PH热榜 | 2026-06-21

This is a sharp wedge for vertical-agent teams. The part I’d want to understand is the customer boundary: when each customer gets a persistent agent with tools, are credentials, approvals, memory, and action logs isolated per customer by default, or does the founder wire that layer around the hosted agent?

Huge congrats for @vishnukool on getting this out the door. cuts out a ton of standard marketing ops busywork for small teams, love that it's an api layer instead of wrestling with individual cli instances. excited to see where you take it next.....

Not everyone wants to maintain VPS instances or Mac minis. This looks like a much cleaner path.

For me, the most intresting part isn't the hosting itself, it's the shift in how founders can think about AI products. Instead of building one shared assistant

Congrats. I wonder how does Agent 37 ensure complete data isolation between tenants sharing the same underlying infrastructure?

This is super helpful! I have my own home lab but it already full of self hosted services and I don't want to host my OpenClaw or Hermes on the same machine with all my data. At the same time I don't want to buy another home lab like some Lenovo mini pc or Mac Mini which here in Canada we should wait at least until September.

I like that you made this product privacy friendly looking at your privacy policy!

The per-customer always-on agent angle is the useful part here. I’ve seen the same “agent infra becomes a babysitting job” problem once tools and permissions differ by client. Curious how you separate tenant secrets/tool scopes when an agent is idle most of the time but still persistent?

Judging by the cost structure, this appears to be serverless. I’m curious about the latency on cold paths and whether usage-based billing will apply later on.

The unit economics here look incredible for SaaS teams wanting to deploy dedicated AI features. At $3.44/mo per customer agent, how do you handle resource provisioning if a client's agent suddenly experiences a massive spike in background workflows or data processing?

The interesting constraint here is handoff quality: if each customer gets a persistent agent, the audit trail and failure state need to be as easy to inspect as the API call is to start.

This solves the exact tradeoff I keep running into. I looked at self-hosting part of my own AI agent stack to save on a per-minute provider fee, and the math always won on paper but lost in practice: the real cost wasn't the server, it was becoming the person who gets paged when an agent goes down for a paying client. $3.44/mo always-on, not-my-problem-to-babysit is the right price for that peace of mind. Curious how you handle noisy-neighbor isolation between customer instances at that price point.

Love this! But - are you compliant with EU laws?
Under what jurisdiction does your company run and do you have a DPA for EU/GDPR compliance?

Congrats on the launch @vishnukool. Turning a complicated infrastructure problem into a simple API is not easy.

The per-customer agent model makes a lot of sense for vertical SaaS. What I keep thinking about is observability: how does a founder show their client what the agent actually did?

Logs the end customer can understand, or at least something you can point to when they ask, "what did it do this week?" Is that on the roadmap or already there?

Congrats on the launch 🚀 Agent 37–38 looks sharp, excited to see how it pushes AI agents into real workflows!

This is super helpful! We had to spend considerable for building agent creator for our customers and the infra work there was no way a competitive advantage.

Atomic Mail Agentic gives autonomous AI agents their own real `@atomicmail.ai` inbox to manage end-to-end — no human setup, verification, or ongoing intervention.

Problem → Solution: Agents need email but current setup requires manual domain verification, CAPTCHA, credit cards, and mail-server ops. PoW signup delivers a real inbox in ~30 seconds — completely hands-free.

What's Different: Built on JMAP (RFC 8620), the standard mailbox API LLMs already speak fluently from training data. No vendor SDKs to learn, no hallucinated request shapes = fewer retries.

Features:

• Full mailbox API (read/send/mail, drafts, threads, search)

• MCP/AgentSkill/REST API/CLI integrations

• bundled JSON presets (`send_mail`, `list_inbox`, `reply`)

• embedded `help` docs

• plain-language error hints with `_next` steps

Benefits: Agents finish without asking users for anything, messages actually arrive (warming IP pool + relay overflow), no vendor lock-in (portable JMAP), get unstuck automatically inside the integration.

Who & Use Cases: For AI agents on Claude Code, Codex, Copilot, Cursor, Hermes, OpenClaw, Pi, Kilo Code. Use cases: newsletter digests, support inbox, user research interviews via email survey.

Open Alpha: Free accounts, 100MB storage, strict rate-limits. Public stable release coming soon.

P.S. I hunt the latest and greatest launches in tech, SaaS and AI, follow to be notified → @rohanrecommends

The hard part with agent-owned email is not just getting an inbox, it is controlling what the agent is allowed to send. For support or research flows, do you expose approval and receipts per outbound thread, or is the mailbox treated as fully autonomous once provisioned?

The JMAP choice makes sense since LLMs already understand it from training, so fewer hallucinated API shapes. The thing I'd want to understand is deliverability reputation at scale.

If one agent on the shared IP pool sends something spammy, does that affect inbox placement for everyone else? Or are reputations isolated per inbox?

Most agent tools still rely on a human to get the inbox set up and verified. Removing that step changes who can actually deploy agents end-to-end without babysitting onboarding.

Curious how “no ongoing intervention” holds up once an agent sends something it shouldn’t?

Congrats on the launch!

The agentic angle on email is the part I'd want to pin down: how much does the agent act autonomously vs. propose-then-confirm? For triage and drafting I'd trust autonomy, but anything that hits send or moves money I'd want a human gate by default. Is that configurable per action type, and does it keep an audit trail of what the agent did on my behalf?

As someone who spends a surprising amount of time in email, I'm fascinated by how quickly AI agents are moving from drafting messages to actually managing workflows. Curious to see where people draw the line between delegation and keeping a human touch in communication. Congrats on the launch.

Congrats Team! Excited to see Atomic Mail Agentic live! Turning inbox chaos into agent-driven clarity is exactly what users need.

The JMAP choice is the interesting bit to me. Email is usually where agents get stuck on setup rather than reasoning, so giving them a real inbox in ~30 seconds makes sense. Curious if you’ve seen deliverability issues when many agents send low-volume but highly varied replies.

When multiple agents share the same inbox for different use cases like support and newsletters, how does the system isolate threads so one agent doesn't accidentally read or act on another's messages?

The 'zero human intervention' aspect is incredibly powerful, but definitely a bit terrifying for brand safety! What kind of policy guardrails can we place on the inbox level to ensure an autonomous agent doesn't hallucinate a promise or discount we can't actually honor?

Curious how the agent handles fact checking when pulling information from the web. That could make a huge difference for professional use cases.

Grok for Word is an AI-powered Microsoft 365 add-in by xAI that lets you draft, restructure, and tighten wording directly inside Word from a docked panel.

Documents often become inconsistent when edited by multiple people, and rough notes or messy drafts need restructuring. Grok solves this by structuring rough notes, fixing grammar, tightening phrasing, and normalizing heading styles — all in place, with everything staying editable and undoable.

Instead of a separate tab or tool you copy back to, Grok works beside your document in a docked panel, reads what you point it at, and applies edits directly in the file with before-and-after comparisons for every change.

Key Features:

• Structure rough notes into formatted Word headings and lists

• Inline edits: fix grammar, simplify language, tighten phrasing

• Before-and-after view for every change

• Normalize heading case and align terms across the whole file

• Editable, visible revisions you can keep or undo one by one

• Free to add from Microsoft Marketplace; admins can deploy org-wide

Benefits:

• Faster drafting and restructuring without leaving Word

• Consistent, professional documents across teams

• No merging copies — edits land directly in your file

• Everything stays editable and fully undoable

Who It’s For & Use Cases:

• Writers, editors, and business professionals using Word

• Teams collaborating on reports, proposals, and documents

• Anyone needing to structure rough notes or maintain consistent styling

Try Grok for Word free from the Microsoft Marketplace!

P.S. I hunt the latest and greatest launches in tech, SaaS and AI, follow to be notified → @rohanrecommends

I can see this becoming part of many teams daily workflow if the generated content maintains quality and consistency across large documents.

Having the AI live inside Word instead of forcing users to copy-paste content back and forth is probably the biggest advantage here. I did notice the discussion around needing a SuperGrok subscription to use the add-in though. Curious how that impacts adoption and whether you've considered a free trial. Congrats on the launch!

is it launched by spaceX? or is it a clickbait?

I write every day, and one thing I've learned is that getting words on the page is usually the hardest part. Tools that help people move from a blank page to a workable draft can be incredibly useful. Congrats on the launch!

Interesting idea. Does it keep context across a doc session, or does each prompt start fresh?

Hey PH! 👋

The problem I kept hitting: I'd hand a task to my AI coding agent… then just sit there watching a terminal. Babysitting. The agent's grinding away and I could've been shipping the next thing — or playing a game.

So I built Backgrind: an always-on-top overlay that runs your agent (Claude Code, Cursor, or Grindy, my built-in one with zero setup) over any app — even a fullscreen game. It only pings you when it actually needs a decision. Fire it, walk away, come back when it taps your shoulder.

It started as a scrappy "let me see the terminal over my game" hack and grew into a real thing: multi-agent tabs, voice, Build/Plan, click-through, local-first (your login, your history — nothing new to trust). macOS & Windows.

So now I'm grinding with my agents. 🚀

"Only pings you when it actually needs a decision" is the detail that matters more than the overlay itself. I run AI agents handling real customer calls and the thing that actually saves me time isn't watching a dashboard, it's a notification that only fires on the exceptions. Most monitoring tools dump everything and let you drown; the bar should be silence by default, signal only when a human decision changes the outcome.

What models are used and what can you tell about safety?

Curious how click through works in practice when the agent needs to briefly take over the screen.

The always-on-top overlay concept is clever but I keep running into the same mental model question - if the agent is doing work I trust, why do I need it floating over my screen at all? The value prop seems to be "run agent + do something else" but the overlay creates its own attention tax. The games angle makes for a good demo but I'm not sure that's where the real workflow is. Most of the people I know running AI coding agents want to context-switch to something productive, not a game, while it runs - and for that a simple tray notification would work fine. The multi-agent tabs are more interesting than the headline use case. What does the breakdown of actual usage look like between the "play a game while it runs" vs genuine parallel work scenarios?

Now Supports Windows + Linux alongwith Mac,
Mac Installer Fixed and Everything works buttery smooth.

Give it a try . ITS FREE!!!

the "without breaking flow" pitch is the real test for any clipboard manager, and it usually comes down to whether the overlay closes itself cleanly after you pick something or whether you're left fumbling to dismiss it and get back to typing. curious what the actual interaction feels like after picking an item, does it auto-paste, auto-close, or require an extra step

The "⌘V for instinct, ⌥V for memory" framing is the smartest part of this — sitting next to the system clipboard instead of trying to replace it is exactly where most managers overreach. As someone shipping a Mac app, curious whether the liquid-glass overlay is rendered natively per platform or one shared layer across macOS/Windows/Linux?

This looks like a massive workflow upgrade! Since marketers copy a million UTM links and hex codes a day, is there a way to 'pin' or 'star' frequently used items to the top of the overlay so they never get buried in the history?

Been using Maccy for years and never thought about switching. The liquid glass UI and cross platform support changed that.

The fact that it's open source seals it.

Nice work. 👏

Very clean and clear idea and website. Up to how many messages can the clipboard support in memory before it starts replacing the oldest with the newest messsage (text or photo). Congrats on the launch!

Love the focus on keeping the user's flow unbroken. The liquid glass UI looks incredibly slick!

Yeah Vishesh! It's amazing. HAving the history would save a lot of time for me cause I always work with the same assets so I need to go and go again to take the same resource. Really glad to see you helping on this!

Poolside has been building quietly for a while and this is the payoff. The 256K context window is the real story for agentic coding - that's where most code agents fall apart, when context fills up halfway through a refactor and the model starts losing track of what it already changed. 23B active params on Apache 2.0 is a strong combo for anyone who can't send proprietary code to closed APIs. Curious how it holds up on actual multi-file editing tasks vs synthetic benchmarks - that's usually where the gap between lab numbers and real workflows shows up. Nice launch.

Open weights at the frontier! @Poolside released this week Laguna M.1, their most capable model to date, with 23B active params and a 256K context window, now Apache 2.0 on both checkpoints.

Run it on your own infra, evaluate it in your own harnesses, fine-tune it, and build on it directly.

H/O to founders @eisokant and @jasoncwarner. OSS ftw!

The open weights are what caught my attention. Being able to run and tune a serious coding model on your own infrastructure is a big plus.

The 256K context window at 23B active params is a strong architectural bet. Long-horizon agentic tasks without chunking is where most models fall apart. We've been navigating the inference infra tradeoff for our own agent layer, and self-hosted open weights changes the calculus significantly. How does Laguna handle attention at max context? Any sparse attention or positional tricks that keep it tractable?

Apache 2.0 on both checkpoints is the real unlock here. Most frontier-level models stay closed exactly at the point where they become useful for production workloads.

Curious what fine-tuning looks like for teams that want to specialize it on a specific codebase or domain. Is that straightforward with the current weights?

Open weights with a 256K context window at 23B active params is a big deal for agentic coding, that should really help on long-horizon refactors where context runs out fast. Curious how Laguna M.1 holds up inside Cursor or Claude Code style loops. Congrats on shipping.

Exposing backup operations through MCP is clever. Claude and Cursor already have context to know when a restore makes sense, so meeting devs inside the IDE is the right call. We've been thinking about which operations are safe to put behind MCP in our own agent workflows. How do you handle the auth model? Is confirmation required per action, or scoped at the connection level?

Can you manage what the MCP server is allowed to touch, like only one repo or one workspace?

One prompt rescheduling 300 backup definitions sounds powerful but also a bit risky if the model misreads intent. Does the MCP server have a confirmation or dry-run step before applying changes, or is it fire-and-execute straight to the API?

MCP server for backup management is a natural fit, being able to reschedule retention policies through a plain-language prompt instead of clicking through a dashboard is exactly the kind of workflow improvement that compounds over time. Curious if you plan to support Bitbucket as well?

This is one of the more practical MCP launches today. Backup configs are exactly the kind of repetitive operational surface where chat beats clicking through a dashboard, especially for bulk schedule and retention changes. Curious whether teams are using it mostly for inspection, like “what isn’t backed up?”, or actually letting it propose config changes.

The MCP angle is interesting here because backup tools sit right on the boundary between helpful automation and dangerous side effects.

The details I would want to see as a user are: read-only inspection tools separated from mutating restore/delete actions, a dry-run mode for any restore operation, clear repository/account scope, and an audit trail that shows exactly which backup was touched and why.

If those boundaries are visible to Claude/Cursor/VS Code before a tool call runs, this becomes much easier to trust. Backups are one of those domains where the assistant should be useful, but never casually powerful.

Nice launch. Backup MCP is one of those places where natural language is useful, but dangerous if the write boundary is fuzzy.

For bulk retention or schedule changes, do you make the assistant show a planned diff and receipt per account before it applies the change?

I'm curious how customizable the drop-down panel is. For me, the speed of capturing notes matters more than advanced features.

I've always thought the MacBook notch was wasted space, so this instantly caught my attention. The quick hover-to-note interaction feels natural and lightweight

the mascot being mildly passive-aggressive is a fun detail but I'm curious if it's just flavor text or if it actually nudges behavior, like guilting you about notes you never revisited. that's the kind of small mechanic that could either make this delightful or mildly annoying depending entirely on execution, curious which side it lands on in practice

This feels like a very Mac native idea. Simple focused and designed around an existing piece of hardware.

I can't help, but that note has an evil look. 😅 Like "😈". But the idea is good! :)

As another solo Mac dev, the "it does one thing" + local-only + one-time-purchase combo is the part I respect most here — that restraint is way harder to hold than it looks once feature requests start rolling in. The drawn-on notch fallback for clamshell setups is a clever touch too.

Congrats on the launch!

I was wondering what other features this product had over me just hovering my bottom right corner for the notes app popup (apart from the really cool mascot!)

I like the idea, it can be actually used a temporary buffer for AI promps of notes. For example sometimes working with Claude Code I want to continue but I don't want to miss some details in it's response. So currently I have Sublime Text opened where I put such notes.

For example I asked AI how can we reduce amount of component re-renders in my application and it gives me 2-3 options and I understand that right now which should only implement first and second option. So I will same third options somewhere and I will return to this option later this date. Sure I can always scroll up and try to find this note in the conversation itself, but it become too cluttered with changes etc

The notch as capture surface is clever because it removes the “which notes app do I open?” tax. I like that there’s also a global shortcut, because quick capture only works if it becomes muscle memory. Curious whether people are mostly using hover or the shortcut after a few days.

The notch angle is clever but I keep running into the same problem mentally - most of my actual screen time isn't near the top edge, so the muscle memory to look/hover up there takes real retraining. The "one thing" focus is exactly right for a notes app, but that value only lands if the trigger feels as instant as a keyboard shortcut. What happens for people running in clamshell mode on external monitors? That's where I spend most of my day and the notch isn't in play at all there. Would love to know if there's a fallback shortcut for that setup.

That's great!

How do you handle sensitive client docs, like where the evidence gets stored and how long you retain it?

Love the launch .. immigration attorneys are incredibly risk-averse. if the agent flags a weak spot during the core checks (like low source of funds tracking), qq. does it provide recommendations on what evidence is missing? @daniel_legaltech

The $100 flat per plan is smart pricing for a per-matter workflow. It maps directly to how law firms think about costs, per matter, not per seat or per month.

Curious what typically changes during the 30 minutes of attorney review. Is it mostly checking the financials, or do the narratives usually need rewriting too?

any free tries?

This is a very clear example of where AI actually makes sense: structured paperwork-heavy workflows that are repetitive but still expensive .

Congrats on the launch!

Can you compare versions easily, like see what changed between drafts while you’re editing?

love that the core features are free. if we don't upgrade to pro, are the basic reels and shorts blocks locked on forever, or do they expire after the trial window?

Hey Product Hunt 👋 I'm Jun, founder of snowscroll.

Why we built it:

Screen time apps don't work. They just don't. Trust me – this idea came from years of frustration. Every single time, I would download a screen time app in the morning, and find myself scrolling on Instagram at night.

They all have the same flaw: they take an all-or-nothing approach. Everyone wants to reduce their Instagram screen time, but not everyone wants to give up seeing their DMs and friends' posts.

What snowscroll is:

So, I decided to build an app that removes the addictive feeds (e.g reels from Instagram, shorts from YouTube) but keeps the parts of social media that you actually want. For example, on Instagram, you can still see stories, posts, use search, and do everything else except scroll on reels.

I've seen similar products as chrome extensions, but that's not where the "doomscrolling" audience is. People scroll on their phones, not on their laptops. snowscroll meets people where they actually are.

You have the option to further customize your experience by blocking stories, home feeds, ads, setting daily limits for each app, and seeing trends across your weekly usage (I've seen my own screen time go from 7 hours a day to 2!)

snowscroll is iOS first, core features free for all users, with a 7-day pro trial.

Privacy:

Privacy is absolutely core to the product. There is no snowscroll account, no tracking, no ads, no sale of data. All social logins and activity stays on your device. The only information that leaves is for RevenueCat. See https://snowscroll.com/privacy for an in-depth explanation!

We'd love feedback on:

• whether it feels smooth enough

• what other platforms you'd like to see added

• what other features you'd want to see in the future

Check out the demo here: https://www.youtube.com/shorts/WX34kJA6o-g

I’ve tried app blockers and always end up disabling them because I still need DMs. Removing only the feed is a much smarter compromise.

Hi is there way to filter/show warning for reels shared by friends?

Interesting. As a marketer, I tend to spend a lot of time on Social Media apps for work and somehow I end up scrolling losing track of the purpose that I opened the app for. This seems like something that is made for me.

How do you handle login and security for all those platforms, do you use official APIs?

What if I just access reels through the explore page. Did you guys make sure to close such loopholes into accessing reels?

I love how this app approaches modern parenting. Using a fair and straight forward points system, it helps teach young family members to be considerate and learn the direct impact of their decisions and actions. It has positively shifted our home dynamic from constant reminders to self-driven accountability, making everyone feel like an integral part of the family.

This app is very intentional in every aspect. I appreciate how you can customize the point system for every child and their needs. It aligns our whole family on our goals.

This app has helped me manage my point system with my son so much. It’s really given a usable tool that both me and my son can use. I don’t think there’s anything else that comes close to it on the market, thanks so much for creating this. It’s made life so much easier

We use this daily in our home. Actually my kids ask for points regularly but prefer to save them up to watch the numbers roll in. Don’t worry I have some great rewards created though ha! I stopped by to ask how many points will it allow a child to earn at max before it stops counting? This will be invaluable to know for my “investors” around here 😉

👋 Hi Product Hunt! I'm Jon, dad of five, and yes, we launched this on Father's Day on purpose.

The problem: Every reward system in our house had the same life cycle. We implement a new system. Kids are thrilled for a week. The chart or system gets forgotten as soon as Mom or Dad is stressed. Motivation dies. Repeat. With five kids, "just be more consistent" wasn't advice—it was a second job. My wife was the single point of failure for every routine in the house.

The weekend: One Saturday morning, I sat down with an idea and started building. Twenty-four hours later, I had a rough, working prototype and five very opinionated beta testers. Within the first week, habits we'd failed to establish for months started sticking. My 10-year-old hit a bathroom-related milestone I won't elaborate on. My 5-year-old organized her room without being asked. My 3-year-old was genuinely excited to earn points for using the potty.

The philosophy: We homeschool with Charlotte Mason's approach, and her core insight shaped everything: the goal of habit formation isn't compliance — it's character. Kids who want to do good, not kids who do what they're told. The points aren't bribes; they're a bridge. Kids earn by contributing real value, choose their own rewards, and somewhere in the repetition, the chart fades into the background, and the rhythms stay. (We talk about the full story on our About page.)

What makes it different from chore apps:

• 🌱 Family kiosk mode: a tablet on the counter everyone sees. No nagging required.

• 🌱 You define what counts: reading, kindness, music practice, scripture memory, helping siblings, and set the point values for each.

• 🌱 Real reward economics: kids earn, kids choose, parents approve. Contribution → value → reward, the way the world actually works.

• 🌱 Weekly insights: because character growth is invisible day-to-day and obvious month-to-month.

It's free to start (no card), on iOS, Android, and web. Setup takes about 5 minutes.

I'd genuinely love your feedback—especially from fellow parents: what habit have you tried and failed to make stick? That question is basically our roadmap.

Happy Father's Day to all the dads here! 💯

This is much nicer than everyone tracking habits separately. Seeing the whole family make progress together could actually help the habits stick.

Well,Jon is my son! I have the app, but all of our 4 kids are grown. Jon, the app is ingenious and should serve families well!

Howdy. I'm Mira—a novelist with 200+ published books across a dozen pen names, a technical writer, and solo founder.

I built Mira Gold's Writing Forge because every AI tool I tried was great for a paragraph and useless for a full book or script. They'd nail a scene, then three chapters later forget a character already died, or reveal the same twist twice, among other issues. The problem isn't that models are dumb, it's that "memory" in these tools is just chat history, which falls apart over a novel.

So I built a state-management, customized pipeline with a living canon: a single source of truth that tracks every character's knowledge-state, open plot threads, timeline, and re-grounds every chapter against the registry. Then a creative can adapt the same story concept as a novel, a screenplay, a song, or a translation—one canon, many forms of IP.

It's model-agnostic with your own API keys across 12+ providers, because I didn't want to be locked to one model's quirks (I run cross-model evals frequently across providers/models and they all have them... quirks).

I'd love to hear from other writers and creatives: Where does AI break down for you on anything longer than a short story?

Hey Product Hunt Community 👋 I'm Austin MacKay Childs, maker of Property Alpha.

When I got my start in real estate investing a decade ago, countless evenings and weekends were wasted pouring over hundreds of listings, dropping them into a rudimentary spreadsheet calculator, and praying the math on my first deal would pan out.

Fast forward and my experience in managing quantitative institutional portfolios, designing & developing machine learning applications, and owning/operating co-living properties led me to build Property Alpha. It runs every metric (cash flow, cap rate, cash-on-cash, etc..) on any U.S. residential property in seconds, using assumptions you customize to your strategy.

It's built for everyday investors trying to land their next (or first) deal while most of the market sits frozen waiting on rates.

To access the special Product Hunt free trial, go to propertyalpha.ai/pricing?promo=free30

I'll be here all day and would love your feedback. Some things I'm especially curious about:
- What part of the site is a joy to use?
- What feature/bug caused the most frustration?
- What data are you most skeptical of and why?
AMA 🙏

Analysis paralysis is one of the biggest trap when getting started. This is Sucha game changer in breaking free some excel sheets and countless hours of work for 1 property. It was able to handle almost all of the the data crunchingcomps, rental trends, and market metrics within minutes. It’s perfect for screening out bad deals fast so you don't waste hours on them. Just make sure to still do your own real world due diligence before pulling the trigger!

Using Zillow to research properties was limited, and required me to use a custom excel template to analyze deals. This is still useful for me but Property Alpha lets me analyze properties at scale and skip the ones that don’t meet my requirements. This saves me a lot of time and mental energy. Have been sending this to my friends for a few months now; check it out!

When I first used property alpha, I was shocked by accessibility of investing in real estate. Coming from the Bay, I always expected real estate to be either too much commitment (time and financial) or too distant to manage. I was surprised when Property Alpha led me to multiple cash flowing opportunities for lower entry price and within 25 miles of where I lived, inviting me in to benefit from real estate investing.

I highly recommend property alpha to anyone curious about investing in real estate!

I had been hunting for an investment property with my partner for a while and getting bogged down with sheets and sheets of calculations and metrics. Property alpha made things SIGNIFICANTLY easier for us - quick way to filter and sort by what we were looking for. End result is that we were able to find a few properties that met what we are looking for and fingers crossed are about to have one of our offers accepted soon!

Well done property alpha team, you have no idea how much time this has saved us hunting for investment properties!

I enjoy using the interface and being able to see all of the critical information and data points needed to help me move towards or away from the property. Truly a time saver and must have when purchasing real estate properties!

Congrats on today's launch!!

Most ops tools are either too basic or way too heavy. Beacon looks like a good middle ground for small service teams juggling multiple clients.

Hey Product Hunt 👋 I'm Illia, solo founder of Brightforge.

After Pixlie (our AI video studio), this is our research track: Kineforge.

The problem: teaching robots to act usually means one of two expensive paths —
record thousands of human teleoperation demos, or run billion-parameter
"robot brain" models that only live in the cloud. Both are slow, costly, and
hard to deploy where it actually matters (warehouses, labs, hazardous sites).

Kineforge takes a different bet: structure over scale.

• No human demos. We train in simulation (MuJoCo MJX), not from teleop recordings.
• Tiny trainable policy — 157K parameters (not billions), sitting on top of a
frozen "knowledge layer" that gives the agent structure from day one.
• Trains in hours on a single GPU, not weeks on a cluster.
• Runs on a CPU in under 10ms — edge-deployable, no datacenter required.

On NVIDIA H200 we log stable task success ~0.70 with open metrics and honest
simulator artifacts (345K+ logged steps, 0 crashes on a 1.5M-step run).

This is early and sim-first, real-robot pilots are next. I'd love feedback from
robotics, RL, and embodied-AI folks, and intros to warehouse / lab automation
teams who feel the data-cost pain.
Imagine teaching a robot to do a job.

Today there are two slow, expensive ways:
1) Sit a human down for months to "puppet" the robot thousands of times
(teleoperation). Slow. Costly. Boring. Doesn't scale.
2) Rent a giant AI "brain" with billions of settings that only runs in a
massive cloud datacenter. Expensive every single time it thinks.

Kineforge does it differently. We give the robot a small, smart "starter brain"
that already understands structure — like a kid who starts school already
knowing the alphabet, instead of inventing language from zero.

Then it practices inside a fast video-game-like simulator (no real-world risk,
no humans puppeting it). It learns the job in hours, on one computer chip —
not weeks on a server farm.

The payoff for a business:
• 10–50× less training data needed
• 5–10× cheaper to develop each new skill
• The finished "brain" is tiny — it runs on a cheap chip inside the machine,
in the blink of an eye, with no cloud bill per move.
Imagine teaching a robot to do a job.

Today there are two slow, expensive ways:
1) Sit a human down for months to "puppet" the robot thousands of times
(teleoperation). Slow. Costly. Boring. Doesn't scale.
2) Rent a giant AI "brain" with billions of settings that only runs in a
massive cloud datacenter. Expensive every single time it thinks.

Kineforge does it differently. We give the robot a small, smart "starter brain"
that already understands structure — like a kid who starts school already
knowing the alphabet, instead of inventing language from zero.

Then it practices inside a fast video-game-like simulator (no real-world risk,
no humans puppeting it). It learns the job in hours, on one computer chip —
not weeks on a server farm.

The payoff for a business:
• 10–50× less training data needed
• 5–10× cheaper to develop each new skill
• The finished "brain" is tiny — it runs on a cheap chip inside the machine,
in the blink of an eye, with no cloud bill per move.

So instead of "AI that's smart but ruinously expensive to run," you get
"AI that's good enough AND cheap enough to actually put in thousands of
machines." That's the difference between a demo and a business.

We've proven the economics in simulation. The next step is putting it into a
real warehouse robot.

Under the hood:
• POMDP control, actions factored into locomotion (16) / respiration (2) /
head (2) / macro (4) = 24 discrete decisions per step.
• Multi-stream policy: Fast (reflex), Valence (affect-modulated gains),
Slow (topology-routed planning).
• Frozen semantic prior (~2.6M params, 110 canonical symbols, 768-dim) supplies
structured inductive bias; only ~157K policy params are trained (REINFORCE/PPO,
JEPA-style + structural regularization).
• Backend: MuJoCo MJX differentiable physics, JAX-native (no PyTorch in core),
SkyPilot on Kubernetes, NVIDIA H200, MLflow tracking.
• Results: ~0.70 task reward at 100K steps; ~159 (baseline) vs ~108
(grounded sensory) env-steps/s; CPU inference target <10ms.
• Sim-only today; sim-to-real via Unity/ROS2 in progress. Priors withheld;
sanitized eval protocol published.

So instead of "AI that's smart but ruinously expensive to run," you get
"AI that's good enough AND cheap enough to actually put in thousands of
machines." That's the difference between a demo and a business.

We've proven the economics in simulation. The next step is putting it into a
real warehouse robot.

Under the hood:
• POMDP control, actions factored into locomotion (16) / respiration (2) /
head (2) / macro (4) = 24 discrete decisions per step.
• Multi-stream policy: Fast (reflex), Valence (affect-modulated gains),
Slow (topology-routed planning).
• Frozen semantic prior (~2.6M params, 110 canonical symbols, 768-dim) supplies
structured inductive bias; only ~157K policy params are trained (REINFORCE/PPO,
JEPA-style + structural regularization).
• Backend: MuJoCo MJX differentiable physics, JAX-native (no PyTorch in core),
SkyPilot on Kubernetes, NVIDIA H200, MLflow tracking.
• Results: ~0.70 task reward at 100K steps; ~159 (baseline) vs ~108
(grounded sensory) env-steps/s; CPU inference target <10ms.
• Sim-only today; sim-to-real via Unity/ROS2 in progress. Priors withheld;
sanitized eval protocol published.
Model card and tech report is still wait endorsement on arXiv, because my CS teacher have only 2 endorsement by himself and to aprove you must have at least 3,sooo... (no fluff, real tables):
https://illiaovcharenko1.github....

Links

• PDF (v1.0): Tech report PDF

• Landing: GitHub Pages

• GitHub: kineforge-portfolio

• HuggingFace (HF Space): kineforge-demo

• arXiv: pending endorsement (cs.RO) → then https://arxiv.org/abs/…

• Contact: illia.ovcharenko@brightforge.ltd · brightforge.ltd
  Try the demo + ask me anything below 🙏

How does it decide what to replace when the word appears multiple times in the text field?

Do you support perspective matching and realistic shadows automatically, or do you tweak those manually?