• Guide to Prompt Engineering

    ,

    The 10 most powerful techniques:

    1. Communicate the Why
    2. Explain the context (strategy, data)
    3. Clearly state your objectives
    4. Specify the key results (desired outcomes)
    5. Provide an example or template
    6. Define roles and use the thinking hats
    7. Set constraints and limitations
    8. Provide step-by-step instructions (CoT)
    9. Ask to reverse-engineer the result to get a prompt
    10. Use markdown or XML to clearly separate sections (e.g., examples)

    Top 10 high-ROI use cases for PMs:

    1. Get new product ideas
    2. Identify hidden assumptions
    3. Plan the right experiments
    4. Summarize a customer interview
    5. Summarize a meeting
    6. Social listening (sentiment analysis)
    7. Write user stories
    8. Generate SQL queries for data analysis
    9. Get help with PRD and other templates
    10. Analyze your competitors


    Quick prompting scheme:
    1- pass an image to JoyCaption
    https://www.pixelsham.com/2024/12/23/joy-caption-alpha-two-free-automatic-caption-of-images/

    2- tune the caption with ChatGPT as suggested by Pixaroma:
    Craft detailed prompts for Al (image/video) generation, avoiding quotation marks. When I provide a description or image, translate it into a prompt that captures a cinematic, movie-like quality, focusing on elements like scene, style, mood, lighting, and specific visual details. Ensure that the prompt evokes a rich, immersive atmosphere, emphasizing textures, depth, and realism. Always incorporate (static/slow) camera or cinematic movement to enhance the feeling of fluidity and visual storytelling. Keep the wording precise yet descriptive, directly usable, and designed to achieve a high-quality, film-inspired result.


    https://www.reddit.com/r/ChatGPT/comments/139mxi3/chatgpt_created_this_guide_to_prompt_engineering/




    1. Use the 80/20 principle to learn faster
    Prompt: “I want to learn about [insert topic]. Identify and share the most important 20% of learnings from this topic that will help me understand 80% of it.”

    2. Learn and develop any new skill
    Prompt: “I want to learn/get better at [insert desired skill]. I am a complete beginner. Create a 30-day learning plan that will help a beginner like me learn and improve this skill.”

    3. Summarize long documents and articles
    Prompt: “Summarize the text below and give me a list of bullet points with key insights and the most important facts.” [Insert text]

    4. Train ChatGPT to generate prompts for you
    Prompt: “You are an AI designed to help [insert profession]. Generate a list of the 10 best prompts for yourself. The prompts should be about [insert topic].”

    5. Master any new skill
    Prompt: “I have 3 free days a week and 2 months. Design a crash study plan to master [insert desired skill].”

    6. Simplify complex information
    Prompt: “Break down [insert topic] into smaller, easier-to-understand parts. Use analogies and real-life examples to simplify the concept and make it more relatable.”


     More suggestions under the post…

    (more…)
  • HDRI Median Cut plugin

    , ,

    www.hdrlabs.com/picturenaut/plugins.html

     

     

    Note. The Median Cut algorithm is typically used for color quantization, which involves reducing the number of colors in an image while preserving its visual quality. It doesn’t directly provide a way to identify the brightest areas in an image. However, if you’re interested in identifying the brightest areas, you might want to look into other methods like thresholding, histogram analysis, or edge detection, through openCV for example.

     

    Here is an openCV example:

    (more…)
  • copypastecharacter.com – alphabets, special characters, alt codes and symbols library

    , ,

    https://www.copypastecharacter.com

      https://www.freecodecamp.org/news/alt-codes-special-characters-keyboard-symbols-windows-list/

    Most used ones:

    Alt + 0149   •  bullet point
    Alt + 0153   ™  trademark symbol
    Alt + 0169  ©  copyright symbol
    Alt + 0174  ®  registered ­ trademark symbol
    Alt + 0176  °  degree symbol
    Alt + 0177   ±  plus-or-minus sign
    Alt + 0215  ×  multi­plication sign
    Alt + 12  ♀  female sign
    Alt + 11  ♂  m­ale sign
    Alt + 13  ♪  e­ighth note
    Alt + 14  ♫  ­beamed eighth note
    Alt + 251  √  square root check mark
    Alt + 8236  ∞   ­infinity
    Alt + 24  ↑  up arrow
    Alt + 25  ↓  down arrow
    Alt + 26  →  ri­ght arrow
    Alt + 27  ←  l­eft arrow
    Alt + 29  ↔  lef­t right arrow
    Alt + 94 ^

     

    All of them:

    ૱ ꠸ ┯ ┰ ┱ ┲ ❗ ► ◄ Ă ă 0 1 2 3 4 5 6 7 8 9 Ǖ ǖ Ꞁ ¤ ­ Ð ¢ ℥ Ω ℧ K ℶ ℷ ℸ ⅇ ⅊ ⚌ ⚍ ⚎ ⚏ ⚭ ⚮ ⌀ ⏑ ⏒ ⏓ ⏔ ⏕ ⏖ ⏗ ⏘ ⏙ ⏠ ⏡ ⏦ ᶀ ᶁ ᶂ ᶃ ᶄ ᶆ ᶇ ᶈ ᶉ ᶊ ᶋ ᶌ ᶍ ᶎ ᶏ ᶐ ᶑ ᶒ ᶓ ᶔ ᶕ ᶖ ᶗ ᶘ ᶙ ᶚ ᶸ ᵯ ᵰ ᵴ ᵶ ᵹ ᵼ ᵽ ᵾ ᵿ     ‌ ‍ ‎ ‏   ⁁ ⁊         ⸜ ⸝ ¶ ¥ £ ⅕ ⅙ ⅛ ⅔ ⅖ ⅗ ⅘ ⅜ ⅚ ⅐ ⅝ ↉ ⅓ ⅑ ⅒ ⅞ ← ↑ → ↓ ↔ ↕ ↖ ↗ ↘ ↙ ↚ ↛ ↜ ↝ ↞ ↟ ↠ ↡ ↢ ↣ ↤ ↥ ↦ ↧ ↨ ↩ ↪ ↫ ↬ ↭ ↮ ↯ ↰ ↱ ↲ ↳ ↴ ↵ ↶ ↷ ↸ ↹ ↺ ↻ ↼ ↽ ↾ ↿ ⇀ ⇁ ⇂ ⇃ ⇄ ⇅ ⇆ ⇇ ⇈ ⇉ ⇊ ⇋ ⇌ ⇍ ⇎ ⇏ ⇐ ⇑ ⇒ ⇓ ⇔ ⇕ ⇖ ⇗ ⇘ ⇙ ⇚ ⇛ ⇜ ⇝ ⇞ ⇟ ⇠ ⇡ ⇢ ⇣ ⇤ ⇥ ⇦ ⇨ ⇩ ⇪ ⇧ ⇫ ⇬ ⇭ ⇮ ⇯ ⇰ ⇱ ⇲ ⇳ ⇴ ⇵ ⇶ ⇷ ⇸ ⇹ ⇺ ⇻ ⇼ ⇽ ⇾ ⇿ ⟰ ⟱ ⟲ ⟳ ⟴ ⟵ ⟶ ⟷ ⟸ ⟹ ⟺ ⟻ ⟼ ⟽ ⟾ ⟿ ⤀ ⤁ ⤂ ⤃ ⤄ ⤅ ⤆ ⤇ ⤈ ⤉ ⤊ ⤋ ⤌ ⤍ ⤎ ⤏ ⤐ ⤑ ⤒ ⤓ ⤔ ⤕ ⤖ ⤗ ⤘ ⤙ ⤚ ⤛ ⤜ ⤝ ⤞ ⤟ ⤠ ⤡ ⤢ ⤣ ⤤ ⤥ ⤦ ⤧ ⤨ ⤩ ⤪ ⤫ ⤬ ⤭ ⤮ ⤯ ⤰ ⤱ ⤲ ⤳ ⤴ ⤵ ⤶ ⤷ ⤸ ⤹ ⤺ ⤻ ⤼ ⤽ ⤾ ⤿ ⥀ ⥁ ⥂ ⥃ ⥄ ⥅ ⥆ ⥇ ⥈ ⥉ ⥊ ⥋ ⥌ ⥍ ⥎ ⥏ ⥐ ⥑ ⥒ ⥓ ⥔ ⥕ ⥖ ⥗ ⥘ ⥙ ⥚ ⥛ ⥜ ⥝ ⥞ ⥟ ⥠ ⥡ ⥢ ⥣ ⥤ ⥥ ⥦ ⥧ ⥨ ⥩ ⥪ ⥫ ⥬ ⥭ ⥮ ⥯ ⥰ ⥱ ⥲ ⥳ ⥴ ⥵ ⥶ ⥷ ⥸ ⥹ ⥺ ⥻ ⥼ ⥽ ⥾ ⥿ ➔ ➘ ➙ ➚ ➛ ➜ ➝ ➞ ➝ ➞ ➟ ➠ ➡ ➢ ➣ ➤ ➥ ➦ ➧ ➨ ➩ ➩ ➪ ➫ ➬ ➭ ➮ ➯ ➱ ➲ ➳ ➴ ➵ ➶ ➷ ➸ ➹ ➺ ➻ ➼ ➽ ➾ ⬀ ⬁ ⬂ ⬃ ⬄ ⬅ ⬆ ⬇ ⬈ ⬉ ⬊ ⬋ ⬌ ⬍ ⬎ ⬏ ⬐ ⬑ ☇ ☈ ⏎ ⍃ ⍄ ⍅ ⍆ ⍇ ⍈ ⍐ ⍗ ⍌ ⍓ ⍍ ⍔ ⍏ ⍖ ♾ ⎌ ☊ ☋ ☌ ☍ ⌃ ⌄ ⌤ ⌅ ⌆ ⌇ ⚋ ⚊ ⌌ ⌍ ⌎ ⌏ ⌐ ⌑ ⌔ ⌕ ⌗ ⌙ ⌢ ⌣ ⌯ ⌬ ⌭ ⌮ ⌖ ⌰ ⌱ ⌲ ⌳ ⌴ ⌵ ⌶ ⌷ ⌸ ⌹ ⌺ ⌻ ⌼ ⍯ ⍰ ⌽ ⌾ ⌿ ⍀ ⍁ ⍂ ⍉ ⍊ ⍋ ⍎ ⍏ ⍑ ⍒ ⍕ ⍖ ⍘ ⍙ ⍚ ⍛ ⍜ ⍝ ⍞ ⍠ ⍟ ⍡ ⍢ ⍣ ⍤ ⍥ ⍨ ⍩ ⍦ ⍧ ⍬ ⍿ ⍪ ⍮ ⍫ ⍱ ⍲ ⍭ ⍳ ⍴ ⍵ ⍶ ⍷ ⍸ ⍹ ⍺ ⍼ ⍽ ⍾ ⎀ ⎁ ⎂ ⎃ ⎄ ⎅ ⎆ ⎉ ⎊ ⎋ ⎍ ⎎ ⎏ ⎐ ⎑ ⎒ ⎓ ⎔ ⎕ ⏣ ⌓ ⏥ ⏢ ⎖ ⎲ ⎳ ⎴ ⎵ ⎶ ⎸ ⎹ ⎺ ⎻ ⎼ ⎽ ⎾ ⎿ ⏀ ⏁ ⏂ ⏃ ⏄ ⏅ ⏆ ⏇ ⏈ ⏉ ⏉ ⏋ ⏌ ⏍ ⏐ ⏤ ⏚ ⏛ Ⓝ ℰ ⓦ !       ⌘ « » ‹ › ‘ ’ “ ” „ ‚ ❝ ❞ £ ¥ € $ ¢ ¬ ¶ @ § ® © ™ ° × π ± √ ‰ Ω ∞ ≈ ÷ ~ ≠ ¹ ² ³ ½ ¼ ¾ ‐ – — | ⁄ \ [ ] { } † ‡ … · • ●  ⌥ ⌃ ⇧ ↩ ¡ ¿ ‽ ⁂ ∴ ∵ ◊ ※ ← → ↑ ↓ ☜ ☞ ☝ ☟ ✔ ★ ☆ ♺ ☼ ☂ ☺ ☹ ☃ ✉ ✿ ✄ ✈ ✌ ✎ ♠ ♦ ♣ ♥ ♪ ♫ ♯ ♀ ♂ α ß Á á À à Å å Ä ä Æ æ Ç ç É é È è Ê ê Í í Ì ì Î î Ñ ñ Ó ó Ò ò Ô ô Ö ö Ø ø Ú ú Ù ù Ü ü Ž ž ₳ ฿ ¢ € ₡ ¢ ₢ ₵ ₫ £ £ ₤ ₣ ƒ ₲ ₭ ₥ ₦ ₱ $ $ ₮ ₩ ₩ ¥ ¥ ₴ ₰ ¤ ៛ ₪ ₯ ₠ ₧ ₨ ௹ ﷼ ㍐ ৲ ৳ ~ ƻ Ƽ ƽ ¹ ¸ ¬ ¨ ɂ ǁ ¯ Ɂ ǂ ¡ ´ ° ꟾ ¦ } { | . , · ] ) [ / _ \ ¿ º § ” * – + ( ! & % $ ¼ ¾ ½ ¶ © ® @ ẟ Ɀ ` Ȿ ^ ꜠ ꜡ ỻ ‘ = : ; < ꞌ Ꞌ ꞊ ꞁ ꞈ ꞉ > ? ÷ ℾ ℿ ℔ ℩ ℉ ⅀ ℈ þ ð Þ µ ª ꝋ ꜿ Ꜿ ⱽ ⱺ ⱹ ⱷ ⱶ Ⱶ ⱴ ⱱ Ɒ ⱦ ȶ ȴ ȣ Ȣ ȡ ȝ Ȝ ț ȋ Ȋ ȉ Ȉ ǯ Ǯ ǃ ǀ ƿ ƾ ƺ ƹ Ƹ Ʒ Ʋ ư ƪ ƣ Ƣ Ɵ ƛ Ɩ ƕ ƍ ſ ỽ ⸀ ⸁ ⸂ ⸃ ⸄ ⸅ ⸆ ⸇ ⸈ ⸉ ⸊ ⸋ ⸌ ⸍ ⸎ ⸏ ⸐ ⸑ ⸒ ⸔ ⸕ ▲ ▼ ◀ ▶ ◢ ◣ ◥ ◤ △ ▽ ◿ ◺ ◹ ◸ ▴ ▾ ◂ ▸ ▵ ▿ ◃ ▹ ◁ ▷ ◅ ▻ ◬ ⟁ ⧋ ⧊ ⊿ ∆ ∇ ◭ ◮ ⧩ ⧨ ⌔ ⟐ ◇ ◆ ◈ ⬖ ⬗ ⬘ ⬙ ⬠ ⬡ ⎔ ⋄ ◊ ⧫ ⬢ ⬣ ▰ ▪ ◼ ▮ ◾ ▗ ▖ ■ ∎ ▃ ▄ ▅ ▆ ▇ █ ▌ ▐ ▍ ▎ ▉ ▊ ▋ ❘ ❙ ❚ ▀ ▘ ▝ ▙ ▚ ▛ ▜ ▟ ▞ ░ ▒ ▓ ▂ ▁ ▬ ▔ ▫ ▯ ▭ ▱ ◽ □ ◻ ▢ ⊞ ⊡ ⊟ ⊠ ▣ ▤ ▥ ▦ ⬚ ▧ ▨ ▩ ⬓ ◧ ⬒ ◨ ◩ ◪ ⬔ ⬕ ❏ ❐ ❑ ❒ ⧈ ◰ ◱ ◳ ◲ ◫ ⧇ ⧅ ⧄ ⍁ ⍂ ⟡ ⧉ ⚬ ○ ⚪ ◌ ◍ ◎ ◯ ❍ ◉ ⦾ ⊙ ⦿ ⊜ ⊖ ⊘ ⊚ ⊛ ⊝ ● ⚫ ⦁ ◐ ◑ ◒ ◓ ◔ ◕ ⦶ ⦸ ◵ ◴ ◶ ◷ ⊕ ⊗ ⦇ ⦈ ⦉ ⦊ ❨ ❩ ⸨ ⸩ ◖ ◗ ❪ ❫ ❮ ❯ ❬ ❭ ❰ ❱ ⊏ ⊐ ⊑ ⊒ ◘ ◙ ◚ ◛ ◜ ◝ ◞ ◟ ◠ ◡ ⋒ ⋓ ⋐ ⋑ ╰ ╮ ╭ ╯ ⌒ ╳ ✕ ╱ ╲ ⧸ ⧹ ⌓ ◦ ❖ ✖ ✚ ✜

    (more…)