... claude. trust it for writing code i can review? absolutely. trust it to deploy code without me looking? not yet the pattern i see working: ai does the heavy lifting on content generation and code writing, humans set the guardrails and make the judgment calls. the tools that respect this boundary are the ones i actually use daily the products where ai just... does whatever with no structure? those are the ones i don't trust Comment from ... ... honestly i think the trust issue comes down to scope. i trust ai way more when its doing one specific thing really well vs

... inside ChatGPT, Claude, Gemini, etc. Comment from Zypressen(@zypressen): Exactly. When it s your own money, you learn fast. But in a team? That surprise bill isn t just a cost it s a credibility hit . That s why guardrails like hard caps and auto-pause aren t nice-to-have they re non-negotiable for orgs . Comment from Dansaki Samuel(@dansaki_samuel): I m seeing more teams move toward a hybrid model rather than pure usage-based. A flat ... ... difference from what I ve seen isn t the pricing model itself, but how visible and controllable usage feels (clear limits, alerts, and caps). When teams can t predict spend, churn follows quickly. Curious how you re thinking about guardrails

... Dashboard Monitor runs, providers, agent usage, and success metrics via a local UI. Why this matters: AutomatosX focuses on  orchestration , not chat. It plans tasks, routes work through agents and workflows, cross-checks outputs across models, and enforces guardrails which makes AI outputs more reliable, explainable, and repeatable for real projects. Get started npm install -g @defai.digital/automatosx ax setup ax init CLI Commands # Multi-model discussion with synthesis ax discuss "REST vs

... sparked an interesting thought experiment: how likely is it that we ll see AGI (AI that thinks and learns like a human) within the next five years? The report highlights a fascinating divergence: the view from money vs. the view from science. Money : the probability inferred from trillions poured into data centers, Nvidia chips, and servers. Investors seem to be betting that AGI is inevitable. Science : the probability inferred from research papers and AI development models. Experts are far more ... ... might be ahead of reality. Expect volatility, overpromised outcomes, and valuable lessons along the way. Meanwhile, AI continues reshaping work, attention, and human behavior. I d love to hear the community s perspective: Are you seeing this money vs

... accuracy matters most tools that work great once, then forget everything I m here on Product Hunt to learn from other builders, share what we re discovering the hard way, and exchange notes on where AI is actually useful versus where it still needs guardrails

... earlier 4o model is a kind of hesitation when it comes to certain topics, be it macroeconomics, politics, or anything nuanced. It almost feels like GPT-5 is holding back. I get the need for guardrails (and for good reason), but sometimes it feels like it s afraid of getting misinterpreted, almost like a human. How many of you have noticed it? Are you still subscribed to the earlier models? Comment from D. Ferencha(@d_ferencha): I would say that ... ... from twinkal Shah(@twinkal_shah1): Yes, I've noticed that GPT 5 has more restriction than earlier and also halluciating a lot nowadays. Comment from Deniz Sert(@realdenizsert): Yes, I've noticed this. OpenAI seems to have put more guardrails

... Project X Incident Runbook while composing. * Scoped & read-only: Chips are TTL (time-to-live) bound, no auto write-back; your current project s memory stays clean. * Inline provenance: Replies show which chip informed which part. * Admin guardrails: Allow/deny lists, approvals, label-based redaction (PII/secrets), and audit logs. Why it helps * Cut repeat explanations across repos/teams. * Reuse proven auth/logging/CI/CD patterns instead of reinventing. * Faster onboarding by bridging Architecture + Conventions. * Safer incident response: pull the right runbook just for this ... ... into a new hire s first week. Open questions (feedback welcome!) * Security model: Is read-only + TTL + provenance enough? What else is mandatory? * UX: Where should chips live in the composer to avoid friction? * Policy defaults: Org opt-in vs

... cart right inside chat + clear funnel/AOV analytics. I d love your take on three things: Boundaries: where should an AI stop (e.g., add-to-cart ok, checkout no)? Trust signals: what proof would you need (copy, receipts, guardrails, undo)? Onboarding bar: if this took <10 minutes to connect, would you try it? If helpful, here s a 30 45s demo clip: LinkedIn demo link I ll reply to every comment today. If you run a Shopify/Woo store and want ... ... stops at add-to-cart; checkout is always human-approved but it can assist in filling the details if human approves it. Trust: instant mini-receipt in chat (item/price/size/returns) + undo window (e.g., 30 min) and an action log. Nudges vs

... started testing Watchflow s rule engine across some of the most active GitHub repositories: vscode langchain react pytorch kubernetes Only vscode

... building SmartDial for outbound sales teams and shipping fast. Right now using Firebase, and it s getting tricky balancing speed with stability. Curious how do you manage staging vs prod environments when you re still validating and iterating weekly? Do you set both up properly from day one, or fake it until things stabilize? Comment from Brandon Rubinshtein(@brandonshtein): @rolambuilds , I can only speak from my own experience. Our product flow always has the customer in mind. First, we speak ... ... wondering, why would you fake anything? Comment from Abdellah Rolam(@rolambuilds): @new_user___1542025a3013bd22f1e340b  Good point and to clarify, it s not really faking. It s more about being scrappy early on. We used the same environment with guardrails