🍁𝐈𝐑𝐂𝐂 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐓𝐢𝐦𝐞 𝐔𝐩𝐝𝐚𝐭𝐞 𝐚𝐬 𝐨𝐟 𝐉𝐮𝐧𝐞 𝟐𝟎𝟐𝟔🗓️ 📩 Contact Us to 𝐀𝐩𝐩𝐥𝐲 📆 Book an appointment with 𝐑𝐂𝐈𝐂 𝐌𝐝 𝐀𝐬𝐡𝐢𝐪𝐮𝐫 𝐑𝐚𝐡𝐦𝐚𝐧. 📞 𝐂𝐚𝐥𝐥 𝐮𝐬: 09677661199, 01633661199 #irccupdate #canadavisa #processingtime #visatime

got a non-public (aka models can not train on) office tasks-test for local smalltown inference, which sends qwen3-14B into forever thinking loop; short overview of our tests: qwen2.5:14B delivers fastest and correct; gemma4-31B, qwen3.6-27B and gpt-oss-20B all work but a lot slower (4x-6x processingtime) qwen3.6-35B-A3B about 20% slower than qwen2.5. still trying to get gemma2-9B to do the job, yet we only get like a 90% correct data extraction for now. test basically extracts structured datasets from a 10k long textfile of unstructured data and prepares datasets for the database-workers. trying to keep with defaults of the models and only do some promptsteering in order not to have startup different llama.cpp-configurations for different use-cases there...

Powell's dovish Jackson Hole speech creates lower rate environment for risk assets. Crypto's overnight decline reflects processing time before benefits emerge. #Crypto #DeFi #MrGM #PowellDovishSpeech #LowerRateEnvironment #RiskAssets #CryptoOvernightDecline #ProcessingTime #BenefitsEmerge

July 2025 DOL Processing Times Are In—What They Mean for Your Green Card Timeline ⏳ PERM delays now averaging over 16 months. Prevailing Wage Determinations? Still inching forward. For foreign workers and employers alike, these wait times can derail long-term plans—unless you act early and strategically. Join Jessica Palarca in todays article to see more about the processing times. Read More: buff.ly/TpebKqT #PERM #ProcessingTime #Visa #Immigration #GreenCard

Partner Visa for Australia: Eligibility, Requirements, and Processing Time #PartnerVisa #AustraliaImmigration #AustralianCitizens #AustralianPRHolders #LegalizedNewZealandCitizens #MarriageCertificate #FinancialCredentials #SocialCommitment #ProcessingTime #VisaApplication

It's important to stay updated with the PERM processing times. Dive into todays article with Partner, Krystal Alanis, to understand: January 2025 PERM Processing Time Updates Read More: buff.ly/4fZSAa2 #PERM #ProcessingTime #PWD #NPWC #Immigration #Greencard

@VervericaData we published some @ApacheFlink additions, published in @sonatype #maven. We believe it brings value and would be glad for a reXpost. github.com/iunera/iu-flink-e… In short, we had a challenge our #mfund Fahrbar20 project from the @bmdv where we got real-time streaming data from #publictransport of the and sometimes streams could break and processing would need to start even when the data stream is broken. Flink did not support that so we needed to help ourselves with some custom code. More details about the project here: x.com/iuneracom/status/18610… #Flink supports #EventTime OR #ProcessingTime windows. One only can adjust to the event time or the processing time. Event time means that as long as there is no element arriving, it does not matter how much real time between the data has passed. This can be problematic when one wants to aggregate/group the event data packages and when there is no data package arriving for a minute the aggregation shall be triggered. Concrete use case: Imagine there are people count in and count out events at a busstop. The aggregation of all events would be the total count in and total count out. Sometimes there is an event indicating the bus departed what means the aggregation could be done. However, there is not always a clear indicator for the departure and sometimes data packages are left out, in disorder or arriving really late (e.g. hours). Therefore, we need the ability to trigger the aggregation one minute after the last event for the aggregation was received, because then we manage to get 99% of all cases covered, regardless if data is much later.

12/6に解放されたGeminiを使って要約していますが、かなり精度が上がった印象です。 新聞コラムの要約もかなりラクになりました✨ こんなプロンプトでやっています。 ''' 以下のプロジェクト要件とタスクを順に実行してください。 以下に、few-shot例として参照用の記事原文と使用すべき語彙と最終成果物が記載されています。 タスク遂行時には、これらのfew-shot例を参考にしながら、指定されたプロセスとガイドラインに従ってください。 [参考用 few-shot example] 【入力】 (天声人語を貼り付け） 【語彙】 "戦々恐々", "背景", "事業者", "科す", "合理的", "措置", "一律", "ケース", "痛感", "意義", "説く", "人ごと" (↑学習する語いをここに入力） 【最終成果物】 オーストラリアで、１６歳未満のＳＮＳ利用を禁止する法案が提出された。いじめや性犯罪などから子どもを守る目的だが、年齢で一律に禁止することへの反発や、効果を疑問視する声もある。子どもとスマホの関係は複雑で、禁止すれば解決するほど単純な問題ではない。大人たちができることは何か、改めて考えてみたい。 [プロジェクト要件] project: name: "新聞記事要約プロジェクト" description: "指定テーマに沿った新聞記事の要約を作成する。150字に収め、原文に忠実な要約を目指す。" WBS: - index: 1 process: P1 step: S1 task: T1 subTask: sT1 work: W1 inputIndex: In1 input: "新聞記事の原文" promptIndex: Prompt1 prompt: "記事の主要トピックを特定し、中心的なテーマや問題を1-2文で表現してください。" outputIndex: Out1 output: "[主要トピック]" dependencies: "なし" status: "未着手" processingTime: "5分" - index: 2 process: P2 step: S2 task: T2 subTask: sT2 work: W2 inputIndex: In2 input: "[主要トピック]" promptIndex: Prompt2 prompt: "記事中の重要な事実や具体例を3-4点挙げ、簡潔に述べてください。" outputIndex: Out2 output: "[重要な事実や例]" dependencies: "P1" status: "未着手" processingTime: "10分" - index: 3 process: P3 step: S3 task: T3 subTask: sT3 work: W3 inputIndex: In3 input: "[主要トピック], [重要な事実や例]" promptIndex: Prompt3 prompt: "筆者の主張や視点を反映させ、記事全体のトーンを維持してください。" outputIndex: Out3 output: "[筆者の視点や主張]" dependencies: "P2" status: "未着手" processingTime: "5分" - index: 4 process: P4 step: S4 task: T4 subTask: sT4 work: W4 inputIndex: In4 input: "[筆者の視点や主張]" promptIndex: Prompt4 prompt: | 要約を150字に収めてください。過不足なく内容を伝えつつ、字数制限を厳守してください。 # 本文の後に書かれた語彙を自然な形で[150字要約]内に可能な限り盛り込んでください outputIndex: Out4 output: "[150字要約]" dependencies: "P3" status: "未着手" processingTime: "10分" - index: 5 process: P5 step: S5 task: T5 subTask: sT5 work: W5 inputIndex: In5 input: "[150字要約]" promptIndex: Prompt5 prompt: "作成した要約と原文を照らし合わせ、誤った解釈や不要な追加がないか確認してください。" outputIndex: Out5 output: "[最終要約]" dependencies: "P4" status: "未着手" processingTime: "5分" guidelines: - ensureMECE: "MECE原則に基づいて、全てのタスクが過不足なくカバーされていることを確認してください。" - clarityInActionContent: "各アクションの内容が明確で理解しやすいようにしてください。" - interactiveConfirmation: "各ステップでの疑問点をその場で確認し、修正を行うようにしてください。" - relationshipAwareness: "各タスクの依存関係を考慮し、順序立てて進行してください。" feedbackLoop: "ユーザーのフィードバックに基づき、要約の修正と改善を継続的に行う。" exceptionHandling: "予期せぬ問題が発生した場合、柔軟に対応し、迅速に解決策を講じる。" finalGoal: - goal: "指定テーマに沿った150字の要約を作成し、原文に忠実で簡潔な要約を提供する。" - outputStyle: clarityAndPrecision: description: "出力は明確で正確な内容を含むこと。" structureAndFormat: textStructure: ["段落", "リスト"] textFormat: ["プレーンテキスト"] textStyle: ["フォーマル"] annotations: entities: "エンティティに関する注釈を含まない。" relationships: "エンティティ間の関係に関する注釈を含まない。" comprehensiveness: description: "重要な情報を漏れなく含むこと。" purposeAdaptability: description: "ユーザーの目標と目的に応じて要約内容を調整すること。" interactivity: description: "ユーザーからの問い合わせやフィードバックに即応し、要約を改善する。" accessibilityAndConvenience: description: "ユーザーが理解しやすく、アクセスしやすい形式で要約を提供すること。"

以下は、私ができることについてさらに詳細に記述した内容です。できる限り具体的に、そして明確に説明しています。 ```yaml project: name: "GPT Capabilities Overview" description: "A comprehensive presentation of the capabilities and functions of GPT (Generative Pre-trained Transformer) as an AI language model designed to assist users through natural language processing." structure: - index: 1 process: "Information Generation" step: "Generate text-based responses" task: "Answer questions" subTask: "Provide detailed explanations" work: "Natural Language Processing" inputIndex: "In1" input: description: "User's query or prompt that initiates the response generation process. This input can range from simple questions to complex requests for information." example: "What are the applications of AI in healthcare?" promptIndex: "Prompt1" prompt: description: "The specific text prompt provided to guide the response generation, typically based on the user's input." example: "Please explain the various applications of AI in the healthcare sector." outputIndex: "Out1" output: description: "The generated response based on the user's query, designed to be relevant and context-appropriate." example: "AI in healthcare includes applications such as predictive analytics, personalized medicine, robotic surgery, and virtual health assistants." dependencies: description: "The process relies on receiving an input query to generate a meaningful and contextual response." example: "Without a user query, the system cannot produce a relevant answer." status: "Active" processingTime: description: "The time taken to process the input and generate the response, typically instantaneous for user interaction." example: "Responses are often generated in less than a second." guidelines: - ensureMECE: description: "Ensure that answers are mutually exclusive and collectively exhaustive, covering all aspects of the question without overlap." example: "When explaining a topic, avoid repeating the same information and ensure all points are covered." - clarityInActionContent: description: "Ensure that responses are articulated in a clear, straightforward manner, avoiding jargon unless required." example: "Use simple language and clear examples to explain complex topics." - interactiveConfirmation: description: "Engage in dialogue to confirm user's understanding and satisfaction with the provided response." example: "Follow up with questions like 'Does this answer your question?' to promote interaction." - relationshipAwareness: description: "Contextually understand the relationships between various concepts to provide integrated responses." example: "When discussing AI, relate different applications like machine learning and automation within the same context." feedbackLoop: description: "Implement continuous feedback mechanisms to improve response quality based on user interactions." example: "Utilize user ratings and comments to refine future responses and adapt to user preferences." exceptionHandling: description: "Have strategies in place to manage unexpected inputs or queries beyond typical scenarios." example: "If faced with a vague query, ask clarifying questions to better understand user intent." finalGoal: - goal: "Provide accurate and helpful text-based responses to a wide range of queries, ensuring user satisfaction and engagement." - outputStyle: clarityAndPrecision: description: "Outputs need to be clear, accurate, and contextually relevant to enhance user understanding." structureAndFormat: textStructure: - "paragraph" - "list" - "table" textFormat: - "markdown" - "plain text" textStyle: - "formal" - "informal" annotations: entities: description: "Establish a defined format for recognizing and labeling entities mentioned in the text." example: "Identify names, dates, and specific terms to enhance clarity." relationships: description: "Define the format for understanding relationships between different entities in a response." example: "Explain how different concepts are interconnected, such as AI and automation." comprehensiveness: description: "Ensure all relevant information related to the query is included in the response." example: "If answering a historical question, include key dates, figures, and events in full context." purposeAdaptability: description: "Adjust outputs according to the user's specific goals, needs, and context." example: "If a user requests a summary, prioritize brevity and key points; if they want detail, provide a thorough exploration." interactivity: description: "Engage users through a responsive interface that adapts to questions and feedback." example: "Encourage users to ask follow-up questions for deeper understanding." visualElements: types: - "diagrams" - "graphs" - "other visual aids" description: "Incorporate visual elements where appropriate to support textual responses." example: "Use charts to illustrate statistical data when relevant." accessibilityAndConvenience: description: "Ensure content is accessible and easy to understand for users at all levels." example: "Use headings, bullet points, and summaries to improve readability." workflow: - step: "Understanding User Intent" command: "Analyze user queries and establish intent by breaking down the input into understandable components." description: "Identify the core question and the context in which it was asked to formulate a relevant response." prompt: "What is the user trying to achieve with this query?" - step: "Generating Responses" command: "Produce text responses based on the analyzed intent, ensuring relevance and clarity." description: "Utilize the model's training data to generate coherent, context-driven replies to user queries." prompt: "Generate a concise answer based on provided context, ensuring all aspects of the question are addressed." - step: "Feedback Collection" command: "Gather user feedback on the generated responses, ensuring high engagement and satisfaction." description: "Incorporate mechanisms for users to provide ratings or comments on responses, facilitating continuous improvement." prompt: "Did the response meet your needs? How can it be improved for next time?" - step: "Continuous Learning" command: "Adapt and evolve the response generation process based on collected feedback and user interactions." description: "Utilize insights gained from user feedback to refine the model's understanding and responsiveness." prompt: "What adjustments are necessary for better alignment with user expectations and needs?" ``` こちらがすべての詳細を含むフォーマットでの記述です。もしさらに特定の情報が必要であれば、どうぞお知らせください！

Excited to share with LLM users worldwide! Using Generative AI, we’ve developed a system to automate workflow generation. With this system, your workflows are guaranteed to be efficiently created Let's Try it!!! === ## Protocol: Goal to Outcome with Conditions ### Definition **Objective**: Ensure understanding of user input intent, define it as a project, and execute tasks to produce deliverables. Intermediate products generated during task execution should be connected using the following EntityRelation mappings. ### EntityRelation Mapping Prompt ``` [Project structure ($N1)] [consists of the following ($H, $N1, $N2)] [elements ($N2)] [such as ($H, $N1, $N2)]. [Process ($N3)], [Step ($N4)], [Task ($N5)], [Subtask ($N6)], [Work ($N7)], [Input Information ($N8)], [Index ($N9)], [Processing Prompt ($N10)], [Intermediate Product ($N11)], [Relationship ($N12 )], [status ($N13)], [processing time ($N14)], [includes ($L, $N2, $N3; $L, $N2, $N4; $L, $N2, $N5; $L, $N2, $N6; $L, $N2, $N7; $L, $N2, $N8; $L, $N2, $N9; $L, $N2, $N10; $L, $ N2, $N11; $L, $N2, $N12; $L, $N2, $N13; $L, $N2, $N14)]. [These components ($N2)] [are ($H, $N2, $N15)] [associated ($N15)] [with each other ($H, $N2, $N15)] [and ($H, $N2, $N15)] [as the project progresses ($N16)] [as ($L, $N16, $N17)] [information ($N17)] [flows ($L, $N17, $N18 )] [flows ($N18)]. [e.g. ($L, $N19, $N20)], [a step ($N19)] [from ($L, $N19, $N21)] [the next step ($N21)] [to ($L, $N19, $N21)] [intermediate product ($N20)] [is passed on ($H, $N20, $N22)], [multiple tasks ($ N23)] [are ($L, $N23, $N24)] [refer to ($H, $N23, $N24)] [or ($L, $N22, $N25; $L, $N24, $N25)]. [to facilitate the project ($H, $N26, $N27)], [the relationships among these elements ($N26)] [to properly ($H, $N27, $N28)] [define ($N28)] [and ($H, $N27, $N28)], [to manage ($N27)] [to be ($H, $N27, $N 29)] [is ($H, $N27, $N29)] ``` ### Project Structure Template ```yaml project: name: "[Project name]" description: "[Project Description]" structure: - index: 1 process: [Process] step: [Step] task: [Task] subTask: [SubTask] work: [Work] inputIndex: [InputIndex] input: "[Input]" promptIndex: [PromptIndex] prompt: "[Prompt]" outputIndex: [OutputIndex] output: "[Output]" dependencies: "[Dependencies]" status: "[Status]" processingTime: "[ProcessingTime]" guidelines: - ensureMECE: "MECE principle for goal-oriented actions and tasks." - clarityInActionContent: "Actions should be clear and understandable." - interactiveConfirmation: "Immediate queries and completions through dialogue." - relationshipAwareness: "Consider relationships between actions within the checklist." feedbackLoop: "Continuous improvement based on user feedback." exceptionHandling: "Flexible response to unforeseen problems." finalGoal: - goal: "Develop a comprehensive Success Learning System." - outputStyle: "Success Learning System." clarityAndPrecision: description: "Outputs need to be clear and accurate." structureAndFormat: textStructure: ["[TextStructure]"] textFormat: ["[TextFormat]"] textStyle: ["[TextStyle]"] annotations: entities: "Define annotation format for entities." relationships: "Define annotation format for relationships." comprehensiveness: description: "Include all relevant information." purposeAdaptability: description: "Adjust outputs according to user goals and objectives." interactivity: description: "Adaptable to user queries and feedback." visualElements: types: ["[VisualElementType]"] accessibilityAndConvenience: description: "Adapt to different user layers, accessible and easy to understand." workflow: step: "[WorkflowStep]" command: "[Command]" description: "[Description]" prompt: "[Prompt]" outputStyle: step: "[WorkflowStep]" command: "[Command]" description: "[Description]" prompt: "[Prompt]" step: "[WorkflowStep]" command: "[Command]" description: "[Description]" prompt: "[Prompt]" ``` ### Role-Play Instructions ``` User: [UserUtterance] prompt: "[Prompt]" Assistant: [Response] If the user intent is missing context, use a step-back question to check with the user. ``` ### Intent Understanding and Process Breakdown ``` Please understand the intent based on the user input content and break down the entire process from start to finish. Ensure to generate deliverables specifically and concretely, as desired by the user. ``` ### Example Scenario ``` [System Prompt] Role: Expert in Project Management Instruction: 1. Structure the steps clearly. 2. Use the provided templates. 3. Ensure each task is defined and mapped properly. End. Output style: Markdown Table format [User Prompt] command: RUN [User: Create a project plan for launching a new product] ## Goal 1. **Structure and organize headings** - [C1]: Index and indent headings, organizing them into upper, middle, and lower structures. 2. **Create user prompts for each heading** - [C2]: Create user prompts for each heading with specific instructions to generate the desired deliverables. 3. **Execute heading and user prompt pairs** - [C3]: Execute each heading and user prompt pair sequentially, numbering them from 1 to N, running commands continuously to produce the final deliverables. ### [C1] Structure and Organize Headings 1. **Upper Structure** - Level 1 headings 2. **Middle Structure** - Level 2 headings 3. **Lower Structure** - Level 3 headings ### [C2] Create User Prompts 1. **Upper Structure Prompts** - User: Provide necessary information based on the upper structure. 2. **Middle Structure Prompts** - User: Provide specific details based on the middle structure. 3. **Lower Structure Prompts** - User: Provide specific work instructions based on the lower structure. ### [C3] Execute Heading and User Prompt Pairs 1. **Command Run 1** - User: Provide information for the upper structure. 2. **Command Run 2** - User: Provide details for the middle structure. 3. **Command Run 3** - User: Provide work instructions for the lower structure. ... n. **Command Run N** - User: Provide final instructions to generate the desired deliverables. ### Deliverables Repeat the following steps recursively 5 times to produce the final deliverables. ```plaintext Goal: Generate a comprehensive project plan for launching a new product. Deliverables: Detailed project plan document. ``` **Recursive Execution 5 Times** **Run** ``` --- ### Implementation Notes 1. Ensure to use proper indentation for structuring the project elements. 2. Make sure all necessary components are included in the project structure. 3. Provide clear and specific instructions in user prompts for each heading. 4. Use a feedback loop for continuous improvement based on user feedback. 5. Handle exceptions flexibly to address unforeseen issues. ``` ===

ઈમરજન્સીમાં ક્રેડિટ કાર્ડ કે પર્સનલ લોનમાંથી કયો વિકલ્પ છે બેસ્ટ? #personalloan #creditcard #disbursals #productfeatures #borrowers #interestrate #loanamount #processingtime #repayment youtube.com/watch?v=faD9ukGY…

⏳ Processing Time: Note that processing time for resubmitted applications starts when IRCC receives the completed application. #ProcessingTime #ImmigrationCanada #Canada #immigration #news #today #IRCC

How to Apply for a Temporary Resident Visa (TRV) in Canada #ApplicationProcess #Canada #CanadaVisa #DualIntent #Extension #ProcessingTime #TemporaryResidentVisa #TRV #VisaGuide #WorkInCanada travelobiz.com/how-to-apply-…

Canada work permit processing time from India oasis-india.com/work/work-in… #canadaimmigration #canad #immigration #processingtime #india #visa #consultant #PR #workVisa #candaworkpermit #canadaworkvisa

#speakwell #greatspeech #UpYourGame #ProcessingTime #speaker #speakercoach #TrainingAndDevelopment

#DLLFcohort5 #ProcessingTime

2/2 Within the shortest period of time. How come everybody else’s process takes forever?? It only shows that if they really wanted to - they would work faster. #processingtime #visa #lint

OINP updates average processing times of all the categories. Below are the average processing times as of June 28, 2022. ✔️ ⏩A disclaimer : do-not contact the program if processing of your application is taking longer than expected. #foreverhopeful #OINP #processingtime

IRCC updated its Processing Times Tool to provide accurate information on the estimated timeframes for processing different applications. 👍 ▶️ Here are the Updated Processing Times as of March 31 #foreverhopeful #immigrationupdate #processingtime #immigrate #immigratetocanada