@BlockSecTeam thanks for the thorough writeup! did you review past audits? i've been browsing through them, and @trailofbits seems to have flagged a very closely related issue, but the report does not specify whether it was fixed or not (cc @Montyly)
I created a VSCode extension to simulate your local contracts as if they were already deployed on-chain
It also allows you to create "Solidity notebooks". This is useful when you want to throw together some Solidity code and test what it would do
It's in beta. Details below
If you want to use a French product go for it! But don’t spread misinfo in the process.
Signal is independently audited, open source, and our protocol has been tested for >10yrs.
We are serious about responsible disclosure and we prioritize all reports to [email protected]
I spent the last month building llamafile which is the fattest executable file format ever. It lets you turn LLM weights into runnable llama.cpp binaries using cosmo libc. https://t.co/OLuijZXKz9 Blog post: https://t.co/hNJXeN41vO
I’ll be serious for a second and take a crack at this. This is how I would describe Ethereum alignment and a lot of people say I’m a trusted source on these matters and then they’re like “omg Gwart launch an L2” and I’m like no I can’t, the mechanism design world needs me, it
@rioujeanpierre@Thinker_View Vous oubliez de préciser qu'il faudra toujours de l'eau pour la majeure partie des réacteurs puisque celui-ci vient en complément des actuels car il utilise leurs déchets
In "Uptober" edition of the Paraswap Dao Recap, 📈
💰 80.3 ETH was distributed to about 3.7k PSP Stakers and 618K sePSP1 was paid as gas refunds.
🤝 Dexguru, Defi Saver and 4 others Integrated Paraswap.
Paraswap launches ios beta testing.
🧵
@paraswap DAO paid contributors program tagged #Paratrooper renewal proposal is live on Snapshot.
This proposal determines what becomes of the initiative and the current cohort.
https://t.co/UhXuSWrDnM
A key way you can make your LLM/RAG chatbot more “advanced” over complex data sources is to add a router - dynamically decide which data to query / which parameters to use using LLMs.
It’s a key step towards LLM workflow automation. It sounds simple, but there’s some trickiness:
✅ Figuring out the right prompt (pick one choice? Multiple choices?)
✅ Add in structured output parsing
✅ How to integrate with function calling APIs
✅ [For multi-routing] how to combine results
@llama_index has out of the box Router modules, but in the spirit of “do it yourself” we have a tutorial showing how you can tackle these challenges with your own Router module!
https://t.co/2AxK6ROwwv
Our in-house router modules do this and more (retrieval augmentation, callback handling, etc.). For more details check out the guide: https://t.co/Egs6cyOP99