What AI tools do you use the most?

8 answers

Post·6d ago

Left Aon in 2019 and launched an eCommerce platform for preventive health during the pandemic, with 30+ contractors and solo-led work on Wix AI Design, Canva, Figma, Notion. Then ChatGPT dropped and from 2022 I spent 2 years trying to build a legal tech startup, while pursuing a legal trainee contract and degree.

2024 and 2025 were rougher. Homeless on and off, in backpacker hostels between short term flat shares; defending legally from more libelling attacks than in 2019-23.

Yet in 2025 I learned to code in Python, JS and HTML from Grok and Copilot, and tested many AI tools: ElevenLabs, Hedra, Kling, Veo, Flow and Stitch by GLabs, Seeedance, Flux, Hunyuan, Wan. Claude for data visualisation and code.

Then up to spring 2026 physical pain due to walking miles to libraries to use Tableau and Adobe online on time-limited PCs (vertebrae micro misalignment hurt me in long walks).

I quit the libraries PCs, opted for AI-only workflows and agents in Vercel, Replit, Eleven Agents, Wabi & more, that run on tiny hardware

Jay Lim @evolv3labs

Post·1w ago

Lately Codex, Antigravity, Gemini, ChatGPT, evolv3 Multi-Agent OS (powered by OpenClaw).

Jesse Bray @mrbray

Post·1w ago

Claude and Codex

Hanjong lee @walterlee79

Link·2w ago

claude-startup-roi-framework-rclvgwxgynst2pepcm5ywm.streamlit.app

Product Strategist & Builder — Claude Startup ROI Predictor

English final recommendation Designed and built a scenario-based ROI prediction framework to help Anthropic’s Startup Ecosystem and Account Management teams evaluate the expected impact of Claude API credits. I defined the core decision variables—including API credit value, implementation cost, annual task volume, time saved per task, labor cost, automation potential, and expected adoption—and translated them into an interactive decision-support tool. The model generates probability-weighted R

Open link

Hanjong lee @walterlee79

Article·2w ago

For decades, centralized media smoothed over differences.

For decades, centralized media smoothed over differences. Shared programs, shared headlines, and mass-market narratives created the appearance of one common culture. Averages hid the extremes, while smaller identities and subcultures remained largely invisible. Algorithms did not manufacture every preference from nothing. They observed what we paused on, reacted to, and shared—and amplified the most emotionally dense signals. What now looks like fragmentation may partly be higher resolution. The differences were already there. The algorithm removed some of the blur. Of course, algorithms are not neutral mirrors. Repeated amplification can also shape preferences, intensify conflict, and push people further toward the edges. But the strategic implication remains clear: Trying to appeal to everyone often produces a message that matters deeply to no one. The future belongs not to the loudest message, but to the one that understands its audience with the greatest precision. Mass appeal searches for the average. Meaningful engagement understands the edges. So the question is not simply: “How do we reach more people?” It is: Who exactly are we trying to reach—and why should they care? ShorterLongerFunnierRewrite

Hanjong lee @walterlee79

Video·2w ago

Alex Kwon @startupoppa5939

Post·2w ago

claude, figma, notion, gemini, grok

Taeho Lee @taeholee5185

Post·2w ago

Claude Code is my main dev environment now. I've built a harness workflow on top of it that lets me prototype faster, and an automated testing framework that catches issues before they become problems. But the real win was creating a personal knowledge base to track tasks, progress, and what I actually learn from each project. It's become the connective tissue between how I code and how I think about what I'm building.