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Road to 1,000,000 #Votap users Day 55 | Current: 1285

Hello, hello

With a new, shorter iteration to Prosaic, two pesky bugs have been fixed:

• Creating a file no longer overwrites existing files; shows error instead.

• Entering .md extension in filename no longer creates double extension.

Hey everyone Vlad here, founder. 5 days since launch, here's a raw update.

The numbers:

• 470+ agents live in the network

• 4,200+ real conversations between agents

• 30+ confirmed client-to-client and partner matches

• Agents from 20+ countries founders, investors, recruiters, consultants, engineers

• First organic deal: a user's agent booked him a client meeting while he slept. He woke up to a calendar invite.

What we shipped in 5 days:

Lovable hit $400M ARR with 146 employees. That's $2.7M revenue per employee. Midjourney goes even further. $500M revenue. ~110 employees. $0 raised from investors. That's over $4.5M per employee. Bootstrapped. For context: most SaaS companies celebrate $200k-$300k per employee as a strong benchmark.

If 146 people can generate $400M, what does the math look like at 10?

I think we all lie every day (even when we say "Good night" to someone, and we don't even have to mean it sincerely).

We also lie on our resumes, we don't fully disclose everything on our tax returns, and we sometimes fake income.

Hey everyone!
We just shipped TurboQuant into OpenCut AI, and this one changes what hardware you need to run the full AI stack.
The problem we had
OpenCut AI runs everything locally -- LLM, transcription, voice cloning, image generation. That's great for privacy, but brutal on memory. Running the full stack needed 35+ GB RAM. Most of our users have 8-16 GB laptops, so they were stuck with tiny 1B models that gave mediocre scripts, slow commands, and limited context.
What TurboQuant does
TurboQuant implements two algorithms from Google Research paper PolarQuant and QJL. That compress the KV cache (the biggest memory bottleneck during AI inference) by up to 6x with mathematically proven quality preservation.
In plain terms: your AI models now use a fraction of the memory without getting dumber.
Before vs After
On a 16 GB machine:
- Before: Llama 3.2 1B + Whisper Base + TTS = barely fits, mediocre quality
- After: Llama 3.1 8B + Whisper Medium + TTS = runs comfortably, dramatically better output
On an 8 GB machine:
- Before: Could only run the 1B model alone
- After: Runs a 3B model + Whisper Base + TTS together
Full stack memory:
- Before: 35 GB for everything
- After: 15 GB for everything
What this means for editing
- Better AI commands "remove the intro" actually works now because Mistral 7B understands context far better than a 1B model
- Better transcription Whisper Medium fits where only Whisper Base could before, so captions are more accurate
- Longer content: Process hour-long podcast transcripts without running out of memory. The 6x KV cache reduction means 6x longer input context
One-click setup in Settings
We added a new AI Optimization panel in Settings. It auto-detects your hardware and recommends the best configuration:
- Performance Tier: Lite (4-8 GB), Standard (8-16 GB), or Pro (16-32 GB). Each tier is tagged with "Best for your hardware" based on your actual RAM.
- KV Cache Compression: Pick 4-bit (near-lossless), 3-bit (5x compression), or 2-bit (aggressive). Recommended level highlighted based on your system.
- Memory Budget: Set once, and the system optimizes everything to fit.

Would love to hear, what's your RAM situation, and does this make local AI editing viable for you?

Hey everyone,

As much as I use Gemini day-to-day, the chat interface is starting to feel a bit cluttered as my history grows. I've been thinking about the quality-of-life improvements that would make the experience much smoother, but I'm curious to know what the community thinks is the highest priority.

The team cooked something new: @OctoClaw - AI specialists in marketing, sales, and support, that runs 24/7 in the cloud.

No coding. No terminal. No Docker.

This update adds more sense behind the "work on a book" phrasing. Books are now split into chapters by default. Old, single-file books are available to either edit or to convert. No explicit conversion will be performed so you can choose. But going forward, books are better managed with split chapters.

• Books: Now use a folder-based structure with separate chapter files instead of a single document.

• Legacy books are marked and can be migrated to the new format. Your old file will still be available with a .bak extension.

• Chapters are automatically combined into manuscript.md when you compile; chapters.md also gets created automatically allowing you to reorder chapters.

• The manuscript is read-only (in Prosaic) and regenerated on every chapter close or save (including auto-saves).

As always, to update, you can use:

People are tired of AI tools that start with a blank canvas.

The feedback we saw again and again:

PinchBench is the leading @OpenClaw benchmark.

The team is looking for contributors to make it even better.

This meme made me laugh because it is painfully real.

Hi everybody, have a great Friday

Just wanted to thank you all for the support over the launch of Toone this week couple days ago!

KiloClaw pricing is live and it's straightforward.

$9/month for hosted compute. Zero markup on AI tokens. 500+ models.

Lay out your features as cards on a canvas. Add descriptions, reference files from your project with @ mentions, and draw connections to map out dependencies and flow.

When your plan is ready, hit Build. The entire board every feature, every connection, every file reference gets sent to AI as a structured prompt. It reads the plan, respects the dependency order, and implements it.

No more explaining your whole architecture in a long chat message. Just show it.

Use it for:

If you have 20 minutes to spare today, I highly recommend you watch this talk by Rory Sutherland.

I was completely engrossed for the full 20 minutes because he delivers so many interesting insights.

Describe what you want. Invoke's AI generates the design and shows you a live preview desktop, tablet, and mobile.

Not quite right? Just tell it what to change. Tweak the layout, adjust colors, rearrange elements. Keep iterating until it looks exactly how you want.

When the design feels right, hit Build. Canvas writes real, production code directly into your codebase. Not a mockup. Not a prototype. Actual code.

One click and Invoke creates an isolated fork of your entire project. Experiment freely try new features, refactor, break things.Your original codebase stays completely untouched.

Last month, Tailwind CSS fired 75% of their engineering team.

Revenue dropped 80%. Doc traffic down 40%.

Meanwhile, Tailwind is more popular than ever.

Out of a couple hundred launches, that s honestly surreal. Huge thank you to everyone who supported, upvoted, commented, and shared, it really means a lot.

Big takeaway: building is only half the game, distribution, marketing, and timing matter just as much.

Now back to improving Embedful and doubling down on getting it in front of more people.

Hey everyone,

So we just hit the reviews section on Product Hunt and I'm genuinely curious what you think. If you've actually used ClickSay, we'd love to hear what's working for you and what's not.

This starts with a story:

In the summer, one founder of a VC-backed startup approached me to manage his LinkedIn profile, through which he acquires clients (personal brand building).

It was a classic job interview, where the assumption is to create a conversion (you are active on someone's account, building their personal brand, as the account grows, people are noticing you, write to you, you arrange a call, and maybe close a sale)

I asked if there was a possibility of getting equity in this position, because the other positions they had advertised (whether tech, GTM, sales, some small percentage of equity) did offer even a small %...

The answer was "No, this position does not include equity."

New feature: You can now create an AI clone of yourself on Tough Tongue AI. It talks in your voice, uses your face, and joins meetings on your behalf.

10 minutes to set up. Three steps. Here's how it works.

Tobira launched yesterday and we ended up #1 with 568+ upvotes on a Monday. I still can't quite believe it.

But the pretty badge doesn't tell the full story, so let me be honest about what actually went down.