“you can see everything” reminds me kurosawa the director Rashonmon wrote in his book:
“Human beings are unable to be honest with themselves about themselves. They cannot talk about themselves without embellishing.”
>>btw had therano keeps raising like today’s ai buzz would those powerful figures behind her scream for scams ?
“Human beings are unable to be honest with themselves about themselves. They cannot talk about themselves without embellishing.”
>>btw had therano keeps raising like today’s ai buzz would those powerful figures behind her scream for scams ?
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為學日益， 為道日減
"He who pursues learning will increase every day;
He who pursues Tao will decrease every day.
-- Tao-te-Ching
everyday i automated a bit of my logistics in life and work, eventually I will fall into just meditation and senses. Maybe that's the Way/Tao
"He who pursues learning will increase every day;
He who pursues Tao will decrease every day.
-- Tao-te-Ching
everyday i automated a bit of my logistics in life and work, eventually I will fall into just meditation and senses. Maybe that's the Way/Tao
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xiao sun retweeted
Can AI accelerate its own development? And how would we know if it's working?
Recursive self-improvement is one of the most fascinating open questions in AI. Thrilled to have @lilianweng join us at SGLang Summit to explore what's possible, what's hard, and what comes next.
Recursive self-improvement is one of the most fascinating open questions in AI. Thrilled to have @lilianweng join us at SGLang Summit to explore what's possible, what's hard, and what comes next.
We’re excited to welcome @lilianweng to SGLang Summit for a fireside chat on Recursive Self-Improvement!
She’ll explore how AI can help build its own successors, what it takes to make that work, and where the process runs into limits.
If you’ve followed her writing on Lil’Log, ov 12. We can’t wait to see you there!
🗓️ Nov 12-13
📍 Fort Mason, SF
🎟️ Get your pass: sglang.io/summit
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dota games seem full of poorly trained ai, and dota bans you for leaving the game early no wonder it’s dying
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xiao sun retweeted
No one changes the world alone
Introducing Ampersand - the first model built for us. Ampersand is a collaborator trained to help people achieve their individual and collective goals. In multi-month simulations, users collaborating with Ampersand accomplished more than with Fable ls
Available to everyone through our API
Introducing Ampersand - the first model built for us. Ampersand is a collaborator trained to help people achieve their individual and collective goals. In multi-month simulations, users collaborating with Ampersand accomplished more than with Fable ls
Available to everyone through our API
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xiao sun retweeted
“Lack of electricity” is an oversimplification. Everything is hard about bringing massive compute online at extremely high speed.
If there was twice as much time, this would be relatively easy, but there is not.
If there was twice as much time, this would be relatively easy, but there is not.
Former $GOOGL employee on why data center capacity and electricity are the real bottlenecks, not GPU production itself, and how unreliable some of the neoclouds are ($AMZN, $GOOGL, $MSFT, $NVDA):
1. According to the expert, open-source models are closing the gap with frontier cted, with customers increasingly replacing Anthropic and OpenAI models in multi-agent architectures with open-source alternatives starting from Q2 of this year. The shift started with simple tasks like tool calling and MCP, but has moved up the stack to low-level orchestration.
2. The expert identifies capacity as the biggest constraint at the moment, with certain companies' traffic growing 5x while GPU availability, particularly in the U.S. where latency requirements are tightest, simply cannot keep up. Sourcing capacity overseas in Asia or Australia is technically possible but adds latency that most customers will not accept.
3. Hyperscalers offer reliability but are expensive, require multi-year commitments of three or more years, and have limited availability of the latest hardware, with customers already chasing B300s. AI-native companies tend to gravitate toward neoclouds due to hyperscaler pricing and contract rigidity, but neoclouds come with their own reliability issues, with GPUs dropping off unexpectedly and requiring engineering resources to manage fleet stability around the clock.
4. The third tier consists of smaller independent operators who have invested in GPU racks, seeing the opportunity to lease out capacity and recoup costs within a year or two, with the remainder being pure profit. The expert describes these as essentially unusable in practice, with operators who do nothing more than find land, install racks, connect electricity, and set up cooling, with no real cloud management, maintenance, or operational expertise behind them.
5. The expert expects overall GPU availability to increase meaningfully over the next year or two, with $NVDA continuing to ramp production. The real bottleneck is not GPUs themselves but data center capacity and electricity in the U.S. On the supply side, the expert also sees players $SPCX beginning to lease out spare capacity to the market, with more likely to follow.
6. The expert notes that U.S.-based neoclouds typically require 20-30% prepayment upfront, with the remainder paid monthly or quarterly over the contract term, and rebates negotiated at the end for any reliability incidents like traffic loss. APAC-based providers operate differently, with no prepayment required at all.
found on @AlphaSenseInc
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most didn’t realize when you can build any trouble or inertia you want to get out of with an app through convo with a bot, you are like a desperate chimp given tools, like a weak archer get a magic arrow won’t fall down
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xiao sun retweeted
Introducing SGLang Low-Latency Mode, built with @NVIDIAAI and @AMD.
On DeepSeek-V4.1-Flash with DSpark, it delivers up to 1.38× P90 per-user token speed on NVIDIA GB300 (671 to 928 tok/s) and 1.56× on AMD MI355X (332 to 516 tok/s) in @SemiAnalysis_'s AgentX trace replay.
overlapping kernels cut decoder layer time from 96.8 to 68.4 µs
- AMD: 2 persistent kernels per layer replace dozens of small ones, with cross-GPU communication overlapped inside them.
- New plugin system: custom kernels plug in from external libraries, with automatic fallback.
- Low-latency kernels for GLM-5.3, K3, and MiniMax M3 are in progress and will plug in next.
On DeepSeek-V4.1-Flash with DSpark, it delivers up to 1.38× P90 per-user token speed on NVIDIA GB300 (671 to 928 tok/s) and 1.56× on AMD MI355X (332 to 516 tok/s) in @SemiAnalysis_'s AgentX trace replay.
overlapping kernels cut decoder layer time from 96.8 to 68.4 µs
- AMD: 2 persistent kernels per layer replace dozens of small ones, with cross-GPU communication overlapped inside them.
- New plugin system: custom kernels plug in from external libraries, with automatic fallback.
- Low-latency kernels for GLM-5.3, K3, and MiniMax M3 are in progress and will plug in next.
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