@GorodkovVi85373 · X

- load testing made easy even without enginnering team. Faster, cheaper, distributional

@ShipItLokesh · X

@javimosch · X

@AppMailpilot · X

if you are doing bulk outreach by emails - lets warmup your emails for lifetime (for only early users) - each time now you don't have to worry about your mails if they are landing in spam go and break servers of

@kitbasedev · X

We just launched, check us out at

Formulas AI — 帮助用户生成 Excel 公式，基于 DeepSeek-V3 的 AI 工具产品（需要用户自己填入key）

@jingconan · X

Hi! Let's Connect! I am building which helps founders to turn ideas to reusable workflows

@Grow0nX · X

- Best X content creator friend/tool

@st0yanov · X

u/maybeImakemoney · Reddit

I built the thing. Now I am not sure the base use case is one people will pay for. Founder here. This started as a side learning project to see whether an LLM could answer questions about Excel data, back when they could not do it well. I built the first version on n8n, with workflows that ingested files, generated metadata with an LLM, and answered questions against the converted data plus that metadata. Then I started using it for my own analysis and report generation, saw that the time sav

chenglong-hn · HN

Data visualizations are the bridge between user and data. But building AI agents that can generate visualizations reliably can be very tricky: - simple chart specs can be reliable, but generated charts are often of low quality due to reliance on system defaults; - complex chart specs with explicit details can produce good-looking charts, but they are verbose and agents can struggle with reliability We figured out it is a limitation on the language issue (not just AI capability thing) -- current visualization languages are a bit too low-level for AI agents, requiring them to explicitly make visual decisions that are supposed to be handled by a good compiler. Flint is a visualization intermediate language to address this issue, allow AI agents to solve this last-mile human-agent interaction problem. It provides a simple semantic-type based specification, and contains a layout optimization engine that can produce good-looking charts (filled with derived low-level details) from simple

@AIWMCX1 · X

Consultants don’t need more noise. AIWMC Quantis helps turn messy business ideas into clear scenarios, runway views, and risk signals — so you can spend less time rebuilding analysis and more time giving the client a real answer.