Almost every founder I spoke to during research said the exact same thing. "We know our HR is a mess, we just haven't had time to fix it."
That one line changed how I'm building Office Bee.
The problem isn't that people don't know better tools exist. It's that switching feels like more work than just living with the spreadsheet. So nothing changes until something actually breaks.
We're building around that reality. Minimal setup, no long onboarding, just clean people ops that works from day one for startups that have outgrown spreadsheets but don't need a full enterprise system yet.
I am (the developer of) Polish ! A is a free, open-source Chrome extension that lets you redesign any webpage you're on - powered by AI, with accessibility features baked in. Ever looked at a website and thought "this could really use some work"? Yeah. Same here. Now you have a tool that can change it - just in the way you want - think of it as Pimp My Ride, but for the internet.
We get very different answers depending on who we ask.
Some people are completely on board. They want the data, the analysis, the verdict, and they'll factor it in alongside everything else they know. For them, AI is just a faster, more rigorous way to get to the same place they were heading anyway.
No scripts. No fake demos. Real calls, real buyers, real browser sessions.
Here's what we learned building ClinqAI: the hardest moment in B2B sales isn't getting the meeting - it's the live demo. Your SE is stretched across 6 calls a day, half-prepped, fumbling through edge-case questions while the buyer loses confidence.
Most AI tools give you an output and ask you to trust it. We'd rather show you the work. When you type a ticker into CoreSight, here's exactly what happens in the background:
Resolving SEC CIK: Every public company has a unique identifier in the SEC database. The agent finds it first, so everything that follows pulls from the right source.
Fetching SEC filings: 10-K, 10-Q. The raw financial truth, straight from the source, not a third-party summary.
Getting market price: Live data. The analysis reflects what the stock actually costs right now, not yesterday's close.
Extracting financial statements: Income statement, balance sheet, cash flow. Structured and ready for analysis.
Computing metrics: P/E, P/S, P/FCF, margins, debt ratios. The numbers that actually tell you if a stock is priced fairly.
Searching web for context: numbers don't exist in a vacuum. The agent searches for recent news, product launches, and market developments that could affect the analysis.
Generating AI analysis: Everything gets synthesized into a valuation verdict with a bull case, bear case, and clear reasoning.
Populating spreadsheet: The output lands in a structured spreadsheet you can explore, edit, and build on.
A few months ago, we launched CoreSight here as a McKinsey-in-a-box platform for founders and operators. You showed up, gave us great feedback, and we've been building since.
This time, we're focused on a feature we've been quietly using ourselves: Analyze a stock.
I have tried many of the QA platforms out there and one thing I noticed is that they are all way too overly complicated. They are also quite expensive. My platform's highest level subscription plan is less than half the cost of some basic plans for other QA platforms and I offer just as much. Ninjatest is a platform I've been working on for the past six months and have launched it recently. We offer advanced analytics for both automated and manual testing.
For automated testing, we support both JUnit and TestNG XML results files. You can upload your automated testing results and store them on our servers securely. We offer the ability to organize automated test runs, and have advanced reporting as well. You can generate a report for both automated and manual test runs and either save it to the server or download the PDF. We use AWS S3 pre-signed URLs for added security so links expire after a short time.
i built an app to create audiobooks, a user-friendly and full automatic, there are other options but require manual work, and have complex dashboards.
the ai model using behind it is different from most TTS you find, it's an LLM based, so you get the different voice pace, emotions etc. each time you generate the audiobook, it means less robotic and non-flat voices.
I just launched Stackwatch a usage monitor for dev teams & Solo devs that tracks GitHub Actions minutes, Vercel bandwidth, Railway and Supabase limits in one place and alerts you before you hit them.
The idea came from a painfully relatable moment: we hit our GitHub Actions limit mid-deploy on a Friday. No warning, no heads-up just a broken pipeline and a confused team. The fix took 10 minutes, but finding why everything was broken took 45.
The problem isn't that these limits exist. It's that the tools don't talk to each other, so you're either checking 5 dashboards manually or finding out you've crossed a limit when something breaks in production.
