... model disagree sharply, that disagreement won t just get averaged away. We ll surface it as a trust signal. 3. Reversibility Recommendations will include how hard they are to undo. Save 40% is a very different decision when rollback takes 4 minutes versus

... here's what we do, here's where we're unreliable, here's the thing we put in front of you before it goes out. That's a harder page to write and a much harder one for a competitor to copy, because it means you actually went and found your failure modes. Curious whether anyone has tried this and what it did to conversion. My guess is it costs you signups and buys you retention. Comment from Venkat ... ... your point running in both directions. Publishing the failure mode is exactly what let a stranger audit it in public, and the audit was worth more than the compliment was. Comment from Alper Tayfur(@alpertayfurr): @asadmalik901  Exactly. Rollback

... controls all had to become part of the delivery process. Now I think the next phase is emerging: AI delivery operations. That could include: Continuous model and prompt evaluations Policy-as-code Progressive model rollouts Runtime AI governance Automated rollback based on quality signals Full lineage across code, model, prompt, and data AI agents supporting incident investigation Human approval for high-risk changes The future AI deployment pipeline may not ask only: Is this release technically healthy? It may also ...

... easy to mess up. 2. Every refund was cash gone for good, even when store credit would've made the customer just as happy. So I built: Pricely schedule a sale once, it auto-reverts (or one-click early rollback). - Creditly auto-issues store credit/cashback instead of a cash refund. Both $9/mo, 14-day trial, both very early I'm the only user right now, which is exactly why I'm here. If you run a Shopify store or have ...

... things that make it different from just scraping a page: - Every refresh is saved as its own version. Nothing gets overwritten. - You can diff any two versions to see exactly what changed, added, removed, modified. - One-click rollback if a refresh comes back bad. - Refreshes nightly by default, or on demand via a ping URL, webhook, or your own cron. - Every dataset ships with an MCP server on every plan, including free, so an agent can query it directly ...

... from Omri Ben-Shoham(@omri_ben_shoham1): solid list. I'd add one that's easy to skip if all your agent examples are text/API-based: does the action happen in the real world where it can't be undone. rollback plans work great for a database write or a sent email you can follow up on, but a phone call the agent already made and the person already heard has no rollback

... there's no shared webhooks folder they both sit under. so the glob fix doesn't even apply to the three i have today, they were never colocated in the first place. which proves your enumeration vs property point before i even get to a fourth provider. the property i actually care about isn't "is this file in the webhooks directory," it's "does this handler get called by an external party with money attached to the payload ... ... agent didn't just act, it acted with someone's credentials, someone's access, someone's blast radius. and when it goes wrong, the audit trail usually can't tell you what the agent was actually authorised to do versus

... work. The work is actually: research the platform's culture, learn its unwritten rules, draft, get roasted internally, redraft, post at the right hour, then watch a number. It's a deploy with no logs and no rollback. Lesson 3: Being not-a-marketer is weirdly an asset. The stiff, honest, engineer-toned posts always outperform everything where I try to sound smooth. People can smell polish and they don't trust it. My lack of skill is the brand ...

... that generated tests may validate the implementation rather than the actual requirements. A test suite can pass while the product still behaves incorrectly. 5. Deployment and Operations AI can assist with Dockerfiles, Terraform configurations, CI/CD pipelines, YAML files, rollback strategies, and log analysis. However, infrastructure mistakes can have serious consequences. AI-generated deployment configurations still require careful review and controlled execution. 6. Maintenance and Documentation AI can generate API documentation, code comments, release notes, and refactoring recommendations. This may reduce ...

... card or drops data: I review personally, every time. No exceptions. The half I think makers under-use: instead of only raising confidence, I lower the cost of being wrong. Low-risk changes ship straight to prod because my rollback is one click, and a monitoring loop watches production, reproduces real breakage in a browser, and files a ticket automatically so a transient blip fixes itself and I'm not the smoke detector. Honest caveat: this is safe because ... ... solo with a small user base and instant rollback

... crying wolf? Comment from Jernej Jan Ko ica(@jernej_jan_kocica): Different domain, same problem, and it breaks the taxonomy a bit. My agents don't write code, they talk to customers. Every list in this thread sorts by reversible vs destructive. Deploys, db writes, rm. By that sorting, "send a reply to a customer" is about as safe as it gets. It's text. It's not a deploy, nothing gets erased, no invariant breaks. It's also ... ... thing in my whole system I cannot undo. There is no rollback

... good thread on useful topic to the community Comment from Roopesh Donde(@roopesh_donde): @nolan_vu I take a lot of time to align with the coding agent. Directly telling agent what to do leads to unexpected results and rollbacks. I follow what you mentioned, except I commit big. Vibe coding gets overwhelming, though. If the product is complex, I have to track multiple things, and often just describing does not cut it. Comment from Sabber Ahamed(@sabber_ahamed): @galdayan ...

... self-hosting your memory layer? Comment from Gal Dayan(@galdayan): @rahilpirani  that's the answer I was hoping for - additive-only migrations mean I can't get stuck mid-upgrade with a half-migrated schema and no rollback. good to know the backup command exists too, that's the thing I'd actually reach for the one time something did go wrong. thanks for the detail. Comment from Rahil Pirani(@rahilpirani): @galdayan Good question: no manual steps. When ...

... risk surfaces, but for T1/debugging I still want a proper human review before letting it ship anything. Comment from Max Musing(@maxmusing): @galdayan  even better way to put it than how I did. Low blast radius + fast rollback changes the calculus a lot. Comment from Max Musing(@maxmusing): @roopesh_donde  thankfully AIs are great at understanding and explaining code! Comment from Saman Baloch(@saman_baloch): For me, the real question isn't whether I understand every line ... ... being about whether i understand the code and started being about whether i understand the blast radius if it's wrong. we let agents write plenty of stuff nobody reads line by line, but only in places with fast rollback

... demand for tighter IaC integration is real, and the community articulated why: Ahmad's @blah_mad framing was precise before deploying an agent-generated IaC change, he wants a "handoff receipt": source sim run, changed resources, blast radius, rollback path. The episode history and final config are stable today. Blast radius as a named field is on the roadmap. The CI integration pattern came from the community, not from us. Jimmy Xu @jimmy_benhsu synthesized it: use Cloud World Model ...

... When it comes to AI Memory, everyone's arguing "RAG vs. grep" like it's a religious war. It's not. It's just a cost curve. I've gotten this question so many times that I thought I'd just share my thoughts. So here goes: Vector search wins when your corpus is massive, messy, and unstructured. Thousands of docs, no clean boundaries, meaning matters more than exact words. Filesystem plus grep wins when your corpus is structured ... ... Curious how you think about that distinction. Comment from Rose Florean(@rose_florean): @hira_siddiqui1  ok thanks Comment from Aleksander Brousseau(@aleksander_brousseau): Could the real competition actually be between retrieval quality and memory quality instead of grep vs

... click to approve and run the experiment part. I think the transition to AI-run companies will depend less on full autonomy at first and more on trustworthy approval loops: clear recommendation, clear expected business impact, clear rollback if it goes wrong. That is where operators will start to trust agents with real work. Comment from Brandon TK Beesman(@thys_beesman): Fascinating vision. How do you see trust being built around AI making proactive business decisions, especially when it suggests ...

... solving: AI agents can write code, but they still run wild. They loop, waste tokens, change files, wait for approval, and leave teams guessing what happened. MartinLoop adds the missing control layer: budgets, safety checks, verifier gates, rollback, notifications, and run receipts so every agent run is easier to trust. Product Hunt: https://www.producthunt.com/prod... Website: https://martinloop.com appreciate any upvotes and Stars on github, (download the open source and if its useful first ofc) Comment from Andrew Simachev(@ra3orbladez ... ... without now. Comment from Stacy Wycoff(@stacywycof83995): @nolan_vu  Thanks Nolan, really appreciate you reading it closely. The confidence grading came straight from beta feedback, people have been burned by tools that state guesses as fact, so "Verified" vs

... None of these are bugs. They're what happens when memory is append-only and nobody's pruning it. The right model: . Every update is a commit. You can diff what it knows now vs. six months ago, roll back a wrong inference, audit what changed after a bad interaction. "The AI knows me" should mean something specific and inspectable, not something you have to trust on faith. Versioned memory isn't a nice ... ... have. It's how you build AI you can actually correct. p.s. this is exactly what we're building these days at AI Context Flow: version history for your memories, viewable, inspectable, and rollback

... CSSContainerRule doesn't compile. Had to use duck-typing @container has conditionText but isn't a CSSMediaRule: if ( !(rule instanceof CSSMediaRule) && !(rule instanceof CSSSupportsRule) && (rule as any).conditionText !== undefined ) { // this is @container } Inline styles override rollback When rolling back changes via history, styles returned to the file, but (element as HTMLElement).style kept the overwritten inline values, which overrode the restored styles from the file. CSS specificity: inline styles always win over stylesheet rules. Fix explicitly ... ... clear the inline property on rollback

... your @Grok subscription directly inside @Kilo Code . No separate API key. No need to pay per token. Just connect your account and instantly get the latest Grok models (including Grok Build for agentic coding) right inside @VS Code , @JetBrains , or the terminal. @Kilo Code is the all-in-one open-source agentic engineering platform. It offers specialized modes for planning, coding, debugging, and orchestration, along with browser automation, MCP extensibility, and support for 500+ models. Kilo is designed for real ... ... beginner as myself, it takes time. thanks for the encouragement! Comment from rongze(@kevinzrzgg): The interesting part is not just using another model inside Kilo Code, it s keeping the same repo workflow around it: rules, tests, review, and rollback

... stick to what works? Comment from GuoleiZuo(@studentzuo): I like the pain today, not hypothetical pain next year rule. I d add one more filter: what does this tool make reversible? A new framework that reduces lock-in, shortens rollback, or keeps data portable is very different from one that only feels faster in week one. My usual test would be: can one engineer prove the bottleneck, migrate one narrow path, and rollback

... would try an AI organizer much faster if every move came with a compact receipt: what category decision it made, what confidence it had, and a clean undo path for the whole batch. Plain-English control is great, but rollback is what makes people comfortable using it on real work folders. Comment from Gowtham V(@gowtham_v007): Let me know in comments how are you keeping your folders organized now on your mac? Comment from Gowtham V(@gowtham_v007): @prafull ...

... lifecycle without storing anything yet. That includes: issuing a future state receipt binding state to tenant/workspace/store/operator context showing fingerprint-only receipts previewing one-time-use state consumption previewing replay rejection previewing expired state rejection previewing session mismatch rejection previewing rollback posture showing the future migration structure before it is created This is intentionally not live OAuth yet. No real Shopify tokens are stored. No authorization codes are stored. No raw OAuth state is displayed. No raw token is displayed ... ... gate. Even after state persistence works, OAuth exchange should remain disabled until specifically reviewed and unlocked. The exchange path should only activate after: state vault is working callback validation is working tenant binding is working token vault is ready rollback

... lifecycle without storing anything yet. That includes: issuing a future state receipt binding state to tenant/workspace/store/operator context showing fingerprint-only receipts previewing one-time-use state consumption previewing replay rejection previewing expired state rejection previewing session mismatch rejection previewing rollback posture showing the future migration structure before it is created This is intentionally not live OAuth yet. No real Shopify tokens are stored. No authorization codes are stored. No raw OAuth state is displayed. No raw token is displayed ... ... gate. Even after state persistence works, OAuth exchange should remain disabled until specifically reviewed and unlocked. The exchange path should only activate after: state vault is working callback validation is working tenant binding is working token vault is ready rollback