PH热榜 | 2026-06-30

Hey all! I'm Chris, Mobile Lead at Cursor, excited to hear what you think of our new app. We'll be around to answer questions throughout the day!

Voice input from the phone sounds useful, but I’d be nervous sending a messy spoken prompt straight to an agent. Does Cursor turn it into an editable brief first, or does the agent start from the raw voice request?

Can I switch seamlessly between an agent running in the cloud and one running on my local machine, or do they have separate workflows?

The mobile part I keep thinking about is the review step. Kicking off agents from a phone is easy, but merging on the go means approving diffs on a screen where you can't really read a diff, and every time we've moved approvals to a phone people start rubber-stamping. So the agent's summary of what it changed becomes the real gate, not the diff. Does the app surface anything risk-ranked, like this touched auth or deleted tests, or is review just scrolling the raw changes?

Love it! Really like the possibility to start working on my mac and continue working in the app.

one suggestion from me would be to add a quick activity timeline showing everything an agent has done while I was away. I think that would make it even easier to catch up before approving changes.

Great launch 👏 I’m curious how many developers will end up using the phone for quick reviews versus actually starting new coding tasks.

The always on cloud agents caught my attention . I'd love to hear how they're performing for people using them on real projects using them on real projects.

Congrats on the iOS launch! 🚀

The most useful part for me is not full coding on mobile, but starting agents, checking progress, and reviewing work while away from the laptop.

Curious how Cursor handles mobile review safety. Does it highlight risky changes like auth, payments, database migrations, or deleted tests before someone approves or merges from a small screen?

Finally I can ship in bathroom.

This is an interesting shift . Mobile won’t replace the desktop IDE , but being able to review code, manage agents , and keep projects moving on the go could be real productivity boost .

The idea of running coding agents from a phone is pretty interesting. How much of a project can you realistically manage from the iOS app before you need to switch back to a desktop?

Exciting, congrats on the launch! Did y'all discover any use cases for the mobile app during testing that you didn't envision before building?

This is a really helpful update! I used Cursor on my computer, and I really like how efficient it is; it saved a lot of time for me. The ios version can definitely help me to continue working when I step away from the computer. I can manually review it on my phone, but when I go back to desk, will it automatically pick up what I did on my phone, and synchronous message history?

I really like the direction here. Being able to kick off an agent from my phone instead of opening my laptop sounds super convenient for quick tasks.

I was pretty skeptical about leaving my VS Code setup, but since Cursor is a direct fork, it imported my entire environment in seconds. Zero downtime. It genuinely feels like working with an active engineering partner

Hello Chris, congratulations on the launch. I have been waiting for something like this because ideas usually come to me when I am away from my desk. Looking forward to trying it.

The phone surface makes sense if it is treated as a control plane, not a tiny replacement for the main coding environment. The useful moments are reviewing a diff, unblocking an agent, checking a dev server, or capturing context while away from the desk with repo permissions still obvious.

I love this idea of using cursor from iPhone. We can truly go mobile, and work can still continue on a system siting at office or home.

The mobile angle for coding agents is interesting, especially with Cursor already being associated with dev workflows. How much of the iOS experience is meant for actually editing code versus steering an agent through prompts, reviews, and approvals? I’d also be curious whether the app is optimized for existing projects or if it supports starting lightweight tasks from scratch while on the go.

the part i want to try is approving agent permissions from the phone. usually a Claude Code session blocks on a prompt while i'm away from the desk and i don't notice for an hour. does iOS push a notification when an agent needs approval or do you have to open the app to check

So great!

@cbrauchli A theme across the reviews seems to be that people want more visibility into which files and symbols the agent actually pulls into context on big codebases, but also a way to steer it. Any plans to expose and let users control context selection more directly ?

Artifacts as the review surface instead of the raw diff matches what we landed on too. The thing that decides whether they actually gate: are the screenshots and logs from an independent run, or the agent narrating its own work? We had demo clips that looked green because the agent captured the happy path it just wrote, and the regression sat one screen over. Tying review to CI output like you mentioned to Prashant is the version I'd trust from a phone.

Background agents are the interesting part. When iOS backgrounds Cursor, do cloud agents keep running independently and push a notification when done? Or does the conection drop and you lose the session?

Hey Product Hunt 👋

Today, I'm excited to share the launch of our new Skills Marketplace for AI-powered data analysis. It's the culmination of a decade-plus of work by the Databox team.

Nine years ago, I left HubSpot to become CEO of Databox, a tiny startup no one had heard of. I did it because they were tackling a problem I knew needed solving: making data-informed decisions easy for anyone, not just analysts.

Back then, just pulling the data together was the hard part. It was scattered across a dozen tools, and getting it into one place took real work before anyone could make sense of it.

We've largely solved that part.

The bottleneck moved downstream, to the analysis, and AI looked like the answer.

Last year, prospects started telling us they were using it for their data analysis. The output looked confident, but they kept finding the math wasn't always right. We weren't surprised once we saw how they were doing it: feeding the AI partial data by uploading CSVs, screen-grabbing charts from different tools, or wiring up one MCP server at a time.

The fix isn't to stop using AI. It's to connect the AI to something that aggregates your data, applies your real metric definitions, and runs the math, so what comes back is accurate, not just confident. That's what Databox does, whether you use Genie, our built-in AI analyst, or connect your own LLM through our MCP server.

We launched both a few months back, and already a quarter of our customers use it every week.

The more they used it, the clearer the next problem got. Running the analysis was no longer the hard part. Knowing which analysis to run was.

We realized we could speed up adoption by giving people proven skills instead of a blank chat box. So we built the Skills Marketplace.

It's a free library of pre-built AI analytics skills that connect to live data and return a finished report in under a minute. Each skill is a specific analytical workflow that already knows which metrics to pull, what to compare against, what to flag, and how to structure the output.

A few examples of what's in there:

• A weekly GA4 traffic report: sessions, channel breakdown, top pages, conversion rate, anomaly flags, all from live data

• A cross-channel paid ads summary across Google, Meta, and LinkedIn from one trigger

• Recurring workflows that deliver to Slack or email on a schedule, so no one has to remember to run them

We launched with 20+ skills and workflows. All free to download.

Take a look at what we built and let us know if it's helpful for you. 🙏

PS — What's the one report your team rebuilds by hand every week that you wish just ran itself? Drop it in the comments. We're prioritizing what to build next based on these replies.

PPS — A growing group of AI experts are building skills for our marketplace, getting exposure to our audience of 100Ks of people, including customers and partners. Want in? Reply below and we'll reach out with instructions.

The insight behind the Skills Marketplace didn't come from a product roadmap. It came from watching how people actually used Databox MCP after we launched it.

Teams connected their data sources, started asking questions in Claude, and got good answers. But then they hit a wall. They had to figure out which questions to ask, how to structure the analysis, what to compare against. The blank canvas problem. The AI was capable - but the analytical workflow still lived in someone's head.

That's what a skill solves. It's not a prompt. It's a complete analytical workflow - the questions, the structure, the context, the output format - packaged into a single file. The expertise is baked in. Someone who has never written an analytics report in their life can trigger a GA4 skill and get back something a senior analyst would be proud to send.

We built the first skills ourselves to prove the format. Then something interesting happened - people started asking if they could build their own and share them. That's when we knew the marketplace was the right move.

What excites me most about where this goes:

• Skills as a new content format. A blog post explains how to analyze paid ads. A skill just does it. The knowledge is executable, not just readable.

• The quality bar is structural. Every skill ships with a metric map, setup guide, and troubleshooting flow. The output is grounded in live data. It's not a prompt someone shared on Twitter - it's something that holds up against real numbers.

• It compounds. Every new skill added to the marketplace is another reason to connect another data source. More sources, richer context, better output. The whole system gets more useful over time.

We're launching with 20+ skills and workflows today. That number is going to grow fast.

The bigger vision: a world where every repeating analytical task a marketing or ops team runs has a skill that does it automatically - grounded in real data, structured for sharing, trusted enough to act on. We think that world is closer than most people expect.

Every team has a reporting workflow that takes longer than it should. Pull the data, format the numbers, write the summary, send it to the right people. Skills Marketplace replaces that cycle. Download a skill built by someone who knows the work, run it on your live Databox data, get the finished analysis in under a minute. For recurring reports, set up the n8n workflow and it runs on a schedule without anyone triggering it. The ROI is clearest on the reports your team produces every single week.

I tested the cross-channel paid ads skill across a few accounts with Google Ads, Facebook Ads, and LinkedIn Ads all connected. The output covered ROAS by platform, CTR trends, budget pacing, and anomaly flags - all in one pass, in under a minute. No manual data pull. No prompt engineering. Just the analysis a paid ads analyst would have produced, ready to send. For anyone doing this kind of reporting weekly across multiple clients, the time difference is not incremental. It's the entire task removed.

From a product perspective this feels like a pivot from dashboards to workflow. That’s a smart move because dashboards are becoming table stakes. The real value now is decision automation not visualization.

I want to give a proper shout out to the partners who have built skills. Their skills are really well written and extremely practical.

These partners have spent years (if not decades) doing this work by hand for themselves and clients. They've now encoded that experience and judgement into these skills for the rest of us to run.

It's extremely valuable and generous of them!

A few worth checking out...

Rick Kranz (AI Marketing Labs, https://ai-marketinglabs.com) has built a whole library of analytics skills. His Sales Pulse skill connects to your CRM through Databox, pulls 14+ pipeline, activity, and funnel metrics, compares two rolling four-week periods, and surfaces the 3-4 findings that actually matter, like deal concentration risk and coverage ratio, before it shows you a single chart. It's pipeline interpretation, not another dashboard. https://databox.com/skills-marketplace/skill/sales-pulse-dashboard

Manav Mehra (Wagman Digital, https://wagmandigital.com) spent 18 years in finance before building his Quick Financial Health Check. It pulls P&L, balance sheet, cash, and AR/AP through Databox and outputs a monthly financial dashboard for services firms: cash flow projection, collections aging, missing-invoice detection. If a number isn't there, it shows a visible data gap instead of inventing one. That discipline is the whole point. https://databox.com/skills-marketplace/skill/quick-financial-health-check

Vasundhara Gupta (Atidiv, https://atidiv.com) built Morning Marketing Summary for people who don't want to open dashboards at all. It reviews your marketing data every morning and emails or Slacks a plain-English briefing under 250 words: what happened, what matters, and the 2-4 moves to make. It learns your channels and targets on the first run and tailors every summary after that. https://databox.com/skills-marketplace/workflow/morning-marketing-summary

Keith Gutierrez (Modgility, https://modgility.com) built Content Performance MAX, which turns any URL into a ready-to-review SEO metadata rewrite in about two minutes. It uses GA4 and Search Console data in Databox to decide which pages are even worth optimizing first, then drafts new titles, H1s, and meta descriptions with a human approval gate before anything touches your CMS. https://databox.com/skills-marketplace/skill/content-performance-max-starter

Marc Woodland (Winbox, https://www.winbox.co) is a LinkedIn Ads specialist who co-hosts the LinkedIn Ads Insider podcast, and he built Monthly LinkedIn Ads Analytics for the thing every team struggles with: a great workflow one person builds shouldn't die on their laptop. His skill plugs the Databox MCP straight into Claude, pulls your LinkedIn Ads campaign data into the chat with no exports or spreadsheets, and produces a clean one-page monthly report benchmarked on reach, relevance, engagement, and pipeline. https://databox.com/skills-marketplace/skill/monthly-linkedin-ads-analytics

Laszlo Fazakas (Arcanian, https://arcanian.com) built Set Baseline to kill a trap I see constantly: treating last month as a baseline. For a seasonal business, December is not a fair reference for January. His skill pulls enough real history to see the seasonal pattern, then states the baseline as a seasonality-adjusted range with the math and a confidence level, so "did it work?" finally has something honest to compare against. https://databox.com/skills-marketplace/skill/set-baseline

Today's launch is a really important milestone. But, the marketplace is bound to get better every time someone with real expertise adds a skill.

So, if you've built something on the Databox MCP, or want to, reply here and we'll get you in

The biggest benefit of the Skills Marketplace is expert-grade analysis on demand. Anyone can run analysis at a senior analyst's level, surfacing insights they might not have known to look for otherwise.

It's easy to set up, too. Connect your sources, set the cadence, and your weekly paid ads report or SEO monitor runs on its own, pulling live data, running the analysis, and delivering the finished result to Slack or email. No one has to open a dashboard.

This is 100% where reporting is headed 🚀

One thing that stands out about Skills Marketplace is that the free download includes more than just the skill file. You get a setup guide PDF, a metric map showing exactly which data sources and metrics are used, and a troubleshooting flow. The first run is designed to produce a usable output. That setup investment - about 5 minutes per skill - pays off every time you run it after that, especially for recurring reports where the structure is the same every cycle.

The scenario I see most often: a team has good data in Databox, they've started using Claude for work, but the analytics outputs are still generic because the AI doesn't know how to frame the analysis for their channel. Skills Marketplace solves exactly that. Each skill brings the expert framing the AI was missing. Run it on your live data and the output is specific to your numbers and structured the way someone who does this work would structure it.

The editable skill format is underrated. You get a specialist's starting point - the metrics, comparisons, and output structure someone who does this work would use - and you can tune it to how your team actually reports. Most teams have specific definitions or cadences that differ from the default. Starting from expert framing and adapting it is a much faster path than building from a blank prompt. That flexibility is what makes the skills useful beyond the first run.

I spend more time deciding which report to build than actually reviewing it, so this approach caught my attention. Having analysis already structured around real metrics sounds like it could save a lot of back and forth.

Really useful idea. Do the skills handle schema changes in connected sources without breaking reports?

I have tried a few analytics AI tools before, but many still expect me to figure out the right prompts. I like that your approach starts with proven workflows instead. That feels like a more natural way for teams to adopt AI without a lot of trial and error.

The metric-map-fails-loudly default is the right call, but the case that bit us with shareable skill files was the quiet one: a source renames a field and quietly repoints it at slightly different semantics, so the map still resolves structurally and the run succeeds with subtly wrong numbers. Do skills pin a schema version or hash the source's field definitions, or is the metric map matched purely by name?

That sounds like a product I could definitely use. Can these skills work with other AI tools besides Claude, or is it Claude-only for now?

I'm still learning data analytics, so this looks interesting. Would you recommend these AI skills for beginners who are trying to understand their data, or are they better suited for experienced teams?

One thing that stands out is the setup experience. Each skill includes a metric map showing the required data sources and metrics, along with a setup guide and troubleshooting documentation. Getting from download to a first result typically takes less than 30 minutes.

For teams used to analytics integrations that can take days to configure, that's a noticeable difference. The goal is for the first run to produce something useful right away, and in practice the skills are built to make that happen.

The marketplace is probably the most interesting part of this. My first thought was maintenance. If a skill gets updated less often than the tools or metrics it depends on, how does a team know? Is there some way to flag outdated skills before they quietly start producing misleading reports? Congrats on the launch!

The "knowledge as an executable skill file instead of a blog post" framing really stuck with me, so I'm curious what stops the marketplace from filling up with thirty near identical GA4 skills once partners pile in, is there any curation layer or do the best ones just float up by usage?

This seems really interesting because I feel like most people don't struggle with getting an answer from AI they struggle with asking the right prompt. I'm curious what was the first report that almost every customer kept asking for. I always find those patterns interesting because they usually tell you what people actually care about, not just what they say they want.

This reminds me a bit of Zapier templates, except for analytics workflows. Different category, similar onboarding advantage.

Congrats on being #2 Product of the Day. It would be awesome if the marketplace eventually supported community ratings and reviews for each skill or does it support already?

Love it! How do you control the quality of the skills in the marketplace so that it doesn't become flooded with low-quality skills over time?

Congratulations on the launch. Does it handle messy real-world data where a company may have custom definitions?

The biggest issue I've had with AI reports is trusting the numbers, not generating the words. This seems to tackle the right problem. Congrats on shipping.

Really like that you're shipping these as skills rather than another dashboard I have to go learn. The "drop it in and ask" angle fits how people actually poke at their data.

Great tool. Definitely gonna try.

Hey Product Hunt — Ben here, founder of Lightning Rod Labs.

Frontier AI is powerful, but it is not built for forecasting. Frontier models are trained to produce plausible text, not well-calibrated probabilities about what will actually happen. They are also expensive to run inside agentic workflows, where bots may need to forecast thousands of markets, events, or decisions.

We trained Foresight to make better predictions at lower inference cost.

Foresight is an AI forecasting API with better accuracy at a lower inference cost. It is trained using our Future-as-Label method (Spotlight at the ICML 2026 AI Forecasting Workshop), which uses real-world outcomes over time for training. Instead of hand-labeling datasets or imitating generic text, Foresight learns from what actually happened.

Foresight beats frontier models 100x larger on live prediction benchmarks, like ProphetArena and ForecastBench, with a particularly large lead in prediction market categories like Sports & Politics.

Our API is OpenAI-compatible, so developers can easily swap it into existing workflows.

Better accuracy. Cheaper inference. OpenAI-compatible API.

Use code PHFORESIGHT for $50 free API credits this month.

We'd love feedback from builders working on forecasting agents, prediction tools, or any workflow where better forecasts matter.

can developers fine-tune forecasts for specific domains like fiance or healthcare?

Really interesting product. Do you see Foresight being used for cybersecurity risk prioritization... for example forecasting whether a vulnerability or exposed service is likely to be exploited within 30/60/90 days based on threat intel, EPSS/KEV, asset criticality, and exposure context? Curious what inputs improve calibration most, and how you handle high-consequence cases where a ‘low probability’ event still needs action.

Love that this is trained on real-world outcomes rather than just text patterns, making it a purpose-built forecasting layer that general LLMs simply cannot replicate.

Have you compared it against prediction markets directly, or only against LLMs?

The calibration angle is the part that actually matters, and the part most forecasting tools skip. When we plugged raw LLM probabilities into a decision loop, the point estimates were fine but the confidence was wildly off at the tails, so the expected-value math downstream was garbage. Two things I'd want to know: do you return a calibration band or just a point probability, and how does calibration hold up under regime shift, when the future stops resembling the outcomes you were scored on?

OpenAI-compatible is a smart wedge — I can point an existing client at it and test forecasting without rewriting my stack. The thing I'd check first: when the model is genuinely uncertain, does the API return a calibrated probability or confidence interval I can threshold on, or just a point prediction I have to trust blindly? For anything I'd wire into a real workflow, knowing when to ignore the forecast matters more than the forecast itself.

how often do you recalibrate the model as new outcomes become available?

Ensembling to get bands around the probabilities is exactly the part I'd reach for, since in a decision loop a miscalibrated tail costs you asymmetrically more than a wrong point estimate. The tension I keep hitting: ensembling N models fights the cheap-inference pitch the moment an agent is forecasting thousands of markets a run. Do you expose the band per-call in the API response, or is it a heavier mode you opt into when the stakes justify the extra passes?

The OpenAI-compatible interface is the right call. It means teams can drop this into existing agent pipelines without touching their orchestration layer. We've hit the same problem with general LLMs hallucinating probabilities. They'll say '70% chance' with no calibration behind it. How do you handle calibration drift as events resolve? Is accuracy validated continuously against a live benchmark, or is it a periodic evaluation cycle?

Congrats on the launch! 🚀 Training data is still one of the biggest bottlenecks in building reliable AI systems, so it's great to see a solution focused on turning real-world data into high-quality, traceable datasets instead of relying on manual labeling. The provenance and quality-scoring features really stand out. Excited to see how teams use this for domain-specific fine-tuning. Wishing you an amazing launch! 👏

One thing I'm curious about: if a lot of forecasting agents end up using the same underlying model, their predictions naturally become more correlated. That's fine for a single application, but it changes things in systems that rely on independent signals. Has that come up with customers using Foresight at scale? Congrats on the launch!

Interesting idea. is there a public demo where developers can try a few forecasts before integrating the API?

Really like this idea! Clean, practical, and solves a challenge that almost every AI team faces. Looking forward to seeing how this evolves—best of luck with the launch! 🚀

The OpenAI-compatible API shape seems like a smart choice here, because forecasting is often something an agent wants to call inside a larger workflow, not a separate dashboard.

One product detail I’d look for as a developer is whether the calibration stays visible after the API call: confidence interval, data/source freshness, and a short reason why the model thinks the probability changed. For agent workflows, the forecast is useful, but knowing when not to trust it may be even more useful.

Predicting anything is a bold promise, and I like that you're not boxing it into one niche like sports or markets. That open-ended framing is fun to play with.

Hey Product Hunters 👋

Akiflow is a task manager + calendars app that automatically imports your tasks and calendars and helps you manage your schedule.
I’m excited to launch our latest integration with Claude / ChatGTP / Cursor (via MCP).

Now you can manage your tasks and calendars (yes, with the Akiflow MCP you’re not limited to one calendar integration anymore 🌝).

Anytime you're chatting on Claude/GPT, you can ask them to add tasks/events into Akiflow, or to check your schedule. You can also now use LLMs to help you review your days or help you plan your next week! You can find more ideas on our website!

Since our last launch here on PH, we added a lot of new features including:
- Transcripts and Meeting Assistant
- Subtasks
- Siri Integrations
- Mobile Widgets
- Daily Dashboard
and much more..

It’s fantastic!! Congrats on the launch!

MCP looks simply amazing.

I’m starting to use it to manage my calendars via LLM, and it’s incredible. ❤️

Akiflow has already changed my life since I started using it, and this is another step in continuing to do so.

🛠️ Very excited to bring this launch, especially our new Model Context Protocol (MCP) integration. MCP allows AI models (like Claude) to directly read, create, and manage your Akiflow tasks and events. It bridges the gap between AI reasoning and daily work. Would love to hear your feedback on it.

I already spend a lot of time planning launches, tasks, meetings, and random founder admin across too many places, so being able to tell Claude or ChatGPT to add something directly into my planner feels like a real workflow improvement, not just another AI feature. The most useful part for me would probably be weekly planning. I often have the context in a chat already, but then still need to manually turn it into tasks and calendar blocks somewhere else. Curious how much Akiflow lets the AI change without confirmation. can it only suggest a plan, or can it actually move tasks/events around once connected?

This looks like the kind of tool that can genuinely save time if it fits into your daily workflow. Congrats on the launch, and best of luck with the release! 🚀

Congratulations @sid_akiflow @tadej_jevsevar excited to try this, was curious about one thing, when I complete or reschedule a task inside Akiflow, does it cleanly push back to the source tool (Asana, Linear, Notion, etc.), or is the sync mostly one-way?

Building an MCP integration for this is incredible. As someone who spends all day in Cursor and Claude, being able to pull up my schedule, manage tasks, and block out time without having to constantly shift focus back and forth from my browser is a massive win. Congrats on the launch, Nunzio and team!

With AKiflow i thikn from day 1 love the product love the team!

Already using Akiflow with Claude and it's a game changer for planning. Being able to manage tasks and schedule directly from the AI assistant I'm already working in removes so much friction!!

The best task manager and calendars app, and an excellent Support Team + Slack Community! I have been an Akiflow user and supporter for many years and will continue to be for many more to come! Well done, Akiflow team and @nunziomartinello !

Akiflow has been my planner/task manager for over a year, and I can't be more happy with it. Being able to integrate with calendars, the slot management, weekly planning, and many more, just feel right. Great job, @nunziomartinello and team.

Great launch! Am I correct to assume the MCP-first call is the most important move? I guess most teams would've bolted a chat box into the app and called it AI. Routing the actions out to Claude or Cursor means you skip the "which assistant wins" fight entirely and just live wherever the user already is I guess

I love akiflow. Super excited to test out the new features especially the MCP 😍

Love seeing Akiflow add MCP support!!! I use Akiflow every single day, and it has become a key part of my workflows. This makes it even better, I'm so existed to integrate it even more into my routines with Claude! Congrats on the launch, can’t wait to put it to use!

Does it sync both ways with tools like Chat GPT and Claude, or is it mainly importing tasks into Akiflow?

How does Akiflow’s MCP integration handle scheduling conflicts, permission checks, and two-way calendar sync when users ask AI assistants to create or update tasks and events?

I can tell that Akiflow is, by far, the best calendar, planner, and task manager I've ever used. It has so many options to personalize your own flow that you will be delighted every time you use it. I love the Pomodoro option that allows you to track the actual task with the timer always on display. It really helps me to focus on what I'm doing at that moment.

Also, to set a specific task as my main task for the day or the week is incredible. I can't wait to see how AI will potentiate all its strengths. The only thing that is missing is a monthly review, because I always use the weekly review. Sometimes I go further and I like to do a monthly, a quarterly, and an annual review, but maybe this will be solved with the AI integration.

Congrats for everything, and thanks for creating such a great app. 💪🏼

The evolution this product has had is amazing! I can usually use Trello as a one stop shop for just about everything, but Akiflow gives me all the control I need and integrates with Trello so i have the best of all the worlds. It's one of the few productivity tools that I've used for years and actually stick with because it is easy to use, always adding more useful features, and it saves me time and keeps me sane. I love being able to quickly capture what i need to work on and giving some structure to view my days across my meetings and events and tasks. They just launched their MCP and im excited to do even more with it!

Love that you went MCP-first instead of bolting a chatbot into the app, but I'm curious about the time-block sync edge case, if I have calendar locking off and Claude creates a block through MCP, does Akiflow still treat that as the source of truth even though Google Calendar never sees it?

One thing in mind is that I think I would be watching closely what people would do after they start using this. Are they opening ChatGPT less because everything lives in Akiflow now, or are they just using it as another shortcut? I feel like the answer to that says a lot about whether this becomes part of someone's daily routine or just a nice feature.

The detail about putting extra checks on deletes and adding guests I think can be quite useful and alleviates concerns about important stuff being deleted accidentally. Are these guardrails fixed, or can a user set their own permission rules (e.g. "AI can reschedule freely but never touch anything with external attendees")? And is there an easy way to see/undo what the AI changed after the fact?

the MCP approach to task management makes a lot more sense than building another standalone AI feature. if i'm already in claude working through a problem, being able to say "block 2 hours for this tomorrow" without leaving the conversation removes a real friction point. curious how it handles conflicts though. if i ask claude to schedule something and there's already a meeting in that slot, does it suggest alternatives or just flag the overlap?

I really struggled with this app, mostly around pricing and the lack of API/MCP and had ultimately given up on it a few months back. In this environment, the pricing will need to come down, too many great alternatives.

its paid or free

I have tested Akiflow MCP and loved it. Combining with their desktop and mobile apps which are awesome, Akiflow MCP made planning my life better :)

Great to see the MCP integration. Congrats on the launch! Btw, what happens if the same task exists across multiple apps or gets updated in more than one place?

Curious to see how you balance structure with not making people over-plan their day.

Hey hunters 👋 I'm Rohan and with my co-founder Anton we're the makers behind Supafax - the first assistant you don't have to prompt.

I built Supafax for my girlfriend. For the past three years I’ve seen her slog through email and calendar admin she hates before she can focus on the parts of her job she loves. All the tools she’s tried have taken up more of her time, rather than freeing it up. So I built something for her :)

She works in a sensitive industry so she pretty much had three non-negotiables:

1. We never store her emails - nothing leaves Outlook or Gmail

2. There's no app to download or new email client to use

3. There's no new UX to learn - just email

She essentially wanted an EA, something that just does the admin for her. That's it. And that's Supafax! It handles her email and calendar admin on its own, and only pings her - by email - when it genuinely needs her input. No prompting, no dashboard, no new app to learn.

If you give it a shot today feel free to let loose with constructive feedback, feature requests, bug reports, or anything else! I really hope this tool helps you become more productive in your day to day work life ❤️

Solid one! From my POV, the "no app, nothing leaves Gmail, no new UX to learn" framing gives it a product spine most of usual tools skip. One thing I wonder..."only pings you when it genuinely needs input" is the whole game for anything autonomous, how do you guys draw it, rules per action type?

the "no new app to learn" part is underrated. most email AI tools ask you to switch your whole workflow to their interface and that's where adoption dies. keeping everything inside gmail/outlook and only pinging by email when it needs input is a much smarter UX bet. curious how the draft quality evolves over time though. does it mostly match tone and length from your past emails or do you need to manually correct it for a while before it gets your voice right?

I love my Gmail, but I hate my email. Currently i already have an elaborate labelling setup. What is your recommendation for migrating? Do I switch to a Supafax first setup or does it adapt to my existing setup?

Really excited to try it out!

The 'don't have to prompt' framing is the interesting bet — most inbox AIs still make me babysit every draft. Where does the learning actually live: is the model personalized per user over time, or is it just retrieval over my recent mail stuffed into context each run? And does it act on a full send/modify OAuth scope, or stay read-only until I approve a draft — I'd want to know exactly what it can send on my behalf before it earns that trust.

loved that you built it for your girlfriend. Now it becomes a cool tool. Congrats on the launch!

Very cool and innovative product! And I love that I don't need to download yet another custom client to use it.

Are you planning on supporting other email providers in future? Or supporting IMAP / SMTP connections so that it would work with anything?

This is amazing. I love the ZDR policy because my biggest worry with other email tools is that it reads and stores something confidential. How do you make sure it doesn't mark an important email as cold inbound spam?

The approval step before sending feels like the right safety net. What I'm curious about is what happens after a mistake slips through anyway. Say an email goes out with the wrong tone or misses some context. Does Supafax learn from that automatically, or does the user have to explicitly correct it? Congrats on the launch!

love this! congrats on the launch!

Love the promise of this and will certainly give it a shot - intrigued what you think you've cracked that all the others before (Superhuman, Fyxer...) have failed to. And I do consider they have failed!

Once Supafax starts drafting replies on your behalf, is there a confidence threshold where it holds back and just flags the email for you instead of drafting, or does it always attempt a draft even early on when it barely knows your voice yet?

This looks awesome, @mahendrakerr and Anton! Excited to give it a try and see how it handles my emails over the coming month

Does it learn tone per-contact? I write very differently to my team vs investors, and most tools flatten that into one voice

Really cool !

Keeping everything in email instead of adding another dashboard is a nice touch. Congrats on the launch!

Reading email is probably the most annoying thing on earth and yet the way we do it didn't change since 20 years. I feel like it should have already changed with AI and this seems to be a solid step forward. I'll give it a try.

does it draft differently for different people I’m writing to?

Curious how Supafax handles the “learns how you work” part in practice. Since it’s email-native, is the learning mostly based on past email patterns, explicit instructions from the user, or ongoing corrections over time? I’d also be interested in whether teams can keep personal working styles separate from shared company workflows.

Big congrats on your launch guys! Loved the simple set up and onboarding, especially the "What Supafax already knows about you" section and the routines. Excited to feed back as I do more with it.

Congrats on the launch!! Product looks slick. I love the idea of loops for my email 😎

Awesome app! 💥does it work with outlook too?

This is gonna make sorting through my emails so much easier! Do you also flag and unsubscribe from marketing emails that I've never opted in to begin with?

Seems like this has all the features you want Superhuman to have. Love it

This is so useful!! Do I always have to cc Supafax to schedule a meeting with someone who I meet with often?

Super smart approach, congrats on shipping @mahendrakerr 👏 the zero-storage policy is a huge win, qq does it scan the entire archive or only process new incoming emails?

Having this inside Gmail is great! For follow-ups, do you plan to have integration with notetakers like Granola?

This is so rad!! Does it work with microsoft outlook as well?

@v0 by Vercel Design Systems 2.0 is here.

Import your design system from @GitHub , @Figma, and more. Build with your real components, colors, fonts, and patterns.

Get started: v0.app

Love that this leans on your own components, colors, and fonts instead of spitting out generic AI layouts. Keeping the design system consistent has always been the painful part when you move fast, so this feels genuinely useful for founders shipping their own look.

Importing from GitHub, npm and Storybook is the right set of sources. The question I'd want answered is how strictly it binds to the typed component API after import. When we fed our own component library into a codegen agent, the failures weren't wrong-component picks, they were invented prop names that looked plausible (variant="subtle" when only "soft" shipped) and reaching for deprecated variants Storybook still had stories for. Does 2.0 treat the imported system as a hard schema, or as reference the model can drift from?

Hey Product Hunt 👋 I'm Jerry, founder of Dayflow.

Here's a bitter lesson that everyone learns first-hand: the person who gets promoted isn't the one who did the best work. It's the one who can remember it and come with receipts.

You know the feeling. Your manager asks "what did you ship this quarter," and your mind goes blank. You spent three months heads-down, debugging the gnarly thing, unblocking teammates, rewriting the pipeline that was about to fall over, and now you're squinting at a git log trying to reverse-engineer your own life. The work was real. The memory of it just evaporated.

So the people who are great at narrating their work get ahead, and the people heads-down doing it get overlooked. That always felt backwards.

So we built Dayflow.

Dayflow runs quietly on your Mac, and uses your screen data + AI to turn your day into a clean timeline of what you actually worked on. No timers, no tagging. You just work, and at the end of the day there's an honest record of it.

Come review season, your brag doc is already written. When you wonder where the week went, there's an answer instead of a shrug.

🔒 Local-first and open source. The recording stays on your machine, and the code's on GitHub.
🧠 A witness, not a manager. It's yours, built to help you see your own work, not score you.
⚡ Zero effort. No categorizing, no Pomodoro guilt. It just runs.

🎁 For Product Hunt: a month of Dayflow Pro free. Or use Dayflow for free forever by bringing your own AI, whether that's a local model or plugging in your ChatGPT/Claude subscription.

If you want to see what the product looks like in action, check out this demo: https://app.arcade.software/share/GuqMkbA2GXfFBY62YGfu

I'll be around all day. I'd love to know: when someone asks what you got done this week, do you actually have an answer, or do you go blank too?

This one I’m definitely going to try. :))

I’m working on our launch right now, and a lot of the work does not always look like a clean task list at the end of the day. Sometimes it’s fixing small product details, writing copy, replying to people, checking feedback, making decisions, or solving random problems that disappear from memory two days later.

The “brag doc is already written” angle is strong. even outside promotions, I think founders and small teams also need this just to understand where the week actually went :) Local-first and open source also matters a lot here. if an app is watching my workday, I’d want exactly this kind of trust model. Curious how Dayflow separates meaningful work from noise. does it learn from what I keep/edit in the daily summary over time?

Neat idea keeping it free with a "bring your own AI model"

Cool! But is it safe to install this on my work laptop?

the screen data approach is what i'd want for passive journaling, no manual tagging. curious about the data model though - does it store actual screen captures locally or just extracted text/events? open source helps with trust but i'd want to know what actually persists on disk before installing something that watches my screen all day

The brag-doc pain is painfully real, "what did you ship this quarter" and the mind goes blank is a universal developer experience :)

My question is about the boundary, because Dayflow doesn't see your work, it sees your screen. And my screen on a workday also has the 2am guitar tab I hunted down, a medical search, personal DMs, the apartment listings I shouldn't have been browsing at 3pm. For a brag doc, none of that should ever surface. So how does the separation actually work, an app/domain allowlist I define up front, AI classification of "work vs personal", a manual scrubbing pass? The honest record is only an asset if I trust it won't quietly file my private life into my performance review.

But here's the twist I can't stop thinking about: that same personal stream is a latent memory index. "When did I find that guitar tab again?" is exactly the kind of question I'd kill to answer, and Dayflow technically saw the moment. I build in the private-memories space myself, so I'm genuinely curious, is the timeline queryable by activity type, or grouped beyond the workday view? Because the thing that's a privacy liability for the brag doc (it captures everything) is the exact thing that could make it a personal recall tool. Two products hiding in one capture stream.

Local-first + MIT is the right foundation for either. Congrats on the launch! :)

The screen-capture approach to auto-journaling is smart — most brag-doc tools fail because the manual logging burden kills the habit. Curious how Dayflow handles sensitive content on screen (passwords, private chats) when it's continuously capturing?

The no-timer workflow is what makes this stand out. Work often happens in small scattered moments, so having Dayflow turn that into a clean record for standups and reviews feels practical.

A lot of the discussion is about whether the timeline is accurate. The part I'm more curious about is what happens when it actually gets used. Does the summary go straight into a review or brag doc, or is there always a review/edit step first? That handoff feels just as important as the capture itself. Congrats on the launch!

The brag doc angle is clever, but the thing I keep thinking about is how it tells real focus from a tab you left open, like if I'm heads down in my editor for two hours with a Jira ticket parked in the background, does Dayflow credit the editor work or the ticket that was just sitting there?

The evaporated-memory problem is real, but the part I'd want pinned down is how Dayflow captures the work without me logging anything by hand. Does it passively ingest from sources like git, Slack, or calendar, and since it's open source, do those integrations run locally/self-hosted or does the raw activity get sent to a server? And where does the accumulated work history actually live — a local DB I own, or your cloud?

It would be better to add an X handle field to the website.

Local-first plus run-any-model is the right call for screen data. One thing I'd push on from doing vision-over-screenshots myself: a frame tells you what's visible, not what you actually worked on. Our summaries would confidently claim 'worked on JIRA-1234 for two hours' when the ticket was just a tab left open while the person was heads-down in the editor. Does Dayflow weight by real keyboard or window-focus signal, or infer effort from frame content? That's the line between a believable brag doc and one your manager flags as inflated.

the framing is spot on. the people who get promoted are the ones who can articulate what they did, not necessarily the ones who did the most. i've lost track of genuinely important work just because i didn't write it down in the moment. the local-first approach matters a lot here too because screen data is about as personal as it gets. does the AI summarize at the end of the day automatically or do you review a timeline and pick what's worth keeping?

The tagline says Dayflow helps you get promoted, which is a very specific promise for an open source tool. Is the main workflow more about capturing accomplishments over time, turning work into status updates, or guiding people on what to focus on next? The AI Agents / AI Dictation Apps hints make me wonder how much is automated versus manually curated.

Remembering what you shipped months later is a real pain. The screen capture side is the only thing I’d be careful with: can Dayflow auto-exclude certain apps or websites before they’re analyzed, or is privacy mostly handled by pausing and deleting entries after?

The notch is a great spot for this, I usually have three or four Claude Code sessions going and lose track of which one is blocked waiting on me. Does it show which repo each session belongs to when you have a bunch running? congrats on shipping

Hey Product Hunt 👋 I'm Bren, the solo dev behind AgentPeek.

I basically live in Claude Code and Codex now. The problem: too much time spent babysitting them. Manually checking tabs, usage, servers, whether anything's actually finished, all while I'm just trying to watch some YouTube.

IT DOESN'T HAVE TO BE LIKE THIS!

So I built AgentPeek. It puts every agent's live status right in your MacBook notch. One glance tells you what's running, what needs you, and what just finished. Lovely!

The part I'm most excited about is how little it asks of you:

- Live status in the notch. Running, waiting on you, or done, at a glance.
- Approve or deny permission prompts straight from the notch, without leaving the app you're in.
- Watch each tool call as it happens (reads, edits, commands), each with its own glyph.
- Zero setup. It reads the hooks Claude Code and Codex already fire, so you just open it and it works.

And that's just the start. There are dozens of small quality-of-life touches baked in too, the kind of thing I added because it bugged me as a dev and now I can't work without them.

I built this for my own workflow first, so I'd genuinely love your feedback.

QUESTION: what do you need out of this app?

I'm shipping constantly and focused on practical, time-saving stuff, so tell me what would actually help.

Thanks for checking it out 🙏

p.s. I post what I'm building on X at @brenhubr if you want to follow along and connect.

cool concept. i run a few Claude Code sessions in parallel and lose track fast. does it total up usage across all of them or just show each session separately? a combined view would be the killer feature for me

The notch placement is smart — babysitting Claude Code/Codex runs is exactly the dead time I want back without alt-tabbing to a terminal. How does AgentPeek actually read session state: is it tailing the local CLI logs/JSONL transcripts on disk, or hooking the process, and does any of that session content ever leave the Mac? For a tool watching my coding-agent output, fully-local with nothing phoned home is the dealbreaker either way.

Using the permission hook as the signal is the right call, idle-time heuristics would've flagged half my long tool calls as stuck. The one state that still looks identical from the notch is a tool call genuinely running for twelve minutes versus one that's quietly wedged, since neither trips the approval hook. Do you surface elapsed-time-in-current-tool, or just the blocked vs running split?

I didn't know what mac notch is, and now i downloaded it. any plans to make it hermes compatible? I know session mngment is less of an issue there but some of us have more than two harnesses.

Congrats on the launch. Trying it out right now!

Also very cool landing.

Bren, the notch sits empty most of the day, so quietly putting something useful up there seems very clever. I like that everything it watches stays on my own computer ! Thanks

running 3-4 Claude Code sessions at once is where this starts making real sense. the thing i keep bumping into though - does it show aggregate token spend across all sessions? each session looks fine individually but cumulative burn across 4 parallel agents adds up way faster than expected. a total daily spend view would make the budget awareness actually useful

The local-only session view is the important part here. Once coding agents touch real repos, the hard problem becomes knowing what is running, what is blocked on a human, and what can safely be approved without turning the whole workflow into a terminal babysitting loop.

Pulling sessions into the notch solves the context switching problem that comes with running multiple agents in parallel. The local-only data model is the right call for codebases you can't risk leaking. We've felt the friction of constantly alt-tabbing to check which agent is waiting on approval. How do you hook into Claude Code sessions: process output, local socket, or something else?

The status I actually want at a glance is whether a session is blocked on me or just slow, because a long tool call and a session sitting on a permission prompt look identical from the outside. Running several agents at once, the time I lose isn't checking usage, it's tabbing into a session to find it's been waiting ten minutes for a y/n. If the notch can flag the blocked-on-human state distinct from busy, that alone earns the space. Does it read the actual pending prompt, or infer from idle time?

approving permission prompts from the notch without switching windows is the real feature here. that context switch of jumping to a terminal just to hit y and then going back to what you were doing adds up fast when you're running multiple sessions. does it support any kind of auto-approve rules for low risk operations or is every prompt manual? would love a way to say "always allow file reads in this repo" without having to approve each one individually.

Congrats on AgentPeek — the notch/menu bar angle for Claude Code is really clever.

Saw two r/ClaudeCode threads today that match what you're building:

1) User on 5x sub sees usage around 40%, then gets locked at 100% with almost no warning — classic "usage meter you can't trust" pain.

2) Desktop SSH dev works fine, but UI stuck on "Reconnecting to root@..." — people think the session is broken when it isn't.

Worth a glance if you're looking for early users venting about session/usage visibility. Happy to share the links if useful.

Hey makers!

Here’s what Claude & other browser agents got wrong:
They use software like humans.

They wait for pages.
They stare at screenshots.
They click buttons.
They get stuck in dropdowns.

You’ve probably seen this - impressive for a demo, painful when you actually need work done.

So we built Pluno.

Pluno skips the UI entirely and talks directly to the APIs underneath your web apps. You tell it the outcome, and it gets the task done in the background.

No clicks.
No dropdowns.
No babysitting.

Just: Done ✅

You can use Pluno as a browser extension to update records, extract data, fill forms, run bulk actions, create dashboards, and handle annoying SaaS tasks that normally eat up your day.

We benchmarked Pluno across 312 real-world tasks in 24 tools (incl. HubSpot, Notion, Stripe, Linear), and the results speak for themselves: 34% higher success rate than Claude’s browser extension, at ~14x the speed.

Why we’ve built it? Because we loved the idea of Claude’s browser extension, but in reality it was way too slow to be useful.

For founders, operators, and tiny teams doing the work of ten people:
This one is for you 💜

What I’d love to know: Which browser task would you use to test Pluno’s limits?

P.S. Get 50$ in free credits, today only.

Korbi here - Technical Co-Founder of Pluno.

Here's why we've built Pluno, and what browser agents got wrong:
Imagine an AI voice agent talking to another voice AI to schedule a barber appointment.

Instead of a 3 second interaction of checking which appointments are available and picking one, they'll converse back and forth for 10 minutes.

Same is happening for browser agents using the browser.

They download the UI, make screenshots to see what's visible, and try to reason about clicking the right button.

Instead, they should do what agents do best: Use code, and skip the UI.

Pluno learns the API for each tool you use it on, and can talk directly to the API. It's like having a perfectly designed MCP server for every web app you use.

Try it on the hardest task you can imagine, and let me know if you find one Pluno can't handle - I'll make sure to hook you up with some extra credits in exchange ;)

Is Pluno mainly focused on faster prompting and responses, or does the workflow automation / AI agents side mean it can also take actions across tools?

Been using it internally a lot for creating dashboards on @PostHog, debugging issues with my queries on @Retool, drafting @LinkedIn comments, and exporting data from @leadfeederapp.

What's the most complex task in browser apps you're still doing manually today?

Very cool, Claude has been annoyingly slow on browser tasks, cant wait to fully test pluno!

Wow I'm amazed - this is a legitimately helpful Clippy! Finally something to help me get through the endless nonsense of random burocratic websites to find that one form I need to fill out. Also great for finding a nice holiday spot without having to manually sift through all the options and bookmarking a well curated list. Seems to work like a breeze for quick web deving tasks and checks, too. Overall, very impressive!

Very cool approach to solving computer use, without the huge latency and quality penalty you get from using screenshots and clicking on buttons!

Skipping the UI and going straight to the API feels like the real shift here - not just a speed improvement, but a completely different way of getting work done.

Curious how you handle actions that aren’t easily reversible though. Is there some kind of confirmation or preview layer, or does it just execute once the intent is clear?

Either way, this is a really interesting direction - congrats on the launch!

Just installed the chrome extension and I am already a huge fan! Handled an error-case on a website much faster with pluno instead of taking screenshots and uploading them to Claude. Talking directly to the API makes much more sense. Thanks team pluno for the launch and the increase of efficiency you bring with it

For browser agents, speed only matters if the failure state is inspectable. The handoff should leave behind what it clicked, what it inferred, where it got stuck, and what still needs human approval; otherwise a fast agent just creates faster uncertainty.

huge fan of the browser plugin. i have a 2nd chrome instance always running where it's doing all my chores. surprised at how good it is!

That makes sense for apps where the request-building lives in readable bundle code. Where I'd expect it to get hairy is when the signing isn't static, like a token minted at call-time inside a web worker or a wasm blob, so you can't lift the recipe once and replay it. Do you keep the page's own JS context alive per session to mint each call, or pull the logic out ahead of time?

The talk-to-the-API-directly bet is the right one for speed, but the wall we keep hitting is that a lot of modern web apps don't expose a callable API in any stable sense. The one I automate safelists its persisted GraphQL queries and 500s anything that isn't on the list, so we're forced back to driving real DOM clicks. How does Pluno handle apps where the underlying calls are signed per-session or the operation set is locked to a safelist?

The "skip the UI, talk directly to the APIs underneath, reuse my logged-in session" approach is what makes the speed claim believable — screenshot-clicking was always the bottleneck. Where does that execution actually happen: does the agent run locally in the extension, or does my authenticated session/token get relayed to Pluno's backend to make those API calls? And how does it map each app's private/internal API — reverse-engineered per tool, or something more general that adapts on first run?

Awesome idea excited to try it out and use my own benchmarks. I've found large tasks eat up token limits on Claude relatively quickly

Hi everyone!

Sure, the MEG scanner is still this massive non-portable machine. But the jump in performance is pretty big. Brain2Qwerty v2 gets to 61% word accuracy on average, and 78% for the best participant — where more than half the sentences only had one word wrong or less.

They basically went end-to-end from raw brain signals instead of using the usual hand-crafted pipelines, and it shows.

Meta also released the full training code for v1 & v2, and the v1 dataset. The scaling laws look promising!

Mind dictation soon?

Decoding full sentences from non-invasive signals is wild, and I really like that you went the non-invasive route instead of chasing implants. Feels way more realistic for actual people to ever use. Curious who you picture reaching for this first, like folks who can't type easily, or a broader everyday crowd? What's the first real use case you're most excited to see people try?

Do you mean to tell me that now I can prompt Claude to write my emails while remaining completely still?

This feels like a major leap forward for brain computer interfaces. If accuracy keepsimproving, text input from thought could change accessibility tools completely.

Hey Product Hunt,

I built Tinkerfont because I got tired of guessing what fonts would look like before changing code or design.

So I made something that lets you open a website, swap fonts instantly, and see the results in context. You can scroll around, test different sections, and even save screenshots before deciding if a change is worth making.

A few things worth trying:

- Right-click any text to see what font the page is actually using.
- Open the panel, pick an element, search Bunny Fonts, and apply one with a click.
- Use Pick Area if you only want to change the nav, a hero section, or another specific part of the page.
- Your changes stick when you come back, so you don't have to start over every time.

The font inspector was inspired by Fontanello. I already had the font swapping working, but I really liked how Fontanello exposed font information, so I built something similar into Tinkerfont.

It's totally free on Chrome and Firefox. If you give it a try, I'd really love to hear what you think. Whether something feels missing, confusing, or completely breaks on a site you use, that feedback is incredibly helpful.

Thanks for checking it out.

The "changes stick when you come back" persistence is the part I'd lean on most — swapping a nav or hero font, then re-checking it days later without redoing setup. Where do those saved overrides actually live: local storage keyed per-domain, or synced through an account somewhere? And since Bunny fonts get inlined as data URLs, does the extension read or send any of the page content it touches, or is everything computed in-page?

The live-on-any-site angle is the part I'd actually use, testing type before committing it in the real CSS. Does it let me save a set of font plus size/spacing tweaks to reapply on the next visit, or is each session one-off? And can I export the final CSS to drop straight into the site?

Nice idea. Can the font changes be exported as CSS once you've found the combination you like? Congrats on the launch!

Browser extensions that inject styles into live pages tend to break on sites with strict CSP headers or shadow DOM components. How much of the modern web does this actually work on cleanly, or are there entire categories of sites, like ones built with web components, where this just won't apply fonts properly?

Hey Product Hunt! 👋

I'm Anton, one of the founders of @Load Nova

The more we talked to dispatchers, the more we kept seeing the same thing: dozens of browser tabs, endless copy-pasting, and constant context switching.

Freight moves fast. And in this industry, speed isn't just convenience - it's money. Every minute spent looking for information is a minute not spent making decisions, helping drivers, or booking the next load.

We honestly believe dispatchers deserve better tools. In 2026, you shouldn't have to jump between 15–20 tabs just to understand what's happening with your fleet. The right information should be in one place, ready when you need it.

That's why we built our product.

Not to replace dispatchers but to help you work faster, make better decisions, reduce mistakes, and keep all the context in one workspace.

We're still building, still learning, and we're looking for early users who want to shape the product with us. If you're a dispatcher, carrier, or just curious about where AI in freight is going, we'd love to hear your thoughts.

Thanks for checking us out. We'll be here all day answering questions and collecting every piece of feedback. 🚚

Small note. Dispatching is moving from tabs to AI workspace 😁

The "15-20 tabs" pain point is so real for dispatch teams — having RPM, profit, and live-weather routing in one side panel next to the load board is a smart way to cut the context switching. I'm curious how the route planning handles multi-stop loads where HOS limits force a reset mid-route. Does it factor available driving hours into the suggested plan, or is that still on the dispatcher to layer in?

The broker-email parsing is the piece I'd dig into. Those emails are all over the place: plain text, forwarded chains with the real load buried three replies down, rate cons as PDF attachments. When we built an email-to-structured extractor, the accuracy cliff was always the quoted chains, where the model grabbed a stale rate from an earlier message in the thread. How does Load Nova decide which number in a back-and-forth is the current one, and does it read PDF rate cons or only the body?

Treating the thread as stateful instead of one blob is the right model, that's the thing most extractors get wrong. The case that broke ours was recency not always meaning truth: a broker would forward the original rate con after sending a correction, so the newest message in the thread carried the stale number. Do you weight by message timestamp, or does the model reason about which message is actually the authoritative update?

@anton_mironov this is the kind of thing I wish more people built for unglamorous, high pressure work. Dispatching rarely gets any love, and giving those folks a bit of calm feels genuinely thoughtful.

Role-of-the-evidence instead of latest-wins is exactly the right frame. The part that broke ours was classifying the role itself: a genuine correction and a broker re-sending the original rate con both read as 'just confirming the rate is X', so the model kept treating a forwarded original as a fresh confirmation. Did you base that role call on email structure (reply depth, attachment vs inline) or on the language, and which one ended up more reliable?

This is a nice product. Could you support importing and syncing the article list from Notion?

The article-listening feature on top of clean reading is a smart combo — most read-later apps stop at clean layout, but audio lets you keep up while commuting. Does Oakamo use TTS or a service like ElevenLabs for the audio quality?

As someone who have 100+ tabs open in chrome of all the articles i want to read all the time this seems like really useful, i actually never really thought about a reading app for articles so this feels really amazing cuz once i put them in a tab i never want seem to revisit it, there are so many tabs idek what i have and what i don't so i just end up not reading anything and just adding more tabs. So personally this is smth i really really needed. A library for articles??? A dream frr.

@mountaize You frame the real problem as saving articles and never coming back to them, and you're right ! But beyond the clean reader, what in Oakamo actually pulls people back to finish what they saved ?Do you think this is where the listen feature comes in?

Feels like getting back to high school :)

Loved the aesthetics. I like how instantly it makes me focussed and think on what to write.
Very minimalist and no distractions.
One thing I would suggest to make tying sound off on default. Its Novel to hear the sounds at first, but it quickly becomes a distraction and doubles up with the actual keyboard sounds.

Nice tool for quick jotdowns. UTF-8 support is appreciated for serious note exports in Indian languages.

Love the simplicity. Just what I need to finally write something without any distractions.

Congrats on the launch :) The "opens instantly, no account, works offline" angle is the part I'd lean into hard, that's where it actually feels different from opening a heavy notes app.

Honest question as a fellow maker: the calm writing space is a crowded room (iA Writer, Bear, Obsidian...). What's the one thing you'd want someone to remember Justwrite for? The ambient scenes caught my eye, that feels like the most "yours" feature here.

Wishing you a great launch day.

Local-first + offline + no login is exactly the right set of constraints for a writing tool — most people don't want their private drafts in the cloud. The ambient writing modes are interesting; are they more like background sounds or visual themes that change the feel of the interface?

Locking the writing session with a timer is a smart constraint. Most writing tools add more features, but sometimes the real problem is just staying with the blank page long enough to get words out.

Nice product. Love the simple UI and export feature. Why not give an export to PDF and Word option. Love it though.

I justlike write note for Rol play sessions with friend, write histories and Justwrite it's a good option to do that and save my notes without depend by Internet connections and much better than an antiquated notepad.

Manish, an app that opens and simply lets me write, with nothing quietly saved somewhere I cannot see, is exactly what I keep wishing for. The quiet of it is the whole appeal.

Local-first writing is still underrated for unfinished thinking. Draft notes, specs, prompts, and private business planning often need to exist before they are polished enough for a cloud workspace; offline ownership changes the way people are willing to write honestly.

Privacy and instant access without a login is exactly what I want from a writing app. Congratulations on the launch!

From a product perspective the positioning is clear but the real test will be retention since note apps live or die by long term habit formation

there's something nice about a writing tool that just opens and works. no login, no sync setup, no onboarding flow. the ambient writing modes are a cool touch too. would be great to see some kind of simple export to markdown file so you can move notes into other tools when you need to.

The local-first, no-account approach really resonates — opening instantly without sign-in is exactly what I want from a notes app. Since everything lives locally, how are you thinking about moving notes between devices? Curious whether there's an export/import flow for that without breaking the privacy model.

Hey Product Hunt 👋 I'm Rahman, Co-Founder & CEO of Clade.

We first launched Clade on Telegram - an AI Digital COO that lives where you work, learns your team and clients, and runs the ops you keep forgetting: standups, follow-ups, the message you meant to send three days ago.

Then our users told us where they actually run their agencies. Not just Telegram. So today we're launching on iMessage, Slack, and a brand-new web cockpit. Same Clade, now everywhere you already work.

It learns your voice and drafts in your tone, catches the follow-ups that slip when you're juggling five deliveries at once, and runs your standups. No starting over every morning.

And nothing's hidden. Every fact Clade learns shows its source, its confidence, and a button to fix it. No black-box memory.

What you get free:
• iMessage, Slack, Telegram + new web cockpit
• Drafts in your voice, runs your standups
• Automations + unlimited memory
• Five trust levels, from watching to operating

Quick question for the founders here: which channel does your team actually live in? That's the one we built for.

I'll be here all day - tell me where this still falls short.

Try it: https://clade.sh 🙏

This is interesting. How does it avoid duplicate syncing when the same project runs across Slack and iMessage?

The "no black-box memory — every fact shows source, confidence, and a button to fix it" is the part that would decide it for me, because an ops agent acting on a wrong assumption is worse than no agent. Concretely: if Clade learns something wrong and drafts a follow-up on it before I catch it, does a correction just update the fact, or does it also walk back actions already taken at the "operator" trust level? I'd want to know the blast radius of one bad fact before handing it standups and client follow-ups.

Congrats team, clean execution across three platforms!

congrats on the launch!

Transparent memory is a smart move. Most AI tools "remember," and you never know what they are actually storing.

Curious though, how do the five trust levels work in practice? Does "observer" watch, and "operator" take action?
Asking because for a COO-type tool, the line between helpful and intrusive is thin. 😅

Congrats on the launch.

Building around trust instead of just memory feels like the right call - especially with sources and confidence visible.

Curious what happens if two channels create slightly different versions of the same fact. Does Clade try to resolve that, or just surface it?

Congrats on the launch!

Hey, congrats on the launch, how is it different than just handing the screenshot to an LLM? Does some data of the design get passed also to Bilt? Genuine question, I think this is insane if it works out exactly as the Figma design

wild time to live in. i'm not a designer nor a developer but i'm building real apps that are beautiful as well:D

Been really fun testing this feature, glad it's finally released :)

My favourite part is how accurate it actually is, truly suggest you guys go try it out, we have a pretty generous free plan.

The "you own the code, dropped on GitHub" part is what makes this stand out to me — most no-code-to-app tools lock you in, so giving people a real exportable codebase is a great call. Since the app is built from the actual Figma file rather than a screenshot, how does it handle iterating after the first build? If I tweak spacing or swap a font in Figma later, does re-running the plugin update the existing app cleanly, or does it regenerate from scratch?

Serializing the design tokens and semantics instead of a flat screenshot is the right move, that's where the pixel fidelity actually comes from. The place I'd expect it to wobble is layout intent: a frame built with auto-layout gives you real constraints to map onto native, but plenty of designers hand-position everything and detach components, so there's no structure left to read. Do you derive native layout from auto-layout when it's present, and what happens with frames that are just absolutely-positioned pixels?

woah this looks exciting! but in android studio we have the option to integrate figma automatically. does that not code the front end now with the deep gemini integration? If not then this really could be a new genre of dev tools!

Congrats! I am curious about how much control does the maker have over platform-specific details? For example, can someone steer iOS and Android patterns separately, or is the agent mainly focused on getting a working native app structure from a higher-level prompt?

Not to be a buzzkill, but how many designers are still building full apps in Figma anymore? I can see it being more common on design teams working on established products, but they would have a dev team building the real app, so only prototyping would be valuable. Does this make dev handoffs easier if I have a pixel-perfect prototype so devs can actually transfer the exact styles and components into their code?

When I give LLMs my designs, I attach screenshots but also directly link to the Figma artboard, ideally with an MCP server. I don't design every screen though - just the base designs and AI can usually handle the rest, which is an essential part of the process of actually testing what I've designed so far and getting feedback before designing more screens. I definitely see the value in this for getting pixel-perfect designs the first time with LLMs, but maybe I'm hung up on it being framed as a tool that takes your end-to-end Figma design and publishes it fully rather than it just being a really good design-to-code prototyping tool.

I'll give it a shot though in case I'm missing something!

The "you own the code + ship straight to the App Store" part is the real hook for me. Having just been through Apple's review a couple times, I'm curious how the generated apps hold up against it — do they clear the 4.3 spam/template checks out of the box, or do you still need to add enough real native substance yourself before submitting?

Do you also have support for figma MCP?

Fair enough that they render. The thing I'd actually watch is what happens across screen sizes: absolute coordinates map 1:1 on the fixed Figma canvas, but a native app runs on a 6.1 inch phone and a tablet, so fixed positions either letterbox or drift. When there's no auto-layout to read, does Bilt infer constraints (pin this to the bottom edge, stretch that to width) or does it place things at the literal coordinates and let them sit?

Aakashdhruv, the messy middle of a project is exactly where my desktop falls apart, so one tidy spot for all the loose bits really speaks to me. Pointing back to the real files and never cloning them is a thoughtful touch.

Reference-by-path instead of copying bytes is the design choice I like most here, and also the one I'd stress-test hardest. When we built a paste buffer that held file paths rather than the files, the thing that bit us was the source moving or getting renamed after you'd dropped it in, so the tray entry quietly pointed at nothing. Do you watch the original paths and re-link on move, or does a dropped item go stale once the file relocates?

Congrats on the launch, seems like this could be really useful on those messy projects.

DropK being listed across Menu Bar Apps, Developer Tools, and AI Workflow Automation makes me wonder where the main daily use case sits. Is it primarily meant for developers triggering quick actions from the tray, or more of a general productivity layer for AI dictation / workflow shortcuts? The tagline suggests restraint, so I’d be interested in how you decide what belongs in the tray and what deliberately doesn’t.

Inode-based bookmarks are the right call, and that handles the rename and move case cleanly. The one that bit us with security-scoped bookmarks was atomic saves: an editor that writes a temp file and renames it over the original creates a new inode, so the path is unchanged but the bookmark now points at the old, deleted inode and the entry silently orphans. Do you re-resolve and refresh the bookmark on access, or would a safe-save app break the reference?

The live pad-editing while it plays is the part that sells it for me — rebuilding those Latin Rascals machine-gun stutters by hand keeps the human feel that quantized chops always kill. Can you save/export the edit patterns as audio, or is it purely a live performance thing?

Any plans to surface what members talk about back to the creator?

Are most users creators monetizing a membership, or businesses adding chat to a site?

I run community launches, and the Memberstack-native part is the real unlock — chat that inherits the membership instead of living in a separate Discord with its own login and lost branding. The edge case I'd test first: when someone is downgraded or removed in Memberstack, does Midway revoke their chat access and DM ability in real time, or is there a sync lag where a churned member can still message people? And do their earlier DMs stay visible to the other party once they lose access?

Real-time presence, typing indicators, and read receipts inside an iframe usually means a persistent websocket connection per active user. For a Webflow site that could have hundreds of members active at once, what does that look like cost and infra-wise on your end, is pricing usage-based on concurrent connections, or flat regardless of how active the community gets?

Hi Product Hunt community 👋 Suyog here, founder of Webless.

Before starting Webless, I led marketing teams at Salesforce, Amplitude, and Samsara. Across all three, I kept running into the same frustrating problem: Companies invest heavily in creating content, but prospects and customers still struggle to find the information they need. The content exists. The problem has always been discovery.

What started as a frustrating UX problem has now become a major business problem.

Over the last 12 months, buyer behavior has fundamentally changed:

Website traffic is going down. But visitor intent is going up.

More buyers are discovering companies through AI search, LLMs, and recommendation engines. By the time they land on your website, they’re often highly intentional and already looking for answers.

The problem? Most websites still behave like static brochures.

Visitors have questions. Websites make them click through menus, dig through pages, and hunt for answers. Too often, they leave before finding what they need.

Webless turns your website into an AI-powered buying experience. Visitors can ask questions in plain English, get direct answers instantly, and move through dynamic journeys tailored to their intent, with relevant content, CTAs and next steps surfaced in real time.

Every visitor question became valuable signal:
→ What buyers actually care about
→ Where content gaps exist
→ Which journeys drive conversion

In many ways, Webless became more than just AI search. It became a new source of first-party demand intelligence.

We started by working with leading enterprise brands across fintech, healthcare, media, cybersecurity, and SaaS. As we saw the impact, we kept hearing the same thing:

This shouldn’t be limited to large enterprises. Every company needs this.

That stuck with us.

So today, we’re excited to launch Webless Self-Serve 🚀

Now any team can turn their website into an AI-powered engagement and conversion engine in minutes with no redesign and no heavy implementation.

We’re incredibly excited to share this with the Product Hunt community and would genuinely love your feedback. What’s the biggest challenge you face turning website traffic into pipeline in the AI era?

Hey Product Hunt community 👋 — I'm Mahi, and I work on Webless.

What excites me most about today’s launch is how fast this goes from “paste your URL” to a live AI search experience.

Most teams spend weeks wiring up search, content indexing, and CTAs — Webless gets you there in minutes.

If you’ve ever watched visitors bounce because they couldn’t find the right pricing page, case study, or doc, try asking your site a question through Webless. The answers + dynamic CTAs are what sold me on the product.

Congrats to Suyog, Suhas, Ashwani, Rakesh and the whole team — proud to be part of this one. Would love to hear what you think if you try it on your own site.

If you've ever wanted to add AI search to your website without the complexity, we'd love for you to give it a try and share your thoughts.

looks awesome @suhas_webless, qq how long does it typically take for the ai to ingest all existing website content during setup?

Incredible product built by incredible people.

this is really neat, would save me a lot of time finding and sharing content links to users haha, great work!