Alex Reibman Profile picture
Aug 4 8 tweets 5 min read Twitter logo Read on Twitter
Every week, SF’s top AI engineers and startup founders meet up and show what’s new in AI.

No pitches. No grifting. Just code.

Here’s what some of SF’s best hackers are up to this week at @cerebral_valley 🧵 Image
1/ Insight AI

Trying to validate an idea? Just pick your topic and ideal customer persona, and generate user interviews surveys automatically

Mom test in a box
@prasann_pandya https://t.co/oicG2RHUustwitter.com/i/web/status/1…
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2/ What’s going on?! (name pending)

Ever seen a cool event on a website but couldn’t find the calendar invites?

This chrome extension automatically scrapes events on any website and adds them to your calendar. Way more convenient than adding them manually https://t.co/WLfAJCZgsPtwitter.com/i/web/status/1…
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3/ MyMap

Summarize any website and turn it into a beautiful, easy to digest slide show of the key points
@victorzhrn https://t.co/kvICFGaH6jtwitter.com/i/web/status/1…
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4/ Playtest

Turn any website into an interactive mini-game

Powered by @AnthropicAI’s Claude, make your web content more engaging with 1 line of Js https://t.co/cjcoZwTLEVtwitter.com/i/web/status/1…

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5/ Anything Protocol

AI Zapier to automate your life and business easily. Open source, hackable workflow protocol
@carllippert
6/ Silic

Generative AI for clothing. Design, model, and print brand new garment designs on demand
@AiSilic https://t.co/Iy09V6MnuAtwitter.com/i/web/status/1…

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@AiSilic That’s it for this week

Also, I didn’t present this time, but check out .

Follow @AlexReibman for on-the-ground hack reports and to see what’s up next timeagentops.ai

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More from @AlexReibman

Jul 30
OpenAI has new competition.

And it’s name is Claude.

Live tweeting the finalists of the first ever @AnthropicAI Hackathon at @cerebral_valley

Meet the best of the 200+ hackers demonstrating what’s now possible with 100k context windows (1/n):
Image
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1/ Mythbusters AI

Plugging in Claude with RAG fact checkers to identified confabulations (aka lies) during presidential debates

@pascalwieler @ryboticc @tonyadastra https://t.co/1Bff1fsXqytwitter.com/i/web/status/1…
Image
2/ Dr. Claude

Combining LLMs with evidence-backed diagnoses using Monte Carlo Tree Search (MCTS).

Identify the likelihood of a disease given patient symptoms using data from vetted medical records
@WianStipp @fadynakhla_ @suKruKiymaCi

🥉 3rd place winner https://t.co/4lBmdiJ3Fntwitter.com/i/web/status/1…
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Read 8 tweets
Jul 23
SF’s most talented:
-AI researchers
-data scientists
-software engineers

All came to a mansion see what’s possible with AI agents.

This stuff is insane.

Live tweeting the demos from the AI Agents hackathon at
@agihouse_org w/ @Wing_VC @MultiON_AI 🧵: Image
1/

High Flyers

There are 90+ airlines with loyalty programs. It’s a huge pain in the neck to register for their frequent flyer programs

We built an agent that goes to all of them and automatically creates accounts for you using
@multi_on

feat. Me and
@thymeshirl94824
:)
2/

RealChar

Create virtual characters with user personas by scraping the web with MultiON to understand their personas

Winner 🥇
Read 25 tweets
Oct 13, 2021
The #NobelPrize in economics was just awarded to 3 top economists. #EconTwitter seems to be over it, but the data science/ML community is totally missing out!

Here's why Data Scientists should start paying attention and what they can take away 🧵
The prize was awarded to David Card, @metrics52, and Guido Imbens for their monumental contributions to statistical methodology and causal inference.

They used and developed strategies that were a true paradigm shift bridging the gap between data and causation in economics
One part of the prize went to David Card from UC Berkeley.

Card is most well-known for his famous minimum wage study that paradoxically revealed that an increase in the minimum wage did *not* reduce employment. How?

The study applied a strategy called Difference in Differences
Read 11 tweets
Oct 6, 2021
Big tech teams win because they have the best ML Ops. These teams
- Deploy models at 10x speed
- Spend more time on data science, less on engineering
- Reuse rather than rebuild features

How do they do it? An architecture called a Feature Store. Here's how it works
🧵 1/n Image
In almost every ML/data science project, your team will spend 90-95% of the time building data cleaning scripts and pipelines

Data scientists rarely get to put their skills to work because they spend most of their time outside of modeling Image
Enter: The Feature Store

This specialized architecture has:
- Registry to lookup/reuse previously built features
- Feature lineages
- Batch+stream transformation pipelines
- Offline store for historical lookups (training)
- Online store for low-latency lookups (live inferences) Image
Read 13 tweets

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