@Tech @tech - Twitter Kullanıcısı

3 gün önce

OpenCode seems to have badly underestimated my Astra Pro usage. OpenRouter ended up reporting roughly 3x the cost OpenCode showed me. Has anyone else run into this? Is OpenCode miscalculating token usage/cost, or is there another reason for the gap?

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@tech

3 gün önce

My Life has become cycling through multiple codex instances.

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16 gün önce

The jump from getting a plaque for 100M tokens to treating 1B/week as baseline happened fast.

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@Tech

@tech

19 gün önce

Enterprises should invest in tokens than in knowledge workers

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Takip edebileceğin hesaplar

bc1qwr30ddc04zqp878c0evdrqfx564mmf0dy2w39l

@idontkn85445458

cmljayByb2xsaW5nIGF0IHlvdXIgbW9tJ3MgaG91c2U=

root@alex:~/ #

@offsec_ginger

Hacker Wannabe • Fan of Stitch • #OSCP #GXPN • #infosec #hacking #judo • Standard Disclaimer Jargon

int 0x80 / 盐与胡椒

@BlakSec

Independent Cyber Security Researcher.

@Tech

@tech

21 gün önce

It's mid-2026. We can drop the vibe and call it code again.

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24 gün önce

@blocksec @defcon Different harness will give you diff results

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@tech

yaklaşık 1 ay önce

@natolambert @natolambert congrats on the release 🎉

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yaklaşık 1 ay önce

Zero-shot outfit selection

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yaklaşık 2 ay önce

@sudoingX @poolsideai Good research

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@tech

yaklaşık 2 ay önce

Windows 95 anyone?

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yaklaşık 2 ay önce

No rate limits, no quotas, just always on

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tech retweetledi

NVIDIA Asia Pacific

@NVIDIAAP

yaklaşık 2 ay önce

One DGX Spark on your desk is powerful. Four is a local AI factory. NVIDIA DGX Spark now scales up to four clustered nodes, enabling inference on models up to 700B parameters — with near-linear performance scaling for fine-tuning and reinforcement learning workloads. Memory scales from 128GB on a single node all the way up for the full four-node configuration, supporting large context windows, high concurrency, and multi-agent workflows. For APAC enterprise teams building autonomous AI: each topology is purpose-built for your workload — from rapid local iteration to production-scale inference. And when you're ready to move to the cloud, Tile IR and cuTile Python carry your kernels across with minimal code changes. No rack. No rearchitecting. Just scale. 🔗https://t.co/TPyfQmcN3H

NVIDIAAP's tweet photo. One DGX Spark on your desk is powerful. Four is a local AI factory.

NVIDIA DGX Spark now scales up to four clustered nodes, enabling inference on models up to 700B parameters — with near-linear performance scaling for fine-tuning and reinforcement learning workloads. Memory scales from 128GB on a single node all the way up for the full four-node configuration, supporting large context windows, high concurrency, and multi-agent workflows.

For APAC enterprise teams building autonomous AI: each topology is purpose-built for your workload — from rapid local iteration to production-scale inference. And when you're ready to move to the cloud, Tile IR and cuTile Python carry your kernels across with minimal code changes.

No rack. No rearchitecting. Just scale.

🔗https://t.co/TPyfQmcN3H

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yaklaşık 2 ay önce

@Mathewdoeslife @lloyd094 I have the CRS812, still can't get nvida cluster assistant to work (keeps saying aborted)

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@tech

2 ay önce

@Mathewdoeslife @lloyd094 Which switch? My switch had the 400g port off so I had to buy a serial wire to turn on dhcp to enable the split to 200g, Still running into slight problems though

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2 ay önce

How would that work though, I currently have 4x sparks on a csr812, the max sparks on that switch is 6 if exhausting all usable ports. Are you saying using one of the 200 port and connect that to another 812? Since we added another hop, would that have any latency or issues with expansion?

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