PH热榜 | 2026-08-15

This solves a problem I didn't know I could solve, I always assumed API billing discrepancies were just the cost of doing business. Turns out I was wrong.

Overpaying for failed calls has quietly cost me more than I'd like to admit. I've had timeouts that still consumed tokens on the provider's side and no clean way to catch that pattern until my bill arrived. If this surfaces failure-related overspend specifically, not just total usage, I'd trust the numbers more than any provider dashboard.

Hey PH

We built inferock-bench because we kept paying for AI answers that died mid sentence, and nobody could tell us where the money went.

Providers give you totals. They don't give you the per call receipt you'd need to prove which answer broke, which retry ran, or which token count changed. The company that charges you also decides what counts as a failure and keeps the only detailed records.

inferock-bench runs locally as a proxy in front of OpenAI, Anthropic, Gemini, OpenRouter shaped calls. Point your existing SDK at it (change two settings: apiKey and baseURL), and it captures every call as an independent, per call record. Your provider key never touches our servers, it's used locally only, attached to provider requests.

What it catches:
- Answers cut off mid stream that still got billed
- Empty replies with billed tokens attached
- Token counts that don't match visible output
- Retries that may have silently doubled a charge
- Cache discounts you may be missing on your invoice

Every run reports a receipt: spend observed, bill-bounded money loss, time loss, and a separate "invoice-check exposure" line that never gets summed into money loss, because we don't want a louder headline at the cost of a weaker claim.

Run it in about a minute: npx inferock-bench

Its open source (FSL-1.1-Apache-2.0, converts to Apache-2.0 in 2 years).

Question for this community: has anyone here actually disputed an AI provider bill and gotten a credit? What worked?

I've been burned by silent retries inflating my OpenAI bill before. Having an independent receipt for that would've saved me a painful invoice conversation.

I like the positioning here, it's not trying to replace the provider's billing system, just verify it. That's a smarter pitch than "cost optimization," which every tool claims.

This is relevant to a problem I've had for months. I run agents that call out to multiple models depending on the task complexity and every so often the bill jumps in a way I can't explain from usage alone. If this can pinpoint whether that's failed calls, redundant retries or just legitimate scaling, I'd finally have an answer instead of a guess.

I like that it works as a local proxy. Keeping billing and usage data on the developer's machine feels like a thoughtful design choice.

The failed-calls and retries breakdown is the part I'd use first. One case I keep hitting might not show up there: a tool call that returns 200 with a silently corrupted value. I measured this on Anthropic models, 40 calls, none flagged the value was wrong, so it bills as a clean success and the retry logic never fires. Can the receipt catch a call that looked fine but wasn't? Or is that out of scope by design?

Congrats on the launch @himashwetha_gowda. Good find @fmerian.

Question regarding accuracy, is it possible it might not be able to distinguish a genuinely billable provider failure from valid hidden token usage, such as reasoning, refusal, cache, or tool-call tokens?

Thanks @fmerian for hunting us. Feel free to ask if anybody has any questions about Inferock Bench.

The retry tracking caught my attention. I’ve seen failed requests become surprisingly expensive, so being able to trace each call would be useful to me.

Love that this sits as a local proxy instead of asking me to route traffic through another cloud service. Keeps my API keys and data where they belong.

What I really want to know is how this handles historical data. Can I feed it a month of past logs and get a retroactive receipt or is it strictly forward-looking from install? I ask because the overspending I'm most curious about already happened and I'd love a way to audit it after the fact.

Tracking retries alongside failures is a smart addition. Those hidden retries can quietly become a big part of the bill.

The retry tracking caught my eye. silent retries are probably one of the easiest ways for API costs to creep up without anyone noticing.

The independent receipt idea is really practical. It's nice to have a way to verify token usage instead of relying only on provider dashboards.

The independent receipt idea is genuinely useful. LLM costs are still surprisingly hard to audit at the individual request level.

Does anyone know of any tools that can work with web-based logins (claud.ai etc)

The bill dispute question is interesting. have you personally managed to get a provider to credit a charge after showing them one of these per call receipts or is that still something you are testing?

The two setting setup makes this feel unusally easy to try.

Which API call actually cost me this money? is a question every AI app eventually needs to answer.

changing the baseURL and apiKey instead of rewriting the application is a nice touch. makes this much easier to test on an existing project.

I like that Inferock Bench focuses on evidence rather than simply showing another dashboard. Having a separate record of every API call could make unexpected billing much easier to investigate.

Hi everyone!

GLM-5.2 was already used on-prem by @Hugging Face last month to investigate the autonomous-agent intrusion into its infrastructure, after hosted frontier models ran into cyber-safety restrictions.

GLM-5.3 keeps the exact same base model as 5.2. @Z.ai simply kept scaling the post-training stack, and the gains are pretty substantial. On their internal code benchmark, 5.3 at high effort beats Opus 4.8 while using less than half the output tokens.

Cyber improved even faster. GLM-5.3 now scores 84.5 on CyberGym, and the model has started reasoning through complete exploitation chains. Their disclosure ledger currently tracks 1,097 critical and high-severity findings across 269 open-source projects.

You can try GLM-5.3 now through GLM Coding Plan and ZCode. Open weights are planned in two weeks, after the remaining safety evaluation and hardening.

Less than half the output tokens is the claim I would want to watch in a real agentic loop. On my own app the tokens that hurt were never the ones in the clean runs. They were the retries and the tool calls that went nowhere, and a benchmark never sees those.

I keep my model id behind remote config so a swap does not need a new build to go out. Is 5.2 to 5.3 drop in at the API level, or does the prompting need reworking for the high effort mode?

Really interesting concept! The combination of real-time injury alerts, fantasy advice, and betting picks in iMessage feels super practical. Curious—are WhatsApp or Telegram versions on the roadmap?

What stands out to me is that Big Mike isn't just another betting picks app—it combines news, injuries, fantasy, and betting into one conversational experience. The public record is a nice touch too. Great launch

There's never a shortage of sports mania. The blockbuster trades, the season-derailing injuries, or just a late-season underdog catching a 10-game hot streak. There's always something moving....

What's missing is a way to filter out the noise before kickoffs, tip-offs, or the first pitch...

That's why you need to "Ask Big Mike", your tailored, personal sports companion that texts you (iMessage) daily updates on:

• The latest on your favorite teams, hot off the press

• Real-time injuries and lineup changes before game time

• Custom fantasy adjustments for your actual roster

• Big Mike's Read, his best picks of the day
  

  
  Hey Product Hunt, I'm Michael, founder of Big Mike! 🐻

I've been a sports fan my whole life, tracking the latest news across multiple teams, players, and games in a single day. And for years I watched friends tail "experts" whose betting records nobody could actually verify....

That never sat right with me. So I built Big Mike: your favorite sports companion, texting you morning, evening, or night with the latest changes and the plays worth making across the NFL, CFB, MLB, WNBA, and NBA.

Ask him anything, like "hey big mike, how do we feel about the cubs against the pirates today?" and he'll tell you where the value is and why.

He handles the whole day, not just the bets. An overnight trade drops, a starter tweaks a hamstring in warmups, your flex on your fantasy team suddenly questionable. Big Mike catches it and texts you before it matters, so you're never the last one in the group chat to know.

He's in your fantasy league too. Inactive alerts before lineup lock, start/sit off his own projections, and weekly recaps with the roast included. (Football season loading...)

He can be added to any existing group chat. Add him to the crew's iMessage chat and he keeps up with who said what, answers each of you on your own terms, and drops slips right into the conversation. It should make watching with your crew more fun, not more homework.

When he does call a play, he shows his work. Big Mike builds his own price for every game from the ground up, only steps in when the market's price is provably wrong, and grades himself against the closing line, not just wins and losses. One companion in your texts, with a public record he can't go back and edit.

Every pick is graded in public. His record is just the number: 141–89 (61.3%) on his best plays for the day and counting. Every call dated, timed, and kept in the app forever. Losses sitting right next to the wins. Nothing gets deleted.

It's a live board, not a static list. The app lays out the day by game time (live now, locking soon, tonight, tomorrow) with live scores, how the lines have moved, and closing-line value on everything he's already graded.

Who's Big Mike for?

If you follow a few teams, ride a fantasy league, and maybe bet casually with a group chat full of opinions, this one's built for you. Big Mike shows his work, texts you back like he's part of the squad, and keeps you on top of the latest around the league.

Get started

Download Big Mike and your first week (7-Days) is on the house: https://apps.apple.com/us/app/big-mike-sports-betting-picks/id6762222670

Big Mike's Newsletter: "The Daily Dough": https://thedailydoughwithbigmike.substack.com/

Big Mike Site: https://www.bigmike.ai/

P.S - Bring the crew while you're at it, every friend who joins your squad drops the price for everyone....and you could be missing out on 50% off for everyone right now!

Love the idea of turning sports updates into a group-chat experience. The public track record and transparent grading make this especially interesting. 🚀

Hello Michael, showing the reasoning behind each pick is probably the part I’d pay the most attention to. I’d want to understand why the number looks valuable.

The group chat feature is a fun idea. I can already imagine my friends arguing with Big Mike about every pick

the injury and lineup alerts could be really useful during those last few minutes before a game starts.

I think we are close to getting jackpots in casino games. My dad started using AI for predictions in lottery btw. :D

Making Big Mike part of the group chat is such a fun product decision. Sports predictions are already social, so having the “uncle who always has a take” actually join the conversation feels much more natural than opening another betting app.

in a group chat how does he decide when to speak and when to stay out? thats the part id have got wrong. one bot in a busy thread gets muted fast if it answers everything.

I do a version of this manually for work - scanning a lot of early-stage stuff fast - and the part I'd worry about with "the 1-2 things worth knowing find me, the other 98 never do" is that the best finds I've had were never things I already knew to track. they were adjacent to my interests, not inside them. if Zetik's filtering works off trackers I explicitly set up, how does it surface something genuinely outside those - a new category I didn't know existed yet - versus getting really good at recall on topics I already told it mattered

👋 Hey Product Hunt! I'm Nathan, the builder of Zetik.

Honest origin story: I hunt for quality information every day — and still miss things that matter. Out of every 100 posts I scroll, maybe 1–2 are actually worth my time.

And the signal is everywhere. A story starts in the news, the best take is on X, the real discussion is on Reddit, the technical proof is on GitHub or arXiv, and the best explanation might show up in a podcast three days later.

Piecing it all together is a part-time job. Staying informed shouldn't depend on how much time you have to scroll.

Then it clicked: presidents and CEOs solved this problem a long time ago.

They don't read everything themselves. They have staff who monitor the world, kill the noise, connect the dots, and walk in with a brief.

It's 2026. I use coding agents every day.

Why shouldn't everyone have an information agent?

So we built Zetik — your own chief information staff.

🎙️ Tell it what matters to you.
Say it out loud or type it in. Zetik spins up a tracker and continuously follows the web for you — collecting, cross-checking, analyzing, and briefing you on what actually matters.

📡 It follows the signal, not just the source.
X, Reddit, YouTube, podcasts, GitHub, arXiv, blogs, financial research — Zetik connects information across formats instead of making you chase it yourself.

💡 Insights, not links.
When something changes, you don't get another pile of tabs. You get the story, the context, what changed, and why it matters.

And it remembers what you care about. Your interests, your preferences, and the things you've already told it — so every brief gets more useful over time.

My trackers right now: new LLM papers, promising open-source repos, startup funding, football transfer news, and the next TV series I want to watch.

One information staff. Both halves of my life.

The goal is simple:

The 1–2 things worth knowing find me.
The other 98 never do.

Two questions for you:

1️⃣ How do you manage your information diet today — still scrolling manually, or have you automated part of it?

2️⃣ What's the one thing you're most afraid of missing?

Drop your answers below. I'll be here all day. 🫡

Usually skeptical of 'info agent' launches, but tracking the signal across formats (especially audio to text) instead of just RSS feeds makes a ton of sense. Setting up my first topic now!

@Rohit C — the local-model/BYOK choice is a strong complement to cursor-level assistance. Screen Assist plus Blackboard looks especially useful when a question needs both the current app context and a visual explanation. How are you deciding when Attyn should infer context automatically versus ask the user to scope what it can see?

Hey Product Hunt, I’m Sam 👋

Most LLMs are trained to agree with you (RLHF). Ask one if a bad idea is good and it’ll lean towards yes, taking multiple paragraphs to do so. I wanted a model that questions my assumptions, pushes back when it matters, and stays quiet when it doesn’t. That became nen-1. A fine-tuned model trained to be concise and question assumptions, instead of padded and flattering.

(note: get one month of Nen Pro for free! reminder email 5 days before billing)

NEN MODEL's: nen-1, nen-2, & nen-alia 🍃

• trained differently to frontier models: a refusal of the axis everyone else maximises
• web search optimised for register and user absorption
• models integrated into notes pages and logbook, so you can query nen-1 about your notes or search the web without leaving the page
• composer (⌘+K) for seamless discussion with nen model's, from any page

SPACE: tasks · notes · logbook · habits · memory 🌍

capture & sift:
• a thought arrives at the wrong time…walking, cooking, mid-task
• capture it in working-memory (not every idea needs to become a “thing” immediately)
• when you get some time, press sift to read the pile of captured notes, thoughts, ideas, and a place is proposed for each: a task, a note, a habit, a space…or simply set it down for later
• nothing moves without an ok, everything is recoverable

A brain does two things: it remembers and it thinks. Every app that calls itself a “second brain” only does the first and leaves you to do the filing. Every LLM does the second but it thinks for you, and is trained to agree with you. You're outsourcing your thinking. The purpose of AI does not have to be thinking for you, but to return you to your own thinking.

I’d love feedback, especially on the models but also what you'd like to see in the /space side as well!

Peace & love 🌍 🫶

Looks super interesting! All the best!

Cool idea, I would like to test it, but the sign-up code comes as an 8-digit code instead of a 6-digit code, and I cannot log in 🌝

Hey Product Hunt 👋

I built Joy 🎉 because working on an indie product can feel surprisingly quiet.

The first sale, crossing a download milestone or a even new GitHub star may represent hours of work, but these moments are usually hidden inside dashboards. I wanted them to feel like real wins.

Joy is a native macOS menu bar app that watches for meaningful milestones and celebrates them instantly by throwing beautiful confetti on the screen. It stays out of the way, avoids becoming another analytics dashboard, and focuses on one thing: making progress feel rewarding.

You can customize what gets celebrated, how often it happens, and which celebration style is used. Joy also includes a lightweight Wall of Wins so you can revisit recent milestones without getting lost in charts.

This is the first version, and I would genuinely love your feedback, especially on the integrations and celebration styles you would like to see next.

Thanks for checking out Joy. I hope it adds a little happiness to your building journey 🎉

Turning invisible backend milestones like Stripe sales and Docker pulls into a burst of confetti is such a clever way to make solo building feel less lonely, love it.

nice product. do u have a Windows version too in the roadmap?

Hey Product Hunt! I've long wanted to have more control over which files open where. In my blog drafts folder, I want my Markdown files to open in nvUltra, in my documentation folders I want them to open in VS Code. When I double click an image file, I want to be offered a menu of Preview, Affinity, and Acorn. So I built a tool to do that. I hope you enjoy it!

Hi Product Hunt 👋

I’m Takahiro Ikeuchi, a solo product engineer from Tokyo 🗼 and the maker of Chronock.

I started building Chronock after repeatedly running into the same problem in two different roles.

As a CTO, I spent a lot of time coordinating recruiting interviews. As a coach, I also needed to schedule meetings with people outside my organization. Native calendar features often felt too limited, while broader scheduling suites could be difficult to justify on price. At the same time, keeping my work, personal, and project calendars aligned often meant paying for and managing another tool.

That led me to a simple idea: scheduling and calendar sync are not really separate problems. They are both about keeping your availability trustworthy.

Chronock brings them together in one workspace:

- Create booking pages that check your Google and Outlook calendars

- Sync events between work, personal, and project calendars

- Review connected events in one Integrated Calendar

- Find and share open-time candidates when a booking link is not the right fit

As a Japanese maker, I also wanted multilingual support—including Japanese—to be part of the product from the beginning, rather than an afterthought.

I wanted to create a focused and accessible utility without treating quality as optional.

I’ve spent more than 10 years working as a full-stack engineer and now describe myself as a product engineer. I’m interested in the entire process, from UI design and frontend development to backend systems and cloud infrastructure.

Chronock is built AI-natively. I use coding agents such as Codex and Claude Code throughout development, but I don’t equate AI-native development with handing off engineering judgment. Architecture, design, security, testing, and the final quality bar still require deliberate care.

For me, the real value of AI is not only speed. It helps a very small team avoid unnecessary compromises and stay close to the people using the product.

Chronock is still at an early stage, and this launch is an opportunity to learn. There’s a free plan, and no credit card is required.

I’d especially love your feedback:

- What is the hardest part of managing multiple calendars today?

- Do you prefer sharing a booking link or proposing a few specific times?

- Is anything about Chronock’s setup or positioning unclear?

Thank you for taking a look. I’ll be here throughout the launch and would be happy to answer any questions.

honest answer to your question: the hardest part for me isn't double-booking, it's the placeholder events. I sync work -> personal so my partner sees when I'm busy, but the synced copy is just a blank "Busy" block with none of the context, so I still end up manually checking the original calendar half the time anyway. the sync solved discoverability, not usefulness. on the technical side, since you're syncing between work/personal/project calendars in both directions, how do you stop that from creating a loop - a busy-block synced from A to B that then gets picked up and re-synced back into A as a second, slightly-different event?

A small reflection from launch day: Chronock is AI-native in how it is built, but it isn’t AI-powered in how it works.

That may sound unusual for a new product in 2026. I considered adding a few AI-powered utilities, but I kept asking whether they solved a durable problem — or whether similar capabilities would soon become part of platform assistants such as Gemini.

The core job of Chronock is different. Keeping two calendars synchronized is repetitive, always-on work where correctness matters more than creativity. The process should be boring, predictable, and trustworthy. That calls for deterministic rules and reliable infrastructure rather than probabilistic behavior.

There is a lot of talk that “SaaS is dead” in the AI era. I think focused SaaS still has an important role wherever people need dependable background processes and a neutral layer across platform boundaries.

Synchronizing multiple Google calendars — or bridging Google Calendar and Outlook — is exactly the kind of cross-ecosystem work an independent third party can do well.

I’m curious: do you now expect every new utility to include AI? Where do you still prefer predictable software over an AI-powered experience?

congrats on the launch. for me the biggest frustration isn't really categorization, it's the accounts that live outside the PSD2 world entirely - a couple of my savings and broker accounts still don't have a clean aggregator connection anywhere. one thing I'm curious about: PSD2 consent expires every 90 days by regulation, so how much of the "zero friction" promise survives that reauth cycle in practice - is it a silent background refresh, or does the user have to go log back into their bank every 3 months?

Hey Product Hunt,

Clamshell started with a very ordinary annoyance. I would close my MacBook, leave a build, download, or coding agent running, and come back later to find that the whole thing had stopped.

I tried the usual workarounds. Some only prevent idle sleep. Others change a system-wide setting that is easy to forget. I wanted one small switch I could turn on only when I needed it.

So I built Clamshell. Arm it, close the lid, and your Mac keeps working while the internal display goes dark. Open it again and normal lid behavior comes back. You can arm it from the menu bar, with a shortcut, or by holding Option while closing the lid.

It checks itself again when power changes. I also added a low-battery cutoff because keeping a Mac awake is not very useful if it runs the battery flat.

Setapp gave me a reality check before this launch: more than 500 people are already using Clamshell, and so far I have had one bug report. That bug is fixed.

If you prefer the direct version, the trial lasts seven days and does not ask for a card. After that, it is $9.99 once.

If you run long builds, downloads, local servers, or coding agents, I would genuinely love to hear how it works on your MacBook.

To answer your forum question: lid cracked open like an animal, which is exactly the problem. What I'd want next is the reverse trigger -arm it until a given process exits, so the Mac finishes the build and then actually sleeps, instead of staying awake in my bag/bed/ table until the battery cutoff catches it. Is there anything like that on the roadmap?

Hey Product Hunt 👋!

We built Agent Orchestrator because running 10+ coding agents quicky becomes a context switching nightmare.

Once you have Claude Code, Codex, Cursor, and others working in parallel, the bottleneck becomes coordination: what is each agent doing, which branch is it on, what is blocked, what passed CI, what needs review, and what can merge?

AO gives you one place to manage all of that.

You give it an outcome, it breaks the work into tasks, delegates them across coding agents, and tracks progress on a live Kanban board from task → PR → CI → review → merge.

Our bigger belief is that developers are moving from writing every line themselves to orchestrating increasing amounts of intelligence. The tools we use need to evolve with that.

AO is open source with 9.5K+ Github Stars already.
We’d love for you to try it, push the limits of the number of agents you thought you could manage, and tell us what you think!

Thanks for checking us out ❤️

Have been using Agent Orchestrator for ~5 months now, and it’s become a pretty regular part of how we work.

I contributed to the repo a while back too, so I’ve seen the product evolve from fairly close quarters. What’s been great is that it’s not just something I personally like — people across my team have picked it up and actually use it.

Congrats on the launch! Excited to see where you take this next 🚀

It was amazing experience to use AO ine everyday building projects, shipping AI agents for my hackthon project.

Been using Agent Orchestrator with several AI coding harnesses—Pi, Claude Code, OpenCode, and Codex—and it’s been great having a single layer to orchestrate multiple agents while keeping everything visible and manageable through the Kanban board.

I’ve also been using the iOS app alongside my Tailscale setup, and the whole experience has been surprisingly smooth. It’s nice being able to keep an eye on agents and workflows without being tied to my desk.

Really enjoying the product so far. Looking forward to agents running fully inside sandboxes 😉 — then I can finally close the laptop lid and let them keep working.

If you’re experimenting with multiple coding agents and want a cleaner way to coordinate them, definitely check out Agent Orchestrator. love the team and vibe. Great work! @agent_wrapper

Been using @Agent Orchestrator very often for the past few weeks and been loving the interface and the experience, excited for what's coming ahead!

Have been using AO for almost 2 months and ive been loving it building products has been way easier and im using it every day probably the best things like for real the best would reccomend every builder or like every developer anyone and everyone can use it

Been using Agent Orchestrator for a while now, and it's been a wonderful experience to take my coding workflow to the next level!
Looking forward to the team taking this product to the next level!

AO has soo far been the experience for me! It has simplified my work by manifolds and even reduce my working time and stress!! Great project and has a lot of potential!

Using @Agent Orchestrator from last two - three weeks, till now having a great experience. Btw i have been also using conductor, superset and onorca but this feels something different.

I’ve been loving Agent Orchestrator, especially for vibe coding.

The clean interface + tmux makes it really easy to run multiple agents, switch between them, and keep track of everything with the Kanban board. That part is genuinely great.

A few pain points on Linux though.

On Arch/Omarchy, I usually have to run it manually, and it would be much nicer to have a proper package/normal app installation option.

The Linux UI also still feels a bit rough and broken in places compared to other apps I use.

Overall, really like the product. There’s a lot of room to polish it, but the core idea and workflow are already very useful.

This looks great! Will give it a try.

AO managing fleed of AI Agent is top notch

This dude goes way above and beyond what most smaller lists go. If you want the support on your next launch, put your product here!

Congrats Nick 🚀

Bookmarked for my next launch.

Congrats on the launch Nick! Awesome to see your growth. Whenever I ship something, I’ll be using @nicklaunches :)

Congratulations on the launch!

How do you moderate listings?

👋 Hi Product Hunt! I'm Toan, founder of Mermail.

First of all, a huge thank you to our Hunter, the Product Hunt team, and everyone taking the time to check us out today. ❤️

🚀 Why we built Mermail

Over the past year, we've seen AI agents become incredibly capable at reasoning.

But they still struggle with actually getting things done.

They can write code, answer questions, and plan workflows… yet they can't:
* Verify an email account
* Receive an OTP
* Subscribe to a SaaS tool
* Pay for a service
* Manage invoices or billing emails

We realized the missing piece wasn't another model.

It was infrastructure.

💡 Meet Mermail

Mermail gives AI agents what humans already rely on every day:

📧 A real email inbox
💳 A payment layer
🤖 An MCP server that lets agents interact with real-world services

Instead of building custom integrations for every website, developers can simply connect their AI agent to Mermail and let it operate through a trusted identity.

✨ What you can do today
✅ Give your AI agent its own email identity
✅ Receive verification emails & OTPs
✅ Connect with Claude, Codex, and other MCP-compatible agents
✅ Let agents subscribe to products and manage billing
✅ Handle invoices and payment notifications
✅ Prepare for the next generation of agentic commerce
We're also working on Agent Wallet, which will enable AI agents to manage payments even more autonomously.

⸻

Who is this for?
* AI builders
* MCP developers
* Agent frameworks
* AI startups
* Anyone building autonomous workflows that need real-world identity and payments

⸻

🎁 SPECIAL OFFER — Product Hunt Launch

To celebrate our launch on Product Hunt, get your first month of Mermail for just $1!

You’ll get:
⭐ Premium model access
⭐ Higher quota
⭐ Custom domain
⭐ Priority support

🚀 One month. Full power. Just $1.

This special offer is available for a limited time only. Don't miss it!

⸻

🙏 We'd love your feedback
* What’s the biggest blocker preventing your AI agent from completing real-world tasks?
* What integrations should we build next?
* If you're building AI agents today, would an email identity + payment layer simplify your workflow?
We'll be in the comments all day answering every question.

⸻

A huge shout-out to our incredible team and our early design partners who helped shape Mermail from day one. ❤️
Thank you again to the Product Hunt community for checking us out.
We’re excited to hear your thoughts and build the future of Agentic Commerce together! 🚀

Congrats! This may make agents very useful in every day tasks. How do you ensure the agent doesn't do something unintended due to misunderstanding users request? I mean stuff like spending too much money without approval?

This is a fantastic tool. Helped me a lot with my video editing.

@harshmanwani - I like the concept, but not being able to understand what the costs are before signing up is a huge worry to me. I'm not prepared to give out my email until I understand what this wil cost me long term.

Hey Product Hunt 👋 I'm Lily, co-founder of ResearchMaster AI.

We built this because we kept running into the same problem in our own work.

AI chat tools are great for quick answers, rewriting, and short summaries. But when the question affects a product decision, competitor analysis, market entry, investment memo, or feasibility study, a quick answer usually is not enough.

We still had to collect more sources, compare conflicting information, rebuild the structure, check which claims were actually supported, and then turn everything into a report or slides.

ResearchMaster AI is built for that workflow.

You can start with a product, company, URL, file, or a plain market question. The system turns it into a research plan, searches across professional sources, cross-checks key points where possible, and keeps the evidence attached to the findings.

The output is not just a chat response. It becomes a research workspace with reports, insights, Q&A, slides, and exports you can review or share with a team.

The main use cases we are focusing on are:

• Product and competitor research

• Industry and market research

• Investment and feasibility research

• Market entry and launch decisions

• Internal research that needs sources people can review later

We care about three things in particular:

1. Simple input — you shouldn't need to write a perfect prompt or already know the research framework.

2. A usable report — The report should be clear enough to edit, present, and share.

3. Checkable conclusions — Key claims should point back to sources, and uncertainty or source conflicts should not be hidden.

What I've learned building this is that answers are cheap now - every AI tool can give you one. What's still hard is turning that answer into something you'd actually hand to your team or your investor. That's the gap we're closing. 🎯 

Hunting this one because the "answer vs. deliverable" problem is something I run into constantly with clients - you can get a solid AI answer in seconds now, but turning it into something you'd actually hand to a founder or investor is still real work. Excited to see @yang_lily and team tackle that gap directly instead of just adding another chat wrapper. Wishing you all the success with ResearchMaster AI 🚀

@AutoHDMI - Auto Switch to HDMI  - Caught the pain point. This should have been easily a module in OS itself. I will try on my TCL TV.

@AutoHDMI - Auto Switch to HDMI @taponyourglass really interesting niche product here - I feel like I never even thought this was an issue - I think I will use this at my Grandmas house to make it easier for all the analog family that tend to be over there on a daily basis!

Congrats on the launch and I think the tool is really interesting!

Interesting, how nobody thought of that earlier. The UI screenshots makes it look convenient to use.