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I ve been spending more time vibe coding recently, and I ve started to question something I initially took for granted. Most of the conversation around vibe coding is about speed. Like how quickly you can go from idea to prototype, or how fast you can iterate. And to be fair, that part is real. The barrier to building has clearly dropped.

But the more I use these tools, the more it feels like speed isn t the limiting factor anymore.

The real constraint seems to be taste.

• what do you choose to build?

• what do you keep vs discard?

• what actually feels right vs just working ?

• what is genuinely useful vs just impressive in a demo?

Yesterday, Jay Graber (Former Bluesky's CEO) introduced Attie  the first agentic social app on Atproto.

(currently as an invite-only closed beta)

It should allow users to:

Right now we have scenarios covering things like giving hard feedback, managing up, and pushing back on scope creep, and more. But I'm building out the next set and I'd rather build what people actually need than guess.

So: what's the conversation you keep putting off?

What's the one you replayed in your head after it went sideways?

I came up with this concept after I failed to set New Year s resolutions at the beginning of 2023 and was disappointed that I didn t achieve much.

Since 2024, I ve been practising quarterly goal-setting.

Hey PH community. Yesterday we launched ClawSecure and landed #2 Product of the Day

ClawSecure is the free security scanner for OpenClaw AI agent skills. But I'm not here to pitch. I just want to share real traffic numbers and what I actually learned from our Product Hunt launch, so it's useful for other makers planning theirs

Genuinely curious what the community does here.

We've been talking to hundreds of teams building with Cursor, Claude Code, and other agentic tools and the honest answer from most of them is: "We just run it and hope."

Some do a quick manual click-through. Some write a few spot checks. Some just ship and wait for users to find the bugs.

We built TestSprite to solve exactly this autonomous testing that runs from your PRD and codebase but I'm curious what your actual workflow looks like before you merge.

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

guys we are launching the delta engine, which takes data of what people in elite roles have done to get there and tailors a few projects for you to reach the similar position, we have had 1500+ engineers on our platform from people working as quants in Jane Street, Citadel, IMC Trading, Graviton, Optiver to software companies like Google, Amazon, Uber, Databricks, Snowflake, Twilio, Confluent, Rippling. We even have some researchers from Google Deepmind, Anthropic and other frontier AI labs, you can all get to know what you need to do to your profile to reach where they are.
Obviously your context matters, so we take into account that and give you the delta that you need

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.

Over the last 4 years I have spoken to hundreds of companies.

Here is what I learned:

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.

I m building Blood Sugar Journal a simple diabetes tracker with AI insights.

Trying to understand what actually annoys people in daily tracking.

When I started my first job after school at a small local agency, a project manager once said something like: If someone has three companies on their CV and stayed less than a year in each, it doesn t look good.

I took that to heart. I tried to stay longer in every role, so I wouldn t seem unreliable, even in underpaid jobs I didn t enjoy. I endured it just to make my CV look stable. In hindsight, it was a little bit stupid. (Sometimes a waste of time.)

If you followed Naoma a year ago, you knew us as a sales conversation analytics tool. We connected to your CRM, analyzed rep calls, and surfaced patterns from top performers so the rest of the team could learn from them.

It worked. Teams liked the insights.

But we kept noticing the same thing: the real bottleneck wasn't after the demo. It was getting to the demo in the first place and what happened in those first few minutes before a rep ever joined.

Qualified buyers were waiting 3 6 days for a demo slot. Many dropped off. The ones who showed up often hadn't been properly qualified. Sales reps were spending half their week running intro demos for people who were never a fit.

We've just shipped one of our most requested features. With Listen Mode, any content you save to Recall (articles, podcasts, YouTube videos, PDFs, web pages) can now be summarized and read back to you as audio. Pick from our library of built-in voices, or clone a voice of your own.

I cloned my dad's voice so he can read me my morning podcast summary. Check us out in action below!

Hey PH, we just shipped 0.2.5 and wanted to share what's new.

Breathing sounds

The breathing pause now plays ambient audio synced to each phase of the cycle (inhale, hold, exhale). Two tracks: a singing bowl that shifts harmonics with each phase, and procedurally-generated pink noise shaped like rainfall. You can mute or switch mid-session; your preference is saved.

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If you're a product owner, founder, or builder reading this, I want to talk about something that's costing you money right now.

You probably don't know it's happening.

Here's what I've learned after analyzing 500+ SaaS products, tools, and digital products at Rankfender.

The Mistake

Hi everyone,

I m getting ready to launch Inquir Compute.

It s a serverless platform for AI agents, cron jobs, webhooks, and backend functions, with isolated containers, custom domains, and deployment on your own server.

I d love feedback from anyone who has run into limits with managed serverless or edge runtimes.

I ve discovered a lot of useful products through Product Hunt over time.

Funny thing is, some of them I found way back, but only recently started using when the right use case clicked. I kept bookmarking them. And that s when their real value showed up.

Road to 1,000,000 Votap users Day 62 | Current: 1295

Hey! Been working on something different lately, an AI coding agent that writes its own code. No human commits. It's been evolving itself for 31 days straight.
I'm not sure I'd call it a "product" yet, but it's genuinely usable. 60 commands, 14 LLM providers, runs in your terminal. The difference is it keeps growing on its own. Every day it's a little better than yesterday.

Building a team or want to join a startup? Let's kick 2026 off to a great start.

Founders, teams, and startups drop a comment if you're hiring.

Every AI product I see launching right now is racing to add the most impressive, most complex AI feature they can build. Autonomous agents. Multi-step reasoning. Real-time analysis of everything.

When we started building Murror, we fell into the same trap. We wanted to build the smartest emotional AI possible. Something that could analyze patterns across months of conversations, predict emotional states, generate deep psychological insights.