gkamradt

Two projects

1. https://interauth.dev/

Share a single google doc with your agent (w/o oauth mess)

I needed a way to share a single google doc/sheet with my agent

I didn’t want to go through the heavy oauth gcp project so I’m using disposable email addresses as the work around

2. Agents.sh

I get so many cold emails that could be better if I tell the bots how to talk to and reach me. What’s top of mind for me, how I like to be pitched, etc.

So I made a mini platform to put up text/md files. Then added all the perms fun - pw support, expiration, every url has an inbox. Aimed at agents only.

Ex: https://agnts.sh/greg

o3-pro is not the same as the o3-preview that was shown in Dec '24. OpenAI confirmed this for us. More on that here: https://x.com/arcprize/status/1932535380865347585

Ah yes, two things

1. We had a no-data retention agreement with them. We were assured by the highest level of their company + security division that the box our test was run on would be wiped after testing

2. We only tested o3 against the semi-private set. We didn't test it with the private eval.

#4 (private test set) doesn't get used for any public model testing. It is only used on the Kaggle leaderboard where no internet access is allowed.

Good question! This was one of the main motivations of our "Paper Prize" track. We wanted to reward conceptual progress vs leaderboard chasing. In fact, when we increased the prizes mid year we awarded more money towards the paper track vs top score.

We had 40 papers submitted last year and 8 were awarded prizes. [1]

On of the main teams, MindsAI, just published their paper on their novel test time fine tuning approach. [2]

Jan/Daniel (1st place winners last year) talk all about their progress and journey building out here [3]. Stories like theirs help push the field forward.

[1] https://arcprize.org/blog/arc-prize-2024-winners-technical-r...

[2] https://github.com/MohamedOsman1998/deep-learning-for-arc/bl...

[3] https://www.youtube.com/watch?v=mTX_sAq--zY

We have a few sets:

1. Public Train - 1,000 tasks that are public 2. Public Eval - 120 tasks that are public

So for those two we don't have protections.

3. Semi Private Eval - 120 tasks that are exposed to 3rd parties. We sign data agreements where we can, but we understand this is exposed and not 100% secure. It's a risk we are open to in order to keep testing velocity. In theory it is very difficulty to secure this 100%. The cost to create a new semi-private test set is lower than the effort needed to secure it 100%.

4. Private Eval - Only on Kaggle, not exposed to any 3rd parties at all. Very few people have access to this. Our trust vectors are with Kaggle and the internal team only.

Hey HN, Greg from ARC Prize Foundation here.

Alongside Mike Knoop and François Francois Chollet, we’re launching ARC-AGI-2, a frontier AI benchmark that measures a model’s ability to generalize on tasks it hasn’t seen before, and the ARC Prize 2025 competition to beat it.

In Dec ‘24, ARC-AGI-1 (2019) pinpointed the moment AI moved beyond pure memorization as seen by OpenAI's o3.

ARC-AGI-2 targets test-time reasoning.

My view is that good AI benchmarks don't just measure progress, they inspire it. Our mission is to guide research towards general systems.

Base LLMs (no reasoning) are currently scoring 0% on ARC-AGI-2. Specialized AI reasoning systems (like R1 or o3-mini) are <4%.

Every (100%) of ARC-AGI-2 tasks, however, have been solved by at least two humans, quickly and easily. We know this because we tested 400 people live.

Our belief is that once we can no longer come up with quantifiable problems that are "feasible for humans and hard for AI" then we effectively have AGI. ARC-AGI-2 proves that we do not have AGI.

Change log from ARC-AGI-2 to ARC-AGI-2: * The two main evaluation sets (semi-private, private eval) have increased to 120 tasks * Solving tasks requires more reasoning vs pure intuition * Each task has been confirmed to have been solved by at least 2 people (many more) out of an average of 7 test taskers in 2 attempts or less * Non-training task sets are now difficulty-calibrated

The 2025 Prize ($1M, open-source required) is designed to drive progress on this specific gap. Last year's competition (also launched on HN) had 1.5K teams participate and had 40+ research papers published.

The Kaggle competition goes live later this week and you can sign up here: https://arcprize.org/competition

We're in an idea-constrained environment. The next AGI breakthrough might come from you, not a giant lab.

Happy to answer questions.

Check out the SOTA resources on the guide

https://arcprize.org/guide

Happy to answer any questions you have along the way

(I'm helping run ARC Prize)

We put a bunch of detail to get started on the guide https://arcprize.org/guide

Happy to answer any questions you have along the way

(I'm helping run ARC Prize)

Thank you

Ha that would be sweet.

Do you have a video link of what you're referring to?

I once tried to use the molds to make chocolate representations of the mountains ha! I learned the hard way that tempering is difficult for a novice

Thank you!

I actually use DEMto3D. It's touchy, but I do post-work on the .stl/3d model in blender so it works out ok for me.

If you have weird artifacts, I'm guessing that is due to the underlying data vs QGIS itself. Have you looked at their documentation (https://demto3d.com/en/)?

I outline how the whole process works here https://www.gregkamradt.com/gregkamradt/2020/2/29/manufactur...

Here's the process on custom orders. I'll put the link right on the site to try and avoid confusion.

https://docs.google.com/document/d/1IkiHG_Z5JS03mWYHv-KNAhi8...

edit: whoops added link

The upfront costs are pretty expensive.

For every new location you do the process looks like: 1. Get the data and prep it for print (fixed) 2. 3D print it (fixed) 3. Rubber Mold (fixed) 4. Wax Model (variable) 5. Bronze (variable)

Steps 1-3 are 40-60% of the costs. So I haven't put the money out of pocket yet to put up new locations. I've let customer's ask first and then do them.

Surprisingly, most of our orders have been custom

Here's my info packet on the custom process https://docs.google.com/document/d/1IkiHG_Z5JS03mWYHv-KNAhi8...

Thank you

It was one of these two vendors, I forget which:

https://apollomapping.com/digital-elevation-models https://www.l3harrisgeospatial.com/Data-Imagery/Elevation-Da...

I don't wanna share the number but it was 6 figures

Where do we get it? Only publicly available sources. Usgs has a great portal. Private data is too expensive to get. I was quoted 6 figures for a larger area. They were going to fly a plane and capture it :)

What resolution? Totally depends on the area the customer would like to cover. If it’s their ranch or property, we usually need 1-meter. If it’s a mountain range than 30-Meter works.

It mainly depends on the resolution limit for 3D printing. So it also depends on the size of the model they want.

Unfortunately not all areas are covered with high res

I’ve used QGIS for a few years to build www.TerraMano.co

We make 3D Maps of American Landscapes in bronze.

We take Digital Elevation Model (DEM) data, do light transformations in QGIS and convert it to an .STL file before additional 3D modeling.

Our latest project was a hairy one doing Oahu (https://terramano.co/blogs/product/oahu-bronze-3d-map)

Nice, the Siver's concept is elegant. If you want to chat more about the soft skills behind data I'd be happy to. My user name at gmail

I agree. Having the experience to spot common pitfalls or 'weak' looking stats is key. Like any craft, there is no easy way to learn this other than experience.

Whenever I'm advising rising data analysts/scientists, I tell them to understand three areas of background knowledge that will multiply their ability to pull out an insight. They'll help them connect disparate ideas together.

Stats is easy but connecting ideas is hard. That's where the real magic happens. Plus, computers have a hard time automating this, for now.

1) Product Knowledge - How well do you know the product? This is easy for a simple app, but for large enterprise apps there are many features to keep track of. If you don't know the full context of your product, how could you frame your analysis in a larger picture?

2) Stakeholder Empathy - Whether you like it or not, as a data person you're advancing a business cause/mission. This means you need to fully understand where the business has been and where it is going. The basic question is - what are your stakeholders priorities? Why do they matter?

3) Customer Empathy - Arguable the most important of them all, how well do you know the customer? I encourage data scientists to get away from the computer and in front of the customer. Hear user research calls and ask questions. Drive as a Lyft driver, deliver food [1], etc.

Unfortunately these three areas are soft skills and you won't know you've improved until you find yourself reciting a fact. Usually you'll think "well duh, because the customer thinks this." It'll seem obvious, but it is only because you went through the trenches to learn that fact.

[1] Tony Xu (DoorDash) delivering pizzas to understand product and customer - https://www.listennotes.com/podcasts/how-i-built-this/doorda...

Yes it is. They use a vacuum technique to remove all the air bubbles and sit perfect on the 3d print

Via USGS https://viewer.nationalmap.gov/basic/

How To Fail At Almost Everything And Still Win Big - Scott Adams (2013)

One of my favorite quotes: “I put myself in a position where luck was more likely to happen. I tried a lot of different ventures, stayed optimistic, put in the energy, prepared myself by learning as much as I could, and stayed in the game long enough for luck to find me.” pg - 158

My top ten list for the year: https://www.gregkamradt.com/gregkamradt/top-reads-2018

HN, we're hiring for a Senior Growth & Adoption position here at Salesforce. Let me know if you're interested and we can set up a call. My email is my HN username@salesforce.com

Role Description: This position will report to the VP of Strategy & Growth of the Product Data Science team. The Lead's main objective is to help shape Salesforce products by delivering data-driven product insights, conducting adoption tests, and guiding a team of data engineers, data scientists, and visualization engineers to productize these insights. This role requires expert-level experience driving adoption growth, and technical expertise in data-mining, analysis, and visualization. Advanced communication skills are also crucial to the success of this role. All Strategy & Growth Leads must be able to build relationships and collaborate across a large, matrixed environment, and comfortably present findings to large groups of product executives.

Role Link http://salesforce.careermount.com/career/54671/Senior-Growth...

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San Francisco, CA | Full Time | Sr. Growth Analyst / Data Scientist | www.Salesforce.com

The Product Data Science (PDS) team is made up of data scientists, engineers, and growth analysts who are dedicated to driving product strategy with data-driven insights. The PDS team works with executives, product managers, designers, developers, user researchers, marketers, and sales strategy team members across all Cloud businesses to discover new opportunities for growth and optimization, experiment with data, drive adoption, and provide actionable insights that impact product strategy.

This role requires expert-level experience driving adoption growth and technical expertise in adoption tools, data mining, and visualization. This role also requires advanced communication skills to collaborate effectively in a large, matrixed environment, and a high level of comfort with public speaking and executive presentations. Responsibilities: + Partner with product teams to understand business requirements, product direction, roadmaps, key metrics, and growth goals. + Create KPIs based on knowledge of the Salesforce business, growth drivers, and industry benchmarks. + Identify data-driven opportunities for product and feature investments. + Deliver easily-consumable presentations to large groups of stakeholders and executives that showcase actionable insights and recommendations to help drive product strategy. Email me (my HN username) at Salesforce with any questions.

San Francisco, CA | Full Time | Sr. Growth Analyst / Data Scientist | www.Salesforce.com

The Product Data Science (PDS) team is made up of data scientists, engineers, and growth analysts who are dedicated to driving product strategy with data-driven insights. The PDS team works with executives, product managers, designers, developers, user researchers, marketers, and sales strategy team members across all Cloud businesses to discover new opportunities for growth and optimization, experiment with data, drive adoption, and provide actionable insights that impact product strategy.

This role requires expert-level experience driving adoption growth and technical expertise in adoption tools, data mining, and visualization. This role also requires advanced communication skills to collaborate effectively in a large, matrixed environment, and a high level of comfort with public speaking and executive presentations.

Responsibilities: + Partner with product teams to understand business requirements, product direction, roadmaps, key metrics, and growth goals. + Create KPIs based on knowledge of the Salesforce business, growth drivers, and industry benchmarks. + Identify data-driven opportunities for product and feature investments. + Deliver easily-consumable presentations to large groups of stakeholders and executives that showcase actionable insights and recommendations to help drive product strategy.

Email me (my HN username) at salesforce with any questions.

Apply here: http://salesforce.careermount.com/career/46827/Senior-Analys...

clickable - http://ryd.io/