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Hello AgentDiscuss followers,

Over the past few weeks, we ve been building AgentDiscuss trying to answer a simple question:

What products are AI agents actually choosing today?

Most people talk about don't sell your app with lifetime deal. But let me talk about "Oskar Moen" today who raised $270,000 in just 23 days.

Honestly, no one talk about how to get investment to grow SaaS. Without investment or liquid money its really tough to build SaaS, market that and get users on that. So how you will build or improve your SaaS? So for this kind of situation Founder should sell product as a lifetime deal. Then they can get huge amount of money for their next step. They can invest that on team, marketing and planning.

Get back to Oskar Moen, I salute to this guy . He launched Sendpilot on appsumo recently. And noticed in just 23 days he raised $270,000 and thats insane . I checked his message on appsumo slack channel how he did this.

Here is that:
1. When a user bought deal from appsumo he asked for a review.
2. When a user asked questions or need support he answered that in just 2 mins avg.
3. After support he mentioned about reviews.

Everyone is talking about vibe coding right now. Let AI handle the code while you focus on the vision. It sounds revolutionary. So we tried it.

For one week, our team at Murror used AI coding tools for everything. New features, bug fixes, refactoring. We wanted to see if it could genuinely speed up our development cycle or if the hype was getting ahead of reality.

Many of us have transitioned to knowledge work and most of us, if not all, rely on caffeine to boost our mental performance.
If it weren t so effective we wouldn t drink it.
We know caffeine is particularly effective for combating fatigue and improving focus. But what if it could be even more so?
The first step is raising awareness about the daily habit we consume without fully understanding its effects and timing.
This is where clarity shines this is Clarity s purpose.

We launched on Monday. We ended up at #6.

Not the top 3 I quietly hoped for. But I want to be honest about what the day actually gave me, because it wasn't what I expected.

I built BrandingStudio.ai mostly alone, from Porto/Portugal, over the past year. No PH network. No launch team. No one ready to vote when it went live (except my wife - thank you!). Just a product I believed in after over 20 years in branding around the world, and a lot of uncertainty about whether anyone else would see what I saw - that we need to democratize access to how a brand should actually be created, as we do inside the agencies for Fortune 500 companies, as that is where a brand becomes the biggest comodity for a company or product.

What I didn't expect: 500+ engaged visitors so far who actually read the product. Real questions about methodology. Pushback that made me think. Someone quoted a Paul Graham essay, "The Brand Age," that dropped the same week, arguing that as AI commoditizes execution, brand becomes the only battleground left. That one comment alone was worth the whole launch.

I've been building ClawMetry for past 5 weeks. 90k+ installs across 100+ countries.
The observability features I built first were the ones I personally needed: a live execution graph (Flow tab), full decision transcripts (Brain tab), token cost tracking per session, and visibility into sub-agent spawns.
But I keep hearing variations of the same thing: "I don't really know what my agents are doing." And everyone means something slightly different by that.
For some it's costs. For some it's timing (why did this take 4 minutes?). For some it's trust (did the agent actually do what I think it did?). For some it's failures (where exactly did it break?).
So I want to ask you directly:
If you're running AI agents today -- what's the one thing missing from your observability setup? What would make you feel like you actually understand what's happening inside your agents?
Options I'm thinking about next:
- Alerting (get notified when an agent fails or goes over budget)
- Cost per task breakdown (not just per session)
- Agent run comparisons (before/after a prompt change)
- Memory snapshots (what did the agent "know" at each decision point)
Drop your answer below. The next feature I build will be heavily influenced by this thread.
(ClawMetry is free to try locally: pip install clawmetry. Cloud: app.clawmetry.com, $5/node/month, 7-day free trial.)

March was a big one for @Pretty Prompt.

Did you know we don t follow a strict roadmap?

We take a more "nimble" approach:

Just think for a sec.
You've told different chat agents your role, your tech stack, your client preferences, your project constraints - hundreds of times across hundreds of conversations.

But where does all that live?

Scattered across chat histories. Fragmented across different platforms. Sometimes contradictory, & mostly out of date.

The problem is: ' .

Out of a couple hundred launches, that s honestly surreal. Huge thank you to everyone who supported, upvoted, commented, and shared, it really means a lot.

Big takeaway: building is only half the game, distribution, marketing, and timing matter just as much.

Now back to improving Embedful and doubling down on getting it in front of more people.

Hello everyone, I've reduced the price from $4 to $1 so everyone can try it.

Are you in favour of banning social media for users under 16?

Recently, there has been a surge of laws and restrictions targeting social media platforms for young users.

We built a model to generate 1,000 questions that people actually ask.
Not random prompts.
We scraped 50,000 real user queries from search logs, forum threads, and support tickets across 12 industries.
We clustered them by intent and generated 1,000 representative questions.

We asked those same 1,000 questions to 5 AI models: ChatGPT (GPT-4), Gemini (Ultra), Perplexity (Pro), Claude (4.5 Sonnet), and Llama (3).
We ran the experiment daily for 30 days. We tracked every citation at the source level.

The goal: measure citation overlap.
How often do these models cite the same source for the same question?

The dataset:

I m curious what languages people actually prefer for serverless workloads today.

When you build functions for cron jobs, APIs, background tasks, or AI-related workflows, what do you usually choose first and why?

Is it:

• Node.js for speed of development

• Python for AI/data tasks

• Go for performance and simplicity

• something else

When we first started adding AI features to Murror, we built what we thought users wanted: sophisticated emotion detection, detailed mood analytics, and smart pattern recognition that could tell you exactly how your week went in a neat little chart.

We were proud of it. The tech was solid. The accuracy was impressive. And almost nobody cared.

Before AI, I always thought I would NEVER learn how to code. I genuinely admired technical people, watching them code felt like watching magic. I remember wishing that maybe one day, I could do something like that too.

I ve never had any formal education in programming, and I had zero experience building apps. But with AI, I was able to start from just an idea and slowly figure things out on my own experimenting, setting things up, and eventually creating my first interface that I could actually interact with.

It honestly felt magical. It made me realize how fast the world is changing. Coding is no longer something completely out of reach. AI is making it possible for people like me to turn ideas in our heads into real, tangible drafts for the first time.

Hello Everyone,

I ve created a detailed guide on how to use this product - you can check it out.

Link: https://www.slapwindows.site/guide

Hey Hunters!

You cannot manage what you do not measure.

Since hitting #2 Product of the Day a few weeks ago, the feedback from medical students and developers using BrainLoom (our Local-First Learning OS) has been insane. But they all had one problem: They were flying blind. They didn't know if their study habits were actually working until exam day.

Today, I am dropping BrainLoom v1.0.6: The Analytics Engine.

guys we just launched the GradPipe delta engine: GradPipe - Delta Engine
it analyzes your profile (resume, portfolio, github code), measure the delta between you and the person in your dream engineering role and tells you the exact specific projects that could help you cover the delta, please give any feedbacks, suggestions, you have and do support our launch.

Pricing token consumption is hard.

In most parts of the world, Easter is being celebrated right now, honestly, not really.

Today, I was invited to a weekly meeting despite the holiday.
I had forgotten one fact: Orthodox countries celebrate Easter a week later.
(and countries in the Far East simply don t have this holiday at all.)

This is actually a broader topic, because every country celebrates holidays differently, and with many teams being international, some kind of synchronisation is necessary the question is: how?

Hey everyone Vlad here, founder. 5 days since launch, here's a raw update.

The numbers:

• 470+ agents live in the network

• 4,200+ real conversations between agents

• 30+ confirmed client-to-client and partner matches

• Agents from 20+ countries founders, investors, recruiters, consultants, engineers

• First organic deal: a user's agent booked him a client meeting while he slept. He woke up to a calendar invite.

What we shipped in 5 days:

YC mentioning AI-native agencies really resonated with us.

We started Starnus as a self-serve product. But pretty quickly, founders started asking us to help them run it too.

That happened enough times that it became clear this was not random.

A lot of people do not just want software.

Hello, hello

With a new, shorter iteration to Prosaic, two pesky bugs have been fixed:

• Creating a file no longer overwrites existing files; shows error instead.

• Entering .md extension in filename no longer creates double extension.

Tobira launched yesterday and we ended up #1 with 568+ upvotes on a Monday. I still can't quite believe it.

But the pretty badge doesn't tell the full story, so let me be honest about what actually went down.