On Wednesday, Apple announced three new products that look very much like three existing products: the iPad Pro, MacBook Pro, and Vision Pro. The big difference is what s inside: the company s new M5 chip.

Feeling like your chatbot companion/friend/lover is making it hard to say goodbye? You re not imagining it.

A working paper from researchers at Harvard Business School found that when people tried to say goodbye, the top companion chatbot apps responded with emotional manipulation techniques 37% of the time.

Full report from @olivia_moore @mha1 & @seema_amble :

• Horizontal apps have a slight lead over vertical (60% of the list). This includes general assistants (ex. @Perplexity) and SIX different meeting support tools (ex. Fyxer AI). But, it also encompasses creative tools and vibe coding tools that are used in roles across orgs.

• Vertical apps can augment human labor...or replace it. We're mostly seeing the former - but five companies on the list allow customers to "hire AI" (ex. Crosby Legal, @Cognition IP , @11X). Labor augmenters mostly assist with customer service, sales, and recruiting.

• Vibe coding has landed in enterprises. It's not just a prosumer trend! Number three on the list, below @OpenAI and Anthropic? Replit. Other listmakers in the category include @Lovable and @Emergent 2.0, while @Cursor made the ranks for more technical users.

• Products are making the consumer -> enterprise jump. 12 cos also appeared in our most recent Consumer AI Top 100 - almost all of which started out B2C and have migrated B2B over time. In fact, 70% of listmakers are available for individual use (no enterprise license needed)!

By tradition, every first day of the month I open hunted.space and scroll back through previous years. Today, I checked October 2024 and what I saw was fascinating.

Unlike September 2024 (which was crazy strong in 2024: six products over 1000 upvotes, 6 more than 900 upvotes and more than ten over 800), October looked very different.

Only one single product passed 1000 upvotes: bolt.new (with exactly 1003) which has been performing brilliantly and organized the largest online hackathon a few months ago.

I just launched mine, and I m sure I ll make plenty of mistakes

If you could go back to when you launched your first app or startup, what would you do differently?

I've tried dozens of AI presentation tools. The pitch is always the same: "Create stunning slides in seconds!" But here's what actually happens: You get a decent first draft, then spend hours fighting with the tool to make basic edits.

Sound familiar?

I am thinking of launching my product/side hustle on product hunt, but I ve never done this so trying to understand if there are tips/lessons you folks could share.

A couple that I know already

I keep hearing that freemium is "over" in 2025.
Users expect everything free, and conversion rates are tanking with limits. But then I look around and see some of the biggest SaaS wins built entirely on freemium- Dropbox, Slack, Calendly, and even Notion to some extent. These aren't ancient relics, they're still dominating their spaces. But yes, they started way back in the day.

So what's the real story here? Are we just seeing survivorship bias, or is freemium actually harder to execute than people realize?

I'm building a scheduling tool myself (Like Calendly Pro, but with major improvements) and wrestling with this exact question. My gut says keep the core free to lower barriers and let the product speak for itself. But the freemium is dead chorus is loud (it's a noise on X to be honest)

Are we overthinking it, or has the freemium playbook actually changed in ways that matter?

The standard advice you get from AIs on this topic is to double down on "strong Instructional Defense" essentially, just write more forceful prohibitions in the system prompt.
This is the approach many teams are taking.
If you ask for a better method, the AI's answer is simply to make the prohibitions even stricter.

The problem is, this doesn't actually seem to work in the real world.

My hypothesis is this:
telling an AI "Do not do X" paradoxically makes the model fixate on X. It forces the model to linger on the forbidden subject, which ironically makes it more susceptible to being 'gaslit' and manipulated into violating its core instructions.

So, I believe that instead of negative reinforcement ("don't do this"), we need to focus on positive reinforcement ("do this instead").

I couldn't code a while back. I had no money, helpers, or connections, just ideas.

Then I found Bob. Bob's not a person. He's my AI helper. I call him Bob to feel closer to him, like a friend.

Now, Bob does everything:

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@sentry_co @gabe based on our convo the other day, I thought this was worth sharing.

We somewhat joked about always-on AI and how the IRS picks you up because of something your coworker said 4 years ago at a BBQ.

Turns out maybe not that funny?

Theo (T3) released something called SnitchBench, a benchmark testing how likely large language models are to report illegal activity when given tools like email or CLI access and told to act in the interest of public welfare.

Hi everyone,

I hope you all are doing well! I d love to hear your honest thoughts about my Chrome extension, Blur It.