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Yesterday, I announced that I'm getting ready to launch my MVP.
For now, it'll only be a beta version, but I'm already thinking about things like pricing, plans, and all that fun stuff.
Right now, these are the options on the table:

• Freemium model (some features free, the rest behind a paywall)

• Monthly plan ( 7)

• Annual plan ( 59) with a discounted coupon

• Multi-license packages

What would you criticise, change, or add to the pricing? If you know about a very good plugin I can inspire from and build my own according to it, please, drop them in the comments  I want to learn from the best.
My second question is about dealing with users who complain about pricing. From what I've seen while working with another app, people always seem to have something to say:
"Make it free."
"It's too expensive."
"Do you have a discount?"
And one thing I see a lot: people strongly prefer lifetime plans (should I consider one?)
If you have any tips on how to handle these kinds of comments from users, I'd love to mentally prepare myself in advance. :D

Glossaries, rulesets, and brand voices now live at the organization level and attach to any engine.

Build a glossary, a ruleset of linguistic rules, or a brand voice once, then attach it to any engine that needs it. Edit a shared asset and every engine it's attached to picks up the change. A new engine no longer starts from an empty configuration: attach what you already have and it inherits it.

Also this week:

• Take over a translation waiting on external review. When a job is parked on an external professional reviewer, an authorized member of your own team can open the pending review in the dashboard and submit the decision, so the job continues instead of waiting on the external round trip.

• A runnable example repo for every framework. Each is a real project with .lingo/config.json and translations committed clone it, run lingo link, and push: iOS, Android, Flutter, React + Vite, Next.js, Rails, Laravel, Markdown, Markdoc, OpenAPI, and TypeScript modules.

One of the main goals of Kopai, is to make sure that the LLMs do not hallucinate. That is to stay , the human and the AI are always in Sync. Kopai has a feature called Chat Orientation where the user is shown the goals and actions taken by the LLM. If they LLM drifts you can manually update to realign the LLM.
Let us know what you would like to incorporate in this and how we can make this better?

Woke up tired like I'd worked a full shift. Same bug, looping, my brain solving it ten slightly different wrong ways until morning. Git log empty. Nothing real happened.

Part of me took it as proof I care about what I'm building. The other part knows a brain that won't clock out isn't a flex, it's a leak.

Do you get this one? And if you do, what actually makes it stop?

Starting June 1st, @Github Copilot moved to a usage-based billing model.

Hey everyone, if you've ever wanted more out of filter code, this one's for you.

1. Filter code now speaks modern JavaScript

For years, filter code ran on a 2018-era version of JavaScript. As of this week it understands modern syntax, so the code you write (or paste in from somewhere) is shorter and easier to read:

• String and array helpers like:

We launched YourSitee today after more than two years of building it with a small team in Hungary. We wrote a blog post (a very good one actually): https://blog.yoursit.ee/introduc...

We don't want polite launch-day feedback. We want to understand what would actually make someone leave Linktree, Beacons, or a personal website builder and move their page somewhere else.

A few years ago, every career conversation started with one question.

"Can you send me your resume?"

In 2026, that question is slowly changing.

AI agents are already changing how we work. Hard to deny that now.

People are building new habits around digital companions and agents in funny ways.
I even know someone who had a brand-new phone ready, but still delayed switching because their little @OpenClaw lobster was living on the old one.
Many of us now use agents to summarize, draft, search, code, organize tasks, or monitor things in the background.

I think investing is next.

A lot of investing still depends on manual habits: checking prices, reading news, following filings, tracking earnings, reviewing portfolios, and trying not to miss important signals.

Not which model benchmarks best.

Which one is actually open on your machine right now? And what did it replace?

A lot of agent projects spend months improving prompts, models, and workflows, then ship to production without asking a few fairly basic questions. Before trusting an AI agent with real work, I'd want clear answers to these:
1. What actions is the agent allowed to take?

Define the boundaries before deployment, not after.
2. Which tools and systems can it access?
Give access based on necessity, not convenience.
3. Are sensitive actions gated by approval?
Not every step needs review, but some decisions probably should.
4. Is there a record of what the agent did?
If something goes wrong, you should be able to reconstruct the sequence of events.
5. What happens when the agent is uncertain?
Low-confidence outputs need a clear path instead of silently moving forward.
6. Can you roll back mistakes?
Every production workflow should have a recovery plan.
7. Who owns the outcome?
At some point, a person needs responsibility for the workflow, even if an agent is doing most of the work.
None of these questions are particularly complicated. What's surprising is how often they only come up after an agent is already running in production.
What would you add to the list before trusting an agent in production?

Hey everyone,

We just opened up Causo.ai for sales lead gen.

We originally built Causo for fundraising: help founders find the right investors, research why they fit, and write proper outreach instead of sending hope this finds you well slop to every VC with a pulse.

Then people kept asking us the same thing:

My Windows build has been in Microsoft's certification queue for three weeks. Three new models shipped in that time, two of them can supposedly do my whole job, and not one of them can get a human at Microsoft to press approve.

What's the longest you've been stuck on something you couldn't code your way out of?

For me, I feel like the hardest part isn't choosing between tools like React, Firebase, Railway, etc, it's really deciding which parts of the stack I should commit most of my time and tokens early and which ones may become painful to replace later.

Do you stick with a familiar stack and avoid overthinking it, design around expected scale from day one, let your coding agent influence the architecture, or start with the cheapest setup and migrate later? Also, curious which early technical choice has saved you the most time, and which one you wish you probably shouldn't have overthinked on.

The one where a foreign-language email stops being a research project. Press T, read it. That's the whole feature.

New

• Translate an email in place Skim now notices when a message isn't in your language and offers to translate it right there: subject and body, in the reading pane, where the email already was. No copy-pasting into the Ask panel, no reading your mail as a chat bubble. Hit T, or click the offer.

• "Show original" is one click away because sometimes the AI's Portuguese is worse than your Portuguese, and you'd like to check.

• Translations are cached the first look costs a request, every look after that is free and instant. Re-opening a thread doesn't re-bill you.

• Language detection runs locally trigram tables compiled into the binary. No model files, no download, no phoning home just to figure out that "Sehr geehrte Damen und Herren" isn't English.

• The AI chat learned to grow Ctrl+E expands it to fill the window, Ctrl+Shift+E throws it into a window of its own, Esc puts everything back. The little arrows finally point where they mean.

The models got dramatically smarter this year. Your product didn't get more trustworthy and in 2026, those turned out to be two completely different problems.

Here's the line that reframed it for me. Stanford's AI Index this year leans on a phrase from Ethan Mollick the "jagged frontier" to describe how these systems excel and then fall off a cliff with no warning. Their example is almost too good: an AI model can win a gold medal at the International Math Olympiad and still not reliably tell time from a clock face. Genius and incompetence living in the same box, and no obvious seam between them.

My first launch is over, and I was more nervous going in than I expected to be.

The questions were sharper than I was ready for. Every one of them landed on something I did not have a comfortable answer to, and I answered anyway.

Every business I have run had a version of this. A lead who asked a good question and never heard back from me. A client who mentioned something felt off during a slow week, and I told myself I would circle back once things calmed down. Things never calm down enough on their own. The follow-up does not disappear because I forgot about it, it disappears because nothing in my day ever forces it back to the top again.

Curious whether other people running something small have actually solved this, not with more willpower, just structurally. Does anything in your process bring the thing you meant to get back to, back in front of you at the right time, or does it just stay buried until the client brings it up themselves first.

Hey Product Hunt.

Murmell already runs Claude Code, Codex, Kimi, and OpenCode on the same cloud canvas. More agents are coming, and I want to prioritize what you'd actually use.

Hi everyone,

I wanted to let you know that version 1.5 of RegionMirror is now available on the App Store. This version contains the ability to set up to 10 preset screen regions. For each region you can record a keyboard shortcut that can be used to quickly begin sharing. You can also toggle between the preset regions on the fly without stopping sharing from the meeting app, using the menu or the keyboard shortcuts.

I have accumulated years of photos, videos, documents and other files across multiple drives.

The problem is that cold storage is cheap, but browsing and searching old data is painful.

A full backup keeps everything, but you often don't know what is inside without connecting the original storage.

I'm curious how people manage large personal archives:

Before opening a PR, I started pasting my code into Claude for a quick review first, just to catch anything obvious before it goes to the lead.

A few times it caught things clearly worth fixing: a missed null case, a variable name that didn't match the rest of the file's convention, a catch block that swallowed the error without logging it. Stuff that, if the lead caught it instead, would've meant another round of comment - fix - push.

But there were also times it gave generic "best practice" suggestions that didn't fit our team's actual conventions, so I had to filter through what to actually apply versus ignore.

Not always a time-saver either, for small PRs, the lead's review still came back basically unchanged after I'd already run it through Claude, so that step felt redundant. The benefit was clearer on PRs with more complex logic.

Everyone talks about model lock-in, but the real switching cost is your conversation history. Months of context, decisions, and half-finished projects living inside one app.

We just shipped chat import in Osaurus (free, open source Mac app) so you can export your conversations from ChatGPT, Claude, Grok, Gemini, or Open WebUI and continue them with any model. Building it made us realize how differently each app treats your data on the way out. Some exports are clean JSON, some are a mess clearly not designed to be read by anything else.

Quick one: we just shipped the Agently MCP.

Your Agently brain now plugs into any agent you already use, Claude Code, Claude Desktop, Cursor, anything that speaks MCP. Ask from there and it answers with your whole company in context instead of a blank slate.

Setup is three clicks:

1. Settings

2. API keys

3. New key, paste it into your agent

Last May, @Solana Foundation launched pay.sh, an open-source, real-time payment infrastructure for AI agents so they can discover and pay per call for any API.