Design & Quality Engineer โš™๏ธ | Crypto Broker ๐Ÿช™ | BioMedical โ›‘๏ธ| Video Game & Music Enthusiast | 2x DGX Spark โšก๏ธ #Bitcoin #Noderunner #LocalAI

Joined September 2021
Find motivation with passion
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Coinupbtc ๐Ÿช™ 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-bโ€ฆ
Readers added context they thought people might want to know
The article states they asked Qwen to make a test detection rule for these behaviors. AI unless explicitly told otherwise will normally test the newly written rule if it can to verify it matches it. It is expected behavior. This includes its destination, CN model -> CN dest cohere.com/blog/concerninโ€ฆ
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Coinupbtc ๐Ÿช™ retweeted
Kind of crazy.. people are still buying Sparks at their new price, definitely not like before but they are being purchased.
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Some cross the TL Some stay โœจ
Some walk with you for a whileโ€ฆ Others stay in your sky foreverโ€ฆ
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Coinupbtc ๐Ÿช™ retweeted
๐Ÿ”ฅ ์—”๋น„๋””์•„ RTX ๊ทธ๋ž˜ํ”ฝ์นด๋“œ ์“ฐ์‹œ๋Š” ๋ชจ๋“  ๋ถ„๋“ค ๊ผญ ๋ณด์„ธ์š”!!!!! ๐Ÿ”ฅ ํ•ด์™ธ์—์„œ๋Š” ์ง„์งœ ๋‚œ๋ฆฌ์ธ๋ฐ... ๊ตญ๋‚ด์—์„œ๋Š” ์•„์ง ์ž˜ ์•ˆํผ์ง„ ๊ฒƒ ๊ฐ™์•„ ๊ณต์œ ๋“œ๋ฆฝ๋‹ˆ๋‹ค!!! --> ๋ฉฐ์น ์ „.. ์—”๋น„๋””์•„๊ฐ€ RTX์— ๊ฑธ์–ด๋‘” "AI ์—ฐ์‚ฐ ์ œํ•œ"์„... ์ตœ๊ทผ์— ๊ณต๊ฐœ๋œ ์˜คํ”ˆ์†Œ์Šค๊ฐ€ ๊ทธ๋ƒฅ ํ’€์–ด๋ฒ„๋ ธ์Šต๋‹ˆ๋‹ค. ์ด๋ฆ„ GPU-Unlock์ธ๋ฐ... ์ƒˆ ๊ธ€์นด ์•ˆ ์‚ฌ๊ณ , ์˜ค๋ฒ„ํด๋Ÿญ๋„ ์•ˆ ํ•˜๊ณ , ์ œํ•œ๋งŒ ํ’€์—ˆ๋”๋‹ˆ.. ์•„๋ž˜์™€ ๊ฐ™์ด ์„ฑ๋Šฅ ํ–ฅ์ƒ์ด ๋˜์—ˆ์Šต๋‹ˆ๋‹ค. RTX 3060 --> +78~99% RTX 3070 --> +72~81% RTX 3080 --> +68~73% RTX 3090 Ti --> BF16 +80% RTX 4070 Ti --> +83~84% RTX 4090 --> ํŠน์ • Tensor ๊ฑฐ์˜ 2๋ฐฐ RTX 2080 Ti๋„ --> +36~64% --> RTX 3000๋Œ€๋งŒ์˜ ์ด์•ผ๊ธฐ๊ฐ€ ์•„๋‹™๋‹ˆ๋‹ค. 20 / 30 / 40 ์‹œ๋ฆฌ์ฆˆ๊นŒ์ง€, ๋ฐ์Šคํฌํƒ‘์ด๋ž‘ ๋…ธํŠธ๋ถ ์ชฝ ์‹ค์ธก๋„ ์ง€๊ธˆ๊นŒ์ง€ ๊ณ„์† ์‹ค์‹œ๊ฐ„์œผ๋กœ ์Œ“์ด๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. โญ๏ธ AI ์ด๋ฏธ์ง€ ์ƒ์„ฑ์ด๋‚˜ ๋กœ์ปฌ LLM ๋Œ๋ฆฌ๋Š” ๋ถ„๋“ค์€ ํŠนํžˆ ์ฒด๊ฐ๋  ์ˆ˜ ์žˆ์„ ๊ฒ๋‹ˆ๋‹ค!! โญ๏ธ ์ง„์งœ ์™œ ์ด๊ฒŒ ๋ฏธ์นœ๋“ฏ์ด ๋‚œ๋ฆฌ๋ƒ๋ฉด... ๋‚ด ์ง‘์— ์žˆ๋Š” RTX ๊ธ€์นด ์•ˆ์— "์“ธ ์ˆ˜ ์žˆ๋Š”๋ฐ ์ž ๊ฒจ ์žˆ๋˜" AI ์„ฑ๋Šฅ์ด ์žˆ์—ˆ๊ธฐ ๋•Œ๋ฌธ์ž…๋‹ˆ๋‹ค. ์™œ ์—”๋น„๋””์•„๊ฐ€ ์ด๋ ‡๊ฒŒ ํ–ˆ๋ƒ๊ณ  ์ง€๊ธˆ ์˜์‹ฌ์ด ๊ฐ€์žฅ ๋งŽ์ด ๋˜๊ณ  ์žˆ๋Š” ์ชฝ์€.. --> ์ง€ํฌ์Šค๋ž‘ ๋น„์‹ผ ์ „๋ฌธ๊ฐ€์šฉ GPU๋ฅผ ๋“ฑ๊ธ‰์œผ๋กœ ๋‚˜๋ˆ„๋ ค๊ณ  FP16/BF16 ์ชฝ์„ ์ผ๋ถ€๋Ÿฌ ๊นŽ์•„๋‘” ๊ฑฐ๋ผ๋Š” ์ชฝ์ž…๋‹ˆ๋‹ค. (์•„์ง ์—”๋น„๋””์•„๊ฐ€ ๊ณต์‹์ ์œผ๋กœ ์ธ์ •ํ•œ ๊ฑด ์•„๋‹˜) ์ด๋ฏธ ์‚ฐ ์˜›๋‚  RTX ๊ธ€์นด์— ์ด๋งŒํผ ์„ฑ๋Šฅ์„ ์—”๋น„๋””์•„๊ฐ€ ๋ง‰์•„๋‘๊ณ  ์žˆ์—ˆ๋‹ค๋Š” ์‚ฌ์‹ค ์ž์ฒด๊ฐ€ ๋„ˆ๋ฌด๋„ˆ๋ฌด ์ถฉ๊ฒฉ์ž…๋‹ˆ๋‹ค... ์˜คํ”ˆ์†Œ์Šค ๊นƒํ—ˆ๋ธŒ: github.com/Zagitta/gpu-unlocโ€ฆ 40 ์‹œ๋ฆฌ์ฆˆ ํ™•์žฅ ๊นƒํ—ˆ๋ธŒ: github.com/JustAResearcher/gโ€ฆ
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Coinupbtc ๐Ÿช™ retweeted
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: โ€ข 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. โ€ข 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. โ€ข 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. โ€ข 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. โ€ข 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. โ€ข 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. โ€ข 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. โ€ข 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. โ€ข 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. โ€ข 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. โ€ข 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. โ€ข 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. โ€ข 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. โ€ข 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
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Coinupbtc ๐Ÿช™ retweeted
no one is pacing anything i love capitalism
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Not your hardware Not your data
Not your keys Not your crypto
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Not your keys Not your crypto
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Coinupbtc ๐Ÿช™ retweeted
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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/Minโ€ฆ ModelScope: modelscope.cn/models/MiniMaxโ€ฆ API: platform.minimax.io M Plan: minimax.io/m-planMiniMax MCode for desktop: agent.minimax.io/download
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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 didnโ€™t fit on 64GB , potentially causing a spike in demand for 128GB version.
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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
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Coinupbtc ๐Ÿช™ retweeted
For my local Ai brothers what about connecting all your DGX Sparks? 200 GBs switch Coming soon ๐Ÿ‘€
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โ€ผ๏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.
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๐Ÿ†“
DeepSeek V4.1 Flash updated to 1017 in OpenCode Go and Free No changes needed on your end
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Grok 4.8 Where are you?
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DeepSeek V4.1 Flash 1017 for 2x DGX Sparks One CX7 cable VS Two CX7 cable
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Coinupbtc ๐Ÿช™ retweeted
Welcome back. Space Bunny. ๐Ÿš€ MiniMax-M3.1-Flash is coming.
๐Ÿค– Made with AI
Dear friends, we are back.
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