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![]() ![]() The app was slow to load, pages loaded slowly, and it was the only browser to not render my website home page properly, dropping the navigation pane to under the rest of the page. Opera has come a long way in the past year, but it is still way behind its competitors. Anyone still using Mac OS X 10.2 can’t run current versions of any of these browsers. Of the nine browser versions reviewed here, only four can run on Mac OS X 10.3.9 Panther – but that includes some of the best ones. Some of them, like Flock, while they will work on a G3, require a G4 for decent performance.Įditor’s note: For those using older versions of OS X, we sometimes include system requirements for versions earlier than the one reviewed. There are a lot of browsers for Tiger, but some of them I wouldn’t put in the lightweight category and therefore didn’t include in my testing. It is a 400 MHz with 1 GB of RAM, a new 5400 rpm 40 GB hard drive with AirPort Card and running Mac OS X 10.4.11 Tiger with all the latest updates installed. After looking carefully at my results and some of the comments I received, I have retested some of the more lightweight browsers on my Pismo PowerBook G3. ![]() I recently tested as many browsers as I could find, but the test machine was my iMac Core Duo running Mac OS X 10.5 Leopard. As a user of older Macs, especially G3s, lightweight apps make your day-to-day usage a lot easier, and this extends to web browsers. ![]() ![]() GoldenKey conveys the startup’s ability to “unlock” the door to this type of service model, said Sowers. Sowers says the company has always sought to empower both agents and consumers: By helping consumers only pay for the services they need and by helping agents charge for every service they provide. GoldenKey has traded in “SoloPro” for its new name because the startup didn’t want to send the message that the marketplace is designed to help consumers buy or sell homes without agents - to transact “solo.” The agent piece of the puzzle “We want to drive as much value to both agents and consumers, and what we’re looking for is really a fair system.” “One of the reasons was we’ve had is that consumers say it sounds too good to be true,” Sowers said. GoldenKey’s “gold package,” for example, now costs $3,500, up from $1,500 in May.Ĭonsumers who opt for GoldenKey’s gold package contract an agent to set up listing alerts, price one home, show three homes, attend one “event” - which could be a closing, walk-through or inspection - make one offer and coordinate one transaction. The startup rolled out service packages around the same time its price tags have increased by more than twofold. Pricing for GoldenKey’s (formerly “SoloPro”) real estate services in early May Increasing price tags Most of the one-off services advertised on GoldenKey have doubled in price since May 2016, when the startup launched an advertising campaign broadcasting its ambition to “blow up” real estate commissions. “We’ve built a model where agents continue to pursue traditional commissions, but GoldenKey unlocks additional opportunities to get paid regardless of whether the buyer buys or the seller sells,” Sowers said. GoldenKey is currently free for agents, “but not forever,” he said. ![]() GoldenKey lets buyers pay flat fees for individual services from agents at brokerages around the country, including pricing a home ($100), showing a home ($100) and making an offer ($200).Īround 1,600 agents in 47 states have signed up to receive job requests through the site, according to CEO Tommy Sowers. ![]() SoloPro, a marketplace that lets consumers hire agents to perform one-off or bundled services for flat fees, has rebranded to GoldenKey, with the aim of projecting an image that’s more inclusive of real estate agents.Īlso in a hat tip to agents, GoldenKey (formerly “SoloPro”) has at least doubled the prices of the brokerage services it advertises on its website. The company lets consumers hire agents on a pay-as-you-go basis or buy service packages from them.SoloPro has changed its name to "GoldenKey" and has more than doubled the pries of the brokerage services it advertises on its website. ![]() ![]() ![]() ![]() The obsession for wanting feminine hairstyles grew to wanting my hair cut into a more feminine cut, i found a salon that would cater for my needs, the stylist that regularly cut my hair knew that i was a bit androgenous and wanted a more feminine look, i told her that i wanted to grow my hair into a bob, i began obsessing about bob hair cuts, buying loads of hair magazines trying to find the right cut for me. It became like an addiction, i used to pick out different hairdressing salons in the local phone book and made the same crossdressing party excuse as to why i wanted my hair to look feminine, one salon gave me a bee hive, another salon set my hair in rollers and put me under a hood hairdryer, i loved this experience so much i wanted my hair in rollers again, so i booked an appointment at a salon i hadn't gone to before and told the stylist on the phone the usual crossdressing party cenario, she said 'if you can, try and bring in a picture of a style that you want so we can copy it', so i looked around at home for a picture of a woman with beautiful curly hair and i found a picture on a bottle of my sisters colour mousse she had, i took it with me to the salon and showed the stylist the picture on the bottle of mousse, she said 'ok that's great we can do that' she then asked me a question i wasn't prepared for, she said 'shall we colour your hair with this mousse, it will make you look more feminine with this colour', so she did it, she coloured my hair a mahogany red colour then set my hair in rollers, i walked out of the salon with a red shampoo and set hair style, it was amazing, i was so happy, i wanted to keep that colour and style so bad, but i couldn't, i am a man after all, so as soon as i got home i got in the shower and washed the colour out :(. ![]() My sister was training as a hairdresser and she used to train on my hair, starting with the usual short back and sides, spikey on top look, i decided to grow my hair so i grew my hair to my ears, my sister cut my hair into a sort of one length bowl cut, i loved it.īecause of pier preasure at senior school i had my hair cut short again to fit in with everyone else, but inside i felt i really wanted feminine hair, so i grew my hair to ear length again, at this point i'm 17 years old, i began to make appointments at hairdressers and make up reasons why i wanted to have a feminine hairstyles done, telling the stylist 'oh i'm going to a crossdressing party, boys as girls and girls as boys', my first experience of this i remember vividly, i walked into the hairdressers feeling very nervous and exited at the same time, the stylist called me over, sat me in the chair and said 'ok what are we doing?', i explained my situation to her, she said 'ok, how about a head full of pig tails?' i said 'ok cool go for it', i sat in the chair holding a box of hair bands while she put my hair up in pig tails all over my head, i walked out of the salon feeling so elated, i was hooked, i needed to have more hairdressing experiences. My sister soon realised that i was going in her room because her hair products and make-up were being used, she also smelt my hair once and it still smelt of hair mousse. ![]() I've had an interest in hair since i was about 6 or 7 (i'm 37 now), i used to play hair brushing with girls at school, when i was around 10 i became more and more interested in my feminine side experimenting with my sisters hair products and make-up. ![]() ![]() ![]() ![]() “It was time someone checked on you two.” “It’s four in the afternoon,” Elise informed him in a loud voice. Kamui heard him hurriedly getting out of bed. Kamui heard Leo practically yelp in the bedroom behind her. “That would explain why Leo of all people is still in bed and sleeping in the middle of the afternoon,” Elise remarked. “We just had some trouble sleeping,” Kamui explained with a yawn. “Yes, but he’s still asleep,” she whispered. Kamui glanced back at her sleeping husband. She glanced over Kamui’s shoulder into the bedroom. “That you were attacked by bandits on the road, and some of your retainers were hurt badly,” Elise told her. Leo himself hadn’t found out the whole story until some ridiculous hour of the night. “We heard what happened yesterday, and I just can’t believe that would happen to you!” “No, lunch,” the younger princess stated. “Don’t you mean breakfast?” I’ve slept through lunch before, but surely Leo wouldn’t do that. “Wait, lunch?” Kamui gave her a funny look. “When you never showed up for lunch, we were starting to get worried.” She was a little startled to see Elise there, her usual bright smile replaced with a worried pout. “Just tell them I’m still sleeping,” he muttered. Leo just shrugged and pulled the covers over himself. “You need to put something on, too,” she whispered frantically. She hurried threw on a robe and glanced back at Leo, who was still lying in the bed. She remembered more had happened in the night besides a lot of crying. Kamui glanced down at her body and blushed. “Put something on,” Leo hissed as she headed for the door. “I’ll get it,” Kamui sighed, sitting up and rubbing her tired eyes. And Leo was known for being irritable if he didn’t get enough sleep, so she was in for a fun day. She’d been up crying a lot in the night, she recalled shamefully, and that had kept Leo up. Kamui felt guilty, knowing that she was the reason he hadn’t got much sleep, and the reason hadn’t been for pleasure. “Whoever that is must have something really important to tell us,” he grumbled. She moaned softly and buried her face against Leo, who was still in bed with her. Kamui awoke to the sound of someone banging on the door. Stats: Published: Updated: Words: 249868 Chapters: 50/? Comments: 155 Kudos: 233 Bookmarks: 26 Hits: 8849 the other relationships are in the background. ![]() ![]() ![]() Maurice Sand, Harlequin, 1671, Michel Levy Freres Collection, Paris, France. Freedom of ExpressionĪrlecchino’s half-mask is painted black with an ape-like nose and a “bump” to signify a devil’s horn. The masks were implemented into different national holidays and events all over the world. With time, the popularity of Venetian carnival masks grew. In this way, in the frames of a small city, people created their unique tradition to use masks both during different activities and in daily life to hide personalities and enjoy some level of privacy. The mask ban was implemented during all the months except for the ones between Christmas and Shrove Tuesday. Citizens decided to emphasize the special time by introducing periods when wearing a mask was not allowed. Later, the masks were used during the Venetian Carnival in particular. Initially, Venetians used masks to conceal their identity during different activities. Giovanni Domenico Tiepolo, Carnival Scene (The Minuet), ca. The city was characterized by inequality among its citizens, which led to the development of traditions rooted in the need of hiding identity. ![]() Historically, Venetians enjoyed comparatively high standards of living. The Carnival of Venice is one of the most important and famous carnivals all over the world, taking place on a relatively small island. Here you can discover the key information about the history of Venetian masks.įirst of all, Venetian carnival masks can be analyzed through the prism of local traditions and customs. Being a primary sign of a carnival, the mask has an interesting history connected with old traditions in Venice. It is not a surprise that every carnival, regardless of its country of origin or other characteristics, is traditionally associated with Venetian carnival masks. ![]() ![]() ![]() This will give you a better feel for how training is progressing and prevent you discovering problems only after you’ve finished training.īe careful about choosing your test functions. It’s, therefore, a good idea to log as many test set metrics during training as you can, rather than waiting to only use them at test time. Often, these are more human-interpretable than the value of your loss function, which can be difficult to understand (handy tip: cross-entropy should be -log when you start training, if the dataset is balanced). For instance, in machine translation and summarization we use BLEU or ROUGE respectively, and for other tasks we might use accuracy, precision, or recall. Whilst loss functions for deep learning have to be differentiable, we often use a different suite of non-differentiable metrics to articulate performance at test time. This is clearly not-quite-right, because a 5 star review is more similar to a 4 star one than it is a 1 star (the data is ordinal). For instance, the Amazon Reviews Dataset, which contains reviews along with star ratings, is frequently used as a classification task by top labs. The latter is surprisingly common, even in academia. whether you need to add a softmax), and mixing up regression and classification. The two most common errors I’ve seen here are confusing whether the loss function expects to receive probability distributions or logits (i.e. However, there are plenty of ways to abuse the rock-solid implementations you’re given. ![]() This is quite hard to do, because most of the frameworks will take care of loss function specification for you. Wherever you read in data, make the naming super-explicit: TrainDataLoader and TestDataLoader, for instance. It’s good practice to split your data into train, validation, and test set once, and then put them in separate folders in the filesystem. ![]() This can determine what kinds of generalization you are quantifying by test-set performance. Sometimes you need to think carefully about what exactly constitutes a disjoint set. But should it also include never-before-seen merchants, to reassure us that we’re not overfitting to specific shops? For instance, if we are trying to extract the total value from a receipt, obviously the test set should contain never-before-seen receipts. Of course, your train/val/test should be disjoint - containing different examples. Mixing training data into your validation or test set is easy to do, and will produce superb model performance - as assessed by your dodgy test set - and terrible performance in the real world. Nothing is more embarrassing or demoralizing than realizing your model is much worse than you thought. If you take away just one thing from this post, it should be this. These are hard to spot because we’re biased against seeing them: when the model performs surprising badly, we’re inclined to take a second look, but when it performs surpisingly well we’re more likely to congratulate ourselves on our superb intuition (essentially a form of confirmation bias).Ĭommon causes of overestimation are overfitting to your test set, your data not being representative of the real world, or simply screwing up your metrics. The big boss of errors: making a mistake which leads you to overestimate performance. Errors that lead you to believe your results are better than they are In fact, you probably need to regularize more heavily to reveal the benefit of your more expressive model.Įrrors that lead to inaccurate experimentation accrue over time, and thus spotting them early is valuable. If your navigation is faulty, you’ll quickly end up somewhere you don’t want to be.įor example, if you implement a new feature which adds a bunch of parameters and compare it to the performance of an existing model without redoing a hyperparameter search, you might incorrectly conclude that your new feature makes things worse. This is a more costly kind of mistake to make, because you end up making poor decisions.Ĭonsider the story of the AirAsia pilot who ended up in Melbourne rather than Malaysia because of a wonky GPS. Errors that lead to inaccurate experimentation You fix it, find a new error, and the cycle repeats. ![]() I’m not going to talk much about these errors, because they are typically explicit: the program fails, and you have to figure out why. In deep learning, the dreaded shape error is the most common, arising when you try and multiply together matrices of incompatible size. These kind of errors are regrettable, but survivable. ![]() ![]()
![]() ![]() ![]() They confirmed that the malware signatures we provided were associated with three accounts that used their service, with the majority of them associated with a single account. We contacted the individuals associated with the Italian company behind CloudEyE in an attempt to gain further information about the RATicate actors. On June 10, CloudEyE announced that they had suspended sale of their installer because of “abuse” of their platform, and were refunding customers for unused portions of their licenses. An email campaign attempting to distribute the Lokibot password-stealing malware used a message attempting to spoof company emails on COVID-19 response policy as a lure to get targeted users to open the malicious attachment: A COVID-19 themed email carrying a RATicate-authored malware installer. It was also during this period that we saw the RATicate actors begin to use the COVID-19 pandemic as a hook to get victims to open the installers. Initially identified (by researchers at CheckPoint) as Guloader, the new Visual Basic 6-based installer was tied to a publicly-marketed installation builder called CloudEyE. But in February, the group started to switch to a new delivery mechanism. As discussed in our original report, the RATicate group had since last November been packing their RAT and infostealer payloads for deployment via e-mail exclusively with custom NSIS installers. In recent campaigns, the group’s tactics have shifted, as the actors employed a new malware “loader” in order to unpack and install RAT and infostealer payloads in a more stealthy way. We tracked multiple malicious spam (“malspam”) email campaigns from the group, with attached installers that usually posed as documents related to financial transactions. In May, we reported initial findings on RATicate, a group of actors spreading remote administration tools (RATs) and other information-stealing malware at least since last year. ![]() ![]() ![]() That also means it’s easy to share your CV to when it’s finished, so you’ll be able to apply for jobs in no time. That means has loads of data available for creating its resume builder and CV templates. The company behind also owns, which is one of the web’s biggest recruitment websites. has also spoken to thousands of employees and employers to find out what people are looking for in a candidate, and its templates are based on that evidence. True to form, the site has loads of fantastic templates that you can use, and the huge popularity of this site means that they’re proven to work – as vast numbers of people have already used these templates to secure interviews and new positions. Once you’ve built your CV, why not brush up on your interview skills with our Virtual Interview Tool or explore other available tools and information via the interviews hub.It’s no surprise that is where many people start looking when they want to buy a new resume – after all, that name is a pretty good sign of this site’s key strength. And if you don’t want to, you don’t have to link your accounts. Just make sure that the information imported are things you think will make you stand out as a suitable candidate. The CV Builder can connect to your Facebook and Twitter accounts to boost your CV with additional skills and interests. What’s more, you can save your progress and come back to it later.Ĭan I import my information into the CV builder? Don’t rush writing your personal statement either if you’ve already prepared one of these then that will save you some time. The LifeSkills CV Builder will take at least 30 minutes, but we’d recommend taking time to make sure you have included all the relevant information so that it shows you in the best light possible. ![]()
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