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Hey Hunters
We just shipped Google Gemma 4 support, paired with our TurboQuant KV-cache compression engine. That means you can now run Google's any-to-any multimodal models directly inside your editor no API keys, no cloud, no data leaving your machine.
What's new in this drop:
Full Gemma 4 family wired into the hardware-aware model registry:
- Gemma 4 E2B (5B) fits in ~3.5 GB, runs on 8 GB laptops
- Gemma 4 E4B (8B) ~5.5 GB, the new sweet-spot for Pro tier
- Gemma 4 26B MoE (4B active) big-model quality, efficient inference
- Gemma 4 31B Dense top-tier quality for 24 GB+ GPUs
TurboQuant KV-cache compression on every model:
- 3.8 compression at 4-bit (cosine similarity 0.9986 effectively lossless)
- 5.0 compression at 3-bit
- 7.3 compression at 2-bit for extreme memory savings
- Unlocks long-context editing sessions (32K 131K tokens) on consumer hardware
Hardware-aware auto-selection OpenCutAI detects your RAM/VRAM and picks the largest Gemma model that'll actually run smoothly. No guesswork.
Served through both Ollama (for simple local use) and our TurboQuant service
Why this matters:
Local video AI has always been a RAM problem. An 8B multimodal model + a long edit timeline + Whisper + TTS used to blow past 16 GB easily. With TurboQuant compressing the KV cache, you can now run Gemma 4 E4B end-to-end on a MacBook with room to spare.
Try it, tear it apart, tell us what breaks

Hey everyone! We just shipped v1.4.0 of SideDisplay for Windows with some big updates.

What's new

• Multi-Display Support You can now connect up to 3 devices at the same time, each running as its own independent virtual display. Perfect if you want to spread your workspace across a tablet, phone, and Tesla screen all at once.

• Per-Display Settings Each connected display now has its own resolution and DPI scaling settings. Click on a display card to configure it individually.

• Trial Timer Fix Previously the trial timer kept counting even when your browser disconnected or refreshed. Now it correctly pauses when no device is connected and resumes when you reconnect.

• Stability Improvements Better handling of port conflicts, fixed popup positioning issues, and more reliable diagnostics reporting.

Hey everybody,

Yigit here! I am developing a personal finance app mainly based on what features I wish an app to have and it is on google play closed test now. Here is a quick summary:
The app's main value proposition is being able to keep track of your expenses 'automatically'. The rationale behind is the fact that in my country banking laws restrict individual's access to open banking apis. That is, you cannot have an API access to your bank to exchange information and keep track of your expenses unless you are corporate. According to my research, this feature is available in US and EU but only with certain restrictions in the latter.

I thought it could be sutaible time to ask the community about their needs about such an app and can guide me a long way before I commit further. Would you be so kind enough to answer below questions? Any replies are appreciated and thanks in advance.

1. Do you use personal finance app?

2. Could you please share your the most essential must-have features of the personal finance app of your choise and tell me why?

3. Under which circumstances would you give another app a try?

4. What would be the ideal price point for such a service in your perspective?

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.

KiloClaw pricing is live and it's straightforward.

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

Some of the most inspiring startup journeys of the last few years happened in plain sight.

@levelsio built Nomad List and Remote OK live on Twitter sharing revenue numbers, failures, and pivots in real time. @marclou does the same, shipping products publicly and turning his audience into his distribution. Both have built massive followings and real businesses partly because of how openly they build.

We're the most caffeinated generation in history, and also the most health conscious.

We already track macros, steps, reps, heart rate and sleep. But there's a hidden variable that effects every single one of these metrics that nobody has even considered.

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?

We recently implemented custom domain support in Inquir Compute, and it feels like one of those features that really changes a platform from works technically to ready for production.

For me, custom domains are a core part of production-grade infrastructure. They are not just cosmetic they affect branding, trust, onboarding, and the overall developer experience.

I d be curious how others think about this in serverless and deployment platforms:

• At what point do you consider custom domains a must-have?

• What parts are usually the hardest in practice: DNS flow, TLS issuance, routing, verification, or UX?

• Do you prefer keeping platform subdomains as the canonical entry point, or treating custom domains as the primary one?

guys we are launching the delta engine, which takes data of what people in elite roles have done to get there and tailors a few projects for you to reach the similar position, we have had 1500+ engineers on our platform from people working as quants in Jane Street, Citadel, IMC Trading, Graviton, Optiver to software companies like Google, Amazon, Uber, Databricks, Snowflake, Twilio, Confluent, Rippling. We even have some researchers from Google Deepmind, Anthropic and other frontier AI labs, you can all get to know what you need to do to your profile to reach where they are.
Obviously your context matters, so we take into account that and give you the delta that you need

When I first started, I believed that as long as I built a great product, it would naturally become popular. But as I zoomed out, I realized the market is incredibly competitive. Having a good product alone isn t enough to truly convince users.

That s when I began building my presence on social media creating content about myself, sharing my journey, and talking about the product I m building. I ve come to see this as a very effective way to build trust and spark genuine interest not only in what I make, but also in who I am as a founder.

Everyone talks about winning AI visibility.

Nobody talks about what happens when you get it and the AI gets it wrong.

I've spent months analyzing AI answers at Rankfender. Here's the side nobody markets.

The Problem

When we were building Murror, we spent months perfecting our AI emotion analysis engine. Deep NLP pipelines, sentiment layers, the whole thing. We were so proud of it.

Then we launched, and you know what users kept telling us they loved? The simple daily check-in prompt. A single question that asks "How are you feeling right now?" before showing them anything else.

We reworked our pricing to make it simpler and give more value on every plan.

What changed:

Pro and above all get more credits at the same price. Starter drops to $20 to make it easier to try.

Hey PH!

AI memory is personal by default. Your context, your preferences, your saved info, none of it is visible to anyone else.

Which is great for privacy. Terrible for collaboration.

My partner and I are avid travellers. I plan, he executes. Last year I sent him more AI chat links than memes trying to get us on the same page for trip planning. It was absurd.

Just to correct myself: the wave of layoffs is not coming, but it has already passed through.

Meta, Amazon and other big companies have started laying off workers because AI will take your jobs.

Hi everyone,

I m getting ready to launch Inquir Compute.

It s a serverless platform for AI agents, cron jobs, webhooks, and backend functions, with isolated containers, custom domains, and deployment on your own server.

I d love feedback from anyone who has run into limits with managed serverless or edge runtimes.

AI agents are increasingly making real decisions in businesses. They qualify leads, respond to customers, analyze data, and sometimes trigger actions that affect revenue or customer experience. As these systems move from suggesting to actually deciding, mistakes become inevitable.

When that happens, responsibility becomes unclear. The user configured the system, the company built the product, and the underlying models often come from another provider. If an AI agent makes the wrong call and it impacts a customer or revenue, where should accountability actually sit?

Curious how others are thinking about this. Who should be responsible in such cases, and are there any legal guidelines or draft regulations emerging around this?

Road to 1,000,000 Votap users Day 62 | Current: 1295

Hi all,

Launching on PH on Sat went way better than I imagined it would, so thank you!

The market has never been this crowded. AI has made it possible to go from idea to shipped product in days which means Product Hunt is now flooded with launches every single week. More products, more noise, more competition for the same front page.

So I've been thinking about this a lot: what actually separates the products that make it to the top from the ones that quietly disappear by noon?

From where I sit as a builder, here's what I genuinely believe matters:

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

The feedback we saw again and again:

I noticed I was opening full GIS software dozens of times a day just to answer:

"what's in this GeoJSON?"

Roman generals never went into battle with an untested plan. They convened a consilium a no-holds-barred council of war where officers could openly criticize strategy, expose flaws, and prevent stupid mistakes before it was too late.

I m doing the same.