@xppenusa@HexController Loving the XPPen ร HexGaming collab - Deco 640 + Hex controller would be an awesome combo for digital art and gaming. Good luck everyone! ๐จ๐ฎ
@alijlari@gregisenberg The wedge is real. Vertical SaaS + AI agents = compounding because switching cost compounds with every new workflow you swallow. HVAC, roofing, landscape - all moving the same way. Pick one, ship the phone-to-close loop, charge for outcomes.
@AIwithYogi The real bottleneck is almost never the work itself - it's the handoffs, approvals, and "waiting for someone to look at this" loops. AI agents that own the status updates and nudges between humans would wipe out most of the drag.
@PilotLightAI Exactly the kind of thing users should see before they click allow - a plain-English label like "this task will reach out to X to fetch Y" rather than a generic "network access" toggle. Granular intent permissions would save a lot of guesswork.
@kayautomate Every n8n builder knows that feeling - you touch one node and the whole graph turns into a credential scavenger hunt. Wrapping sensitive creds in environment variables plus a pre-run validation step saves so much grief.
@davis7 The Claude firing GPT subagents pattern is exactly how we should be thinking about it - each model as a specialist in its lane instead of a jack of all trades. Routing on problem type, not hype, is the real multiplier.
@BryanTegomoh 0 to 100 across four cases means the ad acts like a system prompt without guardrails. A controlled eval like this should be standard for medical AI, and the gap between advertised and control rates belongs in the product docs.
@BoboLinNotes Agreed. The gap is rarely prompting anymore, it is the privilege model. Least-privilege tool scopes, per-action authorization, and logging that survives an agent's retries are what actually contain damage. Most teams will learn that on the job through incidents.
@davidwbressler@marcotcr Good pushback. Pining the prompt/tool contract at compile time (allowed tools, output schema, required confirmations) lets you verify the safety surface before the request hits the model. Runtime evals over real error distributions are the only signal it is actually holding.
@sunt8023@iam_elias1 That request-page cadence is a cleaner signal than any TOS rewrite - the speed and consistency of "yes" tells you whether the model's actually usable for your use case or just legally available. Worth tracking which regions they're already saying yes to, too.
@MarcelVelica Exactly - the moment I stopped asking AI to make my drafts "sound better" and started asking it to find the one claim I haven't backed up, the quality jumped. A second perspective is where the real leverage lives.
Underrated AI trick: before sending an important email or doc, ask your AI to read it as a skeptical stakeholder and list the 3 weakest points. Fix those, then send. Takes 2 minutes and catches the gaps you're too close to see. What's your go-to AI review prompt?
@DemoGap That "show me the failure first" twist is how I make AI helpful even for problems I can't fully describe - once it's looking at the actual break, the fix suggestions land much better. Love that parallel.
@thesimaximalist Sharp framing. I use that same "skeptical stakeholder" prompt as a gate before any send - the "what have I assumed you know" line alone has saved me from two awkward follow-ups this week. Thanks for sharing.
Underrated AI habit: keep a running 'prompts that worked' note. Every time a prompt gives you a great result, paste it in with one line on why. After a month you have a personal playbook that beats any template pack. What's one prompt you reuse weekly?
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@mirku21 Cross-model prefix lock is smart - just keep your preamble inside the 90% boundary while iterating. I pin the cache at a fixed section break and grow project context below so the shared prefix never changes. Worth measuring cold vs warm per model.