+2

Artificial Intelligence Models.md

··· 9 - [LLMs are good at the things that computers are bad at, and bad at the things that computers are good at](https://www.ben-evans.com/benedictevans/2025/2/17/the-deep-research-problem). Also good at things that don't have wrong answers. 10 - Context is king. Managing the context window effectively is crucial for getting good results. 11 - Add websites as context with [jina.ai](https://jina.ai/) or [pure.md](https://pure.md/) 0 0 12 - LLMs amplify existing expertise rather than replacing it. 13 - Be aware of training cut-off dates when using LLMs. 14

··· 9 - [LLMs are good at the things that computers are bad at, and bad at the things that computers are good at](https://www.ben-evans.com/benedictevans/2025/2/17/the-deep-research-problem). Also good at things that don't have wrong answers. 10 - Context is king. Managing the context window effectively is crucial for getting good results. 11 - Add websites as context with [jina.ai](https://jina.ai/) or [pure.md](https://pure.md/) 12 + - Context is easy to mess: [context poisoning, context distraction, context confusion, context clash](https://www.dbreunig.com/2025/06/22/how-contexts-fail-and-how-to-fix-them.html), ... 13 + - [Context Engineering](https://www.philschmid.de/context-engineering) is the discipline of designing and building dynamic systems that provides the right information and tools, in the right format, at the right time, to give a LLM everything it needs to accomplish a task. 14 - LLMs amplify existing expertise rather than replacing it. 15 - Be aware of training cut-off dates when using LLMs. 16

+1 -1

Automation.md

··· 10 3. Create automation. Create a script that runs everything. 11 - Drive standards through automation and building internal tools/scripts rather than through extensive [[documentation]]. 12 - Standards can help to reduce this friction but take time. Before jumping into standards processes, [consider other ways to encourage consistency](https://blog.ldodds.com/2023/09/18/consistency-before-standards/). 13 - - If you jump into standards, [make them open, modular, interoperable, customizable and, extensible](https://voltrondata.com/codex/standards-over-silos#1-3-5-composable-systems-are-MICE). 14 - Makefiles are a great way to document and consolidate different projects of a team. Each project should have a [`make` that runs it](https://gagor.pro/2024/02/how-i-stopped-worrying-and-loved-makefiles/), and perhaps a `make deploy` to deploy it. Language and tool independent!

··· 10 3. Create automation. Create a script that runs everything. 11 - Drive standards through automation and building internal tools/scripts rather than through extensive [[documentation]]. 12 - Standards can help to reduce this friction but take time. Before jumping into standards processes, [consider other ways to encourage consistency](https://blog.ldodds.com/2023/09/18/consistency-before-standards/). 13 + - If you jump into standards, [make them open, modular, interoperable, customizable and, extensible](http://web.archive.org/web/20250327201845/https://voltrondata.com/codex/standards-over-silos). 14 - Makefiles are a great way to document and consolidate different projects of a team. Each project should have a [`make` that runs it](https://gagor.pro/2024/02/how-i-stopped-worrying-and-loved-makefiles/), and perhaps a `make deploy` to deploy it. Language and tool independent!

+1 -1

COVID-19.md

··· 7 - [Flatten The Curve](https://www.flattenthecurve.com/) 8 - As someone said, social distancing measures to work must be done when it seems to be overreacting. And if they work, it will seem as if we overreacted. 9 - [The Basic Dance Steps Everybody Can Follow](https://medium.com/@tomaspueyo/coronavirus-the-basic-dance-steps-everybody-can-follow-b3d216daa343). 10 - - [Coronavirus Info-Database](https://www.lesswrong.com/coronavirus-link-database). An attempt to organize the disparate papers, articles and links that are spread all over the internet regarding the COVID-19 pandemic. 11 - [Justified Practical Advice](https://www.lesswrong.com/posts/LwcKYR8bykM6vDHyo/coronavirus-justified-practical-advice-thread) and [What should we do once infected with COVID-19](https://www.lesswrong.com/posts/F3q7eL7pdQqhWFTYh/what-should-we-do-once-infected-with-covid-19#NR3wH8DxZX2eBBvG7) are useful once infected. 12 - [Distant Socializing, during Physical Distancing](https://github.com/Pezmc/distant-socializing/blob/master/README.md) - Suggested distant socializing tools, games and activities to help keep in touch with family, friends and loved ones during social distancing and the coronavirus (COVID-19) pandemic. 13 - There are lots of [[Social Games]] out there!

··· 7 - [Flatten The Curve](https://www.flattenthecurve.com/) 8 - As someone said, social distancing measures to work must be done when it seems to be overreacting. And if they work, it will seem as if we overreacted. 9 - [The Basic Dance Steps Everybody Can Follow](https://medium.com/@tomaspueyo/coronavirus-the-basic-dance-steps-everybody-can-follow-b3d216daa343). 10 + - [Coronavirus Info-Database](http://web.archive.org/web/20250331225624/https://www.lesswrong.com/coronavirus-link-database). An attempt to organize the disparate papers, articles and links that are spread all over the internet regarding the COVID-19 pandemic. 11 - [Justified Practical Advice](https://www.lesswrong.com/posts/LwcKYR8bykM6vDHyo/coronavirus-justified-practical-advice-thread) and [What should we do once infected with COVID-19](https://www.lesswrong.com/posts/F3q7eL7pdQqhWFTYh/what-should-we-do-once-infected-with-covid-19#NR3wH8DxZX2eBBvG7) are useful once infected. 12 - [Distant Socializing, during Physical Distancing](https://github.com/Pezmc/distant-socializing/blob/master/README.md) - Suggested distant socializing tools, games and activities to help keep in touch with family, friends and loved ones during social distancing and the coronavirus (COVID-19) pandemic. 13 - There are lots of [[Social Games]] out there!

+2 -2

Data/Experimentation.md

··· 41 - Business problem 42 - Supporting data 43 - Required tracking 44 - - [Expected outcome](https://about.gitlab.com/handbook/business-technology/data-team/experimentation-best-practices/): 45 - Define the primary metric that we want the experimentation to move. This is the metric we use to define the rollout scenario. 46 - Define the secondary metric(s): There are metrics that help us validate and understand in detail why and how the primary metric was impacted- 47 - Define Guardrail metrics: This helps us ensure we are not harming the business in the long run for short-term gains. ··· 65 ## Resources 66 67 - [GrowthBook](https://growthbook.io/). Open source A/B testing. 68 - - [GitLab Product Experimentation](https://about.gitlab.com/handbook/product/product-analysis/experimentation/) and [Growth Experimentation](https://about.gitlab.com/handbook/engineering/development/growth/experimentation/) handbooks.

··· 41 - Business problem 42 - Supporting data 43 - Required tracking 44 + - [Expected outcome](http://web.archive.org/web/20231128193004/https://about.gitlab.com/handbook/product/product-analysis/experimentation/): 45 - Define the primary metric that we want the experimentation to move. This is the metric we use to define the rollout scenario. 46 - Define the secondary metric(s): There are metrics that help us validate and understand in detail why and how the primary metric was impacted- 47 - Define Guardrail metrics: This helps us ensure we are not harming the business in the long run for short-term gains. ··· 65 ## Resources 66 67 - [GrowthBook](https://growthbook.io/). Open source A/B testing. 68 + - [GitLab Product Experimentation](http://web.archive.org/web/20231128193004/https://about.gitlab.com/handbook/product/product-analysis/experimentation/) and [Growth Experimentation](https://about.gitlab.com/handbook/engineering/development/growth/experimentation/) handbooks.

+1 -1

Fallacies.md

··· 2 3 - A logical fallacy is a flaw in reasoning, a flaw in [[thinking]]. 4 - Logical fallacies are like tricks or illusions of thought, and they're often sneakily used by politicians and the media to fool people. 5 - - They are [tools](https://yourlogicalfallacyis.com/) designed to help us identify and call out dodgy logic wherever it may raise its ugly, incoherent head.

··· 2 3 - A logical fallacy is a flaw in reasoning, a flaw in [[thinking]]. 4 - Logical fallacies are like tricks or illusions of thought, and they're often sneakily used by politicians and the media to fool people. 5 + - They are [tools](http://web.archive.org/web/20250619031154/https://yourlogicalfallacyis.com/) designed to help us identify and call out dodgy logic wherever it may raise its ugly, incoherent head.

+2 -2

Open Data.md

··· 24 25 In the blockchain example, data is Open, Verifiable, and Useful. And yet, the main provider of data is Dune, a company that captured most of the data layer. Users can run `cryo` but there are no incentives for them to share the data. There isn't a matchmaking market for data and people are forced to repeat the same work. 26 27 - Open Source code has made a huge impact in the world. Let's make Open Data do the same! Open data is, essentially, public infrastructure (similar to roads, bridges, or the internet). Let's make it possible for [anyone to fork and re-publish fixed, cleaned, reformatted datasets as easily as we do the same things with code](https://juan.benet.ai/blog/2014-02-21-data-management-problems/). 28 29 This document is a collection of ideas and principles to make Open Data more accessible, maintainable, and useful. Also, recognizing that a lot of people are already working on this, there are some amazing datasets, tools, and organizations out there, and, that Open Data is a people problem at 80%. This document is biased towards the technical side of things, as I think that's where I can contribute the most. I believe we can do much more with the available data. 30 ··· 42 - Data watermarking, fingerprinting, and provenance tracking with blockchains. 43 - Better CPUs, compression algorithms, and storage technologies. 44 45 - These trends are already making its way towards movements like [DeSci](https://ethereum.org/en/desci/) or smaller projects like [Py-Code Datasets](https://py-code.org/datasets). But, we still need more tooling around data to improve interoperability as much as possible. Lots of companies have figured out how to make the most of their datasets. **We should use similar tooling and approaches companies are using to manage the open datasets that surrounds us**. A sort of [Data Operating system](https://data-operating-system.com/). 46 47 One of the biggest problem in open data today is the fact that organizations treat data portals as graveyards where data goes to die. Keeping these datasets up to date is core concern (data has marginal temporal value), alongside using the data for operational purposes and showcasing it to the public. 48

··· 24 25 In the blockchain example, data is Open, Verifiable, and Useful. And yet, the main provider of data is Dune, a company that captured most of the data layer. Users can run `cryo` but there are no incentives for them to share the data. There isn't a matchmaking market for data and people are forced to repeat the same work. 26 27 + Open Source code has made a huge impact in the world. Let's make Open Data do the same! Open data is, essentially, public infrastructure (similar to roads, bridges, or the internet). Let's make it possible for [anyone to fork and re-publish fixed, cleaned, reformatted datasets as easily as we do the same things with code](http://web.archive.org/web/20240719190206/https://juan.benet.ai/blog/2014-02-21-data-management-problems/). 28 29 This document is a collection of ideas and principles to make Open Data more accessible, maintainable, and useful. Also, recognizing that a lot of people are already working on this, there are some amazing datasets, tools, and organizations out there, and, that Open Data is a people problem at 80%. This document is biased towards the technical side of things, as I think that's where I can contribute the most. I believe we can do much more with the available data. 30 ··· 42 - Data watermarking, fingerprinting, and provenance tracking with blockchains. 43 - Better CPUs, compression algorithms, and storage technologies. 44 45 + These trends are already making its way towards movements like [DeSci](https://ethereum.org/en/desci/) or smaller projects like [Py-Code Datasets](https://py-code.org/datasets). But, we still need more tooling around data to improve interoperability as much as possible. Lots of companies have figured out how to make the most of their datasets. **We should use similar tooling and approaches companies are using to manage the open datasets that surrounds us**. A sort of [Data Operating system](http://web.archive.org/web/20250316031339/https://data-operating-system.com/). 46 47 One of the biggest problem in open data today is the fact that organizations treat data portals as graveyards where data goes to die. Keeping these datasets up to date is core concern (data has marginal temporal value), alongside using the data for operational purposes and showcasing it to the public. 48

+1 -1

Organizations.md

··· 79 ## Resources 80 81 - [Kool-aid factory](https://koolaidfactory.com/). A series of zines about the ways organizations coordinate. 82 - - [Toolboxtoolbox](https://www.toolboxtoolbox.com/index.html). A curated list of the best business, design, and organisational change toolboxes built by some of the most influential companies, institutions and thinkers. 83 - [The Compendium](https://thecompendium.cards/) - A collection of cards about organizations, systems, and processes. 84 85 ### Startups

··· 79 ## Resources 80 81 - [Kool-aid factory](https://koolaidfactory.com/). A series of zines about the ways organizations coordinate. 82 + - [Toolboxtoolbox](http://web.archive.org/web/20250611231851/https://www.toolboxtoolbox.com/index.html). A curated list of the best business, design, and organisational change toolboxes built by some of the most influential companies, institutions and thinkers. 83 - [The Compendium](https://thecompendium.cards/) - A collection of cards about organizations, systems, and processes. 84 85 ### Startups