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“A good engineer knows when not to use AI.” I built a semi-automated workflow to summarize my live streams. With it, I can record and edit a stream in just one day of work. Not everyone has time to watch an entire stream, so creating a summary of the main moments is important for people who want to watch it later. Many tools create Shorts from videos by analyzing transcripts, but they are expensive and do not work well for visually heavy content such as gameplay. Gemini can analyze both video and audio and has a long context window, so I decided to use Google AI Studio to identify important moments. I also created scripts to automate parts of the video-editing process. Some of the scripts can be executed by an agent, and the prompts can be turned into Skills when using the API. Step-by-step: 1. I record the video with separate tracks for the microphone and background audio. I keep the microphone on the first track so the LLM does not identify only the background audio. 2. I compress and cut the video with `ffmpeg`, then extract the audio tracks. Google AI Studio has implicit video requirements: files must be under 400MB and under one hour long. The AI analyzes only one frame per second, so I can also lower the FPS to save space. I use the audio tracks later in the workflow. 3. I upload the processed videos to Google Drive, which makes them easier to use in Google AI Studio. 4. I use Gemini with temperature 1 and a high thinking level to select the important moments. I add the system prompt and specify which part of the video I am uploading: "The video is part X of the stream. Please make a structured script according to the system instructions." This helps Gemini understand what types of moments may appear in the video. The system prompt was created for gameplay presentations but can be adapted for other content. 5. I use Gemini Pro with temperature 1 and a high thinking level to find the timestamps for the selected moments. I keep timestamp generation separate from moment selection so Gemini has more thinking time for each task. Gemini Pro works better than Flash when handling time. I add the system prompt along with a copy of the response from the previous step. - As an extra check, after execution I continue the conversation with: "Check if the analysis was cut off too early (context truncation), ignoring that the video continued and generating false positives for timestamps. Check the last events especially." 6. I send the data to Blender. I create a JSON file containing the AI’s response and use a script to add the original video, the microphone track, and the background audio track. The script cuts and marks the important moments based on the JSON. Because it is not possible to send multiple audio tracks from a single video, I send the tracks separately. I also make sure to use the correct FPS, either 30 or 60. 7. I edit the video manually. The AI’s timestamps are not perfect, so I may add or remove sections, or correct a position that the AI identified incorrectly. I then render the video. 8. As an extra, I can automate standardized edits. For example, I use three different camera positions, so I add clips to three different tracks depending on the position I want. I use a script to change the position and scale of the clips on those tracks. 9. As another extra, I transcribe the audio. I mute the background audio and save only the microphone audio as an MP3, then use Whisper to transcribe it. I can use the transcript as YouTube subtitles or embed it directly into the video. I use Whisper-WebUI with Log probability Threshold -0.5, No Speech Threshold 0.5, Patience 2, and Hotwords `\u003cmy name and terms in other languages I usually use\u003e`. I use an LLM to translate the transcript into other languages. 10. Finally, I create tags and titles. I upload the transcript to Google Drive and use it in Google AI Studio with a system prompt to generate tags and titles for the video. I also use the assistant in Google AI Studio to analyze and summarize my channel for use in LLMs. This helps me choose better titles and tags.

I kept forgetting who I had lent books to, and spreadsheets felt like overkill. So I built Runo, a book-lending app for friends, without traditional coding. Runo (runo.club) is a web app where I can catalog my home library, get a unique shareable link, and let friends browse my books and request to borrow them. I can approve or decline each request with one click. Step-by-step: 1. I used Claude to think through the feature set, data model, and UX flow before writing a single prompt. This helped me avoid building the wrong thing first and gave me a clear blueprint for the Lovable prompts that followed. 2. I used Claude to create precise, narrowly scoped prompts for Lovable, with each prompt focused on a single change so existing functionality would be less likely to break. This significantly conserved Lovable credits. The React, TypeScript, and Supabase stack came out of the box. 3. I added two ways to scan books instead of requiring users to type titles manually: - Barcode scanner: Point the camera at an ISBN barcode to autofill the title, author, and cover using the Open Library API. - Cover photo scanner: Photograph the cover so Gemini 2.5 Flash can extract the title and author in under 2 seconds. 4. I routed the cover scanner through a Supabase Edge Function using Lovable’s AI Gateway, so the API key never touches the client bundle. 5. For every bug fix and feature, I followed the same iteration loop: describe the problem to Claude, get a precise Lovable prompt, push the changes to GitHub, and let Lovable auto-sync them. Claude and Lovable’s bidirectional GitHub sync made the process feel like pair programming. Tools used: Lovable, Claude (Sonnet), Gemini 2.5 Flash (via Lovable AI Gateway), Supabase, and GitHub. Live at: runo.club — public beta and free to use. Feedback welcome. #appbuilding #vibecoding #buildinpublic #lovable #nocode

I kept struggling to stay consistent with social media for my own projects. Writing captions, choosing hashtags, designing graphics, and scheduling content across platforms was taking hours every week—time I would rather spend building. So I built AutoKonnekt. You give it one sentence or a website URL, and it generates a full month of on-brand social posts, including captions, hashtags, and AI-generated images. It then schedules them across Instagram, Facebook, LinkedIn, TikTok, Pinterest, X, and Threads. The hardest part was getting the AI to sound like a specific brand’s voice instead of producing generic marketing copy. That took a lot of iteration on the prompting side. It’s live now with a free plan if anyone wants to try it: https://autokonnekt.com I’d love to hear what the community thinks. For those of you managing social media manually, which part takes the most time? I’m trying to figure out what to build next. Step-by-step: 1. I enter one sentence describing the project or provide a website URL. 2. AutoKonnekt generates a month of on-brand social posts with captions, hashtags, and AI-generated images. 3. I use the generated content across Instagram, Facebook, LinkedIn, TikTok, Pinterest, X, and Threads. 4. AutoKonnekt schedules the posts across those platforms. 5. I iterate on the prompting to make the content sound like the specific brand instead of generic marketing copy.