#technicalwriting — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #technicalwriting, aggregated by home.social.
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"If you are thinking about using an AI agent for documentation, here is what I think matters most.
Teach the agent, do not just instruct it. A prompt that says "write documentation for this feature" produces generic content. A skill that defines your voice, your formatting rules, your page structure, and your verification checklist produces documentation that sounds like your team wrote it. The upfront investment in the skill pays off on every subsequent page.
Make screenshots reproducible. Manual screenshots are the first thing that goes stale. A declarative manifest that can regenerate every screenshot in one command is worth the engineering effort. It changes screenshots from a one-time cost to a maintained artifact.
Phase your work. Even if you are using an agent, "write all the docs" is not a plan. Break it into phases with clear scope and clear deliverables. This gives you stopping points, review points, and the ability to course-correct.
Expect things to break. OCR will misread text. The UI will change mid-sprint. Preview URLs will go stale. The difference between a frustrating experience and a productive one is whether you encode the fix into a skill so it never happens again.
Review everything. The agent does not replace your judgment. It replaces the mechanical work. You still need to read every page, check every screenshot, and verify that the documentation matches what the user actually sees. The agent writes the first draft. You make it right."
https://dev.to/debs_obrien/how-i-documented-an-entire-product-in-4-days-with-an-ai-agent-3338
#TechnicalWriting #SoftwareDocumentation #AI #GenerativeAI #AIAgents #AgenticAI #LLMs
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"If you are thinking about using an AI agent for documentation, here is what I think matters most.
Teach the agent, do not just instruct it. A prompt that says "write documentation for this feature" produces generic content. A skill that defines your voice, your formatting rules, your page structure, and your verification checklist produces documentation that sounds like your team wrote it. The upfront investment in the skill pays off on every subsequent page.
Make screenshots reproducible. Manual screenshots are the first thing that goes stale. A declarative manifest that can regenerate every screenshot in one command is worth the engineering effort. It changes screenshots from a one-time cost to a maintained artifact.
Phase your work. Even if you are using an agent, "write all the docs" is not a plan. Break it into phases with clear scope and clear deliverables. This gives you stopping points, review points, and the ability to course-correct.
Expect things to break. OCR will misread text. The UI will change mid-sprint. Preview URLs will go stale. The difference between a frustrating experience and a productive one is whether you encode the fix into a skill so it never happens again.
Review everything. The agent does not replace your judgment. It replaces the mechanical work. You still need to read every page, check every screenshot, and verify that the documentation matches what the user actually sees. The agent writes the first draft. You make it right."
https://dev.to/debs_obrien/how-i-documented-an-entire-product-in-4-days-with-an-ai-agent-3338
#TechnicalWriting #SoftwareDocumentation #AI #GenerativeAI #AIAgents #AgenticAI #LLMs
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"If you are thinking about using an AI agent for documentation, here is what I think matters most.
Teach the agent, do not just instruct it. A prompt that says "write documentation for this feature" produces generic content. A skill that defines your voice, your formatting rules, your page structure, and your verification checklist produces documentation that sounds like your team wrote it. The upfront investment in the skill pays off on every subsequent page.
Make screenshots reproducible. Manual screenshots are the first thing that goes stale. A declarative manifest that can regenerate every screenshot in one command is worth the engineering effort. It changes screenshots from a one-time cost to a maintained artifact.
Phase your work. Even if you are using an agent, "write all the docs" is not a plan. Break it into phases with clear scope and clear deliverables. This gives you stopping points, review points, and the ability to course-correct.
Expect things to break. OCR will misread text. The UI will change mid-sprint. Preview URLs will go stale. The difference between a frustrating experience and a productive one is whether you encode the fix into a skill so it never happens again.
Review everything. The agent does not replace your judgment. It replaces the mechanical work. You still need to read every page, check every screenshot, and verify that the documentation matches what the user actually sees. The agent writes the first draft. You make it right."
https://dev.to/debs_obrien/how-i-documented-an-entire-product-in-4-days-with-an-ai-agent-3338
#TechnicalWriting #SoftwareDocumentation #AI #GenerativeAI #AIAgents #AgenticAI #LLMs
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"If you are thinking about using an AI agent for documentation, here is what I think matters most.
Teach the agent, do not just instruct it. A prompt that says "write documentation for this feature" produces generic content. A skill that defines your voice, your formatting rules, your page structure, and your verification checklist produces documentation that sounds like your team wrote it. The upfront investment in the skill pays off on every subsequent page.
Make screenshots reproducible. Manual screenshots are the first thing that goes stale. A declarative manifest that can regenerate every screenshot in one command is worth the engineering effort. It changes screenshots from a one-time cost to a maintained artifact.
Phase your work. Even if you are using an agent, "write all the docs" is not a plan. Break it into phases with clear scope and clear deliverables. This gives you stopping points, review points, and the ability to course-correct.
Expect things to break. OCR will misread text. The UI will change mid-sprint. Preview URLs will go stale. The difference between a frustrating experience and a productive one is whether you encode the fix into a skill so it never happens again.
Review everything. The agent does not replace your judgment. It replaces the mechanical work. You still need to read every page, check every screenshot, and verify that the documentation matches what the user actually sees. The agent writes the first draft. You make it right."
https://dev.to/debs_obrien/how-i-documented-an-entire-product-in-4-days-with-an-ai-agent-3338
#TechnicalWriting #SoftwareDocumentation #AI #GenerativeAI #AIAgents #AgenticAI #LLMs
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"If you are thinking about using an AI agent for documentation, here is what I think matters most.
Teach the agent, do not just instruct it. A prompt that says "write documentation for this feature" produces generic content. A skill that defines your voice, your formatting rules, your page structure, and your verification checklist produces documentation that sounds like your team wrote it. The upfront investment in the skill pays off on every subsequent page.
Make screenshots reproducible. Manual screenshots are the first thing that goes stale. A declarative manifest that can regenerate every screenshot in one command is worth the engineering effort. It changes screenshots from a one-time cost to a maintained artifact.
Phase your work. Even if you are using an agent, "write all the docs" is not a plan. Break it into phases with clear scope and clear deliverables. This gives you stopping points, review points, and the ability to course-correct.
Expect things to break. OCR will misread text. The UI will change mid-sprint. Preview URLs will go stale. The difference between a frustrating experience and a productive one is whether you encode the fix into a skill so it never happens again.
Review everything. The agent does not replace your judgment. It replaces the mechanical work. You still need to read every page, check every screenshot, and verify that the documentation matches what the user actually sees. The agent writes the first draft. You make it right."
https://dev.to/debs_obrien/how-i-documented-an-entire-product-in-4-days-with-an-ai-agent-3338
#TechnicalWriting #SoftwareDocumentation #AI #GenerativeAI #AIAgents #AgenticAI #LLMs
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"The future of enterprise technical documentation will not belong to organizations that merely generate more content with AI. It will belong to organizations that build semantically governed, operationally validated, and explainable knowledge ecosystems around AI generation.
Large language models are remarkable language-generation systems, but they remain fundamentally probabilistic, and no amount of vector-based probabilistic augmentation, recursive prompt gymnastics, or trillions of additional parameters magically transforms probabilistic token prediction into deterministic operational intelligence — regardless of what the AI snake-oil salesmen on LinkedIn insist between inspirational rocket-ship emojis. LLMs predict statistically likely outputs. They do not inherently understand operational correctness, governance policy, procedural safety, rollback integrity, regulatory compliance, or whether the “helpful” configuration change they just suggested is going to quietly detonate a production Kubernetes cluster at 2:13 a.m. while everyone is asleep and the on-call engineer is reconsidering their career choices.
That is not a moral failure of AI. It is simply the architectural reality of probabilistic systems pretending to perform deterministic operational reasoning often enough to make people dangerously optimistic.
This is precisely why deterministic models and governance matter.
Structured content, semantic markup, metadata governance, provenance tracking, DOM Graph RAG, iiRDS frameworks, knowledge graphs, RDF and OWL ontologies, context graphs, deterministic inference engines, orchestration platforms, Docs-as-Tests automation, and runtime observability together create something fundamentally different from prompt engineering. They create governed operational ecosystems capable of supporting trustworthy enterprise AI at scale."
#AI #GenerativeAI #DocsAsTests #LLMs #AgenticAI #DITAXML #AIAgents #TechnicalWriting #SoftwareDocumentation
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"The future of enterprise technical documentation will not belong to organizations that merely generate more content with AI. It will belong to organizations that build semantically governed, operationally validated, and explainable knowledge ecosystems around AI generation.
Large language models are remarkable language-generation systems, but they remain fundamentally probabilistic, and no amount of vector-based probabilistic augmentation, recursive prompt gymnastics, or trillions of additional parameters magically transforms probabilistic token prediction into deterministic operational intelligence — regardless of what the AI snake-oil salesmen on LinkedIn insist between inspirational rocket-ship emojis. LLMs predict statistically likely outputs. They do not inherently understand operational correctness, governance policy, procedural safety, rollback integrity, regulatory compliance, or whether the “helpful” configuration change they just suggested is going to quietly detonate a production Kubernetes cluster at 2:13 a.m. while everyone is asleep and the on-call engineer is reconsidering their career choices.
That is not a moral failure of AI. It is simply the architectural reality of probabilistic systems pretending to perform deterministic operational reasoning often enough to make people dangerously optimistic.
This is precisely why deterministic models and governance matter.
Structured content, semantic markup, metadata governance, provenance tracking, DOM Graph RAG, iiRDS frameworks, knowledge graphs, RDF and OWL ontologies, context graphs, deterministic inference engines, orchestration platforms, Docs-as-Tests automation, and runtime observability together create something fundamentally different from prompt engineering. They create governed operational ecosystems capable of supporting trustworthy enterprise AI at scale."
#AI #GenerativeAI #DocsAsTests #LLMs #AgenticAI #DITAXML #AIAgents #TechnicalWriting #SoftwareDocumentation
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"The future of enterprise technical documentation will not belong to organizations that merely generate more content with AI. It will belong to organizations that build semantically governed, operationally validated, and explainable knowledge ecosystems around AI generation.
Large language models are remarkable language-generation systems, but they remain fundamentally probabilistic, and no amount of vector-based probabilistic augmentation, recursive prompt gymnastics, or trillions of additional parameters magically transforms probabilistic token prediction into deterministic operational intelligence — regardless of what the AI snake-oil salesmen on LinkedIn insist between inspirational rocket-ship emojis. LLMs predict statistically likely outputs. They do not inherently understand operational correctness, governance policy, procedural safety, rollback integrity, regulatory compliance, or whether the “helpful” configuration change they just suggested is going to quietly detonate a production Kubernetes cluster at 2:13 a.m. while everyone is asleep and the on-call engineer is reconsidering their career choices.
That is not a moral failure of AI. It is simply the architectural reality of probabilistic systems pretending to perform deterministic operational reasoning often enough to make people dangerously optimistic.
This is precisely why deterministic models and governance matter.
Structured content, semantic markup, metadata governance, provenance tracking, DOM Graph RAG, iiRDS frameworks, knowledge graphs, RDF and OWL ontologies, context graphs, deterministic inference engines, orchestration platforms, Docs-as-Tests automation, and runtime observability together create something fundamentally different from prompt engineering. They create governed operational ecosystems capable of supporting trustworthy enterprise AI at scale."
#AI #GenerativeAI #DocsAsTests #LLMs #AgenticAI #DITAXML #AIAgents #TechnicalWriting #SoftwareDocumentation
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"The future of enterprise technical documentation will not belong to organizations that merely generate more content with AI. It will belong to organizations that build semantically governed, operationally validated, and explainable knowledge ecosystems around AI generation.
Large language models are remarkable language-generation systems, but they remain fundamentally probabilistic, and no amount of vector-based probabilistic augmentation, recursive prompt gymnastics, or trillions of additional parameters magically transforms probabilistic token prediction into deterministic operational intelligence — regardless of what the AI snake-oil salesmen on LinkedIn insist between inspirational rocket-ship emojis. LLMs predict statistically likely outputs. They do not inherently understand operational correctness, governance policy, procedural safety, rollback integrity, regulatory compliance, or whether the “helpful” configuration change they just suggested is going to quietly detonate a production Kubernetes cluster at 2:13 a.m. while everyone is asleep and the on-call engineer is reconsidering their career choices.
That is not a moral failure of AI. It is simply the architectural reality of probabilistic systems pretending to perform deterministic operational reasoning often enough to make people dangerously optimistic.
This is precisely why deterministic models and governance matter.
Structured content, semantic markup, metadata governance, provenance tracking, DOM Graph RAG, iiRDS frameworks, knowledge graphs, RDF and OWL ontologies, context graphs, deterministic inference engines, orchestration platforms, Docs-as-Tests automation, and runtime observability together create something fundamentally different from prompt engineering. They create governed operational ecosystems capable of supporting trustworthy enterprise AI at scale."
#AI #GenerativeAI #DocsAsTests #LLMs #AgenticAI #DITAXML #AIAgents #TechnicalWriting #SoftwareDocumentation
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"The future of enterprise technical documentation will not belong to organizations that merely generate more content with AI. It will belong to organizations that build semantically governed, operationally validated, and explainable knowledge ecosystems around AI generation.
Large language models are remarkable language-generation systems, but they remain fundamentally probabilistic, and no amount of vector-based probabilistic augmentation, recursive prompt gymnastics, or trillions of additional parameters magically transforms probabilistic token prediction into deterministic operational intelligence — regardless of what the AI snake-oil salesmen on LinkedIn insist between inspirational rocket-ship emojis. LLMs predict statistically likely outputs. They do not inherently understand operational correctness, governance policy, procedural safety, rollback integrity, regulatory compliance, or whether the “helpful” configuration change they just suggested is going to quietly detonate a production Kubernetes cluster at 2:13 a.m. while everyone is asleep and the on-call engineer is reconsidering their career choices.
That is not a moral failure of AI. It is simply the architectural reality of probabilistic systems pretending to perform deterministic operational reasoning often enough to make people dangerously optimistic.
This is precisely why deterministic models and governance matter.
Structured content, semantic markup, metadata governance, provenance tracking, DOM Graph RAG, iiRDS frameworks, knowledge graphs, RDF and OWL ontologies, context graphs, deterministic inference engines, orchestration platforms, Docs-as-Tests automation, and runtime observability together create something fundamentally different from prompt engineering. They create governed operational ecosystems capable of supporting trustworthy enterprise AI at scale."
#AI #GenerativeAI #DocsAsTests #LLMs #AgenticAI #DITAXML #AIAgents #TechnicalWriting #SoftwareDocumentation
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I'm streaming KDE docs:
I'm streaming to both Owncast and Twitch right now.
Today I'll continue working on updating plasmoid development docs.
Be sure to join and ask any questions related to KDE and I'll try my best to answer them.
Every single stream I do is an Ask Me Anything KDE Edition ™️
@kde #KDE #Linux #Documentation #TechnicalWriting #FurryStreamer #FurryVTuber #VTuber #Owncast #Twitch
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I'm streaming KDE docs:
I'm streaming to both Owncast and Twitch right now.
Today I'll continue working on updating plasmoid development docs.
Be sure to join and ask any questions related to KDE and I'll try my best to answer them.
Every single stream I do is an Ask Me Anything KDE Edition ™️
@kde #KDE #Linux #Documentation #TechnicalWriting #FurryStreamer #FurryVTuber #VTuber #Owncast #Twitch
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I'm streaming KDE docs:
I'm streaming to both Owncast and Twitch right now.
Today I'll continue working on updating plasmoid development docs.
Be sure to join and ask any questions related to KDE and I'll try my best to answer them.
Every single stream I do is an Ask Me Anything KDE Edition ™️
@kde #KDE #Linux #Documentation #TechnicalWriting #FurryStreamer #FurryVTuber #VTuber #Owncast #Twitch
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I'm streaming KDE docs:
I'm streaming to both Owncast and Twitch right now.
Today I'll continue working on updating plasmoid development docs.
Be sure to join and ask any questions related to KDE and I'll try my best to answer them.
Every single stream I do is an Ask Me Anything KDE Edition ™️
@kde #KDE #Linux #Documentation #TechnicalWriting #FurryStreamer #FurryVTuber #VTuber #Owncast #Twitch
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Random plug for a documentation system I really enjoy and got to talk about with someone today.
If you struggle to write (or think about) good technical documentation like I do, you might benefit from learning about Diátaxis. https://diataxis.fr/
It breaks down the different reasons people go to learn and acquire information and it really helped me think about how I want to present information.
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'Good documentation is a tell-tale sign of a great product and a company that puts users first. There exist good products with bad or no documentation, but there are very few poor products with great documentation.'
--John Gruber, https://daringfireball.net/linked/2026/05/12/kagi-snaps
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I'm streaming KDE docs:
I'm streaming to both Owncast and Twitch right now.
Today I'll continue working on updating plasmoid development docs.
Be sure to join and ask any questions related to KDE and I'll try my best to answer them.
Every single stream I do is an Ask Me Anything KDE Edition ™️
@kde #KDE #Linux #Documentation #TechnicalWriting #FurryStreamer #FurryVTuber #VTuber #Owncast #Twitch
-
I'm streaming KDE docs:
I'm streaming to both Owncast and Twitch right now.
Today I'll continue working on updating plasmoid development docs.
Be sure to join and ask any questions related to KDE and I'll try my best to answer them.
Every single stream I do is an Ask Me Anything KDE Edition ™️
@kde #KDE #Linux #Documentation #TechnicalWriting #FurryStreamer #FurryVTuber #VTuber #Owncast #Twitch
-
I'm streaming KDE docs:
I'm streaming to both Owncast and Twitch right now.
Today I'll continue working on updating plasmoid development docs.
Be sure to join and ask any questions related to KDE and I'll try my best to answer them.
Every single stream I do is an Ask Me Anything KDE Edition ™️
@kde #KDE #Linux #Documentation #TechnicalWriting #FurryStreamer #FurryVTuber #VTuber #Owncast #Twitch
-
I'm streaming KDE docs:
I'm streaming to both Owncast and Twitch right now.
Today I'll continue working on updating plasmoid development docs.
Be sure to join and ask any questions related to KDE and I'll try my best to answer them.
Every single stream I do is an Ask Me Anything KDE Edition ™️
@kde #KDE #Linux #Documentation #TechnicalWriting #FurryStreamer #FurryVTuber #VTuber #Owncast #Twitch
-
I'm streaming KDE docs:
I'm streaming to both Owncast and Twitch right now.
Today I'll continue working on updating plasmoid development docs.
Be sure to join and ask any questions related to KDE and I'll try my best to answer them.
Every single stream I do is an Ask Me Anything KDE Edition ™️
@kde #KDE #Linux #Documentation #TechnicalWriting #FurryStreamer #FurryVTuber #VTuber #Owncast #Twitch
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The introduction of Claude Code in my technical writing workflow has drastically shifted the main bottleneck. It used to be during the exploration and discovery phase, either on my own or with the help of an SME. Now that Claude Code assists in that phase, all my documentation requests reach the final review stage much faster. This means more requests reach the final review stage for SMEs, who are struggling to keep up. When the SME is a developer, it adds to their workload, especially with the increase in code reviews as other developers also produce more. These are interesting times.
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The introduction of Claude Code in my technical writing workflow has drastically shifted the main bottleneck. It used to be during the exploration and discovery phase, either on my own or with the help of an SME. Now that Claude Code assists in that phase, all my documentation requests reach the final review stage much faster. This means more requests reach the final review stage for SMEs, who are struggling to keep up. When the SME is a developer, it adds to their workload, especially with the increase in code reviews as other developers also produce more. These are interesting times.
-
The introduction of Claude Code in my technical writing workflow has drastically shifted the main bottleneck. It used to be during the exploration and discovery phase, either on my own or with the help of an SME. Now that Claude Code assists in that phase, all my documentation requests reach the final review stage much faster. This means more requests reach the final review stage for SMEs, who are struggling to keep up. When the SME is a developer, it adds to their workload, especially with the increase in code reviews as other developers also produce more. These are interesting times.
-
The introduction of Claude Code in my technical writing workflow has drastically shifted the main bottleneck. It used to be during the exploration and discovery phase, either on my own or with the help of an SME. Now that Claude Code assists in that phase, all my documentation requests reach the final review stage much faster. This means more requests reach the final review stage for SMEs, who are struggling to keep up. When the SME is a developer, it adds to their workload, especially with the increase in code reviews as other developers also produce more. These are interesting times.
-
The introduction of Claude Code in my technical writing workflow has drastically shifted the main bottleneck. It used to be during the exploration and discovery phase, either on my own or with the help of an SME. Now that Claude Code assists in that phase, all my documentation requests reach the final review stage much faster. This means more requests reach the final review stage for SMEs, who are struggling to keep up. When the SME is a developer, it adds to their workload, especially with the increase in code reviews as other developers also produce more. These are interesting times.
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Stop learning in silence. Sharing your journey deepens understanding, builds quiet confidence, and lets opportunities find you. Discover how learning in public. https://hackernoon.com/how-learning-in-public-speeds-up-developer-growth #technicalwriting
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Stop learning in silence. Sharing your journey deepens understanding, builds quiet confidence, and lets opportunities find you. Discover how learning in public. https://hackernoon.com/how-learning-in-public-speeds-up-developer-growth #technicalwriting
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I would love to see more technical articles and books written with the same level of clarity.
"Index 1,600,000,000 Keys with Automata and Rust" (2015) by Andrew Gallant.
https://burntsushi.net/transducers/
#article #rustlang #TechnicalWriting #automaton #datastructures #algorithms
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I would love to see more technical articles and books written with the same level of clarity.
"Index 1,600,000,000 Keys with Automata and Rust" (2015) by Andrew Gallant.
https://burntsushi.net/transducers/
#article #rustlang #TechnicalWriting #automaton #datastructures #algorithms
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I would love to see more technical articles and books written with the same level of clarity.
"Index 1,600,000,000 Keys with Automata and Rust" (2015) by Andrew Gallant.
https://burntsushi.net/transducers/
#article #rustlang #TechnicalWriting #automaton #datastructures #algorithms
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I would love to see more technical articles and books written with the same level of clarity.
"Index 1,600,000,000 Keys with Automata and Rust" (2015) by Andrew Gallant.
https://burntsushi.net/transducers/
#article #rustlang #TechnicalWriting #automaton #datastructures #algorithms
-
I would love to see more technical articles and books written with the same level of clarity.
"Index 1,600,000,000 Keys with Automata and Rust" (2015) by Andrew Gallant.
https://burntsushi.net/transducers/
#article #rustlang #TechnicalWriting #automaton #datastructures #algorithms
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Food for thought: When documenting the behavior of a highly customizable and configurable product or feature based on questions from a single customer, a technical writer should always consider the scope of the final documentation artifact. This requires making a clear distinction between what is specific to that customer and what is generalizable and easily reproducible by all.
If we don't enforce that strict separation, we run the risk of transforming the artifact into a laundry list of uncontextualized items that are mostly unintelligible for the other customers. In those cases involving very specific scenarios, sometimes what the customer really needs is not necessarily more documentation but rather a detailed technical conversation from our engineering team with their developers explaining how they can by themselves find answers for those questions whenever they need them :)
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Food for thought: When documenting the behavior of a highly customizable and configurable product or feature based on questions from a single customer, a technical writer should always consider the scope of the final documentation artifact. This requires making a clear distinction between what is specific to that customer and what is generalizable and easily reproducible by all.
If we don't enforce that strict separation, we run the risk of transforming the artifact into a laundry list of uncontextualized items that are mostly unintelligible for the other customers. In those cases involving very specific scenarios, sometimes what the customer really needs is not necessarily more documentation but rather a detailed technical conversation from our engineering team with their developers explaining how they can by themselves find answers for those questions whenever they need them :)
-
Food for thought: When documenting the behavior of a highly customizable and configurable product or feature based on questions from a single customer, a technical writer should always consider the scope of the final documentation artifact. This requires making a clear distinction between what is specific to that customer and what is generalizable and easily reproducible by all.
If we don't enforce that strict separation, we run the risk of transforming the artifact into a laundry list of uncontextualized items that are mostly unintelligible for the other customers. In those cases involving very specific scenarios, sometimes what the customer really needs is not necessarily more documentation but rather a detailed technical conversation from our engineering team with their developers explaining how they can by themselves find answers for those questions whenever they need them :)
-
Food for thought: When documenting the behavior of a highly customizable and configurable product or feature based on questions from a single customer, a technical writer should always consider the scope of the final documentation artifact. This requires making a clear distinction between what is specific to that customer and what is generalizable and easily reproducible by all.
If we don't enforce that strict separation, we run the risk of transforming the artifact into a laundry list of uncontextualized items that are mostly unintelligible for the other customers. In those cases involving very specific scenarios, sometimes what the customer really needs is not necessarily more documentation but rather a detailed technical conversation from our engineering team with their developers explaining how they can by themselves find answers for those questions whenever they need them :)
-
Food for thought: When documenting the behavior of a highly customizable and configurable product or feature based on questions from a single customer, a technical writer should always consider the scope of the final documentation artifact. This requires making a clear distinction between what is specific to that customer and what is generalizable and easily reproducible by all.
If we don't enforce that strict separation, we run the risk of transforming the artifact into a laundry list of uncontextualized items that are mostly unintelligible for the other customers. In those cases involving very specific scenarios, sometimes what the customer really needs is not necessarily more documentation but rather a detailed technical conversation from our engineering team with their developers explaining how they can by themselves find answers for those questions whenever they need them :)
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A practical, honest guide to technical writing for developers. Start with problems you solved, write like you speak, and build a skill that compounds over time. https://hackernoon.com/a-practical-guide-to-technical-writing-for-developers-who-want-to-start-sharing-what-they-know #technicalwriting
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A practical, honest guide to technical writing for developers. Start with problems you solved, write like you speak, and build a skill that compounds over time. https://hackernoon.com/a-practical-guide-to-technical-writing-for-developers-who-want-to-start-sharing-what-they-know #technicalwriting
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A practical, honest guide to technical writing for developers. Start with problems you solved, write like you speak, and build a skill that compounds over time. https://hackernoon.com/a-practical-guide-to-technical-writing-for-developers-who-want-to-start-sharing-what-they-know #technicalwriting
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A practical, honest guide to technical writing for developers. Start with problems you solved, write like you speak, and build a skill that compounds over time. https://hackernoon.com/a-practical-guide-to-technical-writing-for-developers-who-want-to-start-sharing-what-they-know #technicalwriting
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A practical, honest guide to technical writing for developers. Start with problems you solved, write like you speak, and build a skill that compounds over time. https://hackernoon.com/a-practical-guide-to-technical-writing-for-developers-who-want-to-start-sharing-what-they-know #technicalwriting
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"Back in 2024 I wrote that AI helps me remove boring work at the margins. This is fine for a lone writer, but how to scale this to an entire team of technical writers? How to make the system helpful but not intrusive? These are all questions I’m starting to answer now, partly through experimentation, but also through dialogue with practitioners and colleagues. One answer I’m testing these days relies on GitHub Agentic Workflows.
Following Four modes of AI-augmented technical writing, I thought of a way of distributing tooling effort across all modes through a tiered system where each level holds a different relationship with the writer. The result is four tiers: intake, local assistance, automated governance, and an MCP server that provides reliable knowledge to all. The idea is that AI assists the writer not just while writing, but also before and after they work on docs."
https://passo.uno/agentic-workflows-for-docs/
#TechnicalWriting #AI #GenerativeAI #LLMs #AIAgents #AgenticAI #AgenticWorkflows #SoftwareDocumentation #GitHub #DocsAsCode
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"Back in 2024 I wrote that AI helps me remove boring work at the margins. This is fine for a lone writer, but how to scale this to an entire team of technical writers? How to make the system helpful but not intrusive? These are all questions I’m starting to answer now, partly through experimentation, but also through dialogue with practitioners and colleagues. One answer I’m testing these days relies on GitHub Agentic Workflows.
Following Four modes of AI-augmented technical writing, I thought of a way of distributing tooling effort across all modes through a tiered system where each level holds a different relationship with the writer. The result is four tiers: intake, local assistance, automated governance, and an MCP server that provides reliable knowledge to all. The idea is that AI assists the writer not just while writing, but also before and after they work on docs."
https://passo.uno/agentic-workflows-for-docs/
#TechnicalWriting #AI #GenerativeAI #LLMs #AIAgents #AgenticAI #AgenticWorkflows #SoftwareDocumentation #GitHub #DocsAsCode
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"Back in 2024 I wrote that AI helps me remove boring work at the margins. This is fine for a lone writer, but how to scale this to an entire team of technical writers? How to make the system helpful but not intrusive? These are all questions I’m starting to answer now, partly through experimentation, but also through dialogue with practitioners and colleagues. One answer I’m testing these days relies on GitHub Agentic Workflows.
Following Four modes of AI-augmented technical writing, I thought of a way of distributing tooling effort across all modes through a tiered system where each level holds a different relationship with the writer. The result is four tiers: intake, local assistance, automated governance, and an MCP server that provides reliable knowledge to all. The idea is that AI assists the writer not just while writing, but also before and after they work on docs."
https://passo.uno/agentic-workflows-for-docs/
#TechnicalWriting #AI #GenerativeAI #LLMs #AIAgents #AgenticAI #AgenticWorkflows #SoftwareDocumentation #GitHub #DocsAsCode
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"Back in 2024 I wrote that AI helps me remove boring work at the margins. This is fine for a lone writer, but how to scale this to an entire team of technical writers? How to make the system helpful but not intrusive? These are all questions I’m starting to answer now, partly through experimentation, but also through dialogue with practitioners and colleagues. One answer I’m testing these days relies on GitHub Agentic Workflows.
Following Four modes of AI-augmented technical writing, I thought of a way of distributing tooling effort across all modes through a tiered system where each level holds a different relationship with the writer. The result is four tiers: intake, local assistance, automated governance, and an MCP server that provides reliable knowledge to all. The idea is that AI assists the writer not just while writing, but also before and after they work on docs."
https://passo.uno/agentic-workflows-for-docs/
#TechnicalWriting #AI #GenerativeAI #LLMs #AIAgents #AgenticAI #AgenticWorkflows #SoftwareDocumentation #GitHub #DocsAsCode
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"Back in 2024 I wrote that AI helps me remove boring work at the margins. This is fine for a lone writer, but how to scale this to an entire team of technical writers? How to make the system helpful but not intrusive? These are all questions I’m starting to answer now, partly through experimentation, but also through dialogue with practitioners and colleagues. One answer I’m testing these days relies on GitHub Agentic Workflows.
Following Four modes of AI-augmented technical writing, I thought of a way of distributing tooling effort across all modes through a tiered system where each level holds a different relationship with the writer. The result is four tiers: intake, local assistance, automated governance, and an MCP server that provides reliable knowledge to all. The idea is that AI assists the writer not just while writing, but also before and after they work on docs."
https://passo.uno/agentic-workflows-for-docs/
#TechnicalWriting #AI #GenerativeAI #LLMs #AIAgents #AgenticAI #AgenticWorkflows #SoftwareDocumentation #GitHub #DocsAsCode
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#WriteTheDocs just released a bunch of new videos from their most recent conference and unfortunately some of them shill AI, but one video caught my attention and is definitely worth watching: The Most Human Documentation.
It talks about the things to avoid in order to not be mistaken by machine generation, and what it means for your #TechnicalWriting and #Documentation to feel human. The talk comments on this from a cultural perspective and it matches what I've been thinking about recently.
With the rise of LLMs and their gross effect on online text now there's humans that work on humanizing machine content, be it with deterministic tools or with humans. In the end humans prefer content that feels human, and human content continues to provide more value.
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#WriteTheDocs just released a bunch of new videos from their most recent conference and unfortunately some of them shill AI, but one video caught my attention and is definitely worth watching: The Most Human Documentation.
It talks about the things to avoid in order to not be mistaken by machine generation, and what it means for your #TechnicalWriting and #Documentation to feel human. The talk comments on this from a cultural perspective and it matches what I've been thinking about recently.
With the rise of LLMs and their gross effect on online text now there's humans that work on humanizing machine content, be it with deterministic tools or with humans. In the end humans prefer content that feels human, and human content continues to provide more value.