Content
@
https://warpcast.com/~/channel/aichannel
0 reply
0 recast
0 reaction
shoni.eth
@alexpaden
call video: https://youtu.be/eFifjVYhjmE SUMMARY: 1/3 — Structured AI Workflows & Data Scale Alex demoed using Cursor with XML/MD structured workflows to auto-summarize docs, craft project pitches, and efficiently manage tasks. Key lesson: clearly defined context beats raw prompting. 🔗 https://github.com/alexpaden/identity-ai Jason emphasized Snowflake’s power to handle massive datasets without complex queries or niche engineering hires—essential for billion-dollar-scale data like property platforms.
1 reply
0 recast
5 reactions
shoni.eth
@alexpaden
2/3 — Specialized Tools & Autonomous Coding Discussed MCP-friendly tools enhancing AI workflows: HyperChat (custom agents) 🔗 https://github.com/BigSweetPotatoStudio/HyperChat Goose (autonomous workflows) 🔗 https://github.com/block/goose Continue.dev (OSS Cursor alt) 🔗 https://www.continue.dev/ Dean showcased Devon AI, an autonomous coding assistant that built a voice-reactive dancing horse app live. Costly (~$500/mo) but powerful. 🔗 https://dancing-horse-app-py9anonp.devinapps.com/
1 reply
0 recast
3 reactions
shoni.eth
@alexpaden
3/3 — Emerging Hardware & Future Takeaway Nvidia’s "Digits" GPU boxes (~$3K) may soon enable local inference of 200B+ param models. Combined with Google's Titans (a promising transformer alternative), this could reshape local AI dev. 🔗 https://medium.com/@sahin.samia/google-titans-model-explained-the-future-of-memory-driven-ai-architectures-109ed6b4a7d8 Key takeaway: custom workflows + specialized agents > relying solely on big models. Open-source tooling is rapidly catching enterprise solutions.
0 reply
0 recast
0 reaction
