FEATURED STORY

BioCoder strikes again

New issue: bioreactors and food production, modeling a worm's brain on a computer and letting it drive a robot, and more.

BioCoderCover_Page_01The fifth issue of BioCoder is here! We’ve made it into our second year: this revolution is in full swing.

Rather than talk about how great this issue is (though it is great), I’d like to ask a couple of questions. Post your answers in the comments; we won’t necessarily reply, but we will will read them and take them into account.

  • We are always interested in new content, and we’ll take a look at almost anything you send to BioCoder@oreilly.com. In particular, we’d like to get more content from the many biohacker labs, incubators, etc. We know there’s a lot of amazing experimentation out there. But we don’t know what it is; we only see the proverbial tip of the iceberg. What’s the best way to find out what’s going on?
  • While we’ve started BioCoder as a quarterly newsletter, that’s a format that already feels a bit stodgy. Would you be better served if BioCoder went web-native? Rather than publishing eight or 10 articles every three months, we’d publish three or four articles a month online. Would that be more useful? Or do you like things the way they are?

And yes, we do have a great issue, with articles about a low-cost MiniPCR, bioreactors and food production, and what happens when you model a worm’s brain on a computer and let it drive a robot. Plus, an interview with Kyle Taylor of the glowing plant project, the next installment in a series on lab safety, and much more. Read more…

Comment

Commodity data analytics for health care

Predixion service could signal a trend for smaller health facilities.

Analytics are expensive and labor intensive; we need them to be routine and ubiquitous. I complained earlier this year that analytics are hard for health care providers to muster because there’s a shortage of analysts and because every data-driven decision takes huge expertise.

Currently, only major health care institutions such as Geisinger, the Mayo Clinic, and Kaiser Permanente incorporate analytics into day-to-day decisions. Research facilities employ analytics teams for clinical research, but perhaps not so much for day-to-day operations. Large health care providers can afford departments of analysts, but most facilities — including those forming accountable care organizations — cannot.

Imagine that you are running a large hospital and are awake nights worrying about the Medicare penalty for readmitting patients within 30 days of their discharge. Now imagine you have access to analytics that can identify about 40 measures that combine to predict a readmission, and a convenient interface is available to tell clinicians in a simple way which patients are most at risk of readmission. Better still, the interface suggests specific interventions to reduce readmissions risk: giving the patient a 30-day supply of medication, arranging transportation to rehab appointments, etc. Read more…

Comment

Intoxicating machines

The revolutionary thing about desktop machines is that they'll make experimentation easier.

Carvey_Screenshot_cropped

Cropped screenshot of Carvey. Source: the Carvey Kickstarter campaign.

“Mr. Frankel, who started this program, began to suffer from the computer disease that anybody who works with computers now knows about,” [Richard] Feynman later explained. “The trouble with computers is you play with them.”
— George Dyson, describing the beginning of the Manhattan Project’s computing effort in Turing’s Cathedral.

I’ve been reading George Dyson’s terrific history of the early development of the digital computer, and the quote above struck me. Even when they were little more than room-sized adding machines that had to be painstakingly programmed with punchcards, computers offered an intoxicating way to experiment. Most programmers can probably remember their first few scripts and the thrilling feeling of performing millions of operations in seconds. Computers let us take some abstracted human process and repeat it quickly, at almost no cost, with easy modification along the way. Read more…

Comment: 1

Open data for open lands

Recreation.gov should be a platform, not a silo.

President Obama’s well-publicized national open data policy (pdf) makes it clear that government data is a valuable public resource for which the government should be making efforts to maximize access and use. This policy was based on lessons from previous government open data success stories, such as weather data and GPS, which form the basis for countless commercial services that we take for granted today and that deliver enormous value to society. (You can see an impressive list of companies reliant on open government data via GovLab’s Open Data 500 project.)

Based on this open data policy, I’ve been encouraging entrepreneurs to invest their time and ingenuity to explore entrepreneurial opportunities based on government data. I’ve even invested (through O’Reilly AlphaTech Ventures) in one such start-up, Hipcamp, which provides user-friendly interfaces to making reservations at national and state parks.

A better system is sorely needed. The current reservation system, managed by the Active Network / Reserve America is clunky and almost unusable. Hipcamp changes all that, making it a breeze to reserve camping spots. Read more…

Comment

What happens when fashion meets data: The O’Reilly Radar Podcast

Liza Kindred on the evolving role of data in fashion and the growing relationship between tech and fashion companies.

Editor’s note: you can subscribe to the O’Reilly Radar Podcast through iTunes, SoundCloud, or directly through our podcast’s RSS feed.

In this podcast episode, I talk with Liza Kindred, founder of Third Wave Fashion and author of the new free report “Fashioning Data: How fashion industry leaders innovate with data and what you can learn from what they know.” Kindred addresses the evolving role data and analytics are playing in the fashion industry, and the emerging connections between technology and fashion companies. “One of the things that fashion is doing better than maybe any other industry,” Kindred says, “is facilitating conversations with users.”

Gathering and analyzing user data creates opportunities for the fashion and tech industries alike. One example of this is the trend toward customization. Read more…

Comment

Resume Driven Development

Before you ask HR to find a developer skilled in a particular tool or language, think about who you really want in that seat.

Crossed_Wires_Howard_Lake_Flickr

I had a conversation recently with Martin Thompson (@mjpt777), a London-based developer who specializes in performance and low-latency systems. I learned about Martin through Kevlin Henney’s Tweets about his recent talk at Goto Aarhus.

We talked about a disturbing trend in software development: Resume Driven Development, or RDD. Resume Driven Development happens when your group needs to hire a developer. It’s very hard to tell a non-technical HR person that you need someone who can make good decisions about software architecture, someone who knows the difference between clean code and messy code, and someone who’s able to look at a code base and see what’s unnecessary and what can be simplified. We frequently can’t do that ourselves. So management says, “oh, we just added Redis to the application, so we’ll need a Redis developer.” That’s great — it’s easy to throw out resumes that don’t say Redis; it’s easy to look for certifications; and sooner or later, you have a Redis developer at a desk. Maybe even a good one.

And what does your Redis developer do? He does Redis, of course. So, you’re bound to have an application with a lot of Redis in it. Whenever he sees a problem that can be solved with Redis, that’s what he’ll do. It’s what you hired him for. You’re happy; he’s happy. Except your application is now being optimized to fit the resumes of the people you hired, not the requirements of your users. Read more…

Comments: 4