... brand tells people why you exist and your product proves you deserve their attention. In AI, trust gets the first click, product quality gets the second. :)) Comment from Sai Tharun Kakirala(@sai_tharun_kakirala): We went product-first with HelloAria and I think it was the right call for us - we needed real users to tell us what the product even was. But in hindsight, building even a small personal brand in the AI productivity space in the months ... ... will help a lot to your personal brand too if you are a known maker. Comment from Nika(@busmark_w_nika): @justinlee020617  that is build in public but I have seen how many people stopped doing it because

... reading Nika's thread here about free vs paid features. Really made me think. Link: https://www.producthunt.com/p/ge... ( shout-out to @busmark_w_nika ! ) She talks about giving generalized advice for free, but charging for specific, tailored help. That's a good framework. But most product owners figure this out after they build, not before. That's backwards. Your free tier should be designed before you write a single line of code. Because it's not just a marketing tool ... ... where exactly to draw that line. Comment from Sai Tharun Kakirala(@sai_tharun_kakirala): The classic tension. Give too little and people do not understand why they should pay; give too much and they never upgrade. For us with Hello

... thought we knew We assumed enterprise customers wanted the same things as our small business users, just more of it. More security. More control. More features. We never talked to them. We read blog posts. We looked at competitor pricing pages. We guessed. Here's what we missed: Procurement cycles. Enterprise deals take 6-12 months. We had 30-day sales cycles. We weren't built for that. Security reviews. We needed SOC2. We didn't have it. Customers asked ... ... that assumptions are incredibly expensive. Building for the customer you want instead of the customer you have can drain months of focus and budget. What really stands out is the contrast between 47 real requests for API limits vs

... months ago, we ran an experiment with our own data. At Rankfender, we tracked 5 of our own competitors across 8 AI systems. We log their share of voice, citation velocity, content gaps, platform variance. Months of raw numbers sitting in a dashboard. I pulled 6 months of data and fed it into Claude. One question: "Based on this, who is most likely to overtake us in the next 6 months? Show your work. Use the data. Don't summarize ... ... Give me the numbers." The answer changed how I think about competition. The dataset: 5 competitors

... caches, local backups, and app support files can accumulate so quietly. Reminds me of a similar problem we see with AI assistants invisible background processes (syncing, logging, model files) that users never know about until storage is gone. With HelloAria, our WhatsApp/Telegram AI day manager, we were very intentional about keeping the app footprint lean precisely because we knew users would be sensitive to unexplained resource usage. Good explainer post. The more people understand what is actually in that ... ... exactly the manual pain I wanted to eliminate with OptiClear. I love the parallel you drew with AI assistants. As background processes become more complex, storage transparency becomes even more vital. It s great to hear that you kept

... once and one that becomes part of your actual workflow. The concept of "approvals before execution" is underrated it keeps automation safe without removing the human in the loop. We ve been thinking a lot about that balance at HelloAria (our AI day manager, launching soon on PH) how much can an AI do autonomously

... actually good is the slow part, and that's still entirely a human job one im trying to fix. Comment from Sai Tharun Kakirala(@sai_tharun_kakirala): Great question, and honestly something I think about every day building HelloAria (our AI assistant that manages your day via WhatsApp and iOS). Right now, AI works for us - it handles reminders, task management, calendar syncing, all conversationally. The humans helping AI when stuck model is fascinating - suggests a hybrid where value ... ... replacing humans wholesale, it is humans doing micro-tasks without context or agency. The positive version is AI handling the cognitive overhead so humans can focus on creative, relational, irreplaceable work. That is the bet we are making with

... really important observation. Engagement metrics often optimize for stickiness over actual value they measure how often people come back, not whether the product is making their life better. We have been thinking about this a lot with HelloAria. It is an AI assistant that runs inside WhatsApp/Telegram and manages your day. The temptation is to track DAU, session length, message count. But the metric that actually matters is: did someone accomplish what they set out to do that ... ... hide behind. If the product promise is clarity, confidence, or better decisions, then measuring time spent can easily reward the wrong behavior. Comment from Mona Truong(@monatruong_murror): @sai_tharun_kakirala   Really appreciate this perspective, Sai and

... months figuring out how to track this properly and turn it into a service agencies can actually sell. Not some complicated AI audit. Just a simple monthly report that shows clients where they stand in AI search, how their competitors are doing, and what to do about it. Agencies adding this are charging between $200 and $500 extra per month per client for it. The conversation is easy because the data is new and clients have never seen it before ... ... malithmcrdev): @judit10  You are pointing at the real gap here. Most agencies are currently stuck in layer 1. They are starting to package AI visibility reports that show where a brand stands, who is being mentioned, and what competitors

... makes so much sense - the week is done, the discipline loosens, and the habit that felt automatic Mon-Thu suddenly feels optional. The Wednesday spike is more interesting to me - mid-week energy crash? I see something similar with HelloAria users (AI productivity assistant via WhatsApp). Engagement dips mid-week if there is no anchoring trigger like a meeting or deadline. The products that beat this seem to do two things: make the streak feel like a running ... ... rather than a daily checkbox, and build social accountability into the experience. Curious if you found that streak loss is more about forgetting

... most important and challenging experiences you ll have as a founder is fundraising. I pitched VCs across London and San Francisco. Some spent 15 seconds on my deck. Some took the call just to diligence a competitor. One told me to ask for more money. Oh, and I found out your pitch is probably being recorded and run through AI. This is the high-impact version of all of those lessons to save you time, energy, and emotional damage. Please ... ... eyes light up. Notice when someone leans in. Pay attention to which anecdotes connect universally, and which ones don t. Sometimes VCs see patterns you don t. They ve seen hundreds or thousands of pitches. They might notice a competitor

... Curious what the big unlock in the new version will be. Hoping for better AI integration and maybe smarter app suggestions based on time of day or current project. On the AI-in-workflow front, we have been building HelloAria (launching April 10th on PH) which manages your day entirely through WhatsApp/Telegram same core problem: your AI assistant should meet you in the tools you already use, not force you into a new app. Raycast does this brilliantly ...

... tharun_kakirala): Honest answer: I don t feel my job is at risk, but I m acutely aware that what the job looks like is changing fast. Building in the AI space, I see both sides. We re building HelloAria, an AI day-management assistant (WhatsApp/iOS), and our whole premise is that AI should handle the cognitive overhead of managing your schedule, reminders, and daily tasks so humans can focus on the things only humans can do relationships, creative ...

... underrated as a retention mechanism - they create a habit loop without requiring daily active use. The best ones feel like a personal performance review, not a marketing email. Congrats on shipping this! One thing I have noticed building HelloAria (AI productivity assistant via WhatsApp and iOS): users who get a weekly summary of what they accomplished - tasks completed, goals tracked, time saved - have significantly better 30-day retention than those who do not. The data narrative matters as much ...

... more competitive than people realize because the evaluators know the market deeply. Building in public, getting on lists like this, it is all part of making the startup journey legible to others. We have been seeing something similar with HelloAria as we approach our Product Hunt launch (we launched April 10th), the PH community recognition has been huge for building credibility. ~3k users and counting! Cheering you and @arthur_romanov ...

... think about that? Comment from Sai Tharun Kakirala(@sai_tharun_kakirala): Love this collab! Special leaderboard days are a great way to surface momentum around specific ecosystems and give smaller teams a fighting chance to stand out. We launched HelloAria on Product Hunt on April 10th an AI assistant that manages your day via WhatsApp/Telegram. The launch day energy here is honestly unmatched. The concentrated attention from a focused community can do more for early traction than weeks ...

... Most habit trackers treat privacy as a legal checkbox; you re making it a core architectural constraint. That s the kind of commitment that actually earns trust. What s interesting is the contrast with AI-assisted productivity tools. At HelloAria, our AI assistant that works over WhatsApp/Telegram, users share a lot of context with the AI to make it useful which creates the opposite tension. We ve had long internal conversations about what gets stored, what stays ephemeral ... ... zero-knowledge, which means you hold the key. The customers who choose us choose us partly because of that constraint, not despite it. The worry never fully goes away, but I try to reframe it because what's the

... Last month, I did something that felt slightly insane. I took our product description, fed it into ChatGPT, and asked it to build a competitor. Not a parody. A real competitor

... from Sai Tharun Kakirala(@sai_tharun_kakirala): 100% agree. Names change the relationship. They set expectations, invite projection, and create a kind of implicit contract about what the agent will and will not do. We named ours "Aria" for Hello

... time and executed beautifully on it. The knowledge infrastructure for AI framing makes total sense as a category. Docs are essentially the training data and grounding layer for any AI that has to understand your product. As someone building HelloAria (AI productivity assistant, pre-Series A), watching rounds like this is both inspiring and grounding. It is a reminder that the AI infrastructure layer is still being built out - and that well-designed, developer-loved tools have a massive ...

... shift from "tell the agent what to do" to "give the agent intent and let it plan" that's actually closer to how human teams work. We're navigating a version of this in a totally different domain with HelloAria, our AI personal assistant (WhatsApp/Telegram/iOS). Users don t give

... voices nail individual word pronunciation but miss the natural flow of how humans accelerate or slow down across a sentence based on meaning and emphasis. It ends up feeling like someone reading words rather than saying them. Building HelloAria (text-based AI assistant via WhatsApp and iOS), we deliberately stayed text-first partly for this reason - the text medium has more tolerance for AI-style communication than voice does. But the teams cracking the voice problem are doing something ...

... about it publicly. The part about sunk cost is so real - every founder I know has had at least one moment where they kept going not because the product was working but because stopping felt like admitting failure. Building HelloAria, we have had moments where signals were mixed and it would have been easy to rationalize continuing in the wrong direction. What saved us was focusing on actual retention signals rather than vanity metrics. Users who kept coming back ...

... subscriptions no longer cover usage on third-party tools like @OpenClaw . If you were running your agent with @Claude by Anthropic , curious what option you'd pick: would you pay the extra bundle? pick another provider? or use an alternative like @KiloClaw or @Zo Computer ? Comment from Dan(@dansaas): I'm using it via API call still, but just as my orchestrator. The decision from Anthropic finally pushed me to getting a DGX Spark and get more serious about local ... ... teams will rediscover that the model choice and the execution environment should be evaluated separately. Sometimes the cheapest model is not the cheapest workflow once you include retries, weak guardrails, or bad ergonomics. Would be interesting to hear which alternatives

... months, our most requested feature at Murror was a chat function. Users wanted to talk to the AI the way they talk to a friend. It seemed obvious. Every competitor had it. Every feedback form mentioned it. So we built it. And within two weeks, our core metrics started dropping. Session length went down. Return rate went down. The thing users said they wanted was actively making the product worse. Here is what we discovered when we dug into ... ... product creates. That is a much harder level of product thinking than feature voting. Curious if there was a specific metric that made the problem undeniable first. Comment from Luca Ardito(@luca_ardito): This also shows why copying a competitor