mijail

Discovery calls are the initial stage in a selling process. I am biased but "selling" to a customer provides a much more honest picture of the things they truly care about then a generic customer interview. Despite most folks aversion to sales, if you have a compelling story and can narrow in to a general area of pain (which you have already) most people are very willing to hop on a call and tell you exactly the things you need to know. Be sincere, open to their expertise but be willing to challenge and ask prodding questions. This helps test their conviction and set the weights of what will get them to influence internally to purchase - the true kpi of most products. You might feel goofy since they you don't have anything to truly sell yet. Rest assured, your buyers will be very willing to cooperate if you deliver and you will stand out from the crowded majority of those who don't.

@williamcotton - saw this the other day in the prompt engineering post - I enjoyed it. Sent you a message on your site but something looks broken. If you use linkedin I requested to connect.

The "model" is not the hard part! If a pretrained model is generalized enough and valuable enough then it can exist as simple API or a runtime, it stops being a "model." If you have the engineering chops to deploy models in production for your application then deploying a pretrained model is trivial. If its valuable enough to the business then squeezing a few more points by fine tuning the model is worth it.

Kudos to her. Conservation tech is a challenging and exciting test bed for "hackers" because it's a hyper dimensional chessboard of constraints and strategies. It's easy to reduce the solution to "just do this." From one end, poaching is lucrative in areas with massive economic disparity. And from the other, climate change and the pressure we put on natural resources is unbalancing ecosystems driving human wildlife conflict.

A project I've worked on is Trailguard AI, ran by resolve.ngo, the principal is Eric Dinnerstein who is the former chief scientist at WWF. I can say they our running a start up in every sense of the word but the result is beautiful: a satelite/ gsm connected, battery powered AI enabled cryptic trail cam that can be used as a swiss army knife from animal censuses, to poacher detection, illegal logging truck detection and preventing human wildlife content (detect tiger + sound alarm).

Good article on trailguard: https://globalconservation.org/news/tech-parks-trailguard-ai...

The measure of VC is growth and if you are a component to larger systems or programs you will inevitably be throttled. If you are a platform and can own the end to end then you should take VC capital and invest heavily in well connected BD's that are known by the PM's and have a nuanced ability to navigate the orgs.

Palantir (who is end to end) has opened up VC appetites for defense and you see a certain signaling pattern to the VC backed defense start ups vs the traditional small defense business upstart.

I work at a "start up" that develops a component so we took a strategic investment from a defense contractor and that has enabled us to develop core technology and grow commercially while being patient with the arduous cycles of programs.

"Over the years I have come to the conclusion that the real heroes of ideas are not the people who have them – they are the people who buy them."

Absolutely love this.

As a thought experiment this is cool but from a practical perspective it’s too focused on a specific architecture and if anything adding perturbations might (slightly) help the training process.

From the thought experiment side. I think the moral implications cut both ways. Mass image recognition is not always bad - think about content moderation or the transfer of images of abuse. As a society we want AI to flag these things.

Kudos to whoever thought of this at sfdc. This is a visionary acquisition and it makes sense on so many levels. From the fabric of slack's unique sales model to what this means for enterprise communication.

great perspective! the specific models might not be better but the people building them and the system will be getting better with more "data."

In the section around core technology. I'm curious about this statement:

"As we continue to collect data over time, the system will become more robust."

This is one of these statements that sound true and I know Karpathy coined the term "operational vacation" but it's not clear more data always equals more robust system.

Fully Agree with this insight. Superior UI/UX will make a crappy model look like the magical future we imagine. When you flip on a snapchat face filter nobody thinks about the quality or accuracy of the object detection, facial landmark localization and the active shape model that's doing the lifting. I guess this is what they call the "AI effect."

Great engineers are also really great salespeople.

The problem is that engineers often feel like they have to put on their salesperson hat on and it all goes out the door.

Sales is a "state," the sales system you use varies greatly based on what phase you are as a company. The same tactics don't apply when you are looking for product market fit vs when you have reached it.

My favorite joke on this is "The answer is deep learning, now whats the problem?"

I'm happy a VC is providing these insights. If the economics of AI don't make sense by helping increase profits or cutting costs... it's going to be a long road to reaching the "promised land."

I'm biased but in a lot of industries synthetic data has the potential to balance the costs from the perspective of data acquisition and preparation as well as model testing.

This article doesn't focus too much on the edge side of things but one pattern I'm seeing is that edge deployment can be notoriously resource intensive and time consuming.

Can anyone provide the background or framework of how I should see the business value of gpt3.

Are there businesses that have a tremendous needs for the possibilities it provides?

I've seen use cases that some NLG companies provide like sports and stock summaries but what world should I imagine where this is transformative?

(Disclosure: I work at one)

There’s a few working specifically on simulating the autonomous vehicle domain. air sim is unique because you can do both ground and air. From the business side of things I’ve seen enterprises find it as a good way of proving the value of simulation. The company I work for CVEDIA, develops SynCity which we use both for simulating autonomous systems and generating training data for object recognition and classification tasks.

What's the current sentiment on synthetic data? It may be a self serving question since I work at synthetic data company but I'm curious for fear of being stuck in an echo chamber.

What kind of sales? What's the product? Is it Software? Hardware? B2B? B2C? Self serve or high touch? Have you found product market fit or are you still trying to figure it out?

A lot like tech there are tools for every problem and in sales there is a playbook for every kind of product.

Can customers receive a print copy or is it only in PDF?

Why the log in? I don't trust you enough to give you all of my information. Let me play with it first then ask me to sign in after we trust each other.

You should be selling the benefits of poker, not poker itself. Poker education is a pretty competitive field. Since it seems that you are involved in the start up world why not give your marketing an angle: "Poker for Start Ups"

Not only do you offer a unique skill that is highly relevant to start ups. You also create an interesting networking opportunity.

Do they have guarantee? If you aren't satisfied we will give you back your tweet all you pay is shipping and handling.

That's an outstanding list of problems that are all amazing start up ideas. This is where real estate can really be disrupted.

These guys, http://bermanvc.com/ are really smart. They invest in real estate related technologies and even invested in YC alum 42floors.

They are the "1-click" brokers. The experience doesn't mean that they aren't a part of a complex system. We usually applaud efficient systems don't we?

15% is justified because people pay it.

New York real estate is a system. You have to learn the system to hack it.

A lot of real estate start ups are trying to "Avoid the Broker." The system isn't as broken as we may think. Landlords enjoy minimal vacancy. Brokers get paid generously. Tenants find apartments.

Complaining about a 15% broker fee is like complaining about the gas for your Lamborghini.

Pitching to a programmer is the best from of natural selection. Someone should make a webform that directs non programmers through The Wax on Wax Off of being an effective non programmer. There is equal contribution that can occur with non programmers but it takes quiet a bit more effort than most non techs think.

Touché

This was on HN a few weeks ago. http://news.ycombinator.com/item?id=4415508 What's the obsession with cartel technology?

Super good looking site! From my understanding you are an agent that does everything except the showing. I think your last point is kind of key and may need to be fleshed out more. I agree that tenants like meeting the landlord, however, landlord may like meeting tenants but they usually are unable to. Landlords are in the business of owning property. They use a service provider like brokers to make their life more convenient, so they can focus on the core of their business. The first few points are administrative tasks the real burden is to schedule appointments and show up for appointments that are usually a waste of time (that's why brokers are bitter!)Perhaps facilitating the different aspect of these appointments for landlords can help disrupt the status quo.