The meaning of KEEP COMPANY WITH is to spend time with (someone) —sometimes used figuratively. How to use keep company with in a sentence.

Keep company with Definition & Meaning - Merriam-Webster

KEEP SOMEONE COMPANY meaning: 1. to stay with someone so that they are not alone: 2. to stay with someone so that they are not…. Learn more.

KEEP SOMEONE COMPANY definition | Cambridge English Dictionary

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Keep

keep

Good day! My name is Robert. I work for and today I'm sharing some things I think other people would love to know about the Google Keep app. Zoo Web Designs Take A Voice Memo You can take notes on the app using just your voice! Just tap on the microphone icon to speak your note. Keep will both transcribe and record everything you say so even if Google misses some words, you'll still have your voice recording. Transcribe Photo To Text If you import a photo with text on it, Keep can grab that text. When you tap on the photo, you should see a three-dot menu icon in the top right corner. You can then select "Grab image text". Draw On Photos You can use Keep to highlight, color, and draw on your photos. Either import or take a photo then select the paintbrush icon. All you need to do is select your pen style and draw on your photo without affecting the original file. Sync Your Notes with Docs On Your PC Keep is fully integrated with Google's office suite. If you have a file open in Docs or Slides, tap the Keep logo in the sidebar at the right to see a list of your saved notes. Tap a file top open it. You can then copy, cut, or edit anything that's on it. It will automatically sync! If you want to create a Google document out of a note, hold your finger on the note and select "Copy to Google Docs". Collaborate With A Friend Or Colleague It's pretty easy to invite someone to collaborate. Go to the settings sidebar and enable sharing. When you're in a note, tap on the three-dot menu at the bottom and select "Collaborator". You will then be able to select anyone from your address book to link your note with their app. Whenever someone edits it, the other collaborators get notified. Get The Chrome Extension Downloading Keep's extension on the chrome web store will put a small icon next to your address bar which will provide a direct link to your account. By clicking on it, you can save URLs, photos, text, and even take notes while you're using the browser! Use Labels, Colors, Pins For Organization When you long press a note on Keep, it will bring up a series of options to organize your notes. You can color-code, pin, or label your notes! Set A Time or Location Reminder Did you know you could actually turn your note into a reminder? All you need to do is tap the bell icon and it will allow you to set a reminder for a date and time or a location!

10 Things You Didn't Know You Could Do With Google Keep

Today, we are excited to announce the support of continuous updates for long-running pipelines in CocoIndex. This powerful feature automatically applies incremental source changes to keep your index up-to-date with minimal latency. With continuous updates, your indexes remain synchronized with your source data in real time, ensuring that your applications always have access to the most current information without the performance overhead of full reindexing. If you like our work, it would mean a lot to us if you could support Cocoindex on Github with a star. Thank you so much with a warm coconut hug 🥥🤗. Cocoindex on Github When to use it? It fits into situations that you need to access the fresh target data continuously in most of the time. How does it work? It continuously captures changes from the source data and updates the target data accordingly. It's long-running and only stops when being aborted explicitly. A data source may enable one or multiple change capture mechanisms: CocoIndex supports two main categories of change detection mechanisms: General Mechanism:

Refresh Interval: A universal approach applicable to all data sources, allowing periodic checks for changes by scanning and comparing the current state with the previous state.


Source-Specific Mechanisms:

Push Change Notification: Many data sources offer built-in mechanisms for real-time change notification which CocoIndex can subscribe to (coming soon!)
Recent Changes Poll: Some data sources provide APIs for tracking recently modified entries, allowing efficient detection of changes; for example, Google Drive. General Mechanism:

Refresh Interval: A universal approach applicable to all data sources, allowing periodic checks for changes by scanning and comparing the current state with the previous state. General Mechanism Refresh Interval: A universal approach applicable to all data sources, allowing periodic checks for changes by scanning and comparing the current state with the previous state. Refresh Interval: A universal approach applicable to all data sources, allowing periodic checks for changes by scanning and comparing the current state with the previous state. Refresh Interval Source-Specific Mechanisms:

Push Change Notification: Many data sources offer built-in mechanisms for real-time change notification which CocoIndex can subscribe to (coming soon!)
Recent Changes Poll: Some data sources provide APIs for tracking recently modified entries, allowing efficient detection of changes; for example, Google Drive. Source-Specific Mechanisms Push Change Notification: Many data sources offer built-in mechanisms for real-time change notification which CocoIndex can subscribe to (coming soon!)
Recent Changes Poll: Some data sources provide APIs for tracking recently modified entries, allowing efficient detection of changes; for example, Google Drive. Push Change Notification: Many data sources offer built-in mechanisms for real-time change notification which CocoIndex can subscribe to (coming soon!) Push Change Notification Recent Changes Poll: Some data sources provide APIs for tracking recently modified entries, allowing efficient detection of changes; for example, Google Drive. Recent Changes Poll These mechanisms work together to ensure CocoIndex can detect and process changes as they happen, maintaining your index in perfect sync with source data with minimal latency and resource usage. Under the hood, after the change is detected, CocoIndex will use its incremental processing mechanism to update the target data. incremental processing mechanism How to enable continuous updates? Here is an example of how to enable continuous updates for Google Drive. It is pretty simple: example 1. Configure the change capture mechanisms for the source @cocoindex.flow_def(name="GoogleDriveIndex")
def my_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope):
    data_scope["documents"] = flow_builder.add_source(
        cocoindex.sources.GoogleDrive(
            service_account_credential_path=credential_path,
            root_folder_ids=root_folder_ids,
            recent_changes_poll_interval=datetime.timedelta(seconds=10)),
        refresh_interval=datetime.timedelta(minutes=1)) @cocoindex.flow_def(name="GoogleDriveIndex")
def my_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope):
    data_scope["documents"] = flow_builder.add_source(
        cocoindex.sources.GoogleDrive(
            service_account_credential_path=credential_path,
            root_folder_ids=root_folder_ids,
            recent_changes_poll_interval=datetime.timedelta(seconds=10)),
        refresh_interval=datetime.timedelta(minutes=1)) In this example, we've configured two change detection mechanisms: recent_changes_poll_interval=datetime.timedelta(seconds=10): This is a Google Drive-specific mechanism that uses the Drive API's changes endpoint to efficiently detect modifications every 10 seconds. This is effecient fast scan to capture all latest modified files, and we could set it to a short interval to get fresher data. It doesn't capture file deletions, so we need the fallback mechanism to ensure all changes are eventually captured.


refresh_interval=datetime.timedelta(minutes=1): This is the universal fallback mechanism that performs a complete scan of the data source every minute. This is to scan all the files, to ensure all the changes - including the deleted files, are captured.
The refresh_interval parameter is particularly important as it serves as a safety net to ensure all changes are eventually captured, even if source-specific mechanisms miss something. It works by:

Periodically scanning the list of entries (e.g. file list with metadata like last modified time) at the specified interval
Comparing the current list with the previously known list
Identifying any differences (additions, modifications, deletions)
Triggering updates only for the changed items recent_changes_poll_interval=datetime.timedelta(seconds=10): This is a Google Drive-specific mechanism that uses the Drive API's changes endpoint to efficiently detect modifications every 10 seconds. This is effecient fast scan to capture all latest modified files, and we could set it to a short interval to get fresher data. It doesn't capture file deletions, so we need the fallback mechanism to ensure all changes are eventually captured. recent_changes_poll_interval=datetime.timedelta(seconds=10): This is a Google Drive-specific mechanism that uses the Drive API's changes endpoint to efficiently detect modifications every 10 seconds. This is effecient fast scan to capture all latest modified files, and we could set it to a short interval to get fresher data. It doesn't capture file deletions, so we need the fallback mechanism to ensure all changes are eventually captured. recent_changes_poll_interval=datetime.timedelta(seconds=10) refresh_interval=datetime.timedelta(minutes=1): This is the universal fallback mechanism that performs a complete scan of the data source every minute. This is to scan all the files, to ensure all the changes - including the deleted files, are captured.
The refresh_interval parameter is particularly important as it serves as a safety net to ensure all changes are eventually captured, even if source-specific mechanisms miss something. It works by:

Periodically scanning the list of entries (e.g. file list with metadata like last modified time) at the specified interval
Comparing the current list with the previously known list
Identifying any differences (additions, modifications, deletions)
Triggering updates only for the changed items refresh_interval=datetime.timedelta(minutes=1): This is the universal fallback mechanism that performs a complete scan of the data source every minute. This is to scan all the files, to ensure all the changes - including the deleted files, are captured. refresh_interval=datetime.timedelta(minutes=1) The refresh_interval parameter is particularly important as it serves as a safety net to ensure all changes are eventually captured, even if source-specific mechanisms miss something. It works by: refresh_interval Periodically scanning the list of entries (e.g. file list with metadata like last modified time) at the specified interval
Comparing the current list with the previously known list
Identifying any differences (additions, modifications, deletions)
Triggering updates only for the changed items Periodically scanning the list of entries (e.g. file list with metadata like last modified time) at the specified interval Comparing the current list with the previously known list Identifying any differences (additions, modifications, deletions) Triggering updates only for the changed items While source-specific mechanisms like recent_changes_poll_interval are more efficient for near real-time updates, the refresh_interval provides comprehensive coverage. We recommend setting it to a reasonable value based on your freshness requirements and resource constraints - shorter intervals provide fresher data but consume more resources. recent_changes_poll_interval refresh_interval You can read the full documentation: for source specific change detection mechanisms here. Different sources have different mechanisms supported.
for the universal fallback mechanism here. for source specific change detection mechanisms here. Different sources have different mechanisms supported. here for the universal fallback mechanism here. here 2. Run the flow in live update mode Option 1: CLI Add a @cocoindex.main_fn() decorator to your main function, so CocoIndex CLI will take over the control (when cocoindex is the first command line argument). @cocoindex.main_fn() @cocoindex.main_fn()
def main():
    pass

if __name__ == "__main__":
  main() @cocoindex.main_fn()
def main():
    pass

if __name__ == "__main__":
  main() To run the CLI with live update mode, you can use the following command: python main.py cocoindex update -L python main.py cocoindex update -L This will start the flow in live update mode, which will continuously capture changes from the source and update the target data accordingly. Option 2: Python API You can create cocoindex.FlowLiveUpdater. For example, cocoindex.FlowLiveUpdater @cocoindex.main_fn()
async def main():
    my_updater = cocoindex.FlowLiveUpdater(demo_flow)
    await my_updater.wait()
    ...

if __name__ == "__main__":
    asyncio.run(main()) @cocoindex.main_fn()
async def main():
    my_updater = cocoindex.FlowLiveUpdater(demo_flow)
    await my_updater.wait()
    ...

if __name__ == "__main__":
    asyncio.run(main()) And you run with the flow with python main.py python main.py You can also use the updater as a context manager. It will abort and wait for the updater to finish automatically when the context is exited. See full documentation here. here Now you are all set! It is super simple to get started and have continuous updates for your data. Get started now quickstart guide 🚀 quickstart guide It would mean a lot to us if you could support Cocoindex on Github with a star if you like our work. Thank you so much with a warm coconut hug 🥥🤗. Cocoindex on Github

Keep Your Indexes Fresh With This Real-time Pipeline

CocoIndex https://github.com/cocoindex-io/cocoindex is a ETL framework that helps you to turn your data ready for AI in realtime. https://github.com/cocoindex-io/cocoindex The essential part for supporting robust and efficient update is because of incremental update. In CocoIndex, users declare the transformation, and don't need to worry about the work to keep index and source in sync.  In this blog we would like to share how we handled the incremental update. If you like our work, it would mean a lot if you could support us ❤️ with a github star! https://github.com/cocoindex-io/cocoindex https://github.com/cocoindex-io/cocoindex CocoIndex creates & maintains an index, and keeps the derived index up to date based on source update, with minimal computation and changes. That makes it suitable for ETL/RAG or any transformation tasks stays low latency between source and index updates, and also minimizes the computation cost. What is Incremental Updates? Figuring out what exactly need to be updated, and only update that without having to recompute everything throughout. How does it work? You don't really need to do anything special, just focus on define the transformation need. CocoIndex automatically tracks the lineage of your data and maintains a cache of computation results. When you update your source data, CocoIndex will: Identify which parts of the data have changed


Only recompute transformations for the changed data


Reuse cached results for unchanged data


Update the index with minimal changes Identify which parts of the data have changed Identify which parts of the data have changed Only recompute transformations for the changed data Only recompute transformations for the changed data Reuse cached results for unchanged data Reuse cached results for unchanged data Update the index with minimal changes Update the index with minimal changes And CocoIndex will handle the incremental updates for you. CocoIndex provide two modes with pipeline with simple configuration: batch mode - for one time update.
live update mode - long running pipeline, with live update. Both modes runs with incremental updates. batch mode - for one time update. live update mode - long running pipeline, with live update. Both modes runs with incremental updates. Who needs Incremental Updates? Many people may think incremental updates is only beneficial for large scale data, thinking carefully, it really depends on the cost and requirement for data freshness. Google processed large scale data, and google has huge resources for it. Your data scale is much less than Google, but your resource provision is also much less than Google. Real condition for incremental update needs is: high freshness requirement, for example, you client has GDPR compliance, you need to update the index with the latest data.
transformation cost is significant higher than retrieval itself high freshness requirement, for example, you client has GDPR compliance, you need to update the index with the latest data. transformation cost is significant higher than retrieval itself Overall, say T is your most acceptable staleness, if you don't want to recompute the whole thing repeatedly every T, then you need incremental more or less. What exactly is incremental updates, with examples Well, we could take a look at a few examples to understand how it works. Example 1: Update a document Consider this scenario: I have a document. Initially, it's split into 5 chunks, resulting in 5 rows with their embeddings in the index.
After it's updated. 3 of them are exactly same as previous; others have changed I have a document. Initially, it's split into 5 chunks, resulting in 5 rows with their embeddings in the index. After it's updated. 3 of them are exactly same as previous; others have changed So we need to keep 3 rows, remove 2 previously existing rows, and add 2 new rows. These need to happen behind the scene: Ideally, we only recompute embeddings for the 4 new rows, and reuse it for 3 unchanged chunks. This can save computation cost especially when the embedding API charge by usage. CocoIndex achieves this by maintaining a cache for heavy steps like embedding, and on the input for a transformation step isn't changed, the output will be reused.
Besides, we also maintain a lineage tracking in the internal storage. It keeps track of which rows in the index are derived from the previous version of this document, to make sure stale versions are properly removed. Ideally, we only recompute embeddings for the 4 new rows, and reuse it for 3 unchanged chunks. This can save computation cost especially when the embedding API charge by usage. CocoIndex achieves this by maintaining a cache for heavy steps like embedding, and on the input for a transformation step isn't changed, the output will be reused. Besides, we also maintain a lineage tracking in the internal storage. It keeps track of which rows in the index are derived from the previous version of this document, to make sure stale versions are properly removed. CocoIndex takes care of this. Example 2: Delete a document Contining with the same example. If we delete the document later, we need to delete all 7 rows derived from the document. Again, this needs to be based on the lineage tracking maintained by CocoIndex. Example 3: Change of the transformation flow The transformation flow may also be changed, for example, the chunking logic is upgraded, or a parameter passed to the chunker is adjusted. This may result in the following scenario: Before the change, the document is split into 5 chunks, resulting in 5 rows with their embeddings in the index.
After the change, they become 6 chunks: 4 of previous chunks remain unchanged, the remaining 1 is split into 2 smaller chunks. Before the change, the document is split into 5 chunks, resulting in 5 rows with their embeddings in the index. After the change, they become 6 chunks: 4 of previous chunks remain unchanged, the remaining 1 is split into 2 smaller chunks. This falls into a similar situation as document update (example 1), and CocoIndex will take care of it. The approach is similar, while this involves some additional considerations: We can still safely reuse embeddings for 4 unchanged chunks by the caching mechanism: this needs an prequisite that the logic and spec for embedding is unchanged. If the changed part is the embedding logic or spec, we will recompute the embeddings for everything. CocoIndex is able to see if the logic or spec for a operation step is changed from the cached version, by putting these additional information in the cache key.
To remove stale rows in the target index, the lineage tracking works well again. Note that some other systems handle stale output deletions on source update/deletion by replaying the transformation logic on the previous version of input: this only works well when transformation is fully deterministic and never upgraded. CocoIndex's lineage tracking based approach doesn't have this limitation: it's robust to transformation logic non-determinism and changes. We can still safely reuse embeddings for 4 unchanged chunks by the caching mechanism: this needs an prequisite that the logic and spec for embedding is unchanged. If the changed part is the embedding logic or spec, we will recompute the embeddings for everything. CocoIndex is able to see if the logic or spec for a operation step is changed from the cached version, by putting these additional information in the cache key. To remove stale rows in the target index, the lineage tracking works well again. Note that some other systems handle stale output deletions on source update/deletion by replaying the transformation logic on the previous version of input: this only works well when transformation is fully deterministic and never upgraded. CocoIndex's lineage tracking based approach doesn't have this limitation: it's robust to transformation logic non-determinism and changes. Example 4: Multiple inputs involved: Merge / Lookup / Clustering All examples above are simple cases: each single input row (e.g. a document) is involved independently during each specific transformation. CocoIndex is a highly customizable framework, not only limited to simple chunking and embedding. It allows users for more complex advanced transformations, such as: Merge. For example, you're bulding an index for "all AI products", and you want to combine information from multiple sources, some product exists in one source and some in multiple. For each product, you want to combine information from different sources.
Lookup. For example, you also have a data source about company information. During you transformation for each product, you want to enrich it with information of the company building the product, so a lookup for the company information is needed.
Clustering. For example, you want to cluster different products into scenarios, and create cluster-level summaries based on information of products in the cluster. Merge. For example, you're bulding an index for "all AI products", and you want to combine information from multiple sources, some product exists in one source and some in multiple. For each product, you want to combine information from different sources. Merge Lookup. For example, you also have a data source about company information. During you transformation for each product, you want to enrich it with information of the company building the product, so a lookup for the company information is needed. Lookup Clustering. For example, you want to cluster different products into scenarios, and create cluster-level summaries based on information of products in the cluster. Clustering The common theme is that during transformation, multiple input rows (coming from single or multiple sources) need to be involved at the same time. Once a single input row is updated or deleted, CocoIndex will need to fetch other related rows from the the same or other sources. Here which other rows are needed is based on which are involved in the transformations. CocoIndex keeps track of such relationships, and will fetch related rows and trigger necessary reprocessings incrementally. Change Data Capture (CDC) 1. When source supports push change Some source connectors support push change. For example, Google Drive supports drive-level changelog and send change notifications to your public URL, which is applicable for team drive and personal drive (only by OAuth, service account not supported). When a file is created, updated, or deleted, CocoIndex could compute based on the diff. 2. Metadata-based, last modified only Some source connectors don't support push change, but provide metadata and file system operations that list most recent changed files. For example, Google Drive with service account. CocoIndex could monitor the change based on last modified vs last poll time, periodic trigger to check modified. However this cannot capture full change, for example a file has been deleted. 3. Metadata-based, Fullscan Some source connectors have limited capabilities with listing files, but provide metadata that list all files. For example, with local files, we'd need to traverse all files in all directories and subdirectories recursively to get the full list. When the number of files is large, it's expensive to traverse all files. Cache In CocoIndex, every piece of the lego block in the pipeline can be cached. Custom functions can take a paramter cache. When True, the executor will cache the result of the function for reuse during reprocessing. We recommend to set this to True for any function that is computationally intensive. Output will be reused if all these unchanged: spec (if exists), input data, behavior of the function. For this purpose, a behavior_version needs to be provided, and should increase on behavior changes. For example, this enables cache for a standalone function, see full code example here: @cocoindex.op.executor_class(gpu=True, cache=True, behavior_version=1)
class PdfToMarkdownExecutor:
    """Executor for PdfToMarkdown."""
      ... @cocoindex.op.executor_class(gpu=True, cache=True, behavior_version=1)
class PdfToMarkdownExecutor:
    """Executor for PdfToMarkdown."""
      ...

Why Recompute Everything When You Can Use This Solution to Keep Your AI Index Fresh Automatically

Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 1.1 Related literature 1.1 Related literature Types of Metrics and Their Hypothesis and 2.1 Types of metrics
2.2 Hypotheses for different types of metrics


Type I and Type II Error Rates for Decision Rules including Superiority and Non-Inferiority Tests
3.1 The composite hypotheses of superiority and non-inferiority tests
3.2 Bounding the type I and type II error rates for UI and IU testing
3.3 Bounding the error rates for a decision rule including both success and guardrail metrics
3.4 Power corrections for non-inferiority testing


Extending the Decision Rule with Deterioration and Quality Metrics


Monte Carlo Simulation Study
5.1 Results


Discussion and Conclusions Types of Metrics and Their Hypothesis and 2.1 Types of metrics
2.2 Hypotheses for different types of metrics Types of Metrics and Their Hypothesis and 2.1 Types of metrics Types of Metrics and Their Hypothesis and 2.1 Types of metrics 2.2 Hypotheses for different types of metrics 2.2 Hypotheses for different types of metrics Type I and Type II Error Rates for Decision Rules including Superiority and Non-Inferiority Tests
3.1 The composite hypotheses of superiority and non-inferiority tests
3.2 Bounding the type I and type II error rates for UI and IU testing
3.3 Bounding the error rates for a decision rule including both success and guardrail metrics
3.4 Power corrections for non-inferiority testing Type I and Type II Error Rates for Decision Rules including Superiority and Non-Inferiority Tests Type I and Type II Error Rates for Decision Rules including Superiority and Non-Inferiority Tests 3.1 The composite hypotheses of superiority and non-inferiority tests 3.1 The composite hypotheses of superiority and non-inferiority tests 3.2 Bounding the type I and type II error rates for UI and IU testing 3.2 Bounding the type I and type II error rates for UI and IU testing 3.3 Bounding the error rates for a decision rule including both success and guardrail metrics 3.3 Bounding the error rates for a decision rule including both success and guardrail metrics 3.4 Power corrections for non-inferiority testing 3.4 Power corrections for non-inferiority testing Extending the Decision Rule with Deterioration and Quality Metrics Extending the Decision Rule with Deterioration and Quality Metrics Extending the Decision Rule with Deterioration and Quality Metrics Monte Carlo Simulation Study
5.1 Results Monte Carlo Simulation Study Monte Carlo Simulation Study 5.1 Results 5.1 Results Discussion and Conclusions Discussion and Conclusions Discussion and Conclusions APPENDIX A: IMPROVING THE EFFICIENCY OF PROPOSITION 4.1 WITH ADDITIONAL ASSUMPTIONS APPENDIX A: APPENDIX A: IMPROVING THE EFFICIENCY OF PROPOSITION 4.1 WITH ADDITIONAL ASSUMPTIONS APPENDIX B: EXAMPLES OF GLOBAL FALSE AND TRUE POSITIVE RATES APPENDIX B: APPENDIX B: EXAMPLES OF GLOBAL FALSE AND TRUE POSITIVE RATES APPENDIX C: A NOTE ON SEQUENTIAL TESTING OF DETERIORATION APPENDIX C: APPENDIX C: A NOTE ON SEQUENTIAL TESTING OF DETERIORATION APPENDIX D: USING NYHOLT’S METHOD OF EFFICIENT NUMBER OF INDEPENDENT TESTS APPENDIX D: APPENDIX D: USING NYHOLT’S METHOD OF EFFICIENT NUMBER OF INDEPENDENT TESTS Acknowledgments and References Acknowledgments and References 4. EXTENDING THE DECISION RULE WITH DETERIORATION AND QUALITY METRICS Deterioration tests are inferiority tests that aim to capture regressions in metrics. They can be applied to metrics already present as a success or a guardrail metric, or to metrics that are only included in this category as deterioration metrics. The test checks if the metric is significantly deteriorating. That is, if the treatment group is significantly inferior to the control group with respect to the metric of interest. Deterioration tests for success and guardrail metrics attempt to identify significant regressions, which would, if they exist, speak against the success of the experiment. Neither the superiority test used for success metrics or the non-inferiority test used for guardrail metrics would on its own indicate a regression. Knowing when regressions occur is essential when running experiments. In practice, in addition to success, guardrail and detrioriation metrics, also experiment-quality metrics are used. For example, at Spotify, we include a set of tests and metrics that evaluate the quality of the experiment. These include a test for balanced traffic through a sample ratio mismatch test, and a test for pre-exposure bias. By including deterioration and quality tests in the decision rule, the complexity to manage the risks of an incorrect decision increases. Decision Rule 2 formalizes the complete decision rule that is used at Spotify. DECISION RULE 2. Ship the change if and only if: at least one success metric is significantly superior


all guardrail metrics are significantly non-inferior


none of the success, guardrail or deterioration metrics are significantly inferior


none of the quality tests are significantly rejecting quality at least one success metric is significantly superior at least one success metric is significantly superior all guardrail metrics are significantly non-inferior all guardrail metrics are significantly non-inferior none of the success, guardrail or deterioration metrics are significantly inferior none of the success, guardrail or deterioration metrics are significantly inferior none of the quality tests are significantly rejecting quality none of the quality tests are significantly rejecting quality That is, ship if and only if the global superiority/noninferiority null hypothesis is rejected in favor of the alternative hypothesis, the inferiority null hypothesis is not rejected for any metric, and no quality test is significant. Proposition 4.1 displays the corresponding correction, to bound the error rates of Decision Rule 2. This directly implies that In other words, the effect of the deterioration tests can only make the false positive rate lower than α. This concludes the first part of the proof. Plugging this into the above yield To find the corrected β for achieving the intended type II error rate, we solve The condition that α_ < β should be interpreted as that is only possible to correct the false negative rate (and thus ensuring the intended power) for decision rules that include deterioration and/or quality tests as long as the intended false negative rate for the decision is larger than the intended false positive rate for those tests. This is quite natural, if we add a large enough chance of rejecting no deterioration or quality, even when there is no deteriration or problem with the quality, this will at some point (for some α_) limit our ability to find a positive decision, regardless of the sample size. For success and guardrail metrics, there is a dependency between the rejection of the deterioration test and the superiority or non-inferiority test for any given metric, respectively. That is, if the superiority or non-inferiority test is rejected, this affects the probability of the deterioration test to be rejected too. It is possible to utilize this dependency to improve the efficiency of Proposition 4.1 slightly by making additional assumptions about the relation between α, α_, and β. See Appendix A for details. Authors:
(1) Mårten Schultzberg, Experimentation Platform team, Spotify, Stockholm, Sweden;
(2) Sebastian Ankargren, Experimentation Platform team, Spotify, Stockholm, Sweden;
(3) Mattias Frånberg, Experimentation Platform team, Spotify, Stockholm, Sweden. Authors: Authors: (1) Mårten Schultzberg, Experimentation Platform team, Spotify, Stockholm, Sweden; (2) Sebastian Ankargren, Experimentation Platform team, Spotify, Stockholm, Sweden; (3) Mattias Frånberg, Experimentation Platform team, Spotify, Stockholm, Sweden. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

How Companies Decide Which Product Changes to Keep or Scrap

In recent years, artificial intelligence (AI) has become an essential part of many industries, with health care being no exception. Its ability to enhance practices, streamline processes and provide real-time insights is reshaping the way care facilities operate. But how does AI improve patient safety in hospitals? Enhancing Surgical Precision with AI Enhancing Surgical Precision with AI Enhancing Surgical Precision with AI All surgical procedures require an incredibly high level of precision because even the smallest error can lead to complications. Although surgeons are highly trained professionals, they can benefit from technological assistance to enhance their accuracy and decision-making during difficult surgeries. AI video analytics is emerging as a vital tool in improving surgeries. Automated video systems can analyze live feeds of the surgical area, offering guidance to the surgical team throughout the procedure. By using advanced image recognition and machine learning algorithms, these systems can highlight critical areas of focus like tissue differentiation and even surgical tool positioning. live feeds of the surgical area live feeds of the surgical area AI can also track the team’s movements, identifying any deviations from optimal techniques or patterns. This feedback loop can help them correct any potential mistakes before they escalate, enhancing the overall precision of the operation. can also track the team’s movements can also track the team’s movements In high-stakes surgeries, AI video analytics offers an extra layer of support, providing objective analysis and real-time feedback to ensure only the most favorable outcomes. This boosts patient safety and may offer shorter recovery times and better overall outcomes. The Role of AI Video in Monitoring Sedation Levels The Role of AI Video in Monitoring Sedation Levels The Role of AI Video in Monitoring Sedation Levels The Role of AI Video in Monitoring Sedation Levels Both over- and under-sedation can lead to serious complications, including respiratory distress or waking up during surgery. Monitoring the medication’s delivery is critical for operations that require general anesthesia. Not only does it come with more risks, but it’s necessary for more intensive procedures, and waking up during these can be very dangerous. can lead to serious complications can lead to serious complications Traditionally, clinicians rely on various tools and their own observations to assess a patient’s sedation status. However, these methods can sometimes be subjective or prone to human error. AI-powered video monitoring is making waves in this area. By tracking subtle facial cues like blink rate, pupil dilation and facial muscle movement, AI systems can provide data on a patient’s sedation. They analyze video feeds and use algorithms to detect signs of over- or under-sedation. For instance, a decrease in blink rate could indicate a patient is too deeply sedated, while rapid blinking or facial tension may signal insufficient sedation. can provide data on can provide data on If the system detects irregularities in sedation, alerts can be sent to the medical team so they’re able to correct the issue. This makes it possible for clinicians to adjust sedation levels quickly and reduce the risk of complications. AI in Post-Surgery and Recovery AI in Post-Surgery and Recovery AI in Post-Surgery and Recovery AI in Post-Surgery and Recovery The period following surgery is essential for a patient’s recovery because it involves careful monitoring to ensure no complications occur. It is also important to ensure the healing process goes smoothly. Traditionally, post-surgery recovery relies on routine check-ups and patient self-reporting, but AI video analytics is now making this process more efficient and accurate. AI systems can now monitor patients during recovery by tracking their movements, alertness and overall well-being. For instance, video analysis can detect if a patient is attempting to get out of bed too early, which could lead to falls or injury. By analyzing facial expressions and body language, the system can also identify signs of pain or discomfort that might otherwise go unnoticed, which allows medical staff to respond more quickly to a patient’s needs. Automation technology can also monitor key recovery indicators like respiratory patterns or heart rate through video feeds. These can alert the medical team to irregularities like changes in breathing or excessive movement, which might indicate the need for intervention. can also monitor key recovery indicators can also monitor key recovery indicators AI-powered video can help track post-surgical physical therapy progress. For example, it can ensure patients are following the prescribed rehabilitation exercises, providing feedback to both them and clinicians. It can also measure the range of motion or symmetry in movements to support recovery efforts and prevent setbacks. Using AI in post-surgery care can help hospitals ensure faster, more accurate recovery monitoring, reducing complications and improving overall patient outcomes. are following the prescribed rehabilitation exercises are following the prescribed rehabilitation exercises Reducing Human Error and Improving Patient Care Reducing Human Error and Improving Patient Care Reducing Human Error and Improving Patient Care Reducing Human Error and Improving Patient Care Even the most skilled professionals are susceptible to human error, especially when exposed to high-stress environments like operating theatres. Fatigue, distractions, and the volume of patients can lead to mistakes in monitoring, medication administration and patient assessments. AI video analytics help reduce these errors by providing an extra layer of oversight and alerts that support decision-making. One of the largest areas where AI minimizes human error is in medication management. Automated systems can monitor whether the correct dosages and types of medication are being administered to ensure nurses or pharmacists follow proper protocols. This can be particularly useful in preventing dosage miscalculations. correct dosages and types of medication correct dosages and types of medication AI video also enhances patient safety by ensuring compliance with hospital hygiene protocols. Hand hygiene is one of the simplest factors in preventing further medical issues. AI-driven video monitoring can track whether health care workers sanitize their hands before and after patient interactions, reducing the risk of hospital-acquired infections. sanitize their hands before and after sanitize their hands before and after Strengthening Hospital Security Strengthening Hospital Security Strengthening Hospital Security Strengthening Hospital Security Hospitals are high-traffic environments where security challenges are constant. From unauthorized access to missing medical equipment, maintaining a safe and secure facility is essential for both patients and staff. Traditional security measures like CCTV cameras and on-site security personnel have limitations. AI-powered video surveillance enhances hospital security by providing real-time monitoring, automated alerts and predictive threat detection. by providing real-time monitoring by providing real-time monitoring One of the biggest advantages of AI-driven security is its ability to detect unusual behavior before it escalates into a security threat. Video analytics can identify loitering, unauthorized access or even the presence of concealed weapons. Unlike traditional security cameras, it analyzes video feeds continuously, flagging potential threats instantly so hospital security teams can respond faster. AI vision can also improve access control by integrating facial recognition and badge verification systems. This ensures only authorized personnel enter restricted zones like operating rooms, ICUs and pharmaceutical storage areas. It can also track visitors, logging their movements to prevent security breaches or unauthorized patient interactions. by integrating facial recognition by integrating facial recognition Another critical security challenge in hospitals is asset protection. Expensive medical equipment can get misplaced or stolen in the field, either through innocent or malicious means. AI-powered surveillance can track these assets using object recognition, ensuring essential tools are always available when necessary. can get misplaced or stolen can get misplaced or stolen Such video systems also play a role in emergency response. If a patient or visitor collapses, AI can instantly detect the fall and alert nearby medical staff. In cases of violence or distress, AI-driven cameras can identify escalating situations and notify security before they get out of control. Using automation for video surveillance can significantly enhance hospitals’ security infrastructure, ensuring a safer environment for patients, staff and visitors. How Does AI Improve Patient Safety in Hospitals? AI-powered video monitoring is transforming hospital safety in once-unimaginable ways. From ensuring patients receive the right level of sedation to enhancing surgical precision, supporting post-op recovery and tightening hospital security, AI is proving to be an invaluable tool in modern health care. As hospitals continue to evolve, automation-driven video technology will play an even greater role in keeping patients safe, streamlining operations and preventing potential crises before they unfold. In an environment where every second counts, having an extra set of “eyes” that never blink can make all the difference.

Hospitals Are Now Considering Using AI to Keep Patients Safe

Keeping your Twitter account active and engaging requires constant effort, but not everyone has the time to manually retweet relevant content throughout the day. This is where a Twitter Auto Retweet Bot becomes essential. By automating the retweeting process, a Twitter Auto Retweeter ensures that important posts get the exposure they deserve without requiring manual intervention. Whether you're a brand, influencer, or community manager, using automation helps maintain an active presence, improve engagement, and boost visibility on the platform. Twitter Auto Retweeter Twitter Auto Retweeter Additionally, a Twitter Auto Reposter can be set up to retweet specific hashtags, accounts, or keywords, making it a valuable tool for content curation and trend monitoring. Instead of constantly checking Twitter for updates, automation allows you to focus on other critical aspects of your social media strategy while ensuring your feed remains fresh and relevant. Circleboom: The Best Twitter Auto Retweet Bot with Smart Delay Options When it comes to automating Twitter engagement, Circleboom offers the best Twitter Auto Retweet Bot with advanced features like delayed retweets. Unlike basic automation tools, Circleboom allows users to set a specific time delay before retweeting, ensuring a more natural and organic engagement strategy. With Circleboom’s Twitter Auto Retweeter, you can automatically retweet content based on hashtags, keywords, or specific accounts while maintaining control over timing. This feature is particularly useful for businesses, influencers, and brands looking to enhance their visibility without appearing overly robotic. automatically retweet automatically retweet Additionally, Circleboom’s Twitter Auto Reposter helps users strategically space out retweets, preventing spam-like activity and increasing post effectiveness. Whether you’re managing a personal brand or a corporate account, Circleboom’s intelligent auto-retweeting options make it the go-to choice for seamless Twitter automation. Now, let me please show you how Circleboom makes it possible to auto retweet your X posts after a delay: Step #1: First of all, you should be a Circleboomer. Go to your browser and find Circleboom Publish. Step #1: Circleboom Publish Circleboom Publish You need a few seconds to create a new account if you are new to our platform. Step #2: The first page displays options for Twitter, Facebook, LinkedIn, Google My Business, Instagram, YouTube, Pinterest, Threads, TikTok, and Bluesky. Step #2: You can connect your accounts to the Circleboom Publish dashboard to manage multiple X accounts and other platforms all in one place! Step #3: Go to the left menu and select the X Dashboard. Step #3: Here, you can access advanced Twitter publishing features, including the AI Tweet Generator! AI Tweet Generator AI Tweet Generator AI Tweet Generator Step #4: Here, you need to create your tweet. Step #4: As mentioned earlier, you can use our advanced AI Tweet Generator to assist with crafting your next tweet. To speed things up, I'm sharing a blog post! Below, you'll find the retweet/repost icon. Clicking this icon will open the auto-retweet/auto-repost settings. Step #5: You can set the delay between each repost. Step #5: Additionally, you can customize the timing between auto-repost and auto-unrepost. As you can see, you can set it to repeat the loop after a few hours. Once you're done, you can share it immediately or schedule it for later. With Circleboom, you can also cross-post your tweets on Bluesky, Threads, LinkedIn, and Facebook simultaneously! A Comparison: Circleboom vs. PhantomBuster: The Best Twitter Auto Retweet Bot for Your Needs When looking for the best Twitter Auto Retweet Bot, two popular options stand out: Circleboom and PhantomBuster’s Auto Retweeter . Both tools offer automation for retweeting content, but they cater to different user needs. Circleboom Circleboom PhantomBuster’s Auto Retweeter PhantomBuster Circleboom provides a smart Twitter Auto Retweeter with an advanced delay feature, allowing users to schedule retweets at natural intervals. This prevents spam-like activity and enhances engagement by making automation appear more organic. It's ideal for brands, influencers, and businesses looking for a reliable, user-friendly solution with built-in safety measures. Circleboom Twitter Auto Retweeter On the other hand, PhantomBuster’s Auto Retweeter focuses on data-driven automation, offering integration with various APIs and customized workflow automation. While it’s a powerful tool for advanced users, it may require a steeper learning curve for those unfamiliar with automation scripts. PhantomBuster’s Auto Retweeter For users seeking a Twitter Auto Reposter that is easy to set up, provides delayed retweets, and ensures account safety, Circleboom stands out as the top choice. However, for those looking for deep customization and integration capabilities, PhantomBuster’s Auto Retweeter is a solid alternative. Other Auto Retweeters There are other alternatives auto retweet your X posts. Now, let me look at them shortly: Tactycs Retweeter For those looking for an AI-driven approach to Twitter automation, Tactycs Retweeter offers a powerful solution. Unlike traditional Twitter Auto Retweet Bots, Tactycs uses artificial intelligence to optimize retweets based on engagement patterns, content relevance, and audience behavior. This ensures that automated retweets add value rather than appearing random or spammy. By leveraging data-driven insights, Tactycs’ Twitter Auto Retweeter helps users maximize their reach while maintaining a strategic posting schedule. Tactycs Retweeter Twitter Auto Retweet Bots Tactycs’ Twitter Auto Retweeter Additionally, Tactycs’ Twitter Auto Reposter provides full customization options, allowing users to define specific hashtags, keywords, or accounts to retweet from. This makes it ideal for businesses, marketers, and influencers looking to streamline content curation while keeping their feeds active. With AI-powered automation and strategic retweeting, Tactycs Retweeter stands out as a smart alternative for those who want to enhance engagement without manual effort. Tactycs’ Twitter Auto Reposter Tactycs Retweeter Typefully Typefully offers a clean and efficient Twitter Auto Retweet Bot designed specifically for users who want to automate retweets without complexity. With its simple yet powerful interface, Typefully allows users to set up auto-retweets for their own X posts, ensuring that important tweets gain more visibility over time. This feature is especially useful for creators, brands, and professionals looking to extend the lifespan of their content and reach a broader audience. Typefully Twitter Auto Retweet Bot Unlike bulk automation tools, Typefully’s Twitter Auto Retweeter focuses on a streamlined experience, making it easy to schedule retweets at specific intervals. The platform is ideal for users who prioritize simplicity and efficiency while maintaining full control over their automation settings. Whether you want to reinforce key messages or maximize engagement, Typefully’s Twitter Auto Reposter provides a hassle-free way to enhance your content strategy on X. Typefully’s Twitter Auto Retweeter Typefully’s Twitter Auto Reposter Hypefury For users aiming to maximize engagement and grow their audience, Hypefury’s Twitter Auto Retweet Bot offers a strategic automation solution. Designed for content creators, influencers, and businesses, Hypefury’s auto-retweeter helps amplify tweets by automatically retweeting them at scheduled intervals. This ensures that your most valuable posts remain visible, reaching more users and increasing engagement without requiring constant manual effort. Hypefury’s Twitter Auto Retweet Bot What sets Hypefury’s Twitter Auto Retweeter apart is its seamless integration with content scheduling and engagement strategies. The platform allows users to set up automatic retweets for their own posts, reinforcing key messages and keeping their feed active. With smart automation, Hypefury’s Twitter Auto Reposter helps users build a stronger presence on Twitter by maximizing tweet visibility while maintaining a natural posting rhythm. Hypefury’s Twitter Auto Retweeter Hypefury’s Twitter Auto Reposter Final Words Automating retweets is a powerful strategy for increasing engagement, extending content reach, and maintaining an active Twitter presence. Whether you're an influencer, brand, or business, finding the right Twitter Auto Retweet Bot depends on your specific goals and preferred automation style. Circleboom stands out for its intuitive interface and smart delay feature, ensuring natural and effective retweeting. PhantomBuster provides a more technical approach, ideal for users who want API-powered automation. Tactycs Retweeter leverages AI to optimize retweets based on engagement and relevance, while Typefully offers a minimalist and user-friendly solution for auto-retweeting X posts. Finally, Hypefury focuses on growth-driven automation, making it a great choice for those looking to maximize tweet visibility. Each Twitter Auto Reposter has unique strengths, so the best choice depends on your specific needs—whether it’s simplicity, AI optimization, API integration, or content growth strategies. No matter which tool you choose, automating retweets can help you maintain a dynamic and engaging Twitter presence with minimal effort. Twitter Auto Reposter

The Best Auto-Retweet Tools to Keep Your X Account Active

የእርስዎን X መለያ ንቁ ለማድረግ በጣም ጥሩው በራስ-ሰር ዳግም የሚጫኑ መሳሪያዎች

Лепшыя інструменты аўтаматычнага рэтвіту для падтрымання актыўнасці вашага ўліковага запісу X

Най-добрите инструменти за автоматично ретуитване, за да поддържате вашия X акаунт активен

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Las mejores herramientas de retuit automático para mantener activa tu cuenta X

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X アカウントをアクティブに保つための最高の自動リツイート ツール

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Ny Fitaovana Auto-Retweet tsara indrindra hitazonana ny kaontinao X ho mavitrika

Najlepsze narzędzia do automatycznego retweetowania, dzięki którym Twoje konto X będzie aktywne

As melhores ferramentas de retuíte automático para manter sua conta X ativa

Ang Pinakamahusay na Auto-Retweet Tools para Panatilihing Aktibo ang Iyong X Account

Have you ever been in the middle of an important task, only for your Mac to dim the screen or go to sleep at the worst possible moment? Whether you’re giving a presentation, running a long download, monitoring real-time data, or just stepping away briefly, there are plenty of situations where you might need your Mac to stay awake. Fortunately, there are multiple ways to prevent your Mac from sleeping — some built-in, and others requiring a little extra help. Let’s explore the best options. Adjust Energy Saver Settings Adjust Energy Saver Settings One way to prevent your Mac from sleeping is to modify the system settings: For macOS Ventura and later: For macOS Ventura and later: Open System Settings → Displays → Advanced
Enable Prevent automatic sleeping when the display is off Open System Settings → Displays → Advanced Enable Prevent automatic sleeping when the display is off For macOS Monterey and earlier: For macOS Monterey and earlier: Go to System Preferences → Energy Saver
Adjust the Turn display off after slider to “Never” Go to System Preferences → Energy Saver Adjust the Turn display off after slider to “Never” This method works well if you only need to keep the system awake while plugged in. However, it doesn’t always prevent applications from detecting inactivity. Use Terminal Command Use Terminal Command For those comfortable with the command line, macOS provides a built-in tool called caffeinate that prevents sleep temporarily. How to use it: Open Terminal
Type: caffeinate -d Open Terminal Type: caffeinate -d This keeps your Mac awake until you close the Terminal window. To stop it, press Control + C or close the Terminal window. You can also use additional options like caffeinate -t 3600 to keep the Mac awake for an hour. External Hardware Solutions Some users opt for external hardware-based mouse jigglers, which are small USB devices that move the cursor automatically. These are plug-and-play solutions that don’t require software installation. While effective, they’re less flexible compared to software options. Third-Party Apps for Keeping Your Mac Awake Sometimes, built-in settings aren’t enough, especially if certain applications check for activity beyond system-level sleep prevention. That’s where third-party apps come in. Mouse Jiggler — For Simulating Activity Mouse Jiggler — For Simulating Activity Unlike traditional sleep prevention apps, Mouse Jiggler works by simulating actual cursor movement, which some applications and policies require to detect activity. When this is useful: When this is useful: • Remote Desktop & Virtual Machines — Apps like Citrix, VMware, and Microsoft RDP may log you out if there’s no mouse or keyboard activity. • Web Applications & Security Portals — Some platforms enforce automatic logouts unless movement is detected. • Live Monitoring Dashboards — Stock trading platforms, analytics dashboards, or real-time monitoring tools often pause or time out without user interaction. • Software Testing & Automation — Developers and IT professionals can use it to simulate user input during testing. Mouse Jiggler also includes Silent Mode, which moves the cursor invisibly to avoid interfering with workflows, along with customizable deactivation options. Download for free from the App Store: https://apps.apple.com/app/id6740313656 Download for free from the App Store https://apps.apple.com/app/id6740313656 Which Method Is Right for You? The best way to keep your Mac awake depends on your needs: ✅ For presentations, sleep prevention or preventing logouts in remote sessions → Mouse Jiggler provides actual movement detection. ✅ For command-line users → Terminal’s caffeinate command is a quick and easy option. ✅ For a plug-and-play solution → A hardware jiggler might be a good choice. Keeping your Mac awake doesn’t have to be complicated. Whether you prefer a built-in option or a third-party tool, there’s a solution that fits your workflow. What’s your preferred way to keep your Mac awake? Let us know in the comments! 🚀

How to Adjust Your Mac's Energy Saver Settings to Keep It Awake For Longer

Motivation is a huge thing when it comes to learning programming. Oh man, it is. I knew that when I was starting, but I wasn’t aware of just how important it would be later. I actually wonder if I’d even be able to do this if it weren’t for my schedule and the kind of isolated life I lead at the moment. schedule No, I’m not writing this from prison… well, kind of. Haha. I’ve been learning programming—mostly JavaScript and Ruby from The Odin Project for over a year now, and I’ve noticed some interesting patterns. These patterns started emerging from the very beginning when I was going through the foundations course, but at the time, I wasn’t really aware of them. Looking back, I think this might be something useful for people who are just starting. The Odin Project Before I get into these patterns, I want to mention that everything I’ve learned is based on my experience with The Odin Project’s curriculum, so it might not be the same if you’re learning from other resources. However, the human brain learns in similar ways across different fields, so chances are, you’ll find this relatable. The Two Motivation Patterns: “Draining Necessary Boredom” and “Dopamine Hit High” You can probably guess which one’s the low and which one’s the high. The “Draining Necessary Boredom” Phase The “Draining Necessary Boredom” Phase This happens when a new concept is introduced, and we’re suddenly thrown into documentation, long articles, and theoretical explanations. These texts are usually lengthy, written in overly complex language, and, honestly, boring as hell. At this stage, it’s a struggle just to stay focused. Our brain wants to procrastinate, we find our eyes glazing over the text. It’s a battle. But here’s the thing: I never skipped any of those documents or long articles provided by Odin. Why? Because I had already learned from other experiences (outside of programming) that this effort always pays off. So, even though I was almost falling asleep in front of my screen, I forced myself to push through—even when I didn’t understand half of what I was reading. Some topics required more reading than others, but trust me, this part is extremely important. Even if you don’t understand everything clearly, just read through it till the end and carry on. You might ask, Why not just watch a video or find an easier explanation? Well, here’s why: First: There’s usually an easier explanation (like a funny article or a well-made video) at the end of the lesson. (Again, thanks, Odin!) First: Second: By reading the full documentation, you’ll remember where to find the information when you get stuck in the future. Second: Third: And this one might sound stupid, but it’s not—MAGIC! Yes, dear reader, magic. Third: Because when you finally get to the project phase and sit down to plan out your code, you’ll realize you actually remember things from those boring lessons. And it happens every single time, for every single project. Okay, okay—it’s not magic. It’s just how the human brain works. But still, seeing it happen feels like magic. And knowing this keeps me motivated—especially when I start doubting myself. The Lifetime Plan: My “Why” As I said in this article, I always wanted to be independent—to have my own thing rather than working for someone else. this article Computers have always been a huge part of my life, and I’ve always wanted to build an online business. I’ve already had several attempts at affiliate marketing—which taught me a lot, even though they didn’t work out financially. I knew that if I wanted to take things to the next level, I had to learn how to code. And I also knew one thing for sure: If I started switching directions and picking up 100 different things, nothing would actually work out. I had to stick to ONE thing and just carry on. I chose to learn to code so that I could start building apps that solve problems and make money. This, or freelancing first to build an agency later. But honestly? I’d rather build apps. Now, you can already guess how this affects my motivation, right? Especially on a Monday morning. Haha. Finishing The Odin Project & Finally Building My Own Apps The third motivation boost is knowing that once I finish The Odin Project, I’ll finally start building those apps I’ve planned for so long. Right now, I have at least 60 app ideas saved up—waiting for me to validate and build them. I hate seeing all these people pushing through on X while I’m still grinding through lessons… But hey, it’s part of the process. And I deeply believe it’s better to spend proper time learning than to rush into something and make nasty mistakes just because of lack of technical knowledge. That doesn’t mean technical knowledge is everything—because it’s not. But you get my point. Final Thoughts So yeah, these three things have been working out for me so far! Trusting the process (even when it’s boring).


Having a strong reason WHY.


Pushing toward a clear goal: building my own apps. Trusting the process (even when it’s boring). Trusting the process (even when it’s boring). Having a strong reason WHY. Having a strong reason WHY. Pushing toward a clear goal: building my own apps. Pushing toward a clear goal: building my own apps. Now, I’m curious, what keeps YOU motivated? I feel like everyone has an interesting story behind their motivation. Drop yours in the comments below!

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