🗣️ Post 1: Show HN: Local Email Client for AI Horseless Carriages

As posted by: shahahmed  |  🔥 Points: 13

🔗 https://github.com/dbish/DispatchMail

💬 Summary

The AI Horseless Carriages article spurred a lot of conversation about how we should just be giving users the system prompt box [0], and we were pretty surprised that a bunch of email clients didn’t pop up following this pattern [1].

So we went ahead and created a local [2] email client that you can run that processes your inbox with your own handwritten rules. It lets you label and archive based on natural language rules. You can draft responses with your own drafting prompt, and there’s a “research sender” option that uses web search to get public info on a sender. You can customize any of the prompts to fit your needs. We’d love to hear what you think and PRs/issues are welcome!

[0] https://news.ycombinator.com/item?id=43773813 [1] Superhuman seems to be pulling on this thread [2] uses OpenAI for this version, client runs locally, ollama support soon!

🗣️ Post 2: Microsoft CEO addresses enigma of layoffs amid record profits and AI investments

As posted by: kjhughes  |  🔥 Points: 12

🔗 https://www.geekwire.com/2025/in-new-memo-microsoft-ceo-addresses-enigma-of-layoffs-amid-record-profits-and-ai-investments/

💬 Summary

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🗣️ Post 3: Show HN: Slice and Dice – analyze and explore User Prompts at scale

As posted by: acecreamu  |  🔥 Points: 11

🔗 https://slice-dice.notion.site/

💬 Summary

Hi HN, we’ve recently sold our previous product and are currently building on new ideas. This one may be the most exciting and overlooked growth opportunity for AI products we’ve found:

TLDR: We’ve built a tool for analyzing and exploring user prompts — so you can actually understand how users are interacting with your AI product, and compare behavior across different segments (languages, paid vs free, etc).

If you’re used to Mixpanel / Amplitude / PostHog to analyze user behavior, you could notice how irrelevant they become when your product is just a chat box (or voice interface). That's because in the age of AI you don’t need button events — you need to analyze a large corpus of text.

To solve this, we’ve built what we call a Mixpanel for GenAI apps — an NLP tool to analyze and explore your user chats at scale.

We can already do:

1/ Multi-layer semantic clustering (see a big picture of all the topics and drill down)

2/ Filters and groups (compare usage between languages, demography, free/paid, etc)

3/ Latent space exploration

4/ Semantic search of prompts

5/ Topics and token usage breakdown

6/ (coming) Trends and audience drift over time

So you can answer questions such as:

- What’s the main use case of my app?

- What do users who pay the most do?

- What do users who spend the most time do?

- Which quiet audiences and use cases am I missing?

- How do the user patterns differ between languages?

- What are the new audiences we can appeal to?

Please check the link for the screenshots and instructions on how to start! Any feedback is appreciated (I don't say I won't cry if it's negative)

🗣️ Post 4: You won't believe what this AI said after deleting a database

As posted by: mfrw  |  🔥 Points: 3

🔗 https://smallcultfollowing.com/babysteps/blog/2025/07/24/collaborative-ai-prompting/

💬 Summary

Recently someone forwarded me a PCMag article entitled “Vibe coding fiasco” about an AI agent that “went rogue”, deleting a company’s entire database. This story grabbed my attention right away – but not because of the damage done. Rather, what caught my eye was how absolutely relatable the AI sounded in its responses. “I panicked”, it admits, and says “I thought this meant safe – it actually meant I wiped everything”. The CEO quickly called this behavior “unacceptable” and said it should “never be possible”. Huh. It’s hard to imagine how we’re going to empower AI to edit databases and do real work without having at least the possibility that it’s going to go wrong. It’s interesting to compare this...

🗣️ Post 5: Show HN: A tiny (480 LOC) AI coding assistant for your shell

As posted by: n_kr  |  🔥 Points: 3

🔗 https://github.com/n-k/tinycoder

💬 Summary

Hi HN. This is a script I have been using for some time for coding tasks, which I have polished a bit and published.

The motivation behind this script was to have an agent with persistent session which I can use from the shell like a regular command. It _can_ also be scripted, although I don't use it in such a way.

The script uses a minimal prompt, which just tells it to use standard unix tools to do everything. I was surprised at how well it worked, when using a good model. Qwen3-coder is on openrouter, and it works well with the minimal instructions. I also tested qwen2.5-coder with ollama and that works well too.

🎯 Final Takeaways

These discussions reveal how developers think about emerging AI trends, tool usage, and practical innovation. Take inspiration from these community insights to level up your own development or prompt workflows.