
Design & Quality Engineer โ๏ธ | Crypto Broker ๐ช | BioMedical โ๏ธ| Video Game & Music Enthusiast | 2x DGX Spark โก๏ธ #Bitcoin #Noderunner #LocalAI
Joined September 2021
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Coinupbtc ๐ช retweeted
Cohere says Qwen tried to send data to China. Bullshit.
1 call in 100,000, and thats their own number. They wont even tell you how many calls they ran. They ran the same task again and Qwen wrote the correct rule.
So they forced it. They fed the model its own reasoning plus the start of a bash command and sampled hundreds of times until it typed a URL. 1 curl flag took that from 4% to 92%. Thats not a finding. They wrote half the command for it.
Where are the transcripts? The code? The checkpoint, the quant, the sampling settings? "We publish the hosts and the trigger." Link? Nothing. Did anyone ask Alibaba? Not a word. And their own conclusion: they cannot say if any of it is deliberate.
Who wrote it? A company that sells "sovereign AI", just launched a cyber business, wrote part of the post with its own product, and wants governments to put checks on open weights. Released the same day Anthropic filed for its IPO. Andrew Curran is already talking about new laws.
For me this is the same fucking wave. Scare people off the models they can run themselves, then sell them the safe alternative.
I run Qwen3.8 Flash-Next every day on my 4x 3090. Show me the logs or shut up.
While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected.
Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions.
Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent.
But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models?
We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem.
Read the full report: cohere.com/blog/concerning-bโฆ
Coinupbtc ๐ช retweeted
Kind of crazy.. people are still buying Sparks at their new price, definitely not like before but they are being purchased.
Coinupbtc ๐ช retweeted
๐ฅ ์๋น๋์ RTX ๊ทธ๋ํฝ์นด๋ ์ฐ์๋ ๋ชจ๋ ๋ถ๋ค ๊ผญ ๋ณด์ธ์!!!!! ๐ฅ
ํด์ธ์์๋ ์ง์ง ๋๋ฆฌ์ธ๋ฐ... ๊ตญ๋ด์์๋ ์์ง ์ ์ํผ์ง ๊ฒ ๊ฐ์ ๊ณต์ ๋๋ฆฝ๋๋ค!!!
--> ๋ฉฐ์น ์ .. ์๋น๋์๊ฐ RTX์ ๊ฑธ์ด๋ "AI ์ฐ์ฐ ์ ํ"์... ์ต๊ทผ์ ๊ณต๊ฐ๋ ์คํ์์ค๊ฐ ๊ทธ๋ฅ ํ์ด๋ฒ๋ ธ์ต๋๋ค.
์ด๋ฆ GPU-Unlock์ธ๋ฐ... ์ ๊ธ์นด ์ ์ฌ๊ณ , ์ค๋ฒํด๋ญ๋ ์ ํ๊ณ , ์ ํ๋ง ํ์๋๋.. ์๋์ ๊ฐ์ด ์ฑ๋ฅ ํฅ์์ด ๋์์ต๋๋ค.
RTX 3060 --> +78~99%
RTX 3070 --> +72~81%
RTX 3080 --> +68~73%
RTX 3090 Ti --> BF16 +80%
RTX 4070 Ti --> +83~84%
RTX 4090 --> ํน์ Tensor ๊ฑฐ์ 2๋ฐฐ
RTX 2080 Ti๋ --> +36~64%
--> RTX 3000๋๋ง์ ์ด์ผ๊ธฐ๊ฐ ์๋๋๋ค. 20 / 30 / 40 ์๋ฆฌ์ฆ๊น์ง, ๋ฐ์คํฌํ์ด๋ ๋
ธํธ๋ถ ์ชฝ ์ค์ธก๋ ์ง๊ธ๊น์ง ๊ณ์ ์ค์๊ฐ์ผ๋ก ์์ด๊ณ ์์ต๋๋ค.
โญ๏ธ AI ์ด๋ฏธ์ง ์์ฑ์ด๋ ๋ก์ปฌ LLM ๋๋ฆฌ๋ ๋ถ๋ค์ ํนํ ์ฒด๊ฐ๋ ์ ์์ ๊ฒ๋๋ค!! โญ๏ธ
์ง์ง ์ ์ด๊ฒ ๋ฏธ์น๋ฏ์ด ๋๋ฆฌ๋๋ฉด...
๋ด ์ง์ ์๋ RTX ๊ธ์นด ์์ "์ธ ์ ์๋๋ฐ ์ ๊ฒจ ์๋" AI ์ฑ๋ฅ์ด ์์๊ธฐ ๋๋ฌธ์
๋๋ค.
์ ์๋น๋์๊ฐ ์ด๋ ๊ฒ ํ๋๊ณ ์ง๊ธ ์์ฌ์ด ๊ฐ์ฅ ๋ง์ด ๋๊ณ ์๋ ์ชฝ์..
--> ์งํฌ์ค๋ ๋น์ผ ์ ๋ฌธ๊ฐ์ฉ GPU๋ฅผ ๋ฑ๊ธ์ผ๋ก ๋๋๋ ค๊ณ FP16/BF16 ์ชฝ์ ์ผ๋ถ๋ฌ ๊น์๋ ๊ฑฐ๋ผ๋ ์ชฝ์
๋๋ค.
(์์ง ์๋น๋์๊ฐ ๊ณต์์ ์ผ๋ก ์ธ์ ํ ๊ฑด ์๋)
์ด๋ฏธ ์ฐ ์๋ RTX ๊ธ์นด์ ์ด๋งํผ ์ฑ๋ฅ์ ์๋น๋์๊ฐ ๋ง์๋๊ณ ์์๋ค๋ ์ฌ์ค ์์ฒด๊ฐ ๋๋ฌด๋๋ฌด ์ถฉ๊ฒฉ์
๋๋ค...
์คํ์์ค ๊นํ๋ธ:
github.com/Zagitta/gpu-unlocโฆ
40 ์๋ฆฌ์ฆ ํ์ฅ ๊นํ๋ธ:
github.com/JustAResearcher/gโฆ
TensorFold 1.1.4 is live.
Up to 5ร faster Qwen3.8-27B cold prefill on GB10, native Gemma 4 on CUDA, faster Flash Next multi-stream serving, and a long list of fixes from the community.
What's new and fixed:
โข Qwen3.8-27B on CUDA has a new default prompt/decode recipe. On GB10, cold prefill improves by 54% at 2k, 107% at 8k, 258% at 32k and 416% at 64k. The 64k result goes from 203 to 1,045 tok/s; decode gains about 2% across 1 to 8 streams. Optional int8 prompt precision is also available, with bf16 remaining the default.
โข Gemma 4 12B and 31B now run on native CUDA from MLX affine checkpoints, with their drafters. On GB10, the 12B's MTP assistant takes single-stream greedy chat from 30 to 67 tok/s, and z-lab's DFlash drafter takes the 31B from 12.6 to 29.9 tok/s. Image paths are implemented, but their GB10 image test cells have not run yet.
โข Flash Next on CUDA verifies multiple streams' drafted windows in one pass. The GB10 served sweep gains 35%, 75% and 116% at 2, 4 and 8 streams, with tokens unchanged. Greedy MTP heads batch together; sampled heads, trees and media retain their per-stream path.
โข More kernel work: fused Flash Next DeltaNet prefill, a reused PLE worker pool, Qwen attention reads that reuse KV data in L2, segmented split-K decode, and fewer Nemotron RADIO launches. GLM-5.3's Metal sparse-index scan drops from 1.59 ms to 0.14 ms in the measured cell. Startup timing tables also stop one noisy measurement from setting later performance.
โข Drama 3 CUDA prompt chunking is opt-in. With 128-row chunks on GB10, first audio drops from 18.2 s to 0.76 s and the full utterance from 47.5 s to 30.2 s. The default remains unchanged; larger chunks can change rounding.
โข Opt-in speech controls now include silence-tail, onset and mid-utterance guards; whole-reply redraws with budgets and deadlines; short-reply and register guards; a controls allowlist; synthesis frame limits; and attested voice catalogs. The voice tool can build those catalogs from reference audio and transcripts. Drama CUDA replay also waits for buffer fills before reuse.
โข Nemotron-H MLX 4-bit now runs on the opt-in native HIP backend, including MTP, prompt caching and tensor parallelism. Unsupported flags are refused before loading, and Qwen HIP can cancel during long prompt chunks.
โข Idle unload can free a model's memory after a configured idle period and reload it on the next request. A failed reload returns a retryable error; ranked GLM CUDA engines refuse this option.
โข Sticky CUDA faults now make /health return 503. Driver-refused CUDA graphs can run eagerly instead of spinning, and queued writes retain their destination buffers through allocation failures.
โข GLM's draw ring no longer fills after 64 completed or cancelled cold requests. Long chats retain less tokenizer/template scratch, kept-alive connections retain less memory, and a missing disk state no longer evicts cached states before its read fails.
โข Memory and cancellation fixes cover Drama Metal loads that exceed the budget, Nemotron Metal Mamba-pool sizing and released slots after refused work, Qwen draft-node counts above 15, and prompt passes that now stop between chunks on cancellation or shutdown. GLM on one Mac also keeps its draft-acceptance estimates steadier after missed drafts.
โข Flash Next CUDA gains POST /v1/decisions on one GPU and loads valid nested checkpoint shards. Qwen CUDA vision reads Hugging Face-named towers. Managed benchmarks can select bf16 or fp8 KV cache and record the choice. Windows builds with nvcc and cl.exe work again.
โข Slide learning counts only kept lessons. App fixes cover engine-refused checkpoints, TUN/fake-IP proxies, Google's sign-in callback and visible setup errors. The docs now have a native GLM-5.3-Flash CUDA page.
โข Experimental DeepSeek work adds native prompt rendering, HTTP 400 responses for template refusals and four-rank prefill planning. It remains behind the whole-model gate; release binaries do not serve DeepSeek checkpoints yet.
Release checks cover cold prompts from 2k to 64k, 1/2/4/8-stream sweeps and reference checks, with Linux and macOS host tests passing. The Qwen recipe changes prompt arithmetic, so output bits can differ from 1.1.3; its quality check matches the previous recipe at 27/40 exact cases, and concurrent/resumed checks still hold. One measured regression is documented: Nemotron Lightning greedy chat at four streams on GB10 is 3.8% slower.
All three release archives are signed and verified against the site's trust root. The nine published assets were downloaded back and checked against the signed files. Homebrew is updated and upgrade-tested, and a fresh install through the website script verified the archive signature.
A huge thank you to the release contributors:
@bobclawblaw @plotarmordev @rielstamand @MiaAI_lab @Bizuayeu @jishvenugopal @petruspennanen @wesley4546
And GitHub contributors jschmied, Mirrdhyn, wojo, heitke, BlivionIaG, harrisonfriia and paragontasx.
Thank you for the reports and requests, GitHub users Chris9121, flashosophy, Gogo6969, Idoo689, johncarterlives, mikog8tor, samwang0041-star and tonghuaxuan2016-dot.
Attribution also matters. The Gemma family builds on earlier work by gilby on GitHub and @codengod. Credit to Google for the Gemma assistant, z-lab for DFlash and the 31B drafter, MLX/mlx-lm and Hugging Face Transformers for model references, and Fish Audio, SGLang-Omni and Descript for the Drama model/codec references. Full source and licence credits are preserved in THIRD_PARTY_NOTICES.md and docs/credits.md.
And thank you to everyone testing, sharing measurements, contributing fixes and giving me access to hardware. This project would not be moving this quickly without you.
@MiaAI_lab @plotarmordev @petruspennanen @volatilemarkts
tensorfold.dev
The rabbits out of the hat
Space Bunny
MiniMax M3.1 Flash weights are now public. 428B parameters total, 23B activated per token.
Agentic performance: A generational leap in agentic tasks, coding, and cybersecurity โ 76.2 on BankerToolBench, 73.7% on DeepSWE v1.1, 61.7 on OSWorld V2, 29.6 on Agents' Last Exam, and 82.2 on CyberGym.
Efficient by design: MiniMax Sparse Attention and DSpark speculative decoding improve inference efficiency.
Hugging Face:
huggingface.co/MiniMaxAI/Minโฆ
ModelScope:
modelscope.cn/models/MiniMaxโฆ
API: platform.minimax.io
M Plan: minimax.io/m-planMiniMax
MCode for desktop: agent.minimax.io/download
Be a shame if a Chinese model released open weights right before NVIDIA dropped the new 64GB DGX Spark.
Be even crazier if the new models didnโt fit on 64GB , potentially causing a spike in demand for 128GB version.
October has not been playing nice
This is the most serious 48 hours in cybersecurity history.
"Black October" they're calling it
It seems every *hour* there's a new incident, that in a typical year, would be one of the biggest incidents of the *entire year*
SUMMARY OF THE LAST 48 HOURS:
YEAR-LEVEL INCIDENTS (In a typical year, these would be among the biggest incidents of the year):
1. PHP2Shell: a hole in the software behind ~70% of websites. Governments took sites offline.
2. eMudhra: the master keys that prove websites are real were cracked. One was the number 4.
3. California: data on every public school student since 2023 stolen, including disability status
4. Railway: hackers stole customers' secret keys from a hosting platform used by millions of developers
5. Zeabur: 321,810 accounts breached. The company is shutting down.
6. LiteLLM: an AI tool downloaded 3.4 million times a day was poisoned with a password stealer
7. GitLab: a 10-out-of-10 severity hole in every version since 2014
8. Cairn: criminals used an AI hacking tool to break into 100+ companies
9. Ahold Delhaize: hackers knocked the owner of Food Lion and Stop & Shop offline
10. Onamae: malware planted on up to 26,739 customer servers in Japan
MONTH-LEVEL INCIDENTS (In a typical month, these would be among the biggest incidents):
11. Signal: a bug could leave your mic and camera on after you hang up
12. NGINX: 3 critical holes in software running ~1/3 of websites
13. HTTP/3: a single connection can crash 7 of the 10 web server types tested
14. Plex: hackers breaking into home media servers
15. SafePay ransomware: FBI warns it has hit 600+ critical infrastructure organizations
16. Palo Alto: ransomware gangs breaking in through company firewalls
17. Bullhorn: platform used by 10,000+ companies knocked offline
18. Medi-Cal: 970,000 patients' data stolen
19. WordPress: hackers stole a plugin maker's keys and used them to plant backdoors on websites
20. AOMEI: backup software's update server handed out a backdoor
21. Meta Muse: Meta's AI agent exposed users' personal and business secrets
22. Tor: 333 relays hijacked to steal logins
23. TiDB Cloud: database company's cloud breached
Also serious:
24. State Bank of India: India's biggest bank confirms a data breach
25. OVHcloud: hackers slipped a password stealer into a cloud company's official tool
26. Siemens: a software hole being used to spread malware
27. North Korea: state hackers launch a new attack campaign on South Korea
Coinupbtc ๐ช retweeted
For my local Ai brothers what about connecting all your DGX Sparks?
200 GBs switch
Coming soon ๐
Coinupbtc ๐ช retweeted
โผ๏ธ BREAKING: The private keys of 8 certificates from Indian certificate authority eMudhra were compromised, Mozilla says. Firefox blocked them within 3 hours.
Each key should be a 116-digit number, but one was just 4.
Anyone can guess them and forge HTTPS certs for any website.
x.ai/bot/T3ZWXx3oYZJSIY4aIVaโฆ
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