Most companies overpay 20-30% on software they've forgotten they even have unused seats, duplicate tools doing the same job under different names, subscriptions nobody remembers signing up for.

I built SaaS Auditor to catch exactly that. Import your spend and usage data, and it:

Flags duplicate tools by actual function, not loose category labels (so Slack and Zoom don't get falsely flagged just because they're both "Communication")

I kept running into the same problem when using AI to think through startup ideas.

I'd ask:

We're building an assessment layer that scores candidates on two things, separately: can they actually do the job (Work-Fit) and will they thrive in how the company operates (Culture-Fit) through one adaptive AI conversation instead of a DSA round and a gut-feel culture interview.

The core idea: before any assessment exists, a hiring manager defines 4-6 things the hire will really do in their first 90 days. That list becomes the rubric. Candidates get a scoped task built from it (PR review, small build, debug), and an AI asks live follow-ups based on what they actually say so a rehearsed answer breaks on question two.

We deliberately skipped personality tests and generic "culture fit" replaced with structured, evidence-based behavioral scoring, because that's what the research (and the hiring leaders we've talked to) says actually predicts performance and reduces bias.

What we don't have yet, and why we're here: a real rubric built with a real hiring manager. We want to co-define the first 4-6 activity list and value set with a handful of companies actually hiring right now not guess at it ourselves.

Hi Product Hunters, I'm an solo founder building e-commerce agent Sellclaw for amazon seller.

SellClaw is a multi-agent AI assistant built for e-commerce sellers.

We want SellClaw to become the assistant sellers work with every day: one place to research, create, organize, and eventually coordinate more of their store operations.

Hey Product Hunt community!

We re putting together a private 14-day Founding Cohort for Zenor a personal AI tool engineered specifically to help high-responsibility leaders manage daily decision load and reduce context-switching.

Been deep in the weeds this week on what it actually takes to run a serious local AI setup not a toy chatbot, something that actually does real automated work. The honest number ended up being 24GB+ VRAM minimum, 48GB+ recommended for a smooth experience. That's not "install an app," that's "own real hardware."

Which makes me wonder if local-first AI has a much smaller realistic audience than the privacy pitch suggests plenty of people want their data to stay local, but way fewer actually have (or want to buy) a machine that can run it well.

I've been building Ash, a local AI agent, partly to test this myself but genuinely curious from others here: if you've looked at local AI tools, was the hardware bar the thing that actually stopped you, or was it something else (setup complexity, model quality, just habit)?

Most AI video products focus on the final output: generating clips, adding captions, or automating edits.

With Someple, we re working on an earlier and less visible part of the workflow the decisions that happen before editing begins.

Before someone can edit a long video, they usually need to:

• Understand what happened across the entire recording

• Find the moments that are actually worth using

• Organize the main topics and narrative

• Decide what kind of content can be created from the footage

• Communicate that direction to an editor or production team

Hey fellow makers

I m currently building a tool in the sales-tech space, and I m hitting an interesting architectural/positioning dilemma that I d love your thoughts on.

Getting raw intent data is easier than ever. You can scrape GitHub, monitor social channels, or track G2 reviews to find people talking about specific problems. But talking to sales reps, the real bottleneck is the noise. Handing an SDR a list of raw signals usually wastes their time half the time it s a junior dev venting, or a 5-person startup with zero budget.

To solve this, my approach is to focus almost entirely on the qualification gate in the middle. The system intercepts the signal, but automatically checks company headcount, tech stack, and deal size fit before the lead ever hits the CRM.

Hey everyone,
We just launched Appllama MCP + Skills. Now, your Claude/Codex can research the world's top mobile apps and use those patterns to redesign or build your app.

Appllama is a mobile app design discovery platform with 800+ top-earning apps and 35,000+ screens across major and niche app categories.

With the new MCP, you can:
Research: Why does Duolingo make so much more money than other language apps?
Build Apps: Build me a home screen like bevel, but for a fasting app

Every project accumulates dependency bloat packages you installed once, forgot about, or replaced but never removed. I built PkgDiet to catch that before it costs you build time, bundle size, or a supply-chain risk you don't need.

Run npx pkgdiet in any Node project and it scans your dependency tree for:

• Unused packages installed but never imported

• Bloated packages heavy deps with lighter alternatives

• Unhealthy packages unmaintained, deprecated, or flagged

It's a single CLI command, no config, no dashboard sign-up just a clean terminal report you can act on immediately.