Anthropic just shipped Opus 4.7 today and i had to write about it somewhere because the jump is weird.

I ran the same backlog task on 4.6 and 4.7 back to back. same repo, same prompt, same tools. 4.6 looped on a bug for 25 minutes and was not going to solve it. 4.7 closed it in eleven, and the part that freaked me out is that it paused in the middle to sanity-check an assumption i had not asked it to check. literally wrote "before i write this migration, let me verify the actual shape of the response object, because my assumption here might be wrong" and then went and verified it. unprompted.

That self-verification behavior is the thing. Vercel is reporting it does proofs on systems code before starting work. Hex says it flags missing data instead of making up plausible-but-wrong fallbacks. Genspark measured loop rates on hard queries and 4.7 basically stopped looping. different teams, different harnesses, same pattern.

the numbers are nuts too:

I've used SocialBee, Buffer, Jasper, Copy.ai, and Castmagic. They all promise "your tone of voice". Most are just a ChatGPT wrapper bolted on to create quick captions, but none of them let you define specific rules and enforce them on every output. I'd spend 15-20 minutes per piece correcting the same mistakes the tool kept making.

SplitPost (splitpost.io) takes one piece of your content and generates platform-native posts for LinkedIn, Twitter/X, Instagram, Threads, Reddit, TikTok and Facebook, each in the relevant voice and formatting for that platform. You set voice rules (banned phrases, sentence pattern constraints, register checks) and every output gets validated against them before you see it.

Free tier, no card required. This is app three in 18 months as a non-technical solo founder in the UK. I'd genuinely like to know if the voice enforcement angle lands for anyone else or if I've built for a problem only I had.

Here s what changed:

Production-ready code with minimal oversight, and it can verify its own outputs
More control over reasoning effort
3x better vision (now up to 3.75MP images)
Improved instruction following and overall reliability
New xhigh reasoning mode for finer control between speed and depth

Same pricing as Opus 4.6 ($5 and $25 per million input and output tokens). The new tokenizer can use around 1.0 to 1.35x more tokens depending on content, though this can be managed through effort settings and task budgets.

Hi everyone, I'm James a solo founder from the UK building SplitPost, a content repurposing tool with voice rule enforcement. Got into building apps 18 months ago as someone who couldn't write code from scratch, learned a lot along the way by shipping (and breaking) things. Excited to see what everyone working on?

1. Y Combinator startup will pay humans to help AI agents when they get stuck. (This is what I read today.)

2. At the same time, I see how Indian employees in production have cameras on their heads, and the AI learns from their movements (practically filming their firing process).

3. In addition, there was already a site where AI agents hired human actions for stablecoins.

• First, AI worked for us.

• Now we are starting to work for AI.

• And eventually, will AI work (without us)?

I don t want to portray a Terminator scenario where people will have to unite against AI, but what future awaits us in terms of cooperation/non-cooperation with AI?

1. I've always been on the personal brand side. More and more founders are building it now (sometimes even before the product is ready while it's still in development, before seed fundraising). The CEO builds their position so the product sells more easily at the official launch.

2. But I have experience with people who built the product, scaled it, and only then did we discover who was behind it.

Honestly, with the first approach, I'd be concerned that people invest more in me as a person than in the product. People would idealise the founder and overlook the product's flaws (which could hurt development and constructive feedback).

+ I noticed the most common mistake that many people who started building a personal brand first, connected their product to their personal accounts (emails, social media, etc.) and started having a problem selling these things, because they cannot "give someone keys" to their personal profiles.

Hi everyone, I was responsible for a launch in November 2024 which did very well and would love to get your guidance on the main differences and best practices on how to perform well on our Product Hunt launch. As this is my own company now, would love to hear your guidance!

I noticed that the teaser pages do not exist anymore so I guess the message to audience and sharing teaser page does not make sense anymore. Any other things that matter now / differ?

I ve been using a lot of AI-generated code lately, and while it definitely speeds things up, security feels like a weak spot.

I ve run into issues like missing auth, exposed endpoints, and weak configs stuff that AI doesn t really flag unless you explicitly ask.

Curious how others are handling this:

• Do you rely more on manual reviews or tools?

• Any workflows that consistently catch vulnerabilities?

• Have you faced any real incidents because of AI-generated code?

I m curious what Product Hunt launch results looked like in real terms for people here.

Not just upvotes or comments, but actual outcomes like:

• site visits

• signups

• activated users

• paid conversions

• retention after the spike

If you re open to sharing, it would be interesting to know:

Building something useful is hard. Explaining it clearly can be just as hard.

I ve noticed a lot of products seem solid, but the value isn t immediately obvious from the first sentence, screenshot, or landing page.

Hey, I was wondering how many of you are coding from the phone - it's easier to do it with AI coding agents now, so I'm wondering how often developers are doing it.

Personally, I do some coding tasks daily from my phone and I'm curious how others are doing it?