Gianmario Spacagna

Chief Scientist at Helixa, Machine Learning savvy, motorcyclist and social sciences enthusiast.

[Andrew Ng @AndrewYNg]

I’m delighted to announce that AI Fund has closed $190M for our new fund, in an oversubscribed round. I look forward to working with many more builders to create new companies that serve humanity.

AI Fund isn’t a traditional venture capital firm that invests in existing businesses. Instead, we are a venture builder (also called a venture studio): We co-found AI companies, so our team is directly involved in writing code, talking to customers to get feedback, iterating on product designs, preparing market analyses, and so on. We have a lot of fun building multiple AI products at a time, and thus live daily the emerging AI startup best practices.

Many factors go into the success of a startup. But if I had to pick just one, it would be speed. Startups live or die based on their ability to make good decisions and execute fast, which has been a recurring theme of my posts here as well.

If you are building an AI startup, here are some ideas to consider:
- A startup with a small team that pursues one focused, concrete idea can move really fast. Rather than hedging, it is often better to pursue one hypothesis (for example, build one concrete product) but also be willing to switch quickly to a different hypothesis (say, change what features you decide to build) if the data that comes back indicates the original hypothesis is flawed. Concreteness gets you speed!
- A subject matter expert’s gut is remarkably good at making quick decisions. Obviously, there’s a role for data and user studies as well. But if you’re deciding whether to build feature A or B, or to sell first to user persona X or Y, sometimes a domain expert’s gut will point to a quick decision that you can execute and validate or falsify. Trusting a domain expert’s gut gets you speed!
- AI-assisted coding is making prototyping faster than ever before. Yes, AI assistance is speeding up building reliable, enterprise-grade applications and maintaining legacy codebases. But the acceleration it brings to building stand-alone prototypes is far greater. This is because stand-alone prototypes have low requirements for reliability, integration, or even security (if, say, you run them in a sandbox environment). This lets us prototype and test at a ferocious velocity. AI-assisted coding (including vibe coding, where you might barely look at the code) gets you speed!
- Finally, with faster prototyping, the bottleneck shifts to getting feedback from users. A single learning cycle might consist of (i) building a prototype and (ii) getting user feedback to inform the next iteration. Since (i) is now much faster than before, accelerating (ii) is growing in importance. This means teams that are skilled at finding prospective customers and getting their feedback in hours/days rather than weeks can go faster. For example, when building consumer products, I routinely approach strangers (in a respectful way) in public places to ask if they’re willing to give feedback on a prototype I’m working on. (Gathering feedback is more complex for enterprise products, because prospective customers are harder to track down.) Quick user feedback gets you speed!

In addition to speed, a second criterion that I find important for startup success is deep knowledge of the technology. Because AI technology is evolving rapidly, a team with a deep technical understanding of what AI can and cannot do, and when to use what tool, will make better decisions. This creates meaningful differentiation and saves wasting time in blind alleys. A good technical understanding, too, gets you speed!

I’m grateful to AI Fund’s investors, team, and entrepreneur partners for working with us. There is much ahead to build!

[Original text: https://t.co/I1nkYeTkFA ]

[jacob @jacobmtucker]

I'll be patiently waiting to see what Entourage Spark has in store. As a stellar dev rel and sole writer of @StoryProtocol docs, I hope they integrate us first.

Check out the article here: https://t.co/46shGec7Hn

Entourage Spark: https://t.co/4AkGPZh1nD

[Gianmario Spacagna @gm_spacagna]

@SlackHQ I think this helps and should solve the problem. Thanks.

[Gianmario Spacagna @gm_spacagna]

@SlackHQ how can I remove the white dot from the side panel with the list of workspaces in the desktop application (mac os)? Thanks

[Gianmario Spacagna @gm_spacagna]

@SlackHQ Well there are certain workspaces like saas product communities that use slack for connecting developers. Unfortunately there are too many conversations going on and it is annoying to have the white dot visible all the time.

[Gianmario Spacagna @gm_spacagna]

Everything ready for the Sunday's #carbonara #pecorinoromano #tuorloduovo #guanciale #pepenero #bucatini #montepulcianodabruzzo 🥚🥓🧀🍝🍷 @ Tempera, Abruzzi, Italy https://t.co/7SUfVAiAR8

[Gianmario Spacagna @gm_spacagna]

A novel approach to Document Embedding using Partition Averaging on Bag of Words https://t.co/n4ran9JULm

[Gianmario Spacagna @gm_spacagna]

Manifold clustering in the embedding space using UMAP and GMM https://t.co/dH5iVIM58q

[Gianmario Spacagna @gm_spacagna]

Our menu for Christmas Eve dinner. Happy holidays everyone! #christmasevedinner #merrychristmas @ Tempera, Abruzzi, Italy https://t.co/q6xMysd2xB

[Gianmario Spacagna @gm_spacagna]

Having tested both Boruta and Shap separately I can confidently believe is the way to go.

#featureselection
https://t.co/wdqgvwcQCQ

[Gianmario Spacagna @gm_spacagna]

If you're a trader - check this out. https://t.co/MOGz75m9mo

[Gianmario Spacagna @gm_spacagna]

@lmioulet providing a comparison of Docker containers Vs. lambda functions for real-time machine learning applications @DataScienceMi. Watch the live: https://t.co/x9SPouY4rE

[Gianmario Spacagna @gm_spacagna]

I just published The Manager’s Non-Technical Guide to Machine Learning https://t.co/4PFl7wEppa

[Gianmario Spacagna @gm_spacagna]

Next Tuesday, join the other members of the @DataScienceMi community at this live YouTube event about serverless architectures and engineering practices for machine learning applications.

https://t.co/h1LzhrqYXo

#machinelearning #mlops #engineering #serverless #webinar

[Gianmario Spacagna @gm_spacagna]

Foto appena pubblicata https://t.co/bxIJWlE0MC

[Gianmario Spacagna @gm_spacagna]

@dl_weekly @fritzlabs I thought it might be interested for the newsletter. Thanks

[Gianmario Spacagna @gm_spacagna]

Read my tutorial on extracting rich embedding features from pictures using PyTorch and the ResNeXt-WSL model trained on 940 million of Instagram public images https://t.co/sS3cwlWEeg

[Gianmario Spacagna @gm_spacagna]

Read my tutorial on extracting rich embedding features from pictures using PyTorch and the ResNeXt-WSL model trained on 940 million of Instagram public images https://t.co/sS3cwlWEeg

[AI at Meta @AIatMeta]

Producing high quality forecasting is not an easy problem for machines or analysts. We’ve built NeuralProphet using #deeplearning to do time series modeling, and help solve this. It’s a PyTorch-based version of our Prophet forecasting package. Code: https://t.co/ltfckBU9Lg

[Gianmario Spacagna @gm_spacagna]

In this whitepaper, we will analyze what are the fundamentals concepts that enable organizations to become successful machine learning enterprises. https://t.co/x8Wfec448n

[Gianmario Spacagna @gm_spacagna]

What am I going to cook today? Any suggestions? 🤔🍝🥓🧀🍅🍷 #bucatiniallamatriciana @ Tempera, Abruzzi, Italy https://t.co/LUoW9TWr6z