PH热榜 | 2026-07-02

We are using Context.dev and we love it! Recommended

We have been using Context.dev at Notra for a bit now and its great, much cheaper than Firecrawl which we used before and with no concurrent browser limits. The team also provides top tier support and listens to our crazy ideas! 10/10 left no crumbs

the markdown output came back clean enough that i barely had to clean it up before feeding it into my agent. brand extraction on a few random sites was surprisingly on point too, definitely beats maintaining my own scrapers.

Been using this to replace a scraper that kept breaking. One API call, clean markdown back. The brand data extraction (logos, colors, fonts) is surprisingly useful for onboarding flows. Handles JS-heavy sites better than I expected. 5000+ customers is a decent trust signal. Some niche sites still struggle, but overall solid.

🚀🌚

The tricky part on that hash is that 'material' is consumer-specific: a price flip matters to a catalog agent, a nav reshuffle doesn't, but an agent watching layout wants the reverse. If you can surface the structured diff and let the caller pick which spans count, with your materiality hash as the sensible default, you sidestep everyone fighting one baked-in definition of meaningful. Glad it's already on the roadmap.

@yahia_bakour3 Congrats on the launch! We’ve been using your APIs for almost half a year now and they’ve been really reliable, fast, and delivering great results. Quality and speed are what matter most to us, and you’ve nailed both. Thank you, Yahia, for everything!

I can already think of a ton of use case for this. Congrats on the launch Yahia!!

Have been a user for 9 months haven't had any problems.

Would recommend

How does it actually handle sites that load content dynamically with JavaScript, does it run a real browser under the hood or just hit the raw HTML and miss half the page?

Didn’t know I’d love scraping websites, extracting style guides, and pulling font data until I tried context.dev.

There’s a surprising amount of knowledge to gain from doing things like this.

How does the brand data extraction actually work under the hood when a site uses lazy loading or dynamically renders its content client-side?

Plugged it into a side project and the brand extraction returned logos and fonts on the first try, which honestly surprised me for a 10 minute setup.

The agent-native onboarding is the part that stopped me, most APIs assume a human reads docs and wires a key in manually. Letting a coding agent paste one line, sign itself up, and grab a key end to end is a genuinely different distribution bet.

That's also my real question: what stops that flow from becoming a free-tier abuse vector? If an agent can self-provision with no human in the loop, nothing stops another agent from looping ten signups to dodge rate limits. Is there verification or a review gate before a self-signed-up key actually starts working?

Congrats on the launch!

@yahia_bakour3 The product looks solid, but I’m interested in the competitive side. If someone already has Firecrawl or a similar API in production, what’s the biggest reason they decide to move to Context.dev? I’d love to know which feature or capability ends up being the deciding factor in real customer deployments rather than just in demos.

Awesome product Yahia! Glad I never have to handle scraping on my own again!

Congrats on the launch! Happy to have your MCP deployed on us!🫡

Putting crawl, Markdown cleanup, brand data, and schema extraction behind one API is a useful shape for agent builders! The place I would care about most is freshness, because stale web context can quietly poison a workflow. Do responses include enough source and timestamp detail for an app to decide when to reuse context and when to fetch again?

Typed SDKs across TS/Python/Ruby plus sub-10-min integration is a great combo. Curious how you handle caching/rate limits when a customer wants to enrich brand data for thousands of domains in one batch - queued async or synchronous per call?

A customer here , product is very dope and save us lot of money and time.
If you are founder Yahia is kind of founder who go through every message and try to make himself available.

Right, I wasn't doubting the render fidelity, I meant determinism across fetches. Same URL scraped today vs next week: if the live DOM reorders a section, the markdown shape moves with it and an agent that indexed against the first shape drifts. Do you expose a content hash or a diff between fetches, so a pipeline can tell 'page actually changed' from 'page just reordered'? That's the bit that decides whether I wire it into an agent loop or keep it a one-off pull.

Treating web context as a first-class infrastructure concern rather than a DIY afterthought is exactly the kind of abstraction AI-native apps have been missing.

Thanks Yahia, this is game changer.

Nice work! The UI looks clean. I'm curious, what was the biggest challenge while building this?

Been using context.dev for a personal project since back when it was brand.dev. I'm not technical, I just build with AI, so being easy to use with an agent was my number one thing. Product aside, Yahia is just a really nice guy. Gave me some free credits, then topped me up again when I ran out. We're now a customer at the company level too, and I'll definitely be reaching for it on more projects going forward.

Amazing product, but more than that, amazing founder.

Context.dev solved an exact problem we had and we've actually shifted a lot of our architecture around their service because it's so good.

But beyond that, since I became a customer the founder Yahia has offered such amazing support that we have become friends. He's an intelligent, kind hearted guy and gives me a lot of advice on my business as we go through growth pains.

I highly recommend context and the ability to use something made by Yahia!

Big fan of Context.dev using it to personalize all our sales outreach!

Been a happy customer for a while and case study. Best and most affordable for brand extraction APIs for highly personalized experiences in my SaaS and marketing.

Expanding usage to full scraping soon!

Hey Product Hunt 👋

I'm Steven, Co-founder of Fypro.

Fypro turns your TikTok followers into paying customers you actually own.

We built this for creators who've grown a real audience — but haven't turned that trust into a business of their own yet.

If you post on TikTok and want your followers to become buyers, an email list, and real income, this is for you.

✍️ Here's how it works:

1. Drop your TikTok handle. Fypro reads your account — your niche, your followers, and what's already working.

2. Get your plan: what to post next, and what to sell.

3. Fypro builds it for you — your site, your store, and your content. Review, tweak, and go live.

Why you want Fypro:

• Followers → customers: the audience you built becomes buyers you keep

• Everything in one place: your site, store, content, and customer list

• A store that stocks itself: trending products matched to your niche, margin shown on every pick

• Content in your own voice: from product to finished video in minutes

• Own what you build: emails and purchase history that are yours to keep, not just followers on a feed

🎉 To celebrate our launch, use code PHPRO1 to get your first month of Pro for just $1!

💬 Join our Discord to get close to the Fypro team — share your account, ask us anything, and we'll help you set up your store: https://discord.gg/vXhq7mPDbB

Come drop your handle and build with us 🚀

I would love to see Instagram and YouTube support too. That would make Fypro even more useful.

I like that everything is built from a TikTok handle. I think keeping the setup simple will help more creators get started.

Congrats on the launch!
For creators who've never run an email list before, does Fypro help with the actual nurture side too, or is building the list as far as it goes?

For a creator who's already got a Shopify store running, does Fypro work alongside it or is it more of a replace-everything setup?

Huge congrats on shipping this. The creator economy has no shortage of content tools, but monetization tools are still underserved.

Congrats on the launch! Feels like you're solving one of the biggest problems in the creator economy today.

Huge congrats. Is the content generation optimized around conversion goals or audience growth goals?

someone i know has been trying to turn their TikTok audience into a small business. I'm definitely sending this their way because it seems built for exactly that challenge.

Does the system continuously update recommendations as audience behavior changes?

Really interesting approach. Does the generated storefront keep updating as new TikTok content is posted?

How does Fypro differentiate itself from traditional creator management platforms?

Dropped in my handle and it pulled in my old videos without me having to upload anything, the site looked surprisingly on-brand within a couple minutes. Wish the email capture popup was a bit less aggressive but overall way easier than I expected.

The "audience you built becomes buyers you keep" framing is the part I wish more creators understood before they hit follower burnout. Watching a lot of DTC brands right now the pattern is the same at every stage, TikTok drives eyeballs, then everyone realizes eyeballs on TikTok aren't the same asset as email addresses on your own list. Getting people to make that translation before they need it is the hard part.

The AI-writes-in-your-voice claim is what I'd stress-test hardest. Voice for a creator isn't just word choice, it's rhythm and specific reference points that only work because their audience knows them. Trained on 4M viral TikToks probably captures the format layer well but voice is more parasocial than pattern-based. Curious how much of the voice replication is "match their content style" versus "match how they talk to their specific audience", because the second is a much harder problem and I don't know if it's solvable without deep sample data per creator.

Also congrats on the 2,000 creators. That's not a demo number, that's real evidence.

how does the matching actually work when it picks products for your niche, is it pulling from a database you choose or do you connect your own supplier somehow?

Hello Steven, I know quite a few creators who have a good following but no real way to monetize it outside brand deals. This looks like it could help bridge that gap.

congratulations! the product recommendations sound smart. do you also estimate demand before suggesting products? or mainly calculate margins?

Have you seen any common traits among the creators getting the highest conversion rates with Fypro?

How does Fypro decide which products are the best match for a creator's audience and niche?

Can creators connect their own product catalog if they already have an existing business?

can creators already selling products migrate their existing store? or is Fypro mainly designed for starting from scratch?

Useful product! But I personally think people on the Internet nowadays don't really like commerce promotion videos, how do you help creators monetize without making their content feel too salesy or damaging audience trust?

Congratulations

When a follower buys, who handles fulfillment and shipping — the creator, or is that built in?

Turning content into an actual business is a much bigger challenge than growing folowers. I like that you're tackling that part.

how does it actually pick the products it puts in your store, do you pick from a catalog or does it just auto-match stuff from your niche without any approval step

Really curious to see what creators build with this. Feels like the kind of product that could change how creators approach monetization.

This feels especially valuable for creators who aren't naturally entrepreneurial.

Smart move focusing on commercialization rather than chasing content generation trends.

Congratulations! the audience to business journey is a great vision.does Fypro help optimize existing products too?or mainly recommend new ones?

Very interesting one! Wondering how does Needle tell a deal that stalled from one that went quiet on purpose? Is it pulling that from CRM notes? Call transcripts?

How do you handle permissions?

'Proactive' is the interesting word here - most agents wait to be asked, and the value is in the ones that surface the thing you didn't know to ask about. How do you keep the proactive pings from becoming noise? That threshold (helpful vs annoying) is the hardest dial to tune in any agent I've shipped. Congrats on the launch.

the deal nudges actually got me to follow up on stuff i'd completely forgotten about, which is rare for any tool i've tested

Finally gave this a spin in our Slack and the pre-call prep nudge saved me from walking into a discovery call blind. The stalled-deal drafts are a bit generic but honestly useful as a starting point.

Super excited to finally see this out in the open 🚀

One thing that stood out while building Needle is how much of a rep’s day is spent on everything except selling: research, CRM updates, follow-ups, finding the right context, preparing for calls. That’s exactly the problem we wanted to solve.

Proud of what the team has built, and we’re just getting started. We’d love to hear what the Product Hunt community thinks!

Congrats on the launch!

1. Curious how Needle decides that something is a signal in the first place. Is it mostly based on rules the team sets, or does it infer patterns from HubSpot, email and call context?

2. And once a rep acts on a suggestion, can that close the loop? Meaning, can Needle learn which plays worked, which got ignored, and turn that into better GTM moves over time?

This looks genuinely useful - taking the admin work off a sales pipeline is exactly where I would want the help. One question before trying it: is it usable for a small, early team, or does it assume an established sales org already on HubSpot and Gong?

Wow, great stuff! Congrats on the launch!
Love the video 🖤

Hyped!

Built our GTM from scratch using Needle.

Now we have superpowers 🦸

LFGO 🚀

The Slack/Teams angle makes a lot of sense to me. From my perspective, a lot of GTM tools end up living outside the flow of work, so people either forget to use them or only check them when something has already gone cold.

How would you avoid this becoming noisy though? If Needle is proactive, how do you decide what is worth surfacing versus what just becomes another notification people start ignoring?

Proactive feels right, it pinged me about a stalled deal before I even opened HubSpot that morning and drafted a solid follow-up. The Slack-first setup keeps it out of my way.

most ai waits to be asked. this one talks first. that flip is where the trust question gets real. reactive is easy because the worst case is a bad reply. proactive is scary because the worst case is a bad action nobody signed off on. the design job is making sure the actions are the ones the human would have taken 5 minutes later anyway.

curious how you handle disagreement. when needle drafts follow-up and the rep would have written it differently, does the rep edit and move on, or does needle learn to write more like that rep over time?

super hyped to build this!

I like the vision but I'd probably want to start with recommendations before giving an AI permission to take actions automatically. Is there a gradual adoption path for more cautious teams?

the part that gives me pause is "tidies the CRM on its own" - CRM data being wrong silently is worse than it being stale, because nobody double checks a field that looks filled in. is there an audit trail showing what it changed and why, or do you just have to trust the drafts before they go out

Interesting approach 🙂. The 'mirrors your permissions' model avoids a lot of the config headache. Curious how it handles document-heavy steps (proposals, contracts, PDFs) in the pipeline. That's often the messiest part of automating a sales workflow.

A proactive GTM agent only works if the handoff is legible. The important part is not that it finds every possible signal; it is that sales/support can see why this account matters, what changed, and what action is safe to take next.

Congrats on the launch! The Slack and Teams-first angle makes a lot of sense here, especially for the “act before someone opens the CRM” moments.

Curious how you handle trust when Needle suggests an action. Do reps get a clear reason for why something was surfaced, like the signal, source, and suggested next step, or is the goal to keep it mostly invisible unless they ask?

Congrats on the launch @jan_heimes ! Needle feels strongest where sales teams usually lose momentum.

Curious how you recommend a team rolls this out in the first 2 weeks.

The proactive angle is the interesting part since most of these just wait to be prompted. What actually triggers Needle to act: configurable rules/thresholds (deal untouched N days, no reply on a thread), or a model deciding on its own when something matters? And when it fires, does it act autonomously (send the follow-up, update the HubSpot field) or draft it and wait for the rep to approve? Trying to gauge how much CRM-hygiene work I could hand off on day one vs still babysitting.

The reactive-to-proactive shift is where the interesting failure modes start. When AI just answers questions, wrong answers are annoying but recoverable. When AI acts before you ask, wrong actions cost you deals and worse, they cost you credibility with the prospect who now thinks your team is uncoordinated.

Running my own cold email ops solo right now (162 leads, personalized Loom video sequence) and the manual version of what Needle does is roughly 40% of my week, pipeline watching, follow-up drafting, CRM hygiene. The math on offloading that is real. But the trust boundary Mustafa raised is exactly right, the piece that has to feel right is "would this have been the follow-up I'd have written." Not just competent, but on-voice.

Curious about the calibration period, does Needle need a warm-up phase where it drafts and you approve before it moves to autonomous action, or is it comfortable acting on day 1 with just permission scoping?

Congrats! How are you connecting to all the 3rd party tools? @onur_o ?

the fact that it pings you in slack before a call with a quick brief is genuinely useful, not just another ai wrapper hype thing

Nice work Jan! Congrats on the launch

Great stuff! GTM is a pain, automating it is great

Curious how it handles the noise though - does it learn from feedback when a flagged "stalled deal" is actually just going slow on purpose, or does it keep pinging you the same way every time?

I lead the creative operations side at Winclap Studio — the team that actually produces the assets.

Before PixFit existed, we had great designers, solid processes, and still spent an embarrassing amount of time on mechanical adaptation work. The kind where a talented person is manually resizing the same creative into 12 formats at 11pm. Not because they lacked tools — because none of the tools truly understood the workflow behind the creative, not just the creative itself.

That frustration is where PixFit started. We built it first for ourselves, tested it with our own production load, and broke it enough times to know what actually had to work.

What I'm most proud of: we didn't ship a tool that promises 100% automation and quietly fails. We designed for the real number — the 70% you can automate confidently — and built a proper human review layer for the rest. That honesty in the product design is what makes it trustworthy at scale.

If your team is producing performance creatives across formats for multiple brands, you'll feel this immediately. Happy to share how we've been running it internally if anyone wants to dig in 🙏

So the human fallback only kicks in after three AI attempts fail... how do you actually detect that an output "failed" versus just handing me something that technically fits the safe zones but looks off? Curious whether that's a confidence score or purely me hitting a button.

This can be pretty helpful and time-saving. When I want to change something, it can take a pretty amount of time to customise it.

Hi Marco! Having a tool to turn 1 idea into multiple ad formats is life saving. I have one question. Do multiple formats have any negative impact in pixel quality? Congrats on the launch!

La verdad que la herramienta me facilitó un montón parte de la operatividad de mi trabajo diario. Me pareció muy amigable y fácil de usar! Me sirvió más de lo que esperaba! Súper recomendado!

Been waiting for something like this. The worst part of running paid is the creative bottleneck - one good asset and then hours reformatting it into 20 placements before you can actually launch. Curious how PixFit handles the weird ones (Stories vs. feed vs. display banners). Congrats on the launch 🚀

We built PixFit around a problem that sounds simple, but quietly consumes a huge amount of creative-production time:

You have one approved campaign asset. Now you need to rebuild it for every ratio, placement and platform — Meta, Google, TikTok — while preserving the hierarchy, branding, safe zones, logos, CTAs and legal copy.

But the part we cared about most was making it useful in real production, not just impressive in a demo.

AI handles the first pass and automatically retries or fixes most problematic outputs. When it still cannot produce something that meets the required standard, a senior creative steps in and finishes it.

95% resolved with AI.
5% refined by humans.
0% shipped unfinished.

You can try it with your own creative — the free trial includes five resizes and requires no credit card.

Give it something difficult. See where it works, where it struggles and tell us what you would change. That feedback is exactly what we want from this launch.

Nice idea, especially the human fallback. How much flexibility is there to tweak the AI generated creatives before exporting them? Also, i sthe human fallback included in the pricing or charged separately? Congratulations!

the human fallback after 3 attempts is the right call honestly, most of these tools pretend the AI output is always good enough and quietly ship the bad crops. curious what the actual failure rate looks like once you have real usage data, not just your own campaigns

Amazing! Tired of waiting days for my agency to resize my assets

resizing ad creatives across formats is genuinely one of those tasks that eats hours and adds zero creative value. the platform safe zones detail is what makes this useful, anyone can crop an image but knowing where tiktok puts its UI overlays vs where meta does is the actual knowledge being automated. how does it handle text-heavy creatives where a straight resize would break the layout? does it reflow elements or just scale everything proportionally?

Really interesting product!!! Curious.....what's the biggest challenge in keeping creatives brand-consistent across so many different ad formats?

Tried the tool, works clean!

What about videos? that would be great ;)

How does it handle brand fonts and custom typography when generating all those platform variants?

This tool is amazing. Does it only support TikTok today?

Finally a tool that gets ad resizing right. Batch processing 30+ formats in one click is insane – saves our design team days of work. Brand consistency across platforms is solid. The human fallback option is a nice safety net, though we haven't needed it much. Some complex layouts still need manual tweaks, but for 80% of our work, it's been a game changer.

The auto-safe-zone logic looks incredibly thoughtful, especially the way it preserves focal points across formats rather than just cropping blindly. That kind of attention is exactly what senior designers actually need.

Hey Product Hunt!

I'm Jacob, the founder and CEO of Macro (macro.com) - an open source, all-in-one workspace.

In our last startup, we ran on Slack + Linear + Notion + Superhuman + 17 other tools. All of these are fine individually but as we scaled it became chaotic to manage, and information was everywhere.

We built Macro to replace siloed apps and put them all into a unified workspace with shared AI memory.

Already Macro has:

• Email inspired by Superhuman, with better AI for triaging ("Signal vs. Noise")

• Notion-like documents with fast CRDT's instead of last-write-wins, @linked to everything

• Messaging, like Slack, but more focused for deep work

• Linear-like tasks but deeply integrated with channels, auto-created and auto-assigned

• Video calls with Google Meet performance that are transcribed and added to your team

• A unified brain for all of this, in one place, and much more

We've also chosen to be open source in order to keep customizability at the core of our company. You can check out the repo here https://github.com/macro-inc/macro and see what we're building in the Pull Requests tab.

Macro is an ambitious project. But that's also what makes it so useful and fun to work on.

Give it a try: https://macro.com/

This is one of those products that immediately makes sense. Bringing docs, tasks, communication, and AI into a single workspace could eliminate so much context switching. The shared AI memory is an especially interesting idea. Looking forward to trying it out—congrats on the launch and best of luck! 🚀

I've been waiting for a workspace where AI actually understands everything happening across emails, docs, and tasks. This looks like a promising step in that direction. Best of luck with the launch!

How does the team-level memory actually work across all those different tools, especially for things like email threads from a few months back that I barely remember starting?

Huge shoutout for going open source with this 👏 @jacob_beckerman qq Is there a native desktop app with global shortcuts for quick capture, or is everything running out of the browser for now?

The shared memory part is the bit I’m most curious about. Feels like a lot of tools are getting better at storing more context, but more context is not always the same as useful context. How does Macro decide what is actually worth remembering? Is it mostly things I tell it to save, or does it start picking up patterns from how I work over time?

the super-app graveyard is real. slack tried to be your inbox, docs, calls, CRM. teams too. both ended up as chat with a lot of tabs nobody clicks. the thing that would actually make one of these work is the connective tissue between the parts. team memory is that, if it knows who said what where.

genuine question: is the memory a permission-scoped graph or a flat corpus? that's where team products either become invaluable or become HR nightmares.

Shared memory is the hook here, but the trust boundary feels just as important. Can teams choose which channels, docs, or calls get added to agent memory, or is everything in the workspace queryable by default?

The unified surface demos well, but the hard part is team memory respecting permissions at retrieval time. If I query 'the workspace' and the answer lives in a doc or DM I'm not on, does retrieval enforce ACLs per chunk, or is the index shared and you filter after? We built agent memory over mixed-permission sources and the lesson was that access control has to live inside retrieval, not the prompt, or the model will happily quote something the user was never allowed to see. Being open source makes that auditable, which is a real plus.

finally an app that actually unifies my inbox and tasks without feeling clunky, the memory search pulled up a thread from last month instantly

This is very interesting, and I like the unified experience. But does that mean I have to move out of Notion and rebuild everything?

how does team-level memory actually work when people leave or join a workspace, does it retain or wipe their context

Finally tried Macro this morning and the team memory feature actually works like advertised. Asked it to pull up last week's client thread and got the email, doc, and follow up tasks in one view.

How does team-level memory actually work across different tools like email and code, and is it really seamless or do you have to manually tag what gets stored?

How does team-level memory actually work across different apps — does it just index everything or is there a way to exclude certain conversations or docs from being searchable by the rest of the team?

the team-level memory idea is genuinely clever - being able to query your whole workspace instead of digging through tabs sounds like a real quality of life upgrade

Finally tried Macro after hearing about it for weeks and the team memory feature is the real deal. Pulled up everything I needed across docs and messages without jumping tabs.

Team-level memory that lets you query your whole workspace is the compelling part - most unified-workspace tools stop at search. Does the memory span everything (docs, calls, CRM) from day one, or is there a lookback window before it kicks in?

Open source is a smart trust move for a workspace that wants to hold email, docs, tasks, calls, and CRM in one place! The adoption challenge is that most teams will not move their whole stack at once. Are early users starting with Macro as an email client first, or are they bringing docs and tasks in from day one?

The constant hopping between apps is one of those small daily drains you stop noticing, so seeing it all pulled into one calm place is a relief.

finally something that pulls my chaotic slack, gmail, and notion stuff into one place without me copy-pasting between tabs. the team memory search is genuinely useful for digging up old context

How does the team-level memory actually work across different tools like email and code—does it pull context live or do you need to keep everything inside Macro for it to be useful?

replacing 17 tools with one app is the pitch every all-in-one workspace makes, and it usually means each individual piece ends up 80% as good as the dedicated tool it replaced. what's actually best in class here vs just "good enough to not need Slack anymore" - email triage, docs, or something else

Inheriting the launching user's permissions is a sane default. Where it got us was memory writes: once the agent summarizes something into shared memory, a teammate with lower access can pull that derived summary later even if they were never allowed to see the source it came from. Does a memory entry carry the ACL of its most-restricted source, or does it just inherit the channel it was created in?

Hey Product Hunt 👋

Annie here, co-founder of Build Club.

Today we’re launching Solaris - an AI-native transformation platform that helps organisations turn AI access into real team capability.

We built Solaris because most companies already have AI tools inside the business (ChatGPT, Claude, Copilot, Gemini and more are already being used somewhere).

But usage is often scattered, shallow and inconsistent.

A few early adopters move quickly, while the rest of the organisation is unsure what to use, how to use it, or where AI actually fits into their work.

Solaris is designed to make AI adoption more structured.

It starts with an AI fluency baseline, so leaders can understand where teams are today: who is confident, who is stuck, what tools people are using, and where the biggest capability gaps are.

From there, each team gets guided through role-based learning pathways and practical AI use cases based on how they actually work. Instead of generic AI training, Solaris helps employees see where AI can improve their day-to-day workflows across functions like sales, ops, marketing, finance, HR, customer support and leadership.

Teams then submit real workflow experiments each week, turning learning into hands-on application. Internal champions are upskilled to support adoption inside the company, share examples, and help AI behaviour spread beyond the early adopters.

Leaders get visibility into adoption over time: which teams are progressing, what workflows are being tested, where support is needed, and how AI fluency is improving across the business.

The goal is simple:

AI should not be another tool sitting unused inside the organisation.

It should become part of how teams think, build, communicate and work.

We have seen this pattern again and again through Build Club, Campus, Manus Academy and our enterprise AI programs. One-off training is not enough. Real transformation needs baselining, tailored learning, practical workflows, champions and measurement.

That is what Solaris brings together in one platform.

We’re onboarding organisations now and would love your feedback, roasts and intros.

Explore Solaris: https://solaris.buildclub.ai/

Question for you: What is the hardest part of getting teams to actually adopt AI at work?

Built with ❤️,
The Build Club team - Annie, Kevin, Talin, Clinton, David and Andrew

What's the metric you point to when a company asks if this actually worked? Like what does 'AI-native' look like 90 days in vs day 1?

Wherever you're starting from, Solaris meets your team where they are. Get your team started on their journey to becoming AI fluent and beyond 🚀

Build Club helps anyone leverage AI. Solaris helps any company become AI-native.

Super excited to usher in an era of AI-native companies!

How does the community actually work day to day — is it mostly async discussions, weekly calls, or something else? Trying to figure out if it fits a busy schedule or if regular attendance is expected.

Joined a build session last week and the feedback loop felt really natural, people jump in with code reviews and resource links right when you need them. Surprised how active the Discord is even for niche AI topics.

How does the free tier actually hold up compared to paid AI learning platforms — do you get hands-on project feedback from the community or is it more self-paced content?

Hi Annie, learning by actually building something has always stuck with me far more than watching a tutorial I forget by lunch. The idea of staying current while getting my hands dirty is really appealing.

Congrats on the launch, and really like the angle here.

I like that this is framed around adoption and not just training. Feels like a lot of companies can do an AI workshop, everyone nods along, and then a week later people are mostly back to the same old habits.

I guess what I’m wondering is: what actually changes the day after the training? Like what stops people from just going back to their old way of working?

Love how clean the community hub layout is, everything you need to dive into a lesson or connect with other builders is right there without feeling cluttered.

Congratulations on your launch!

interesting approach starting with the fluency test before prescribing learning paths. curious how you calibrate what "fluent" actually means for different roles — the bar for a product manager vs an engineer using AI is pretty different in practice. do you differentiate by function or is it a universal baseline?

If two teammates are reviewing the same conversation at the same time, how does Banger prevent duplicate replies or conflicts?

Hey Product Hunt 👋 I'm Tiago.

I’ve spent the last few years building chat apps, first at Beeper, including Beeper Mini, and then at Automattic. Most of my work has been deep app engineering: native clients, messaging surfaces, sync, reliability.

One thing I kept running into is that email still feels built for one person, even though a lot of company work happens there. Support, sales, invoices, recruiting, partnerships. It all lands in the inbox, but teams still end up sharing passwords, forwarding threads around, or pasting drafts into Slack when they want someone else to review a reply.

So I left my job and started MuchBetterApps with a friend who has spent years building and operating infrastructure. Banger is the first thing we’re building.

The basic idea is shared email with review built in. You can have shared mailboxes, real permissions instead of one shared login, and a way to put an email up for review before it sends. That can be for a teammate’s draft, or for something an AI agent wrote. The closest analogy is probably a pull request, but for email.

One important detail: for custom domains, we are not just putting a nicer UI on top of someone else’s email product. We built our own mail infrastructure for receiving and sending email, including the domain setup layer. We are not relying on AWS SES, SendGrid, Postmark, Mailgun, or another hosted email service for that part. There is a lot of unglamorous work in deliverability, routing, queues, DNS setup, bounces, retries, and abuse prevention, but I think owning this layer matters if we want to build the kind of email product we have in mind.

Today we're launching the native Mac app. It supports your own domains and Google Workspace. For new domains, we’re trying to make setup less annoying. For existing Google Workspace teams, the goal is to add the collaboration layer that Gmail does not really have.

This is early access, so it’s not the whole vision yet. There are 500 open spots now and we plan to open more as the infrastructure scales.

I’ll be around in the comments. I’d love to hear what you like, what feels unclear, and what you think we’re missing. Thanks for checking it out.

How do you envision the AI agents handling email threads with multiple stakeholders or complex customer support issues?

I appreciate that you built your own email infrastructure instead of just adding another interface on top of existing services. That is a much bigger undertaking than most people realize.

The "pull request for email" framing is exactly right. I build support and sales agents for ecommerce stores, and

review-before-send is the single thing that makes owners comfortable letting an agent anywhere near their inbox. So, it

is great to see it treated as a first-class feature instead of a bolt-on.

The question that I think decides whether this scales: what happens after a reviewer has approved 50 routine drafts in

a row and starts rubber-stamping? Do you plan per-agent or per-thread-type trust levels (auto-send the routine stuff,

always hold refunds, pricing, anything with money), or sampled review once an agent earns confidence? That dial

between safety and approval fatigue is the hardest part of this pattern in my experience.

Also, respect for owning the mail infrastructure yourselves. Deliverability, bounces and abuse handling are unglamorous, but that layer is usually where products like this live or die.

Congrats on the launch, Tiago.

Congrats! QQ, this is only for MacOS or any future plans to release on Win?

Finally a shared inbox that doesn't feel like a hacked together web app. Loved the scoped AI access, draft stayed put until I hit send which is the right default.

Building your own send/receive infrastructure instead of layering on SES or Postmark is the choice most teams talk themselves out of because the ROI takes years to show. But it's the one that separates products that stay flexible from ones that hit ceilings the moment their upstream provider decides to change something.

The "pull request for email" analogy is the tightest way to state that pattern I've heard. Review-before-send with AI drafts is exactly the missing layer, I've watched teams try to bolt this on with Slack channels, forwarded threads, and shared Google Docs, and it collapses within a month because the review context doesn't survive being ripped out of email.

Genuine question, for someone running cold email operations across multiple mailboxes and warmed domains, does Banger's shared inbox model extend to that use case, or is it strictly for shared team inboxes like support@ and sales@? The reason I ask: cold email is one of the places where "one draft, three eyes on it before it goes" would materially reduce misfires, but the current tooling assumes single-operator workflows.

Shared mailboxes where agents triage and draft but a human reviews before send is exactly the setup I'd want for a support@/founder@ inbox — the 'forward the thread into Slack for a second opinion' dance is real. One thing I'd test first: does the review-before-send gate apply to every agent-drafted reply forever, or can you whitelist specific labels/intents (say, shipping-status replies) for auto-send once you trust the triage? And is an agent's scoped access set per-mailbox, or can you restrict it to certain threads/labels within a single inbox?

finally a shared inbox that feels built for mac, the scoped ai access for drafting is genuinely useful and the kanban board for threads makes support way easier to follow

finally a mac app that doesn't feel like a chrome wrapper for shared inboxes. love that ai drafts sit waiting for approval before sending, keeps things sane for support@

how does the AI scoping actually work in practice, like can two agents see different drafts of the same thread or is everything shared across the team

How do the AI agents actually decide when to draft a reply vs just label something, and can I set per-agent rules or do they all run on the same defaults?

Shared inboxes are a good place for AI because the work is repetitive but the accountability is still human. The guardrail I would want is a clean separation between draft, suggested action, and actual send, especially for support or billing threads.

How scoped exactly is the agent access — can it send on its own in some scenarios, or does every outbound always wait on a human click?

Scoped access + human review before send is the right trust boundary for mixing AI agents into shared inboxes. How granular does the scoping get - can one agent draft-only on support@ while another gets broader access on sales@, or is it set per-mailbox rather than per-agent?

shared password / forwarding threads around for a support@ inbox is such a specific, real pain that never gets fixed properly. glad you kept a review step before send instead of letting agents fire off replies on their own, that's the part that would actually make me trust it with a customer-facing mailbox

This is very interesting. We're building an AI agent for outreach and encountered this issue. Will explore how it works. Congrats on the launch!

The Mailbook integration seems really useful. Having contacts ready instead of entering addresses every time could save quite a bit of effort

Congrats on the launch. I never expected postal mail and AI agents to come together, but this actually makes a lot of sense for businesses that still rely on physical communication.

Love it! 😍 How are you thinking about pricing the MCP side? Is it just pay per letter/postcard sent, or will there also be something like a monthly/API plan if people start wiring this into their own tools? Mostly asking because this feels like something I’d try once manually, then immediately want to automate if it works.

How does Pieter Post actually handle the physical delivery part, do you print and mail things on my behalf or is there some kind of kiosk pickup I need to visit nearby?

Letting an agent create a checkout link before anything mails is a clever gate, but I'm curious what the agent actually sees back after it uploads a postcard image. Does it get any confirmation of how the final print looks, or is it flying blind on the physical artifact once payment clears?

Connecting AI agents to physical mail is a genuinely underexplored

space. The irreversibility angle is the interesting design challenge

here — curious if you're adding a confirmation step before anything

actually ships, since a hallucinated address isn't a retry, it's a

stranger's mailbox.

Would love to see a "draft mode" where agents prepare everything but

a human approves before it goes physical.

That tiny detail of skipping the stamp-licking step is honestly such a nice touch, love how clean the whole flow feels from envelope to sent.

Physical mail as an MCP tool is a fun edge because it's one of the few agent actions that's genuinely irreversible once it's in the postbox. What's the confirm boundary here: does a human have to click through the checkout link, or can an agent with a saved payment method quote-and-send in one shot? For a tool that spends money on a physical artifact I'd want the resolved address read back and a hard human gate, since a hallucinated recipient isn't a retry, it's a stranger opening my letter.

Loved how quick it was to send a letter from my phone, and the tracking kept me from worrying if it actually arrived.

Really nice concept, way more convenient than running to the post office. The way it just works behind the scenes and you forget it's even mail feels seamless.

That stamp-licking problem is such a classic annoyance, love how clean and focused the whole concept is. The branding feels really thoughtful too, the name and tone make it feel friendly instead of corporate.

Gotta say, the branding here is genuinely charming. That name "Pieter Post" with the classic envelope vibe really sells the whole concept before you even read what it does. Smart move leaning into the postal heritage while modernizing the actual experience.

MCP for postal mail is a pretty novel idea! I can imagine a use-case where you'd want your agent to send out postal advertisements or have it send your friends and family holiday cards or even handle the shipping for small online businesses. I'm curious, is letter/package tracking also included w/ the PieterPost MCP tool? Because that could also be an interesting feature to have.

Love seeing MCP tools that result in real physical objects. Good stuff!

Two things for me. First, the fully resolved postal address read back verbatim, plus which Mailbook entry it matched, since 'John in London' quietly resolving to the wrong saved contact is the failure I'd never catch. Second, an idempotency key on the send, so if the agent's tool call times out and retries I get one postcard and not two. Duplicate physical sends are the money version of a double-submit.

Finally tried Pieter Post for a birthday card to my grandma and it worked like a charm. The whole process took under a minute and the handwriting on the envelope actually looks legit.

Uploaded a letter from my phone yesterday and it showed up at my mom's place two days later, tracking included. Genuinely didn't expect the whole process to feel that painless.

There is something lovely about a real letter landing in someone's hands, and making that as effortless as tapping out a message is a delightful little bridge between the physical and the everyday. Nicely done, Pieter.

That's a pretty wide access scope for a free tool - Gmail, LinkedIn, Instagram, Twitter, Outlook, plus friends' connections. What happens to that data once it's mapped my network? Is the graph stored on your end for future searches, or does it get processed and discarded after each job paste?

Hey Product Hunt 👋

I built Sidedoor because I kept seeing the same thing: people grinding through hundreds of cold job applications while sitting one or two connections away from someone who could have just gotten them in.

The referral path was always there. They just couldn't see it.

Paste any job posting and Sidedoor maps your network to find who can refer you, across Gmail, LinkedIn, Instagram, Twitter, Outlook, and friends' connections. Not just people you know directly, but people your people know too. Sidedoor is powered by Happenstance (YC W24).

I'd love your feedback, especially if you try it and find someone unexpected. Drop a comment and let me know what you think!

the thesis is right. cold applications lose to warm intros. every hire that actually closes came from someone the recruiter already trusted.

but reading every social graph a user has ever touched to find the "right" referrer is basically linkedin premium without asking. the referrer half of the marketplace didn't opt in, they just got tagged.

genuine question: how do you handle the case where the found "referrer" has never actually met the user? does the referrer know they're being pinged, and can they opt out of showing up?

How does Sidedoor actually find my friends’ connections across LinkedIn and socials without me manually granting access to all those accounts?

This is a great idea - LinkedIn is becoming worse and worse and something straightforward like this cuts more directly.

the idea of scanning LinkedIn connections and Gmail threads together for warm intros is genuinely clever, and pulling it off for free feels almost suspicious in the best way

Finally tried this and it surfaced a former coworker I completely forgot could vouch for me. Kinda wild how it just pulls the connections out of nowhere without making you dig.

This is a cool idea! I've done this manually a few times with searching thru linkedin but I definitely will use this next time I know someone who is looking for a job!

And in case you want one, here's a free QR code you can use that goes to your site:

how does it actually find referrals without getting flagged by gmail or linkedin for scraping? seems like a thin line to walk but i want to trust it works

How do you handle data privacy and security for users' email and social media connections, especially when accessing their friends' connections?

Hello Jerry, cool idea. The tool checks if someone from my personal network (social media, etc.) works a the company who published the job post so they can refer me? That could be quite useful for a lot of people. Thanks for the launch!

Found three solid referrals for a role I had almost given up on, including someone I hadn't talked to in years. Kind of wild how it pulls from places I forgot to check.

This solves a problem I've definitely run into. I usually end up searching LinkedIn manually, so having everything in one place sounds really useful.

This is an interesting approach. I'm currently exploring career opportunities myself, so I'm curious how do you decide which connection is the best person to reach out to when there are several possible referral paths?

The part that got me is how often the person who could vouch for you is already somewhere in your circle and you just never realized it. That quiet nudge toward a warm intro feels genuinely useful.

The idea sounds brilliant, but I know that Linked In is quite strict about using side tools and block accounts for using such tools. Did you solve this somehow?

Wow, this is clever! It can also be used for freelancing. When I switched to freelancing full time in 2021, I could use something like this. I did everything manually. I went through my emails, Facebook friends, and LinkedIn. I then checked who can help me out.

Worth checking out!

This is clever, especially the second-degree connection piece. Most job search tools just show you your direct network, but the referral that actually matters is usually two hops away.

Quick question: when you surface those unexpected connections, how does it prioritize them? Like if Sidedoor finds 10 people who could refer you, does it weight by recency of contact, strength of connection, or something else? Wondering how useful the ranking is in practice when you're trying to actually reach out.

@jerry_feng This is very cool and will make job hunt easy.
To login, Gmail is good but it should be Linkedin or a job portal by default.

my only concern is the subscription for individual developers. A lower priced personal tier or a lightweight plan for solo builders might encourage more people to give it a try after the trial ends.

The case I would test hard is stale or wrong memory, not just recall. If one agent records a bad debugging hypothesis and another agent asks about the same repo tomorrow, can I mark that capture as superseded or incorrect so it stops being retrieved?

For coding agents, I would want each memory hit to show source session, repo/branch, timestamp, and whether it was later contradicted. Local storage is a good default, but stale local facts can still send the next agent down the wrong path.

Hello, this solves a problem I run into quite often. I keep repeating the same project context every time I change AI tools. Having one shared memory across them all feels like a much cleaner workflow

This tracks. The wall I hit doing hybrid retrieval over raw terminal exchanges is that tool output, the file dumps and stack traces, dominates the keyword side and matches great while carrying zero reasoning, and the actual 'why' lives in the model prose the vector side catches. Ended up down-weighting tool-output spans so the small budget didn't get eaten by noise that scored relevant. Do you tag span type at capture, or leave it to the ranker?

As a solo dev the prompt.toml part hits home lol — I keep a CLAUDE.md of rules and just watch its relevance decay the deeper a session goes, so injecting them every turn is way better. Quick q on the phone-sync PWA: can the agent keep chugging on a long task while I'm away from my desk, or is it read-only until I'm back at the terminal?

The part that sells me is that it sits as the terminal the agents already run in and captures passively — the 'bring the new session up to speed' dance is exactly the tax I want gone. Since captures stay local, does secret/token redaction happen at capture time, or does raw terminal output (API keys, env dumps) land in the searchable index as-is? And when an agent pulls context back over MCP, is retrieval scoped per-project/repo, or does it serve the whole corpus so unrelated work bleeds into the prompt?

love that it keeps everything local and still makes the corpus searchable across tools, the MCP piece is a really thoughtful bridge between agents and your own history.

That's pretty interesting! I wonder if this would help w/ distillation of the model?

how does it actually hook into the different CLI agents, do you need to run them through a wrapper or does it just sit and watch the terminal output?

The cross-tool angle hides a mismatch I keep hitting: memory written by one model's sense of what mattered, read back by a different one. A summary of Claude's own debugging reasoning isn't always legible to Codex, which had a different plan for the same repo. You mentioned raw turns stay the source of truth, which is the right call, so the pressure moves to retrieval budget: what's the cap on turns pulled per query? Pull back 15 old sessions and you've rebuilt the exact context wall you're routing around.

the fact that everything stays local and still feeds back to your agents via MCP is a really thoughtful bit of craft. local-first capture that actually closes the loop with the tools feels rare.

finally a way to stop losing track of what claude told me in that one terminal two weeks ago, the local index feels snappy and the mcp handoff back to my agents actually worked on the first try

this is the exact workflow i have, burning through Claude then hopping to Codex mid problem. one thing that worries me: terminal sessions end up full of API keys, .env dumps, stack traces with internal urls. if you're capturing and indexing everything by default, is there any redaction before it hits the searchable corpus, or is that on me to be careful about what i paste

finally a way to stop losing track of what claude said in that one terminal session two days ago. the local index idea is solid

The context loss between agents is real and nobody talks about it

enough. I've been using Claude Code heavily and the moment you hit

a usage limit mid-session the mental overhead of rebuilding context

somewhere else is brutal — you spend the first 10 messages just

catching the new agent up instead of actually solving the problem.

The MCP angle is the part that makes this different from just

"searchable logs." Agents querying each other's past turns rather

than starting cold is a genuinely different model. Curious how the

retrieval quality holds up on longer sessions — does it surface

the right past context or do you find yourself still needing to

manually point it at the right conversation?

Also the prompt.toml injection is underrated. Maintaining consistent

rules and persona across agent switches without copy-pasting is

something I'd use daily.

The part that got me: it serves the captured history back to the agents over MCP. Most "agent memory" tools stop at making things searchable for the human.

I run Claude Code plus a couple of other CLI agents side by side, and they constantly re-derive context the other one already figured out. One local corpus they can all query is exactly the right shape for this.

And keeping captures on-machine instead of phoning home — nice call 👌

If I'm working across multiple client projects that shouldn't ever mix, is the corpus scoped per project/repo by default, or is it one global memory that I'd have to manually wall off? Worried about an agent on project A accidentally surfacing something it learned while I was working on project B.

the piece i keep waiting for. every agent has its own memory silo which means every session gets rebuilt from scratch. cross-tool memory should be a standard everyone shares. also the fact that it stays local instead of getting phoned home makes this ok to leave running.

question for v2: does the index know when two conversations are about the same thing but happened in different tools, or is it just full text search? that dedupe is where this gets scary useful.

The context loss when you switch from Claude Code to another agent mid problem is exactly what kills me, so pulling it all into one searchable memory over MCP is a great idea. Does it keep the full transcript searchable or summarize once a session gets big? Congrats on shipping.

how does it actually capture the conversation from agents like Codex and Copilot, do you have to wrap the calls or does it hook into the terminal session itself?

finally something that pulls all my agent chats into one place without sending data anywhere, the MCP search back into claude worked surprisingly well on my session history

How does the indexing actually work under the hood when the agents stream output live, do you buffer the full transcript and re-embed after each turn or keep an incremental vector index in sync as the conversation grows?

Hello Product Hunt! I’m thrilled to introduce EasyAR Mega to the community today. 🚀
For years, AR development has been trapped in a "small-scale bottleneck." Building large-space AR navigation or city-scale AR marketing required massive budgets, specialized hardware, and months of tweaking.
We built EasyAR Mega to democratize spatial computing. We wanted to give every indie developer, creator, and studio the superpower to turn the physical world into a digital playground using just a smartphone or a consumer-grade 360 camera.
Whether you want to build:
🏛️ An interactive AR tour guide for an ancient museum,
🛍️ An immersive cyber-punk shopping mall assistant,
🎮 Or a massive city-wide multiplayer AR game...
EasyAR Mega has your back with out-of-the-box Unity plugins, WeChat mini-program integrations, and full XR headset compatibility.
🎁 Special Product Hunt Gift:
We would love to hear your feedback! Let us know what you'd like to build in the comments below. To celebrate our launch, everyone can enjoy our free trial credits starting today!
Let's build the Spatial Internet together. What will you create first? 👇

https://discord.gg/YNfVsYs56

Q: We care deeply about privacy and data compliance. Can we deploy this in the US, Europe, or Japan?

A: Absolutely. We are fully aware of regional compliance requirements (like GDPR in Europe or specific data localization laws in the US and Japan). EasyAR Mega is built with a flexible architecture that supports regional server deployment and strict data compliance framework isolation, ensuring that map data and user positioning requests are processed legally and securely within your target market.

Q: What exactly is EasyAR Mega, and how is it different from standard SLAM SDKs?

A: Standard AR SDKs (like basic SLAM) are built for room-scale or tabletop experiences, which easily drift in large open areas. EasyAR Mega is a city-scale Visual Positioning System (VPS) platform. It uses cloud-based spatial computing to bind digital content to persistent, real-world physical coordinates (from shopping malls to entire city blocks) with centimeter-level accuracy

Q: Is there a free tier or trial available for Indie developers or startups?

A: Yes! We love indie creators. We offer a generous free trial quota that includes free credits for cloud mapping and positioning requests so you can test and build your proof-of-concept without touching your wallet. You can scale up to our usage-based pay-as-you-go tiers as your project grows.

Q: How long does the cloud mapping process take after uploading the data?

A: For most standard locations (like a retail store, museum, or outdoor square), our automated cloud reconstruction engine will deliver a highly accurate, deployable 3D spatial map within 24 hours.

Curious how well EasyAR Mega holds up when an area gets a lot of seasonal changes, like trees leafing out or storefronts swapping signage — does it drift or need re-mapping often?

Curious how this handles GPS-denied indoor spaces like a multi-floor mall, and whether the centimeter accuracy holds up across different phone models or only flagships?

Q: Which platforms and XR headsets are supported out of the box?

A: We believe in "Build Once, Deploy Anywhere." EasyAR Mega provides robust Unity plugins and natively supports:

Mobile: iOS, Android, and WeChat Mini-Program

XR Headsets: Apple Vision Pro, XREAL, Rokid, Meta Quest（in development）, and Pico.

Q: What kind of hardware do I need to scan an environment and create a map? Do I need expensive LiDAR?

A: No, you don't need expensive specialized gear! EasyAR Mega offers ultimate flexibility in data acquisition. You can use:

Smartphones: Standard iOS or Android devices.

Consumer hardware: Consumer-grade panoramic cameras (e.g., GoPro Max).

Professional gear: High-end laser scanners (like XGRIDS) for ultra-massive or highly complex industrial environments.

How does the centimeter accuracy hold up in changing lighting or crowded outdoor scenes, and is the VPS data something I capture myself or does it need to come from your prebuilt maps?

Hey Product Hunt,

I built Macuse because AI assistants are getting incredibly good at reasoning, but on macOS they still often hit a wall: they can answer questions, but they cannot reliably act across the apps where your work actually lives.

Macuse is a native macOS app that turns your Mac apps into local tools for AI assistants.

It runs as a local MCP server, connecting Claude, Codex, Cursor, Raycast, and any MCP-compatible client to your Mac. Your AI can manage Calendar events, read and draft Mail, work with Notes and Reminders, search Contacts, send Messages, and use Computer Use to click, type, scroll, and navigate real app interfaces.

A few things I cared about while building it:

• Local-first: your Mac app data is processed locally
• Permissioned: every connection requires approval and can be revoked
• Multi-client: one Macuse setup works across your AI tools
• Native integrations: Calendar, Mail, Notes, Reminders, Messages, Contacts, Shortcuts, Maps, and more
• Computer Use: control apps that do not have APIs, without taking over your active cursor/window

The goal is simple: make your AI assistant useful inside the Mac apps you already use every day.

I’d love feedback from Mac users, MCP builders, and anyone experimenting with AI agents on desktop workflows.

Love how it stays truly native on macOS instead of wrapping everything in an Electron shell. Tying Calendar, Mail, and Notes together through one MCP layer is the kind of plumbing I have been waiting for.

The 'every connection requires approval and can be revoked' line is what matters most to me here, but how granular is that approval? Is it per-app (grant Calendar but withhold Messages/Mail), or one grant per client that then covers everything Macuse can touch? And for outbound actions like sending a Message or letting a Mail draft actually go out, is that gated separately each time, or does the initial connection approval cover silent sends?

finally something that lets me ask claude to pull events from my calendar without weird workarounds, super clean mac feel too

Finally a clean way to let Claude actually touch my Mac apps without weird workarounds. Setup with Calendar and Mail took like two minutes and the permissions prompt felt transparent instead of sketchy.

How does Macuse handle permissions when giving an AI client access to Mail and Messages, and is there any sandboxing or audit log so I can see exactly what got read or sent?

Finally a clean way to let Claude actually read my Mail and Calendar without weird workarounds. The MCP setup was painless and everything stayed local.

Finally something that lets Claude actually touch my Mac apps without me copy-pasting between windows. Calendar and Mail worked right away once I granted the permissions, and the MCP setup was painless.

Does Computer Use actually click around in your real apps without sandbox issues, or do you need to grant a bunch of accessibility permissions first?

mcp calls and computer-use have very different blast radii — reading a calendar is a scopable tool call, but 'real app control' via computer use is an unscoped click that does whatever the frontmost app can. the gate between those two is the hard part

the fact that it sits natively in the menu bar and just hands AI tools straight access to calendar and mail without weird workarounds is genuinely thoughtful engineering

Hello yuexunjiang, I like the design a lot, it looks elegant. I have a quick question. What about the privacy issue? This AI agent will have access to all my emails, messages, reminders, prompts from my macOS apps? Congrats on the launch, let's connect!

How does it handle permissions when it needs to actually click around in apps via Computer Use, especially for things that need accessibility access every launch?

How does it actually handle permissions when an AI wants to send a message or move a calendar event, do you get a prompt each time or is there a way to set trusted rules upfront?

Hi Yuexun, the reassuring part for me is that it stays helpful without ever feeling like it might run off and do something behind my back. That sense of staying in the driver's seat matters a lot to me.

connecting MCP clients to native mac apps is the missing layer for local workflows. right now claude can browse the web and run code but can't touch the calendar sitting right there on the same machine. the messages and mail access is where it gets interesting and also where it gets risky. what does the permission model look like? per-app grants, per-action approval, or one big trust decision at install? that choice basically determines whether people feel safe using it for anything real.

This looks great for the AI can reason but can't reach my apps problem. One thing I'm curious about: when Computer Use is driving clicks and typing in an app without an API, how do you handle it if it clicks the wrong thing in Mail or Messages before you can stop it? Is there some kind of review or undo step, or does it just fire at full speed once permission is granted?

Kris from @Basedash: AI data analyst here! Super excited about this launch. We built this because we realized that we often wanted to act on all the insights we were getting from our own product. But carrying out these write tasks is still pretty tedious for a non-technical user, whereas technical teams need guardrails in place before non-coders like me can update the database. So we built both :)

Now the agent writes the fix, shows you exactly what it's about to run, and waits for your approval. One click. All with tons of controls. Admins enable edits per connection. Every tool gets its own permission level. Routine operations run automatically, sensitive ones pause for a human, dangerous ones stay blocked. Your rules, enforced every time.

We've run this internally for weeks and it has already changed how our own team operates. The person closest to the customer fixes the issue.

Happy to answer anything!

Love that the natural language input sits front and center instead of burying it behind a SQL editor or settings panel. The "describe what you want" framing makes it feel like a creative tool rather than another BI dashboard.

Finally something that lets me skip the SQL step entirely. Connected my Postgres in a couple minutes and asked for a churn chart by plan, which came out surprisingly clean without any fiddling.

Curious how this handles more complex queries that need joins across multiple tables - does it figure out the relationships on its own or do you have to map those out somewhere first?

Connected a Postgres database and asked it to show weekly churn by plan. The chart generated in seconds and was actually accurate, which I was not expecting.

How does it handle complex joins across multiple tables when generating charts from natural language?

Connecting my Postgres was painless and the natural language chart builder nailed the visualization on the first try. Wish the dashboard sharing flow was a bit smoother though.

The conversational UI for generating charts feels really considered, you can tell the team obsessed over the small stuff like how follow-up questions modify the existing visualization instead of starting from scratch.

Connected my Postgres database and asked it to show weekly churn by plan in plain English, and it built the chart in seconds. Way easier than dragging fields around in my usual BI tool.

How does it handle really messy or unstructured data sources, like pulling from a NoSQL database or a third-party API that doesn't return clean tables?

The natural language to chart flow feels really polished, like the team actually thought through what happens when a query doesn't quite match the data. That's rare in AI BI tools.

Preview plus approve/reject covers the intent check nicely. The one that's bitten me: the affected-row count you show at preview is a separate query from the write, so under any concurrent traffic the number I approved and the number that actually changes can drift. Do you run the count and the update inside one transaction, or is that preview count more of an estimate?

An LLM writing and running mutating SQL against a real database is exactly the part I'd want the guardrails on. When it says 'update the state of a hundred items', does it show me the statement and the affected-row count before it runs, and does it wrap the write in a transaction I can roll back? The classic failure is a dropped WHERE turning a 100-row update into 100k, and the model sounds equally confident either way. Preview plus row-count plus rollback is what would get me to flip 'allow edits' on in prod.

How does Flowly actually pull data across all my open tabs and apps at once, is that local on-device stuff or does everything get routed through your servers?

Runs natively on your own machine with your own model keys, plus a persistent memory of your world rather than just a chat log - that's a meaningfully different bet than most desktop agents. How does it decide what's worth remembering vs noise, is that tunable per-user?

Congrats on the launch! Flowly looks really interesting.

I wanted to try the mobile app, but it seems unavailable in Latin America. Is the mobile app currently limited to selected countries, or do you have plans to open availability for LatAm soon?

This is interesting a personal AI with memory could be really useful. Curious how people can manage or edit what it remembers?

Hakan, most helpers happily tell you how to do a task and then leave you to it, so one that quietly carries it out for you is refreshing. The quick shortcut to summon it is a nice touch too.

To answer your self-host question: full offline capability with a local model, plus a visible action log I can actually audit after the fact - not just sandboxing. Right now the biggest trust gap with agents that touch my real data isn't whether they're capable, it's that I usually have no idea something happened until after it already changed. Open sourcing the core is a good first step toward that.

the notch overlay is such a thoughtful choice, feels way less intrusive than another floating widget. really nice execution on something most AI tools get wrong.

The "memory of your world" being a model that tracks what changed and self-corrects is what would make me actually keep an agent around — a flat chat log always rots. Since data stays on-device but you've got Mac + iPhone in sync, how does that sync actually move: peer-to-peer / local network, or through a relay you host, and where does the memory live when one device is offline? And with the core open-sourced, is the memory store a documented local format I can inspect and back up, or an opaque embedded DB?

How does the verified routing actually work in practice, and does it require setting up SPF/DKIM on my domain or is that handled on your end?

the attachment support without forcing an upgrade just to test the real flow is a nice touch, shows respect for the people actually trying to figure out if it works for their use case.

Finally something that handles attachments without making me write a backend from scratch. Took about ten minutes to wire up and the verified routing actually felt safe, not just slapped together.

How does the attachment size limit work on the free plan, and does the API allow sending through custom domains or only the html.contact subdomain for replies?

love that the free plan lets you actually test the real form setup with attachments and verification instead of just a stripped-down demo, shows respect for the people trying it out.

@hellowilly, the small stuff on a simple site is somehow always the most annoying, and a contact form that just quietly works and lands in your inbox removes a real headache. Feels like one less thing to worry about.

Since this is meant for plain HTML forms with no JS framework required, how do the spam controls actually work without something like a CAPTCHA widget? Is it server-side honeypot fields and rate-limiting, or would I still need to bolt on some client-side JS to get real protection against bot spam?

Honestly impressed that the Free plan mirrors the real setup instead of locking core features away, that kind of honesty is rare and it makes the upgrade feel earned rather than forced.

Plugged in a simple contact form and had it sending real emails with attachments in under ten minutes, which is wild for something this no-frills. Love that the free tier actually works the same as the paid one instead of gimping the important stuff.

How does the Free plan actually work in practice, like is there a limit on submissions per month or any kind of branding injected on the emails sent out?

set it up on a static site and the form was actually sending mail within like ten minutes, which never happens for me. the domain allowlist feels like a small thing but it's nice not having to babysit the inbox for spam.

Honestly didn't expect the free tier to actually let me send a real test email with an attachment before any paywall. Routing by domain and the simple export to CSV made it feel like a grown-up tool, not a stripped-down trial.

How does the verified routing actually work in practice, do I need to set up SPF or DKIM on my own domain, or does html.contact handle that for me?

Hey Product Hunt!

Pourav here, PM at CometChat.

Your players can now talk to each other mid-match, right inside the game — no alt-tabbing to other apps, no bolted-on voice app. We built the CometChat Unreal SDK because we kept hearing the same thing from Unreal Engine studios: chat gets added last, and it's always the part nobody budgeted time for.

So we built a native plugin instead of a wrapper. The CometChat Unreal SDK is a real UE5 plugin: a GameInstanceSubsystem that owns the chat lifecycle, latent async nodes with Success/Failure pins for Blueprint, and multicast delegates for incoming events, all firing on the Game Thread so it's safe to update UI directly.
Messaging: 1:1 and group, with history and pagination.

Real-time events: 40+ delegates covering messages, typing, receipts, reactions, presence, calls, and connection state.

Groups and moderation: create, join, leave, member management, and message flagging built in.
UI, if you want it: a drop-in chat panel and toggle button, or build your own on top of the subsystem.
It runs on Windows, macOS, iOS, and Android, works in C++ or pure Blueprint.

Try it, and let us know what you think! We're in the comments all day.

finally tried this with a side project and the moderation controls are way more useful than i expected, caught a spam pattern i'd have missed on my own.

I haven't used this product yet but I use comet chat for other products and I have always had a great experience with them.

How does pricing actually work once you start layering in voice, video, and the AI Agents — is that bundled or do usage-based costs stack up quickly at scale?

How does pricing scale as concurrent users and AI agent usage ramp up on the platform?

The GameInstanceSubsystem owning the chat lifecycle is a smart call for UE5, but I'm wondering how it behaves during seamless travel when the world tears down mid match. Does the connection and message history survive a level transition, or do players effectively rejoin the chat each time?

For competitive lobbies where toxicity is a real problem (think 100-player battle royale), is moderation happening server-side before a message reaches other clients, or is it client-side filtering that a modified client could just bypass? That's usually the actual pain point studios hit with in-game chat, more than wiring up the API itself.

Nice launch. In-game chat is one of those things that sounds simple until you actually have to build it, so having a ready-made SDK here makes a lot of sense.

How does the AI Agents piece actually work under the hood. Is it your own models or can I bring my own and fine tune it for our specific use case.

Finally a way to actually see what my agents are doing under the hood. The replay feature saved me a ton of time figuring out why one tool call was looping.

Forking a run like a git branch is exactly how agent debugging should work. Replay alone rarely helps when the failure came from one weird tool response ten steps in.

Also went through your forum thread on separating real regressions from provider noise — nice to see nondeterminism treated as a first-class problem (first-divergence diff + verdict) instead of being waved away.

One thing I couldn't find though: when everything downstream of the fork runs live, do the agent's tool calls actually execute?

I work on agents with real side effects (checkout, payments, emails), and mocking those from the recording would be the difference between "safe to fork production runs" and not.

finally something that lets me actually see why my agent broke instead of digging through logs. the replay view caught a tool call loop in seconds, super useful.

The first-divergence approach surfaces an input-side problem I've hit in my own agent harness: volatile tokens the harness itself embeds in prompts — timestamps, run ids, sampled examples — make every replay look like it diverges at step 1, before any real regression. My fix was blunt: ban wall-clock and randomness inside the orchestration layer entirely (time gets injected as an argument), so replays are byte-stable by construction. Curious where Retrace draws this line: do you normalize/mask known-volatile spans when computing first divergence, so a timestamp delta doesn't count as a fork point — or is the recommendation to make the harness deterministic upstream, like I did? And if it's masking, is the mask list configurable per project? Feels like the difference between a diff you trust and a diff you learn to ignore.

The failures that actually bite me only show up on a real user's weird input in prod, never when I'm testing locally, so recording a run and replaying it after the fact is the dream. Two Qs: can I ingest traces from a deployed backend (not just a local dev harness), and since those recordings carry real user messages, is there any redaction/PII control before a trace gets stored or shared?

Spent a few minutes replaying a flaky agent run and being able to fork the exact trace to try a different prompt without rerunning the whole thing was honestly a nice surprise. The tool call breakdown finally makes it obvious where my agent was looping.

How does the free tier handle traces that get close to the limit mid-session — does it cut off or let you finish and just throttle new ones?

Love how clean the replay view is, being able to scrub through each LLM call and tool invocation without losing context makes debugging agents feel way less like guesswork.

The replay feature is genuinely useful, I reran a flaky agent run and could pinpoint exactly where it stalled without digging through logs. Free tier is enough to actually evaluate it before committing.

Finally something that makes debugging AI agents less painful. The forking feature let me branch off a stuck trace and rerun it with a different prompt in like a minute. Super practical for anyone shipping agents right now.

this solves a problem every team building agents eventually runs into.

how well does it scale when an agent has dozens of tool calls, nested workflows, and multiple sub-agents? would love to know how you've approached visualizing complex traces.

congrats on the launch!

The replay-from-tape answer makes sense for stopping side effects re-firing, but there's a subtle failure once you fork and swap the model. The new branch might call the same tool with different arguments than the recorded run did, so the taped response is now the answer to a question the new path never asked. Do you match a replay on the tool name only, or on the actual call arguments, and what happens when a forked run makes a tool call that has no matching entry on the tape?

the git branching analogy for forking a run is the right mental model, most "replay" tools stop at showing you what happened instead of letting you actually change the input at the broken step and re-run from there. i've lost hours re-running an entire agent chain from scratch just to test one fix at step 8. does forking work if the tool call at that step had side effects, like a real API write, or only for pure LLM steps

The fork-as-git-branch model is the right call for agent debugging — re-running a whole prompt throws away the exact upstream state that caused the break. The thing I'd need pinned before wiring this into a real stack is side effects: when a forked run re-executes downstream live, does a tool call that writes to a DB or hits a payment/email API actually fire again, or can you stub specific tools so a fork doesn't repeat real-world writes? Being able to mark tools as replay-only vs live seems like the difference between using this on prod agents or only read-only ones.

Replay + fork is exactly how agent debugging should work. Today my 'debugging' is reading transcripts of production calls and guessing which turn derailed it - being able to fork from the exact step and test a fix against the same context would save hours. Does it work with voice agents / live conversation logs, or is it aimed at tool-calling agents? Congrats on the launch.

For Retrace, when you say users can replay and fork runs, does the fork preserve the full context of the original AI agent run, or is it more about starting from a selected point in the trace? I can imagine both being useful for debugging, especially when a bad tool call or prompt change happens midway through a run.

The drag-to-position subtitles directly on the video canvas is such a thoughtful touch. So many tools force you into separate preview windows, so this feels like it actually respects how creative work gets done.

The drag-to-position directly on the video canvas is such a smart workflow choice, makes fine-tuning placement feel natural instead of fighting with sliders.

How does the qsub2 file format handle compatibility with other subtitle tools or editors like Premiere or Final Cut, or is it pretty locked into its own ecosystem?

this looks cool

SwiftUI native on Mac is rare for video tools, nice to see. Dragging subtitles straight on the canvas feels way faster than keyframing in Premiere. Timeline zooming down to 0.1x is genuinely useful for tight caption timing.

the on-canvas drag-and-rotate is the good part — the quiet killer is preview-to-export parity. canvas previews at display res, the burn-in composites at source res, so rotated text anti-aliases differently and slips off the placement you set by hand

Finally tried this and the drag-on-canvas positioning feels really natural, especially with the timeline zoom for fine-tuning cues. Batch styling multiple subs at once is a nice time-saver too.

I like the focus on subtitle editing instead of a full video editor. Can Quick Sub 2 work with subtitles generated by AI tools, so users can fine-tune the styling and timing afterward?

SwiftUI-native and the timeline precision at 0.1x is genuinely useful for tight caption sync. Batch styling across multiple objects worked exactly as advertised, though I wish the canvas allowed nudging with arrow keys for pixel-level tweaks.

The direct-canvas dragging plus batch styles across objects is the part that would actually save me time — positioning subtitles in a timeline-only editor is the tedious bit. Two workflow questions before I'd switch a video over: on export, does it burn the subtitles into the video, or can it also output a separate .srt/.ass sidecar so I can re-edit captions elsewhere? And does the .qsub2 file save styling as reusable presets I can reapply across a whole series, or does each new video start from scratch?

Love that you can drag subtitle objects right on the canvas instead of fighting a properties panel. With that 0.1x to 10x timeline zoom, does the dragging snap to anything at the fine end, or is it pure freehand timing when you're lining up a fast cut?

the direct canvas control is what most subtitle tools get wrong. dragging text exactly where you want it instead of picking from preset positions sounds small but it's the difference between subtitles that work with your footage and ones that fight it. does it handle auto-transcription too or is it purely for styling and placement after you have the text?

Does the dynamic timeline let you snap to frames or audio cues, or is it strictly time-based scrubbing? Trying to gauge how it handles tight lip-sync adjustments.

Hey Product Hunt 👋

What if AI didn’t wait for you to open a chatbot, write a prompt, and explain your context?

That’s the idea behind Adsideo.

Adsideo is a proactive ambient AI layer that understands what you’re doing across your Mac: your screen, meetings, writing, code, tasks, and memory, then helps at the right moment.

Instead of asking you to bring context to AI, Adsideo brings AI to your context.

It can help you:
• turn meeting asks into tasks
• improve prompts before you send them
• draft email replies using thread context
• explain and deep-dive code errors
• answer questions like “what did I work on today?”
• protect private apps/files from being captured

The bigger bet is that most people won’t use AI by opening 10 different tools every day. AI needs to become a natural layer on top of everyday computing.

Adsideo starts with Mac, but the vision is broader: ambient intelligence that works before you ask.

We’re launching early and would love honest feedback from the Product Hunt community.

What would you want an ambient AI layer to understand or help with?

https://adsideo.ai

Interesting idea, and although I would not use today, mainly due to I'm getting frustrated by more and more subscriptions I need to sign up to for tools that may or may not help. I'm bootstrapping my own solo indie developer venture so I have to be very strict on which tools I give my money to. That said I do see the potential this tool provides and I may look again in the future. I do think that for other people happy to agree to yet another subscription this tool could be a game changer, and for that reason you still get my vote.

Best of luck with the Launch.

The “AI before you ask” is really interesting. I’ve seen my colleagues and even I avoid AI tools because we need to think what could be the right prompt which is an extra effort. If Adsideo can make AI useful without requiring people to learn prompting, that could be a big shift.

I'm intrigued by the idea of an ambient AI layer. One potential improvement could be to add a transparency feature, so users can see what context Adsideo is picking up and how it's being used to generate actions.

What safeguards do you use to ensure sensitive info never gets exposed or acted on without explicit permission? And are there easy ways for non-technical users to customize what context Adsideo can access?

Hi everyone!

Have been using ZCode since its first version late last year. With the recent 3.0 upgrade, it's taken a massive leap forward. Here are a few reasons it’s worth a try:

It’s the official harness for GLM-5.2. Yes, try the tuning that understands the true upper bound of GLM-5.2 better than anything else!

It’s built for long-horizon execution. The in-house ZCode Agent keeps your files, terminal output, browser context, and Git state stitched together in the same task loop, so it doesn't lose the thread halfway through a complex build.

It lets you step away from the desk. You can steer the agent, check progress, and kick off tasks from your phone, or directly through messaging bots while the desktop keeps running.

And it’s moving fast!

Congrats on the launch, MIT licensing is a nice touch. Quick question though, are there any rate limits or token caps per day for free users right now, or is the unlimited angle going to hold once traffic picks up?

The reasoning model handled a tricky coding question without any hand-holding, which caught me off guard. Clean interface, no fuss, just straight to the response.

Tried the reasoning model on a tricky coding question and it walked through the logic clearly instead of just spitting out an answer. The bare-bones UI is kind of refreshing honestly.

Reasoning mode actually explains its thinking out loud, which helped me catch where it went off track. Nice to see solid open weights like this finally have a clean free playground to test in.

The minimal UI really lets the models speak for themselves, no clutter or upsells getting in the way of actually trying them out.

Tried the reasoning model on a coding problem and it nailed a tricky edge case I'd been stuck on for an hour. Really appreciate that the base models are MIT-licensed, makes it easy to experiment locally without worrying about restrictions.

Congrats on the launch! 🚀

Long-running coding agents are where things usually get difficult, especially when the agent has to keep file state, terminal output, browser context, and Git changes aligned across one task.

Curious how ZCode handles situations where the agent gets stuck or starts making risky edits. Does it surface checkpoints or risk summaries before continuing?

Curious how the rumination model handles long context sessions compared to the base version?

the minimalist UI actually lets the models breathe instead of competing with them. nice to see a platform that trusts the work to speak for itself.