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7255 discussions

“Uber Against Hunger” Delivers Unused Food To Needy, Hits 1000-Meal Mark - Good News Network

by Romy Ilano

http://www.goodnewsnetwork.org/uber-hunger-delivers-unwanted-food-need/ “Uber Against Hunger” Delivers Unused Food To Needy, Hits 1000-Meal Mark Terry Turner Jul 29, 2016 What could be more perfect than using the “sharing economy” to deliver food that people don’t want to those who desperately need it? That’s what the Unsung app sets out to do. Being tested in Austin, Texas, the app has delivered 1,000 meals that would have otherwise gone into the trash. The Unsung app works kind of like Uber’s ride-sharing service to “hack hunger,” as it’s creators say. RELATED: Tesco to Give All Unsold Food to Charity in its 800 UK Supermarkets Restaurants, grocery stores, or even individuals click on the app to announce they have food to give to the hungry. The app sends the information to nearby volunteer drivers who pick it up and deliver it to people in need. The app will even calculate the value of your food donation so you can declare it as a tax deductible contribution on your income taxes. Every meal that’s donated appears on the app’s live feed. This video shows how the app works. RELATED: Canadian Restaurant Offers Free Meals To Anyone Who Can’t Afford To Pay The app is expected to be available later this year as Unsung works to set up networks of users around the United States. (WATCH the video below from Unsung) – Photos: Unsung.org SEND The Sharing Economy To Your Friends, By Sharing This Story… Sent from my iPhone

8 years, 5 months

Fwd: SSF: Bay Area Robotics Warehouse Blow-out Sale Sat RIGHT NOW

by Patrik D'haeseleer

Happening RIGHT NOW - Bay Area Robotics near SFO is having a clearance sale: ---------- Forwarded message ---------- From: Elise Engelhardt <techelise(a)gmail.com> Date: Fri, Jul 29, 2016 at 11:47 AM Subject: SSF: Bay Area Robotics Warehouse Blow-out Sale Sat (plus help needed) Al (who i know from Homebrew Robotics) is having to shut down his warehouse by Monday so he is having a clearance event on Saturday. There will probably be really good deals. Plus he needs labor help conducting the sale which he will pay for with inventory. From: Albert Margolis <almargolis(a)gmail.com> I have to shrink my operations by 75% over the next 72 hours so I am going to have a blow-out sale tomorrow Saturday 7/30/16 from 10am to 2pm Where: 282 Harbor Way, South San Francisco, 94080 Facebook Link w/pictures: https://www.facebook.com/BayAreaRobotics/ Website Page: https://www.bayarearobotics.com/pages/warehouse-sale-saturday-7-30-16 There will be great prices on great products plus lots of weird stuff that just has to go. The pictures don't really show the scale of the product line. ***************************************** Please share on facebook, mailing lists, etc. I also need help conducting the sale and packing up the leftovers. See below. Please spread the word about that too. ***************************************** I don't really have the resources to pull this off in the available time. If you can make time to help me put stuff out, lookup prices and/or provide customer service tomorrow. I can really use the help. I have 3 days to clear out of a 5000 sq-ft warehouse and desperately need help to make it happen. - Friday 7/29/16: Help put stuff out for a warehouse sale. Lookup prices and mark products. - Saturday 7/30/16: Help at a warehouse sale. - Saturday and Sunday: Load stuff onto pallets and move into storage space. - I am not in a position to pay cash for help but definitely can trade some stuff for your time. https://www.bayarearobotics.com/pages/critical-help-needed Here is my personal Facebook page with the help needed post: https://www.facebook.com/big.al.margolis ***************************************** I will still be in business next week, but as a much smaller, better focused operation. Thanks for your support. Al Margolis owner, www.BayAreaRobotics.com email: al(a)bayarearobotics.com

8 years, 5 months

Kinect2

by Cere Davis

Hey does anyone have a Kinect2 I can borrow for an art project? Thanks, Cere -- Cere Davis CereDavis.com @ceremona <http://Twitter.com/Ceremona> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ GPG Key: http://taffy.findpage.com/~cere/pubkey.asc GPG fingerprint (ID# 73FCA9E6) : F5C7 627B ECBE C735 117B 2278 9A95 4C88 73FC A9E6

8 years, 5 months

FYI: East Bay Mini Maker Faire - Oct 23

by Glenn Meader

East Bay Mini Maker Faire Sunday, October 23, 2016 10 am—5 pm Park Day School campus + Studio One Arts Center in the Temescal district of Oakland, CA http://eastbay.makerfaire.com Makers! Performers! Presenters! Apply now for the 2016 East Bay Mini Maker Faire The 7th Annual East Bay Mini Maker Faire is coming up on Sunday, October 23rd. The annual Call for Makers is open now... Apply to participate at this year’s faire! Science, art, food, craft, engineering, play—there is no limit to the subject matter. Whatever your passion, this is the place to share it. Show off your projects—finished or unfinished! Teach a workshop, sell your work, make a presentation or perform on the stage. REMEMBER, space is FREE for non-commercial exhibitors. Individuals, young makers, "first-timers," maker spaces and collaboratives, clubs or classes, businesses and startups are all welcome to apply. As many of you know, the East Bay Mini Maker Faire is not so terribly "mini"—we're only "small" if you compare us to the MAXI flagship Maker Faire in San Mateo. Over 7,000 people enjoyed the one-day East Bay event last year. There's an incredible array of amazing makers and interesting attendees to meet. We offer makers great exchange, big fun, a community vibe, and a wonderful showcase for their efforts. Apply early to secure your space. The priority round Call for Makers closes September 2nd. More details on content, process and logistics are at our Call for Makers page. If you have questions or ideas, please get in touch via makers(a)ebmakerfaire.com. See you in October! —the East Bay Mini Maker Faire team

8 years, 5 months

We built voice modulation to mask gender in technical interviews. Here’s what happened. – interviewing.io blog

by Romy Ilano

Maybe this is another reason why coed groups may benefit Women more than all women groups? http://blog.interviewing.io/we-built-voice-modulation-to-mask-gender-in-tec… We built voice modulation to mask gender in technical interviews. Here’s what happened. June 29th, 2016 Posted by Aline Lerner on . interviewing.io is a platform where people can practice technical interviewing anonymously and, in the process, find jobs based on their interview performance rather than their resumes. Since we started, we’ve amassed data from thousands of technical interviews, and in this blog, we routinely share some of the surprising stuff we’ve learned. In this post, I’ll talk about what happened when we built real-time voice masking to investigate the magnitude of bias against women in technical interviews. In short, we made men sound like women and women sound like men and looked at how that affected their interview performance. We also looked at what happened when women did poorly in interviews, how drastically that differed from men’s behavior, and why that difference matters for the thorny issue of the gender gap in tech. The setup When an interviewer and an interviewee match on our platform, they meet in a collaborative coding environment with voice, text chat, and a whiteboard and jump right into a technical question. Interview questions on the platform tend to fall into the category of what you’d encounter at a phone screen for a back-end software engineering role, and interviewers typically come from a mix of large companies like Google, Facebook, Twitch, and Yelp, as well as engineering-focused startups like Asana, Mattermark, and others. After every interview, interviewers rate interviewees on a few different dimensions. Feedback form for interviewers As you can see, we ask the interviewer if they would advance their interviewee to the next round. We also ask about a few different aspects of interview performance using a 1-4 scale. On our platform, a score of 3 or above is generally considered good. Women historically haven’t performed as well as men… One of the big motivators to think about voice masking was the increasingly uncomfortable disparity in interview performance on the platform between men and women. At that time, we had amassed over a thousand interviews with enough data to do some comparisons and were surprised to discover that women really were doing worse. Specifically, men were getting advanced to the next round 1.4 times more often than women. Interviewee technical score wasn’t faring that well either — men on the platform had an average technical score of 3 out of 4, as compared to a 2.5 out of 4 for women. Despite these numbers, it was really difficult for me to believe that women were just somehow worse at computers, so when some of our customers asked us to build voice masking to see if that would make a difference in the conversion rates of female candidates, we didn’t need much convincing. … so we built voice masking Since we started working on interviewing.io, in order to achieve true interviewee anonymity, we knew that hiding gender would be something we’d have to deal with eventually but put it off for a while because it wasn’t technically trivial to build a real-time voice modulator. Some early ideas included sending female users a Bane mask. Early voice masking prototype (drawing by Marcin Kanclerz) When the Bane mask thing didn’t work out, we decided we ought to build something within the app, and if you play the videos below, you can get an idea of what voice masking on interviewing.io sounds like. In the first one, I’m talking in my normal voice. And in the second one, I’m modulated to sound like a man. Armed with the ability to hide gender during technical interviews, we were eager to see what the hell was going on and get some insight into why women were consistently underperforming. The experiment The setup for our experiment was simple. Every Tuesday evening at 7 PM Pacific, interviewing.io hosts what we call practice rounds. In these practice rounds, anyone with an account can show up, get matched with an interviewer, and go to town. And during a few of these rounds, we decided to see what would happen to interviewees’ performance when we started messing with their perceived genders. In the spirit of not giving away what we were doing and potentially compromising the experiment, we told both interviewees and interviewers that we were slowly rolling out our new voice masking feature and that they could opt in or out of helping us test it out. Most people opted in, and we informed interviewees that their voice might be masked during a given round and asked them to refrain from sharing their gender with their interviewers. For interviewers, we simply told them that interviewee voices might sound a bit processed. We ended up with 234 total interviews (roughly 2/3 male and 1/3 female interviewees), which fell into one of three categories: Completely unmodulated (useful as a baseline) Modulated without pitch change Modulated with pitch change You might ask why we included the second condition, i.e. modulated interviews that didn’t change the interviewee’s pitch. As you probably noticed, if you played the videos above, the modulated one sounds fairly processed. The last thing we wanted was for interviewers to assume that any processed-sounding interviewee must summarily have been the opposite gender of what they sounded like. So we threw that condition in as a further control. The results After running the experiment, we ended up with some rather surprising results. Contrary to what we expected (and probably contrary to what you expected as well!), masking gender had no effect on interview performance with respect to any of the scoring criteria (would advance to next round, technical ability, problem solving ability). If anything, we started to notice some trends in the opposite direction of what we expected: for technical ability, it appeared that men who were modulated to sound like women did a bit better than unmodulated men and that women who were modulated to sound like men did a bit worse than unmodulated women. Though these trends weren’t statistically significant, I am mentioning them because they were unexpected and definitely something to watch for as we collect more data. On the subject of sample size, we have no delusions that this is the be-all and end-all of pronouncements on the subject of gender and interview performance. We’ll continue to monitor the data as we collect more of it, and it’s very possible that as we do, everything we’ve found will be overturned. I will say, though, that had there been any staggering gender bias on the platform, with a few hundred data points, we would have gotten some kind of result. So that, at least, was encouraging. So if there’s no systemic bias, why are women performing worse? After the experiment was over, I was left scratching my head. If the issue wasn’t interviewer bias, what could it be? I went back and looked at the seniority levels of men vs. women on the platform as well as the kind of work they were doing in their current jobs, and neither of those factors seemed to differ significantly between groups. But there was one nagging thing in the back of my mind. I spend a lot of my time poring over interview data, and I had noticed something peculiar when observing the behavior of female interviewees. Anecdotally, it seemed like women were leaving the platform a lot more often than men. So I ran the numbers. What I learned was pretty shocking. As it happens, women leave interviewing.io roughly 7 times as often as men after they do badly in an interview. And the numbers for two bad interviews aren’t much better. You can see the breakdown of attrition by gender below (the differences between men and women are indeed statistically significant with P < 0.00001). Also note that as much as possible, I corrected for people leaving the platform because they found a job (practicing interviewing isn’t that fun after all, so you’re probably only going to do it if you’re still looking), were just trying out the platform out of curiosity, or they didn’t like something else about their interviewing.io experience. A totally speculative thought experiment So, if these are the kinds of behaviors that happen in the interviewing.io microcosm, how much is applicable to the broader world of software engineering? Please bear with me as I wax hypothetical and try to extrapolate what we’ve seen here to our industry at large. And also, please know that what follows is very speculative, based on not that much data, and could be totally wrong… but you gotta start somewhere. If you consider the attrition data points above, you might want to do what any reasonable person would do in the face of an existential or moral quandary, i.e. fit the data to a curve. An exponential decay curve seemed reasonable for attrition behavior, and you can see what I came up with below. The x-axis is the number of what I like to call “attrition events”, namely things that might happen to you over the course of your computer science studies and subsequent career that might make you want to quit. The y-axis is what portion of people are left after each attrition event. The red curve denotes women, and the blue curve denotes men. See interactive graph with Desmos Now, as I said, this is pretty speculative, but it really got me thinking about what these curves might mean in the broader context of women in computer science. How many “attrition events” does one encounter between primary and secondary education and entering a collegiate program in CS and then starting to embark on a career? So, I don’t know, let’s say there are 8 of these events between getting into programming and looking around for a job. If that’s true, then we need 3 times as many women studying computer science than men to get to the same number in our pipelines. Note that that’s 3 times more than men, not 3 times more than there are now. If we think about how many there are now, which, depending on your source, is between 1/3 and a 1/4 of the number of men, to get to pipeline parity, we actually have to increase the number of women studying computer science by an entire order of magnitude. Prior art, or why maybe this isn’t so nuts after all Since gathering these findings and starting to talk about them a bit in the community, I began to realize that there was some supremely interesting academic work being done on gender differences around self-perception, confidence, and performance. Some of the work below found slightly different trends than we did, but it’s clear that anyone attempting to answer the question of the gender gap in tech would be remiss in not considering the effects of confidence and self-perception in addition to the more salient matter of bias. In a study investigating the effects of perceived performance to likelihood of subsequent engagement, Dunning (of Dunning-Kruger fame) and Ehrlinger administered a scientific reasoning test to male and female undergrads and then asked them how they did. Not surprisingly, though there was no difference in performance between genders, women underrated their own performance more often than men. Afterwards, participants were asked whether they’d like to enter a Science Jeopardy contest on campus in which they could win cash prizes. Again, women were significantly less likely to participate, with participation likelihood being directly correlated with self-perception rather than actual performance. In a different study, sociologists followed a number of male and female STEM students over the course of their college careers via diary entries authored by the students. One prevailing trend that emerged immediately was the difference between how men and women handled the “discovery of their [place in the] pecking order of talent, an initiation that is typical of socialization across the professions.” For women, realizing that they may no longer be at the top of the class and that there were others who were performing better, “the experience [triggered] a more fundamental doubt about their abilities to master the technical constructs of engineering expertise [than men].” And of course, what survey of gender difference research would be complete without an allusion to the wretched annals of dating? When I told the interviewing.io team about the disparity in attrition between genders, the resounding response was along the lines of, “Well, yeah. Just think about dating from a man’s perspective.” Indeed, a study published in the Archives of Sexual Behavior confirms that men treat rejection in dating very differently than women, even going so far as to say that men “reported they would experience a more positive than negative affective response after… being sexually rejected.” Maybe tying coding to sex is a bit tenuous, but, as they say, programming is like sex — one mistake and you have to support it for the rest of your life. Why I’m not depressed by our results and why you shouldn’t be either Prior art aside, I would like to leave off on a high note. I mentioned earlier that men are doing a lot better on the platform than women, but here’s the startling thing. Once you factor out interview data from both men and women who quit after one or two bad interviews, the disparity goes away entirely. So while the attrition numbers aren’t great, I’m massively encouraged by the fact that at least in these findings, it’s not about systemic bias against women or women being bad at computers or whatever. Rather, it’s about women being bad at dusting themselves off after failing, which, despite everything, is probably a lot easier to fix. 1Roughly 15% of our users are female. We want way more, but it’s a start.↩ 2If you want to hear more examples of voice modulation or are just generously down to indulge me in some shameless bragging, we got to demo it on NPR and in Fast Company.↩ 3In addition to asking interviewers how interviewees did, we also ask interviewees to rate themselves. After reading the Dunning and Ehrlinger study, we went back and checked to see what role self-perception played in attrition. In our case, the answer is, I’m afraid, TBD, as we’re going to need more self-ratings to say anything conclusive.↩ Sent from my iPhone

8 years, 5 months

omni haunted house

by robb

hello omninoms, halloween is quickly approaching omni has been in the red for almost a whole year now we are borrowing about $5000 from different members each month to pay our bills if each collective could contibute some time & effort, i think, we could put on a multiday haunted house fundraising benefit to buy the building & renovate the kitchen that could bring in a significant amount of revenue. so what do you think?

8 years, 5 months

Java programming

by Sarah

Hello everyone, This is my first time writing to this list. I am fairly new to Java programming, but eager to learn more. My interest was piqued by the Minecraft modding community and I have started to code Java mods for Minecraft myself at this point. The thing is I would really love to learn more Java at this point so was wondering if there were any Java projects people were working on or anyone willing to help me build my skills. Thanks for your time! -Sarah

8 years, 6 months

new wiki page for circuit board fabrication

by Jake

I have created a wiki page for references and testimonials for circuitboard fabrication companies, as well as assembly services. https://sudoroom.org/wiki/Boardfab please add any information you have on these vendors so that others can skip the learning curve and start making boards. -jake

8 years, 6 months

mediation list access

by Jake

can anyone tell me how to get on the mediation list? i went to the link Robb posted below but I do not have access to join or to view the archives. -jake On Wed, 20 Jul 2016, robb wrote: > johnny has already been informed of his being asked to leave & should > contact the mediation <https://omnicommons.org/lists/listinfo/mediation> > list, which is *not* a publicly archived list, if he wishes to do so.

8 years, 6 months

Re: [sudo-discuss] johnny ban / process?

by Sera Sigma

Email lists are terrible for resolving anything. Much of the conversation had on this thread was already covered in person yesterday, so Johnny's accusation that the individuals present at last week's sudo meeting haven't "even spoke up" is false. Some facts: * This is not a housing conflict, this is an alleged assault/abuse incident. * There is a restraining order against Johnny obtained by Leslie. * When faced with being served with that order, Johnny fled the scene (yesterday at sudo). * Previous mediation for a different incident failed in the eyes of at least one party. * Legal recourse was sought by both parties: Johnny evicted Leslie, Leslie obtained a restraining order against Johnny. As with many conflicts, these legal actions appear to be regarding different issues. Undeniable power dynamics that affect the situation: * Johnny identifies as male, Leslie as female * Johnny was the landlord, Leslie the tenant Current status: * Leslie has agreed to mediation. * Johnny agreed to mediation in person yesterday to me, but has subsequently backed out of this agreement. As we are having a sudo get together tonight, I propose we discuss this in person. Sudo members who think it is important to have a space that is both just and safe for all, drop by and we can pursue this further. Johnny, I prefer real human interaction to process, which why I thought we had made some progress yesterday (before you fled anyway). Based on the content of your last email, and in particular your line "may the OMNI become the ONLY space she feels safe", I ask that you not be present tonight. I do hope that you change your mind and agree to mediation. I would volunteer if necessary. I genuinely empathize with your feeling of being oppressed by legal and other systems. This situation obviously has multiple facets and we are all faced with the human condition of bringing our past pain to every new interaction. I wish you the luck to find your best possible self. -Sigma

8 years, 6 months

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