I've been building ATLAS for 7 months. The idea is simple: instead of charts and opinions, you get the actual probability of making or losing money on any stock.
This is what a simulation looks like: for example Apple, target +5%, 75 days. You can do this for 4,500+ stocks, or get a suggestion of the best stocks with the highest probability for your constraints! The idea is that you can make a more informed decision about your investment ideas!!
GitWhy is the context layer for git. It helps developers save the reasoning behind AI-generated code, link it to commits, and surface it in GitHub PRs for review. It works with Claude Code, Cursor, and other coding agents.
We built GitWhy after running into a simple problem: AI could help write the code, but the reasoning behind it was getting lost. The prompt, trade-offs, and decisions stayed in the chat window, while the team only saw the diff.
I just released my OSS app, this is a PGP workstation with simple and intuitive interface to operate with encrypt/sign/decrypt/verify workflow under PGP keys.
Quick question for the marketers and analytics folks here: How much time are you actually spending getting data into something you can present or share? And does it ever feel like most analytics tools are built for data people, leaving everyone else to figure things out themselves?
We noticed that the process is still surprisingly painful and manual. So we built Crunchy. Drop in your data, get back editable dashboards and insights without the SQL, the setup or the cleaning up charts in PPT at midnight.
We're running our closed beta now and launching on PH mid April, would love to connect with anyone who wishes their analytics tools were just a bit simpler.
I built AiVIS because i got really tired of watching sites rank on Google and still disappear when answers get assembled by ai
that gap is real and most people still are not measuring it properly
AiVIS audits or deeply analyzes whether a site can actually be read trusted and cited by answer engines. not just whether it ranks, not another SEO tool with an Ai badge. Its not just whether it looks good to us as humans but
it shows where structure, trust signals, metadata, schema and content clarity break down then the system ties it back to evidence on the page, not Ai. The LLMS are used for reading content then interpreting the evidence backed results and writing the unbiased/unfluffed summaries. BRAG - Based Retrieval Audit Grading is our unique evidence ledger mechanism.
Looking to complete the first 200 iOS paid users. Any recommendations? B2B is welcomed. Edit your notes quicker with prompts and extract information from voice, videos, live and virtual meetings, screenshots, among others.
