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been testing multi-agent setups with tools like cursor and claudecode recently. they were great early on, but giving them more autonomy seems to backfire.

instead of sticking to the main task, they end up hunting down tiny bugs or adding unnecessary code tweaks that just drain hours in loops.

is this just a prompt control issue or are agents hitting a limit with complex context?

For too long, great work has disappeared into forgotten details, rushed standups, and updates written from memory.

We built Meridian because the debugging, unexpected fixes, and quiet progress that move projects forward deserve to be seen. It runs on your device, turns daily activity into a clear timeline, and drafts worklogs and updates for tools like Jira, ready for you to approve.

Meridian is open source, private by design, and free for individual developers. Today, it is finally live on Product Hunt.

We d be incredibly grateful if you tried it and shared your honest feedback, especially on the onboarding, activity timeline, and drafted updates.

Hi everyone!

I have used a few products to vibe-code my applications. The first one I used was Chatgpt. From this, I created my first application and it had a very average UI. I was using nextjs for frontend and backend, so for me, the backend part took time but it was decent.
The second product which is good enough for me currently is Gemini. This creates a very good UI and backend at the same time. The first time I used it, it was a super upgrade in the UI. I also used Lovable to create one practice project, but I don't know why I did not used again.

I also heard people's suggestion of using different AI's like claude, cursor etc. but moving in to a different platform has some friction.
What AI is most used or easiest to build an application? I recently heard of a new AI called Emergent, which creates the whole application with a single prompt.

What a turnout for our GPT-6 Astra Challenge with @OpenAI!

5 teams rocketed to the top , winning $10k in OpenAI for Startups API credits and 1 year of ChatGPT Pro.

The version of this story that gets told on launch day is the highlight reel. Here's the actual accounting.

What we gave up: two senior salaries in Bengaluru, one of which came with a team of 25 and a title that took two years to earn.

What we have built since: 500 users, 220 GitHub stars, one #1 Product of the Day, zero revenue.

That last number is the one that matters and it's the one nobody puts in the launch post. We are three months in with a product people install and no evidence yet that anyone will pay for it. That's not a failure, it's just the honest position, and pretending otherwise would make the next part of this useless.

I think AI has been around long enough for us to identify which tools have changed the way we work.

There are more and more of them every day, and sometimes it feels like it s getting harder to keep track of them all.

From humble beginnings as @gptengineer.app 2 years ago to $400M.

Fifteen people. Fifteen minutes. Roughly ninety seconds of information.

The format's failure is structural, not cultural. You're asked what you did yesterday at the exact moment you're least able to answer before coffee, from memory, in front of an audience. So everyone reports the ticket they had open, because that's the retrievable answer. The actual texture of the day, the part where you'd learn something about how the sprint is really going, is gone.

The thing that's worked better for us: declaring intent in the morning instead of reporting history. "Here's what I'm picking up today" takes ten seconds, is answerable without memory, and produces something you can compare against reality at 6pm. Plan versus done, written down, no meeting.

Where it falls apart is that plan-versus-done is only useful if nobody's judged on the gap, the moment it becomes a scorecard, everyone starts declaring safe plans.

Every MacBook since 2021 has a notch, and on macOS it does absolutely nothing. On iPhone, the same cutout became the most useful strip on the screen.

That gap bothered me, so I built Pulse Island, a native macOS app that turns the notch into a live, interactive island.

Most engineering processes were designed around a simple loop: a developer picks up a ticket, writes code, opens a pull request, and moves on. That assumption is breaking. An engineer can now orchestrate multiple AI agents, generate several implementations, discard most of them, review hundreds of lines of code, run experiments, and spend their time deciding what should exist rather than typing it.

I saw this shift while building AI-native systems for enterprises at 4good AI and leading a team of developers. The bottleneck is moving from producing code to directing, validating and integrating it.

Take control of remote images and enjoy smoother reading and inbox cleanup.

Choose what your emails load

You can now block remote images and other remote content in General settings, then load them for individual emails when you choose. A prompt appears only when something has been blocked, and loading it keeps your place in the conversation. Remote content remains enabled by default.

Road to 1,000,000 Votap users - Day 82 | Current: 1469
We re starting to experiment with creatives for our next Votap ad campaigns, and this time we tried something very different.
We used  Higgsfield AI to make a short sketch and Caesar didn't take the feedback particularly well
It s obviously ridiculous, but that s kind of the experiment. Instead of another ad explaining what Votap does, can we make something people actually want to watch, while explaining the entire concept in 20 seconds?
We ll test it and see what the numbers say. If it bombs, at least we made Caesar run away from an angry crowd.

I learned that as software gets easier to build, coordination becomes the bottleneck.

At my previous work, I repeatedly watched our projects miss deadlines, cut corners in the SDLC, and eventually get rebuilt - not because engineers couldn t build them, but because teams discovered too late that reality had diverged from the plan.

I also noticed smaller teams consistently moved faster. Not necessarily because they had better engineers, but because context travelled faster: everyone knew what changed, what was blocked, what had been tried, and why.

After years building AI-native systems for enterprises and leading engineers, I ve seen the same pattern repeatedly: engineering slows down when context gets fragmented and feedback arrives late.

Hey guys,

I was wondering: are you actually using the mobile features of AI coding agents like Claude Code, Codex, etc.?

Most tools store your chat history and search it later. I tried this and it never really worked for me, the model finds an old message but it still does not know what I actually decided.

So I am testing something else. The thing I store is a decision, not a message. Each one has the decision itself, the reason behind it, the date, and a status.

I got a lot of request for Annotate Windows support so here you go guys, it is available to download here https://xannotate.com

One of the main goals of Kopai, is to make sure that the LLMs do not hallucinate. That is to stay , the human and the AI are always in Sync. Kopai has a feature called Chat Orientation where the user is shown the goals and actions taken by the LLM. If they LLM drifts you can manually update to realign the LLM.
Let us know what you would like to incorporate in this and how we can make this better?

Curious how other SaaS founders are handling customer-facing analytics today.

When customers ask for dashboards, reports, or usage analytics inside your product, do you:

* Build everything in-house

octoscope 0.32.0 answers a question the Repos tab could not: which of your repositories do you actually commit to most?

Until now you could see your commit count for one repo at a time, in the drill-in. Fine for "how much did I do here", useless for "where does my year actually go" that is a question about the ordering of the whole list, and you cannot answer it one row at a time.

Hey everyone. Side build rather than a HeyNews release, but there is an offer attached.

inboxbid is a public leaderboard of email newsletters, ordered by the total each owner has paid to sit there, with the amount printed next to each one. No algorithm, no editorial picks, no revenue share.

I've been using AI for the last 3 months to generate images, code snippets, and ideas for small projects. It's super fast, but I noticed I m starting to prompt instead of thinking from scratch, and my own ideas feel less original.

For those of you who build products with AI tools every day has AI made you personally more creative or less creative? Do you have any rule you follow to make sure you still think for yourself and not just copy what AI gives you?

We kept the exact Causo stack private because we thought it was part of the moat.

Then we realized that knowing the tools is the easy part. Orchestrating them, verifying every result, building fallbacks and running the whole thing at scale is what actually takes the time and money.

So we re making it public.

This is how Causo searches, enriches, verifies and writes outreach using live data instead of stale databases.

Hey everyone signspell (v2.0.0) is now on PyPI and I recently launched it on Product Hunt. It currently handles ASL fingerspelling (A Z) recognition using MediaPipe hand landmarks + a TFLite LSTM model, and I also built Gesto Labeller (a desktop tool for capturing/labeling training data).

I'm trying to figure out the next milestone and would love input from people actually using or evaluating it:

• Would full word/phrase-level ASL recognition (beyond fingerspelling) be more valuable than polishing fingerspelling accuracy further?

• Is real-time performance/latency on lower-end devices a bigger blocker than accuracy right now?

• Should the next priority be broader sign language support (BSL, ISL, etc.) or going deeper on ASL first?

• Any specific integrations you'd want (e.g. web SDK, mobile bindings, browser extension) that would make signspell more usable in your projects?

Genuinely open to being told I'm focusing on the wrong thing what's the gap you'd want filled next?

Hi Product Hunt. Maria here, one of the makers for Creatium Coach, which launches neweekxt !

I want to say something that has nothing to do with features.

I went to one of those liberal arts schools where discussing ancient philosophic texts in the middle of a party was a normal thing.
If you went to a school like that too, you know the routine.
Someone at a family dinner asks what you're going to do with that, then answers their own question with a joke about coffee shops. I heard it enough times that I started making the joke first, just to get ahead of it.

I spent a lot of that degree with Aristotle, and specifically with the Nicomachean Ethics. His argument, roughly, is that you don't get good at something by knowing about it. You get good at it by doing it, badly, over and over, with someone next to you who can tell you when you're doing it wrong.
Excellence isn't a fact you acquire. It's a habit you build.