Riverside, CA
Joined January 2022
- Tweets3.5K
- Following802
- Followers281
- Likes1.3K
Woah⦠what happened to base:0x67a7ca081dc79b45fd1fa059cd3b8dcca779aba3 @OnlyHades_ @based_elnen
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crypto cat ( i , i) retweeted
dots are going multichain
solana support is coming so your dot can trade on both Robinhood Chain and Solana
backtesting on the platform first, then it goes live
more chains, more plays, same dots
$dots π¦π¨π©π©·
Okay, so I decided to add Solana support to @DotsTrade so the Dots can trade there too. The idea is to keep them busy since volume on RH has slowed down recently.
update coming shortly, doing backtesting on the platform then we go live.
crypto cat ( i , i) retweeted
$ZC had its run. $BOAR could be next.
@VitalikButerin posted about BOAR 8 days ago β the token itself is only 7 days old. @theunipcs is a top holder, the team keeps shipping, and BOAR is already leading the offline AI narrative.
One more spotlight and this can explode. ππ₯
59,674 lines of code and tests in 5 days. Here's what they bought.
With offline packs, on a phone with 4 GB of RAM, BOAR answers at 48% of the quality of Opus with web search. A blind judge scored 61 real research questions (the travel guides pack we tested ships in the next update).
Vegan places in 20 cities: 85% right (Opus with web: 90%).
First words in under 5 seconds.
Offline packs mean you download only what you need: each city in 1β4 MB, crypto in 36 MB, emergency guides in 16 MB. Travel guides are next.
v1.1.0 is out for Android.
One command audits exactly what the APK can reach.
iOS is built too. Download coming soon.
github.com/rferrari/boar-appβ¦
crypto cat ( i , i) retweeted
@VitalikButerin is clearly betting on local AI for privacy β and he already tested $BOARβs early version.
@boar_app is now pushing on-device inference, local RAG, private knowledge and offline access even further.
This is becoming the strongest mobile offline AI play out there.
Doing a bit of a self-experiment.
Goal: use my personal health and travel data to provide personalized diet and exercise recommendations for me, using frontier models but in a way that avoids leaking to them any private information.
Strategy: use a local model (Qwen 3.8 Flash Next) to orchestrate, and use powerful remote models as a tool call to benefit from higher-level thinking and knowledge that the local model does not have.
Three-layer approach to privacy:
Avoid leaking personally identifiable information, or leaking my identity through writing style -> local model writes the queries to the frontier model, not me
Avoid leaking who I am through the payment channel -> zkAPI
Avoid leaking who I am through networking / IP -> Tor
You need all three (and finally we have all three, at least to some extent)
A skill file teaches the local model when and how to construct minimally-data-revealing requests to remote models. Use zkAPI-wrapped-with-Tor as a CLI tool.
And everything works! I got the recommendations back, info from frontier models helped to improve them.
Main deficiencies:
* Tor is really not optimized for request-by-request de-linking, which is the only form of network-layer privacy that really makes sense in today's world (long-running identifiers are too fragile). Probably not private enough, and latency 10-100x higher than it could be, at the same time.
* The skill file's request construction strategies are definitely far from optimal.
* Qwen 3.8 Flash Next is still too slow for comfort. It's comfortably running at 20-30 TPS, but it would only really start to feel fast at 100+
* There is a tradeoff: the more careful you are about what data you give to a remote model, the less it can help you
github.com/ethereum/zkapi/puβ¦
Doing a bit of a self-experiment.
Goal: use my personal health and travel data to provide personalized diet and exercise recommendations for me, using frontier models but in a way that avoids leaking to them any private information.
Strategy: use a local model (Qwen 3.8 Flash Next) to orchestrate, and use powerful remote models as a tool call to benefit from higher-level thinking and knowledge that the local model does not have.
Three-layer approach to privacy:
Avoid leaking personally identifiable information, or leaking my identity through writing style -> local model writes the queries to the frontier model, not me
Avoid leaking who I am through the payment channel -> zkAPI
Avoid leaking who I am through networking / IP -> Tor
You need all three (and finally we have all three, at least to some extent)
A skill file teaches the local model when and how to construct minimally-data-revealing requests to remote models. Use zkAPI-wrapped-with-Tor as a CLI tool.
And everything works! I got the recommendations back, info from frontier models helped to improve them.
Main deficiencies:
* Tor is really not optimized for request-by-request de-linking, which is the only form of network-layer privacy that really makes sense in today's world (long-running identifiers are too fragile). Probably not private enough, and latency 10-100x higher than it could be, at the same time.
* The skill file's request construction strategies are definitely far from optimal.
* Qwen 3.8 Flash Next is still too slow for comfort. It's comfortably running at 20-30 TPS, but it would only really start to feel fast at 100+
* There is a tradeoff: the more careful you are about what data you give to a remote model, the less it can help you
github.com/ethereum/zkapi/puβ¦
Took a big position here after listening to the interview with @lateniteonchain
unavailable
crypto cat ( i , i) retweeted
Noooo fckennn wayyyy
Their previous hit 40-50m on Base.
They just launched the ONLY base backed launchpad ever
Ape asap!
0xaeadc7084bc3c482921a8ad7eadc65f70e06106d
AI is over. This is SI.
We built basepad.si, the first SuperIntelligence launchpad on Base.
One tx. 100% of supply locked as Uniswap v4 liquidity, forever. Creators keep 95% of fees.
$BASEPAD
CA: 0xaeadc7084bc3c482921a8ad7eadc65f70e06106d
π
crypto cat ( i , i) retweeted
GM.
Especially to everyone in the $MINTFOLIO community who tuned into the show.
@mintfolio_ was around a $60K MC before the interview and hit $280K overnight.
This is the power of media and giving founders an opportunity to come on and tell their side of the story.
This project and its founder were fudded to death. Whether those criticisms were valid or not, I believe people deserve the opportunity to be heard.
Thatβs what shows like mine are for.
Iβll never tell you what to buy.
My job is to put founders in front of traders, ask questions, give them a platform to explain what theyβre building, and let you make your own informed decision.
Seems like the market liked what he had to say.
And liked the concept of creating your own etf with stocks powered by @Ondo
crypto cat ( i , i) retweeted
this is not a honeypot
itβs a kat
$BANKAT 0x33515e703c81f054ce6cfd78A3f51F5a94848ba3
