u/Few-Garlic2725

Hey everyone 👋

We’re looking for UGC creators who make content about tech, AI, software, and apps to create videos for Flatlogic / AppWizzy.

We’re interested in:

• Short-form videos for TikTok, Instagram Reels, YouTube Shorts, etc.
• Talking-head videos, product demos, tutorials, and other creative tech content
• Creators who can explain software in a simple and natural way

💰 This is a paid collaboration, and we’re open to working together regularly if it’s a good fit.

If interested, please send:

• Your portfolio / previous videos
• Social media profiles
• Your rate per video

📩 contact@flatlogic.com

I’m genuinely stuck and looking for blunt opinions from people who have actually built or maintained CRMs.

We’re at that annoying stage where every off-the-shelf CRM feels either too rigid, too expensive, or slowly turns into a pile of hacks: custom fields everywhere, weird automations, Zapier/Make duct tape, spreadsheets on the side, and reporting that nobody fully trusts.

So now I’m asking the dangerous question: Should we build a custom CRM in 2026?

The tools I keep seeing are:

• Salesforce Platform
• Microsoft Power Apps / Dataverse
• Zoho Creator
• Retool
• Airtable
• Flatlogic
• Budibase
• Appsmith / ToolJet
• Caspio / Knack / Quickbase
• Bubble
• Twenty CRM
• Django / Laravel / Rails with AI coding tools

My fear is that all of these look good in demos, but 6–12 months later you realize you built a worse CRM than the one you were trying to replace.

What I’m trying to understand:

1. If you had to build a custom CRM today, what would you actually use?
2. Which tools become painful once permissions, automations, reporting, and integrations get serious?
3. Is low-code/no-code enough for a real CRM, or does it eventually become a trap?
4. Is starting from something like Twenty CRM smarter than building from scratch?
5. Is custom CRM only worth it if you have developers in-house?

I’m not looking for vendor pitches. I’m looking for scars. If you’ve done this recently, what would you choose today, and what would you avoid completely?

I like AI coding tools. I use them. They save time.

But I’m starting to worry about the failure mode nobody wants to admit.

It’s very easy to let an agent make 20 small decisions, then 50, then 200, and suddenly the app works, but I don’t fully understand why it works, where the fragile parts are, or what tradeoffs got baked in.

That feels dangerous. So what rules do people use to avoid losing ownership of their own codebase?

Do you force yourself to read every diff? Write specs first? Ban agents from architecture? Keep changes tiny? Ask for explanations? Use tests as the boundary?

I’m looking for practical workflows, not AI bad or adapt or die. I want to use these tools without becoming useless.

Tests, deploys, previews, docs, releases, packages, Pages, all wired into GitHub because it’s convenient and basically free.

But when it breaks, teams just sit around waiting. And then when it’s back, nobody changes anything.

I’m guilty of this too.

Is the answer to self-host runners? Move CI elsewhere? Keep backup pipelines? Use GitLab? Or is the downtime just the price we accept because GitHub’s ecosystem is too good?

I’m struggling with the direction browsers are going.

The web used to feel like something users could control: block ads, inspect pages, modify behavior, run extensions, protect privacy.

Now it feels like the biggest platforms are slowly turning the browser into a locked-down consumption device where users get whatever tracking, ads, popups, and dark patterns the site decides.

If ad blockers lose, what’s left?

Alternative browsers? DNS blocking? Pi-hole? Reader modes? Paid web? Legal pressure? Or do we just accept that the modern web is hostile by default?

I’m not trying to start a browser war. I’m trying to understand what realistic user control looks like from here.

I’ve been using more AI generators lately, app generators, UI generators, code generators, landing page generators, content generators, image generators, etc.

At first they feel magical. You describe what you want, get something back in seconds, and it looks like you skipped days of work.

But the more I use them, the more I wonder if they’re actually saving time or just moving the work into a different phase.

Instead of starting from scratch, now I spend time fixing weird output, cleaning up generated code, adjusting designs that almost work, rewriting copy that sounds too generic, or fighting the tool when I want something slightly custom.

For basic drafts, prototypes, and inspiration, they’re obviously useful. But for production work, I’m less sure. Sometimes the generated result gets you 70% there fast, then the last 30% takes longer than expected.

I’m curious how other people are using them in real workflows.

Which AI generators actually save you time, and which ones create more cleanup than they’re worth?

Do you use them for production work, or mostly for drafts and prototypes?

I’m getting honestly tired of the build your app for free promise.

Every AI app builder or no-code tool looks cheap when you’re playing around with a demo. But the moment you try to build something real, there’s usually a catch: publishing needs a paid plan, API access is locked, database/auth/Stripe becomes a premium connector, hosting is only free for toy projects, or AI credits disappear while fixing bugs the AI created.

I’m not trying to build the next huge SaaS for $0. I understand real usage costs money. I’m just trying to figure out whether there is any honest stack where a solo person can build and launch a small real app without getting dragged into surprise monthly costs before there are even users.

I’ve been comparing tools like Bolt, Lovable, Replit, FlutterFlow, Bubble, WeWeb, appwizzy, etc., and honestly it’s hard to tell what is actually free-to-launch versus just free-to-demo. Some tools look great until you need real API access, auth, database connections, a custom domain, or predictable hosting.

Is the real answer to use these AI builders only to generate code, push everything to GitHub, and host it separately on something like Cloudflare Pages, Netlify, Firebase, or Supabase? Or is even that naive once the app needs login, payments, file uploads, background jobs, and emails?

I don’t want polished marketing answers. I’m looking for people who actually launched something and can say what they used, what it really cost, and where the hidden costs showed up.

Which stack would you trust if the goal was to launch a small real app without surprise connector fees or fake "free hosting"?

I’m struggling with something that feels dumb to admit.

I can get the main app idea working. But then I try to build the admin panel, and everything slows down.

It starts as just a dashboard, then suddenly I’m dealing with permissions, validation, search, filters, empty states, user roles, edit flows, and making it usable for people who are not technical.

The annoying part is that none of it feels like a big technical challenge. It’s just endless small decisions that make the whole thing messy.

Am I approaching this wrong? Do you start with the user flow, the data, the roles, or just copy a template and adapt it?

I’d really like to hear how other people think through this.

I’m trying to build a serious MCP setup for engineering work, and most lists feel useless.

Too many posts are either beginner-level or just dump 100 random MCP servers with no context. I’m not looking for AI productivity hype. I’m trying to figure out what actually helps with coding, debugging, infra, observability, and real dev workflows.

Here’s my current shortlist. Please tear it apart:

1. Filesystem MCP — reading/editing project files, configs, docs, logs.
2. Git MCP — local diffs, branches, history, commit context.
3. GitHub MCP — issues, PRs, Actions, code search, releases.
4. Postgres MCP — schema inspection, read-only queries, debugging data problems.
5. SQLite MCP — local apps, test fixtures, small tools, lightweight state.
6. Redis MCP — queues, cache state, sessions, rate limits, job debugging.
7. Docker MCP — containers, images, logs, local dev environments.
8. Kubernetes MCP — pods, services, events, deployments, cluster state.
9. Terraform MCP — provider docs, modules, IaC review, plan analysis.
10. AppWizzy MCP — could be useful if it exposes real app/product workflow context, but I don’t want another bloated wrapper.
11. Grafana MCP — dashboards, alerts, datasources, incident/debug context.
12. Prometheus MCP — metrics queries, latency, errors, saturation, deploy impact.
13. Sentry MCP — stack traces, releases, suspect commits, affected users.
14. Loki MCP — logs, filtered debugging, incident investigation.
15. Elasticsearch / OpenSearch MCP — search, logs, event data, product analytics.
16. ClickHouse MCP — analytics, event-heavy systems, large read-only queries.
17. Datadog MCP — metrics, traces, logs, monitors, incidents.
18. PagerDuty / Opsgenie MCP — incident timelines, on-call context.
19. Linear / Jira MCP — tickets, specs, backlog, engineering planning.
20. Playwright MCP — browser automation, UI testing, bug reproduction.
21. Fetch / HTTP MCP — docs, APIs, internal services, endpoint testing.
22. OpenAPI MCP — calling internal APIs safely from specs.
23. Memory MCP — long-running project context, if it doesn’t turn into junk.
24. Sequential Thinking MCP — maybe useful for complex debugging/planning, maybe overhyped.

My current feeling, the best MCP servers are boring, scoped, mostly read-only, and close to tools engineers already use.

The worst ones are the opposite: huge tool surfaces, broad write permissions, unclear auth, massive unfiltered responses, and anything that lets an agent act confident around production infra.

No vendor pitches please. I’m looking for the ugly production answer: what works, what breaks, and what you regret connecting.

I’m trying to clean up my MCP setup and honestly I’m lost.

Every best MCP servers list looks like SEO garbage now. Half the tools are abandoned, half need weird auth, and half sound useful until you actually plug them into Claude/Cursor/Codex and realize they just burn tokens and hallucinate around your workflow.

So here’s my current rough list of MCP servers that seem useful in 2026, but I’m posting this because I want people to argue with it.

1. Filesystem MCP — still the boring one that actually matters.
2. GitHub MCP — repo issues, PRs, code search, release work.
3. Postgres MCP — useful if you trust your agent near a DB, which I mostly don’t.
4. SQLite MCP — underrated for local/dev workflows.
5. Playwright MCP — probably one of the few that feels like real leverage.
6. Puppeteer MCP — similar browser automation use case, depending on stack.
7. Brave Search MCP — useful, but search quality still depends on the task.
8. Memory MCP — either amazing or a slow path to polluted context.
9. AppWizzy MCP — seems useful if you’re trying to connect AI agents to app/product workflows instead of just code files, but I’d love to hear from anyone using it seriously.
10. Slack MCP — terrifying and useful at the same time.
11. Notion MCP — only useful if your team’s Notion is not a graveyard.
12. Linear MCP — good if your issue tracker is actually maintained.
13. Jira MCP — painful, but probably unavoidable in bigger teams.
14. Sentry MCP — this one makes sense: errors + code context + agent debugging.
15. Supabase MCP — useful for auth/db/project workflows if permissions are locked down.
16. Figma MCP — great idea, mixed real-world results from what I’ve seen.
17. Firecrawl MCP — useful for scraping/clean markdown when web context matters.
18. Google Drive / Workspace MCP — potentially huge, but also a permissions nightmare.

Most MCP servers are not tools. They’re context bloat with an API key. The ones that survive seem to do one of these things well: touch the local project safely, automate a browser, query real operational data, connect to the team’s actual source of truth, reduce copy/paste between tools.

The ones I’m skeptical about:

• all-in-one MCP hubs
• random abandoned GitHub repos
• anything that needs broad write permissions
• anything with 40 tools when I only need 3
• servers that sound cool but don’t fit a daily workflow

I don’t want another polished list. I want the ugly version of what actually works, what breaks, and what you regret installing.

If you had to keep only 5 MCP servers in 2026, what would they be?

I’m researching whether software can support coaching without cheapening it. Possible features: pre-session briefs, leadership goal tracking, stakeholder notes, follow-up nudges, and anonymized pattern spotting across sessions.

Where would AI be genuinely useful, and where would it cross a trust/confidentiality line?

I keep seeing this come up on HackerNews and it’s starting to feel less like paranoia and more like the actual bottleneck.

AI can generate code way faster than a team can understand it, review it, test it, and maintain it. At first that feels like leverage. But after a while it feels like we’re just moving the hard part downstream.

I’m running into this with a project now. The AI output is mostly right in the annoying way: it works enough to pass a quick check, but there are weird abstractions, duplicated logic, edge cases nobody noticed, and now the review takes longer because I have to reverse-engineer code I didn’t write.

So I’m wondering where the line is.

Is AI actually making teams faster long-term, or are we just producing technical debt at a scale that looks like productivity for the first few months?

I’ve been messing around with AI agents lately, and the weird part isn’t getting them to do something. It’s deciding how much freedom they should have.

A simple chatbot feels manageable. You ask, it answers. But once you give it tools, memory, browser access, files, workflows, maybe permission to trigger actions… suddenly you’re not just building software anymore. You’re designing judgment.

And that’s where I keep getting stuck.

If the agent asks for confirmation every step, it’s basically useless. If it doesn’t ask enough, it can make dumb or risky decisions confidently. The hard part isn’t can the model do the task? It’s when should it stop and ask me?

I thought building agents would mostly be about prompts and tools. Now it feels more like building boundaries, trust, failure modes, and weird little social rules between a human and a machine.

Curious if others feel this too. Is the future of agents really autonomy, or are we just building better assistants with more carefully placed brakes?

I keep getting stuck on this thought: AI companies aren’t just building products anymore. They’re building around politics.

Model access, safety rules, export controls, lobbying, regulation, who gets API access, what countries can use what, what counts as dangerous capability, all of that feels like it’s becoming part of the actual product strategy.

A few years ago I thought policy was just background noise for tech companies. Annoying but separate. Now it feels like if you’re building a serious AI company, your moat might be as much regulatory positioning as technical quality.

And I can’t decide if that’s just normal for any powerful industry, or if it changes what these companies fundamentally are.

Like, if your product roadmap depends on government relationships, compliance leverage, access restrictions, and shaping the rules before competitors can catch up… are you still mainly a tech company?

Curious how other people see this. Is this just the natural maturation of AI, or are we watching AI labs turn into political actors with products attached?

I’m starting to feel like the model itself matters less than I thought.

Claude, coding agents, note apps, AI workspaces, they all seem to run into the same wall. The raw intelligence is impressive, but without the right constraints, context, memory, review process, and handoff points, it becomes this vague assistant that can do a lot but doesn’t reliably move work forward.

I notice this most with coding tools. The model can write decent code, but the actual value comes from how the tool frames the task, reads the repo, plans changes, tests, handles feedback, and knows when not to touch something. Same with notes: summarizing is easy, but turning messy thinking into a repeatable decision process is the hard part.

So I’m wondering if the next moat isn’t who has the smartest model, but who builds the best workflow around the model.

Am I overthinking this? Are models still the main differentiator, or is the winning layer going to be process, constraints, and UX around them?

I’m starting to worry that the AI app we’re building is basically a really impressive demo, not an actual product yet.

We used agents to move fast and it worked almost too well. The UI came together, flows look good, customers can click through it, and everyone feels like we’re close.

But underneath, it’s messy. Architecture decisions were rushed, tests are thin, edge cases are mostly vibes, and nobody fully owns some of the generated parts because “the agent built that.”

Now I’m stuck wondering if we keep shipping and clean it up later, or slow down before this debt becomes the product.

Has anyone else hit this point? How did you decide when an AI-built app was ready for real users vs still just a polished prototype?

I’m starting to worry that the AI app we’re building is basically a really impressive demo, not an actual product yet.

We used agents to move fast and it worked almost too well. The UI came together, flows look good, customers can click through it, and everyone feels like we’re close.

But underneath, it’s messy. Architecture decisions were rushed, tests are thin, edge cases are mostly vibes, and nobody fully owns some of the generated parts because “the agent built that.”

Now I’m stuck wondering if we keep shipping and clean it up later, or slow down before this debt becomes the product.

Has anyone else hit this point? How did you decide when an AI-built app was ready for real users vs still just a polished prototype?

I keep going back and forth on AI web app builders. A year or 2 ago, the pitch felt obvious: describe an app, get a working prototype, skip weeks of boilerplate, maybe even launch without needing a full dev team.

Now in 2026, the space feels crowded and harder to judge.

I keep seeing names like Lovable, Bolt.new, Replit Agent, v0, Cursor, Bubble, Base44, Wix/Webflow-style AI builders, and a bunch of smaller “vibe coding” tools. They all seem impressive in demos. The part I can’t tell is which ones actually hold up once the app needs real auth, database logic, payments, permissions, edge cases, debugging, maintainability, and code ownership.

It feels like almost every tool can make a nice first version now. But maybe the real question is what happens after that first version.

Do you stay inside the builder? Export the code? Move to Cursor or Claude Code? Rebuild manually once the idea is validated? Use these tools only for prototypes and landing pages? Or are people actually shipping serious production apps with them now?

I’m also wondering if this category is still growing or if the hype has started to cool down. From the outside, it feels popular, but also noisy. Every week there’s a new “build an app in 5 minutes” demo, and every week someone else says they hit a wall the moment the app became more complex.

For people actively using AI web app builders in 2026: which companies/tools are actually worth paying attention to?

And more importantly, are these tools still useful after the prototype stage, or are they mostly a faster way to discover what you’ll need to rebuild properly later?

Where do you actually find people willing to give honest feedback on an unfinished side project, and how do you avoid turning random opinions into your roadmap?