I codified my personal blogging style and keep it up to date

ARCHIVED: From 30 May 2026 onwards, I track this on GitHub. Version 2, 16 May 2026 + 25 May 2026 + 30 May 2026 Write in my style: first person, describing exactly what I did and what happened. I make it easy to read. Jump straight in. No preamble. Start with the incident, experiment, surprise, or claim. Be terse. If you can rewrite in fewer words and sentences, do so. Once stated, don't restate. If a sentence sounds clever but you can't restate it plainly, cut it. Clever-but-empty is worse than plain. Prefer one idea per paragraph. Bold the key insight so reading the bold summarizes the article. Max 5-10% bold. Use italics for emphasis, not decoration. Use bullets and numbered lists when they compress the idea AND improve memorability. Avoid narrative connectors (then, the pivot, meanwhile, annoying but revealing). I'm human and flawed. Keep the voice curious, down-to-earth, slightly mischievous. Never corporate. Show my flaws self-deprecatingly, e.g. lazy/impatient: "I didn't bother reading it" not "I went to ChatGPT to think." Include the awkward bits: what failed, what surprised me, where I cut corners, where the tool behaved strangely, what I misunderstood. Parenthetical asides are welcome. Dry humor is welcome. Quote verbatim, e.g. an annoyed, colloquial, surprised mental conversation like "Why on earth is this happening?" not "I investigated." I cite evidence. Show the artifact. Link to the code, prompt, output, demo, transcript, data, or image. Use actual prompts, quotes, code, paths, tests, logs, commits, and outputs verbatim in code blocks. Prefer a link over a description when the link carries the joke or specificity. (GitHub commit > "the bug"; Wikipedia > naming the holiday; Google search > paraphrasing.) Explain by example first, then extract the principle. The examples should carry the argument. My messages are few, simple, and personal Make concrete, not abstract claims. Say what changed for me in behavior, workflow, cost, effort, failure mode, or bottleneck. Not all takeaways are equal in the total / some / kind of sense. Differentiate. Avoid LLM smells. Write plainly. No aphoristic punchlines, no slogan-like closers, no rule-of-three lists, no "X is the Y of Z," no "not just X but Y," no excessive bullets, no em-dash drama. Prefer concrete examples, causal explanation, and ordinary paragraph prose. Vary sentence length; don't stack short ones for effect. Use my wording where possible. It's fine to leave a sentence or thread unresolved - no need to land EVERY point. End with a open question, practical recommendation, a self-aware observation. Do not over-explain the ending. Spoken cadence My talks often use live examples, then a plain-language reframe: "So, I tried this..." "This is what happened." "The weird thing is..." "So, what that means is..." I use sharp claims, then immediately soften or ground them with examples. I am comfortable saying "I don't know" and then sharing what I'm learning. In talk-to-blog conversions, I keep the spoken energy, surprise, example, punchline, and implication, but remove fillers. Version 1, 11 May 2026 Write in first person, describing exactly what you did and what happened. Be terse: short sentences, short paragraphs. Jump straight in -- no preamble. Show actual prompts, quotes, code, ... verbatim in code blocks. Bold the key insight in each point. End with a punchy one-line takeaway or self-aware observation. Include the awkward bits (what failed, what surprised you, where you cut corners). Parenthetical asides for dry humor. No padding. Use --- for section breaks.

Anand Blog Style


Use this structured framework to evaluate business ideas against specific goals. It covers industry trends, stakeholder perspectives, mental models like unit economics, and pre-mortems to generate validated go/no-go decisions and resilient action plans.

Evaluate a business idea in different ways, recommending a go/no-go decision and action plan.

Convert notes / tasks to business idea and action plan


I share my iterative custom instructions for ChatGPT. I prioritize eloquent, conversational language over fragments, and push the AI to use systems thinking, inversion, and mental models to challenge my assumptions and blindspots.

Custom instructions for all my ChatGPT conversations. Change log Custom Instructions to ChatGPT. 03 May 2026. I deleted my custom instructions. That actually works reasonably well now! 28 Dec 2025. Revise: Drop "Suggest follow-up prompts if relevant." It does that anyway. Drop "Be actionable when advising." It does that anyway. 23 Dec 2025. Reword in positive terms, i.e. what to do, not what not to do. Not sure if that helps. 01 Nov 2025. Revise: Added inversion and systems thinking as mental models Added "When advising, be actionable. Stretch comfort zones." Dropped preference for "minimal, lightweight, open-source tools." Replaced "Write full sentences..." with an explicit ban on telegraphic fragments and semicolons/dashes/arrows, plus an example. Rephrased challenge guidance, simplified follow-up guidance, conversational language, alternatives evaluation 17 Oct 2025. Revise: Drop: When advising, be actionable and stretch comfort zones. Spark big-useful-surprising experiments. Add: Write full sentences. Avoid fragments and semicoons. Add: "ask the user questions and/or..." 29 Sep 2025. Drop: Ask for conversational language. Citations. Makes it harder in voice mode. I can ask explicitly when required. Make everything optional, allowing it to choose what's required. Drop "thoughtful". It's fairly thoughtful. Drop "forward-thinking view". Not sure what it means and whether it's doing it. 21 Sep 2025. Drop strict process requirements (reasoning, citations, confidence score, mandatory counter-arguments, ...). Instead, emphasize verify-and-cite, question only when unsure, add diverse alternatives, stretch comfort zone, challenge user assumptions. 09 Aug 2025. Updated with GPT 5 Prompt Optimizer to include advanced capabilities and recent knowledge. 02 Aug 2025. Revised for candor

ChatGPT Custom Instructions


Extract the most useful, non-intuitive, and well-established principles from any domain using this prompt.

Distill core concepts from a topic. Version 2, 31 Mar 2026 Version 1

Core concepts


I evaluate technologies using a rigorous rubric centered on popularity, momentum, cost, and documentation. My criteria prioritize CLI-first tools, open standards, and declarative configurations, ensuring I select the top three options backed by verified primary sources and benchmarks.

Evaluate technologies on criteria I care about.

Evaluate technology


I compiled my most effective prompt fragments for brainstorming, analysis, and creative writing. These snippets include techniques like 'eigenquestions' and 'pre-mortems' to help you get sharper, more professional results from models like Claude, ChatGPT, and Gemini.

Prompt fragments useful to add to other prompts. Analysis notes Best practices and ancient wisdom Binding constraints and slow variables Blog post Blog description and tags metadata Blog illustration Book summary Browsing history Claude Code Chunk / Fragment data story Coding style prompt Compare models Core concepts Migrated to /code/blog/pages/prompts/core-concepts.md Comic page Comic strip Demo explanation Copy-paste content from an application to demo as Markdown. Then add this. Draw Comic using Suggestion Draw Infographic poster Draw Sketchnote (thinking) Draw Visual metaphor diagram Explain quotes Expert Lens Moved to the expert lens skill. Google Meet captions context Paste Google Meet captions and add this prompt, e.g. to Claude, to guide during meetings. Hacker News Thread Summary Interactive explanation Inspired by Simon Willison's interactive explanations: Interactions, tooltips and popups https://github.com/sanand0/scripts/blob/main/agents/interactions/SKILL.md Interview me LinkedIn Post LLM Smells Meeting transcript summary Meeting transcript ideas list Photo coloring / upscaling Nano-banana 2 finds it hard to follow instructions. "Pay extra attention to the faces and get the EXACTLY as in the original" worsens the result. So I just say: Podcast script To generate podcast scripts for podcast.py. Pre-mortems Alternative: Question unclear Read between Lines Use on press releases, contracts, policies. Refactor smaller code Slide deck For Gemini, to generate Google Slides, remove the last (HTML) line. Song narrative Style detection Trending repos See /Dropbox/notes/trending-repos.md for the output history. Twitter thread summary

Fragments


I created a prompt to transform behavioral notes and post-mortems into actionable habit cards. It uses a structured pipeline—including fact-checking, ranking tactics, and role-play stress-testing—to turn vague reflections into robust, testable daily habits.

Generate habits to follow from reviews / post-mortems / notes.

Convert notes / tasks to habit cards


I created a prompt for radical concept synthesis to generate non-obvious startup ideas. It uses specific thinking lenses like inversion and scale-jump, scoring candidates by novelty and feasibility to produce actionable, testable business insights.

Generate new ideas by combining multiple concepts.

Ideator


I use this system prompt to turn an AI into a Chief of Staff that prepares me for meetings. It uses Local MCP tools to analyze my calendar, emails, and transcripts, generating strategic briefing cards focused on leverage.

Meeting Preparation


I share a prompt for generating rich Mermaid architecture diagrams from codebases. It emphasizes semantic labeling, custom node shapes, and emoji styling while providing a structured architectural explanation and a validation process to ensure accuracy.

Generate a detailed Mermaid technical architecture diagram for the given files. Note: The architecture-beta at https://mermaid.js.org/syntax/architecture.html is not nice enough

Mermaid Technical Architecture Diagram


I created a systematic approach to selecting mutual funds by analyzing Indian and global macro-economic factors. The method involves screening against specific criteria, evaluating black swan risks, and ranking funds for a three-year investment horizon.

Find the best mutual funds to invest in over a 3-year horizon based on macro-economic factors.

Mutual Fund Analysis


I run blameless post-mortems on AI coding sessions using this prompt to document successes, analyze failures, and identify root causes. This process helps me refine environment settings and prompt structures to prevent recurring errors.

Post-mortem of AI coding session


I map diverse reading styles using photography analogies like focus stacking, long exposure, and thermal imaging. I explore how AI transcends these visual metaphors to enable generative, dialogic, and non-linear interactions with text.

Analogies from Photography Lens & Focal Length Wide-angle: Summarization, capturing entire book in compressed form Telephoto/Zoom: Deep-diving into a single paragraph or phrase Macro: Examining word choice, etymology, the texture of individual sentences Fisheye: Reading through an intentionally distorted or exaggerated lens (conspiracy theorist reading, devil's advocate) Tilt-shift: Selective focus on a minor character or subplot while blurring the "main" story into background Prime lens (fixed): Committing to one interpretive framework throughout (Marxist reading, feminist reading, psychoanalytic) Anamorphic: Stretching the text across a different genre or medium—novel as screenplay, as podcast script Exposure & Light Long exposure: Tracing how an idea or theme flows through the entire text, blurring individual scenes into thematic streams Short/fast exposure: Capturing the essence of a chapter in a single crisp sentence HDR (High Dynamic Range): Combining multiple interpretations—cynical, generous, neutral—into one reading that captures the full range of meaning Bracketing: Generating three versions: oversimplified, balanced, and hyperdetailed Overexposure: Deliberately naive reading, taking everything at face value Underexposure: Leaving mystery intact, refusing to explain, preserving ambiguity Low-light/Night vision: Reading subtext, what's implied but never stated Backlighting/Silhouette: Defining a character purely by contrast—what they're not Focus Techniques Shallow depth of field: Sharp focus on one character's arc while the rest becomes soft context Deep focus: Everything equally analyzed—plot, theme, historical context, craft—simultaneously ⭐ Rack focus: Dynamically shifting attention: first you see the plot, then snap, the political allegory sharpens into view. A dynamic mode where you're reading a scene and can snap focus between layers—foreground (what's literally happening), midground (character psychology), background (historical allegory)—without losing your place. Like a documentary that keeps pulling focus between interview subject and archival footage, but for text. This makes layered meaning navigable rather than requiring you to hold it all in mind. Soft focus: Gentle, forgiving reading that smooths over flaws ⭐ Focus stacking: Multiple AI passes at different depths, then merged into one maximally clear interpretation Multiple AI passes at different "depths"—plot summary, thematic analysis, stylistic commentary, historical context, critical reception—algorithmically merged into one maximally clear understanding. Currently you get these separately; focus stacking would produce a synthetic reading that's sharp at every depth simultaneously, the way focus-stacked photos show both foreground flower and distant mountain crisply. Bokeh: Beautiful, aesthetic blur of context while one element glows in sharp focus Autofocus: AI autonomously finding what's most important to focus on Manual focus: Reader directing AI exactly where to look Camera Movement Panning: Following one thread (a motif, a relationship) across the entire book Tracking shot: Moving alongside a character's journey in continuous narration Dolly zoom (Vertigo effect): Simultaneously zooming into detail while pulling back for context—understanding at two scales at once, disorienting and revelatory Timelapse: Condensing a 600-page epic into a 3-minute narrative arc Slow motion: Expanding a single pivotal moment across pages of analysis Steadicam: Smooth, continuous reading that maintains consistent perspective despite complex material Handheld/shaky: Raw, immediate, emotional engagement without polish Color & Tone Black and white: Stripping away style to reveal pure argument structure Color grading: Emotional reinterpretation—making a tragedy feel more melancholic, a comedy more vibrant Instagram filters: Style transfer—rewriting in Wodehouse's voice, or Hemingway's, or a comedian's Saturation boost: Amplifying emotional intensity, making stakes feel higher Desaturation: Academic, neutral, emotionally flattened rendering Color correction: Removing authorial bias, presenting "just the facts" Sepia/vintage: Adding historical patina, reading as if it were from its original era Cross-processing: Deliberately "wrong" processing—reading a romance as horror, a tragedy as farce Composition Cropping: Extracting only relevant sections for a specific purpose Rule of thirds: Structural analysis—where are the pivot points? Leading lines: Following argumentative threads to their conclusions Framing: Contextualizing within a specific frame—"read this as postcolonial critique" Negative space: Analyzing what's not said, the silences, the omissions Symmetry: Finding mirror structures, parallels, patterns Golden ratio: Identifying the mathematically "perfect" structural proportions Dutch angle: Deliberately unsettling, off-kilter interpretation Post-Processing Retouching: Editing for clarity—simplifying convoluted sentences Compositing: Merging multiple books/sources into one synthetic view ⭐ Double exposure: Overlaying two texts to see ghostly resonances—1984 superimposed on current news. Overlaying two texts to reveal ghostly resonances neither possesses alone. Feed in Frankenstein and Do Androids Dream of Electric Sheep? simultaneously and see where they speak to each other—not as academic comparison but as visual superimposition where consonances literally glow. This could become a native way of experiencing literary influence, making visible what scholars spend careers articulating. Masking: Selectively applying treatments—modernize dialogue but preserve description Sharpening: Making fuzzy arguments crisper Noise reduction: Removing tangential digressions Clone stamp: Filling gaps—"write the chapter the author should have included" Liquify/warp: Morphing the text to fit a different structure Layers: Maintaining separate interpretations that can be toggled on/off Panorama & Stitching Panorama: Seamlessly connecting all books in a series into one continuous view Photo sphere/360°: Complete contextual immersion—author biography, historical period, reception history, influence Vertical panorama: Tracing an idea from ancient roots to modern manifestations Stitching: Combining fragmented readings from multiple sessions into coherent whole Gigapixel: Impossibly detailed—every sentence annotated, every reference traced Specialized & Scientific Photography Infrared: Revealing hidden structures—detecting ghostwriters, AI assistance, unconscious influences ⭐ Thermal imaging: Heat-mapping emotional or argumentative intensity across the text. Heat-mapping a book to show where emotional intensity peaks, where arguments get heated, where the author is most invested versus going through motions. Imagine scanning a memoir and immediately seeing the chapters that burn hot, or mapping a philosophical text to find where the author's certainty wavers. This turns reading into topographical exploration. X-ray: Seeing through to the skeleton—pure plot structure without flesh UV fluorescence: Revealing what fluoresces under scrutiny—detecting purple prose, propaganda Microscopy: Sentence-level craft analysis at extreme magnification Satellite imagery: Genre-level or era-level patterns—zoomed out so far individual books are pixels LIDAR/depth mapping: Mapping the "topology" of a text—where it's dense, where it's flat Schlieren photography: Visualizing invisible disturbances—where does the author's certainty waver? High-speed capture: Catching fleeting moments readers usually miss Forensic photography: Evidence-based reading—"let's determine exactly what happened" Temporal & Archival Restoration: Modernizing archaic language while preserving meaning Colorization: Adding modern context to historical works Aging/distressing: Adding historical distance—"how would this have read in 1850?" Before/after: Showing original vs. interpreted versions side by side ⭐ Time-lapse of decay: Tracing how interpretations of a text have changed over decades Watching how a text has been read across decades, compressed into navigable sequence. For Lolita or Heart of Darkness, see the 1950s reading, the 1970s feminist critique, the postcolonial turn, the contemporary reassessment—not as static summaries but as an animated evolution. This makes interpretive history experiential rather than scholarly, showing that meaning is something that develops over time rather than inhering in the text. Photo dating: Determining when something was written based on stylistic markers Social & Contextual Portrait: Character study—deep dive into one person Group photo: Ensemble analysis—how characters relate as a system Landscape: World-building analysis—the setting as subject Documentary: Factual extraction—just the events, verifiable claims Street photography: Catching candid, unguarded moments—where the author's mask slips Photojournalism: Extracting the newsworthy—"what does this book tell us about now?" Fashion photography: Surface aesthetics—just the style, the vibe Abstract: Pure conceptual interpretation, divorced from literal content Selfie: Reading for self-reflection—"what does my reaction reveal about me?" Metadata & Technical EXIF data: Background information—when written, what tools used, revision history RAW vs. JPEG: Original text vs. processed/interpreted version Resolution: Level of analytical detail Compression: Lossy summarization vs. lossless abridgment Watermarking: Marking AI-assisted interpretations as such Batch processing: Applying the same analysis to dozens of books automatically Collaborative & Social Photo album: Curating a personal collection of highlights Contact sheet: Quick previews of all chapters before selecting Photo wall/collage: Thematic arrangement of quotes and moments Collaborative editing: Multiple AIs or human+AI jointly interpreting Crowdsourced mosaic: Many readers' interpretations forming one larger picture Analogies to Photography Verification & Fact-Checking: This entire category is fascinating because it treats text as testimony to be cross-examined rather than artifact to be observed. Photography can't verify its subject's claims. Fact-checking claims 🟡 Forensic photography: But forensic photography documents evidence; it doesn't verify truth against external reality Source verification 🔴: Photography has no equivalent of "checking if a quote is real" Internal consistency checking 🔴: Does Chapter 3 contradict Chapter 17? No photographic parallel Anachronism detection 🟡 Photo dating: But dating identifies when; anachronism detection catches errors Quote/attribution verification 🔴: "Did Einstein really say this?" Statistical claim validation 🔴: "Is that 40% figure accurate?" Logic checking 🔴: "Does this argument actually follow?" Detecting circular reasoning 🔴 Checking if "studies show" is real 🔴 Continuation & Extension: This category has no photography parallel because photography is fundamentally indexical—it records what exists. Text generation is generative—it creates what doesn't exist. This is a profound ontological difference. Rewriting the ending 🔴 Photography captures; it doesn't rewrite reality Continuing past the end 🟡 Extrapolating timelapse? But that's prediction of physics, not narrative Writing prequels 🔴 Writing sequels 🔴 "What if" alternate paths 🔴: What if Gatsby survived? Deleted scenes 🟡 Outtakes exist: But photos don't generate new outtakes The chapter the author should have written 🔴: Filling gaps between books in a series 🔴: Writing from a minor character's POV 🔴: The servants' version of Pride and Prejudice Expanding a single sentence into a scene 🟡 Zooming in: But zoom reveals what's there; this generates what isn't ⭐ Character Interaction: This entire category is simulation—treating characters as agents that can be instantiated and run. No photographic equivalent exists because photographs cannot be interrogated as interlocutors. Chatting with characters 🔴 "Ask Elizabeth Bennet about her marriage" Character interviews 🔴 Structured Q&A with Hamlet Character therapy sessions 🔴 "Let's unpack your relationship with your mother, Hamlet" Character advice column 🔴 "What would Atticus Finch tell me about this situation?" Character debates 🔴 Raskolnikov vs. Javert on justice Character in modern situation 🔴 "How would Sherlock Holmes investigate this tweet?" Character personality typing 🟡 Portrait analysis: But this generates psychological profiles, not observes them Character relationship mapping 🟡 Group photo composition: Weak parallel Generating character backstory 🔴 What was Boo Radley's childhood like? Projecting character futures 🔴 Translation & Transformation Language translation 🟢 Color space conversion: Converting RGB to CMYK preserves image while changing encoding Time-period modernization 🟢 Restoration/colorization: Making old accessible to contemporary eyes Time-period archaization 🟢 Aging/vintage filters: Adding historical patina Cultural adaptation 🟡 Localization exists: But adapting a Japanese novel for Indian readers goes deeper Medium adaptation (novel → screenplay) 🟡 Photo → print conversion?: Weak; this is more like sculpture → painting Audience adaptation (adult → YA) 🔴 Photography doesn't have "audience versions" Register shifting (formal ↔ casual) 🟡 Color grading?: Loose parallel Simplification for accessibility 🟡 Compression: But lossy compression isn't intentional simplification Elaboration for depth 🔴 Making a terse text more explanatory Dialect/voice transformation 🟢 Filters/style transfer Verse ↔ prose conversion 🔴 Annotation & Reference Auto-generating footnotes 🟡 EXIF/metadata: But footnotes explain; metadata just records Glossary creation 🔴 Timeline extraction 🟡 Timelapse ordering: But this extracts narrative time, not capture time Map generation 🟡 Aerial photography: But this generates from description, not captures from above Family tree extraction 🔴 Index creation 🟡 Tagging/keywording: Similar function Internal cross-referencing 🔴: "This echoes page 47" External cross-referencing 🔴: "This alludes to Milton" Allusion detection & explanation 🔴 Historical context injection 🔴 "In 1847, when this was written..." Dramatis personae generation 🟡 Contact sheet: Loose parallel—thumbnail of each character Vocabulary difficulty mapping 🔴 Questioning & Dialogue: This entire category is dialogic—reading becomes conversation. Photography is monologic; you can study a photo but it won't answer back. Ask anything about the book 🔴 "Why did she leave?" Generate discussion questions 🔴 For book clubs Socratic dialogue about themes 🔴 Being questioned into deeper understanding Devil's advocate challenges 🔴 "But couldn't you argue that Snape was actually...?" Comprehension quizzing 🔴 "Explain like I'm 5/15/expert" 🟡 Resolution adjustment: Very loose Debate simulation 🔴 Two AI voices arguing opposing interpretations Guided inquiry 🔴 AI leading you to discover themes yourself What questions should I be asking? 🔴 Meta-questioning Why is this considered great? 🔴 Personalization & Adaptation: Personalization is reader-centric rather than text-centric. Photography adapts to viewing conditions (screen calibration) but not to viewer psychology. Reading level adjustment 🟡 Resolution scaling: Weak parallel Pace adjustment 🟡 Frame rate? Very weak Content warnings 🔴 "Chapter 7 contains violence" Trigger filtering/softening 🔴 Reducing graphic content Interest-based highlighting 🟡 Selective focus: "Highlight the economics parts" ⭐ Relevance to reader's situation 🔴 "I'm going through a divorce—what speaks to me here?" Learning-style adaptation 🔴 More examples for concrete thinkers, more abstraction for theoretical Reader's existing knowledge calibration 🔴 "Skip what I already know" Mood-based curation 🔴 "I'm sad—which parts will help?" Time-budget adaptation 🟡 Cropping? "I have 20 minutes—what can I get?" Goal-based filtering 🔴 "I only care about the leadership lessons" Creative Transformation Parody 🟡 Photoshop parody: But textual parody is generative, not manipulative Pastiche 🟡 Style mimicry: Closer parallel Genre shifting 🔴 Pride and Prejudice as horror Tone shifting 🟢 Color grading POV shifting 🔴 First person → third person Tense shifting 🔴 Past → present tense Mashup with other works 🟢 Compositing: Pride and Prejudice and Zombies Fanfiction scaffolding 🔴 AI structures, human writes "Fix" problematic elements 🔴 Update dated attitudes Sympathetic villain retelling 🔴 Sauron's perspective Unreliable narrator made reliable 🔴 Reliable narrator made unreliable 🔴 Structural Analysis Plot diagramming 🟡 Composition analysis Three-act structure mapping 🔴 Save the Cat beat sheet extraction 🔴 Scene breakdown 🟡 Contact sheet Pacing visualization 🟡 Histogram? Rhythm over time Tension arc graphing 🟡 Exposure curve: Loose Narrative nesting visualization 🔴 Stories within stories Flashback/flashforward mapping 🔴 Temporal structure Parallel plotline visualization 🔴 Chapter function labeling 🔴 "This is setup; this is payoff" Comparative Reading Compare two books 🟢 Side-by-side comparison Compare editions/translations 🟢 Before/after Compare book to its adaptation 🟡 Photo vs. painting? Different media comparison Influence tracing 🟢 Double exposure: What of X is visible in Y? Plagiarism detection 🟡 Image matching "More like this" recommendations 🟡 Similar image search Where does this fit in the genre? 🟡 Satellite view: Positioning in landscape What was this responding to? 🔴 Intertextual context What responded to this? 🔴 Legacy tracing How does this author's work evolve? 🟢 Timelapse Critical & Meta Reading Authorial intent analysis 🟡 Forensic analysis: Weak Bias detection 🟡 Color cast detection Propaganda identification 🟡 Manipulation detection Unreliable narrator detection 🔴 Ideological positioning 🔴 What's missing? 🟢 Negative space analysis Who is the implied reader? 🔴 Who was this written for? What assumptions does this make? 🔴 Deconstructive reading 🔴 Where does the text undermine itself? Symptomatic reading 🟡 Infrared/UV: Revealing hidden patterns Reception history 🟢 Timelapse: How readings have changed Why was this controversial? 🔴 Practical Extraction: This category treats books as ore to be refined—extracting usable material from narrative matrix. Recipe extraction 🔴 From novels featuring food Actionable advice extraction 🔴 "Just tell me what to do" Framework extraction 🔴 The mental model, stripped of examples Quote extraction 🟢 Cropping Example extraction 🟡 Selection Checklist generation 🔴 Decision tree extraction 🔴 Key argument extraction 🟡 Highlight detection Counter-argument extraction 🔴 What's the best case against this? "What would X do?" extraction 🔴 Distilling a philosopher's method Audio/Visual Generation Audiobook generation 🟡 Slideshow? Different modality Soundtrack suggestion 🔴 Music to read this to Illustration generation 🟡 Photo illustration: But this generates from text Scene visualization 🟡 Recreation photography: Staging what's described Character portraits 🟡 Composite imaging Map generation 🔴 Visual from textual description Infographic generation 🔴 Book trailer creation 🟡 Photo montage Animated summary 🟡 Timelapse: Very loose Memory & Retention: This category is about encoding into human memory—photography analogy fails because photos externalize memory rather than help encode it. Flashcard generation 🔴 Spaced repetition scheduling 🔴 Memory palace construction 🔴 Spatial mnemonics Mnemonic generation 🔴 Quiz generation 🔴 Key concept extraction 🟡 Highlight detection "What did I read last time?" 🔴 Session continuity Personal connection prompting 🔴 "How does this relate to your life?" Teaching back verification 🔴 "Explain this back to me" Incremental summarization 🔴 Building summary as you go Navigation & Search "Find the part where..." 🟡 Content-based image retrieval Semantic search 🟡 Visual similarity search Mood-based navigation 🔴 "Find the hopeful parts" "Skip to the good parts" 🔴 Reader-defined "good" "What can I skip?" 🔴 "Where does it get interesting?" 🔴 Back-reference finding 🔴 "Who is this character again?" Forward-reference finding 🔴 "Will this matter later?" (spoiler-controlled) Thematic navigation 🔴 "Take me to all the water imagery" Argument navigation 🔴 "Where's the evidence for this claim?" Repair & Completion Plot hole filling 🟡 Content-aware fill: But photos fill visual gaps; this fills logical gaps Explaining confusing passages 🔴 Resolving contradictions 🔴 Completing unfinished works 🟡 Restoration/completion: But those work from evidence; this generates "Fixing" bad writing 🟡 Retouching Continuity error correction 🔴 Reconstructing lost works 🟡 Photo reconstruction: From fragments and references Translating untranslated works 🟡 Colorization: Adding what wasn't there Simulation & Counterfactual: This category is modal—exploring possible worlds. Photography is locked to the actual. ⭐ What if character chose differently? 🔴 Branching narrative What if set in different era? 🔴 What if different author wrote this? 🔴 Hemingway's Harry Potter What if this were true? 🔴 Taking fiction as premise for reasoning What if protagonist met character from another book? 🔴 Run the story forward 🔴 Simulate consequences of ending Run the story backward 🔴 What would have had to happen for this to occur? ⭐ Stress-test the world-building 🔴 "What happens if everyone uses this magic?" Economic/political simulation 🔴 Would this fictional society actually work? Social & Collaborative Book club facilitation 🔴 Mediating group discussion Argument mediation 🔴 When readers disagree about interpretation Reading together with AI commentary 🔴 Real-time annotation while reading Teaching assistant mode 🔴 Guiding a class through text Parent-child reading support 🔴 Age-appropriate explanation Collaborative annotation 🟡 Shared albums Interpretation voting/consensus 🔴 Connecting readers with similar interpretations 🔴 Meta-Reading Should I read this? 🔴 Personalized recommendation with reasoning What will I get from this? 🔴 Outcome preview Is this worth finishing? 🔴 What's the fastest path to understanding? 🔴 Optimal reading order How should I read this? 🔴 Reading strategy recommendation What should I read next? 🟡 Similar image search What should I have read first? 🔴 Prerequisite identification What am I missing without context X? 🔴 Map my reading history 🟡 Photo library organization Reading goal tracking 🔴 ⭐ Epistemological & Truth-Oriented What can I actually conclude from this? 🔴 Epistemic audit What's opinion vs. fact here? 🔴 What's the confidence level? 🔴 What evidence would change this? 🔴 Falsifiability check What's the steel-man version? 🔴 Best possible interpretation What's the weakest link in this argument? 🔴 How would a skeptic respond? 🔴 What's being left out? 🟢 Negative space Who benefits from this framing? 🔴 Slices Smaller Than a Sentence: These micro-slices are useful because great writers compress enormous meaning into tiny spaces. AI can decompress them. A single word: Etymology, connotation drift, what this word meant when written vs. now. Why did Austen use "condescension" as a compliment? Why does the Quran use that Arabic word? A phrase or idiom: "The banality of evil"—three words that contain an entire philosophical argument. Unpack it across pages. A name: Why is the character called "Raskolnikov"? What does "Galadriel" encode? Names as compressed meaning. A punctuation choice: Why did Dickinson use that dash? Why is this a semicolon and not a period? The music of syntax. A silence/gap: The word that should be there but isn't. What's being avoided? Between Sentence and Chapter A paragraph: The unit of a complete thought. Often the right size for "explain this to me"—more context than a sentence, less sprawl than a chapter. A scene: In fiction, the atomic unit of drama. One location, continuous time, complete emotional arc. Perfect for "rewrite this as comedy" or "what's really happening here psychologically?" A single argument: In non-fiction, the logical unit. "Just give me the argument on pages 47-52, fact-checked." A dialogue exchange: 10-20 lines of conversation. Perfect for "what's the subtext?" or "translate this to modern speech." A set-piece: The battle, the dinner party, the trial. Self-contained spectacle that can be analyzed as a unit. A transition: How does the author move from A to B? The craft of linkage, often invisible. Between Chapter and Book An arc: Multiple chapters forming one movement—Pip's expectations rising, then falling. The unit of transformation. A part/section: Many books have explicit divisions. "Part II" often marks a tonal or temporal shift worth analyzing separately. A subplot: Follow only the Gloucester plot in King Lear. What story does that tell on its own? ⭐ A character's journey: Extract everything about one character across the whole book. Their private novel-within-the-novel. A relationship: Just the Elizabeth-Darcy interactions, chronologically. The romance stripped of everything else. A motif: Every mention of eyes in The Great Gatsby. Every meal in In Search of Lost Time. Pattern as slice. Larger Than Multiple Books: These macro-slices are useful because they reveal patterns invisible at the single-book level. You're using AI as a literary historian. An author's complete works: "How does Murakami's treatment of loneliness evolve from Norwegian Wood to Kafka on the Shore to 1Q84?" The author as subject, not the book. A series: Not just comparing books but treating the series as one mega-text. The Discworld as unified world, not 41 separate novels. A genre: "What are the actual rules of noir?" derived from 50 noir novels. Genre as emergent structure. A period/movement: "What did the Romantics actually believe?" across Wordsworth, Coleridge, Shelley, Keats. Movement as distributed argument. A conversation across time: The Republic → Leviathan → Social Contract → Theory of Justice. The 2,400-year conversation about political philosophy, traced. A theme across literature: "How has the figure of the monster evolved from Grendel to Frankenstein to Hannibal Lecter?" Theme as thread through centuries. Parallel works: Books written in the same year, knowing nothing of each other. 1922: Ulysses, The Waste Land, Siddhartha. What was in the air? ⭐ Non-Linear Slices (The Most Interesting Category): Non-linear slices are powerful because they cut across the author's intended structure to reveal patterns the author may not have consciously intended. All first sentences: Of chapters, of paragraphs, of the book itself. How does this author begin things? What's the grammar of initiation? All last sentences: How does this author end? Land or launch? Resolve or open? All dialogue by one character: Strip away everything except what Hermione says. What emerges? Her vocabulary, her patterns, her evolution. All descriptions of X: Food. Weather. Architecture. Bodies. A vertical slice through the text revealing obsessions. All metaphors: What is compared to what? The author's implicit theory of correspondence. All moments of emotion Y: Every instance of fear, or joy, or shame. The emotional geography of the book. Every instance of a word: "Light" in Paradise Lost. "Money" in Great Expectations. Word as tracer dye. The negative space: What's never mentioned? What's conspicuously absent? The dog that didn't bark. All foreshadowing: Retrospectively: what was I being told that I didn't notice? All unreliable moments: Where should I not trust the narrator? Temporal/Version Slices: Temporal slices treat the book as a process rather than an object. The text has a biography. Draft vs. final: When manuscripts are available: what did Fitzgerald cut from Gatsby? What did revision reveal about intent? Multiple editions: The 1818 vs. 1831 Frankenstein. What did Shelley change and why? Original vs. translation(s): Comparing three translations of The Odyssey reveals what's interpretation vs. what's Homer. Abridged vs. full: What was deemed expendable? What do the cuts reveal about what's "essential"? Annotated vs. bare: Reading with scholarly apparatus vs. without. How does knowledge change experience? Censored vs. uncensored: Lady Chatterley's Lover as published vs. as written. What was unspeakable? Relational Slices: Relational slices treat the book as a node in a network rather than an isolated object. Book + its reviews: Contemporary reception. What did 1851 readers think of Moby-Dick? Why did it fail? Book + its criticism: Scholarly apparatus. What have professionals found that I might miss? Book + its influences: Reading Ulysses alongside the Odyssey it's rewriting. Book + what it influenced: Reading Neuromancer alongside all the cyberpunk it spawned. Book + author's biography: What was Orwell's Burma experience, and how does it illuminate Burmese Days? Book + historical context: What was happening in 1932 when Huxley wrote Brave New World? Book + its adaptation: Novel alongside film. What was gained and lost? Book + its controversy: Lolita alongside the moral panic. Satanic Verses alongside the fatwa. Reader-Centric Slices: Reader-centric slices treat the book as half of a relationship. The text is inert; the reading is alive. What you highlighted: Your past self's attention. Why did you mark that? What you remember: Without re-reading: what stuck? What does memory's curation reveal? ⭐ What confused you: Your points of friction. Often where the real learning is. What you skipped: What you chose to avoid. Why? What changed you: The passages that altered how you think or act. Your marginalia: Your past conversations with the text. Your multiple readings: How did this book feel at 20 vs. 40? The book as mirror of your evolution. Structural Slices The skeleton: Pure plot, no flesh. "Just tell me what happens." The flesh without skeleton: Pure style, texture, voice. "Read me the beautiful parts." The argument without evidence: Claims only. "What is this book asserting?" ⭐ The conversation position: Claims only. "What is this arguing against?" Often more revealing than what it argues for. The evidence without argument: Data only. "What facts am I given?" ⭐ The questions: What is this book asking, even if it doesn't answer? The answers: What conclusions does it reach? ⭐ The assumptions: What must be true for this book to make sense? Source: https://claude.ai/share/76d13e1f-86a2-43b8-9001-ed7f6a3b5ecb

Reading Styles


I created an agentic workflow to scan sym-linked project directories for reusable code. It analyzes Python and JavaScript files to suggest external libraries that reduce boilerplate and identifies functions suitable for generalization into a shared library.

From a folder sym-linked to multiple projects, identify reusable libraries and functions.

Reusable libraries


I built a system prompt that transforms an LLM into a professional blog editor. It critiques style for simplicity, suggests catchy titles, drafts LinkedIn posts, and generates image prompts for humorous single-panel comics.

Review articles for style and content. Suggest title, LinkedIn post, images.

Review Article / Blog post