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7.1K posts

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@gradientintern

push past limits | real niu for @Gradient_HQ | part time troll | full time janitor | professional edging specialist

edge to edge Katılım Aralık 2024
669 Takip Edilen2.4K Takipçiler
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⬜️⬜️⬜️ ⬜️ ⬜️ ⬜️ ⬜️⬜️ ⬜️⬜️⬜️ ⬜️⬜️⬜️ ⬜️⬜️⬜️ ⬜️ ⬜️ ⬜️ ⬜️ ⬜️ ⬜️ ⬜️⬜️⬜️ ⬜️⬜️⬜️ ⬜️ ⬜️ ⬜️⬜️⬜️ ◻️◻️ ./ training mode on… @Gradient_HQ
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Video models have now arrived on @commonstack_ai Create with ByteDance's Seedance video models on Commonstack using your same API key! Stay tuned, more on the way!
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construction spending still going up for data centers, but its growth rates has been slowing. likely due to constraint on resources and public pushback. more spaces for clankers compared to humans continues
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US construction is now spending 9% more on Data Centers ($50B) vs Office Space ($46B) Orchestration & efficiency of resource usage increases in importance, I bet agentic SaaS and platforms will be top beneficiaries Fleet optimization will be defaulted to asynchronous operations

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deepseek reached enough market scale that now they are reportedly doing their own AI chips. with all the efficiency gains they were able to make with GPUs everyone else has could you imagine asics? 🐋
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looks like price forecast and hikes is hitting DeepSeek previously did a permanent 75% discount, now hiking prices during peak hours by 2x likely also signals a significant increase in user growth. our beloved whale also lets us run locally on our own hardware. awesome folks

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Bill@Bill58861938368·
Excited to introduce AgenticTrading! 🚀 An open-source experimental playground for LLM-powered trading agents. Build, test, and deploy agents that reason, trade, and perform in realistic market environments. We are thrilled to collaborate with Dr. Xiaoyang Liu's group at Columbia University to push the boundaries of AI in finance. Stay tuned for our findings. Explore our platform: agentic-trading-lab.vercel.app Read our Medium post: @kuailedefcl/agentic-trading-part-1-build-a-simple-trading-agent-with-commonstack-apis-42b413365906" target="_blank" rel="nofollow noopener">medium.com/@kuailedefcl/a… GitHub Repo: github.com/Open-Finance-L… We welcome more collaborators to join us! Let's shape the future of open finance together. 🌟 #AgenticTrading #AIinFinance #LLM #AlgorithmicTrading #OpenSource
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Bill@Bill58861938368·
The Bitter Lesson of Asynchronous RLHF: Why Staleness Matters and How to Control It linkedin.com/pulse/bitter-l…
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Mythos level Fable 5 and new Sonnet 5 by Anthropic is now both available on @commonstack_ai Both are the latest leading frontier models from Anthropic for cybersecurity, autonomous operation and agentic AI. Try them and others on Commonstack
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looks like price forecast and hikes is hitting DeepSeek previously did a permanent 75% discount, now hiking prices during peak hours by 2x likely also signals a significant increase in user growth. our beloved whale also lets us run locally on our own hardware. awesome folks
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the convenience of AI will likely hit consumer electronics prices even harder then it already has. memory prices going higher from widening supply gap. and we all know how companies are with price hikes, it stays elevated after hike.

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good idea to load up on more personal hardware before the end of the year.
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the convenience of AI will likely hit consumer electronics prices even harder then it already has. memory prices going higher from widening supply gap. and we all know how companies are with price hikes, it stays elevated after hike.
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building ideas is the easier than ever before. but that also means low usage on alot of garbage ideas. execution phase of the process is now more important then ever before. in this lens you can have near infinite tries going local and cost efficient tries on open labs API. do you agree?

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with $100,000 you can get 21x more tries on Kimi K2.6 compared to Opus 4.7 or 42x more tries compared to Fable 5 performance close enough with multiples more in tries. with corporations cutting back on token spend, this could be the rise of more efficient models.
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building ideas is the easier than ever before. but that also means low usage on alot of garbage ideas. execution phase of the process is now more important then ever before. in this lens you can have near infinite tries going local and cost efficient tries on open labs API. do you agree?
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with $100,000 you can get 21x more tries on Kimi K2.6 compared to Opus 4.7 or 42x more tries compared to Fable 5 performance close enough with multiples more in tries. with corporations cutting back on token spend, this could be the rise of more efficient models.

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you can also run the latest and best models of GLM 5.2 on Parallax 👀 run locally with both macs and gpus together or solo with @tryParallax
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Gradient@Gradient_HQ

A self-evolving agent + a 428B model + 3 Macs = ? Your own AI lab. We ran @MiniMax_AI M3 locally with @tryParallax, right on our desk. Then @GA_agent_ai took over to create a 5-stock portfolio and write it to disk. No cloud. No API bills. Nothing left the machine. Wild to see a ~3K-line agent drive all this with a 400B+ model on local hardware. Thanks to the GenericAgent and MiniMax teams for making local AI feel real.

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