's strategy is to reduce development hurdles by endorsing

models that train on one to three billion parameters.

So while this may compromise quality in the interim, Zhang is fairly optimistic about

this and says, quote, as computing power becomes more affordable and open source models elevate

in caliber, GitHub and OpenAI's competitive advantage is bound to ebb.

So I think it's going to be interesting to see if he's right about this or not.

Definitely a very interesting area to watch.

If you're looking for an innovative and creative community of people using chat GPT, you need

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Machine-generated transcript that may contain inaccuracies.