📘 Logistics Glossary | #LearnwithGenex Line Item Fill Rate: the % of individual order items fulfilled from stock on the first shipment. Measured at the SKU level, it's a precise gauge of inventory health and a key driver of on-time delivery & customer satisfaction. . #FillRate

Once 1440p 240 Hz AMOLED smartphones come out, I will press "buy" instantly just to confirm that 100% duty cycle/brightness setting 50% strobing via alternate eye rendering (manual / software BFI basically), would double the GPU fillrate perf achieve 2ms MPRT = OK, blur-wise.

Fill rate is more than a metric—it’s a revenue driver. Learn more: delta-driver.com #FillRate #SupplyChain #RevenueGrowth #Manufacturing #DeltaDriver

🚀 Trading Bot Performance Isn't Just About Finding Edge A trading bot can identify great opportunities, but if it fails to get orders filled consistently, much of that edge disappears. That's why I've been investing time into fill rate optimization: ✅ Faster order handling ✅ Improved execution logic ✅ Better market participation ✅ More efficient capital deployment Small execution improvements compound over thousands of trades and can have a meaningful impact on long-term performance. The best trading systems don't just predict well—they execute well. t.me/sei_dev #Polymarket #TradingBot #CryptoTrading #AlgorithmicTrading #FillRate #Automation

💬 Interested in discussing fill rate optimization and execution performance? I've spent a significant amount of time researching and improving fill rates for automated trading systems, and I'm always interested in exchanging ideas with traders, developers, and quantitative researchers. Topics I'm particularly interested in: ⚙️ Order execution optimization 📈 Fill rate improvement strategies 🤖 Trading bot infrastructure 🎯 Market-making and liquidity provision 📊 Algorithmic trading performance Feel free to reach out and connect: 📩 Telegram: t.me/sei_dev Looking forward to discussing ideas, challenges, and best practices with the community. #Polymarket #TradingBot #AlgorithmicTrading #Execution #FillRate #PredictionMarkets #QuantTrading #Automation

🔥 Bot Update Complete! ⚙️ Upgraded my Polymarket trading bot to significantly improve fill rates and execution efficiency. 🚀 Restarted the system and it's already generating strong results. 📈 Better fills. ⚡ Faster execution. 🎯 More opportunities captured. The edge isn't luck—it's constant optimization. 🔗 Track performance live: polymarket.com/@sei-ddev #Polymarket #PredictionMarkets #TradingBot #CryptoTrading #DeFi #AlgorithmicTrading #Automation #MarketMaking #FillRate #Profit

🚀 Bot Update Complete! ⚙️ Just upgraded my Polymarket trading bot to improve fill rates and optimize execution. 🔄 Deployment finished and the bot is already back in action, delivering solid profits. 📈 💡 In trading, consistency and continuous iteration create the edge. 📊 Live performance: polymarket.com/@sei-ddev 🤖 Faster fills ⚡ Better execution 📈 Stronger performance #Polymarket #TradingBot #CryptoTrading #DeFi #PredictionMarkets #AlgorithmicTrading #Automation #Trading #BotUpdate #FillRate

PS2 has INSANE fillrate. You could brute force practically everything, effects-wise. Granted most of those effects ran at half or even quarter resolution. (which wasn't an issue in the CRT days)

Because the insane fillrate of the PS2. Would require too much to get the transparency effects on contemporary hardware without destroying the performance. That’s why you see meshes as transparencies up close on modern hw instead ps2 ate that for breakfast.

⚙️ Game Dev Hack: Transparent materials killing GPU fillrate? Use a Dither node Alpha Clip in Shader Graph instead! You get a faux-fade that writes to the depth buffer & avoids costly overdraw. 📉✨ #UnityTips #TechArt #IndieDev

Totally agree on Alpha to Coverage! we actually switched all vegetation (trees, bushes, flowers) from transparent: true to transparent: false with discard in the fragment shader exactly for this reason. Skipped the back-to-front sort cost and got early-Z back for free. The remaining alpha blending is on the dust particle system(billboarded quads that genuinely need soft edges, so A2C alone won't cut it there). That's where the pixel budget cap mattered most. Mobile is a whole other conversation 😅 fillrate pain hits different there.

Blending high DPI is definitely a performance eater. On mobile GPU's especially. Also, you could use masking (discard) instead of alpha blending, with MSAA Alpha to Coverage. So it still slightly better on fillrate than pure blending, especially that you don't need to sort them like with alpha blending. Quality might not be satisfiable though.

Great point! at native high DPI, MSAA is largely redundant since you already get natural supersampling from the pixel density. Disabling it there is a clean win. In this case the bottleneck was fillrate, alpha-card trees, instanced dust particles, transparent vegetation. so fewer pixels was the faster path. Both approaches are worth knowing. Depends on what's actually eating your frame budget.

Or, if you render with high DPI at native resolution, you can disable MSAA, this will also increase performance, while keeping image sharp. If the bottleneck is fillrate, then reducing resolution - is the way.

What is interesting though is the details they decided to store on a person ( despite it being a mock-up with fantasy names ): /** * 单人全息档案系统 - 标签数据 v2.0 * 310项标签，8大维度，以单人为核心 */ // ==================== 完整标签体系 ==================== const PersonArchiveData = { // 基础信息 basic: { name: '张伟', formerNames: ['张强'], gender: '男', birthDate: '1988-03-15', age: 36, nation: '汉族', politicalStatus: '群众', bloodType: 'O型', height: 175, weight: 70, appearance: '方脸，短发，左臂有纹身', accent: '贵州口音', // 证件信息 idCard: '520102198803151234', idCardIssueDate: '2018-05-20', idCardExpireDate: '2038-05-20', idCardIssueOrg: '贵阳市公安局南明分局', passport: 'E12345678', passportType: '普通', passportIssueDate: '2019-03-10', passportExpireDate: '2029-03-09', hkMacauPermit: 'C12345678', taiwanPermit: 'T12345678', driverLicense: '520101198803151234', driverLicenseType: 'C1', driverLicenseIssueDate: '2010-08-15', birthCertificate: '', marriageCertificate: 'J520102-2015-001234', divorceCertificate: '', // 户籍居住 householdType: '居民户口', householdStatus: '正常', nativePlace: '贵州省贵阳市', birthPlace: '贵州省贵阳市', registeredAddress: '贵州省贵阳市南明区花果园大街1号', registeredProvince: '贵州省', registeredCity: '贵阳市', registeredDistrict: '南明区', registeredPoliceStation: '花果园派出所', currentAddress: '贵州省贵阳市云岩区北京路123号', currentProvince: '贵州省', currentCity: '贵阳市', currentDistrict: '云岩区', currentCommunity: '北京路社区', currentGrid: '第一网格', housingType: '租赁', residenceDuration: '2年', // 联系方式 mobilePhones: ['13800123456', '13900567890'], fixedPhone: '0851-12345678', emergencyContact: '李芳（配偶）', emergencyPhone: '13700111111', email: 'zhangwei1988@qq.com', qq: '123456789', wechat: 'zhangwei88', weibo: '', dingtalk: '', workPhone: '0851-87654321' }, // 社会属性 social: { // 职业信息 occupationCategory: '个体经营', occupationDetail: '建材批发', workUnit: '贵阳市南明区伟强建材经营部', workUnitType: '个体工商', workUnitCode: '92520102MA6JXXXXXX', department: '', position: '经营者', jobTitle: '', employmentDate: '2015-06-01', workYears: 9, monthlyIncome: 15000, annualIncome: 180000, incomeSource: ['经营收入', '投资收益'], workAddress: '贵州省贵阳市南明区花果园建材市场A区12号', workPhone: '0851-12345678', isKeyPosition: false, industry: '批发零售', // 教育背景 educationLevel: '高中', degree: '无', graduateSchool: '贵阳市第一中学', schoolType: '普通', major: '', enrollmentDate: '2003-09-01', graduationDate: '2006-06-30', studentId: '', academicPerformance: '一般', certificates: ['驾驶证C1', '营业执照'], // 家庭关系 maritalStatus: '已婚', marriageDate: '2015-05-01', spouseName: '李芳', spouseIdCard: '520102199005152345', spouseOccupation: '家庭主妇', childrenCount: 1, childrenInfo: [ { name: '张小明', gender: '男', birthDate: '2016-08-20', relation: '儿子' } ], fatherName: '张建国', fatherIdCard: '520102196001011111', motherName: '王秀英', motherIdCard: '520102196202022222', siblingsCount: 1, siblingsInfo: [ { name: '张强', relation: '弟弟', occupation: '公司职员' } ], familyStructure: '核心家庭', familyAssets: '一般', // 社交网络 socialCircles: ['生意伙伴', '老乡', '车友'], closeContacts: ['李强', '王鹏', '赵明'], friendCount: 328, communityParticipation: '低', religiousBelief: '无', politicalParticipation: '无', volunteerActivities: [], onlineCommunities: ['建材交流群', '贵阳车友会'] }, // 行为特征 behavior: { // 通信行为 callFrequency: '频繁', callDurationAvg: '3.5分钟', monthlyCallCount: 450, monthlyCallDuration: 1575, contactCount: 186, frequentContacts: ['李强(建材商)', '王鹏(客户)', '赵明(老乡)'], nightCallRatio: '15%', roamingFrequency: '偶尔', internationalCall: false, smsFrequency: '较少', // 消费行为 consumptionLevel: '品质型', monthlyConsumption: 8500, consumptionCategories: { 餐饮: 2000, 交通: 1500, 购物: 3000, 娱乐: 1000, 其他: 1000 }, paymentPreference: ['微信支付', '支付宝', '银行卡'], creditCards: 3, creditLimit: 150000, loanStatus: [ { type: '房贷', amount: 500000, status: '还款中' } ], shoppingPlatforms: ['淘宝', '京东', '拼多多'], gamblingTendency: '高风险', overseasConsumption: false, // 上网行为 onlineTimeDaily: '6小时', onlineTimeDistribution: { morning: '1小时', afternoon: '2小时', evening: '3小时' }, favoriteApps: ['微信', '抖音', '淘宝', '支付宝', '高德地图'], contentPreference: ['新闻', '娱乐', '汽车', '财经'], socialActivity: '高', gamingBehavior: '偶尔玩手游', liveStreaming: '偶尔观看', vpnUsage: '偶尔', darkWebAccess: false, onlineShopping: '频繁', // 出行行为 travelMode: ['私家车', '出租车'], hasVehicle: true, vehicleCount: 2, vehicleInfo: [ { plate: '贵A·12345', brand: '奥迪', model: 'A6L', color: '黑色' }, { plate: '贵A·67890', brand: '五菱', model: '宏光', color: '银色' } ], driverBehavior: '有超速记录', activityRadius: '市内', frequentRoutes: ['家-建材市场', '家-客户公司'], borderCrossing: '无', hotelFrequency: '偶尔', transportationPreference: ['自驾', '打车'] }, // 轨迹刻画 trajectory: { // 空间轨迹 frequentLocations: [ { name: '家', address: '云岩区北京路123号', type: '住宅', frequency: '每天' }, { name: '建材市场', address: '南明区花果园建材市场', type: '工作', frequency: '每天' }, { name: '客户公司', address: '观山湖区金融城', type: '业务', frequency: '每周3次' }, { name: '某酒店', address: '南明区中华南路', type: '娱乐', frequency: '每月2次' } ], homeLocation: { lat: 26.6470, lng: 106.6302 }, workLocation: { lat: 26.5732, lng: 106.7145 }, activityHotspots: [ { name: '花果园商圈', lat: 26.5732, lng: 106.7145, intensity: '高' }, { name: '北京路', lat: 26.6470, lng: 106.6302, intensity: '高' } ], sensitiveAreasVisited: [ { area: '边境地区', visitCount: 0 }, { area: '机场', visitCount: 5 } ], borderAreasVisited: [], placeTypes: ['住宅', '工作场所', '餐饮', '娱乐场所', '交通枢纽'], // 时间轨迹 dailyRoutine: { wakeUp: '07:00', leaveHome: '08:30', arriveWork: '09:00', lunch: '12:00', leaveWork: '18:00', arriveHome: '19:00', sleep: '23:30' }, wakeUpTime: '07:00', sleepTime: '23:30', workHours: { start: '09:00', end: '18:00' }, anomalyTimeActivity: [ { time: '02:00-04:00', activity: '通话', frequency: 3 } ], overnightFrequency: '偶尔', weekendActivity: '外出应酬', // 轨迹关联 companions: [ { name: '李强', relation: '生意伙伴', coincidenceCount: 45 }, { name: '王鹏', relation: '客户', coincidenceCount: 23 } ], spatiotemporalCoincidence: [ { location: '某酒店', time: '2024-02-15', persons: ['李强', '赵明'] } ], gatheringEvents: [ { location: '花果园酒楼', time: '2024-02-10', participants: 8, type: '聚餐' } ], meetingPatterns: '定期与固定人员会面', groupActivities: ['建材商会', '老乡聚会'] }, // 风险研判 risk: { // 人员类型 focusLevel: '重点人员', personCategory: ['涉毒人员', '前科人员'], criminalRecord: [ { caseType: '贩卖毒品', caseNo: '（2018）黔01刑初123号', sentence: '有期徒刑3年', prison: '贵州省第一监狱', entryDate: '2018-06-15', releaseDate: '2021-06-14', status: '已释放' } ], drugHistory: '吸毒史', drugDetail: { drugType: '冰毒', firstUseDate: '2017-03-01', treatmentHistory: ['强制戒毒2年'], currentStatus: '社区康复' }, terroristRelated: '无', gangRelated: '无', fraudHistory: [], wantedStatus: '无', controlStatus: '已布控', entryBan: false, // 行为预警 abnormalGathering: [ { date: '2024-02-10', location: '花果园酒楼', participants: 8, alertLevel: '中' } ], sensitiveAreaAlert: [], suddenWealth: '可疑', suddenWealthDetail: '近期账户有大额资金流入，来源不明', frequentBorderCrossing: '无', communicationAnomaly: ['夜间频繁通话', '与重点人员联系'], consumptionAnomaly: ['大额现金消费', '频繁出入娱乐场所'], travelAnomaly: ['频繁更换居住地点'], // 关联分析 caseInvolved: [ { caseNo: 'A5201022024020001', caseType: '毒品案件', role: '嫌疑人', date: '2024-02-01' } ], gangMembership: [], keyContacts: [ { name: '李强', relation: '同案人员', contactType: '频繁通话' }, { name: '赵明', relation: '涉毒人员', contactType: '资金往来' } ], moneyLaundering: '中风险', undergroundBank: false, illegalOrganization: [] }, // 感知数据 perception: { // 生物特征 faceFeature: 'FACE_3A7B9C2D4E5F6G7H8I9J0K1L2M3N4O5P', facePhoto: 'available', fingerprint: 'FP_5F6G7H8I9J0K1L2M3N4O5P6Q7R8S9T0', fingerprintCount: 10, dna: 'DNA_A1B2C3D4E5F6G7H8I9J0K1L2M3N4O5P6Q7R8', iris: '', voiceprint: 'VP_9J0K1L2M3N4O5P6Q7R8S9T0U1V2W3X4', gaitFeature: '', palmPrint: '', // 设备特征 macAddresses: ['00:1A:2B:3C:4D:5E', '00:6F:7G:8H:9I:0J'], imei: ['860000011234567', '860000098765432'], imsi: ['460001234567890', '460009876543210'], deviceFingerprint: ['FP_device_001', 'FP_device_002'], phoneBrand: 'iPhone', phoneModel: 'iPhone 14 Pro', osVersion: 'iOS 17.1', installedApps: ['微信', '支付宝', '抖音', '淘宝', '高德地图', '百度'], // 位置特征 cellTower: [ { lac: '12345', ci: '67890', time: '2024-02-22 10:30:00' } ], wifiFingerprint: [ { ssid: 'ChinaNet-5G', bssid: '00:1A:2B:3C:4D:5E', signal: '-65dBm' } ], gpsTrace: [ { lat: 26.6470, lng: 106.6302, time: '2024-02-22 10:30:00' } ], bluetoothId: [], nfcRecords: [] }, // 资产信息 asset: { // 不动产 propertyCount: 1, propertyInfo: [ { address: '贵州省贵阳市南明区花果园大街1号2栋3单元401', area: 89.5, type: '住宅', value: 850000, ownership: '共有（配偶）', mortgage: true } ], propertyValue: 850000, landOwnership: [], // 车辆资产 vehicleCount: 2, vehicleDetails: [ { plate: '贵A·12345', brand: '奥迪', model: 'A6L', year: 2020, value: 350000 }, { plate: '贵A·67890', brand: '五菱', model: '宏光', year: 2018, value: 50000 } ], vehicleValue: 400000, // 金融资产 bankAccounts: [ { bank: '工商银行', account: '6222********1234', balance: 50000 }, { bank: '建设银行', account: '6227********5678', balance: 120000 } ], deposits: 170000, investments: [ { type: '股票', value: 80000 }, { type: '基金', value: 50000 } ], stocks: [ { code: '600519', name: '贵州茅台', shares: 100, value: 150000 } ], funds: [ { name: '某某混合基金', value: 50000 } ], insurance: [ { type: '寿险', value: 200000 } ], // 经营资产 companyShares: [ { company: '伟强建材', share: 100, value: 500000 } ], businessLicense: ['92520102MA6JXXXXXX'], intellectualProperty: [] }, // 健康医疗 health: { // 医疗信息 medicalInsurance: '居民医保', medicalHistory: ['骨折（2015年）'], chronicDisease: [], disabilityStatus: '无', mentalHealth: '正常', hospitalRecords: [ { date: '2015-08-10', hospital: '贵阳市第一人民医院', reason: '右腿骨折' } ], medicationRecords: [], // 健康状况 physicalCondition: '良好', psychologicalStatus: '正常', addictionStatus: ['毒品成瘾（已戒断）'], vaccination: ['新冠疫苗（3针）', '乙肝疫苗'] } }; // 维度配置 const DimensionConfig = { identity: { name: '基础身份', icon: '🆔', color: '#1890ff', categories: { demographic: { name: '人口信息', count: 12 }, document: { name: '证件信息', count: 16 }, residence: { name: '户籍居住', count: 12 }, contact: { name: '联系方式', count: 5 } } }, social: { name: '社会属性', icon: '👥', color: '#52c41a', categories: { occupation: { name: '职业信息', count: 17 }, education: { name: '教育背景', count: 10 }, family: { name: '家庭关系', count: 15 }, network: { name: '社交网络', count: 10 } } }, behavior: { name: '行为特征', icon: '📊', color: '#faad14', categories: { communication: { name: '通信行为', count: 12 }, consumption: { name: '消费行为', count: 14 }, internet: { name: '上网行为', count: 12 }, travel: { name: '出行行为', count: 10 } } }, trajectory: { name: '轨迹刻画', icon: '🗺️', color: '#f5222d', categories: { spatial: { name: '空间轨迹', count: 12 }, temporal: { name: '时间轨迹', count: 10 }, association: { name: '轨迹关联', count: 16 } } }, risk: { name: '风险研判', icon: '⚠️', color: '#722ed1', categories: { personType: { name: '人员类型', count: 14 }, behaviorAlert: { name: '行为预警', count: 14 }, relationAnalysis: { name: '关联分析', count: 14 } } }, perception: { name: '感知数据', icon: '👁️', color: '#eb2f96', categories: { biometric: { name: '生物特征', count: 10 }, device: { name: '设备特征', count: 10 }, location: { name: '位置特征', count: 15 } } }, asset: { name: '资产信息', icon: '💰', color: '#13c2c2', categories: { realEstate: { name: '不动产', count: 8 }, vehicle: { name: '车辆资产', count: 6 }, financial: { name: '金融资产', count: 10 }, business: { name: '经营资产', count: 4 } } }, health: { name: '健康医疗', icon: '🏥', color: '#ff4d4f', categories: { medical: { name: '医疗信息', count: 10 }, healthStatus: { name: '健康状况', count: 12 } } } }; // 统计数据 const ArchiveStats = { totalTags: 310, filledTags: 287, fillRate: '92.6%', riskLevel: '重点人员', riskTags: 42, lastUpdate: '2024-02-22 15:30:00', dataSource: ['公安人口', '通信运营商', '银行', '互联网'] }; // 时间轴数据 const TimelineData = [ { time: '2024-02-22 14:30', type: '通话', content: '与李强通话 5分32秒', location: '云岩区' }, { time: '2024-02-22 12:15', type: '消费', content: '餐饮消费 ¥268', location: '南明区花果园酒楼' }, { time: '2024-02-22 09:00', type: '出行', content: '驾驶贵A·12345从家到建材市场', location: '云岩区→南明区' }, { time: '2024-02-21 22:30', type: '上网', content: '使用抖音 45分钟', location: '云岩区家中' }, { time: '2024-02-21 20:00', type: '消费', content: '超市购物 ¥156', location: '南明区沃尔玛' }, { time: '2024-02-21 14:00', type: '通话', content: '与王鹏通话 12分08秒', location: '南明区建材市场' } ]; export { PersonArchiveData, DimensionConfig, ArchiveStats, TimelineData };

Actually… asking about bloom lighting on the Sega Dreamcast is not a silly question at all, and I was also extremely interested in bloom lighting on the DC as well… The reason you never saw effects like that back in the day is two-fold: 1) Framebuffer post processing effects like bloom and HDR were still extremely new and didn’t really become mainstream until games like Tron for Xbox started doing it around 2004, just after the DCs time. 2) Due to how the PowerVR GPU was architected, with its support for order-independent transparencies, redrawing the framebuffer over the entire screen for effects like bloom or the motion blur we implemented in GTA3 is quite taxing on fillrate. Now to finally answer your question… can DC actually do it? Fuck yeah it can! The same @poiitidis who led our GTA3 and VC ports has done some incredible work exploring these kinds of post-processing effects on DC!

Sad it was this size only but really good R/R on the entry for a 3233% return Thx to @PolyCop_Trader for letting me trade on @Polymarket with this amazing speed and fillrate t.me/PolyCop_BOT?start=ref_I…

64,000 bricks on a single thread. Pixel fillrate and tile cache size are my only bottlenecks now

Yes, but occlusion culling costs fillrate too. The technique works by doing a lower resolution rasterization of simplified geometry. But here: 1. You can't simplify it further 2. Too low res and you get too many false negatives. Too high and you're just drawing twice.

re mina l1 performance: i believe we must find the sweet spot. it has to feel "not slow" - that doesn’t mean solana-level tps. there are many ways to make mina much faster, and we should. focus on perceived speed real throughput where it counts (in addition to some issues we currently face on e.g. mainnet with regards to fillrate)