Everything is changing so fast. As a Millenial having lived through the introduction of PCs (loved my Apple LC II), dial-up internet, iPhones, now AI, can't believe how much has changed. Even AI, in december I remember people being skeptical of AI ads, then 4-5 months later it seems like if you don't do AI ads you're gonna be left behind. At the same time, while I love what I'm able to do with AI, worried about the environmental impacts and wonder if those will catch up to us before we figure things out. For my industry definitely seeing a need to upskill quickly to stay relevant, probably even by the end of the year. Wonder what others are thinking?

I'm setting up a few old computers to use to run local LLMs for subagents, is there a good MCP server to talk to them that people recommend or like? update 5/5/2026 - I was able to install https://mcpservers.org/servers/rawveg/ollama-mcp to use as a MCP bridge - my setup: 1 NUC intel i3 mini computer, 1 Lenovo Laptop with an Nvidia card with 6GB Vram, and my main desktop with Claude Code. - Claude is able to send direct curl requests to the 2 computers - still working on what each is able to do with the models they have but for now they're talking to each other and Claude has friends

Context: I want to pitch a few talks to conferences in my field (game audio), and its not something I do regularly so I don't have established workflow for it. I do write linkedin posts and prep for my DND sessions, but that output is significantly different than a 30m to 1hr talk. I've been doing one approach, which I'll outline below, but I'm wondering if others have done this and have a more efficient way of getting to a final result, the below took me 2 sessions both a few hours each, and I'd love to compress that. Wondering about other approaches or resources to help create a better framework. Current process (captured in a skill after finishing last submission): 1. Claude asks : "What could you talk about from memory right now, without looking anything up?" and "What do you know how to do, or think about, that most people in your field don't?" (this takes a long time) 2. feed it which conference, deadline, and format the talk submission is 3. Claude researches past accepted talks for fit and content 4. We lock in thesis, pillars, and target audience (this takes the longest) 5. we do a draft in this order: Description, takeaway, outline 6. Pre-submission review (this part is easy with humanizer and the conference form submission) Wondering if there are places I could improve? And how would others approach this?

Most people are using AI like a slot machine. Pull the lever, hope for a payout, blame the model when it's generic. The makers I respect run a workshop instead. They've stopped asking AI for things and started telling it what they're building. Three moves: 1. Outcomes, not prompts. A prompt is a wish. A brief is a contract. When AI gets it wrong, your brief was ambiguous. 2. Context is king. Models are interchangeable. Context is yours. A bad prompt with the right context beats a great prompt with none. 3. Train your taste. AI gives you 90% in 10% of the time. Spend the 90% you got back on the trim pass. Your taste is the bottleneck now. Full deep-dive: aris-space.com/stop-prompting-start-defining-outcomes // A<3

I am known to take Claude outputs and put them into ChatGPT for blind-spot checks. Today I decided to create a "council of 5" skill that runs any question, problem, solution, document, etc., through 5 distinct personalities, with 3 rounds of discussion, then a consensus. 1. The professor: peer-reviewed/cited sources only 2. The teacher: logical, wonders, "is this the right question to be asking" 3. The founder: can this be done, and what is the fastest way 4. The outside: zero context, thinks outside of the box 5. The contrarian: hunts for the fatal flaw in everything. Sharing the skill here if it could help anyone.