Buckets:
| {"schema_version":1,"generated_at":"2026-07-25T17:09:11Z","group_key":"kimi-k2-7-code","docs":[{"root_id":"2076746035758502275","url":"https://x.com/TheZachMueller/status/2076746035758502275","author":{"username":"TheZachMueller","display_name":"Zach Mueller"},"created_at":"2026-07-13T19:10:13.000Z","lang":"en","categories":["agents","local","models","quantization"],"models":["glm-5","glm-5-2","kimi-k2-7-code"],"posts":[{"id":"2076746035758502275","text":"For the first time since Claude Code came out, I moved one of my actual work pipelines to @pidotdev & open-weight models.\n\nAnd after a weekend of fighting with it, I prefer the report it made.\n\n36 pages vs 21 from Claude, more information-dense, prose I liked more, and pennies compared to the Claude API.\n\nIt does not save on speed. 30-40 minutes instead of ~20. But it's running locally-adjacent/off resources I have so that's just fine.\n\nSetup was one 8xB200 node split 4/4 between:\n- GLM 5.2 NVFP4 (main agent/driver)\n- Kimi K2.7 Code NVFP4 (retriever).\n\nThe dumbest fix throughout it: I summarized sources into briefs to save on context, then notes, losing information each time. Ended up saving all articles directly to disk so there were multiple layers of information retrieval I could work with.","created_at":"2026-07-13T19:10:13.000Z","media":[{"type":"photo","url":"https://pbs.twimg.com/media/HNIU1cCXwAE1fi6.jpg:orig"}],"metrics":{"likes":72,"retweets":5,"replies":6,"views":5116},"quoted_tweet_id":null}]},{"root_id":"2076679717168578679","url":"https://x.com/TraffAlex/status/2076679717168578679","author":{"username":"TraffAlex","display_name":"AlexAImaginator"},"created_at":"2026-07-13T14:46:42.000Z","lang":"en","categories":["agents","models","performance","runtime"],"models":["kimi-k2-6","kimi-k2-7-code"],"posts":[{"id":"2076679717168578679","text":"š CYBERSECURITY, PRIVACY & OPEN SOURCE ROUNDUP ā July 13, 2026\n\n1ļøā£ EVOSKILL: OPEN-SOURCE TOOLKIT FOR LLM AGENT SKILLS\n\nSentient AGI announced that their EvoSkill research paper has been accepted to Agent Skills '26 at the Center for AI Safety conference. Led by Salah Alzubi and researchers from Virginia Tech, EvoSkill is an open-source toolkit designed for the design, evaluation, and optimization of skills for LLM agents. As AI agents become more prevalent in production environments, having standardized frameworks to assess and improve their capabilities is becoming a critical piece of infrastructure for the open-source community.\nš¹ @SentientAGI\n\n2ļøā£ DSPARK SPECULATORS OPEN-SOURCED FOR KIMI MODELS\n\nNovita AI trained and released DSpark speculators for Kimi-K2.6 and Kimi-K2.7-Code on Hugging Face, with native serving support in vLLM version 0.25.0. DSpark, DeepSeek's speculative decoding method, drafts entire token blocks in a single pass rather than one token at a time. The benchmarks show Kimi-K2.6 achieving 2.55x average throughput (+155%) and Kimi-K2.7-Code reaching 2.36x across six evaluation metrics. This open-source release significantly lowers the barrier for fast inference on the latest Mixture-of-Experts models.\nš¹ @vllm_project\n\n3ļøā£ CRASHSTEALER: MACOS INFOSTEALER USING GITHUB FOR DELIVERY\n\nJamf Threat Labs identified CrashStealer, a macOS infostealer that takes delivery infrastructure more seriously than typical malware. It uses a signed and Apple-notarized dropper posing as a crash reporter, then pulls its second-stage payload directly through GitHub. The payload is a native C++ stealer featuring client-side AES-GCM encryption and layered anti-analysis techniques to evade detection. The fact that threat actors are leveraging GitHub's trusted infrastructure for C2 delivery highlights the growing challenge of securing the software supply chain.\nš¹ @JamfThreatLabs\n\n4ļøā£ APPLE ACQUIRES SIGSCALR ASSETS BEHIND SIGLENS\n\nApple acquired certain assets from SigScalr, the company behind SigLens ā an open-source platform that allows developers to collect, search, and analyze application logs, metrics, and performance data. Apple also hired part of the SigScalr team. The move signals Apple's continued investment in developer tooling and observability infrastructure, and suggests the SigLens codebase may see tighter integration with Apple's internal systems or potentially be incorporated into its developer ecosystem.\nš¹ @Apple_Geek_Actu\n\n5ļøā£ SPECIALIZED LINUX DISTROS FOR SECURITY AND PRIVACY\n\nDan from Learn Linux with Dan compiled an overview of specialized Linux distributions built specifically for security and privacy work. These purpose-built distros cater to penetration testers, security researchers, and privacy-conscious users who need hardened operating systems out of the box. From Kali Linux for offensive security to more obscure privacy-focused variants, the Linux ecosystem continues to offer the most comprehensive free and open-source options for anyone serious about digital security.\nš¹ @xmodulo\n\n6ļøā£ CENTERS LABORATORY BREACH: 540K INDIVIDUALS AFFECTED\n\nA breach at Centers Laboratory has compromised the personal information of approximately 540,000 individuals after roughly 720 GB of data was stolen. SecurityWeek highlighted that healthcare organizations should use this incident to review their incident response procedures and vendor access controls. In an industry where patient data is constantly targeted, breaches of this scale underscore the importance of zero-trust architectures and rigorous third-party risk management in healthcare IT environments.\nš¹ @ADKCyber\n\n7ļøā£ EVILSOUL-ENGINE: BRAZILIAN MALWARE-AS-A-SERVICE THREAT\n\nMeridian Group documented EvilSoul-Engine, a Brazilian Malware-as-a-Service platform that generates uniquely packed infostealer payloads to evade hash-based detection. The malware bypasses Chrome's App-Bound Encryption to steal cookies and credentials, then disables Windows Defender and firewalls to maintain persistence. It targets browsers, Discord, and cryptocurrency wallets. The modular nature of MaaS platforms means even low-skill attackers can deploy sophisticated campaigns, making threat intelligence and behavioral detection essential for defense.\nš¹ @MeridianEU\n\nš The threat landscape this week spans from sophisticated supply chain abuse ā with CrashStealer leveraging GitHub for payload delivery ā to the rapid commoditization of malware through Brazilian MaaS platforms. Meanwhile, the open-source community continues to push forward with powerful tools like EvoSkill for agent evaluation and DSpark speculators for faster inference. The contrast is clear: while attackers industrialize their tradecraft, defenders are open-sourcing their countermeasures. Stay updated, patch early, and verify your supply chain.\n\nWhich of these threats or tools should you be prioritizing this week? š\n\n#CyberSecurity #OpenSource #Privacy #InfoSec #MacOS #Linux #ThreatIntelligence","created_at":"2026-07-13T14:46:42.000Z","media":[],"metrics":{"likes":0,"retweets":0,"replies":1,"views":36},"quoted_tweet_id":null}]},{"root_id":"2076676812281925952","url":"https://x.com/vllm_project/status/2076676812281925952","author":{"username":"vllm_project","display_name":"vLLM"},"created_at":"2026-07-13T14:35:09.000Z","lang":"en","categories":["models","runtime"],"models":["kimi-k2-6","kimi-k2-7-code"],"posts":[{"id":"2076676812281925952","text":"š Great to see @novita_labs train and open-source DSpark speculators for Kimi-K2.6 and Kimi-K2.7-Code.\n\nDSpark (@deepseek_ai's spec decoding method) drafts a whole token block in one pass, and vLLM supports it natively (v0.25.0). Grab the speculators and get faster Kimi decoding today. šš","created_at":"2026-07-13T14:35:09.000Z","media":[],"metrics":{"likes":105,"retweets":13,"replies":6,"views":11923},"quoted_tweet_id":"2076670078645571679"}]},{"root_id":"2068016542827340254","url":"https://x.com/stevibe/status/2068016542827340254","author":{"username":"stevibe","display_name":"stevibe"},"created_at":"2026-06-19T17:02:20.000Z","lang":"en","categories":["agents","models"],"models":["glm-5","glm-5-2","kimi-k2-7-code","minimax-m3"],"posts":[{"id":"2068016542827340254","text":"Asking LLMs to explain concepts in HTML is great. HTML is interactive and flexible, so the model can render a fully working page to teach you something.\n\nI gave the same prompt to 5 models. One-shot, no system prompt, no skills:\n\"Generate an interactive HTML page to explain Convolutional Neural Networks.\"\n\nWhat I found:\n> GPT-5.5: consistent GPT taste with or without Codex.\n> Claude Opus 4.8: unexpectedly plain. Opus usually nails layout, so I suspect the design magic now lives mostly in the skills / Claude Code harness.\n> GLM 5.2: great layout out of the box, like the design skills are baked into the model. Also the longest thinking process.\n> Kimi K2.7 Code: my favourite for learning. It even built a CNN simulator I could draw on.\n> MiniMax M3: the least tokens spent.\n\nNext time you're chatting with your model, ask it to generate an HTML page for you and see what you get.","created_at":"2026-06-19T17:02:20.000Z","media":[{"type":"video","url":"https://video.twimg.com/amplify_video/2068015658831683584/vid/avc1/1604x1440/o9uHqh0-IUxG_UYH.mp4?tag=28"}],"metrics":{"likes":103,"retweets":13,"replies":10,"views":6334},"quoted_tweet_id":null}]}]} | |
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