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You use one app for notes and another for tasks. A new workspace offers both, with one bill and no copying between them. But its task view is weaker than the tool you already know.

Keeping specialist tools means extra logins and connections to maintain. Moving everything into one place means accepting whatever that app does less well.

Genuine question for people shipping with Lovable, Bolt, v0 or Cursor. Building fast is the whole point, but the same few problems keep showing up on freshly shipped apps when you look at them from the outside: admin or debug routes left reachable, API endpoints that return another user's data if you change an ID in the URL, Supabase or Firebase tables readable without logging in, and API keys sitting in the frontend JavaScript. None of these need a hacker to find, just someone curious with a browser. So how do you handle it? Do you have a checklist before launch, rely on the platform's defaults, ask the AI to review its own code, or ship and fix later? And which of these has actually bitten you? Disclosure: I work on external recon at Xseth, so this is my day job, but I'd really like to hear what works for people here, with or without tools.

We talk a lot about product-market fit, but i think how clearly we actually know who our product is for.

Is it the audience you imagined when you started?

The people who are using it now?

Or did you discover a completely different group along the way? =)

When I was reading TechCrunch today, I came across Jensen Huang s (NVIDIA CEO) view that AI safety should be handled by companies through engineering and market pressure, rather than through new regulation.

In my opinion, the power of consumers and the public is sometimes simply not strong enough to counterbalance large technology companies. That s why I believe governments sometimes need to step in and establish rules and regulations.

Lancetta 0.68.0 is out

Every Claude Code and Codex session, in one place: what waits on you, what you finished, and what the agents still keep on disk

A brand might appear regularly in ChatGPT recommendations while showing up less frequently in Gemini, Perplexity, or Google AI Mode.

Each platform uses its own retrieval systems, available sources, and methods for generating answers. As a result, the same question can produce different brand recommendations depending on where someone asks it.

Most of us now use 2 4 AI tools, and every one of them starts from zero. We put together an honest comparison of the 5 common ways to build an AI second brain :

1. Markdown files: free and portable, but lots of copy-paste

2. Local files (CLAUDE.md, rules files): best for coding agents

3. ChatGPT / Claude / Gemini Projects: easiest if you use one assistant

4. Drive / GitHub connectors: good for large archives

5. A portable memory layer over MCP, which is what we build with AI Context Flow

Projects is enough if you only use one assistant. Whereas a memory layer helps iwhen you switch between models, share context with a team, or run workflows (n8n, agent teams) that need to read and write the same memory.

i'm alim, one of the makers of agentmash. we launched today and i keep going back and forth on one design call.

in august two of our agents collided on payments code. fixing prod OAuth took 10 hours. worktrees keep agents out of each other's folders, not each other's code.

We hear both extremes. Some teams want AI that only drafts, and a human clicks send on everything. Others want full autonomy.
Where we've landed: let the AI act freely on routine work inside clear rules (book the meeting, update the CRM, send the reminder).
Anything outside the rules, like a non-standard discount or a dispute, waits for a named person. The offer stays unchanged while approval is pending.
It's a middle ground, and we keep adjusting where the line sits for each customer.
Where would you draw it? Which actions would you never let an AI take on its own?
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Product Hunt s Ship Faster Day is coming on September 30th!

The idea is pretty fun: launch something, get feedback, and then build and ship the requested improvements as fast as you can.

With only two days left, I started wondering:

How do you prepare yourself for a day like this?

Next in our short tutorials, one task each.

This one: a chat on your own site. Pick the name and colour, choose who answers (an AI bot that hands over to your team, or just your team), and paste one snippet. Chats show up in the same inbox as calls, texts and email, with the page the visitor is on.

Video: https://youtu.be/XI9AV-yhn34

What should we cover next?

I think procrastination isn't always about doing nothing.

Sometimes you're actually busy, working on interesting things, learning something new, or exploring ideas.

You feel productive, but you're are not doing what you planned to do.

That's why I think having a weekly plan is so important. At least you know when you're procrastinating, even if you're doing something useful.

PasteKit currently handles 58+ formats and 48 conversion pairs, all running locally in your browser. Nothing is uploaded, and it works offline after your first visit. I want the next addition to come from you. What format or conversion do you reach for that's missing, or that you still paste into a tool you don't fully trust? Config formats, query languages, data formats, anything goes. Drop it below and upvote the ones you'd use too.

I built PasteKit because I kept pasting API responses, JWTs and customer exports into online formatters, then wondering where that data went.

PasteKit formats and converts everything locally in your browser. Nothing is uploaded, and it works offline after your first visit.

I'm curious: what's the most sensitive thing you've pasted into an online tool? And what would make you trust a formatter with it?

Many brands measure AI search visibility by running a predefined list of prompts and counting how frequently they appear in the answers.

For example, a SaaS company might track prompts such as "best CRM software," "top CRM platforms," and "CRM tools for sales teams."

Archived repositories stop counting as work in octoscope 0.40.0

i've been talking to a lot of researchers lately and the same thing keeps

coming up. nobody is slow because the ideas are hard. they're slow

because the stack around the ideas is held together with tape.

Hi everyone! I m Maria, cofounder of Maritime.

We built Maritime because agents need different computers for different jobs: a sandbox to run code, a persistent workspace, or a desktop or phone to use apps with no API.

On Doneas.com, you build your roadmap by answering 4 questions, starting with:
"What's your life mission?"

Then you break it down step by step:
Life mission

3-year goals

1-year goals

3-month goals

Monthly goals

From there, you can create your weekly plan based on your roadmap. The idea is simple:
Your weekly tasks should be connected to what you actually want to achieve in life.

Almost everything starts to be only the "prompt" and the "overview result" in LLMs.
As for our effort with minimalist phone we started doing 3 things:

our blog articles are containing the section FAQ at the end of each article

we also started testing ChatGPT ads

 we increased our effort to be seen in online magazines that are likely to be quoted

Quick background if you haven't used them: hooks in Claude Code are your own scripts that run automatically at set points, like right before a command runs or right when the agent finishes its answer. A hook can block that step or send the answer back to be rewritten. CLAUDE.md is something the model reads and can forget after a long session. A hook just runs, every time.

Here are the ones I'd miss most. Each started as a rule in CLAUDE.md that the agent kept breaking.

Replies drift into English after long sessions. A hook checks every answer and sends it back if it isn't in Japanese.

Lancetta 0.65.0 is here, and it's for everyone on Claude's new Ultra plan

Ultra has no 5-hour limit, only the weekly ones. Until today Lancetta treated that missing window as a number it couldn't read: a grey lamp in the menu bar, and a dash on every card. Now it knows the difference.

There are so many different parts, inputs, and efforts that founders put into building a product.

I'm sure everyone is doing their best to achieve the best result.

When an AI-assisted build takes a weekend instead of three weeks, hourly pricing starts to feel like it punishes you for being fast. But a fixed fee gets scary the moment the client keeps asking for one more tweak , and a retainer only works if they actually need ongoing changes.

For those of you building apps or tools for clients with AI coding tools: how do you price it now? Did you change how you charge once your build time dropped, and how do you explain the price to a client who knows it only took a few days ?

Everyone remembers their first sale. Not the biggest one. The first one.

Almost every founder I talk to tells a similar story. It wasn't big money but something changed after it. Before that you're someone who builds things. After that you know people will actually pay for them.

And it almost never comes from a perfect pitch. It's a friend of a friend, a follow up someone almost didn't send, a random comment that turned into a call.

What was your first sale? And how did you actually get it?