Share your best AI workflow. We could show it to 2M+ people.

I built a portal to manage lectures and applications for teaching sociology students at my university. It displays educational content, monitors attendance, supports pedagogical evaluation, and provides training in methodological skills. Step-by-step: 1. I built a portal for managing lectures and applications for sociology students at my university. 2. I added educational content for students to access. 3. I included attendance monitoring. 4. I added tools for pedagogical evaluation. 5. I incorporated training in methodological skills.

I use this workflow when I want fresh creative input for my visual work and want to expand my inspiration library in Eagle without collapsing into randomness. The starting point is not a generic prompt like “show me inspiring artists.” I give the AI examples of images, artists, materials, traditions, or visual details that already catch my attention. The AI first looks for patterns in what I respond to: color, material, composition, atmosphere, craft techniques, cultural traditions, natural structures, architectural forms, and more. From there, we branch outward. I explore adjacent references across disciplines, cultures, periods, and materials rather than looking only for visually similar work. I react freely to the suggestions: “This texture, yes.” “This artist, no.” “More of that construction method.” “Less decorative.” “Stranger.” “Older.” “Softer.” Those reactions steer the next round of research. One research session, for example, moved through Indian stepwells; Ajrakh and Bandhani textiles from Gujarat and Kutch; Tibetan/Ladakhi painted textiles; Miao baby carriers; Dong wooden bridges; art from Papua; Russian lubok prints; and even 11th-century goldworking in Panama. These references do not belong to one tidy category, period, or geography. That is precisely the point. The session was driven by visual responses and emerging connections involving color, surface, construction, ornament, repetition, symbolism, material, and technique. Each discovery created a new branch to follow. AI made it possible to move laterally across disciplines, cultures, and centuries without losing the thread of what was visually interesting to me. I then selected only the references that genuinely sparked something and added those to Eagle. The result is not just a collection of “similar images,” but a deliberately expanded visual vocabulary. The human remains the curator. AI expands the search radius; taste decides what enters the library. Sometimes there is no initial research question at all. A joke, misheard word, object, memory, or accidental phrase becomes the seed. The value is in allowing associative drift to continue long enough for a real curiosity trail to emerge. > The research does not always begin with a topic. Sometimes the topic is discovered during the wandering. Step-by-step: 1. I start with a few visual references I genuinely like. 2. I ask AI to identify recurring visual qualities and possible connections. 3. I explore adjacent references across disciplines, cultures, periods, and materials rather than searching only for visually similar work. 4. I react freely to the suggestions, noting what I want more or less of, such as a particular texture, artist, construction method, decorative quality, age, or atmosphere. 5. I let those reactions steer the next round of research. 6. I open the references and collect only the images that actually trigger something. 7. I save those images in Eagle as a curated visual library rather than an indiscriminate image dump. 8. I add useful context through folders, collections, tags, or notes so the references can resurface later.

I built AIEnhancer to solve a problem I often encountered when working with low-quality images. Many images contain useful content but are too small, blurry, or lacking in detail to reuse effectively. The project uses AI-based image processing to improve image resolution and recover visual details. I wanted to make the workflow simple: upload an image, process it, and receive an enhanced version without needing professional image-editing software. One challenge was finding the right balance between sharpening details and avoiding artificial-looking results. During development, I experimented with different enhancement approaches and focused on keeping the output natural. I'm still interested in improving enhancement quality for different types of images. I'd like to hear how other developers handle image restoration and super-resolution, especially for difficult or heavily compressed images. Step-by-step: 1. I identified the problem of reusing images that were too small, blurry, or lacking in detail. 2. I built AIEnhancer to process low-quality images with AI-based image enhancement. 3. I designed the workflow around uploading an image, processing it, and receiving an enhanced version. 4. I experimented with different enhancement approaches to improve resolution and recover visual details. 5. I evaluated the results for a balance between sharper details and a natural appearance. 6. I continued exploring ways to improve enhancement quality for different image types, including difficult or heavily compressed images.

I design songs by giving different vocal parts different emotional jobs rather than writing one continuous lyric and deciding afterward who sings each section. I start with contrast. One voice might carry rhythm, detail, tension, or narrative through rap, while another opens the song emotionally through a melodic chorus. A sung section can create space after a dense verse, and a rap section can sharpen the story after a more atmospheric refrain. The contrast is functional as well as musical. Different voices can represent different perspectives, emotional registers, or levels of intimacy. The chorus can express the feeling that the verse struggles to name, while the verse can complicate what the chorus makes sound simple. A bridge can temporarily change the relationship between them. This is why songs that combine rap and fully sung passages can work: the parts are doing different jobs instead of simply stitching two styles together. The song “Airplanes” is useful as a structural inspiration because of its contrast between rhythmic narrative and a melodic, emotionally open refrain. The goal, however, is not imitation. It is to understand why that contrast works and build a new song around its own voices, themes, and emotional architecture. AI can help me develop lyrics, transitions, vocal roles, perspective shifts, and the points where the song needs to open up or contract. The result is a song designed not only around verses and choruses, but also around the relationship between different ways of speaking and singing. Step-by-step: 1. I identify the different vocal modes the song will use, such as rap, melodic singing, or a bridge. 2. I assign each vocal part a distinct emotional or narrative job, such as carrying rhythm, creating tension, opening emotionally, or introducing another perspective. 3. I use contrast to create movement: a sung section can follow a dense verse, while a rap section can sharpen the story after an atmospheric refrain. 4. I decide how the voices relate to one another, including what the chorus expresses, what the verse complicates, and whether the bridge changes their relationship. 5. I study the contrast between rhythmic narrative and melodic refrain in “Airplanes” as structural inspiration without imitating it. 6. I use AI to develop lyrics, transitions, vocal roles, perspective shifts, and the points where the song should open up or contract. 7. I shape the final song around the relationship between its different ways of speaking and singing, not only around its verses and choruses.

I wanted a publicly viewable leaderboard on the web, with input coming from creators. I prompted Gemini: " Can you design good looking leaderboards using Google documents. I want three: all time, rolling 30 days and rolling 90 days. They should be different tabs on the same document. They should be easy to administer/update. Show me a mockup and describe how it would work." After I was satisfied that it would be easy to launch and maintain, I got more specific. Because it was for creative types, I wanted more than a basic spreadsheet ranking. Gemini responded: "It features profile avatars, gamified badges, high-contrast dark visual styling, and clear ranking tabs for All-Time, Rolling 30 Days, and Rolling 90 Days..." That's what AI calls creative, I guess. Later in the chat, Gemini suggested achievement levels with a more creative feel: - 👑 Century Virtuoso / Platinum Mic - 🔥 Monthly On-Fire / Speedster - 🎙️ Heartstring Weaver - 🌟 Smile Architect For the podium treatment, rows 1, 2, and 3 feature custom metallic highlights—Warm Gold, Silver Slate, and Bronze Copper—with circular numerical rank badges to make top contributors stand out instantly. Gemini heavily integrated Google Docs and Google Sheets. The system uses Google Apps Script to update the leaderboard in real time based on creator activity. Step-by-step: 1. I asked Gemini to design three leaderboards—All-Time, Rolling 30 Days, and Rolling 90 Days—as separate tabs in the same Google document. 2. I asked for the leaderboards to be easy to administer and update, along with a mockup and an explanation of how they would work. 3. After reviewing the initial concept, I clarified that the leaderboard was for creative types and should be more distinctive than a basic spreadsheet ranking. 4. Gemini added profile avatars, gamified badges, high-contrast dark visual styling, and clear ranking tabs. 5. I incorporated creative achievement levels, including Century Virtuoso / Platinum Mic, Monthly On-Fire / Speedster, Heartstring Weaver, and Smile Architect. 6. I used Warm Gold, Silver Slate, and Bronze Copper highlights for the top three rows, along with circular numerical rank badges. 7. I connected the system to Google Docs and Google Sheets and used Google Apps Script to update it in real time based on creator activity.

I use conversation not only to generate ideas, but to pressure-test them until they become internally consistent and practically usable. The workflow starts with a philosophical idea, intuition, or principle that is still partly unfinished. I explain the core thought in ordinary language, and AI helps unfold what is already inside it: assumptions, consequences, tensions, possible interpretations, and links between different parts of the idea. Unlike a purely creative brainstorming process, the conversation does not simply keep expanding. AI also interrupts the momentum with questions that require deeper thinking: - What exactly does this concept mean in practice? - Which parts are essential, and which are only examples? - Does this principle still hold in edge cases? - What happens when two values conflict? - Is there a contradiction between this statement and an earlier one? - Is the system describing how things are, how they should be, or both? - Who has responsibility at each point? - What would make the system fail? - Can the idea be translated into an actual process, structure, or decision rule? My answers then change the model. Sometimes I clarify what I meant. Sometimes a question reveals that two parts of the idea do not fit together yet. Sometimes the tension turns out to be useful and becomes part of the concept itself. The workflow gradually moves through several layers: intuition → articulation → questioning → contradiction testing → refinement → system design. Once the philosophical core is stable enough, AI helps translate it into practical suggestions: structures, rules, decision points, roles, safeguards, workflows, or alternative system designs. The important part is that the practical system is not invented separately from the philosophy. It is derived from it. That makes the process useful for ideas that are too complex for a single prompt and too interconnected to solve by simply asking AI to “make a framework.” The final result is not merely a polished explanation of an idea. It is a concept that has been repeatedly examined from different angles and then turned into something that could actually guide decisions or system design. > AI is useful here not because it agrees with the idea, but because it helps expose what the idea requires in order to remain coherent. At the end of the process, AI can also create drawings, diagrams, or schematic visualizations of the core concepts on request, making the structure easier to understand and communicate. Step-by-step: 1. I explain an unfinished philosophical idea, intuition, or principle in ordinary language. 2. I ask AI to unfold the assumptions, consequences, tensions, possible interpretations, and connections already contained in the idea. 3. I use targeted questions to examine practical meaning, essential elements, edge cases, conflicting values, contradictions, responsibility, failure conditions, and possible decision rules. 4. I revise the model based on my answers, clarifying ambiguous points and identifying contradictions or useful tensions. 5. I move the idea through the sequence of articulation, questioning, contradiction testing, refinement, and system design. 6. Once the philosophical core is stable enough, I ask AI to derive practical structures, rules, decision points, roles, safeguards, workflows, or alternative system designs from it. 7. I ask AI to create drawings, diagrams, or schematic visualizations of the core concepts when I need to understand or communicate the structure more easily.

I wanted lyrics for original music that did not rely on random invented syllables, so I developed a fictional “crystal language” with its own sound, atmosphere, recurring words, and internal logic. The starting point was musical rather than academic. I considered which sounds belonged in this world, which consonants felt sharp, soft, ancient, luminous, or fragile, which vowels sang well, and which word shapes fit the music’s rhythm and emotional tone. As I repeated sounds, they began to acquire repeated meanings. Words started to relate to one another, and certain phonetic patterns became associated with ideas, emotions, or imagery. The language gradually developed enough consistency that listeners could feel it belonged to one world, even without understanding it. That internal structure keeps the invented lyrics from sounding like decorative nonsense. The words begin to carry memory: a phrase can reappear later and mean something, and a sound used in one song can echo another. The fictional language becomes part of the music’s identity. The aim is not necessarily to construct a complete language with full grammar. It is to create enough recurring logic that the lyrics feel intentional, recognizable, and alive—not gibberish with nice vowels, but a tiny musical culture. Step-by-step: 1. I started with the music and defined the kind of world its language needed to evoke. 2. I selected sounds, consonants, vowels, and word shapes based on their emotional qualities, singability, rhythm, and atmosphere. 3. I repeated sounds until some began to acquire recurring meanings. 4. I connected related words and associated phonetic patterns with ideas, emotions, or imagery. 5. I reused phrases and sounds across songs so they could carry memory and create echoes between pieces. 6. I developed only as much structure as necessary for the lyrics to feel intentional, recognizable, and alive.

I use AI to turn my watch history into a personal recommendation engine that can answer situational questions such as, “What should I watch tonight?” Generic recommendation algorithms know what is popular and what resembles something I clicked before. They usually know much less about why I want to watch something tonight. For this workflow, I give AI two different datasets: - Already watched: Evidence of my actual viewing history and taste. - Want to watch: Shows my curiosity, intentions, and unexplored directions. This should not automatically be treated as proof that I will like something. AI analyzes both lists for patterns such as genre, themes, emotional intensity, pacing, humor, visual atmosphere, storytelling style, cultural interests, darkness versus comfort, realism versus imagination, and other recurring preferences. Instead of reducing everything to genres, the workflow builds a descriptive taste profile. It keeps confirmed preferences separate from hypotheses based on the watchlist and uses my later reactions to refine the profile. When I want a recommendation, I add my current context: available time, mood, energy, desired emotional intensity, whether I want something comforting or challenging, and whether I want a movie or an episode. The system matches that temporary context against my longer-term taste profile and the available watchlist. So instead of asking: > “Recommend me a good series.” I can ask: > “I have about 90 minutes, my brain is tired, I want something comforting but not stupid, and I don't want anything emotionally brutal tonight.” The recommendation is based on three layers at once: past taste, future curiosity, and present state. Over time, the system becomes less like a recommendation list and more like a personal cultural navigation tool. Step-by-step: 1. I import or paste my watched films and series. 2. I add a separate list of things I still want to watch. 3. I ask AI to analyze recurring themes and less obvious connections. 4. I build a descriptive taste profile rather than reducing everything to genres. 5. I keep confirmed preferences separate from hypotheses based on the watchlist. 6. I use my later reactions to refine the profile. 7. When choosing something to watch, I add the current context, including available time, mood, energy, desired emotional intensity, whether I want something comforting or challenging, and whether I want a movie or an episode. 8. I match that temporary context against my longer-term taste profile and the available watchlist.

I built an AI workflow to automate guest-posting outreach. I use Claude to analyze target website requirements, draft personalized pitch emails for editors, and optimize my responses so I can pursue high-quality backlinks more efficiently. Step-by-step: 1. I provide Claude with the requirements for each target website. 2. I use Claude to analyze those requirements and identify what to address in the outreach. 3. I ask Claude to draft a personalized pitch email for the editor. 4. I use Claude to optimize my responses during the outreach process.

I’ve been building AIEraser, a browser-based tool for removing unwanted elements from photos. One challenge has been making reconstructed areas blend naturally with nearby textures, especially when removing larger objects from detailed backgrounds. Early versions often left blurred patches, so I focused on improving contextual reconstruction while preserving the image’s original dimensions and sharpness. I also learned that selection flexibility matters. Some users prefer brushing over irregular objects, while others find box or automatic selection faster, so I added all three approaches. I’d appreciate feedback from anyone who has worked on image inpainting or object-removal tools. What types of images or backgrounds usually expose the biggest weaknesses in these models? I’m particularly interested in difficult test cases and suggestions for evaluating output quality. Step-by-step: 1. I built AIEraser as a browser-based tool for removing unwanted elements from photos. 2. I tested object removal on detailed backgrounds and identified blurred patches as a weakness, especially when removing larger objects. 3. I focused on improving contextual reconstruction while preserving the original image dimensions and sharpness. 4. I added brushing, box selection, and automatic selection to support different user preferences and object shapes. 5. I’m seeking difficult image and background test cases, along with suggestions for evaluating the quality of the output.