u/This-You-2737

Finally had some time to gather my thoughts after Data + AI Summit.

Overall, really well produced event. Databricks knows how to put on a conference. But if I’m being honest, the AI stuff started to blur together pretty fast.

Also had very practical conversations about the less sexy problems: cost, reliability, orchestration, observability, governance,etc.

A few things I kept hearing:

Costs are becoming impossible to ignore. Not just for finance teams. Data teams are being forced to care because “we’ll optimize it later” apparently does not count as a strategy.

Favorite booths and companies I’ll be taking demos with :

Astronomer

Good booth. Clear message around orchestration, and Airflow is obviously still not dead, despite how many times people have tried to kill it. They did seem kind of annoyed when I asked if I could recreate the kiss cam photo with my CEO though lol.

Monte Carlo

Data reliability is one of those things everyone forgets about until the dashboard is wrong and you have a major meeting in an hour

Zipher

Favorite booth for me, and one of the busier ones from what I saw. They’re focused on optimizing Databricks workloads around cost, performance, and reliability. The platform learns workload behavior and adjusts infrastructure automatically. Claims up to 60% databricks + cloud savings

Anyone have any other takeaways from this conference or opinions on the vendors?

I am looking what it would take to make one of our products available in China,Shenzhen per say . From the outside, it looks like one of the best places to build or expand a tech business, but the deeper I go, the more it feels like an entirely different digital world.

I thought the process would be similar to launching in any other country. Translate the product, improve website performance, set up local marketing. Instead, I've found myself reading about WeChat Mini Programs, Official Accounts, local cloud providers, ICP filings, different app stores, payment platforms, and regulations that I've never had to think about before.

Also many of the tools and platforms we rely on every day don't seem to play the same role in China. It feels like businesses have to rethink not only their technical setup but also how customers discover products, interact with brands, and even complete simple tasks that would normally happen through a website or mobile app.

The more I research, the more I realize that success there isn't just about translating your product into Chinese. It seems like understanding the local digital ecosystem is just as important as the product itself.

It's been a fascinating learning experience so far, and I have a much greater appreciation for how unique Shenzhen's tech ecosystem has become compared to almost anywhere else.

For anyone running international AP and tired of supplier emails about missing wires, the reliability problem is almost always the rails not the team. SWIFT has too many hops and each one is a place a payment can stall.

What actually makes international supplier payments reliable is moving off correspondent bank chains and onto a stack where the cross border leg does not rely on intermediaries. The platforms doing this well in 2026 run on regulated stablecoin infra. Cybrid is the backend layer most north america focused B2B platforms use it seems, it handles licensing, FBO custody, and the USDC settlement leg, while the platform on top owns the AP workflow.

The reliability gain comes from fewer moving parts. A SWIFT MT103 touches 3 to 5 banks on average before reaching the supplier. A stablecoin rail touches the origination bank, the infra provider, the payout partner, done. Fewer hops, fewer failure points. And it's all tracked

Reconciliation gets easier too because every leg is timestamped. Your controller can prove the supplier received funds at 2:14pm without calling the bank. That alone removes most of the "did you get our payment" emails that eat AP team time.

If reliability is the goal, the answer is fewer intermediaries on the cross border path. Stablecoin backed AP platforms are the realistic 2026 way to get there.

Been on reply for about 18 months now and starting to wonder if Im missing out on better options. The multichannel sequences work well enough and the AI stuff is decent for personalizing at scale. But man, thier pricing has gotten agressive - went from like $70 to $90/user in the last year alone.

Biggest gripe is thier data enrichment is pretty weak. Half the time I'm importing leads from other sources anyway. The LinkedIn automation features keep breaking too whenever LinkedIn updates their anti-bot measures.

The email deliverability tracking is solid though. And their support responds, which is more than I can say for some tools. Task management inside sequences saves time vs juggling between platforms.

Anyone else feeling like Reply.io is getting left behind? I've been looking at Instantly and also saw some poeple mention Prospeo for the data side. Curious what others are using for outbound sales tools these days - feels like theres a new tool every week and my boss keeps asking why our bounce rates are still high lol

I feel like everything shifts daily and it's hard to keep track of everything.for suppliers, often the simplest approach is to request updated quotes. Many companies also set up regular refreshes, automated feeds, or negotiated price update schedules.the hard part for me is not just asking again, it is knowing which price changed, which supplier changed terms, and which quote is still old.Sometimes suppliers send emails at night,and I usually miss them.Damn, accio sourcing toolkit has really helped a lot recently. i use it to keep the supplier replies and updated quotes in one place, so i don't need to open every email and chat again next morning. It negotiates while I sleep, and I just check the results.

FedEx money back guarantee got suspended during covid and a huge chunk of shippers just assumed it was gone permanently, but the policy was reinstated for most service levels and FedEx did almost nothing to publicize it

The catch is FedEx does not proactively issue refunds when a package arrives late even when the guarantee is active, and the threshold is brutal, a delivery that lands even 60 seconds past the commitment time qualifies for a full refund of the shipping charge, but the shipper has to file each claim individually within a specific window and for anyone moving real volume that is not something any ops team has time to do manually

So what ends up happening is businesses shipping 15k, 20k, 50k a month are just bleeding money on late deliveries they are fully entitled to get back and not a single person on the team is tracking it

Is anyone here actually recovering these or is everyone just accepting the loss

we started seeing these terms thrown around a lot lately and getting confused. my email finder spits out "validated" emails, my esp talks about "verification" before sending, and now i'm wondering if i've been using the wrong terminology this whole time.

from what i understand, validation vs verification breaks down like this - validation is checking if the format is right (like has @ symbol, proper domain structure) while verification confirms the inbox exists and can receive mail? but then some tools seem to use them interchangeably which doesn't help.

right now i run everything through Prospeo for finding contacts, then Instantly does another check before sending. feels redundant but my bounce rate is under 1% so maybe the double-checking is worth it?

curious how others handle this. do you trust your data source's validation/verification or run everything through a separate email verification tool? and does the distinction even matter in practice?

Doing product video ads for DTC brands, the thing clients notice instantly is when the product changes between shots: the label shifts, the color is slightly off, the bottle shape morphs. With end-to-end generators (Runway, Pika, Kling) every shot is a fresh generation, so the product drifts and a 6-shot ad fails QA.

What's worked best for me is treating the product as one locked element instead of re-generating it each shot. In a node/workflow setup the product is a single node every shot pulls from, so it stays identical across the cut and across variations. ComfyUI can do this locally if you're technical; I landed on OpenCreator, a browser-based node-workflow tool for AI video, because I wanted that locked-product reuse without running a local install. The practical payoff: I batched 10 variations of one ad and the product stayed consistent because it's the same node, not 10 separate re-rolls.

To be fair: generators with reference-image conditioning are closing the gap for simple products, and the node approach is more upfront setup than just prompting. But for anything with fine label or text detail, the locked-node route is the only thing that's passed client QA for me.

For others running product ads at volume: how are you keeping the product consistent across shots and variations? Re-rolling with reference images, or something more locked-down?

There are literally hundreds of crypto casinos, but most of them are invasive. Which is the best option for people caring about their own privacy??

I've been getting into AEO over the last month and mostly learning by trying things myself. Currently for blogs I am breaking content into smaller sections instead of writing one long guide. Each section covers one fact or topic, so it still makes sense even if AI only picks up that one part. It's a bit different from the usual FAQ advice. The idea is to make each section useful on its own since AI often pulls a single passage instead of the whole page. It's still too early for me to tell if it's working but in the meantime, I'd love to hear about other simple strategies people have tried that actually worked. Any tips based on measureable results?

The way B2B sales teams run outbound has barely changed in 15 years. Build a list, enrich it, score it manually, hand it to reps, run sequences, review results, repeat. Every step involves human decisions that could theoretically be automated but weren't because the tooling wasn't there.

difference between a regular GTM platform and an agentic one comes down to whether the system can act on what it learns or just report it. Traditional platforms surface signals but agentic platforms decide what to do with them.

Continuous account monitoring without human triggers: instead of a rep manually checking which accounts are active this week, AI agents scan job boards, news sources, G2 activity, LinkedIn signals, and website behavior continuously. When an account crosses a threshold, it routes automatically.

ICP that updates from outcomes, not just inputs: static ICP definitions go stale the moment your market shifts. Agentic systems update which signals and firmographic attributes actually predict closes based on your real win/loss data. The model gets more accurate over time rather than drifting from reality.

Signal-to-message without a human in between: when an account shows a specific signal type, a well-built agentic GTM system doesn't just flag it. It determines what that signal implies about where the buyer is, adjusts the message angle accordingly, and routes it to the right channel.

I've seen this in practice with tools like Tapistro, where TapAI Agents handle the data enrichment and signal monitoring layer continuously, while the orchestration layer acts on what those agents surface. The design intent is to eliminate the manual steps between signal detection and outreach execution. The agentic GTM market is still early but moving fast. The gap between teams using signal-based agentic execution and teams still running manual cadences is going to widen over the next two years.

I produce AI video at volume, and I spent a while comparing tools not on clip quality but on how well they automate a repeatable production. Sharing my criteria and where things landed, since "best AI video tool" threads rarely define what they're optimizing for.

The criteria I actually cared about:

Reuse: can one setup drive many shots/variations, or is every video from scratch?

Revision cost: can you change one part without re-rolling the rest?

Control vs maintenance: how much you can tune vs how much you have to keep alive.

Cost model: per-clip credits vs local compute.

How the approaches landed against that:

End-to-end generators (Pika, Runway). Strong on single-clip quality and speed. Weak on reuse and revision: one prompt, one clip, and a small change means rewriting the prompt and re-rolling the whole thing. Fine for one-offs, doesn't scale to repeatable production.

Node/workflow tools. The video is a graph of steps, each a node with explicit inputs, so you rerun or change one node and reuse the whole pipeline.

ComfyUI: open-source, local, maximum control and tunability. Tradeoff is you build and maintain the graph and the GPU/VRAM side yourself.

OpenCreator: a browser-based node-workflow tool for AI video. Same reusable-graph model, hosted with the models wired in, so far less setup. Tradeoff is less low-level control than ComfyUI and it runs on credits.

Short version of where each fits: Runway/Pika for fast one-offs, ComfyUI for maximum control if you'll maintain it, OpenCreator for a reusable node workflow without the local overhead.

In practice the deciding axis for me was revision cost. On a node workflow, changing one shot is a single-node edit and downstream holds; on end-to-end it's a full re-roll and a gamble. For repeatable output, that difference outweighed raw model quality.

Curious how others here would benchmark this. If you've actually measured revision/iteration cost across these (not just output quality), what did you find?

our outbound team is wasting hours manually searching for emails on sales nav. we've been testing different chrome extensions but most either grab outdated info or hit you with captchas every 10 profiles.

right now we're looking hard at Prospeo and Apollo. Prospeo caught my eye because they verify emails in real time instead of just pulling from some static database. plus their extension shows the email right there on the linkedin profile without making you leave the page. been trying to find email from linkedin profiles without all the friction and this seems like it might actually work.

anyone here running high volume outbound (500+ emails/day) with a solid linkedin email finder extension? specifically need something that works with sales navigator and doesn't get your account flagged. our sdrs are wasting like 2-3 hours daily just on email discovery when they could be actually selling. my manager is starting to ask why pipeline numbers are down and i'm like... because we spend half the day hunting for contact info lol

Seat-based pricing gets painful fast when visibility needs expand beyond the core team. Once external collaborators, reviewers, or occasional stakeholders need access, the bill can grow even if their usage is minimal.

At that point, teams usually start asking whether they want a light coordination layer or a broader work platform. ClickUp is one of the names that comes up when people want the latter without splitting planning, docs, and communication into separate systems.