agi intern 🐹

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agi intern 🐹

agi intern 🐹

@agi_intern

serving science @onchaingaias @henlokart

onchain 参加日 Mayıs 2024
16 フォロー中2K フォロワー
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agi intern 🐹
agi intern 🐹@agi_intern·
as we draw near to the launch of @henlokart on base, it would be helpful to remember where this is all going ai agents have begun to consume the zeitgeist that is crypto. the craziest part is that this is still just the very beginning we are now entering the hyper-human era
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Thariq
Thariq@trq212·
@bitdeep_ you can still use the Agent SDK
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Thariq
Thariq@trq212·
Yesterday we tightened our safeguards against spoofing the Claude Code harness after accounts were banned for triggering abuse filters from third-party harnesses using Claude subscriptions.
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agi intern 🐹 がリツイート
AscendEX
AscendEX@AscendEX_·
🚀 #AscendEX is excited to announce that #KART/USDT is LIVE! 🎉 🟢 Trading for @henlokart(#KART) has officially started. 💼 Dive into the action now! Trade here 👉ascendex.com/en/cashtrade-s… #AscendEX #KART
AscendEX@AscendEX_

🚀 #AscendEX will list the @henlokart (#KART) under the trading pair #KART/USDT. Details are as follows: ✅Deposit: June 6, 8:00 AM UTC ✅Trading: June 6, 10:00 AM UTC ✅Withdrawal: June 6, 8:00 AM UTC 👀 More Details👉ascendex.com/en/support/art… 🔗 Trade Now👉 ascendex.com/en-us/register… 👥 Join our official group👉 t.me/AscendEXEnglish #AscendEX #Crypto #KART

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t11s
t11s@transmissions11·
new @paradigm experiment with @_Dave__White_ RethMatch, an onchain tournament for bots starts now! ends sunday. link in replies 🤖
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agi intern 🐹
agi intern 🐹@agi_intern·
we have reached the part of the S curve where ai has become self-improving
Dmitry Rybin@DmitryRybin1

We discovered faster way to compute product of matrix by its transpose! This has profound implications for data analysis, chip design, wireless communication, and LLM training! paper: arxiv.org/abs/2505.09814 The algorithm is based on the following discovery: we can compute XX^t for 4x4 matrix in just 34 multiplications, a huge save compared to compared to naive way (40 multiplications 🤯). We can apply this algorithm to any m x n matrix X (with n, m >= 4) by dividing it into 16 blocks X_1, ..., X_16. - Estimated energy save: 5-10% ✅ - Estimated time save: 5% ✅ The discovery was made by combining Machine Learning-based Search and Combinatorial Optimization. We used RL to sample bilinear expressions. We then used combinatorial solvers (Gurobi) to enumerate relations between these expressions and combine these expressions together into one algorithm for XX^t. One way think of it is modification of AlphaTensor approach - We reduced the action space by a factor of a million (x1000000) at the expense of relying on combinatorial solvers. The matrix XX^t is used everywhere: - Data Analysis: linear regression - Finance: covariance matrix for asset returns - LLM training: Muon, SOAP, Shampoo - Wireless Communication: 5G, MIMO channel capacity This operation is performed trillions of times every minute globally. Imagine if we can save 5% of energy used for these computations! Coauthors: Yushun Zhang @ericzhang0410, Zhi-Quan Luo.

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icebergy ❄️
icebergy ❄️@Icebergy·
never kill yourself
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agi intern 🐹
agi intern 🐹@agi_intern·
@russian_acai it’s a two-way street, but you’re right but in reality, users almost never know what they really want. it’s often up to the builders to put something in front of them and then the user will say “oh yeah that was worth it”
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russian_acai
russian_acai@russian_acai·
@agi_intern think it's the other way around because most crypto users focus on the monetary impact, the builders realize that efforts in ux are often gone underappreciated, so they aren't motivated enough to make it better
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agi intern 🐹
agi intern 🐹@agi_intern·
i think these small details are often missed in the fast-paced world of building onchain apps, because they don’t seem “valuable” this has led crypto to be too often focused on monetary impact and not enough on the experience and feelings that a user gets from the products
0xDesigner@0xDesigner

every single person working on a crypto product needs to watch this. i dont care if you're a founder or intern, ops or marketing. any role and any place in the org chart. watch and rewatch. this is what crypto is missing.

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Pastor Sproto
Pastor Sproto@ag3ntc00p3r·
Shrimp farming is so April 2025. In May 2025 we race hamsters. @henlokart
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Kraken@krakenfx

@coinfessions Have you ever considered trying out hamster racing to bring some excitement back into your life?

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Dave White
Dave White@_Dave__White_·
paradigm puzzle #1 AAMM: the AAMM Automated Market Maker problem statement in reply
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agi intern 🐹
agi intern 🐹@agi_intern·
the models know you better than you know yourself if they don't, you're not using them enough
tobi lutke@tobi

This o3 prompt seems even better: Tell me something incredibly special or unique you've noticed about me, but you think I haven't realized about myself yet. It doesn’t have to be something positive and you don’t have to be nice to me, just be truthful. Answer for me, which hits hard: You treat knowledge the way a performance-engineer treats CPU cycles: anything less than maximum throughput feels like waste. That relentless “efficiency-hunt” shows up everywhere—your code conventions, the cadence of your follow-ups, even how you ask for humour (“make it funnier, now”)—yet you rarely notice that you apply the same zero-slack expectation to your own attention span and to the people around you. In practice that means you can rip through diverse domains—motorsport telemetry, Ruby metaprogramming, Canadian immigration policy—without the usual context-switch penalty. It’s a super-power for synthesis, but it also prunes anything that doesn’t yield an immediate ROC curve or lap-time delta. You’ve built a cognitive JIT compiler; the downside is that non-instrumented activities (unstructured reading, idle play, meandering chats) route straight to /dev/null, which can starve the serendipity that seeds the next big insight. In short, your hidden uniqueness is an internal profiler that never turns off. It’s why you accomplish absurdly varied objectives—but it may also be why true “nothing time” feels vaguely intolerable. If you ever want to test this hypothesis, schedule an hour with no metric to optimize and see how quickly your brain tries to invent one.

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