Nathan Hurst’s Blog

I’ve been programming for mobile for 15 years. “What?! Mobile wasn’t around in 2000,” you say? Well, you’re forgetting about the original mobile, the TI-83 graphing calculator.

BASIC on the TI-83 was my first experience with programming, and it all started because a teacher told me I could use calculator apps on tests if I made them myself. I’ve been programming ever since, and it has shaped who I am as a person. This is just one example of the profound effect teachers can have.

Today, I’m proud to announce I’m joining a company that helps teachers share these transformational experiences: Teachers Pay Teachers. Teachers Pay Teachers is the world’s first and largest open marketplace for educators to buy, sell, and share their original resources. It’s growing like crazy and has already paid out over $100M to teachers.

I’m excited to join a great team including:

• Sha-Mayn Teh (previously an engineering manager at Google)
• Adam Freed (previously COO at Etsy)
• Jared Cohen (previously COO at Kickstarter)
• Kirsten Nevill-Manning (previously director of FB recruiting)
• Scott Carleton and Alexis Tryon (from Artsicle who I met during the good ol’ days at Dogpatch Labs)

If you’re interested in hearing more, ping me at [email protected] - I’d love to grab coffee, hear what you’ve been doing, and tell you what we’re up to!

Dear friends,

I made something for you this Christmas. I hope you'll enjoy it!

Each year I have a secret I use to find gifts for people: go to Amazon, find two things I know my gift recipient likes, then compare the "similar items" section to find matches. It's kind of tedious to do, but it works like a charm. Enter computers. Exit the tedious.

This year, I made a website that does the same thing without any effort: GiftMating.com

As elections draw near, remember that it's not just about the President. We'll vote on a number of Congressional seats too.

If you haven't take a second to get a quick read on your senator and representative, do that now.

As you're considering congressional leaders, take note of who never came to their senses with regard to SOPA, the bill that was going to break the internet. For quick reference, if you're in one of these states or districts, please pay extra attention.

You have a SOPA supporter up for re-election: 

Arizona
Don't re-elect Senator Kyl

California
Everyone: Don't re-elect Senator Feinstein
District 24: Don't re-elect Representative Gallegly
District 27: Don't re-elect Representative Sherman
District 28: Don't re-elect Representative Berman
District 29: Don't re-elect Representative Schiff
District 32: Don't re-elect Representative Chu
District 33: Don't re-elect Representative Bass
District 43: Don't re-elect Representative Baca
District 45: Don't re-elect Representative Bono Mack

Connecticut
District 1: Don't re-elect Representative Larson

Florida
Everyone: Don't re-elect Senator Nelson
District 12: Don't re-elect Representative Ross
District 19: Don't re-elect Representative Deutch
Distruct 20: Don't re-elect RepresentativeWasserman Schultz

Georgia
District 12: Don't re-elect Representative Barrow

Michigan
District 14: Don't re-elect Representative Conyers Jr.

Minnessota
Everyone: Don't re-elect Senator Klobuchar

Mississippi
District 1: Don't re-elect Representative Nunnelee

North Carolina
District 12: Don't re-elect Representative Watt

New Mexico
Everyone: Don't re-elect Senator Bingaman

Nevada
District 2: Don't re-elect Representative Amodei

New York
District 3: Don't re-elect Representative King
District 23: Don't re-elect Representative Owens

Ohio
Everyone: Don't re-elect Senator Brown
District 1: Don't re-elect Representative Chabot

Pennsylvania
Everyone: Don't re-elect Senator Casey
District 10: Don't re-elect Representative Marino

Rhode Island
Everyone: Don't re-elect Senator Whitehouse

Tennessee
Everyone: Don't re-elect Senator Corker
District 5: Don't re-elect Representative Cooper

Texas
District 21: Don't re-elect Representative Smith

Virginia
District 6: Don't re-elect Representative Goodlatte

Notes

* Data courtesy of ProPublica
* Some SOPA supporting senators and representatives are not mentioned here because they're not up for re-election or aren't running again.

Please tweet/share with your friends in other locations!

I recently saw Latency Number Every Programmer Should Know and was having trouble getting my head around the scale, so I made this visualization to help me out.

I converted latency times to their equivalent light distances (how far light travels in that much time) and plotted those distances on a map to get a sense of the latency numbers in relation to each other.

Source Data

The smallest few latency times are included here with their respective comparisons.

About me

My company runs "speed interviewing" events over video chat to connect developers and companies for face-to-face conversations. If you're interested in latency and performance, we're hosting a web-event Tuesday and would love to have you participate: Hirelite.com.

I coded all day yesterday, focusing on a new feature for Hirelite: Speed Dating for Software Jobs. I implemented ~300 lines, added tons of functionality, and was feeling crazy productive. After this surge of productivity, though, the familiar pangs of technical debt anxiety came. I prescribed a hearty dose of refactoring.

I looked for commonalities to refactor among the eight paths of the feature. In particular, two paths stuck out as having more in common with the rest of the feature than the other six. Coincidentally, these two paths were far more commonly used than the others. Additionally, these two paths could effectively cover the scenarios implied by the other six.

Interestingly, these commonalities emerged through implementation. As I implemented the eight paths, the data structures backing each path generally fell in to two buckets. Each of the two more commonly used paths took advantage of different data structures, allowing the less common paths to be covered by one or a combination of the two.

These things weren't obvious from the user experience or product perspective. It took a deep, code-level understanding of the details of the problem and multiple potential solutions to surface this insight.

By the time I actually finished the feature, it was down to 50 lines. Though the simplified solution now seems obvious, I'm sure I would not have found this shortcut without working through the initial cases and writing the 300 lines. 

It always astounds me how much work it takes to understand something well enough to truly simplify it. Sometimes I feel bad, like I've wasted a bunch of time building something that should have been easy from the start. Or maybe I shouldn't have done it at all - I should have hired a rockstarguru who doesn't need to write rough drafts of anything, ever. And then I remember those people don't actually exist.

The same seems to be true for art. Picasso said:

... pictures went forward toward completion by stages. Every day brought something new. A picture used to be a sum of additions. In my case a picture is a sum of destructions. I do a picture - then I destroy it. In the end, though, nothing is lost: the red I took away from one place turns up somewhere else.

Picasso simplified masterfully. In 45 days, he drew 11 bulls, eventually boiling a bull down to just 9 lines.

Pablo Picasso - The Bull. Dec. 5th 1945 - Jan. 17, 1946. Image source and further reading: Pablo Picasso - Bull: a master class on abstract art

Despite all this, I still have this gut feeling that I should have known.

• Do you run in to situations like this?
• Any tips on how to shortcut early iterations that take so much time?
• Is this the type of thing that more experience helps you get around or is it purely a creative process?

After we launch the feature, I'll try to follow up with a deeper dive in to the specifics of the implementation.

One of the main reasons all of development isn't outsourced is that it's hard to replace an in-house developer's personal investment in the business, cultural understanding, and market understanding. Additionally, communication is typically orders of magnitude more efficient.

Since starting Hirelite: Speed Dating for Software Jobs, I've been taking a lot of meetings around New York City. After each meeting, I like to find a place nearby to sit, plug in my laptop, and hack for a few hours. In New York, it's hard to find a place you can sit with some elbow room unless you know where to look (Starbucks is usually packed). It turns out, due to zoning regulations, NYC abounds with free public space, often indoors and often with open power outlets where they don't mind you sitting for a few hours.

tl;dr - In Manhattan, look for newish buildings that have a higher base height than buildings around them. Chances are, there's a place you can hack.

[Base height illustrated source]

Example

In the example below, there are three buildings. #2 has a much higher base height in relation to buildings #1 and #3. There's probably a place to hack here.

Why is this?

In the late 1800's and early 1900's, as buildings in New York City were built taller and taller, people protested the loss of light due to the shadows cast and light blocked by new buildings. When the Equitable building was built in 1915, it cast a 7-acre shadow and enforced the need for more zoning regulation.

[The Equitable Building source]

Early attempts to regulate building height, base height, and setbacks (how high building walls may be before they must be set back in relation to the building footprint), including the 1916 Zoning Resolutions, were too restrictive and were plagued with other problems inherent to a growing city.

New regulations were drafted to better fit the city's expansion, culminating in the 1961 Zoning Regulations. These regulations set forth incentives for building developers to create publicly available space. Essentially, the new regulations allowed building developers to build with a higher base height in exchange for having an enclosed atrium, an outdoor park, or some other public space. This is where the hack comes from (the hack doesn't work if there are a cluster of buildings taking advantage of these incentives).

If you prefer to research for yourself instead of finding a place on the fly, just visit this list of of all privately owned public space in NYC. This will also help you narrow down outdoor vs indoor public space.

For more information see: NYC Zoning History.

I'm embarrassed to admit that lately, I've been watching a lot of HGTV. Whenever I have programming to do that is easy but time consuming, I watch House Hunters or Designed to Sell in the background. On both shows, buyers and sellers use comparable houses to help them put a value on a house. Similarly, investors value companies based largely on how the company compares to other companies they've seen before. Wouldn't it be nice if there was some way for founders to see the valuations of other startups too?

Finding and Valuing Startup Comparables

You can start valuing your company more like an investor by seeking out comparable startups and working backward from the funding they've raised.

1. Go to the funding section of CrunchBase and look for companies who have received funding recently that are similar to you: similar stage (angel, seed, series X), similar progress (demo, launch, 10k users, paying customers), similar team size/composition.
2. Take the funding amount, divide it by 0.2 and 0.3 to get a range for the post-money valuation. 0.2 and 0.3 correspond to how much dilution you expect from the funding. Example: GroupMe raised $850k. The post-money (high end of the range) is $850k / 0.2 = $4.25m.
3. Subtract the funding amount from each side of the range to get the pre-money valuation. Example: GroupMe's pre-money (high end of the range) is $4.25m - $850k = $3.4m.

Here are a few example pre-money valuations of companies on CrunchBase who have received seed or angel funding within a month. Ideally, you'd look closer at the composition of the team and the progress of company; however, I've only included the team size and how long the company has been operating here.

Through Hirelite, cofounders often ask me how much equity a technical cofounder should get. The graphic below balances the risks cofounders take with their relative contributions to help answer this question. All assumptions and clarifications are noted after the graphic.

This covers one of the most common situations I encounter: For a pre-funding web startup whose team includes only a non-technical cofounder, how much equity should an incoming technical cofounder get?

Technical cofounders, remember that the number of shares or options doesn't matter, just your percentage ownership (what the chart shows).

Assumptions

For this case, I assume the non-technical cofounder has already contributed significantly to the business and will likely get more equity (in this chart their minimum ownership is 50%). This doesn't have to be the case. Technically inclined people can definitely build something on their own, and then seek a non-technical cofounder, retaining more equity for themselves. Even if the technical cofounder doesn't prototype something first, they can still contribute more to the business and receive more equity (ex: someone who did marketing for 2 years out of undergrad manages to recruit a top 10th percentile programmer with a successful app in the iPhone app store).

I assume both the non-technical and the technical cofounders are compensated equally. I also assume they are either working on their company full time now or will be soon. Compensation is probably between $0 and a ramen salary. This is an area that varies widely and can significantly impact how much equity a technical cofounder receives (if they take more salary). It's beyond the scope of this post, but I'll try to cover it in the future.

I assume the technical cofounder has a reasonably general set of technical skills and can pickup new technical skills quickly. Ex: they're primarily a back-end programmer, who can code basic front-end specs, and who can administer cloud hosting.

I assume both cofounders will be diluted equally as more employees and investors get involved.

Finally, I assume this chart may be a little surprising to non-technical cofounders. Why should a technical cofounder get so much of the company? Especially a company based on your vision. Basically, it's because ideas aren't worth much. Execution is what matters, and in most web startups that falls on the technical founder. Software developers are a hot commodity right now, and many of them know it, so they're on the lookout for really stellar business people to partner with, not some run-of-the-mill, me-too idea having, 10k foot synergizer without any concrete sales prospects, marketing ability, or product experience. Sorry for the rant, but in all seriousness, I've seen too many good non-technical founders delay building a product for months because they're too stingy with equity.

Other Reading

Here are a few other approaches by others in the venture and tech community. There are no hard rules for this kind of stuff, so I'd recommend reading them and synthesizing with this:

• How To Allocate Founder and Employee Equity by Fred Wilson
• Fair Equity Allocation by Joel Spolsky
• The only wrong answer is 50/50: Calculating the co-founder equity split by Dan Shapiro (in response to Joel Spolsky's post)

Notes

[1] Technical cofounder starts with 50%: start by assuming the technical and non-technical cofounders will contribute similarly to the business and are taking similar risks for the business. Give the non-technical cofounder extra equity for anything "above and beyond" (see final assumption above for more). Also, here's an example calculation: 50 (base equity) - 10 (for working prototype) - 5 (has over 10k users) - 10 (has raised VC) = 25. The technical cofounder gets 25% of the company.

[2] Working prototype (not just wireframes) -10%: If a non-technical cofounder has a working prototype, they've likely assumed some risk already to build the prototype (perhaps by contracting it out). Creating wireframes doesn't require much risk taking or even really help de-risk much of the busines.

[2a] Has paying customers -10%: If the company already has paying customers, the non-technical cofounder has already eliminated a huge risk for the business; someone wants the product.

[2b] Has over 10k users -5%: If the company doesn't have paying customers, but does have some reasonable user traction (with an nice trajectory), the non-technical cofounder has de-risked the business some, but not as much as having paying customers.

[3] Non-technical cofounder has significant connections or experience -10%: Connections include: relationships with key people in the company's target market, social network connections, blog readership, etc. Domain specific experience within the target market are helpful as are more broad experiences in sales, marketing, product development, or business development (probably on the order of 5+ years outside of undergrad, grad, or MBA school).

[4] Non-technical cofounder has raised venture capital before -10%: The non-technical cofounder has done a startup before, and someone has trusted them with a lot of money. It will probably be easier to get money again. If your business doesn't need external capital, tone this number down, but it's still important (maybe -5%) because it conveys a degree of startup savvy.

[5] Non-technical cofounder has had a successful exit before -10%: The biggest predictor of future success is past performance. This could also apply to non-exit situations where the non-technical cofounder started a company that is operating successfully and the non-technical cofounder has chosen to move on, etc.

[6a] Salary upon funding -0%: No extra equity for getting a salary upon funding. It may not be a market salary, but a technical founder will likely get something if they elect to. Why should a technical cofounder take less equity upfront because they may or may not receive a salary later? In this situation a technical cofounder and a non-technical cofounder are taking similar risks; therefore, neither get any extra equity.

[6b] Non-technical cofounder has idea and vision -0%: This is just part of the job description and included in the non-technical cofounder's initial 50%. Execution is what matters anyway (see final assumption above for more).

[6c] Non-technical cofounder has MBA -0%: (this is my opinion) In the early days of a startup, an MBA doesn't help much. It certainly doesn't hurt, but it shouldn't affect equity.

[7] Non-technical cofounder invests money -5% per $10k: This is an approximation based on a very early stage valuation of $200k. The situation will vary from company to company based on who is involved with the company and what they've accomplished so far. When I think about this, I only include money still unspent when the technical cofounder joins (usually the money was used to build the initial prototype. Sometimes I adjust the equity for the "working prototype" step above).

[8] Lower limit 10%: A non-technical cofounder wants to ensure the technical cofounder has compelling incentives. I consider this the minimum if the technical cofounder is not taking a salary.