Workspace URL examples

Mike's Notes

Today, I'm diving deep into the existing configuration settings for industry domain-based applications. They were done for Pipi 6 and 7, which is a while ago. They now need to be edited and migrated into Pipi 9. Work on creating the workspace UI can then begin. Eventually, Pipi 9 will no longer be headless.

Resources

• https://www.blog.ajabbi.com/2025/09/workspace-url-naming-pattern.html
• https://www.blog.ajabbi.com/2020/01/pipi-6-2017-2019.html
• https://www.blog.ajabbi.com/2021/03/pipi-7-2020.html
• https://learn.ajabbi.com/eng/9/docs/index.html

References

• Reference

Repository

• Home > Ajabbi Research > Library >
• Home > Handbook > 

Last Updated

25/10/2025

Workspace URL examples

By: Mike Peters

On a Sandy Beach: 01/10/2025

Mike is the inventor and architect of Pipi and the founder of Ajabbi.

This is a working draft and subject to change as I experiment. I need to test this across multiple diverse industries to ensure it works automatically and reliably.

Note: The URLs below don't link to anything.

General Notes

• Industry object names can be aliased to conform to industry-specific terms, depending on the parent industry name. Hence, Stock in a Plant Nursery has a different meaning than Rolling Stock in Rail.
• Applying i18n will change the URLs. However, the underlying ASCI code names remain the same.
• Unique ASCI code names to avoid namespace collisions.
• These URLs are for logged-in users.
• Style Guide: Use plural or singular names? Are they all nouns?
• Each of these industry names has a corresponding three-letter code. They could also be used for the URLs. eg, cst/ for construction, but not very user-friendly.
• Ajabbi subdomain name options: workspace, app, cloud, or wsp. I think I will go with "cloud". It is shorter than "workspace". The term workspace can be used as a noun to describe what cloud.ajabbi.com is.

Industry names

Second draft revision of the Pipi 6 industry names used for English i18n URLs. The final revision will be imported into Pipi 9 for testing purposes. The nouns are singular.

• Agriculture: agriculture/
• Art: art/
• Aviation: aviation/
• Conservation: conservation/
• Construction: construction/
• Drainage: drainage/
• Electricity Supply: electricity-supply/
• Forestry: forestry/
• GLAM (Galleries-Libraries-Archives-Museums): glam/
• Learning: learn/
• Health: health/
• Horticulture: horticulture/
• Port: port/
• Rail: rail/
• Research: research/
• Road: road/
• Screen (was Film): screen/
• Sewer: sewer/
• Transport: transport/
• Water Supply: water-supply/
• Website: website/
• Zoo: zoo/

Industry objects

First draft revision of the Pipi 7 industry names used for URLs. Industry domains can be combined with industry objects, provided that this is allowed by schema constraints. Final revision will be imported into Pipi 9.

• Task: task/
• Settings: settings/
• Person: person/
• Script: script/
• Storyboard: storyboard/
• Shot list: shot/
• Shooting schedule: schedule/
• Prop: prop/
• Location: location/
• Location: location/l/
• Set: set/
• Crew: crew/
• Wardrobe: wardrobe/
• Rolling Stock: rolling-stock/
• Budget: budget/
• Loan: loan/
• Mail: email/
• Mail: email/inbox/
• Mail: email/inbox/i/
• Patient: patient/

Default Enterprise "e" deployment examples

The URL pattern is /e/industry name/industry object/

• demo.cloud.ajabbi.com/eng/9/e/aviation/aircraft/
• demo.cloud.ajabbi.com/eng/9/e/aviation/airport/
• demo.cloud.ajabbi.com/eng/9/e/aviation/airspace/
• demo.cloud.ajabbi.com/eng/9/e/aviation/cargo/
• demo.cloud.ajabbi.com/eng/9/e/aviation/flight/
• demo.cloud.ajabbi.com/eng/9/e/aviation/passenger/
• demo.cloud.ajabbi.com/eng/9/e/glam/collection/
• demo.cloud.ajabbi.com/eng/9/e/glam/loan/
• demo.cloud.ajabbi.com/eng/9/e/glam/9/event/
• demo.cloud.ajabbi.com/eng/9/e/rail/booking/
• demo.cloud.ajabbi.com/eng/9/e/rail/freight/
• demo.cloud.ajabbi.com/eng/9/e/rail/rolling-stock/new/
• demo.cloud.ajabbi.com/eng/9/e/rail/track/
• demo.cloud.ajabbi.com/eng/9/e/screen/budget/
• demo.cloud.ajabbi.com/eng/9/e/screen/location/
• demo.cloud.ajabbi.com/eng/9/e/screen/scritp/
• demo.cloud.ajabbi.com/eng/9/e/sewer/network/
• demo.cloud.ajabbi.com/eng/9/e/website/wiki/page-edit/

Additional examples

Use more levels if required.

• demo.cloud.ajabbi.com/eng/9/e/health/email/inbox/
• demo.cloud.ajabbi.com/eng/9/e/health/email/inbox/i/

Workspace URL naming pattern

Mike's Notes

I'm working out a pattern to use for naming workspace URLs. This is part of the current build roadmap.

Resources

• https://en.wikipedia.org/wiki/Google_Workspace
• https://en.wikipedia.org/wiki/Zoho_Office_Suite
• https://www.blog.ajabbi.com/2025/09/the-internal-control-hierarchy-of-pipi.html
• https://www.blog.ajabbi.com/2025/09/localisation-options.html
• https://developer.ajabbi.com/eng/9/report/roadmap.html
• https://help.forcepoint.com/fpone/deploy/rhtml/application/guid-ee073056-4fe1-40f5-b44e-3bf21491f0bb.html
• https://learn.microsoft.com/en-us/microsoft-365/enterprise/urls-and-ip-address-ranges?view=o365-worldwide
• https://support.google.com/a/answer/53340?hl=en
• https://web.dev/blog/baseline-urlpattern
• https://developer.mozilla.org/en-US/docs/Web/API/URLPattern

References

• Reference

Repository

• Home > Ajabbi Research > Library >
• Home > Handbook > 

Last Updated

21/11/2025

Workspace URL naming pattern

By: Mike Peters

On a Sandy Beach: 30/09/2025

Mike is the inventor and architect of Pipi and the founder of Ajabbi.

Logged-in Ajabbi users will be able to use web-based applications called a workspace. Each application requires a URL that follows a predefined pattern.

Again, this is a work in progress and is likely to change, especially as it addresses performance, usability, security, and privacy issues.

Here are examples used by other companies.

Google Workspace example URLs

• https://calendar.google.com/calendar/u/0/r/week
• https://calendar.google.com/calendar/u/0/r/month
• https://mail.google.com/mail/u/0/#inbox
• https://mail.google.com/mail/u/0/#sent
• https://draft.blogger.com/blog/posts/jsdksjdksJK;Sjk;SJDKsjd/
• https://draft.blogger.com/blog/post/edit/hhddfddfd/
• https://docs.google.com/document/d/5fd8f5/
• https://contacts.google.com/directory
• https://contacts.google.com/person/123456789/
• https://groups.google.com/all-groups
• https://groups.google.com/g/ontolog-forum
• https://groups.google.com/g/ontolog-forum/c/coj8JqR6nzw

Zoho Office Suite example URLs

• https://www.zoho.com/mail/
• https://accounts.zoho.com.au/signin?

Service Now example URLs

• <instance>.service-now.com/now/cmdb/relationship-health-dashboard/
• https://www.servicenow.com/docs/bundle/zurich-healthcare-life-sciences/page/product/healthcare-life-sciences/concept/hcls-cto-care-team-portal.html
• <instance>.service-now.com/now/servicenow-studio/home
• https://www.servicenow.com/docs/bundle/zurich-application-development/page/administer/ui-builder/concept/ui-builder-overview.html

MuleSoft example URLs

• https://docs.mulesoft.com/exchange/to-describe-an-asset

---

Note: The URLs below don't link to anything.

Ajabbi workspace domain

Note: workspace. or app. or cloud. or wsp/ ? I have decided on cloud.

The default naked domain URL is

• https;//cloud.ajabbi.com/

The user account code name will be added as a URL before the domain.

• https;//example.cloud.ajabbi.com/

Domain redirection enables

• https://cloud.example.com/

Ajabbi Workspace proposed available URL patterns

A lot of customisation will be possible for user accounts.

• https://cloud.ajabbi.com/eng/9/e/calendar/
• https://example.cloud.ajabbi.com/eng/9/e/calendar/
• https://example.com/cloud/eng/9/e/calendar/
• https://example.com/eng-uk/cloud/9/e/calendar/
• https://app.example.com/eng-uk/e/calendar/
• https://calendar.example.com/eng-uk/
• https://en.example.com/workspace/e/calendar/
• https://fr.example.com/espace/e/calendrier/

Workspace application directories

Each application has directories associated with different tasks.

Mail

• inbox/
• draft/
• sent/

Some simple examples using mail.

• https://cloud.ajabbi.com/eng/9/e/email/inbox/
• https://cloud.ajabbi.com/eng/9/e/email/draft/12345678/

Security concerns

Long, meaningless code will be used to name endpoints similar to those used by Google.

• https;//cloud.ajabbi.com/eng/9/e/email/draft/hnjsdhtrhxn79snrfusni9c5/

To do next

1. Define the code names to use with all the workspace applications
2. Build some static web-based workspace mockups
3. Make some examples in other languages and scripts
4. Share with volunteer testers
5. Reiterate till people are happy
6. Build a working demo at
• https://demo.cloud.ajabbi.com/
7. Provide a Template Engine template for the Pipi Render Engine to render on demand from the Pipi Deployment Engine.
8. Automate the deployment of workspaces for logged-in users.

Ajabbi high-level navigation options

Mike's Notes

Ajabbi is the home of Pipi. Today's task has been to think about the common navigation bar used across Ajabbi.com and make it more useful.

The common navigation bar has been added to the top of this blog website as an experiment.

Resources

• Resource

References

• Reference

Repository

• Home > Ajabbi Research > Library >
• Home > Handbook > 

Last Updated

02/02/2026

Ajabbi high-level navigation options

By: Mike Peters

On a Sandy Beach: 29/09/2025

Mike is the inventor and architect of Pipi and the founder of Ajabbi.

Common navigation bar

The ajabbi.com website uses a common navigation bar across all its subdomain sites. It deliberately resembles a ribbon. It's inspired by PostHog's previous website navigation.

For logged-in users, a toolbar will be added to each sub-menu.

The bar comprises 8 menu items, 6 of which are permanent and 2 are contextual. Choosing any permanent menu item brings up a sub-menu and 2 possible contextual menus. There are spaces for a total of 12 contextual menus (6x2).

Sub-menu

There is room for up to 8 sub-menu items.

8 menu items x 8 sub-menu items gives a total of 64 possible sub-menu items.

Existing permanent menu items (6)

• Ajabbi.com
• Learn
• Community
• Handbook
• Foundation
• Research

Existing contextual menu items (12 possible)

• Developer
• pipiWiki
• i18n
• Project
• Design

Possible menu items

• Example
• i18n
• API
• Schema
• Help
• Blog
• Cloud

Audience

Are these menu options helpful for website visitors? Who are the groups by audience/task/need?

• Ajabbi.com (customers)
• Blog (readers, curious about why)
• Community (users)
• Developers (building stuff)
• Foundation (supporting open-source)
• Researcher (standards & science behind Pipi)

---

Changes to be made to the common navigation bar

Each menu item choice has 2 contextual items.

Ajabbi.com

• Ajabbi.com
• About
• Legal
• Privacy
• News
• Press Releases
• Pricing
• Learn
• Community
• Handbook
• Foundation
• Research
• Developer
• i18n

Blog

• Ajabbi.com
• Learn
• Community
• Handbook
• Foundation
• Research
• Developer
• Blog
• Newsletter

Community

• Ajabbi.com
• Learn
• Reference
• Docs
• Guides
• Tutorials
• Demo
• Community
• Handbook
• Foundation
• Research
• Project
• i18n

Design

• Ajabbi.com
• Learn
• Community
• Handbook
• Foundation
• Research
• Design
• Acessibility
• Components
• Content
• Data Visualisation
• Foundations
• Objects
• Style Guide
• Tokens
• Usability
• i18n

Developer

• Ajabbi.com
• Learn
• Community
• Handbook
• Foundation
• Research
• Developer
• Reports
• Support
• Tools
• Translate
• i18n

Learn

• Ajabbi.com
• Learn
• Reference
• Docs
• Guides
• Tutorials
• Demo
• Community
• Handbook
• Foundation
• Research
• pipiWiki
• i18n

Foundation

• Ajabbi.com
• Learn
• Community
• Handbook
• Foundation
• Mission
• Board
• Program
• Events
• User Groups
• Research
• TBA1
• i18n

Handbook

• Ajabbi.com
• Learn
• Community
• Handbook
• Ajabbi
• Handbook
• Design
• Documentation
• Engineering
• Product
• Publication
• Teams
• Foundation
• Research
• Design
• i18n

i18n

Ajabbi.com

• Learn
• Community
• Handbook
• Foundation
• Research
• Design
• i18n
• Languages
• Downloads

pipiWiki

• Ajabbi.com
• Learn
• Community
• Handbook
• Foundation
• Research
• pipiWiki
• Recently Added
• Interaction
• Toolbox
• Platform
• i18n

Project

• Ajabbi.com
• Learn
• Community
• Handbook
• Foundation
• Research
• Project
• Current
• Planned
• Completed
• Workshops
• Status
• FAQ
• i18n

Research

• Ajabbi.com
• Learn
• Community
• Handbook
• Foundation
• Research
• Research
• News & Events
• People
• Complex Systems
• About
• TBA1
• i18n

On a Sandy Beach, database version 2 is underway

Mike's Notes

In May, after manually reformatting every page and post of "On a Sandy Beach," I wrote.

"A blogging module needs to be built and added to Pipi 9 CMS. This could then be used to create blog posts using an underlying database, which could be modified to be more useful."

Resources

• https://www.blog.ajabbi.com/2025/05/on-sandy-beach-reformat-is-done-whats.html
• https://www.blog.ajabbi.com/2025/09/ajabbi-research-monthly-newsletter.html
• https://developers.google.com/blogger/docs/3.0/using
• https://developers.google.com/blogger/docs/3.0/reference
• https://developers.google.com/blogger/docs/3.0/libraries

References

• Reference

Repository

• Home > Ajabbi Research > Library >
• Home > Handbook > 

Last Updated

28/09/2025

On a Sandy Beach, database version 2 is underway

By: Mike Peters

On a Sandy Beach: 28/09/2025

Mike is the inventor and architect of Pipi and the founder of Ajabbi.

The datamodel version 2 to support blogging is now being built. It is designed to support On a Sandy Beach. Yesterday, this Blogger post index was scraped and imported to initially populate the database.

In future, the blogging module will also support other blogs/newsletters, including the Ajabbi Research Monthly Newsletter, which begins next month in October on Substack.

The new database and blogger will be synced while other jobs are completed, including;

• The tags need consolidating
• The same tags will form a topic map and be used across Ajabbi
• etc

Data Model version 1 (current)

• Mike's Note
• Resources
• References
• Repository links
• Date Updated
• Title
• Page Url
• Author
• Source publication
• Date Created
• Author description
• Body of the article
• Tags
• Comments

Data Model version 2 (now being built)

• Title
• Page Url
• Site-wide Navigation
• Site-wide Breadcrumb
• Mike's Note
• Author
• Source publication
• Date Created
• Author description
• Body of the article
• References
• Further Reading (replacing References)
• Articles
• See Also (cross-links to Ajabbi.com website pages, replacing Repository URL)
• External Links (replacing Resources)
• Keywords (replacing Tags)
• Sharing
• Updated
• Forum (replacing Comments)

The Bitter Lesson

Mike's Notes

I found this article written by Rich Sutton in today's Gary Marcus Substack.

I agree with both Rich and Gary, LLMs don't have a world model, and that's a point of failure. LLMs are great for translating between languages. They are overhyped and contributing to a speculative bubble. There will be tears.

Resources

• http://www.incompleteideas.net/
• http://www.incompleteideas.net/IncIdeas/BitterLesson.html
• https://garymarcus.substack.com/

References

• Reference

Repository

• Home > Ajabbi Research > Library > Subscriptions > Marcus on AI
• Home > Handbook > 

Last Updated

27/09/2025

The Bitter Lesson

By: Rich Sutton

Incomplete Ideas: 13/03/2019

I am seeking to identify general computational principles underlying what we mean by intelligence and goal-directed behavior. I start with the interaction between the intelligent agent and its environment. Goals, choices, and sources of information are all defined in terms of this interaction. In some sense it is the only thing that is real, and from it all our sense of the world is created. How is this done? How can interaction lead to better behavior, better perception, better models of the world? What are the computational issues in doing this efficiently and in realtime? These are the sort of questions that I ask in trying to understand what it means to be intelligent, to predict and influence the world, to learn, perceive, act, and think..

The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin. The ultimate reason for this is Moore's law, or rather its generalization of continued exponentially falling cost per unit of computation. Most AI research has been conducted as if the computation available to the agent were constant (in which case leveraging human knowledge would be one of the only ways to improve performance) but, over a slightly longer time than a typical research project, massively more computation inevitably becomes available. Seeking an improvement that makes a difference in the shorter term, researchers seek to leverage their human knowledge of the domain, but the only thing that matters in the long run is the leveraging of computation. These two need not run counter to each other, but in practice they tend to. Time spent on one is time not spent on the other. There are psychological commitments to investment in one approach or the other. And the human-knowledge approach tends to complicate methods in ways that make them less suited to taking advantage of general methods leveraging computation.  There were many examples of AI researchers' belated learning of this bitter lesson, and it is instructive to review some of the most prominent.

In computer chess, the methods that defeated the world champion, Kasparov, in 1997, were based on massive, deep search. At the time, this was looked upon with dismay by the majority of computer chess researchers who had pursued methods that leveraged human understanding of the special structure of chess. When a simpler, search-based approach with special hardware and software proved vastly more effective, these human-knowledge-based chess researchers were not good losers. They said that ``brute force" search may have won this time, but it was not a general strategy, and anyway it was not how people played chess. These researchers wanted methods based on human input to win and were disappointed when they did not.

A similar pattern of research progress was seen in computer Go, only delayed by a further 20 years.

Enormous initial efforts went into avoiding search by taking advantage of human knowledge, or of the special features of the game, but all those efforts proved irrelevant, or worse, once search was applied effectively at scale. Also important was the use of learning by self play to learn a value function (as it was in many other games and even in chess, although learning did not play a big role in the 1997 program that first beat a world champion). Learning by self play, and learning in general, is like search in that it enables massive computation to be brought to bear. Search and learning are the two most important classes of techniques for utilizing massive amounts of computation in AI research.

In computer Go, as in computer chess, researchers' initial effort was directed towards utilizing human understanding (so that less search was needed) and only much later was much greater success had by embracing search and learning.

In speech recognition, there was an early competition, sponsored by DARPA, in the 1970s. Entrants included a host of special methods that took advantage of human knowledge---knowledge of words, of phonemes, of the human vocal tract, etc. On the other side were newer methods that were more statistical in nature and did much more computation, based on hidden Markov models (HMMs).

Again, the statistical methods won out over the human-knowledge-based methods. This led to a major change in all of natural language processing, gradually over decades, where statistics and computation came to dominate the field. The recent rise of deep learning in speech recognition is the most recent step in this consistent direction. Deep learning methods rely even less on human knowledge, and use even more computation, together with learning on huge training sets, to produce dramatically better speech recognition systems. As in the games, researchers always tried to make systems that worked the way the researchers thought their own minds worked---they tried to put that knowledge in their systems---but it proved ultimately counterproductive, and a colossal waste of researcher's time, when, through Moore's law, massive computation became available and a means was found to put it to good use.

In computer vision, there has been a similar pattern. Early methods conceived of vision as searching for edges, or generalized cylinders, or in terms of SIFT features. But today all this is discarded.

Modern deep-learning neural networks use only the notions of convolution and certain kinds of invariances, and perform much better.

This is a big lesson. As a field, we still have not thoroughly learned it, as we are continuing to make the same kind of mistakes. To see this, and to effectively resist it, we have to understand the appeal of these mistakes. We have to learn the bitter lesson that building in how we think we think does not work in the long run. The bitter lesson is based on the historical observations that 1) AI researchers have often tried to build knowledge into their agents, 2) this always helps in the short term, and is personally satisfying to the researcher, but 3) in the long run it plateaus and even inhibits further progress, and 4) breakthrough progress eventually arrives by an opposing approach based on scaling computation by search and learning. The eventual success is tinged with bitterness, and often incompletely digested, because it is success over a favored, human-centric approach.

One thing that should be learned from the bitter lesson is the great power of general purpose methods, of methods that continue to scale with increased computation even as the available computation becomes very great. The two methods that seem to scale arbitrarily in this way are search and learning.

The second general point to be learned from the bitter lesson is that the actual contents of minds are tremendously, irredeemably complex; we should stop trying to find simple ways to think about the contents of minds, such as simple ways to think about space, objects, multiple agents, or symmetries.

All these are part of the arbitrary, intrinsically-complex, outside world. They are not what should be built in, as their complexity is endless; instead we should build in only the meta-methods that can find and capture this arbitrary complexity. Essential to these methods is that they can find good approximations, but the search for them should be by our methods, not by us. We want AI agents that can discover like we can, not which contain what we have discovered. Building in our discoveries only makes it harder to see how the discovering process can be done

Data Colada Table of Contents

Mike's Notes

Data Colada is a remarkable effort by Uri Simonsohn, Leif Nelson and Joe Simmons on topics such as fake data, research design, meta-analysis, and the reproducibility of science, among others.

Here is the table of contents of Data Colada.

Resources

• https://datacolada.org/

References

• Reference

Repository

• Home > Ajabbi Research > Library >
• Home > Handbook > 

Last Updated

26/09/2025

Data Colada Table of Contents

By: Uri Simonsohn, Leif Nelson and Joe Simmons.

Data Colada: 26/09/2025

Thinking about evidence and vice versa.

---

Table of Contents

About Research Design

• [31] Women are taller than men: Misusing Occam’s Razor to lobotomize discussions of alternative explanations
• [36] How to Study Discrimination (or Anything) With Names; If You Must
• [46] Controlling the Weather
• [51] Greg vs. Jamal: Why Didn’t Bertrand and Mullainathan (2004) Replicate?
• [124] "Complexity": 75% of participants missed comprehension questions in AER paper critiquing Prospect Theory

About Research Tips

• [10] Reviewers are asking for it
• [34] My Links Will Outlive You
• [40] Reducing Fraud in Science
• [69] Eight things I do to make my open research more findable and understandable

Comment on media coverage

• [65] Spotlight on Science Journalism: The Health Benefits of Volunteering

Credibility Lab

• [95] Groundhog: Addressing The Threat That R Poses To Reproducible Research
• [100] Groundhog 2.0: Further addressing the threat R poses to reproducible research

Data Replicada

• [81] Data Replicada
• [82] Data Replicada #1: Do Elevated Viewpoints Increase Risk Taking?
• [83] Data Replicada #2: Do Self-Construal and Group Size Influence How People Make Choices on Behalf of a Group?
• [84] Data Replicada #3: Does Self-Concept Uncertainty Influence Magazine Subscription Choice?
• [85] Data Replicada #4: The Problem of Hidden Confounds
• [87] Data Replicada #5: Do Human-Like Products Inspire More Holistic Judgments?
• [89] Data Replicada #6: The Problem of (Weird) Differential Attrition
• [90] Data Replicada #7: Does Displaying Multiple Copies of a Product Increase Its Perceived Effectiveness?
• [92] Data Replicada #8: Is The Left-Digit Bias Stronger When Prices Are Presented Side-By-Side?
• [94] Data Replicada #9: Are Progression Ads More Credible?
• [97] Data Replicada #10: Does Goal Conflict Affect Time Spent on Work and Leisure?

Discuss own paper

• [30] Trim-and-Fill is Full of It (bias)
• [43] Rain & Happiness: Why Didn’t Schwarz & Clore (1983) ‘Replicate’ ?
• [45] Ambitious P-Hacking and P-Curve 4.0
• [49] P-Curve Won’t Do Your Laundry, But Will Identify Replicable Findings
• [62] Two-lines: The First Valid Test of U-Shaped Relationships
• [73] Don't Trust Internal Meta-Analysis
• [75] Intentionally Biased: People Purposely Don't Ignore Information They "Should" Ignore
• [126] Stimulus Plots

Discuss Paper by Others

• [1] "Just Posting It" works, leads to new retraction in Psychology
• [3] A New Way To Increase Charitable Donations: Does It Replicate?
• [11] “Exactly”: The Most Famous Framing Effect Is Robust To Precise Wording
• [25] Maybe people actually enjoy being alone with their thoughts
• [38] A Better Explanation Of The Endowment Effect
• [50] Teenagers in Bikinis: Interpreting Police-Shooting Data
• [54] The 90x75x50 heuristic: Noisy & Wasteful Sample Sizes In The “Social Science Replication Project”
• [60] Forthcoming in JPSP: A Non-Diagnostic Audit of Psychological Research
• [74] In Press at Psychological Science: A New 'Nudge' Supported by Implausible Data
• [79] Experimentation Aversion: Reconciling the Evidence
• [82] Data Replicada #1: Do Elevated Viewpoints Increase Risk Taking?
• [83] Data Replicada #2: Do Self-Construal and Group Size Influence How People Make Choices on Behalf of a Group?
• [84] Data Replicada #3: Does Self-Concept Uncertainty Influence Magazine Subscription Choice?
• [85] Data Replicada #4: The Problem of Hidden Confounds
• [86] The Data Colada Seminar Series
• [87] Data Replicada #5: Do Human-Like Products Inspire More Holistic Judgments?
• [89] Data Replicada #6: The Problem of (Weird) Differential Attrition
• [90] Data Replicada #7: Does Displaying Multiple Copies of a Product Increase Its Perceived Effectiveness?
• [92] Data Replicada #8: Is The Left-Digit Bias Stronger When Prices Are Presented Side-By-Side?
• [94] Data Replicada #9: Are Progression Ads More Credible?
• [96] Madam Speaker: Are Female Presenters Treated Worse in Econ Seminars?
• [97] Data Replicada #10: Does Goal Conflict Affect Time Spent on Work and Leisure?
• [98] Evidence of Fraud in an Influential Field Experiment About Dishonesty
• [99] Hyping Fisher: The Most Cited 2019 QJE Paper Relied on an Outdated Stata Default to Conclude Regression <i>p</i>-values Are Inadequate
• [101] Transparency Makes Research Evaluable: Evaluating a Field Experiment on Crime Published in <i>Nature</i>
• [119] A Hidden Confound in a <i>Psych Methods</i> Pre‑registrations Critique
• [121] Dear Political Scientists: Don't Bin, GAM Instead
• [122] Arresting Flexibility: A QJE field experiment on police behavior with about 40 outcome variables
• [124] "Complexity": 75% of participants missed comprehension questions in AER paper critiquing Prospect Theory
• [125] "Complexity" 2: Don't be mean to the median
• [127] Meaningless Means #4: Correcting Scientific Misinformation
• [128] LinkedOut: The Best Published Audit Study, And Its Interesting Shortcoming

Effect size

• [18] MTurk vs. The Lab: Either Way We Need Big Samples
• [20] We cannot afford to study effect size in the lab
• [28] Confidence Intervals Don't Change How We Think about Data
• [63] "Many Labs" Overestimated The Importance of Hidden Moderators
• [70] How Many Studies Have Not Been Run? Why We Still Think the Average Effect Does Not Exist

Fake data

• [1] "Just Posting It" works, leads to new retraction in Psychology
• [19] Fake Data: Mendel vs. Stapel
• [21] Fake-Data Colada: Excessive Linearity
• [40] Reducing Fraud in Science
• [74] In Press at Psychological Science: A New 'Nudge' Supported by Implausible Data
• [77] Number-Bunching: A New Tool for Forensic Data Analysis
• [98] Evidence of Fraud in an Influential Field Experiment About Dishonesty
• [109] Data Falsificada (Part 1): "Clusterfake"
• [110] Data Falsificada (Part 2): "My Class Year Is Harvard"
• [111] Data Falsificada (Part 3): "The Cheaters Are Out of Order"
• [112] Data Falsificada (Part 4): "Forgetting The Words"
• [114] Exhibits 3, 4, and 5
• [117] The Impersonator: The Fake Data Were Coming From Inside the Lab
• [118] Harvard’s Gino Report Reveals How A Dataset Was Altered

file-drawer

• [24] P-curve vs. Excessive Significance Test
• [55] The file-drawer problem is unfixable, and that’s OK
• [58] The Funnel Plot is Invalid Because of This Crazy Assumption: r(n,d)=0
• [59] PET-PEESE Is Not Like Homeopathy
• [71] The (Surprising?) Shape of the File Drawer
• [72] Metacritic Has A (File-Drawer) Problem
• [73] Don't Trust Internal Meta-Analysis

Interactions

• [17] No-way Interactions
• [57] Interactions in Logit Regressions: Why Positive May Mean Negative
• [80] Interaction Effects Need Interaction Controls
• [123] Dear Political Scientists: The binning estimator violates ceteris paribus

Just fun

• [5] The Consistency of Random Numbers
• [8] Adventures in the Assessment of Animal Speed and Morality
• [9] Titleogy: Some facts about titles
• [15] Citing Prospect Theory
• [22] You know what's on our shopping list
• [32] Spotify Has Trouble With A Marketing Research Exam
• [56] TWARKing: Test-Weighting After Results are Known
• [72] Metacritic Has A (File-Drawer) Problem
• [120] Off-Label Smirnov: How Many Subjects Show an Effect in Between-Subjects Experiments?

Lawsuits

• [113] Data Litigada: Thank You (And An Update)
• [114] Exhibits 3, 4, and 5
• [116] Our (First?) Day In Court
• [118] Harvard’s Gino Report Reveals How A Dataset Was Altered

Meta Analysis

• [30] Trim-and-Fill is Full of It (bias)
• [33] "The" Effect Size Does Not Exist
• [58] The Funnel Plot is Invalid Because of This Crazy Assumption: r(n,d)=0
• [59] PET-PEESE Is Not Like Homeopathy
• [63] "Many Labs" Overestimated The Importance of Hidden Moderators
• [76] Heterogeneity Is Replicable: Evidence From Maluma, MTurk, and Many Labs
• [104] Meaningless Means: Some Fundamental Problems With Meta-Analytic Averages
• [105] Meaningless Means #1: The Average Effect<BR> of Nudging Is d = .43
• [106] Meaningless Means #2: The Average Effect of Nudging in Academic Publications is 8.7%
• [107] Meaningless Means #3: The Truth About Lies
• [127] Meaningless Means #4: Correcting Scientific Misinformation

Music

• [2] Using Personal Listening Habits to Identify Personal Music Preferences
• [32] Spotify Has Trouble With A Marketing Research Exam
• [72] Metacritic Has A (File-Drawer) Problem

On Bayesian Stats

• [13] Posterior-Hacking
• [35] The Default Bayesian Test is Prejudiced Against Small Effects
• [42] Accepting the Null: Where to Draw the Line?
• [78a] If you think p-values are problematic, wait until you understand Bayes Factors
• [78b] Hyp-Chart, the Missing Link Between P-values and Bayes Factors
• [78c] Bayes Factors in Ten Recent Psych Science Papers

Opinion

• [52] Menschplaining: Three Ideas for Civil Criticism
• [53] What I Want Our Field To Prioritize

p-curve

• [24] P-curve vs. Excessive Significance Test
• [37] Power Posing: Reassessing The Evidence Behind The Most Popular TED Talk
• [41] Falsely Reassuring: Analyses of ALL p-values
• [45] Ambitious P-Hacking and P-Curve 4.0
• [49] P-Curve Won’t Do Your Laundry, But Will Identify Replicable Findings
• [59] PET-PEESE Is Not Like Homeopathy
• [60] Forthcoming in JPSP: A Non-Diagnostic Audit of Psychological Research
• [61] Why p-curve excludes ps>.05
• [66] Outliers: Evaluating A New P-Curve Of Power Poses
• [67] P-curve Handles Heterogeneity Just Fine
• [91] p-hacking fast and slow: Evaluating a forthcoming AER paper deeming some econ literatures less trustworthy
• [129] P-curve works in practice, but would it work if you dropped a piano on it?

p-hacking

• [29] Help! Someone Thinks I p-hacked
• [48] P-hacked Hypotheses Are Deceivingly Robust
• [68] Pilot-Dropping Backfires (So Daryl Bem Probably Did Not Do It)
• [73] Don't Trust Internal Meta-Analysis
• [91] p-hacking fast and slow: Evaluating a forthcoming AER paper deeming some econ literatures less trustworthy
• [122] Arresting Flexibility: A QJE field experiment on police behavior with about 40 outcome variables

Preregistration

• [12] Preregistration: Not just for the Empiro-zealots
• [44] AsPredicted: Pre-registration Made Easy
• [55] The file-drawer problem is unfixable, and that’s OK
• [64] How To Properly Preregister A Study
• [101] Transparency Makes Research Evaluable: Evaluating a Field Experiment on Crime Published in <i>Nature</i>
• [115] Preregistration Prevalence
• [119] A Hidden Confound in a <i>Psych Methods</i> Pre‑registrations Critique
• [122] Arresting Flexibility: A QJE field experiment on police behavior with about 40 outcome variables

Replication

• [4] The Folly of Powering Replications Based on Observed Effect Size
• [7] Forthcoming in the American Economic Review: A Misdiagnosed Failure-to-Replicate
• [11] “Exactly”: The Most Famous Framing Effect Is Robust To Precise Wording
• [23] Ceiling Effects and Replications
• [37] Power Posing: Reassessing The Evidence Behind The Most Popular TED Talk
• [38] A Better Explanation Of The Endowment Effect
• [43] Rain & Happiness: Why Didn’t Schwarz & Clore (1983) ‘Replicate’ ?
• [47] Evaluating Replications: 40% Full ≠ 60% Empty
• [51] Greg vs. Jamal: Why Didn’t Bertrand and Mullainathan (2004) Replicate?
• [54] The 90x75x50 heuristic: Noisy & Wasteful Sample Sizes In The “Social Science Replication Project”
• [76] Heterogeneity Is Replicable: Evidence From Maluma, MTurk, and Many Labs
• [82] Data Replicada #1: Do Elevated Viewpoints Increase Risk Taking?
• [83] Data Replicada #2: Do Self-Construal and Group Size Influence How People Make Choices on Behalf of a Group?
• [84] Data Replicada #3: Does Self-Concept Uncertainty Influence Magazine Subscription Choice?
• [85] Data Replicada #4: The Problem of Hidden Confounds
• [87] Data Replicada #5: Do Human-Like Products Inspire More Holistic Judgments?
• [89] Data Replicada #6: The Problem of (Weird) Differential Attrition
• [90] Data Replicada #7: Does Displaying Multiple Copies of a Product Increase Its Perceived Effectiveness?
• [92] Data Replicada #8: Is The Left-Digit Bias Stronger When Prices Are Presented Side-By-Side?
• [94] Data Replicada #9: Are Progression Ads More Credible?
• [97] Data Replicada #10: Does Goal Conflict Affect Time Spent on Work and Leisure?

Reproducibility

• [100] Groundhog 2.0: Further addressing the threat R poses to reproducible research
• [108] MRAN is Dead, long live GRAN

Researchbox

• [93] ResearchBox: Open Research Made Easy

Statistical Power

• [6] Samples Can't Be Too Large
• [18] MTurk vs. The Lab: Either Way We Need Big Samples
• [26] What If Games Were Shorter?
• [54] The 90x75x50 heuristic: Noisy & Wasteful Sample Sizes In The “Social Science Replication Project”

Teaching

• [2] Using Personal Listening Habits to Identify Personal Music Preferences
• [26] What If Games Were Shorter?
• [56] TWARKing: Test-Weighting After Results are Known

Unexpectedly Difficult Statistical Concepts

• [17] No-way Interactions
• [20] We cannot afford to study effect size in the lab
• [27] Thirty-somethings are Shrinking and Other U-Shaped Challenges
• [33] "The" Effect Size Does Not Exist
• [39] Power Naps: When do Within-Subject Comparisons Help vs Hurt (yes, hurt) Power?
• [41] Falsely Reassuring: Analyses of ALL p-values
• [42] Accepting the Null: Where to Draw the Line?
• [46] Controlling the Weather
• [50] Teenagers in Bikinis: Interpreting Police-Shooting Data
• [57] Interactions in Logit Regressions: Why Positive May Mean Negative
• [70] How Many Studies Have Not Been Run? Why We Still Think the Average Effect Does Not Exist
• [71] The (Surprising?) Shape of the File Drawer
• [79] Experimentation Aversion: Reconciling the Evidence
• [80] Interaction Effects Need Interaction Controls
• [88] The Hot-Hand Artifact for Dummies & Behavioral Scientists
• [91] p-hacking fast and slow: Evaluating a forthcoming AER paper deeming some econ literatures less trustworthy
• [99] Hyping Fisher: The Most Cited 2019 QJE Paper Relied on an Outdated Stata Default to Conclude Regression <i>p</i>-values Are Inadequate
• [103] Mediation Analysis is Counterintuitively Invalid
• [120] Off-Label Smirnov: How Many Subjects Show an Effect in Between-Subjects Experiments?
• [121] Dear Political Scientists: Don't Bin, GAM Instead
• [123] Dear Political Scientists: The binning estimator violates ceteris paribus

Further UI customisation

Mike's Notes

Something I figured out last night. This will be very easy to implement.

Resources

• Resource

References

• Reference

Repository

• Home > Ajabbi Research > Library >
• Home > Handbook > 

Last Updated

25/09/2025

Further UI customisation

By: Mike Peters

On a Sandy Beach: 25/09/2025

Mike is the inventor and architect of Pipi and the founder of Ajabbi.

Enterprise Account

The various Ajabbi Appl UI will come out of the box with industry-standard language strings used for terms in the ribbon, menus, controls, navigation, and other elements. However, the option of customising these strings will be available for all Enterprise accounts.

These changes can be saved as a template, shared and applied to any owned Deployment Workspace. The underlying terms still remain and can be easily restored.

Healthcare example

In healthcare, SNOMED provides a comprehensive list of terms, ensuring that any healthcare app utilises these terms. But an Enterprise account will be able to override any of these terms. This may be necessary if SNOMED is currently being implemented to replace an existing system. Another example would be healthcare not covered by SNOMED.