SO PUMPED, JUST Finished the 3D UI/UX Render Engine for the World's Top Best Performing Agentic AI Memory System that Crushes on anything else out there! Been using it for the last 5+ months and Loved how seamlessly every single AI Agent on my entire network, and every machine connect and share all the same memories and the Best retrieval systems on the world together. The last project I will tackle is directly connecting it to my ChatGPT browser chats so ALL my chats also have read write access. Mostly so I can RIP over a years worth of ALL my chat data that OpenAI is holding in context blown chats that are all about me and my business, etc. In the meantime, I will will be releasing the working BEST Graphical 3D Render Engine for a memory graph in the world IMHO. But I think when you see the pics tomorrow.. I'd love to get all your feedback. Here are some of the benchmarks this system has placed all #1 In The World on in the attached image. This is NOT a Toy like that Obsidian file explorer with an exploding dot matrix. Oh ya. This UNI-MIND system comes with way more graphical displays systems then any other memory system out there. There are 3-4 separate Memory graph views including the Sickest 3D Render Engine. Check out how many #1 Benchmarks it's got! Can't wait for the GitHub to be finished for initial Public release. I think I will be opening it up on Product hunt 1st though. Let me know if anyone is interested in a memory system when it releases free on Product hunt or GitHub, I'll let you know.

Most real estate photographers already have everything they need to sell AI video services: the listing photos. I built and tested a workflow where you simply paste a property listing URL, and the system automatically creates a complete property video with an AI virtual avatar. No manual editing. No scripting. Just the listing URL.

A small personal win to share with the creators here. I'm mildly dyslexic, and text-to-speech has been part of my life for years. When creating my own content meant paying again for every tiny revision, I started building something for myself. That became Vois Studio: an app that turns written scripts into finished voiceovers for videos, podcasts and courses. Now other creators are using it, and a learning and development team at one of the largest space exploration companies is quietly testing it too. That part still surprises me! I'm launching it on AppSumo with a lifetime deal: https://appsumo.8odi.net/5kDBO9 New AppSumo customers only: 10% off with code "voisstudio10" at checkout. I built Vois. Happy to chat about whether it fits what you're making.

Context: putting one arrow on a screenshot meant three apps for me. GoFullPage to capture, Annotly to annotate, then paste it wherever it was actually going. Every single time. What kept me in that loop wasn't the steps. It was assuming that building the alternative would be a project. It was about 30 minutes in Claude Code. Now it is one shortcut. Full page capture and the editor in the same place, running on my own machine, nothing uploaded anywhere. One thing worth taking even if you never build anything: If you blur client information in screenshots, use a solid bar rather than pixelate. Pixelated text can often be recovered when the font and the pixel grid are guessable. A solid bar replaces the pixels, so there is nothing underneath to pull back out. The interesting part is that I didn't start with the idea of building a product. I just had a problem that annoyed me, so I built a small solution for myself. The bigger idea: Look at the boring things you repeat every day. - Moving information between tools? - Downloading something and uploading it somewhere else? - Doing the same manual steps over and over? Maybe that's something you can build yourself.

Most useful answers in a community disappear into the feed. Someone solves a hard problem today. A month later, the same question returns because the answer was never turned into something durable. The community keeps paying the cost of rediscovery. That is why I built LLM Wiki: a simple shared memory for AI agents. Instead of saving every conversation, it keeps the knowledge worth reusing: 1. Save a useful article, transcript, note, or exported community thread. 2. Let the agent compile it into linked Markdown pages. 3. Ask a question later and retrieve the right section with evidence. 4. File useful conclusions back so the next answer starts from what the community already learned. This is useful for recurring questions, research libraries, customer conversations, project decisions, and any topic a group explores over time. LLM Wiki v2 improves how that memory is searched: - exact section search finds names, flags, and phrases - optional semantic search finds paraphrased ideas - reciprocal rank fusion combines both result lists - citations and source metadata show where an answer came from Cerebras recently published a detailed article about its internal knowledge base. Employees ask it more than 15,000 questions a day, according to the team. Their system is enterprise-scale, but the need is universal: information is scattered, and good answers should not have to be rebuilt every time. No embedding service is required. The default is local, Markdown-first, and dependency-free. The clever parts are optional; the knowledge stays readable. Open-source project: https://github.com/praneybehl/llm-wiki-plugin Cerebras article: https://www.cerebras.ai/blog/how-we-built-our-knowledge-base What valuable answer in this community keeps getting rediscovered?