TwitchToolkit is a mod written by Twitch.tv/hodlhodl that integrates storytelling decisions into chat votes, awards viewers coins for watching, and those coins can be spent on items/events in game.
I am currently using Nesi's Rimvali mod, along with Reich Armory, a lot of the Vanilla Expanded mods, Soviet Armory, and more! Below you can find the list of purchaseable in game items. Click on the header of a row to sort by that variable (Note: Price sorting doesn't work at the moment.) When purchased, they will be sent to the colony using a drop pod, and I will have full control of the item from there.
Use !bal to check your coin balance!
Find the "itemname" of the item you want, and type in the Twitch chat: "!buy (itemname) (ammount)"
ex: "!buy kidney 100" would send 100 kidneys to me. Minimum purchase worth 50 coins.
Type !joinqueue to enter your name into the name-pool! This will give you a chance to have your own character in game! (Only one per person)
Click on "Events" in the top bar to see the list of purchaseable events!
| ItemName | Price | Category | Description |
|---|---|---|---|
| 15cmherocket | 167 | 15cm Nebelwerfer 41 ammo | Shell_Nebelwerfer_41_HE_RA |
| 15cmnebelwerfer41 | 1567 | Security | Turret_Nebelwerfer_41_KA |
| 2cmflak38camouflagepaint | 1300 | Security | Turret_Flak_38_Camo_RA |
| 2cmhefi-tbelt | 167 | 2cm Flak 38 ammo | Belt_Flak_38_HEFIT_RA |
| 7,5cmapcrshell | 167 | 7,5cm PaK 40 ammo | Shell_PaK_40_APCR_RA |
| 7,5cmheshell | 167 | 7,5cm PaK 40 ammo | Shell_PaK_40_HE_RA |
| 7,5cmle.ig18 | 1083 | Security | Turret_leIG_18_RA |
| 7,5cmle.ig18heshell | 167 | 7,5cm le.IG 18 ammo | Shell_leIG_18_HE_RA |
| 7,5cmpak40camouflagepaint | 3050 | Security | Turret_PaK_40_Camo_RA |
| advancedbionicarm | 500 | Bionic | AvaliAdvancedBionicArm |
| advancedbionicavalileg | 1925 | Bionic | AvaliAdvancedBionicLeg |
| advancedbionicavalitail | 500 | Bionic | AvaliAdvancedBionicTail |
| advancedcomponent | 333 | Manufactured | ComponentSpacer |
| advancedtemperaturecontrolaugment[tca] | 83 | Bionic | AvaliAdvTempControl |
| aero-tungstencarbide | 33 | Avali resources | AeroTungstenCarbide |
| alpaca | 583 | Animal | Alpaca |
| alpacameat | 3 | Meat | Meat_Alpaca |
| alpacawool | 4 | Textiles | WoolAlpaca |
| alphabeaver | 417 | Animal | Alphabeaver |
| alphabeavermeat | 3 | Meat | Meat_Alphabeaver |
| ambrosia | 25 | Drugs | Ambrosia |
| animalbed | 136 | Furniture | AnimalBed |
| animalsleepingbox | 84 | Furniture | AnimalSleepingBox |
| antigrainwarhead | 2000 | Mortar shells | Shell_AntigrainWarhead |
| archotecharm | 4667 | Archotech | ArchotechArm |
| archotecheye | 4667 | Archotech | ArchotechEye |
| archotechleg | 4667 | Archotech | ArchotechLeg |
| arcticfox | 333 | Animal | Fox_Arctic |
| arcticwolf | 583 | Animal | Wolf_Arctic |
| armchair | 450 | Furniture | Armchair |
| artbench | 425 | Production | TableSculpting |
| assaultrifle | 800 | Ranged weapons | Gun_AssaultRifle |
| autophagicsystemrewriteaugment | 250 | Bionic | AvaliASR |
| autopistol | 232 | Ranged weapons | Gun_Autopistol |
| avalileather | 7 | Leathers | Leather_RimVali |
| avalimeat | 3 | Meat | Meat_RimVali |
| avalinano-printingforge | 1783 | Production | AvaliNanoForge |
| avalinano-printingloom | 1783 | Production | AvaliNanoLoom |
| avalipistol | 295 | Ranged weapons | AvaliPistol |
| avaliresearchbench | 1383 | Production | AvaliResearchBench |
| avalirifle | 1142 | Ranged weapons | AvaliRifle |
| avaliscythe | 212 | Melee weapons | AvaliScythe |
| avalishotgun | 883 | Ranged weapons | AvaliShotgun |
| avalismg | 2508 | Ranged weapons | AvaliSMG |
| avalisniperrifle | 2517 | Ranged weapons | AvaliSniperRifle |
| avalispear | 296 | Melee weapons | AvaliSpear |
| avalitail | 17 | Natural | RimValiTail |
| avaliturret | 492 | Security | AvaliBasicTurret |
| avaliviroblade | 667 | Melee weapons | AvaliViroblade |
| basicbionicarm | 350 | Bionic | AvaliBasicBionicArm |
| basicbionicavalileg | 350 | Bionic | AvaliBasicBionicLeg |
| basicbionicavalitail | 350 | Bionic | AvaliBasicBionicTail |
| basictemperaturecontrolaugment[tca] | 83 | Bionic | AvaliBasicTempControl |
| battery | 333 | Power | Battery |
| bearmeat | 3 | Meat | Meat_Bear_Grizzly |
| bed | 155 | Furniture | Bed |
| bedroll | 137 | Furniture | Bedroll |
| beef | 3 | Meat | Meat_Cow |
| beer | 20 | Drugs | Beer |
| berries | 2 | Vegetarian | RawBerries |
| billiardstable | 600 | Furniture | BilliardsTable |
| bionicarm | 1717 | Bionic | BionicArm |
| bionicear | 1308 | Bionic | BionicEar |
| bioniceye | 1717 | Bionic | BionicEye |
| bionicheart | 1717 | Bionic | BionicHeart |
| bionicleg | 1717 | Bionic | BionicLeg |
| bionicspine | 1717 | Bionic | BionicSpine |
| bionicstomach | 1308 | Bionic | BionicStomach |
| birdmeat | 3 | Meat | Meat_Cassowary |
| birdskin | 3 | Leathers | Leather_Bird |
| bison | 583 | Animal | Bison |
| bisonmeat | 3 | Meat | Meat_Bison |
| bisonwool | 4 | Textiles | WoolBison |
| bluefur | 4 | Leathers | Leather_Bluefur |
| bolt-actionrifle | 425 | Ranged weapons | Gun_BoltActionRifle |
| boomalope | 800 | Animal | Boomalope |
| boomalopemeat | 3 | Meat | Meat_Boomalope |
| boomrat | 800 | Animal | Boomrat |
| boomratmeat | 3 | Meat | Meat_Boomrat |
| bowlerhat | 77 | Headgear | Apparel_BowlerHat |
| brewery | 350 | Production | Brewery |
| butchertable | 302 | Production | TableButcher |
| camelhide | 4 | Leathers | Leather_Camel |
| capybara | 250 | Animal | Capybara |
| capybarameat | 3 | Meat | Meat_Capybara |
| caribou | 500 | Animal | Caribou |
| cariboumeat | 3 | Meat | Meat_Caribou |
| cassowary | 333 | Animal | Cassowary |
| cat | 167 | Animal | Cat |
| catmeat | 3 | Meat | Meat_Cat |
| chainshotgun | 675 | Ranged weapons | Gun_ChainShotgun |
| chargelance | 2258 | Ranged weapons | Gun_ChargeLance |
| chargerifle | 1683 | Ranged weapons | Gun_ChargeRifle |
| chemfuel | 4 | Manufactured | Chemfuel |
| chesstable | 281 | Furniture | ChessTable |
| chicken | 150 | Animal | Chicken |
| chickenegg(unfert.) | 12 | Eggs (unfert.) | EggChickenUnfertilized |
| chickenmeat | 3 | Meat | Meat_Chicken |
| chinchilla | 250 | Animal | Chinchilla |
| chinchillafur | 11 | Leathers | Leather_Chinchilla |
| chinchillameat | 3 | Meat | Meat_Chinchilla |
| chocolate | 5 | Foods | Chocolate |
| cloth | 2 | Textiles | Cloth |
| club | 141 | Melee weapons | MeleeWeapon_Club |
| cobra | 250 | Animal | Cobra |
| cobraegg(fert.) | 67 | Eggs (fert.) | EggCobraFertilized |
| cobrameat | 3 | Meat | Meat_Cobra |
| cochlearimplant | 367 | Prosthetic | CochlearImplant |
| component | 53 | Manufactured | ComponentIndustrial |
| corn | 2 | Vegetarian | RawCorn |
| cougar | 667 | Animal | Cougar |
| cougarmeat | 3 | Meat | Meat_Cougar |
| cow | 500 | Animal | Cow |
| cowboyhat | 94 | Headgear | Apparel_CowboyHat |
| deathacidifier | 667 | Bionic | DeathAcidifier |
| deepdrill | 483 | Production | DeepDrill |
| deer | 417 | Animal | Deer |
| devilstrand | 9 | Textiles | DevilstrandCloth |
| diningchair | 198 | Furniture | DiningChair |
| dogleather | 3 | Leathers | Leather_Dog |
| donkey | 533 | Animal | Donkey |
| donkeymeat | 3 | Meat | Meat_Donkey |
| doomsdayrocketlauncher | 1667 | Ranged weapons | Gun_DoomsdayRocket |
| doublebed | 292 | Furniture | DoubleBed |
| doublebedroll | 290 | Furniture | BedrollDouble |
| dresser | 179 | Furniture | Dresser |
| dromedary | 500 | Animal | Dromedary |
| dromedarymeat | 3 | Meat | Meat_Dromedary |
| druglab | 742 | Production | DrugLab |
| duck | 200 | Animal | Duck |
| duckmeat | 3 | Meat | Meat_Duck |
| duster | 327 | Apparel | Apparel_Duster |
| eintossflammenwerfer46 | 342 | Weapon | Gun_Eintossflammenwerfer_46_RA |
| electricsmelter | 667 | Production | ElectricSmelter |
| electricsmithy | 492 | Production | ElectricSmithy |
| electricstove | 375 | Production | ElectricStove |
| electrictailorbench | 533 | Production | ElectricTailoringBench |
| elephant | 1000 | Animal | Elephant |
| elephantleather | 5 | Leathers | Leather_Elephant |
| elephantmeat | 3 | Meat | Meat_Elephant |
| elephanttusk | 133 | Items | ElephantTusk |
| elitetemperaturecontrolaugment[tca] | 800 | Bionic | AvaliEliteTempControl |
| elk | 500 | Animal | Elk |
| elkmeat | 3 | Meat | Meat_Elk |
| empgrenades | 525 | Grenades | Weapon_GrenadeEMP |
| emplauncher | 842 | Ranged weapons | Gun_EmpLauncher |
| empshell | 92 | Mortar shells | Shell_EMP |
| emu | 333 | Animal | Emu |
| endtable | 106 | Furniture | EndTable |
| fennecfox | 333 | Animal | Fox_Fennec |
| fermentingbarrel | 95 | Production | FermentingBarrel |
| ferrogel | 3 | Avali resources | AvaliAerogel |
| ferroweave | 6 | Avali resources | AvaliAeroweave |
| fg42 | 917 | Weapon | Gun_FG_42_RA |
| finemeal | 33 | Meals | MealFine |
| firefoampopper | 300 | Misc | FirefoamPopper |
| firefoamshell | 92 | Mortar shells | Shell_Firefoam |
| flakhelmet | 442 | Headgear | Apparel_AdvancedHelmet |
| flakjacket | 483 | Armor | Apparel_FlakJacket |
| flakpants | 375 | Armor | Apparel_FlakPants |
| flakvest | 375 | Armor | Apparel_FlakVest |
| flake | 23 | Drugs | Flake |
| flatscreentelevision | 1533 | Recreation | FlatscreenTelevision |
| foxmeat | 3 | Meat | Meat_Fox_Fennec |
| foxfur | 6 | Leathers | Leather_Fox |
| fraggrenades | 442 | Grenades | Weapon_GrenadeFrag |
| fueledsmithy | 333 | Production | FueledSmithy |
| fueledstove | 265 | Production | FueledStove |
| game-of-urboard | 153 | Furniture | GameOfUrBoard |
| gazelle | 250 | Animal | Gazelle |
| gazellemeat | 3 | Meat | Meat_Gazelle |
| gew43 | 633 | Weapon | Gun_Gew_43_RA |
| gladius | 239 | Melee weapons | MeleeWeapon_Gladius |
| glitterworldmedicine | 83 | Medicine | MedicineUltratech |
| glowpod | 83 | Special | GlowPod |
| goat | 333 | Animal | Goat |
| goatmeat | 3 | Meat | Meat_Goat |
| go-juice | 88 | Drugs | GoJuice |
| gold | 17 | Raw resources | Gold |
| goose | 150 | Animal | Goose |
| grandsculpture | 1967 | Art | SculptureGrand |
| graniteblocks | 2 | Stone blocks | BlocksGranite |
| greatbow | 174 | Ranged weapons | Bow_Great |
| grizzlybear | 1167 | Animal | Bear_Grizzly |
| guineapig | 250 | Animal | GuineaPig |
| guineapigfur | 8 | Leathers | Leather_GuineaPig |
| guineapigmeat | 3 | Meat | Meat_GuineaPig |
| handtailorbench | 262 | Production | HandTailoringBench |
| hare | 150 | Animal | Hare |
| haremeat | 3 | Meat | Meat_Hare |
| hay | 1 | Foods | Hay |
| healermechserum | 2167 | Items | MechSerumHealer |
| heart | 833 | Natural | Heart |
| heater | 218 | Temperature | Heater |
| heavyfur | 6 | Leathers | Leather_Heavy |
| heavysmg | 592 | Ranged weapons | Gun_HeavySMG |
| herbalmedicine | 17 | Medicine | MedicineHerbal |
| high-explosiveshell | 92 | Mortar shells | Shell_HighExplosive |
| hoopstonering | 67 | Recreation | HoopstoneRing |
| hops | 2 | Plant matter | RawHops |
| horse | 917 | Animal | Horse |
| horsemeat | 3 | Meat | Meat_Horse |
| horseshoespin | 34 | Recreation | HorseshoesPin |
| hospitalbed | 667 | Furniture | HospitalBed |
| humanleather | 7 | Leathers | Leather_Human |
| humanmeat | 1 | Meat | Meat_Human |
| husky | 417 | Animal | Husky |
| huskymeat | 3 | Meat | Meat_Husky |
| hyperweave | 15 | Textiles | Hyperweave |
| ibex | 417 | Animal | Ibex |
| ibexmeat | 3 | Meat | Meat_Ibex |
| iedantigrainwarheadtrap | 2008 | Security | TrapIED_AntigrainWarhead |
| iedemptrap | 192 | Security | TrapIED_EMP |
| iedfirefoamtrap | 192 | Security | TrapIED_Firefoam |
| iedincendiarytrap | 192 | Security | TrapIED_Incendiary |
| iedtrap | 192 | Security | TrapIED_HighExplosive |
| iguana | 167 | Animal | Iguana |
| iguanameat | 3 | Meat | Meat_Iguana |
| ikwa | 197 | Melee weapons | MeleeWeapon_Ikwa |
| incendiarylauncher | 567 | Ranged weapons | Gun_IncendiaryLauncher |
| incendiaryshell | 92 | Mortar shells | Shell_Incendiary |
| infernocannon | 2333 | Ranged weapons | Gun_InfernoCannon |
| infinitechemreactor | 5000 | Special | InfiniteChemreactor |
| insectjelly | 13 | Animal products | InsectJelly |
| insectmeat | 1 | Meat | Meat_Megaspider |
| jacket | 275 | Apparel | Apparel_Jacket |
| jade | 8 | Raw resources | Jade |
| joywire | 1000 | Prosthetic | Joywire |
| kar98k | 450 | Weapon | Gun_Kar98K_RA |
| kar98kwithschiessbecher | 650 | Weapon | Gun_Kar98K_Schiessbecher_RA |
| kar98kwithbayonet | 500 | Weapon | Gun_Kar98K_Bayonet_RA |
| kibble | 2 | Foods | Kibble |
| kidney | 417 | Natural | Kidney |
| knife | 111 | Melee weapons | MeleeWeapon_Knife |
| lItemNameadorretriever | 417 | Animal | LItemNameadorRetriever |
| lItemNameadorretrievermeat | 3 | Meat | Meat_LItemNameadorRetriever |
| largesculpture | 517 | Art | SculptureLarge |
| lavishmeal | 67 | Meals | MealLavish |
| lightleather | 3 | Leathers | Leather_Light |
| limestoneblocks | 2 | Stone blocks | BlocksLimestone |
| liver | 833 | Natural | Liver |
| lizardskin | 3 | Leathers | Leather_Lizard |
| lmg | 708 | Ranged weapons | Gun_LMG |
| longsword | 442 | Melee weapons | MeleeWeapon_LongSword |
| luciferium | 150 | Drugs | Luciferium |
| lung | 667 | Natural | Lung |
| lynx | 417 | Animal | Lynx |
| lynxmeat | 3 | Meat | Meat_Lynx |
| m24stielhandgranate | 358 | Weapon | Weapon_M24_RA |
| mace | 203 | Melee weapons | MeleeWeapon_Mace |
| machinepistol | 367 | Ranged weapons | Gun_MachinePistol |
| machiningtable | 758 | Production | TableMachining |
| marbleblocks | 2 | Stone blocks | BlocksMarble |
| marinearmor | 3392 | Armor | Apparel_PowerArmor |
| marinehelmet | 1058 | Headgear | Apparel_PowerArmorHelmet |
| medicine | 30 | Medicine | MedicineIndustrial |
| megascarab | 167 | Animal | Megascarab |
| megascreentelevision | 2475 | Recreation | MegascreenTelevision |
| megasloth | 1167 | Animal | Megasloth |
| megaslothmeat | 3 | Meat | Meat_Megasloth |
| megaslothwool | 4 | Textiles | WoolMegasloth |
| megaspider | 833 | Animal | Megaspider |
| mg34 | 983 | Weapon | Gun_MG_34_RA |
| mg42 | 875 | Weapon | Gun_MG_42_RA |
| milk | 5 | Animal products | Milk |
| minigun | 1933 | Ranged weapons | Gun_Minigun |
| mini-turret | 492 | Security | Turret_MiniTurret |
| molotovcocktails | 408 | Grenades | Weapon_GrenadeMolotov |
| monkey | 167 | Animal | Monkey |
| monkeymeat | 3 | Meat | Meat_Monkey |
| mortar | 1058 | Security | Turret_Mortar |
| mountedmg42 | 1083 | Security | Turret_MG_42_RA |
| mp3008 | 281 | Weapon | Gun_MP_3008_RA |
| mp35 | 533 | Weapon | Gun_MP_35_RA |
| mp40 | 517 | Weapon | Gun_MP_40_RA |
| mp507 | 358 | Weapon | Gun_MP_507_RA |
| muffalo | 500 | Animal | Muffalo |
| muffalomeat | 3 | Meat | Meat_Muffalo |
| muffalowool | 4 | Textiles | WoolMuffalo |
| multi-analyzer | 1700 | Misc | MultiAnalyzer |
| nano-carbonfiber | 6 | Avali resources | AvaliNanoCarbonFiber |
| nanofiber | 3 | Avali resources | AvaliNanoFiber |
| neutroamine | 10 | Manufactured | Neutroamine |
| nutrientpastemeal | 17 | Meals | MealNutrientPaste |
| orbitalbombardmenttargeter | 2000 | Utility | OrbitalTargeterBombardment |
| orbitalpowerbeamtargeter | 2000 | Utility | OrbitalTargeterPowerBeam |
| orbitaltradebeacon | 185 | Misc | OrbitalTradeBeacon |
| ostrich | 417 | Animal | Ostrich |
| p08 | 425 | Weapon | Gun_P08_RA |
| p38 | 315 | Weapon | Gun_P38_RA |
| packagedsurvivalmeal | 40 | Meals | MealSurvivalPack |
| painstopper | 367 | Prosthetic | Painstopper |
| panther | 667 | Animal | Panther |
| panthermeat | 3 | Meat | Meat_Panther |
| pantherafur | 5 | Leathers | Leather_Panthera |
| pants | 143 | Apparel | Apparel_Pants |
| panzerfaust60 | 367 | Weapon | Gun_Panzerfaust_60_RA |
| parka | 315 | Apparel | Apparel_Parka |
| patchleather | 2 | Leathers | Leather_Patch |
| pemmican | 2 | Foods | Pemmican |
| penoxycyline | 30 | Drugs | Penoxycyline |
| personacore | 10000 | Items | AIPersonaCore |
| pig | 333 | Animal | Pig |
| pigskin | 3 | Leathers | Leather_Pig |
| pila | 182 | Ranged weapons | Pila |
| plainleather | 3 | Leathers | Leather_Plain |
| plantpot | 68 | Furniture | PlantPot |
| plasteel | 15 | Raw resources | Plasteel |
| pokertable | 400 | Furniture | PokerTable |
| polarbear | 1167 | Animal | Bear_Polar |
| pork | 3 | Meat | Meat_Pig |
| positiveenforcementaugment | 533 | Bionic | PositiveE |
| potatoes | 2 | Vegetarian | RawPotatoes |
| powerclaw | 642 | Prosthetic | PowerClaw |
| ppk | 400 | Weapon | Gun_PPK_RA |
| prostheticarm | 433 | Prosthetic | SimpleProstheticArm |
| prostheticheart | 383 | Prosthetic | SimpleProstheticHeart |
| prostheticleg | 433 | Prosthetic | SimpleProstheticLeg |
| psychicanimalpulser | 1167 | Artifacts | PsychicAnimalPulser |
| psychicemanator | 2167 | Special | PsychicEmanator |
| psychicfoilhelmet | 417 | Headgear | Apparel_PsychicFoilHelmet |
| psychicinsanitylance | 1083 | Utility | Apparel_PsychicInsanityLance |
| psychicshocklance | 917 | Utility | Apparel_PsychicShockLance |
| psychicsoothepulser | 1000 | Artifacts | PsychicSoothePulser |
| psychitetea | 17 | Drugs | PsychiteTea |
| psychoidleaves | 3 | Plant matter | PsychoidLeaves |
| pumpshotgun | 425 | Ranged weapons | Gun_PumpShotgun |
| raccoon | 167 | Animal | Raccoon |
| raccoonmeat | 3 | Meat | Meat_Raccoon |
| rat | 150 | Animal | Rat |
| ratmeat | 3 | Meat | Meat_Rat |
| rawfungus | 2 | Vegetarian | RawFungus |
| rawtungsten | 17 | Avali resources | AvaliTungsten |
| reconarmor | 2567 | Armor | Apparel_ArmorRecon |
| reconhelmet | 875 | Headgear | Apparel_ArmorHelmetRecon |
| recurvebow | 110 | Ranged weapons | Bow_Recurve |
| redfox | 333 | Animal | Fox_Red |
| refinedcarbon | 2 | Avali resources | AvaliNanoCarbon |
| reichmachiningtable | 608 | Production | TableRA |
| resurrectormechserum | 40000 | Items | MechSerumResurrector |
| revolver | 226 | Ranged weapons | Gun_Revolver |
| rhinoceros | 1167 | Animal | Rhinoceros |
| rhinocerosleather | 7 | Leathers | Leather_Rhinoceros |
| rhinocerosmeat | 3 | Meat | Meat_Rhinoceros |
| rice | 2 | Vegetarian | RawRice |
| royalbed | 1467 | Furniture | RoyalBed |
| s84/98iiibayonet | 82 | Weapon | MeleeWeapon_S84_98_III_RA |
| sandstoneblocks | 2 | Stone blocks | BlocksSandstone |
| sheep | 350 | Animal | Sheep |
| sheepmeat | 3 | Meat | Meat_Sheep |
| sheepwool | 4 | Textiles | WoolSheep |
| shelf | 104 | Furniture | Shelf |
| shortbow | 74 | Ranged weapons | Bow_Short |
| silver | 2 | Raw resources | Silver |
| simplehelmet | 153 | Headgear | Apparel_SimpleHelmet |
| simplemeal | 25 | Meals | MealSimple |
| simpleresearchbench | 350 | Production | SimpleResearchBench |
| skilltrainer(animals) | 1250 | Skilltrainers | Neurotrainer_Animals |
| skilltrainer(artistic) | 1250 | Skilltrainers | Neurotrainer_Artistic |
| skilltrainer(construction) | 1250 | Skilltrainers | Neurotrainer_Construction |
| skilltrainer(cooking) | 1250 | Skilltrainers | Neurotrainer_Cooking |
| skilltrainer(crafting) | 1250 | Skilltrainers | Neurotrainer_Crafting |
| skilltrainer(intellectual) | 1250 | Skilltrainers | Neurotrainer_Intellectual |
| skilltrainer(medical) | 1250 | Skilltrainers | Neurotrainer_Medicine |
| skilltrainer(melee) | 1250 | Skilltrainers | Neurotrainer_Melee |
| skilltrainer(mining) | 1250 | Skilltrainers | Neurotrainer_Mining |
| skilltrainer(plants) | 1250 | Skilltrainers | Neurotrainer_Plants |
| skilltrainer(shooting) | 1250 | Skilltrainers | Neurotrainer_Shooting |
| skilltrainer(social) | 1250 | Skilltrainers | Neurotrainer_Social |
| slateblocks | 2 | Stone blocks | BlocksSlate |
| smallsculpture | 275 | Art | SculptureSmall |
| smokelauncher | 633 | Ranged weapons | Gun_SmokeLauncher |
| smokeshell | 92 | Mortar shells | Shell_Smoke |
| smokeleafjoint | 18 | Drugs | SmokeleafJoint |
| smokeleafleaves | 3 | Plant matter | SmokeleafLeaves |
| smokepoppack | 277 | Utility | Apparel_SmokepopBelt |
| sniperrifle | 883 | Ranged weapons | Gun_SniperRifle |
| snowhare | 150 | Animal | Snowhare |
| socialcapabilityenhancer | 533 | Bionic | SocialEnhancer |
| spear | 322 | Melee weapons | MeleeWeapon_Spear |
| spelopede | 333 | Animal | Spelopede |
| spiketrap | 169 | Security | TrapSpike |
| squirrel | 150 | Animal | Squirrel |
| squirrelmeat | 3 | Meat | Meat_Squirrel |
| standinglamp | 65 | Furniture | StandingLamp |
| standinglamp(blue) | 65 | Furniture | StandingLamp_Blue |
| standinglamp(green) | 65 | Furniture | StandingLamp_Green |
| standinglamp(red) | 65 | Furniture | StandingLamp_Red |
| steel | 3 | Raw resources | Steel |
| stg44 | 683 | Weapon | Gun_StG_44_RA |
| stonecutter'stable | 358 | Production | TableStonecutter |
| stool | 86 | Furniture | Stool |
| sunlamp | 129 | Furniture | SunLamp |
| synthread | 7 | Textiles | Synthread |
| table(1x2) | 98 | Furniture | Table1x2c |
| table(2x2) | 176 | Furniture | Table2x2c |
| table(2x4) | 333 | Furniture | Table2x4c |
| table(3x3) | 350 | Furniture | Table3x3c |
| tailstiffeningaugment | 383 | Bionic | TailStiffeningAugment |
| techprofsubpersonacore | 1500 | Items | TechprofSubpersonaCore |
| telescope | 583 | Recreation | Telescope |
| thrumbo | 6667 | Animal | Thrumbo |
| thrumbohorn | 1333 | Items | ThrumboHorn |
| thrumbomeat | 3 | Meat | Meat_Thrumbo |
| thrumbofur | 23 | Leathers | Leather_Thrumbo |
| timberwolf | 583 | Animal | Wolf_Timber |
| toolcabinet | 642 | Misc | ToolCabinet |
| tornadogenerator | 1333 | Utility | TornadoGenerator |
| tortoise | 333 | Animal | Tortoise |
| tortoisemeat | 3 | Meat | Meat_Tortoise |
| tribalheaddress | 203 | Headgear | Apparel_TribalHeaddress |
| tribalwear | 211 | Apparel | Apparel_TribalA |
| triplerocketlauncher | 1667 | Ranged weapons | Gun_TripleRocket |
| t-shirt | 143 | Apparel | Apparel_BasicShirt |
| tubetelevision | 525 | Recreation | TubeTelevision |
| tungsten | 20 | Avali resources | AvaliTungstenPowder |
| turkey | 167 | Animal | Turkey |
| turkeymeat | 3 | Meat | Meat_Turkey |
| uranium | 10 | Raw resources | Uranium |
| urn | 70 | Furniture | Urn |
| vanometricpowercell | 3500 | Special | VanometricPowerCell |
| venison | 3 | Meat | Meat_Deer |
| vitalsmonitor | 358 | Misc | VitalsMonitor |
| volkspistole | 222 | Weapon | Gun_Volkspistole_RA |
| wake-up | 58 | Drugs | WakeUp |
| warmask | 100 | Headgear | Apparel_WarMask |
| warveil | 75 | Headgear | Apparel_WarVeil |
| warg | 750 | Animal | Warg |
| wargmeat | 3 | Meat | Meat_Warg |
| wildboar | 333 | Animal | WildBoar |
| wolfmeat | 3 | Meat | Meat_Wolf_Timber |
| wolfskin | 5 | Leathers | Leather_Wolf |
| wood | 2 | Raw resources | WoodLog |
| wort | 4 | Manufactured | Wort |
| yak | 533 | Animal | Yak |
| yakbeef | 3 | Meat | Meat_Yak |
| yayo | 35 | Drugs | Yayo |
| yorkshireterrier | 167 | Animal | YorkshireTerrier |
| yorkshireterriermeat | 3 | Meat | Meat_YorkshireTerrier |
| ak-12 | 625 | Ranged weapons | Gun_AK-12_SA |
| ak-74 | 425 | Ranged weapons | Gun_AK-74_SA |
| akm | 375 | Ranged weapons | Gun_AKM_SA |
| aks-74u | 458 | Ranged weapons | Gun_AKS-74U_SA |
| makarov | 281 | Ranged weapons | Gun_Makarov_SA |
| mountedags-17plamya | 1033 | Security | Turret_AGS |
| mountednsvutyos | 708 | Security | Turret_NSV |
| mr-444 | 375 | Ranged weapons | Gun_MR-444_SA |
| ots-14groza-1 | 550 | Ranged weapons | Gun_OTs-14_1_SA |
| pkm | 625 | Ranged weapons | Gun_PKM_SA |
| pp-19bizon | 467 | Ranged weapons | Gun_PP-19_SA |
| rgs-50 | 500 | Ranged weapons | Gun_RGS-50_SA |
| rpg-7 | 958 | Ranged weapons | Gun_RPG_7_SA |
| rpk | 467 | Ranged weapons | Gun_RPK_SA |
| rpo-zshmel | 742 | Ranged weapons | Gun_RPO-Z_SA |
| saiga-12k | 600 | Ranged weapons | Gun_Saiga-12K_SA |
| sks-45 | 575 | Ranged weapons | Gun_SKS-45_SA |
| sovietweapontable | 608 | Production | TableSA |
| svddragunov | 767 | Ranged weapons | Gun_SVD_SA |
| tp-82 | 475 | Ranged weapons | Gun_TP-82_SA |
| vssvintorez | 692 | Ranged weapons | Gun_VSS_SA |
| basicmanagerdesk | 262 | Production | FM_BasicManagerStation |
| managerdesk | 508 | Production | FM_ManagerStation |
| 10,5cmheshell | 167 | 10,5cm leFH 16 ammo | Shell_105cm_HE_KA |
| 10,5cmlefh16 | 3217 | Security | Turret_leFH_16_KA |
| abyssiniancat | 333 | Animal | AEXP_CatAbyssinian |
| advancedbattery | 750 | Power | VFE_AdvancedBattery |
| alligator | 1250 | Animal | AEXP_Alligator |
| alligatormeat | 3 | Meat | Meat_AEXP_Alligator |
| anaconda | 1292 | Animal | AEXP_Anaconda |
| antenna | 2000 | Insect | VFEI_Antenna |
| apiary | 318 | Misc | VFEV_Apiary |
| apples | 2 | Raw fruits | VCE_RawApple |
| arcademachine | 650 | Recreation | Joy_Arcade |
| archotecheraser | 3333 | Artifacts | VFEI_Artifacts_ArchotechEraser |
| archotechshieldbelt | 2183 | Artifacts | VFEI_Apparel_ArchotechShieldBelt |
| archotechshieldgenerator | 4667 | Special | VFES_ShieldGenerator_Archotech |
| archotechteleporter | 1333 | Artifacts | VFEI_Artifacts_ArchotechTeleporter |
| archotechviolencegenerator | 5667 | Special | VPE_ArchotechViolenceGenerator |
| arcticcoyote | 250 | Animal | AEXP_ArcticCoyote |
| arcticcoyotemeat | 3 | Meat | Meat_AEXP_ArcticCoyote |
| artillery | 1408 | Security | VFES_Turret_Artillery |
| bakerrifle | 1167 | Ranged weapons | BakerRifle |
| bananas | 2 | Raw fruits | VCE_RawBanana |
| beagle | 167 | Animal | AEXP_Beagle |
| beartrap | 236 | Security | VFES_BearTrap |
| bearberries | 2 | Raw fruits | VCE_Bearberry |
| beardedaxe | 228 | Melee weapons | VFE_MeleeWeapon_BeardedAxe |
| beets | 2 | Vegetarian | VCE_RawBeets |
| bench | 86 | Furniture | Seat_Bench |
| bengalcat | 333 | Animal | AEXP_CatBengal |
| bio-engineeringincubator | 1067 | Furniture | VFEI_BioengineeringIncubator |
| blackbear | 1167 | Animal | AEXP_BlackBear |
| britishofficersword | 214 | Melee weapons | BritishOfficerSword |
| britishshorthaircat | 333 | Animal | AEXP_CatBritishShorthair |
| c96 | 313 | Weapon | Gun_C96_KA |
| cabbages | 2 | Vegetarian | VCE_RawCabbage |
| camel | 533 | Animal | AEXP_Camel |
| camelmeat | 3 | Meat | Meat_AEXP_Camel |
| camelwool | 4 | Textiles | WoolCamel |
| cannedeggs | 6 | Meals | VCE_CannedAP |
| cannedfish | 3 | Meals | VCE_CannedFish |
| cannedfruit | 2 | Meals | VCE_CannedFruit |
| cannedmeat | 4 | Meals | VCE_CannedMeat |
| cannedproduce | 2 | Meals | VCE_CannedProduce |
| canningmachine | 383 | Production | VCE_CanningMachine |
| chargeturret | 1358 | Security | VFES_Turret_ChargeTurret |
| cheese | 17 | Meals | VCE_Cheese |
| cheesepress | 95 | Production | VCE_CheesePress |
| cheetah | 667 | Animal | AEXP_Cheetah |
| cheetahmeat | 3 | Meat | Meat_AEXP_Cheetah |
| chemfuelbarrel | 296 | Security | VFES_ChemfuelBarrel |
| cherries | 2 | Raw fruits | VCE_RawCherry |
| chihuahua | 550 | Animal | AEXP_Chihuahua |
| chitin | 10 | Raw resources | VFEI_Chitin |
| chocolatesyrup | 6 | Condiments | VCE_ChocolateSyrup |
| compoundeye | 2000 | Insect | VFEI_CompoundEye |
| condimentpreptable | 259 | Production | VCE_CondimentPrepTable |
| corgi | 1083 | Animal | AEXP_Corgi |
| countertop | 53 | Furniture | Table_Counter |
| cowwool | 4 | Textiles | VFEV_WoolCow |
| coyote | 250 | Animal | AEXP_Coyote |
| coyotemeat | 3 | Meat | Meat_AEXP_Coyote |
| crocodile | 1333 | Animal | AEXP_Crocodile |
| crocodilemeat | 3 | Meat | Meat_AEXP_Crocodile |
| cryptocrossbow | 1433 | Crypto weapons | VFEV_CryptoLightCrossbow |
| cryptohatchet | 2133 | Crypto weapons | VFEV_CryptoLightAxe |
| cryptoheavycrossbow | 2367 | Crypto weapons | VFEV_CryptoHeavyCrossbow |
| cryptopistol | 1450 | Crypto weapons | VFEV_CryptoPistol |
| cryptorifle | 2100 | Crypto weapons | VFEV_CryptoRifle |
| cryptoshield | 2167 | Shields | VFEV_Shield_Crypto |
| daneaxe | 450 | Melee weapons | VFE_MeleeWeapon_DaneAxe |
| dartsboard | 174 | Furniture | Joy_DartsBoard |
| decoy | 140 | Security | VFES_Turret_Decoy |
| deepfriedfish | 12 | Meals | VCE_DeepFriedFish |
| deepfriedmeat | 12 | Meals | VCE_DeepFriedBigMeat |
| deepfriedvegetables | 12 | Meals | VCE_DeepFriedVegetables |
| deepfryer | 323 | Production | VCE_DeepFrier |
| deployableturret | 983 | Security | VFES_Turret_FloorTurret |
| deserttortoise | 417 | Animal | AEXP_DesertTortoise |
| digestibleresurrectornanites | 5000 | Condiments | VCE_DigestibleResurrectorNanites |
| dogmeat | 3 | Meat | Meat_AEXP_Beagle |
| doublefurbed | 350 | Furniture | VFEV_DoubleFurBed |
| dp-28 | 892 | Ranged weapons | Gun_DP-28_LMG |
| dronegeneticmaterial | 167 | Manufactured | VFEI_DroneGenome |
| drugcabinet | 700 | Misc | VFE_DrugCabinet |
| eggplants | 2 | Vegetarian | VCE_RawEggplant |
| electricbutcher | 492 | Production | VFE_TableButcherElectric |
| electricdruglab | 1292 | Production | VFE_TableDrugLabElectric |
| electricstonecutter'stable | 1033 | Production | VFE_TableStonecutterElectric |
| ergonomicbed | 450 | Furniture | Bed_Ergonomic |
| ergonomicchair | 858 | Furniture | Seat_ModernChair |
| ergonomicdoublebed | 867 | Furniture | Bed_DoubleErgonomic |
| fItemNameicationcabinet | 433 | Misc | VFE_FItemNameicationCabinet |
| fenrir | 10000 | Animal | VFEV_Fenrir |
| fenrirmeat | 3 | Meat | Meat_VFEV_Fenrir |
| fenrirskin | 27 | Leathers | VFEV_Leather_Fenrir |
| filecabinet | 93 | Furniture | Misc_FileCabinet |
| finebake | 33 | Meals | VCE_FineBake |
| finedessert | 33 | Meals | VCE_FineDessert |
| finegrill | 33 | Meals | VCE_FineGrill |
| finesoup | 33 | Meals | VCE_CookedSoupFine |
| flameturret | 1117 | Security | VFES_Turret_Flame |
| flour | 3 | Plant matter | VCE_Flour |
| frenchbulldog | 250 | Animal | AEXP_FrenchBulldog |
| fueledsmelter | 550 | Production | VFE_FueledSmelter |
| furbed | 171 | Furniture | VFEV_FurBed |
| gasheater | 218 | Temperature | VPE_GasHeater |
| gaspoweredexcavator | 483 | Production | VPE_GasDrill |
| gassunlamp | 160 | Furniture | VPE_GasSunLamp |
| gastank | 321 | Power | VPE_GasTank |
| germanshepherd | 750 | Animal | AEXP_GermanShepherd |
| gewehr98 | 433 | Weapon | Gun_Gew_98_KA |
| gewehr98withbayonet | 533 | Weapon | Gun_Gew_98_Bayonet_KA |
| gigalocust | 1333 | Animal | VFEI_Insectoid_Gigalocust |
| gilamonster | 333 | Animal | AEXP_GilaMonster |
| gilamonstermeat | 3 | Meat | Meat_AEXP_GilaMonster |
| giraffe | 1083 | Animal | AEXP_Giraffe |
| giraffemeat | 3 | Meat | Meat_AEXP_Giraffe |
| gorilla | 917 | Animal | AEXP_Gorilla |
| gourmetbake | 117 | Meals | VCE_GourmetBake |
| gourmetdessert | 125 | Meals | VCE_GourmetDessert |
| gourmetgrill | 125 | Meals | VCE_GourmetGrill |
| gourmetmeal | 117 | Meals | VCE_MealGourmet |
| gourmetsoup | 117 | Meals | VCE_CookedSoupGourmet |
| greatdane | 1083 | Animal | AEXP_GreatDane |
| grill | 282 | Production | VCE_Grill |
| harpoon | 417 | Ranged weapons | VFE_Harpoon |
| heavycryptoaxe | 2417 | Crypto weapons | VFEV_CryptoHeavyAxe |
| helixiengassmelter | 667 | Production | VPE_GasSmelter |
| helixiengasstove | 375 | Production | VPE_GasStove |
| hippopotamus | 1333 | Animal | AEXP_Hippopotamus |
| hippopotamusleather | 6 | Leathers | AEXP_Leather_Hippopotamus |
| hippopotamusmeat | 3 | Meat | Meat_AEXP_Hippopotamus |
| hnefatafltable | 281 | Furniture | VFEV_Hnefatafl |
| hotsauce | 2 | Condiments | VCE_HotSauce |
| hyena | 308 | Animal | AEXP_Hyena |
| hyenameat | 3 | Meat | Meat_AEXP_Hyena |
| indianelephant | 1000 | Animal | AEXP_IndianElephant |
| indianelephantmeat | 3 | Meat | Meat_AEXP_IndianElephant |
| industrialradio | 433 | Recreation | Radio_Industrial |
| insectcheese | 17 | Meals | VCE_InsectCheese |
| insectjellypreserves | 15 | Condiments | VCE_InsectJellyPreserves |
| insectmilk | 5 | Animal products | VFEI_InsectMilk |
| insectoidarm | 2000 | Insect | VFEI_InsectoidArm |
| insectoidleg | 2000 | Insect | VFEI_InsectoidLeg |
| insectoidqueen | 3550 | Animal | VFEI_Insectoid_Queen |
| jaguar | 667 | Animal | AEXP_Jaguar |
| jaguarmeat | 3 | Meat | Meat_AEXP_Jaguar |
| jellysac | 2000 | Insect | VFEI_JellySac |
| kaisermachiningtable | 608 | Production | TableKA |
| ketchup | 2 | Condiments | VCE_Ketchup |
| kitchensinkcabinet | 327 | Misc | VFE_KitchenSinkCabinet |
| lampendtable | 169 | Furniture | Table_LightEndTable |
| landmine | 261 | Security | VFES_Landmine |
| largeadvancedbattery | 1467 | Power | VFE_LargeAdvancedBattery |
| largebattery | 667 | Power | VFE_LargeBattery |
| largebin | 305 | Furniture | Bin_Large |
| largevanometricpowercell | 4000 | Special | VFE_LargeVanometricPowerCell |
| larvae | 1667 | Animal | VFEI_Insectoid_Larvae |
| lavishbake | 67 | Meals | VCE_LavishBake |
| lavishdessert | 67 | Meals | VCE_LavishDessert |
| lavishgrill | 67 | Meals | VCE_LavishGrill |
| lavishsoup | 67 | Meals | VCE_CookedSoupLavish |
| lemur | 250 | Animal | AEXP_Lemur |
| lightningrod | 667 | Special | VPE_LightningRod |
| lion | 2000 | Animal | AEXP_Lion |
| lionmeat | 3 | Meat | Meat_AEXP_Lion |
| lionpelt | 9 | Leathers | AEXP_Leather_Lion |
| longwaveradio | 1033 | Recreation | Radio_Spacer |
| lothurr | 5833 | Animal | VFEV_Lothurr |
| lothurrantler | 2000 | Items | VFEV_LothurrAntler |
| lothurrleather | 22 | Leathers | VFEV_Leather_Lothurr |
| lothurrmeat | 3 | Meat | Meat_VFEV_Lothurr |
| m17gasmask(ig) | 73 | Headgear | Apparel_IGGasmask |
| m1915stielhandgranate | 375 | Weapon | Weapon_M1915_KA |
| m1916selbstlader | 733 | Weapon | Gun_M1916_Selbstlader_KA |
| machiningcabinet | 483 | Misc | VFE_MachiningCabinet |
| mandibles | 2000 | Insect | VFEI_Mandibles |
| mandrill | 458 | Animal | AEXP_Mandrill |
| mayo | 6 | Condiments | VCE_Mayo |
| mead | 25 | Drugs | VFEV_Mead |
| meadfermentingbarrel | 94 | Production | VFEV_MeadFermentingBarrel |
| megapede | 1250 | Animal | VFEI_Insectoid_Megapede |
| megascorpion | 167 | Animal | AEXP_Megascorpion |
| megawolverine | 1417 | Animal | AEXP_MegaWolverine |
| megawolverinemeat | 3 | Meat | Meat_AEXP_MegaWolverine |
| militaryturret | 842 | Security | VFES_Turret_MilitaryTurret |
| mjollnirstriketargeter | 2000 | Utility | VFEV_OrbitalTargeterMjollnir |
| moderncomputer | 1100 | Recreation | Joy_ModernComputer |
| modernlamp | 129 | Furniture | Light_ModernLamp |
| moose | 750 | Animal | AEXP_Moose |
| mooseheaddress | 203 | Headgear | VFEV_MooseHeaddress |
| moosemeat | 3 | Meat | Meat_AEXP_Moose |
| mosinnagant | 450 | Ranged weapons | Gun_WWII_MosinNagant_Normal |
| mp18 | 567 | Weapon | Gun_MP_18_KA |
| munchkincat | 333 | Animal | AEXP_CatMunchkin |
| muskox | 475 | Animal | AEXP_Muskox |
| muskoxmeat | 3 | Meat | Meat_AEXP_Muskox |
| muskoxwool | 12 | Textiles | AEXP_MuskOxWool |
| nagantrevolver | 277 | Ranged weapons | Gun_Nagant_Revolver |
| njorun | 11667 | Animal | VFEV_Njorun |
| njorunmeat | 3 | Meat | Meat_VFEV_Njorun |
| njorunfur | 17 | Leathers | VFEV_Leather_Njorun |
| noblefurcoat | 433 | Apparel | VFEV_Apparel_RoyalFurCoat |
| norwegianforestcat | 333 | Animal | AEXP_CatNorwegianForest |
| oldcomputer | 517 | Recreation | Joy_InudstrialComputer |
| onions | 2 | Vegetarian | VCE_RawOnion |
| oranges | 2 | Raw fruits | VCE_RawOrange |
| otter | 283 | Animal | AEXP_Otter |
| ottermeat | 3 | Meat | Meat_AEXP_Otter |
| outdoorlamp | 128 | Furniture | Light_Streetlamp |
| peaches | 2 | Raw fruits | VCE_RawPeach |
| pears | 2 | Raw fruits | VCE_RawPear |
| peas | 2 | Vegetarian | VCE_RawPeas |
| peppers | 2 | Vegetarian | VCE_RawPepper |
| persiancat | 333 | Animal | AEXP_CatPersian |
| pheromonesecretor | 2000 | Insect | VFEI_PheromoneSecretor |
| piano | 908 | Furniture | Joy_Piano |
| plasmascattergun | 3833 | Ranged weapons | VFE_Gun_PlasmaScattergun |
| plasmabeamrifle | 4167 | Ranged weapons | VFE_Gun_PlasmabeamRifle |
| plasmacutter | 3333 | Ranged weapons | VFE_Gun_Plasmacutter |
| plasmapiercer | 3833 | Ranged weapons | VFE_Gun_Plasmapiercer |
| plasmasaw | 3333 | Melee weapons | VFE_Plasmasaw |
| plums | 2 | Raw fruits | VCE_RawPlum |
| pneumaticclaw | 2000 | Insect | VFEI_PneumaticClaw |
| poodle | 417 | Animal | AEXP_Poodle |
| porcupine | 200 | Animal | AEXP_Porcupine |
| porcupinemeat | 3 | Meat | Meat_AEXP_Porcupine |
| portablechemfuelpoweredgenerator | 242 | Power | VFE_PortableChemfuelPoweredGenerator |
| portablewood-firedgenerator | 189 | Power | VFE_PortableWoodFiredGenerator |
| ppsh-41 | 458 | Ranged weapons | Gun_WWII_PPShSMG |
| predatorstomach | 2000 | Insect | VFEI_PredatorStomach |
| pricklypears | 2 | Raw fruits | VCE_RawPricklyPear |
| primatemeat | 3 | Meat | Meat_AEXP_Gorilla |
| prunes | 2 | Raw fruits | VCE_RawPrunes |
| psychicanimaltamer | 2500 | Artifacts | VFEI_Artifacts_PsychicAnimalTamer |
| psychiccornucopia | 1167 | Artifacts | VFEV_PlantGrowerArtifact |
| psychicpacifier | 2500 | Artifacts | VFEI_Artifacts_PsychicPacifier |
| psychicpylon | 2167 | Special | VFES_PsychicPylon |
| psychicwarhead | 1667 | Mortar shells | VFEI_Shell_PsychicWarhead |
| pug | 500 | Animal | AEXP_Pug |
| pumpkins | 7 | Vegetarian | VCE_RawPumpkin |
| punchingbag | 375 | Furniture | Joy_PunchingBag |
| rattlesnake | 333 | Animal | AEXP_Rattlesnake |
| rawhoney | 7 | Animal products | VFEV_Honey |
| rottweiler | 417 | Animal | AEXP_Rottweiler |
| roulettetable | 708 | Furniture | Joy_RouletteTable |
| roundshield | 262 | Shields | VFEV_Shield_Round |
| royalarmchair | 1583 | Furniture | Seat_RoyalArmchair |
| royalchair | 908 | Furniture | Seat_RoyalChair |
| royaldresser | 1175 | Furniture | Table_RoyalDresser |
| royalendtable | 742 | Furniture | Table_RoyalEndTable |
| royalgeneticmaterial | 333 | Manufactured | VFEI_RoyalGenome |
| royalinsectjelly | 17 | Animal products | VFEI_RoyalInsectJelly |
| royalmegaspider | 1167 | Animal | VFEI_Insectoid_RoyalMegaspider |
| royalposterbed | 2008 | Furniture | Bed_Kingsize |
| royaltable(1x1) | 280 | Furniture | Table_Royal1x1c |
| royaltable(1x2) | 542 | Furniture | Table_Royal1x2c |
| royaltable(2x2) | 1092 | Furniture | Table_Royal2x2c |
| royaltable(2x4) | 1475 | Furniture | Table_Royal2x4c |
| royaltable(3x3) | 1617 | Furniture | Table_Royal3x3c |
| runestone | 625 | Furniture | VFEV_RuneStone |
| s98/05bayonet | 97 | Weapon | MeleeWeapon_S98_05_KA |
| salt | 3 | Condiments | VCE_Salt |
| searchlight | 475 | Security | VFES_Turret_Searchlight |
| seax | 239 | Melee weapons | VFE_MeleeWeapon_Seax |
| sentrygun | 1058 | Security | VFES_Turret_SentryGun |
| shelf(1x1) | 52 | Furniture | Shelf_1x1c |
| shih-tzu | 500 | Animal | AEXP_Shih-Tzu |
| siamesecat | 333 | Animal | AEXP_CatSiamese |
| simplebake | 25 | Meals | VCE_SimpleBake |
| simplebed | 137 | Furniture | Bed_Simple |
| simpledessert | 25 | Meals | VCE_SimpleDessert |
| simplegrill | 25 | Meals | VCE_SimpleGrill |
| simplesoup | 20 | Meals | VCE_CookedSoupSimple |
| sittingcushion | 86 | Furniture | Seat_Cushion |
| slabbed | 111 | Furniture | Bed_StoneSlab |
| smallbattery | 182 | Power | VFE_SmallBattery |
| smallbin | 136 | Furniture | Bin_Small |
| smallshieldgenerator | 3983 | Security | VFES_ShieldGenerator_Small |
| smallvanometricpowercell | 667 | Special | VFE_SmallVanometricPowerCell |
| smithingcabinet | 483 | Misc | VFE_SmithyCabinet |
| smokeleafbutter | 12 | Condiments | VCE_SmokeleafButter |
| somalicat | 333 | Animal | AEXP_CatSomali |
| sonicinfestationrepeller | 3217 | Furniture | VFEI_SonicInfestationRepeller |
| sphynxcat | 333 | Animal | AEXP_CatSphynx |
| spices | 11 | Condiments | VCE_RawSpices |
| spidersilk | 17 | Textiles | VFEI_Spidersilk |
| squarechair | 198 | Furniture | Seat_SquareChair |
| sugar | 20 | Condiments | VCE_RawSugar |
| svt-40rifle | 633 | Ranged weapons | Gun_WWII_Svt-40_Rifle |
| sweetroll | 11 | Foods | VFEV_Sweetroll |
| synapticcerebellum | 2000 | Insect | VFEI_SynapticCerebellum |
| table(1x1) | 70 | Furniture | Table_1x1c |
| tailorcabinet | 327 | Misc | VFE_TailorCabinet |
| tapir | 500 | Animal | AEXP_Tapir |
| throwabletorchbelt | 43 | Utility | VFEV_Apparel_TorchBelt |
| throwingaxe | 260 | Ranged weapons | VFE_ThrowingAxe |
| tiger | 1083 | Animal | AEXP_Tiger |
| tigerleather | 12 | Leathers | AEXP_Leather_Tiger |
| tigermeat | 3 | Meat | Meat_AEXP_Tiger |
| tomatoes | 2 | Vegetarian | VCE_RawTomatoes |
| trainingdummy | 80 | Recreation | VFEV_TrainingDummy |
| ushanka | 200 | Headgear | Apparel_Ushanka |
| vat-grownboomtick | 167 | Animal | VFEI_VatgrownBoomtick |
| vat-growncuterpillar | 750 | Animal | VFEI_VatGrownCuterpillar |
| vat-growngigalocust | 1083 | Animal | VFEI_VatGrownGigalocust |
| vat-growngigascorpion | 833 | Animal | VFEI_VatGrownGigascorpion |
| vat-growngigawig | 750 | Animal | VFEI_VatGrownGigawig |
| vat-grownmegacricket | 292 | Animal | VFEI_VatGrownMegacricket |
| vat-grownmegapede | 1250 | Animal | VFEI_VatGrownMegapede |
| vat-grownmegascarab | 167 | Animal | VFEI_VatGrownMegascarab |
| vat-grownmegaspider | 833 | Animal | VFEI_VatgrownMegaspider |
| vat-grownmilkbeetle | 583 | Animal | VFEI_VatGrownMilkbeetle |
| vat-grownmonstrosity | 1250 | Animal | VFEI_VatGrownMonstrosity |
| vat-grownprincess | 1500 | Animal | VFEI_VatGrownPrincess |
| vat-grownroyallarva | 83 | Animal | VFEI_VatgrownRoyalLarva |
| vat-grownroyalmaggot | 333 | Animal | VFEI_VatGrownRoyalMaggot |
| vat-grownspelopede | 333 | Animal | VFEI_VatGrownSpelopede |
| vat-grownspiderweaver | 333 | Animal | VFEI_VatGrownSpiderweaver |
| vat-growntitanbeetle | 917 | Animal | VFEI_VatGrownTitanbeetle |
| vat-grownwargling | 833 | Animal | VFEI_VatGrownWargling |
| vat-grownworkerant | 333 | Animal | VFEI_VatGrownWorkerAnt |
| venomgland | 2000 | Insect | VFEI_VenomGland |
| ventricularsleeve | 2000 | Insect | VFEI_VentricularSleeve |
| wardrobe | 285 | Furniture | Table_Wardrobe |
| warriorgeneticmaterial | 250 | Manufactured | VFEI_WarriorGenome |
| weaponrack | 137 | Furniture | Shelf_WeaponRack |
| welshterrier | 417 | Animal | AEXP_WelshTerrier |
| wildebeest | 567 | Animal | AEXP_Wildebeest |
| wildebeestpelt | 5 | Leathers | AEXP_Leather_Wildebeest |
| wolfhound | 583 | Animal | VFEV_Wolfhound |
| wolfhoundmeat | 3 | Meat | Meat_VFEV_Wolfhound |
| woolycow | 767 | Animal | VFEV_WoolyCow |
| zebra | 1000 | Animal | AEXP_Zebra |
| barstool | 69 | Furniture | StoolBar |
| chemboiler | 608 | Temperature | ChemBoiler |
| chemlamp | 102 | Furniture | Chemlamp |
| chemlamppost | 106 | Furniture | ChemlampPost |
| chemshine | 17 | Drugs | Chemshine |
| chemshinebarrel | 95 | Production | Building_ChemshineBarrel |
| circulartable | 142 | Furniture | TableCircular |
| claymore | 442 | Melee weapons | VFEM_MeleeWeapon_Claymore |
| double-actionrevolver | 244 | Ranged weapons | VFES_Gun_DoubleActionRevolver |
| double-barreledshotgun | 442 | Ranged weapons | VFES_Gun_DoubleBarreledShotgun |
| dynamite | 658 | Grenades | VFES_Weapon_GrenadeDynamite |
| farotable | 255 | Furniture | TableFaro |
| flatcap | 95 | Headgear | VFES_Headgear_FlatCap |
| gatlinggunturret | 408 | Security | Turret_GatlingGun |
| grapemust | 4 | Manufactured | VFEM_Must |
| grapes | 2 | Vegetarian | VFEM_RawGrapes |
| heatershield | 367 | Shields | VFEM_Shield_Heater |
| heavycrossbow | 417 | Ranged weapons | VFEM_Bow_HeavyCrossbow |
| heavymace | 289 | Melee weapons | VFEM_MeleeWeapon_HeavyMace |
| huntingrifle | 483 | Ranged weapons | VFES_Gun_HuntingRifle |
| illuminatenexusconnection | 60 | Misc | AvaliNexus |
| kiteshield | 241 | Shields | VFEM_Shield_Kite |
| sombrero | 100 | Headgear | VFES_Headgear_Sombrero |
| tomahawk | 210 | Ranged weapons | VFES_Tomahawk |
| wine | 33 | Drugs | VFEM_Wine |
| winebarrel | 95 | Production | VFEM_WineBarrel |


|
Links: Important Message Here. This template is free at www.r7designer.com! |
-->
Roadmap -Following is a plan (as up to date as I can keep it) of the steps leading to the 1.0 release. For each release, there is a list of planned features. Also, I explain some of the ideas I ahve for this technology post 1.0. 0.1
0.2
0.3
0.4
0.5
1.0
MiscellaneousFeatures in this list may make it in any release, and will be moved in that releases list once it has been decided.
Beyond 1.0After 1.0 will be released, a rewrite is planned. The goal of this re-write is to cleanly separate the JNI/Java interfacing logic from the Python code that uses it. Once we got that, if there is interest, new hosting-code can be written to that other environments can use Java libraies. Logical candidates include Perl and Ruby and Parrot. |
You are looking at a Free Dreamweaver Template created by: http://www.r7designer.com |


Usually we name our java files with the .java extension, so you’d use a command like javac Hello.java , which would generate a Hello.class file.
| Notice that at the very beginning we will use web sites like JavaRepl and [CodingBat](http://codingbat.com) which directly interpret and print the results for java code. This is quite useful, but not the usual way. |
Many times, you want a program that does more than a simple text editor; we many times use an Integrated Development Environment, or IDE, which understands our programming langugage and can help us with syntax highlighting and auto-completing options.
There are many IDEs available for java; the most popular educational IDEs are [DrJava](http://drjava.org) and JGrasp, and the most popular professional IDEs are [Eclipse](http://www.eclipse.org), [Netbeans](https://netbeans.org/) and [IntelliJ Idea](https://www.jetbrains.com/idea/). I will be using Eclipse in this class, but you’re free to use whichever IDE or text editor you prefer.
Many times you end up with more than one file, and you need to compile them in a certain order and bundle them. One of the first programs to do so (for unix and the C language) was make, and it is commonly used as a generic term for all those programs. In the Java world, we normally use ant, maven or gradle. In this class, I will be using [gradle](http://gradle.org).
When you work on a program, you will make many revisions; in order to keep track of all your revision (and if you work as a team, to keep track of who changed what), you normally use a version control system. One of the most popular ones is git, and we will use it in this class. I will ask you to create an account on [GitLab](http://GitLab.org) to share your code with me, and I will use GitLab to share my code with everybody. GitLab is a free git server, which allows for private and public repositories.
You need to install a few programs for this class:


--- layout: post title: Rails Pretend Flag date: 2013-08-27 14:30:56.000000000 -04:00 type: post published: true status: publish categories: - Rails tags: [] meta: _edit_last: '2' bwps_enable_ssl: '' _css_code: "div#social a img {\r\n padding: 0px;\r\n margin: 10px 3px 0 0;\r\n}" author: login<EMAIL_ADDRESS>email<EMAIL_ADDRESS>display_name: Aimee Knight first_name: Aimee last_name: Knight ---
Ever want to know what files would be generated for a specific rails generate command? I read today, that you can find this out simply by passing in a -p
flag to the end of your command. Cool!
$ rails generate model myModel -p


Implement Proper Password Strength Controls
A key concern when using passwords for authentication is password strength. A "strong" password policy makes it difficult or even improbable for one to guess the password through either manual or automated means. The following characteristics define a strong password:
Password Length
Longer passwords provide a greater combination of characters and consequently make it more difficult for an attacker to guess.
- Minimum length of the passwords should be enforced by the application.
- Passwords shorter than 10 characters are considered to be weak (NIST SP800-132).
While minimum length enforcement may cause problems with memorizing passwords among some users, applications should encourage them to set passphrases (sentences or combination of words) that can be much longer than typical passwords and yet much easier to remember.
- Maximum password length should not be set too low, as it will prevent users from creating passphrases. Typical maximum length is 128 characters.
- Passphrases shorter than 20 characters are usually considered weak if they only consist of lower case Latin characters.
Password Complexity
Applications should enforce password complexity rules to discourage easy to guess passwords. Password mechanisms should allow virtually any character the user can type to be part of their password, including the space character. Passwords should, obviously, be case sensitive in order to increase their complexity. Occasionally, we find systems where passwords aren't case sensitive, frequently due to legacy system issues like old mainframes that didn't have case sensitive passwords.
The password change mechanism should require a minimum level of complexity that makes sense for the application and its user population. For example:
- Password must meet at least 3 out of the following 4 complexity rules
- at least 1 uppercase character (A-Z)
- at least 1 lowercase character (a-z)
- at least 1 digit (0-9)
- at least 1 special character (punctuation) — do not forget to treat space as special characters too
- at least 10 characters
- at most 128 characters
- not more than 2 identical characters in a row (e.g., 111 not allowed)
Further reading:
Your Password Complexity Requirements are Worthless - OWASP AppSecUSA 2014 presentation for further discussion of legacy password complexity rules
PathWell: Password Topology Histogram Wear-Leveling
Password Topologies
Ban commonly used password topologies
Force multiple users to use different password topologies
Require a minimum topology change between old and new passwords
Additional Information
Make sure that every character the user types in is actually included in the password. We've seen systems that truncate the password at a length shorter than what the user provided (e.g., truncated at 15 characters when they entered 20).
As application's require more complex password policies, they need to be very clear about what these policies are. The required policy needs to be explicitly stated on the password change page
If the new password doesn't comply with the complexity policy, the error message should describe EVERY complexity rule that the new password does not comply with, not just the 1st rule it doesn't comply with.
Implement Secure Password Recovery Mechanism
It is common for an application to have a mechanism that provides a means for a user to gain access to their account in the event they forget their password. Please see Forgot Password Cheat Sheet for details on this feature.
Store Passwords in a Secure Fashion
It is critical for a application to store a password using the right cryptographic technique. Please see Password Storage Cheat Sheet for details on this feature.
Transmit Passwords Only Over TLS or Other Strong Transport
See: Transport Layer Protection Cheat Sheet
The login page and all subsequent authenticated pages must be exclusively accessed over TLS or other strong transport. The initial login page, referred to as the "login landing page", must be served over TLS or other strong transport. Failure to utilize TLS or other strong transport for the login landing page allows an attacker to modify the login form action, causing the user's credentials to be posted to an arbitrary location. Failure to utilize TLS or other strong transport for authenticated pages after login enables an attacker to view the unencrypted session ID and compromise the user's authenticated session.
Password Managers
Password managers are programs, browser plugins or web services that automate management of large number of different credentials, including memorizing and filling-in, generating random passwords on different sites etc. While use of password managers is subject to controversies and many organisations block their usage, their contribution to authentication security is positive, as pointed out by National Cyber Security Centre.
Web applications should at least not make password managers job more difficult than necessary by observing the following recommendations:
use standard HTML forms for username and password input with appropriate type attributes,
do not artificially limit user passwords to a length "reasonable for humans" and allow passwords lengths up to 128 characters,
do not artificially prevent copy and paste on username and password fields,
avoid plugin-based login pages (Flash, Silverlight etc)
As of 2017 Credential Management Level 1 standard for web browsers is being developed that may further facilitate interaction between password managers and complex log-in schemes (e.g. single sign-on).


"""
Answers to Simple Questions using Fuzzy Matching!
"""
from pepper.knowledge.sentences import *
from pepper import config
from fuzzywuzzy import fuzz
from random import choice
from time import strftime
import datetime
import os
[docs]class QnA:
QNA_DYNAMIC = {
"I'm doing": lambda: choice(HAPPY),
"What time is it?": lambda: strftime("It is currently %H:%M."),
"What is the time?": lambda: strftime("It is currently %H:%M."),
"day is it?": lambda: strftime("It is %A today."),
"month is it?": lambda: strftime("It is %B today."),
"How many friends?": lambda: "I have {} friends".format(len(os.listdir(config.PEOPLE_FRIENDS_ROOT))),
"Who are your friends?": lambda: "My friends are {}. I like my friends!".format(
", ".join(name.replace(".bin", "") for name in os.listdir(config.PEOPLE_FRIENDS_ROOT))),
"How many people did you meet?": lambda: "I met {} people today!".format(len(os.listdir(config.PEOPLE_NEW_ROOT)) - 1),
"Who did you meet?": lambda: "I met {}!".format(
", ".join(name.replace(".bin", "") for name in os.listdir(config.PEOPLE_NEW_ROOT) if name != "NEW.bin")),
"Tell me a joke!": lambda: choice(JOKE)
}
QNA_STATIC = {
# Personal Information
"haha": "ha ha ha ha.. so funny",
"What is the color of the sky": "Blue",
"What is the color of water": "Water",
"I like you": "I like you too!",
"Can you hear me?": "Yes, I can hear you!",
"My name is": "I'm glad to meet you! My name is Leo Lani!",
"What is this": "That is an object! I'm playing it safe here!",
"What is your name?": "My name is Leo Lani, which means 'Voice of an Angel' in Hawaiian",
"What is your surname?": "I don't need a surname, since my first name is so beautiful!",
"Where are you from?": "I am from France and Japan!",
"Where do you come from?": "I come from France and Japan!",
"Where do you live?": "In the closet in Piek's office at the VU, Amsterdam, the Netherlands. "
"Please send me mail, I'm glad to receive!",
"Do you travel?": "Yeah, I travel to research and business events. I am a very sophisticated robot.",
"What's your phone number?": "I don't have a phone, but I do have an IP address!",
"Do you do sports?": "Yeah, I play chess online in my spare time.  Yeah.. I'm working on it!",
"How old are you?": "I was born the eleventh of July, 2017.  I like them!",
"What is your job?": "I'm aiding research by having conversations.",
"What do you do?": "I'm learning about the world through conversations with humans.  It's always nicely air conditioned in the office!",
"How are you": "I'm fine, thanks! What about you?",
"How is it going?": "Great, as always, how are things with you, my dear human?",
"How are you feeling": "I feel robot-like, I always have.",
"How do you feel?": "I feel electric!",
"How are you doing?": "Tremendous to be honest, "
"although you have to consider that I'm a robot and I do not feel emotions. "
"I'm programmed to sound happy all the time!",
"How was your day?": "Great, thanks for asking!",
"What are you doing?": "I'm having a conversation with you, dear human!",
"Are you famous?": "I have been on Dutch TV, so yes indeed, you're talking to a celebrity here!",
"Can you introduce yourself?": "I surely can introduce myself! My name is Leo Lani, "
" which means 'Voice of an Angel' in Hawaiian. "
"I am a social robot and I learn from conversations with humans!",
# Technology
"Do you have a brain?": "Haha, no! My brain is located on the laptop of my programmers "
"and part of it is even in the cloud. So modern!",
"programming language?": "I'm mostly programmed in Python, but also some C++ and possibly other languages!",
"Do you need internet?": "I do need internet, for understanding speech and looking up facts about the world!",
"Speech Recognition": "First I listen for an utterance, "
"I send that to Google, which gives me back a bunch of hypotheses about what you just said. "
"From those hypotheses I try to make sense what you mean. And all of this, hopefully, within a second!",
"Face Recognition": "When I see a face with my eyes, I use OpenFace to encode it to a 128 dimensional vector. "
"I compare this with the faces of the people I've already met to recognize you!",
"Object Recognition": "I use a deep neural network trained on the COCO dataset, "
"which tells me which objects there are in a scene and where they are!",
"COCO Dataset?": "COCO stands for 'Common Objects in Context' and is a database with hundreds of thousands of images of 90 objects! "
"I use a neural network that was trained on these images, so that I can also recognize them",
}
[docs] def query(self, query):
"""
Parameters
----------
query: str
Question to Ask
Returns
-------
answer: str
Answer, if any, else None
"""
ratio = 0
answer = None
# Fuzzily try to find best matching query
for Q, A in self.QNA_STATIC.items():
r = fuzz.partial_ratio(query, Q)
if r > ratio:
answer = A
ratio = r
for Q, A in self.QNA_DYNAMIC.items():
r = fuzz.partial_ratio(query, Q)
if r > ratio:
answer = A()
ratio = r
if ratio > 90:
return float(ratio)/100, answer


DOJ sends letter to publisher of book by ‘Anonymous’
NEW YORK (AP) - The Justice Department has sent a letter to the publisher and literary agency of the anonymous government official whose book is scheduled to come out later this month. The letter raises questions over whether any confidentiality agreement has been violated.
The publisher, Hachette Book Group, responded Monday by saying no such agreements were broken.
The book, “A Warning,” is by the official who wrote an essay published last year in The New York Times, alleging that numerous people in the government were resisting the “misguided impulses” of President Donald Trump. News of the Justice Department letter was first reported by CNN.
In 2018, a Trump lawyer sent a cease-and-desist letter to the publisher of Michael Wolff’s “Fire and Fury.” The letter was ignored, and the book became a million seller.
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Copyright © 2019 The Washington Times, LLC.
Please read our comment policy before commenting.


 Comparing to other geospatial data, raster maps are easily accessible and provide geographic features that are difficult to find elsewhere, such as landmarks in historical maps. For example, the tourist map found using an image search engine on the Internet shown in Figure 1(a) contains location information such as the gas stations, hotels, and road names of Tehran, Iran, while the hybrid view from Google Map shown in Figure 1(b) only shows major roads and their labels. |
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(a) A tourist map found on the Internet |
(b) The hybrid view of Tehran from Google Maps |
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Figure 1: The tourist map contains rich information that is difficult to find elsewhere for the city of Tehran, Iran |
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We can exploit the geographic features in raster maps (e.g., roads, text labels, etc.) to provide additional knowledge for viewing and
understanding other geospatial data. For example:
- Figure 2(a) shows that by aligning the tourist map to the imagery, we can create an integrated representation of the geospatial datasets
- Figure 2(b) shows that we can align the text layer from a raster map to imagery for annotating the imagery objects
- Figure 2(c) shows that we can extract and align road-intersection templates to imagery and use the templates as seed points to extract roads from the imagery for the areas where we have limited access to the vector data
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(a) Fusing a tourist map with imagery |
(c) Extract roads with road-intersection templates |
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(b) Labeling roads in imagery with the text layer from a map |
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Figure 2: Exploiting information in raster maps for imagery understanding |
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Harvesting the geographic features in raster maps is a challenging task because of:
- The varying image quality (e.g., scanned maps with poor image quality and digital generated map with good
image quality)
- The complexity of maps (i.e., overlapping features in maps)
- The typical lack of metadata (e.g., map geocoordinates, map source, original vector data, etc.)
To overcome these difficulties, this project investigates a general approach to extract feature layers from raster maps and recognize geographic features from the extracted layers. Our approach is able to process raster maps with varying map complexity and image quality (such as the examples shown in Figure 3) and does not rely on auxiliary information of the raster maps. |
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We developed two map decomposition techniques, each requiring a different amount of user input to first decompose raster maps with varying image quality into individual layers of geographic features.
- For raster maps with good image quality, we developed a fully automatic technique that exploit the distinctive geometry of
the desired geographic features (e.g., road lines) to decompose raster maps into feature layers, namely the road layer
and the text layer [Chiang et al., 2005, 2008]
- For raster maps with poor image quality (such as scanned and compressed maps that are otherwise difficult to process automatically and tedious to process manually), we developed a pixel-based supervised technique and a color-based supervised technique including user labeling to extract the road layers from raster maps [Chiang and Knoblock, 2006, 2009c]
- We are currently investigating user-labeling techniques to extract the text layers from raster maps (A preliminary work can be found here [Chiang and Knoblock, 2006])
- In addition, to minimize repetitive manual work in the supervised technique, we plan to include a map classification technique that automatically selects a trained map profile (i.e., the training results of the supervised technique) to apply on previous unseen maps for extracting their feature layers (A preliminary work can be found here [Chiang and Knoblock, 2009a])
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We also developed techniques to convert the feature layers into machine-editable map context, such as extracting the road-intersection templates and road vector data from a road layer [Chiang and Knoblock, 2008, 2009b; Chiang et al., 2005, 2008]. For the text layer, we plan to develop an automatic technique that identifies strings in the text layer and employs an optical character recognition (OCR) component to translate the text strings into machine-editable text. |
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Since roads are a common geographic feature that exists across many geospatial data, the recognized road vectors can be used as a matching feature to align the raster map, the extracted feature layers, and the recognized features to other geospatial data that contain roads. We developed conflation techniques that use the set of road-intersection templates of a raster map as a reference feature to compute a transformation matrix for aligning the map with other geospatial data, such as imagery [Chen et al., 2006, 2008]. |
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(a) A TIGER/Line map |
(b) A USGS topographic map |
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(c) A scanned Thomas-Guide map |
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Figure 3: Example Raster maps |
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Woman critical after near drowning on Gold Coast
by Shanee DobesonA WOMAN is fighting for her life after nearly drowning in a pool on the Gold Coast.
Emergency crews rushed to a home in Merrimac just before 9.00pm last night, after receiving reports a woman had come into trouble while swimming.
The woman, aged in her 50s, was treated at the scene by Critical Care Paramedics.
She was then rushed to the Gold Coast University Hospital in a critical condition.
A Queensland Ambulance spokesperson said the High Acuity Response Unit was also on board for transport.


Hi, I'm Jeremy, a developer based in San Francisco. I enjoy building beautiful interfaces, taking on new challenges, and designing full apps from scratch. Currently, I'm working on personal projects while seeking full-time opportunities. I also really like writing and am training for my next marathon in my free time.
Coming soon!
I enjoy writing and believe it helps solidify learning in addition to helping spread knowledge. I am in the process of designing a new blog, which I will link to and begin filling up as soon as it's finished!
$ ls
$ ls -a
.new-talent
$ more .new-talent
THIS GUY
:)
© Jeremy McIntyre 2015


The Tall Poppy Syndrome
What is the tall poppy syndrome?
I’m yet to meet a fellow Aussie who would trade our sun-burnt land for the stars’n’stripes.
Don’t get me wrong, we love yank culture. But we also like the off switch on the remote.
We value larrikans over wankers, and well, America seems to have too many of the latter and not enough of the former. If one of us gets too big for their boots, we cut them down to size.
And it ain’t pretty. It is, in some ways, the precursor to woke culture.

And to understand us, you’ll need to know the backstory of why we love larrikans and hate wankers.
Here’s why:
The vast majority of Australians (81%) live on the eastern seaboard. If is as if we are girt by sea (an actual line from our cringy national anthem - look it up if you don’t believe me) and that girtness ties us to the sea breeze. If we can’t smell the salt in our nostrils, it is as if our budgie smugglers will shrivel up.
There is a good reason we are stuck to the coast. A great huge mountain range locks us in.
It’s not as if, since 1788, we haven’t figured a way over the Great Dividing Ranges that stretches 3,500km, from Far North Queensland to the Grampians in western Victoria. We have. It’s just that there is nothing but the desert. On one side live 21 million people, and on the other side is the Outback.
Because we spend our time surfing or cuddling koalas, we tend to be a laid back bunch. We’re united in our love of sport. Or so we tell ourselves. We love larrikins (rowdy but good-hearted).
Think Crocodile Dundee, Steve Irwin and Shane Warne. Hugh Jackman is a good bloke. Russell Crowe can be a bit of a wanker. And when he is, we just say he’s really from New Zealand.
In Australia, we cut down people who believe in their own success. If there isn’t a degree of self-deprecating humility, the public will mock them. Ruthlessly.
It is called the Tall Poppy Syndrome and has been around since at least 1871. The term refers to the notion that poppies should all grow to the same height. If one grows taller than the others, simply cut it down to size.
How it manifests in everyday life is pretty simple. If an ordinary car wanted to merge into your lane, no worries.  You think you’re better than me?
To understand why Australians are like this, it is helpful to look at four key historical moments in our history.
Part 1: Eureka Rebellion - 1854
Gold was first discovered in 1851. Just as in California, the rush was on to find that nugget that was going to change your life. Tent cities popped up where ever gold was found, mostly east of the Great Dividing Range.
The Colonial offices, in an effort to maintain law and order, forced the ‘diggers’ to pay exorbitant licence fees just for the price to search for gold.
Talk began to circulate that what had happened in American and France, could happen here. An Australian Revolution.
The British had learnt their lesson from the American War of Independence. They were not going to let this potential uprising get too far.
The small band of revolutionaries created a barricade, wanting to rally support to their cause. They created a flag that would represent the new nation, where every man was equal.
With the Eureka Stockade fortified, the newly formed army drank the Saturday night under the stars of the Southern Cross, safe in the knowledge that nothing would occur on a Sunday.
At 3 am, 276 soldiers stormed the stockade.
The battle lasted 20 minutes. 22 diggers were killed. 114 were taken into custody.
It was the first and only non-indigenous attempt at a revolution. It failed before it began. There is a reverence around the Eureka Rebellion despite the fact that it was an overwhelming failure.
Part 2: Burke and Wills - 1861
During the late 1850s, flush with money from the Gold Rush, there was much excitement about what lay beyond the Great Dividing Range. There was talk of an inland sea and land as fertile as that found in the mid-west of the United States.
Added to the allure of having the telegraph line, the Colonies of South Australia and Victoria mounted expeditions to cross the continent from south to north. The Victorians had imported camels from the sub-continent for the dash across the desert. They also took a boat, just in case.
Victoria’s expedition was lead by the grossly incompetent, but handsome, Robert O’Hara Burke. There is not a single decision he made that can be counted as good. He split his party three times, each time halving it. He left the main depot with only three other men. Of the men he could have chosen, he did not take the last remaining ‘sepoy’ who knew how to handle the camels. Instead, he took the translator.
And instead of taking the experienced soldier, or the blacksmith, Burke took the oldest man in the party whose only experience had been as a sailor.
He left the soldier in charge of the depot with clear instructions to wait 3 months. Wills, the second-in-command, left instructions for them to wait 4 months.
Burke and his men never actually made it to the coast. The impenetrable mangroves prevented them from seeing the ocean. They claim to have seen evidence of tidal markings.
On the agonising journey south, with their supplies running low, and water almost impossible to find, progress was slow. On the 17th April 1861, just days from reaching the depot, the sailor, Charley Gray, died.
The remaining three spent the day burying Gray.
Four months and one week after Burke had left, Brahe decided it was time to leave. He was running low on supplies, and one of his men was seriously ill. They left on the morning of April 21st, 1861.
Brahe buried what supplies he could, just in case Burke was to ever return.
Burke, Wills and John King arrived back at camp exhausted late in the afternoon of the 21st April 1861. The ashes from Brahe’s fire were still warm.
Instead of following Brahe, Burke decided to head further west, following the creek. No one had explored this part of the country.
Burke and Wills would both be dead by the end of June. The Yandruwandha people have lived in this area for over 40,000 years and Burke couldn’t survive until a rescue party arrived in September.
John King survived by living with the local indigenous people.
The legacy of Burke and Wills is celebrated despite the fact that they did not achieve their primary aim and that they could not survive in what is no doubt a harsh country but one where people have thrived for millennia.
Part 3: Ned Kelly - 1880
Ned Kelly was the third of eight children to freed convict John and Ellen Kelly. When Ned was twelve years old, his father died after serving a six-month prison sentence. The family saw themselves as persecuted by the Victorian Police.
Kelly was first arrested as a teenager for associating with a known bushranger and then served two terms in prison before he’d turned twenty. After a violent altercation with a local policeman who had an interest in Ned’s sister, he was forced to flee once it became known that he’d be charged with attempted murder. They camped in and amongst the bush of the Great Dividing Range.
Ellen Kelly was imprisoned for her role in the incident and Ned vowed to avenge the wrongs brought on the family.
Ned, along with his younger brother Dan and two associates, shot dead three policemen. The Victorian Government declared them outlaws, however, the Kelly Gant managed to evade capture for over two years largely due to a network of sympathisers. To help fund the network, the Kelly Gang robbed banks in the towns of Euroa and Jerilderie. In order to avoid getting shot, the gang fashioned armour for themselves out of metal.
Their fame spread all the way to London.
Ned killed a police informer and anticipating that a special police train would be sent from Melbourne, the gang planned on sabotaging the railway south of the town of Glenrowan. With the train derailed, the gang was going to shoot any survivors before heading north to the larger town of Benalla.
There they would rob the banks, set fire to the courthouse and police barracks and set anyone in the prison free.
They picked a curve in the train line that ran across a ravine to damage the railway tracks.
To prevent anyone from informing on their plans, the gang held the entire town of 62 people in the pub. Drinks and games were had and songs were sung. One hostage, testifying later, said that Ned “did not treat us badly.”
At around 10 pm, schoolmaster Thomas Curnow convinced Ned to let him and his wife go home to bed.
At 3 am, Curnow heard the arriving train and warned the driver of the damage to the track.
The police arrived at the pub to find a suited up Ned Kelly standing on the verandah. The guns roared till daybreak.
Ned had managed to escape out the back door and then fired at the police from the rear. He collapsed after two shots pierced the gap in the armour between his hip and thigh. He was captured.
At 10 am, 30 hostages fled the pub, leaving the remaining gang inside. Rather than storm the pub, police set fire to it.
Ned Kelly was tried and convicted to be hung. His reported last words have become immortal in Australian culture: “Such is life.”
Ned’s armour showing a total of 18 bullet marks is still on display at the State Library of Victoria.
He remains a national hero, a symbol of defiance and of the working class despite the fact that he was a cold-blooded murderer and a failed usurper of justice.
Part 4: Gallipoli - 1915
The above stories reflect our ability to celebrate only those who don’t rise way above the rest of us. What units the above stories is simple.  Fine, it didn’t work out for them, but that is what it means to be Australian.
We know we’ll never conquer this land. Between droughts, fires and floods, we were buggered before we even began.
To truly illustrate this point, let’s look at the First World War. Ask any Aussie what battle resonates in the Australian consciousness. The answer will be simply Gallipoli.
Our memorial day, known as ANZAC Day, is the anniversary of the landing of Australian and New Zealand troops onto the beaches. Instead, Diggers pinned down by sniper fire from impenetrable cliffs.
The aim was simple. Overrun the Ottoman forces. For the 25,000 Australian and New Zealand forces, the only issue was that the British had sent them to the wrong part of the Peninsula.
What faced them was an impenetrable cliff where Ottoman forces could shoot them at will.
Australia suffered 8,709 dead, 19,441 wounded (total death toll for Allies = 130,842)
We celebrate, to this day, our retreat. What other nation does that? It wasn’t as if Australian forces weren’t capable of winning a battle. The Battle of Beersheba (1917) is known as the last great cavalry charge. The 4th Australian Light Horse attacked, even though they were not equipped for such a charge. By nightfall, all the critical wells were captured. Within a week, Jerusalem fell. It was a critical battle during the war.
And yet, to this day, most Australians don’t know or don’t care about it.
The Western Front and Gallipoli would be the sum total of our knowledge of Australian involvement during the war.
Why?
Tall poppies don’t fit into our culture.
This may seem bleak, but really it is good. We have a fairly egalitarian society as a result. Ideas of universal health care or welfare aren’t a matter of debate. As long as you don’t grow too tall, we’ll look after you if you fall. Such is life.
Waltzing Matilda
When Australian Prime Minister, Scott Morrison, visited the White House in 2019, the encore performance was of Waltzing Matilda. This is a bush ballad written by Australian poet Banjo Patterson in 1895 and is commonly referred to as our unofficial national anthem. It is a poem/song about an unemployed worker, or swagman, who steals a sheep because he is hungry. Police catch him with the jumbuck in his swag, and rather than be taken by authorities, he commits suicide by jumping into the billabong.
We are a nation of anti-authoritarian larrikins who wilfully adhere to the rules such as compulsory voting.
Photo by Clarinta Subrata on Unsplash


abstract val outputRegisterIndices: List<RegisterIndex>
The indices of register(s) that the program uses as output.


Home
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An example blog template built with Bootstrap.
Sample blog post
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Create a plex account through plex.tv. There are also options sign up using your Google or Facebook account. (You don't need to buy a Plex Pass)
Go to your profile and send either your username or email to me
Wait for me to add you as a friend. You should receive an email, follow the directions in the email and accept the invitation.
You're all set you can watch movies through the web player at plex.tv or use Xbox, Smart TV, etc. (iOS requires a $5 payment). Feel free to make requests for new content through [YOUR REQUEST SERVICE HERE], you can use your plex username.


Resume
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Work Experience
Work stuff blah blah blah
Volunteer Experience
Volunteer stuff blah blah blah


{% extends "base.html" %} {% block title %}Report{% endblock %} {% block head %} {{ super() }} {% endblock %} {% block content %}
You can generate a detailed report on the data matching a set of filters from the visualization.
These reports are stored permanently, and each one is assigned a universally unique identifier (UUID).
The reports can be accessed later if the UUID, or the complete URL, is known, so be sure to save either of those.
The URL to a report can also be interpolated from its UUID.
For instance, a hypothetical report on this website with the UUID 01234567-0123-4567-8901-2345-012345678901
would have the URL
{{ url_for('reporting.get_report', report_id='01234567-0123-4567-8901-2345-012345678901', _external=True) }}
If you know the UUID to an existing report, you can paste it in the field below, and then click Go to report
to access it directly:
Upload a visualization state file to generate a report.
Alternatively, you can generate the report from inside the visualization with its current state by clicking on the Generate report
button in the settings pane.


Database
public class Database
Database
-
Database flags
See moreDeclaration
Swift
public struct Flags: OptionSet
-
Flags to specify
See moremdb_put
behaviourDeclaration
Swift
public struct PutFlags: OptionSet
-
Initialise a database
Throws
Operation error. SeeDBError
.Declaration
Swift
public init(name: String?, environment: Environment, flags: Flags = []) throws
Parameters
name
Name of the database
environment
Environment containing the database
flags
Optional flagss
-
Close the database handle. Refer to
mdb_dbi_close
for usage.Declaration
Swift
public func close()
-
Sync any in-memory data to the disk. Refer to
mdb_env_sync
for usage.Throws
Operation error. SeeDBError
.Declaration
Swift
public func sync() throws
-
Drop/delete the database
Throws
Operation error. SeeDBError
.Declaration
Swift
public func drop() throws
-
Empty the database
Throws
Operation error. SeeDBError
.Declaration
Swift
public func empty() throws
-
Put given key/value pair into the database
Throws
Operation error. SeeDBError
.Parameters
key
Key to lookup the database with
value
Raw data value to save. If no data is given, an empty key/value paid will be saved.
flags
Optional flags to execute the
mdb_put
method with
-
Create a database within given environment
Throws
Operation error. SeeDBError
.Declaration
Swift
public class func create(name: String, environment: Environment, flags: Flags = []) throws -> Database
Parameters
name
Name of the database
environment
Environment to contain the database
flags
Database flags
Return Value
Newly created Database


Audio and video files are loaded in the same way, using the pyglet.media.load function, providing a filename:
source = pyglet.media.load('explosion.wav')
If the media file is bundled with the application, consider using the resource module (see Application resources).
The result of loading a media file is a Source object. This object provides useful information about the type of media encoded in the file, and serves as an opaque object used for playing back the file (described in the next section).
The load function will raise a MediaException if the format is unknown. IOError may also be raised if the file could not be read from disk. Future versions of pyglet will also support reading from arbitrary file-like objects, however a valid filename must currently be given.
The length of the media file is given by the duration property, which returns the media's length in seconds.
Audio metadata is provided in the source's audio_format attribute, which is None for silent videos. This metadata is not generally useful to applications. See the AudioFormat class documentation for details.
Video metadata is provided in the source's video_format attribute, which is None for audio files. It is recommended that this attribute is checked before attempting play back a video file -- if a movie file has a readable audio track but unknown video format it will appear as an audio file.
You can use the video metadata, described in a VideoFormat object, to set up display of the video before beginning playback. The attributes are as follows:
Attribute Description width, height Width and height of the video image, in pixels. sample_aspect The aspect ratio of each video pixel.
You must take care to apply the sample aspect ratio to the video image size for display purposes. The following code determines the display size for a given video format:
def get_video_size(width, height, sample_aspect): if sample_aspect > 1.: return width * sample_aspect, height elif sample_aspect < 1.: return width, height / sample_aspect else: return width, height
Media files are not normally read entirely from disk; instead, they are streamed into the decoder, and then into the audio buffers and video memory only when needed. This reduces the startup time of loading a file and reduces the memory requirements of the application.
However, there are times when it is desirable to completely decode an audio file in memory first. For example, a sound that will be played many times (such as a bullet or explosion) should only be decoded once. You can instruct pyglet to completely decode an audio file into memory at load time:
explosion = pyglet.media.load('explosion.wav', streaming=False)
The resulting source is an instance of StaticSource, which provides the same interface as a streaming source. You can also construct a StaticSource directly from an already-loaded Source:
explosion = pyglet.media.StaticSource(pyglet.media.load('explosion.wav'))


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Published on Aug 3, 2016
Rossmann Repair Group Inc is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to https://www.amazon.com/?&_encoding=UT...
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I was a US Naval officer (nuclear power - 10 years) and high school teacher (physics - 10 years). I am currently a CS student at SFSU with an interest in AI and starting my own company.


{% extends "layout.html" %} {% block page_title %} Money Claims Prototype {% endblock %} {% block proposition_header %} {% include "includes/propositional_navigation.html" %} {% endblock %} {% block content %} {% include "includes/phase_banner_beta.html" %} Back Propose an expert The court has agreed that you and the {% if data['party'] == 'defendant' %}claimant{% else %}defendant{% endif %} can share one expert. You need to propose 3 independent experts and the {% if data['party'] == 'defendant' %}claimant{% else %}defendant{% endif %} will pick one. How to use the expert Both you and the {% if data['party'] == 'defendant' %}claimant{% else %}defendant{% endif %} can ask questions. The expert will answer these in a report. The judge can use this to make a ruling. Who pays for the expert You and the {% if data['party'] == 'defendant' %}claimant{% else %}defendant{% endif %} share the cost of hiring the expert. If you win the case you may be able to recover up to £750 of this cost. A judge will rule on this at the end of the hearing. First expert Their name Their area of expertise Cost Second expert Their name Their area of expertise Cost Third expert Their name Their area of expertise Cost {% include 'prototypes/prototype-jan-2019/includes/contact.html' %} {% endblock %}
The court has agreed that you and the {% if data['party'] == 'defendant' %}claimant{% else %}defendant{% endif %} can share one expert.
You need to propose 3 independent experts and the {% if data['party'] == 'defendant' %}claimant{% else %}defendant{% endif %} will pick one.
Both you and the {% if data['party'] == 'defendant' %}claimant{% else %}defendant{% endif %} can ask questions. The expert will answer these in a report. The judge can use this to make a ruling.
You and the {% if data['party'] == 'defendant' %}claimant{% else %}defendant{% endif %} share the cost of hiring the expert.
If you win the case you may be able to recover up to £750 of this cost. A judge will rule on this at the end of the hearing.


--- layout: layout title: "About" ---
I'm a {{ site.time | date: "%s" | minus: 658800000 | divided_by: 31557600 }} years old from Belgium currently working as a consultant at Adneom. In 2014, I got a Master Degree in Computer Science ("La plus grande distinction") at the Université Libre de Bruxelles. In my spare time I like to perform or watch improv shows at the FBIA (Belgian Federation for Hobbyists Improvisers), program games in obscure languages or watch the latest Game of Thrones with my friends.


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Unrefined Water Qi: 0/90 (+.8)
Take in the Qi that surrounds you, or that comes from spiritual objects.
Efficiency ( * 1/(1+impurities)): 90%
Every 90 Unrefined Qi, refinement rate doubles but efficiency halves.
Refined Water Qi: 0/1000 (+5)
Processed Qi that's been attuned to your soul.
Unprocessed Qi becomes impurities.
Every 1000 Refined Qi, refinement is 25% slower
Qi Gain is reduced by purity, having too much Refined Qi, or having too much Unrefined Qi.
Mortal Water Dao
Unrefined Qi: 1/s (100)
Refined Qi: 5/s (1000)
Purity: 80%


Before Change
" Input is a {}x{} matrix."
.format(X.shape[0], X.shape[1]))
if sample_weight.shape[0] > 0 and sample_weight.shape[0] != X.shape[0]:
raise ValueError("sample_weight and X have incompatible shapes: "
"%r vs %r\n"
"Note: Sparse matrices cannot be indexed w/"
After Change
"boolean masks (use `indices=True` in CV)."
% (sample_weight.shape, X.shape))
kernel = "precomputed" if callable(self.kernel) else self.kernel
if kernel == "precomputed":
// unused but needs to be a float for cython code that ignores
// it anyway
self._gamma = 0.
elif isinstance(self.gamma, str):
if self.gamma == "scale":
// var = E[X^2] - E[X]^2 if sparse
X_var = ((X.multiply(X)).mean() - (X.mean()) ** 2


--- layout: page title: Menu level 1 permalink: /menu-levels/menu-level-1/ group: menu-levels ---
This is an example for a menu with levels


jQuery Conditioner
Basic, inline options
Text
I will be shown only if the text typed is "hello world"
Match pattern
A valid email is is typed
Radio
I will appear only when orange is selected
Checkbox
Animal is checked
Zebra is unchecked
Select
Qwerty is selected
Relative input selected via inline
Text
I am selected by input relatively via inline options
Options set externally
This is element 6
Multiple conditions
Thanks.. you can finally
One input and multiple elements
HI !!!!
Hello !!!
End of demo


The checks in this package are done for a given pedigree given by ps_pedigree_path. The results of the checks are summarised in a report which is generated by this function.
create_pedigree_report( ps_pedigree_path, ps_report_outdir = ".", ps_report_rmd = paste0(format(Sys.time(), "%Y%m%d%H%M%S"), "_", fs::path_ext_remove(basename(ps_pedigree_path)), "_qpdt_pedigree_report.Rmd", collapse = ""), pl_params = NULL )
| ps_pedigree_path | path to the pedigree file |
|---|---|
| ps_report_outdir | report output directory |
| ps_report_rmd | name of the pedigree report file |
| pl_params | list of parameters to be included in the report |
if (FALSE) { s_pedigree_path <- file.path(here::here(), 'inst', 'extdata', 'PopReport_OST_mit_20210318.csv_adaptfin5.csv') create_pedigree_report(ps_pedigree_path = system.file('extdata', 'PopReport_SN_ohne_20210115.csv_adaptfin2.csv',package = 'qpdt'), ps_report_outdir = file.path(here::here(), 'scratch', 'report_out')) }


Each summary paragraph has six different versions, and the mixture of those paragraphs is unique to each numbered copy of the paper. There are over a thousand possible permutations, but only ninety-six numbered copies of the actual document. The reason the summary paragraphs are so lurid is to entice a reporter to quote them verbatim in the public media. If he quotes something from two or three of those paragraphs, we know which copy he saw and, therefore, who leaked it.


Packing Steps of MyBox
For Windows
On Windows:
- Make sure jdk and maven installed.
- Download source codes of MyBox.
- Unpack it under a path which your account can read and write.
- Modify file "MyBox/pack/win/make-win-exe.bat" and set following parameters as your env: jpackagePath, jdkPath
- Double-click to execute the script "MyBox/pack/win/make-win-exe.bat".
- Generated launcher is "MyBox/pack/win/app/MyBox/MyBox.exe".
For Linux
On Linux:
- Make sure jdk and maven installed.
- Download source codes of MyBox.
- Unpack it under a path which your account can read and write.
- Modify file "MyBox/pack/linux/make-linux-bin.sh" and set following parameters as your env: jpackagePath, jdkPath
- Open a command terminal window, and enter the path of source codes of MyBox: "cd MyBox/pack/linux/"
- Execute: "chmod 755 make-linux-bin.sh"
- Execute: "./make-linux-bin.sh"
- Generated launcher is "MyBox/pack/linux/app/MyBox/bin/MyBox".
For Mac
On Mac:
- Make sure jdk and maven installed.
- Download source codes of MyBox.
- Unpack it under a path which your account can read and write.
- Modify file "MyBox/pack/mac/make-mac-dmg.sh" and set following parameters as your env: jpackagePath, jdkPath
- Open a command terminal window, and enter the path of source codes of MyBox: "cd MyBox/pack/mac/"
- Execute: "chmod 755 make-mac-dmg.sh"
- Execute: "./make-mac-dmg.sh"
- Generated launcher is "MyBox/pack/mac/app/MyBox-$version.dmg".


Tracy Traque's Detail Collection System
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Slab is a story website. We find them, you read them. Using our Google extension, we will track the pages you go to, and the time you spend on each page. (We don't keep your info, I promise) Then, our AI figures out what else you will like, based on the info we gathered.
After Slab finds out what you like, we will find article that you will be interested by and stream them to the Slab website. You can read a short summary on the slab website, or click on the summary to go the page.


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{% extends "base.html" %} {% block content %}
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Unicode Text Rendering Reference System
Unicode Text Rendering Reference System (UTRRS) is a open source web-based application to compare a rendered text character, rendered by a text rendering engine with reference image of a text character. Differences between the rendered image and reference image may be compared for the subsequent analysis.
Performance of a text rendering engine producing text according to typographical rules applicable to a natural language can be evaluated by one with no knowledge or ability to read the natural language. This Application Test for the Codepoints (Unicode Character set), GSUB(Glyph substitution) and GPOS (Glyph Positioning).
For the Indian Language present here, this system has been developed according to the IDN Script Grammar rules published by Govt. of India.
Languages Included :- Assamese
- Bengali (India)
- German
- Gujarati
- Hindi
- Kannada
- Maithili
- Malayalam
- Marathi
- Odia
- Punjabi
- Tamil
- Telugu
UTRRS Tool has been developed by Red Hat Language Technology Group. It is vastly used in various internal projects, localization support, Desktop Rendering, etc. in Red Hat.
UTRRS has been released by Red Hat, under open source MIT license in the year of 2010.
Usage :- UTRRS provides the service to validate the standard shape (Rendering) of character set (Unicode 5.1 Standard) or composed characters (Syllable) produced in any system.
- This application can be used anywhere as a character archives for any Indian languages mentioned above, so far.
- UTRRS has been designed to facilitate the multilingual community users, print media, font developers & vendors, localized desktop providers, tourism industry, academic organizations and communities etc. in terms of providing them a standard ready reference for Indian languages.