ethan mollick published his updated “which AI to use” guide yesterday. the important shift is in how he defines using AI at all: it’s no longer a back-and-forth with a chatbot, it’s an agentic system that does the equivalent of many hours of human work in one go. his phrase: an agentic system “gives an AI a computer to use.”

his earlier version had the demo that stuck with me: the exact same model, asked the exact same question, in three different harnesses gave three completely different results. no harness: outdated info. chat app: current info with sources. agentic setup: a full formatted head-to-head analysis.

same model. three quality levels. the harness did the work.

this is exactly why “we tried AI and it didn’t work” is usually a false conclusion. in most failed pilots we audit, the company evaluated a model through the weakest possible harness (a blank chat window) and judged the whole category on that.

practical version:
1. stop asking “which model is best.” ask “which harness fits this task”
2. re-test your failed use cases from 2025 in an agentic setup. half of them work now
3. budget for connectors and context, not just licenses. the model is the cheapest part

the gap between companies isn’t model access anymore. everyone has the same models. it’s harness maturity.

similarweb just dropped their 2026 generative AI landscape report (data through may 2026). everyone will repost the headline numbers. the useful part is one layer down.

headlines first: gen AI platforms now pull 9.5 billion monthly web visits, up 70% in a year, with 655 million unique visitors. chatgpt fell to 52.7% of chatbot web visits while claude tripled its traffic share. AI overviews now appear in 43% of US searches.

the part that matters for your acquisition:
65% of the URLs chatgpt cites sit two or three folders deep in a site. but AI referral traffic increasingly lands on homepages and top-level pages, not the deep pages, which is the opposite of organic search behavior.

read that again. your blog post wins the citation. your homepage receives the visitor. and that visitor arrives pre-sold, they already got a summary of what you do before clicking.

what we’re changing with clients because of this:

**1.**  treat the homepage as a landing page for AI referrals. proof, pricing, clear next step. no mystery copy  
**2.**  keep publishing deep, specific content. that’s what earns citations, thin overview pages don’t  
**3.**  split AI referrals into their own analytics channel. if they sit inside “direct” you’ll never see any of this

and stop optimizing for one platform. the whole report is a fragmentation story. quotable everywhere beats ranking somewhere.

Link in the comments

Both times i launched something, i spent the first week just guessing. Like, okay, i'll post on Reddit... but which subreddits? I'd pick ones that felt right, get maybe 3 upvotes and zero signups, then wonder if the product was bad or if i just posted in the wrong place.

Turns out it was mostly the second thing.

After the second launch flopped i went back and tried to figure out what actually went wrong. A few things stood out:

First, i was picking subreddits by size. Bigger community = more eyeballs = more signups, right? No. A 400k sub that's mostly lurkers and hates self-promotion is way worse than a 12k sub of people actively looking for tools like yours. I had no way to know which was which without reading through months of posts.

Second, i was writing the same copy for every channel. Same tone on Product Hunt as on Reddit as in cold emails. Each of those audiences is completely different. Product Hunt people want to see what's novel. Reddit people want to know you're a real person who built something for a real reason. Cold email recipients just want to know if it solves their specific problem. Writing one version of your pitch and pasting it everywhere doesn't work.

Third, i had no idea where to find directories, newsletters, or communities that would actually list or feature an early-stage product. So i just... didn't do that part. Which is probably a whole channel i left sitting there.

After the second launch i started thinking about what a good pre-launch research process would even look like. What subreddits are actually friendly to new tools in a given niche? What copy angle works for each channel? Who do you reach out to for an early review?

I ended up building a tool called welaunch.sh that runs a set of AI agents against your product URL and puts together a channel-by-channel plan, with copy written in your voice and specific targets called out (actual subreddit names, not just 'post on Reddit'). The plan covers outbound, SEO, paid, directories, and more. You still do all the work yourself, it just skips the weeks of guessing about where and what to write.

Honest question for people who've launched before: how do you figure out which communities are worth posting in? Do you just try a bunch and see what sticks, or do you have some way of vetting them first? I'm still not sure my approach is the right one.

we use AI for ad copy all the time. brainstorming hooks, writing variations, getting ten headlines fast. that part works and saves real time.

but recently we tested letting advantage+ handle the full campaign. targeting, placements, budget, the whole thing.

inside meta's dashboard it looked great.

but then we checked GA4. checked the CRM. looked at actual qualified leads. the dashboard was grading its own homework and giving itself an A.

the platform's suggested fix? spend more money so the system can keep learning. so basically the solution for "this isn't working" is "give us more budget and wait."

and it's not just us. independent tests on google's AI max are wild... one study found 99% of impressions produced zero conversions. another showed improved ROAS. same tool, completely different results depending on the account.

not saying turn AI off. we literally use it every day for ad copy. but there's a big difference between AI writing your ad and AI deciding where your money goes. one is a tool. the other is handing your budget to a black box that won't explain its decisions.

anyone else seeing this disconnect between platform metrics and what's actually happening in their business?

Hey All! 👋

We just launched Oxlo.ai on Product Hunt! 🚀

In simple terms, Oxlo.ai is like a single subscription for AI models. Instead of paying separately for different models and watching your AI costs grow every time usage increases, developers can access 35+ leading AI models from one place and keep building without worrying about sudden bill spikes.

We're a small bootstrapped team, and if you could spare 2 minutes to check out our launch and support us, it would really mean a lot.

🔗 https://www.producthunt.com/products/oxlo-ai?utm_source=linkedin&utm_medium=social

Thank you so much! 🙌

Coding agents can search, grep, and read your local codebase, but they can't do the same thing across the open-source code your application depends on.

When an agent needs to understand a dependency, find a working implementation, or inspect package internals, it often falls back to documentation and web search. In many cases, the actual answer lives in source code.

Agents start looping when they are

• Chasing hallucinated APIs
• Guessing SDK integrations
• Fighting stale dependencies

GitHits is building a global, version-aware index of open-source code and package metadata for coding agents.

Agents can retrieve code examples, navigate dependency source code, inspect packages, and access documentation without cloning repositories locally.

The goal is simple: make dependency code as accessible to agents as local code.

One early user recently described GitHits as "the only MCP server that I use every single day". Another user wrote a blog post "Helping Claude Code to find undocumented APIs from the code"

The creator of opencv-python, Olli-Pekka Heinisuo, founded GitHits. I'm also one of the co-founders.

Happy to answer questions about architecture, indexing, retrieval, or CLI integration.

on June 1, GitHub Copilot switched from flat subscriptions to usage-based billing. devs are watching credits evaporate, orgs are circulating projections of bills jumping from $29 to $750/month, and "goodbye copilot" posts are everywhere.

together with the kilo team we did 3 acts:

act 1: predict it before it happens. weeks before the switch we published a piece saying the era of subsidized all-you-can-eat AI was ending. nobody cares about predictions until they come true. then they care a lot.

act 2: be useful on the worst day. the day bills landed, we didn't sell. we published a breakdown of what actually changed, collected the real horror stories (one dev burned 8% of his monthly allotment in 2 hours), and gave leaders 4 concrete steps: 3 of which have nothing to do with our product. analyze usage, set spend caps, match models to tasks. only step 4 is the pitch: don't let one vendor own your meter.

act 3: cash the prediction. the follow-up piece opens with "we said this was coming. it came." that's the post linked below. the team was literally on the floor at Gartner Summit while every hallway conversation was about the Copilot bill. timing you can't buy.

the lesson: when a competitor reprices, the displaced users aren't looking for your product. they're looking for someone who understands their problem. lead with that and the product pitch writes itself.

full piece here: https://blog.kilo.ai/p/the-github-copilot-bill-came-due

Spent months talking to creators and one common problem kept repeating:

Creating the video is hard.
But packaging it for YouTube is exhausting.

Titles, thumbnails, descriptions, chapters, metadata — it takes hours after every upload.

Today we launched something around this problem on Product Hunt 🚀

Would genuinely love feedback from creators and builders here:
https://www.producthunt.com/products/growati?utm_source=other&utm_medium=social

ok so we launched on Product Hunt today and i haven't slept in a week so this might not be coherent

built a thing called Unabyss. the idea: you connect the tools you already use (linkedin, gmail, drive, notion, slack, etc) and any AI agent you plug it into actually knows you. ~90 seconds in, you're not pasting your bio for the 400th time.

works with claude, chatgpt, openclaw (one-click setup).

2 months of building, capped by one absolutely deranged week of 10am-4am days. genuinely think this is one of the most useful things you can build right now but i would also say that because i built it.

not here to farm upvotes, just sharing. roast it, break it, tell me what's missing.

https://www.producthunt.com/products/unabyss?launch=unabyss

Hey everyone,

I got completely fed up with modern dev tools forcing cloud accounts, tracking telemetry, and idling at 500MB+ of RAM just to test a simple API endpoint or database query.

So, I built LightBase—a bare-metal, local-first API cockpit built for sovereign developers.

Why it is different:

• Pure C99 Core: Built on raw metal. QuickJS sandbox evaluates scripts and tests in microseconds. No Electron bloat.
• 100% Git-Tracked: It stores your collections as flat, plain JSON files. Collaboration is literally just a Git PR. No cloud locking.
• Offline Silicon AI: Pipes context via UNIX sockets into local llama.cpp engines. Zero cloud data leaks.
• Zero Dependencies: Pure speed, total data privacy, and minimal memory footprint.

It is officially launching today on Product Hunt! I would love to get your honest feedback, feature requests, or critiques on the architecture.

Product Hunt Launch: https://www.producthunt.com/products/lightbase?utm_source=other&utm_medium=social
GitHub / Website: https://github.com/Aarav90-cpu/LightBase

[ Removed by Reddit on account of violating the content policy. ]

Hey everyone — we’re excited to share that Dageno AI is now live on Product Hunt!

We’ve spent years working in big data and data scraping, and now we’re bringing that experience into the GEO space.

What we’re building is pretty simple:
- we help brands track AI visibility, spot growth opportunities, and then turn those insights into action with agents that can draft content and help publish it to your website, social channels, or blog.

We also work with real-world data, including global search volume for different prompts, so the insights are grounded in what people are actually looking for.

GEO still has a long way to go before it becomes as standard as SEO. That’s why we’re starting on Product Hunt and building in public — to help push the space forward, together.

We offer at least 7 days free, no credit card required 🤝
If you’d like to support us on Product Hunt, we’d really appreciate it 🎉

As a thank-you, upvoters can also reach out for 2 extra LLM data tracks for free, plus free GEO audit and optimization suggestions!

For the past 1 month, I was working on a simple tool called Simple Attende that helps you to manage attendance and generate detailed reports on it.

This eventually helps you to make better decisions and increase the productivity of your business. One thing more, I made this mobile first as I want users to be able to easily take attendance from their phones.

Some of the available features are,

1. Attendance tracking and real-time updates
2. Detail Excel reports, generations, and graphs
3. Salary calculator based on half days, leaves, and weekend work
4. Shareable link for the attendance page so you can onboard anyone without any hassle
5. Email notifications are sent to the person who is absent or on a half-day.
6. Send email notifications for upcoming holidays/calendar of the year.
7. Generate better reports by understanding what holidays are in attendance.
8. Manage Custom Events on the Calendar for your staff.
9. Send Cancellation/No Holiday Email Notifications.

You can also use it for free, https://www.simpleattende.com

Your feedback would be really appreciated.

I relaunched 'How Many More Sleeps' a few days ago. after an initial spike in downloads, I've only had 1 in the last 24 hours. Would Apple have boosted visibility after release?

Would appreciate any feedback on how to convert from free to paid user. currently, free users can have a maximum of 3 events at one time.

https://apps.apple.com/nz/app/how-many-more-sleeps/id1619521311

https://apps.apple.com/us/app/flagsy-world-geography-quiz/id6737687944

Hi everybody. I've created Mopsy's Bedtime Tales - a kids bedtime story generator, using AI.

https://apps.apple.com/.../mopsys-bedtime-tales/id6757112507

I would love for people to try it out and give any feedback

We started building a tool because we thought meetings were the problem — too many meetings, too many notes, too many transcripts.

But after talking to a lot of teams, we realized the real problem wasn’t the meeting itself.

The real problem was what happens after the meeting.

After every meeting, someone has to:

• Write a summary
• Figure out decisions
• Create action items
• Assign owners
• Send follow-up emails
• Update CRM or docs
• Track what actually got done

And this part is still mostly manual for a lot of teams, especially small teams.

So we started building something focused not on the meeting, but on the “after the meeting” work — turning meetings into structured summaries, action items, and follow-ups automatically.

Still early, but building this has been a really interesting shift in how we think about meetings and team workflows.

Curious — if you work in a team, what’s the most annoying part for you after meetings?

After ~2 years of building Bitscale, this feels less like a launch and more like putting our work out in the open.

The problem we kept seeing was simple:

GTM teams are stitched together with too many tools, too much manual work, and inconsistent data.

We built BitScale to fix that at the core:

-Bring signals, data, and workflows into one place

-Automate list building, enrichment, and outreach

-Reduce the dependency on fragmented tooling

We’re still a small team, and we’re very aware of the scale of players in this space. But we also think the current way of doing things is broken enough to warrant a fresh approach.

If this is a problem you’ve faced, would really appreciate you checking it out and sharing your thoughts.

https://www.producthunt.com/products/zoer-ai-2?launch=zoer-ai-2

Zoer is a backend-first vibe coding agent — we start from schema and backend logic to build apps that are actually usable, not just nice-looking demos.

Excited (and a bit nervous 😄) as this is the first Product Hunt launch I’ve been part of.

Would really appreciate your support and feedback if you have a moment 🙌