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?

I just shared the full walkthrough of the Vois website refresh on LinkedIn. I have attached it here too because the process is more useful than the screenshots alone. The headline is big: less than 1.5 days from idea to deployment, with about 5 billion text-agent tokens used along the way. That total excludes image and video generation. Before I posted it, I asked a friend who is a veteran UI/UX designer to review the work. His response: "I hate you. this is good... I hate you for making it" For context, I am the founder of Vois, and this is my own product and codebase. I ran the refresh like a coordinated AI product team. I stayed responsible for the product direction, visual taste, quality bar, and every ship or reject decision. The setup: • GPT-5.6-sol handled the main orchestration. It broke the work into plans, assigned specialist agents, integrated their output, and kept the release moving. • More than 300 specialist sub-agents using GPT-5.6-terra and Grok-4.5 worked in parallel and in sequence across planning, UX, choreography, visual design, asset creation, copy, frontend development, mobile QA, accessibility, browser testing, performance optimisation, and deployment. • Fable-5 acted as an independent advisor. It reviewed alternatives, challenged decisions, and sent weak work back through another pass. • GPT-image-2 generated the images and characters. Grok Imagine generated the video assets. Their generation usage is not included in the roughly 5 billion text-token total. The text-agent token split: Main agent: 13% Sub-agents: 51.3% Advisor agent: 35.7% What shipped: • a complete visual refresh for Vois • a cinematic, scroll-driven homepage • a consistent design system across the site • dedicated desktop and mobile behavior • generated visual and motion assets • accessibility, performance, browser, and deployment fixes The 5 billion total makes the trade visible. I used far more compute and coordination to compress a large design and engineering cycle into less than 1.5 days.

Hey everyone, building in public and could use honest eyes from people who actually get this space. I'm building a white-label AI front desk that agencies resell to local businesses. I made two completely different landing pages and I genuinely can't pick: A) the classic SaaS page: https://a.konvy.ai B) a scroll story, the page plays as you scroll: https://b.konvy.ai Not selling in this post, just asking for critique: which one would make you keep reading, and where exactly do you drop off? Comment A or B plus one sentence why. Brutal beats polite. Mods: if links aren't allowed here, say the word and I'll delete or move them to a comment.

I emailed 600 people I hadn't spoken to in 14 years. 5 of them became my first paying customers — within 60 minutes. Here's what I built and why. I'm partially dyslexic. Long text has always been a struggle. Since high school I've been converting written content to audio — articles, reports, white papers, ebooks. I kept building tools to do this. Eventually one of them got good enough that content creators started asking for it. A friend wanted it for creating custom bedtime stories for her kids. Another had a stack of ebooks he'd never read — wanted them as audio for his commute. Others were producing YouTube content and tired of paying per-character for cloud voiceover tools. That personal tool became a full desktop voice AI studio. 63 voices, voice cloning, 23 languages, multi-speaker editing, professional mastering. Everything runs locally — no uploading scripts to someone else's server. Then 3 days ago I emailed 600 customers from a product I built in 2012. Plain text, no design. Some of them bought. Revenue before the product even launched publicly. Tonight it goes live. For content creators here — how much of your workflow involves voiceovers? And what's your biggest frustration with the tools you're using now?

What a Friday. For two years, if you were building anything serious with AI, you were building on Claude. Not because it was a rule — because it was the right call. Anthropic set the bar for coding. They set the bar for writing. They set the quiet default that if you cared about quality, you paid the Opus premium and didn't ask questions. I didn't either. The whole builder community ran on Claude for a reason. This week, that changed. GPT-5.5 shipped yesterday. DeepSeek V4 Pro shipped the same day. Inside twenty-four hours, the ceiling on agentic coding went up — and the open-weight floor came within striking distance of the closed frontier. Real contenders. Not "almost there." Actually here. Three things this changes for anyone building, and none of them are in the headlines yet. Coding: The default setting of "Claude writes the code, Claude runs the agents" breaks this week. GPT-5.5 is measurably better on the kind of long-running multi-step agent work that used to be Claude's moat. DeepSeek V4 Pro is within a fraction on real software engineering, at a price point where "run it myself" is genuinely on the table. Every tool in your stack that quietly assumed Anthropic — your IDE integrations, your review agents, your automation glue — is about to get reconsidered. That's good for you. Less lock-in. More leverage. Marketing and writing: The price-per-draft math just flipped. We've been rationing the good model forever — the flagship handles the brand-safe stuff, volume work gets the cheap model, and we've all quietly accepted that frontier-quality writing at scale isn't possible. That's over. Frontier-quality writing at open-weight pricing means every ad variant, every email rewrite, every landing-page test, every personalization loop runs at the top tier. The whole architecture of "one good draft, fifty cheap copies" starts feeling as dated as shared creative. Everything top-tier. Everything personalized. Everything testable. Agentic work: This is the one I am most excited about, and the most under-talked-about. For two years, "multi-model agent stacks" has been a slide in decks. Nobody actually builds them, because there hasn't been a real second option. GPT-5.5 for the reasoning step. DeepSeek V4 Pro for the long-context research step. Claude for the interpretive writing step. A cheap open model for the high-volume structured step. Not one runtime. A pipeline. Composed by you. Owned by you. That stops being a slide and starts being the default next month.