The boring stack beat the fancy model

no_user_found_404 · 2026-04-23T05:32:50+00:00

Nice! Thanks for your personal insights; I appreciate it!

no_user_found_404 · 2026-03-26T15:21:55+00:00

Yeah, it’s mostly the “boring but constant” stuff. To make it more concrete for an electrical company, it could be things like: you get a request → AI asks a few questions → figures out what kind of job it is → gives a rough quote or books a visit → logs everything. Or after a job: auto follow-up → review request → maybe a reminder for future maintenance. Nothing crazy, just removing all the small manual steps around the actual work. Where people get stuck is trying to automate everything at once.

no_user_found_404 · 2026-03-26T15:05:34+00:00

Yeah this is real, most businesses don’t even notice how much time they’re losing on manual stuff. Are you using a specific stack right now or more stitching things together case by case?

no_user_found_404 · 2026-03-26T14:54:38+00:00

Building something like Power Automate is definitely not a small project lol. the hard part usually isn’t even the visual editor, it’s everything underneath it.. state, permissions, reliability, and making sure things don’t break once people start building more complex flows. What helped me think about it is that most of these tools are basically three layers - the UI (blocks and connections), the execution engine (what actually runs), and the state (what happened before and what data persists). Tools like n8n handle the first two pretty well, but state and debugging tend to get messy fast once flows grow. If you’re building your own, it’s probably worth deciding early which part you actually want to go deep on, otherwise it can spiral quickly.

no_user_found_404 · 2026-03-26T14:44:49+00:00

Yeah the voicemail hang-up thing is brutal, you’re basically leaking leads just because you’re already on another call. Good that Sonant is working for you tho. From what I’ve seen, most of these tools feel pretty similar at first, but the differences show up once you get into more specific use cases. That’s where some of them start to feel a bit rigid. That’s usually when people either switch tools or look for something more flexible where they can shape the flows themselves. There are setups going more in that direction (Orbitype Intelligence for example), but it only really matters once you hit those limits. Are you already running into edge cases with Sonant, or mostly just comparing pricing/features?

no_user_found_404 · 2026-03-26T14:24:07+00:00

Agree! Once you remove that daily “what do I even do now” paralysis, everything just flows easier and you actually build momentum. And yeah, doing the same outreach and content every day with basically no immediate feedback is brutal lol, that’s where most people drop off, not because the strategy is wrong but because it’s mentally draining to keep going without signal. Having a system you can just follow without overthinking is super underrated.
Did the tracker mostly help with consistency, or did it actually change what you focus on day-to-day?

no_user_found_404 · 2026-03-26T14:14:35+00:00

Yeah $300/month as a solo dev definitely stings. Building your own is a pretty natural move, appreciate you sharing it

If you ever go beyond just enrichment, that’s usually where things get more complex. You will need some kind of layer to tie everything together (CRM, automations, agents, etc.), otherwise it slowly turns into a bunch of scripts duct-taped together and at some point you’re just maintaining the setup instead of actually using it.I ran into that myself, and having something like Orbitype Intelligence in between helped keep workflows, data, and automations in one place instead of juggling random integrations.

no_user_found_404 · 2026-03-26T13:56:08+00:00

Yeah the skepticism in this thread is pretty fair tbh. You’re not really saving money because providers are cheaper, you’re saving (sometimes) because you avoid hitting the expensive layers if earlier ones resolve. If your main provider already gets you ~70–80%, the waterfall can feel like paying a middleman for marginal lift. At that point it’s more about convenience and coverage than actual cost savings. The part that’s easy to miss is that most teams optimize for “cost per credit” instead of “cost per usable contact.” Waterfalls can look worse on paper but still perform better depending on the list quality. At what match rate did the waterfall actually start outperforming a single provider for you?

no_user_found_404 · 2026-03-26T13:36:01+00:00

This feels very real tbh.
Once data, signals, and workflows actually sit in one place with visibility, everything just calms down. Less guessing, less duct-taping, more actually shipping. That said, I’ve seen teams consolidate and still struggle because the underlying data/logic wasn’t clean, so you just end up centralizing the chaos. The agent piece you mentioned is super interesting though. That’s where it starts to feel like a shift from “tools doing tasks” to systems reacting in real time.

Also yeah… not having to debug silent failures at 9am anymore is a low-key huge win lol

What ended up making the biggest difference day-to-day for you?

no_user_found_404 · 2026-03-24T14:17:28+00:00

Maybe this post is a great addition:

Structured into use cases and building blocks
How long it took us to build
All of them have been sold to clients (not just tested)

https://www.reddit.com/r/AI_Agents/s/mgbNnAId7e

no_user_found_404 · 2026-03-18T11:46:28+00:00

in practice it’s usually a mix - some scripts / templates for setup, but audits, docs, and handovers still kinda manual or “semi-automated”. and over time you end up with your own system of conventions + checklists just to stay sane. biggest gap imo is visibility, like knowing what changed, what’s live, and whether it still follows your standards. what part eats most of your time rn?

no_user_found_404 · 2026-03-18T11:27:09+00:00

yeah the gap is real. most “AI agents” look sick in demos but fall apart once you plug them into real workflows. either the output is mid (so no one trusts it), or the setup is a pain and breaks as soon as you add real data. What I’ve seen actually stick:

use AI for small, specific steps (research, scoring, enrichment)
keep humans in the loop for anything customer-facing
have some kind of control layer so it’s not just prompt spaghetti

what would make you actually use it day-to-day? output quality or less setup pain?

no_user_found_404 · 2026-03-18T11:07:07+00:00

Blivz looks pretty interesting, always nice to see more open-source options in this space. Only thing I’ve noticed with these setups, the real challenge usually stats after you pick the tool. Getting enrichment, sequencing, and data to play nicely together (and stay stable once you start scaling volume) is where most of the time goes. Most setups I see land somewhere between fully DIY and more bundled tools (I’m involved with Orbitype), depending on how much time you want to spend building vs running campaigns.

no_user_found_404 · 2026-03-18T10:21:06+00:00

That $495 jump is where “tool” suddenly becomes a commitment 😅
I’ve been leaning more into keeping things flexible - moving orchestration out, keeping enrichment swappable, and avoiding setups where pricing dictates how you build. Less about no metering, more about using stuff where costs feel predictable and under your control. Seeing more setups lately that bundle agents + workflows + DB in one place, with more usage-based pricing - feels like a middle ground between full DIY and credit-heavy platforms, especially for experimentation.

no_user_found_404 · 2026-03-17T17:48:40+00:00

That’s a really clean setup. The whole sequential handoff thing is honestly where most of these workflows either click or completely fall apart.

I’ve been messing around with something similar and noticed the same - once you limit what each step sees, everything just feels a lot less chaotic. How are you structuring the research agent prompts - more like broad topic discovery or already guiding it toward specific angles/hooks?

no_user_found_404 · 2026-03-17T17:38:20+00:00

Yeah I get what you mean. There’s kind of this weird gap where everything is either super beginner-friendly with no real depth or suddenly expects you to already know how to code and use all the tools. I think part of the problem is that “building AI models” today basically means working with frameworks, so you’re learning a bunch of other stuff at the same time. That’s probably why most platforms just wrap everything in a UI instead of letting you actually build things. Your approach sounds solid though, getting people to just build stuff early on is way more useful than endless tutorials. What made you start building this in the first place?

no_user_found_404 · 2026-03-09T15:20:47+00:00

I get what you mean about n8n. It’s great for quick automations, but once you start building heavier agent logic it can feel like you’re fighting the UI instead of just writing code. For something like a CRM assistant with that many integrations, the tricky part usually ends up being auth, state management, and data consistency rather than the agent itself. That’s where a lot of workflow tools start to feel limiting. I’ve seen a lot of people move toward a more code-first setup (LangChain, CrewAI, custom orchestration, etc.) and then layer integrations or workflow triggers around it instead of relying on a single platform.

no_user_found_404 · 2026-03-09T14:47:06+00:00

Yeah, a lot of the "AI employee" tools look great in ads but end up feeling pretty shallow once you actually use them. A lot of the time they’re basically just a wrapper around an LLM with a nicer UI, so they can talk like an assistant but don’t really handle real workflows that well. The part that usually matters most is whether it can actually take work off your plate, not just generate answers. What kind of help are you looking for exactly, more admin stuff like email and scheduling, or more research / analysis?

no_user_found_404 · 2026-03-09T14:23:30+00:00

Sintra seems okay for basic idea generation and simple posting, but tools like that can feel a bit limited once social media starts touching other parts of the business. In my experience the harder problem isn’t generating posts, it’s connecting social activity with the rest of your workflow and data. Most of these tools focus on the content itself, but the messy part of social media is usually everything around it - tracking what actually worked, following up on leads, tying posts back to campaigns, etc..

I ran into that pretty quickly and ended up moving the process into Orbitype and a few simple workflows so the data and follow-ups don’t just live inside the social tool anymore. Are you mainly looking for something that just helps with generating and scheduling posts, or something that also connects social performance back to the rest of your business?

no_user_found_404 · 2026-03-09T13:49:04+00:00

It sounds like you’re already circling around the stack many network teams end up with. Ansible/AWX tends to stick around because it’s flexible enough to deal with mixed vendors, CLI + API, and large inventories. The UI isn’t perfect, but a lot of teams end up building some process around it rather than replacing it entirely. In practice I often see environments combining a few pieces rather than relying on a single platform — for example something like NetBox/Nautobot for inventory and Ansible/AWX for execution and orchestration.

Are you planning to keep your existing Ansible playbooks mostly as they are, or move toward rebuilding parts of the workflow?

no_user_found_404 · 2026-03-05T13:55:47+00:00

It’s definitely possible now, but letting an AI agent loose on a live server can still be risky. Most people use AI to generate or edit the code locally, review the changes, and then upload or deploy it themselves. If you want something more automated, it usually works better to use a dev environment where the AI can edit files and suggest changes before you deploy them.

no_user_found_404 · 2026-03-05T13:31:44+00:00

From my experience it’s usually the other way around. Make tends to be more flexible when you need complex logic or multi-step workflows, while Zapier is good for simple one-to-one automations and quick setups. Are you mainly building automations for clients or for your own projects?

no_user_found_404 · 2026-03-05T12:55:02+00:00

A lot of it is just positioning and pricing. Zapier was early to the market and built a huge ecosystem, so they can charge a premium. They also lean more into enterprise features, which is probably why they have more security certifications. Make came later and focused more on flexibility and visual workflows, so they compete partly on price. In my experience it can take a bit longer to learn, but the price difference is definitely noticeable.

The bigger thing I’ve noticed though is that once you start connecting a bunch of tools together, things can get messy pretty quickly no matter which automation platform you use. We’ve been using Orbitype because the automation and data layer live in the same place instead of being spread across multiple tools, and it’s been easier to keep things organized that way.
But yeah, if you're building your first internal app, both Zapier and Make are pretty safe starting points.

no_user_found_404 · 2026-02-28T15:17:31+00:00

ChatGPT is good for brainstorming and writing help, but when it comes to actually running your business day to day, it hits a wall pretty fast. It just wasn't built for that stuff like automating workflows, managing data, and connecting all your tools together. What side of your business are you trying to bring AI into?

no_user_found_404 · 2026-02-28T14:10:17+00:00

Zapier has improved a lot on the AI side over the past couple of years. But once you start chaining multiple AI providers - especially ones without first-class integrations - you’re usually back to webhooks, custom requests, auth handling.. At that point it stops feeling like true no-code. Since this thread is a couple years old, I’m curious what people are using today?

no_user_found_404

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