1. Executive Summary

Product / initiative name: KNIEPUNKT Assistant

Interviewed stakeholder role: Dr. André Knie, author of the LinkedIn column “KNIEPUNKT”

Product type: New assistant supporting an existing editorial process and content base. The product builds on 34 existing KNIEPUNKT episodes.

Goal in one sentence:
The assistant should improve and accelerate the production of the KNIEPUNKT column while making quality and success metrics measurable and sustainably improvable.

Short business context:
KNIEPUNKT is a LinkedIn column about current news in the world of AI and the author’s reflections on those developments. The column addresses a highly educated audience of medium-sized company CEOs, decision-makers from the Mittelstand, corporations, and public administration who are interested in moral AI.

Primary target users:
The only active product user is Dr. André Knie as author. Readers, feedback providers, and external experts may provide input, but they are not active user groups of the assistant.

Core functional scope summary:
The assistant should support weekly AI news research, source assessment, storyline development, draft preparation, tonal quality review, redundancy checks against previous KNIEPUNKT episodes, KPI and feedback analysis, teaser experimentation, visual idea generation, cover preparation, and quality assurance before publication.

In scope:

Weekly collection and preparation of relevant AI news
Suggestions for multiple sources per news item
Source quality classification
Storyline and draft preparation
Support for column-style, gloss-like, amusing, culturally literate tone
Detection of missing authorial opinion or missing reader-facing interpretation
Review against previous episodes to reduce redundancy
Length, teaser, topic, and cover experimentation
KPI and feedback collection, interpretation, and regular review
Visual and cover idea development while preserving recognizability
Quality checks before final author approval

Out of scope:

Fully automatic publication or sending of the LinkedIn newsletter/post
Replacing the author’s final editorial judgment
Replacing the author’s personal source review
Replacing the author’s prioritization of news items
Taking the final cover decision away from the author
Producing purely technical deep dives that would make the column boring for the intended audience

Main open questions:

Which KPIs are reliably available from LinkedIn and other feedback sources?
What review cadence should be used for KPI and quality learning?
What concrete length variants should be tested?
What minimum visual identity elements are mandatory for recognizability?
How should qualitative reader feedback be categorized and weighted?
What exact thresholds define “sustainably better” performance?

Main acceptance indicators:

Production is faster than the current 2–3 hours per episode.
Relevant success metrics become measurable.
Key success metrics improve sustainably over time.
Source quality and factual reliability are maintained or improved.
The KNIEPUNKT tone remains recognizable, amusing, opinionated, and suitable for the audience.
The author remains in final editorial control.
2. Goal

One-sentence goal:
Enable the author to produce better, faster, more measurable KNIEPUNKT episodes while preserving the column’s distinctive editorial voice and source quality.

Detailed goal:
The assistant should support the full editorial preparation process for weekly KNIEPUNKT episodes. It should help identify relevant AI news, suggest and assess sources, develop storylines, prepare draft texts, review tone and factual grounding, support visual and cover development, incorporate KPI and reader feedback, and make quality improvement more systematic over time.

Business value:

Reduce production effort from the current 2–3 hour process.
Increase consistency in research, source assessment, editorial quality, and tonal fit.
Make reach and audience resonance more measurable.
Support better decisions about length, teaser text, topics, and cover formats.
Preserve and strengthen the recognizable KNIEPUNKT style.

Intended outcome for users and organization:

The author can produce episodes more efficiently.
The author receives better editorial preparation and quality signals.
The column becomes more data-informed without losing opinion, humor, and cultural character.
Readers receive timely, well-sourced, amusing, and opinionated orientation on major AI developments.
3. Stakeholder Context

Respondent role:
Dr. André Knie, author of the KNIEPUNKT column.

Perspective represented:
Primary author, editor, curator, and final decision-maker for the column.

Relevant organizational or customer context:
The column is published on LinkedIn as a newsletter/post format. Its recipients include medium-sized company CEOs, decision-makers from the Mittelstand, corporations, and public administration who are interested in moral AI.

Whether this spec reflects one interview only:
This specification reflects one stakeholder interview with the author. It should be treated as the author’s current product discovery input and may require later validation after prototype use or after additional KPI/feedback analysis.

4. Target Users

User groups:

Primary active user: Dr. André Knie as author.
Indirect audience: Readers of the KNIEPUNKT column.
Input sources, not users: Readers who provide feedback, external experts, and potential feedback channels such as homepage or email.

User context:
The author produces a weekly AI news column with commentary and visual presentation. The process currently relies on multiple separate tools, manual comparison, manual source validation, and subjective quality assessment.

User needs:

Faster preparation of each episode.
Better research coverage of the week’s relevant AI news.
Stronger source quality and source transparency.
Support for finding a compelling storyline.
Support for maintaining the established column tone.
Avoidance of repeated allegories, themes, and content from earlier episodes.
Better understanding of what increases reach and resonance.
Structured use of qualitative feedback.
Support for visual and cover experimentation.

Pain points:

Current production takes approximately 2–3 hours.
Current work is distributed across several separate tools and duplicated steps.
No systematic use of KPIs to increase reach.
No established method for continuously securing and improving quality.
Positive feedback exists, but little improvement-oriented feedback reaches the author.
LinkedIn itself provides little actionable qualitative feedback.
External feedback currently arrives mainly through direct conversations and is not systematically usable.

Important differences between groups:

The author is the only active user and decision-maker.
Readers are the intended audience, not direct product users.
Feedback providers may contribute signals but should not influence the process without author review.
5. Current Situation / Current Process

The current KNIEPUNKT production process works as follows:

The author analyzes current AI news using Perplexity for relevance and newsworthiness for AI enthusiasts and the target audience.
The author adds personal input from AI news consumed during the week.
Optionally, the author provides an initial storyline if one is already visible across the news.
The author provides input to several LLMs, currently including Claude, Gemini, and ChatGPT, to create multiple storyline options while considering previous KNIEPUNKT episodes.
The author selects one storyline per LLM to create a complete draft.
The author selects the best draft and enriches it with input from other drafts.
The author verifies source content and searches for original sources and/or high-quality sources.
The author finalizes and rereads the text, then enters it into LinkedIn.
Based on the text, the author uses LLMs to generate visual ideas.
The author selects the best visual.
The author integrates the visual into the existing visual template and adds the KNIEPUNKT logo, title, and date.
The author uploads everything to LinkedIn as a newsletter.
The author creates a personal invitation message as a LinkedIn post.
The author schedules the contribution for Sunday at 08:30.

Existing workarounds or manual steps:

Manual comparison of outputs from different LLMs.
Manual verification of sources.
Manual selection and enrichment of drafts.
Manual development and selection of cover ideas.
Manual interpretation of feedback from direct conversations.
Manual publication and scheduling on LinkedIn.

Main gaps, bottlenecks, and failure points:

High production duration.
Duplicated workflow steps across separate tools.
Limited systematic feedback.
Limited KPI-based learning.
Unclear method for quality measurement.
Risk of weak or repetitive storylines.
Risk of unsupported claims or unsuitable sources.
Risk that a text reports news without sufficient authorial interpretation.
Risk that visuals are attractive but insufficiently connected to the episode.

What should remain unchanged:

The author keeps final editorial control.
The author personally reviews sources.
The author prioritizes the news after research.
The author decides the final cover.
The author performs final quality assurance.
The author presses the final publish/send button.
6. Functional Scope

The assistant should support the KNIEPUNKT editorial workflow from weekly topic research to publication preparation, while preserving the author’s final decision authority.

Main capabilities:

Weekly AI news preparation
The assistant should collect and structure relevant current AI news candidates for the column.
Author input incorporation
The assistant should allow the author to add personally relevant news, observations, or an initial storyline.
News relevance support
The assistant should help assess which news items are relevant and newsworthy for KI enthusiasts and the target audience of decision-makers interested in moral AI.
Source suggestion and source quality assessment
The assistant should propose several sources per relevant news item where useful, distinguish primary and high-quality sources from weaker sources, and flag unsuitable sources.
Storyline development
The assistant should generate several possible storylines for an episode and make differences between them visible from an editorial perspective.
Redundancy review against previous episodes
The assistant should consider the existing 34 KNIEPUNKT episodes to reduce repetition in content, allegories, themes, and references.
Draft preparation
The assistant should prepare draft column text based on selected news, sources, storyline, tone expectations, and author input.
Draft enrichment
The assistant should support combining the strongest parts of different draft directions into one improved draft.
Tonal review
The assistant should review whether the draft remains column-like, gloss-like, amusing, culturally literate, opinionated, and appropriate for the target audience.
Factual and source quality review
The assistant should flag unsupported claims, weak sourcing, contradictory sources, unclear facts, or claims that require author verification.
Opinion and interpretation check
The assistant should identify whether the episode contains sufficient authorial opinion and reader-facing interpretation.
Length experimentation support
The assistant should propose length options and help the author learn which episode lengths work well with the audience.
Teaser experimentation support
The assistant should support different teaser text options and help analyze how teaser choices relate to audience response.
KPI and feedback learning
The assistant should collect and structure available success signals such as reach, newsletter open rate, likes, comments, shares, new subscriptions, dwell time, qualitative reader input, topic resonance, and reactions to length, teaser, and cover.
Visual idea and cover support
The assistant should develop visual ideas and cover layout suggestions, including experimental variants, while preserving recognizability of the KNIEPUNKT visual identity.
Publication preparation support
The assistant should help prepare the finalized content package for publication, including article text, teaser or invitation message, source references, and cover recommendation, but must not publish automatically.

Key user interactions:

Author provides weekly personal news input.
Author optionally provides an initial storyline.
Author reviews researched news candidates.
Author prioritizes selected news items.
Author reviews source suggestions and verifies individual sources.
Author selects or adjusts a storyline.
Author reviews and edits the draft.
Author selects cover direction.
Author performs final quality assurance.
Author publishes or schedules the LinkedIn content.

Role-based differences in usage:

Only the author actively uses the assistant.
Feedback providers may submit input through a feedback channel, but they do not interact with the assistant as users.
Readers are considered only through feedback and KPI signals.

Relevant triggers, inputs, outputs, and outcomes:

Trigger: Weekly preparation of a new KNIEPUNKT episode.
Inputs: Current AI news, author-selected news, previous KNIEPUNKT episodes, source candidates, reader feedback, KPI signals, visual identity expectations.
Outputs: Curated news list, source suggestions, source quality notes, storyline options, draft text, quality warnings, teaser options, visual ideas, cover recommendations, KPI/feedback insights.
Outcome: A faster, better prepared, better measured, and editorially stronger KNIEPUNKT episode ready for author approval and LinkedIn publication.
7. Business Rules

Editorial authority rules:

The author must retain final control over all published content.
The author must personally review and prioritize news items.
The author must personally verify individual sources before publication.
The author must decide the final cover.
The assistant must not publish or send the final LinkedIn newsletter/post automatically.

Source quality rules:

Preferred sources include primary sources.
Primary sources may include scientific publications, sources from the originators of the news, and initial sources of discussions.
Initial discussion sources may include Reddit when it is the original or relevant discussion context.
Respected journalistic sources for AI topics are suitable, including The Decoder and Heise.
Established high-quality journalistic or public broadcasting sources are suitable, including ARD, ZDF, DLF, Handelsblatt, Wirtschaftswoche, Die Zeit, and Der Spiegel.
Tabloids are unsuitable.
Unknown media are unsuitable unless the author explicitly accepts them after review.
Purely reproducing or republishing media are unsuitable as primary evidence.
Unsupported claims must be flagged.
Contradictory or unclear factual situations must be flagged.

Tone and content rules:

The column should remain column-like and gloss-like.
The column should amuse readers, including through contradictions, anecdotes, and pointed framing.
The text may include references to Greek and Roman mythology, canonical literature, and classical German literature.
The text should be suitable for a highly educated and broadly culturally literate audience.
The text must not go so deeply into technical detail that it becomes boring for the audience.
The text must not be factually wrong.
The author’s opinion should appear and help contextualize the news.
An episode must not merely summarize news without interpretation.

News selection rules:

News should be relevant to AI enthusiasts and the target audience.
The assistant should help ensure that readers hear about the major topics of the week.
The author’s own weekly news input must be incorporated.
The author’s prioritization overrides the assistant’s suggestions.

Redundancy rules:

The assistant should consider previous KNIEPUNKT episodes.
Repeated allegories, repeated references, and repeated content angles should be reduced or flagged.

Length and teaser rules:

The assistant should propose length options.
The assistant should support experiments with length.
The assistant should support experiments with teaser text.
The assistant should help relate length and teaser choices to later performance signals.

Visual and cover rules:

The assistant may propose strong visual and layout experiments.
Recognizability of KNIEPUNKT must remain important.
Visual decisions should be justifiable and/or informed by expert input where available.
Cover ideas should have a recognizable connection to the episode.
The author makes the final cover decision.

KPI and feedback rules:

Reach is a key KPI.
KPI selection should be reviewed regularly.
The assistant should support additional KPI suggestions.
Qualitative feedback should be treated as a useful input source.
Feedback from a homepage and/or email address may be used as an additional resource, not as a separate user group.

Exception rules:

Too few high-quality sources must be flagged.
Contradictory sources must be flagged.
Unclear factual situations must be flagged.
Weak storylines must be flagged.
Unsuitable tone must be flagged.
Too little current or newsworthy material must be flagged.
Cover ideas without a clear relation to the episode must be flagged.
Missing opinion or missing reader-facing interpretation must be flagged.
8. Business Objects / Functional Data Objects

KNIEPUNKT Episode
A complete weekly column edition, including selected news, storyline, article text, teaser/invitation message, sources, visual direction, cover, date, and publication preparation status.

Previous KNIEPUNKT Episode
One of the existing 34 episodes used to understand tone, recurring references, prior topics, allegories, and potential redundancies.

News Item
A current AI-related development that may be relevant for the weekly episode.

Author News Input
The author’s personal selection of AI news, observations, or impressions gathered during the week.

Source
A piece of evidence or reporting that supports or contextualizes a news item. Sources may be primary, high-quality journalistic, initial discussion sources, unsuitable sources, or sources requiring further review.

Source Quality Assessment
A functional classification of whether a source is suitable, preferred, weak, contradictory, or unsuitable for KNIEPUNKT use.

Storyline
An editorial framing that connects selected news items into a coherent column angle.

Draft
A prepared version of the column text before final author editing and approval.

Tone Profile
The set of editorial qualities expected from KNIEPUNKT: column-like, gloss-like, amusing, culturally literate, opinionated, and suitable for a highly educated audience.

Teaser / Invitation Message
A short LinkedIn-facing message that introduces or invites readers into the episode and may be tested in relation to performance.

Visual Idea
A concept for the episode’s visual representation or cover direction.

Cover
The final visual asset for the episode, including the chosen visual, layout, KNIEPUNKT logo, title, and date.

KPI Signal
A measurable performance indicator such as reach, newsletter open rate, likes, comments, shares, new subscriptions, dwell time, or reactions to length, teaser, and cover.

Qualitative Feedback
Reader or external feedback, including direct conversation input or feedback submitted through a homepage or email address.

Quality Warning
A flagged issue that may affect acceptance of the episode, such as weak sourcing, factual uncertainty, weak storyline, unsuitable tone, missing opinion, or weak cover relevance.

9. In Scope
Support for weekly AI news research.
Incorporation of the author’s personal weekly news input.
Optional incorporation of an initial author-provided storyline.
Assessment of relevance and newsworthiness for the KNIEPUNKT audience.
Suggestion of multiple sources per news item where useful.
Source quality assessment based on business-defined source categories.
Flagging of weak, contradictory, unsupported, or unsuitable sources.
Generation of multiple storyline options.
Support for reducing redundancy with previous KNIEPUNKT episodes.
Preparation of full draft text options.
Support for enriching a chosen draft with useful elements from other drafts.
Review of tone, opinion, factual grounding, and reader-facing interpretation.
Support for length experimentation.
Support for teaser experimentation.
Collection and interpretation of relevant KPIs and feedback signals.
Regular review of KPIs and potential KPI adjustments.
Development of visual ideas and cover layout suggestions.
Support for visual experimentation while maintaining KNIEPUNKT recognizability.
Preparation support for the LinkedIn newsletter and related invitation post.
Quality assurance support before final author approval.
10. Out of Scope
Automatic publication or sending of the LinkedIn newsletter/post.
Removing the author from final quality control.
Automatic final prioritization of news without author decision.
Automatic acceptance of sources without author review.
Automatic final cover selection without author decision.
Treating readers, feedback providers, or experts as active assistant users.
Producing content that is too technically deep for the intended audience.
Using tabloids, unknown media, or purely reproducing media as reliable primary support without explicit author review.
Publishing claims that are unsupported, factually uncertain without disclosure, or based on false sources.
Replacing the author’s opinion with neutral summarization only.
11. Requirements
A. Weekly Research and News Preparation
The assistant shall prepare a weekly list of current AI news candidates relevant to KNIEPUNKT.
The assistant shall allow the author’s personally relevant weekly news input to be included in the preparation.
The assistant shall allow an optional author-provided initial storyline to influence the episode preparation.
The assistant shall distinguish between news relevance for general AI enthusiasts and relevance for the target audience of decision-makers interested in moral AI.
The assistant shall help identify the major AI topics that readers should be aware of for the week.
B. Source Support and Verification Preparation
The assistant shall suggest multiple sources per news item where useful.
The assistant shall identify whether a source appears to be a primary source, a respected AI journalism source, an established high-quality journalistic source, an initial discussion source, or a weaker source.
The assistant shall flag tabloids, unknown media, and purely reproducing media as unsuitable or requiring author review.
The assistant shall flag claims that lack adequate source support.
The assistant shall flag contradictory sources for author review.
The assistant shall flag unclear factual situations before the episode is finalized.
The assistant shall present source suggestions in a way that supports the author’s personal review and prioritization.
C. Storyline and Draft Development
The assistant shall generate multiple storyline options for each episode.
The assistant shall make the editorial difference between storyline options understandable to the author.
The assistant shall support selection and refinement of a preferred storyline.
The assistant shall prepare draft column text based on selected news, sources, storyline, author input, and tone expectations.
The assistant shall support combining strong elements from multiple drafts into one improved draft.
The assistant shall identify weak storylines and explain why they may not be suitable.
D. Tone, Style, and Editorial Quality
The assistant shall evaluate whether a draft matches the KNIEPUNKT tone: column-like, gloss-like, amusing, culturally literate, and opinionated.
The assistant shall support references to Greek mythology, Roman mythology, canonical literature, and classical German literature where suitable.
The assistant shall flag text that becomes too technically detailed for the intended audience.
The assistant shall flag text that lacks authorial opinion.
The assistant shall flag text that lacks reader-facing interpretation.
The assistant shall help illuminate contradictions and anecdotes that make the column amusing.
The assistant shall support the author in filtering news rather than merely listing news.
E. Redundancy Management
The assistant shall consider previous KNIEPUNKT episodes when preparing new storylines and drafts.
The assistant shall flag repeated allegories, repeated references, or repeated content angles.
The assistant shall support variation while preserving the recognizable KNIEPUNKT style.
F. Length and Teaser Experimentation
The assistant shall propose length options for an episode.
The assistant shall help the author experiment with different episode lengths.
The assistant shall propose teaser or invitation text options.
The assistant shall support analysis of how length and teaser choices relate to audience response.
The assistant shall record which length and teaser approaches were used for later comparison.
G. KPI and Feedback Learning
The assistant shall support collection and interpretation of reach-related performance signals.
The assistant shall support consideration of newsletter open rate, where available.
The assistant shall support consideration of likes, comments, shares, new subscriptions, dwell time, qualitative reader comments, topic resonance, and reactions to length, teaser, and cover.
The assistant shall allow additional KPI suggestions to be considered.
The assistant shall support regular review of selected KPIs.
The assistant shall help convert qualitative reader feedback into usable editorial learning.
The assistant shall treat feedback from homepage or email channels as input resources, not as separate user interactions.
H. Visual and Cover Support
The assistant shall propose visual ideas based on the finalized or near-finalized episode text.
The assistant shall propose cover layout variants where useful.
The assistant shall allow strong visual experimentation.
The assistant shall preserve recognizability of the KNIEPUNKT visual identity.
The assistant shall flag cover ideas that lack a clear connection to the episode.
The assistant shall support justifiable visual decisions and may incorporate expert input where available.
The assistant shall leave the final cover decision to the author.
I. Publication Preparation and Control
The assistant shall prepare the content package needed for final author review, including article text, teaser/invitation message, source references, and cover recommendation.
The assistant shall support final quality assurance before publication.
The assistant shall not publish, send, or schedule the LinkedIn newsletter or post without the author’s final action.
The assistant shall make unresolved source, factual, tonal, or visual issues visible before final approval.
12. Open Questions / Items to Clarify
Which exact KPIs are available from LinkedIn for the newsletter and related posts?
How should “reach” be defined for KNIEPUNKT: impressions, unique readers, newsletter opens, post reach, or another metric?
What KPI review cadence should be used?
What minimum improvement threshold qualifies as “sustainably better”?
What length variants should be tested first?
How should teaser experiments be compared when topics differ from week to week?
Which visual identity elements are mandatory for KNIEPUNKT recognizability?
What level of expert input is expected for cover decisions?
How should qualitative reader feedback be categorized?
How should feedback from direct conversations be captured without over-formalizing the author’s process?
What role should a homepage or email feedback channel play in the first version of the assistant?
How should the assistant distinguish between a useful initial discussion source and an unreliable discussion thread?
How strict should the assistant be when flagging unknown but potentially original sources?
Which previous KNIEPUNKT episodes should be treated as the core style reference if some episodes performed better than others?
What exact acceptance baseline should be used for production time reduction?
13. Risks and Ambiguities
“Quality” is important but not yet fully operationalized; without clearer quality dimensions, the assistant may over-optimize for reach or superficial metrics.
KPI availability may be limited, which could restrict the assistant’s ability to measure success.
Reach may be influenced by LinkedIn distribution patterns outside the author’s control, making causal interpretation difficult.
Length, teaser, topic, and cover experiments may overlap, making it hard to isolate what caused improved performance.
The desired tone combines humor, moral seriousness, cultural references, and current AI news; poor balancing could make the column either too frivolous or too dry.
Source quality expectations are high; if source assessment is too loose, factual trust may suffer.
If source assessment is too strict, relevant emerging AI discussions may be excluded too early.
Visual experimentation may weaken recognizability if minimum identity rules are not defined.
Feedback from direct conversations may be biased toward positive or socially desirable responses.
The assistant could accidentally produce polished but generic commentary unless authorial opinion remains explicit.
Without clear review points, the assistant might increase preparation options rather than reduce total production time.
14. Acceptance Perspective

The product should be accepted as successful from a business perspective when the following statements are true:

The author can produce a KNIEPUNKT episode faster than the current 2–3 hour process.
The author still feels in full editorial control.
The assistant reliably prepares relevant weekly AI news candidates.
The assistant improves the availability and clarity of source options.
The assistant helps prevent unsupported claims and weak sourcing.
The assistant helps maintain the recognizable KNIEPUNKT tone.
The assistant flags missing opinion or missing reader-facing interpretation.
The assistant helps reduce repetition across episodes.
The assistant supports meaningful experimentation with length, teaser, topic framing, and cover direction.
The assistant makes relevant performance metrics visible and reviewable.
The assistant helps the author learn from KPIs and qualitative feedback.
Key success metrics become measurable and improve sustainably over time.
The author does not feel that the assistant produces generic AI commentary instead of KNIEPUNKT.
The assistant supports publication preparation but never bypasses final author approval.
15. Glossary
Original term	English explanation	Notes / context
KNIEPUNKT	Name of the LinkedIn column and initiative	A column by Dr. André Knie about current AI news and reflections
Kniepunkt	Individual episode or brand term of the column	Should remain untranslated because it is the column’s proper name
Kolumne	Column	Editorial format with recurring authorial voice
Glosse / glossenhaft	Gloss-like commentary	Amusing, pointed, opinionated, often highlighting contradictions
Mittelstand	German medium-sized business sector	Target audience includes CEOs and decision-makers from this context
moralische KI	Moral AI	The target audience is interested in ethical and moral implications of AI
Rezipienten	Recipients / readers	Refers to the column’s intended audience
Entscheider	Decision-makers	Includes business and public administration leaders
öffentlich-rechtlich	Public broadcasting	Used as an indicator of established, high-quality journalism in Germany
Primärquelle	Primary source	Original source of a claim, research result, discussion, or announcement
Qualitätsquelle	High-quality source	Trusted source suitable for factual support
Boulevard Blätter	Tabloids	Explicitly unsuitable source category
The Decoder	German-language AI journalism source	Named as a suitable AI topic source
Heise	German technology journalism source	Named as a suitable AI topic source
ARD / ZDF / DLF	German public broadcasting sources	Named as suitable established sources
Handelsblatt / Wirtschaftswoche / Die Zeit / Der Spiegel	Established German journalistic sources	Named as suitable high-quality journalistic sources
Storyline	Editorial framing or narrative angle	Connects selected news into a coherent column
Teasertext	Teaser or invitation text	LinkedIn-facing text that introduces the episode
Cover	Episode visual	Includes visual idea, layout, title, logo, and date
Wiedererkennbarkeit	Recognizability	The KNIEPUNKT visual identity should remain recognizable despite experimentation
KPI	Key performance indicator	Includes reach, open rate, likes, comments, shares, subscriptions, dwell time, and feedback signals
Reichweite	Reach	A central success metric, exact operational definition still open
Verweildauer	Dwell time	Potential indicator of reader engagement
Themenresonanz	Topic resonance	How strongly a topic appears to connect with the audience
