[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-gemma-4-26b-a4b-4b-active-moe-for-multimodal-ai-summary":3,"summaries-facets-categories":102,"summary-related-gemma-4-26b-a4b-4b-active-moe-for-multimodal-ai-summary":4507},{"id":4,"title":5,"ai":6,"body":13,"categories":54,"created_at":56,"date_modified":56,"description":47,"extension":57,"faq":56,"featured":58,"kicker_label":56,"meta":59,"navigation":84,"path":85,"published_at":56,"question":56,"scraped_at":86,"seo":87,"sitemap":88,"source_id":89,"source_name":90,"source_type":91,"source_url":92,"stem":93,"tags":94,"thumbnail_url":56,"tldr":99,"tweet":56,"unknown_tags":100,"__hash__":101},"summaries\u002Fsummaries\u002Fgemma-4-26b-a4b-4b-active-moe-for-multimodal-ai-summary.md","Gemma 4 26B A4B: 4B Active MoE for Multimodal AI",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",7989,2096,22810,0.0026233,{"type":14,"value":15,"toc":46},"minimark",[16,21,25,29,32,36,39,43],[17,18,20],"h2",{"id":19},"moe-architecture-delivers-dense-like-speed-at-scale","MoE Architecture Delivers Dense-Like Speed at Scale",[22,23,24],"p",{},"Gemma 4 26B A4B-it employs a Mixture-of-Experts (MoE) design with 25.2B total parameters but only 3.8B active per inference (8 active \u002F 128 total experts + 1 shared), running as fast as a 4B dense model while outperforming Gemma 3 27B. It features 30 layers, 1024-token sliding window, 256K context via hybrid attention (local sliding + global with unified KV and p-RoPE), and 262K vocab. Supports text\u002Fimage inputs (~550M vision params); audio\u002Fvideo on smaller E2B\u002FE4B. Dense siblings: E2B (2.3B effective\u002F5.1B total, 35 layers, 128K context, text\u002Fimage\u002Faudio), E4B (4.5B effective\u002F8B total, 42 layers, same context\u002Fmodalities), 31B (30.7B params, 60 layers, 256K text\u002Fimage). Per-Layer Embeddings (PLE) on small models boost efficiency for on-device use. Native system prompts, function-calling, and configurable thinking modes (\u003C|think|>, \u003C|channel|thought\n\u003Cchannel|>) enable agentic workflows.",[17,26,28],{"id":27},"benchmark-leadership-in-reasoning-coding-multimodality","Benchmark Leadership in Reasoning, Coding, Multimodality",[22,30,31],{},"Instruction-tuned models crush benchmarks: 26B A4B-it scores 82.6% MMLU Pro, 88.3% AIME 2026 (no tools), 77.1% LiveCodeBench v6, 1718 Codeforces ELO, 82.3% GPQA Diamond, 86.3% MMMLU, 73.8% MMMU Pro, 82.4% MATH-Vision; long-context MRCR v2 128K at 44.1%. Larger 31B hits 85.2% MMLU Pro, 89.2% AIME, 80.0% LiveCodeBench, 2150 ELO. Small E4B\u002FE2B: 69.4%\u002F60.0% MMLU Pro, audio CoVoST 35.54%\u002F33.47%. All beat Gemma 3 27B (no think) by wide margins, e.g., Tau2 68.2% vs 16.2%, BigBench Hard 64.8% vs 19.3%. Vision excels in OmniDocBench (0.149 avg edit distance, lower better). Use thinking mode (enable_thinking=True) for complex reasoning; parse with processor.parse_response.",[17,33,35],{"id":34},"transformers-integration-for-text-image-audio-video","Transformers Integration for Text, Image, Audio, Video",[22,37,38],{},"Load via pip install -U transformers torch accelerate; use AutoProcessor\u002FAutoModelForCausalLM (text) or AutoModelForMultimodalLM (multimodal). Example text gen: apply_chat_template on messages with system\u002Fuser roles, generate max_new_tokens=1024, decode\u002Fparse. Multimodal: add {\"type\": \"image\u002Faudio\u002Fvideo\", \"url\": \"path\"} before text in user content; audio needs librosa, video torchcodec\u002Ftorchvision. Sampling: temperature=1.0, top_p=0.95, top_k=64. Audio max 30s, video 60s (1fps). Prompts: image variable resolution\u002Faspect via token budget; audio transcription e.g., \"Transcribe in {LANG}, digits only, no newlines.\" Multi-turn via standard roles. Modality order: non-text before text.",[17,40,42],{"id":41},"safety-gains-with-provenance-focus","Safety Gains with Provenance Focus",[22,44,45],{},"Trained on web\u002Fcode\u002Fimages\u002Faudio (cutoff Jan 2025) with dedup, filtering, PII removal. Matches Gemini safety evals: minimal violations (text\u002Fimage), low unjustified refusals vs Gemma 3. Aligns Google AI Principles; risks like bias\u002Fhallucinations mitigated via evals\u002Fpartnerships. Intended for reasoning\u002Fcoding\u002Fagents\u002Fmultimodal apps; limits: no native video\u002Faudio on large models, potential ethical VLMs issues (fairness, misuse). Apache 2.0 licensed for enterprise\u002Fon-device deployment.",{"title":47,"searchDepth":48,"depth":48,"links":49},"",2,[50,51,52,53],{"id":19,"depth":48,"text":20},{"id":27,"depth":48,"text":28},{"id":34,"depth":48,"text":35},{"id":41,"depth":48,"text":42},[55],"AI & LLMs",null,"md",false,{"content_references":60,"triage":79},[61,66,69,72,76],{"type":62,"title":63,"url":64,"context":65},"other","Gemma 4 Launch Blog","https:\u002F\u002Fblog.google\u002Finnovation-and-ai\u002Ftechnology\u002Fdevelopers-tools\u002Fgemma-4\u002F","mentioned",{"type":62,"title":67,"url":68,"context":65},"Gemma Documentation","https:\u002F\u002Fai.google.dev\u002Fgemma\u002Fdocs\u002Fcore",{"type":62,"title":70,"url":71,"context":65},"Gemma 4 License","https:\u002F\u002Fai.google.dev\u002Fgemma\u002Fdocs\u002Fgemma_4_license",{"type":62,"title":73,"url":74,"context":75},"Google’s AI Principles","https:\u002F\u002Fai.google\u002Fprinciples\u002F","cited",{"type":62,"title":77,"url":78,"context":65},"Google Gemma GitHub","https:\u002F\u002Fgithub.com\u002Fgoogle-gemma",{"relevance":80,"novelty":81,"quality":81,"actionability":81,"composite":82,"reasoning":83},5,4,4.35,"Category: AI & LLMs. The article provides in-depth technical details about the Gemma 4 26B A4B model, including its architecture and performance benchmarks, which are crucial for developers looking to implement AI features. It also includes practical integration instructions, making it actionable for the audience.",true,"\u002Fsummaries\u002Fgemma-4-26b-a4b-4b-active-moe-for-multimodal-ai-summary","2026-04-16 03:04:54",{"title":5,"description":47},{"loc":85},"dd59ec1e4e966fad","__oneoff__","article","https:\u002F\u002Fhuggingface.co\u002Fgg-hf-gg\u002Fgemma-4-26B-A4B-it","summaries\u002Fgemma-4-26b-a4b-4b-active-moe-for-multimodal-ai-summary",[95,96,97,98],"llm","coding","agents","open-source","Gemma 4 26B A4B-it uses 26B total params but activates only 3.8B for fast inference, topping charts in reasoning (MMLU Pro 82.6%), coding (LiveCodeBench 77.1%), and vision tasks with 256K 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V2.5 Pro: Open MoE Excels in Long Agentic Coding",{"provider":7,"model":8,"input_tokens":4512,"output_tokens":4513,"processing_time_ms":4514,"cost_usd":4515},8160,2118,16147,0.00266835,{"type":14,"value":4517,"toc":4539},[4518,4522,4525,4529,4532,4536],[17,4519,4521],{"id":4520},"architecture-enables-long-horizon-agentic-workflows","Architecture Enables Long-Horizon Agentic Workflows",[22,4523,4524],{},"MiMo V2.5 Pro is a 1.02T-parameter Mixture of Experts (MoE) model with 42B active parameters, a 1M token context window, and hybrid attention. It sustains 1000+ tool calls while maintaining coherence, precision instruction-following, and self-correction in multi-step tasks. This makes it ideal for agentic coding and complex workflows like end-to-end app building, outperforming base models in planning persistence. The Pro variant targets software engineering; the base adds native multimodal understanding. MIT-licensed for commercial use, it prioritizes token efficiency, consuming 40-60% fewer tokens than GPT-5.4, Claude Opus 4.6, or Gemini 3.1 Pro at matching performance levels. Benchmarks show top results on SWE-Bench Pro, GPQA, and LiveCodeBench, surpassing DeepSeek V4 and rivaling closed models.",[17,4526,4528],{"id":4527},"low-cost-access-matches-production-needs","Low-Cost Access Matches Production Needs",[22,4530,4531],{},"Run MiMo V2.5 Pro free via Xiaomi AI Studio chat (aistudio.xiaomimimo.com), API at $1 per 1M input tokens\u002F$3 per 1M output, or providers like OpenRouter and Kilo.ai (25 free credits, open-source harness). Skip local inference without multi-GPU setups; Hugging Face hosts weights. Pair with Kilo CLI for agentic tasks to generate browser-based apps in ~3 minutes.",[17,4533,4535],{"id":4534},"test-results-highlight-coding-and-3d-strengths","Test Results Highlight Coding and 3D Strengths",[22,4537,4538],{},"In Kilo CLI tests, MiMo built a functional MacOS clone with SVG icons, animated terminal, apps (Finder, Safari, Messages, Notes, Maps, Photos, Music, Calculator, Calendar, Weather, Settings), Launchpad, and dynamic UI—better than Kimi K2.6 despite incomplete toolbar\u002Fsystem details. A one-shot Minecraft clone included walkable terrain, block breaking\u002Fplacing, inventory, water, clouds, caves\u002Fores (buggy physics). Frontend demos excelled: Slack workspace (channels\u002FDMs, beat GLM 4.7), Amazon product page (beat GLM 5.1), SaaS landing pages with typography\u002Flayout\u002Fanimations. Three.js simulations passed SUV off-road durability (loaded assets\u002Fphysics, beat Gemini 3 Flash), lava lamp (fluid motion), and a 9-channel '90s TV: fireworks particles, night city\u002Fcars, ocean waves, audio visualizer, solar system, ping-pong, fractal tree, bird flock—all in one file with shaders\u002Fprocedural animations. SVG generations: animated pelican-on-bike (minor leg\u002Fpedal sync issue, matched Kimi), dynamic painting (added motion), flapping butterfly (best open-source result, beat MiniMax M2.7). Lost to DeepSeek V4 on 360° product viewer functionality but won overall coherence in long tasks.",{"title":47,"searchDepth":48,"depth":48,"links":4540},[4541,4542,4543],{"id":4520,"depth":48,"text":4521},{"id":4527,"depth":48,"text":4528},{"id":4534,"depth":48,"text":4535},[],{"content_references":4546,"triage":4570},[4547,4551,4554,4558,4561,4564,4567],{"type":4548,"title":4549,"url":4550,"context":65},"tool","MiMo V2.5 Pro","https:\u002F\u002Fmimo.xiaomi.com\u002Fmimo-v2-5-pro",{"type":4548,"title":4552,"url":4553,"context":65},"Xiaomi AI Studio","https:\u002F\u002Faistudio.xiaomimimo.com\u002F#\u002Fc",{"type":4548,"title":4555,"url":4556,"context":4557},"Kilo CLI","https:\u002F\u002Fkilo.ai\u002Fcli","recommended",{"type":4548,"title":4559,"url":4560,"context":65},"Xiaomi API Platform","https:\u002F\u002Fplatform.xiaomimimo.com\u002Fdocs\u002Fen-US\u002Fwelcome",{"type":4548,"title":4562,"url":4563,"context":65},"MiMo V2.5 Hugging Face Collection","https:\u002F\u002Fhuggingface.co\u002Fcollections\u002FXiaomiMiMo\u002Fmimo-v25",{"type":4548,"title":4565,"url":4566,"context":65},"OpenRouter MiMo V2.5 Pro","https:\u002F\u002Fopenrouter.ai\u002Fxiaomi\u002Fmimo-v2.5-pro",{"type":62,"title":4568,"url":4569,"context":65},"loktar00 Twitter Post","https:\u002F\u002Fx.com\u002Floktar00\u002Fstatus\u002F2048903028556648697",{"relevance":4571,"novelty":4571,"quality":81,"actionability":4571,"composite":4572,"reasoning":4573},3,3.25,"Category: AI & LLMs. The article discusses a new AI model, MiMo V2.5 Pro, which is relevant to AI engineering and software development. It provides insights into the model's capabilities and benchmarks, but lacks specific actionable steps for implementation.","\u002Fsummaries\u002Fmimo-v2-5-pro-open-moe-excels-in-long-agentic-codi-summary","2026-04-28 06:39:15","2026-04-28 15:11:20",{"title":4510,"description":47},{"loc":4574},"ecec0c484e97ee96","WorldofAI","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=popj1l3LA-I","summaries\u002Fmimo-v2-5-pro-open-moe-excels-in-long-agentic-codi-summary",[95,97,98,96],"Xiaomi's 1.02T-param MoE model (42B active) with 1M context beats DeepSeek V4 on benchmarks, sustains 1000+ tool calls coherently, uses 40-60% fewer tokens than GPT-5.4\u002FClaude, priced at $1\u002FM input\u002F$3\u002FM output.",[],"AWgb0TXMvN3zdtPFjQBma_hhsBQKMoYPTo6ojtNQk6I",{"id":4588,"title":4589,"ai":4590,"body":4595,"categories":4637,"created_at":56,"date_modified":56,"description":47,"extension":57,"faq":56,"featured":58,"kicker_label":56,"meta":4638,"navigation":84,"path":4653,"published_at":4654,"question":56,"scraped_at":4655,"seo":4656,"sitemap":4657,"source_id":4658,"source_name":4659,"source_type":91,"source_url":4660,"stem":4661,"tags":4662,"thumbnail_url":56,"tldr":4663,"tweet":56,"unknown_tags":4664,"__hash__":4665},"summaries\u002Fsummaries\u002Fdeepseek-v4-98-cheaper-rival-to-gpt-5-5-in-coding--summary.md","DeepSeek V4: 98% Cheaper Rival to GPT-5.5 in Coding\u002FAgents",{"provider":7,"model":8,"input_tokens":4591,"output_tokens":4592,"processing_time_ms":4593,"cost_usd":4594},5845,1930,13669,0.00211135,{"type":14,"value":4596,"toc":4631},[4597,4601,4604,4607,4611,4614,4617,4621,4624,4628],[17,4598,4600],{"id":4599},"massive-moe-architecture-at-fraction-of-frontier-costs","Massive MoE Architecture at Fraction of Frontier Costs",[22,4602,4603],{},"DeepSeek V4 Pro uses 1.6 trillion total parameters but activates only 49 billion per inference via mixture-of-experts (MoE), enabling efficient scaling. V4 Flash is lighter at 284 billion total\u002F13 billion active. Both handle 1 million token context and generate up to 384,000 output tokens. Pricing undercuts leaders: V4 Flash at $0.14\u002FM input, $0.28\u002FM output; V4 Pro at $1.74\u002FM input, $3.48\u002FM output. This makes V4 Pro 98% cheaper than GPT-5.5 Pro ($30 input\u002F$180 output) and V4 Flash 99% below Claude Opus 4.7 ($25 output). Developers gain open MIT-licensed weights on Hugging Face for self-hosting, customization, and hardware optimization, bypassing API rental limits.",[22,4605,4606],{},"New engineering like MHC manifold constrained hyperconnections stabilizes signal propagation over long contexts, and Muon optimizer boosts MoE training efficiency, yielding nearly 2x inference speedups. DeepSeek internally favors V4 Pro as its primary agentic coding model, with 85 developers confirming superior performance over priors.",[17,4608,4610],{"id":4609},"codingmath-dominance-with-narrowing-frontier-gaps","Coding\u002FMath Dominance with Narrowing Frontier Gaps",[22,4612,4613],{},"V4 Pro excels where cost matters most: coding, math, STEM, and agents. It ranks #1 open-source on Vals.ai Vibe Code (10x jump from V3.2's 5 points), beating Kimi 2.6; #3 open-source\u002F#14 overall on arena.ai code arena; #2 overall on Vals.ai index (0.07% behind Kimi 2.6). Specifics: Codeforces 3,206 (human ~23rd percentile); Apex shortlist 90.2% (beats Opus 4.6's 85.9%, GPT-5.4's 78.1%); SWE Verify 80.6% (matches Opus 4.6). DeepSeek claims parity or leads in these vs. open models, trailing frontier by 3-6 months in general reasoning.",[22,4615,4616],{},"Gaps persist: MMLU Pro 87.5% (vs. Gemini 3.1 Pro 91.0%); GPQA Diamond 90.1% (vs. 94.3%); Humanity's Last Exam 37.7% (vs. 44.4%). Strength in structured tasks like GitHub issue resolution and long-context agents positions V4 as 'good enough' for production at 1\u002F100th cost, shifting economics from premium closed models.",[17,4618,4620],{"id":4619},"hardware-independence-defies-export-bans","Hardware Independence Defies Export Bans",[22,4622,4623],{},"V4 optimizes for Nvidia (Blackwell\u002FHopper: 150+ tokens\u002Fsec on GB200 NVL72 via NIM\u002FVLLM\u002FSG Lang) and Huawei Ascend NPUs (1.5-1.73x acceleration on 950 series SuperNode). US Nvidia export curbs since 2022 spurred domestic chip reliance, mainly for inference; training may still use Nvidia. DeepSeek ties future pricing drops to 2026 Ascend scale-up, promising even lower costs. This builds parallel AI stacks: inference on Chinese hardware, CUDA ecosystem intact, proving restrictions accelerate optimization over stagnation.",[17,4625,4627],{"id":4626},"builder-economics-agents-at-scale-text-only-limits","Builder Economics: Agents at Scale, Text-Only Limits",[22,4629,4630],{},"Enterprises save on 1M-token workflows (legal\u002Ffinancial analysis, codebases, agents) at \u003C$4\u002FM output. Solos use V4 Flash for cheap chat\u002Fcoding helpers. Open weights enable full control, but text-only limits multimodal edges (vs. OpenAI\u002FGemini\u002FXiaomi). Real-world: benchmarks shine in code\u002Fagents; daily chat uneven vs. V3.2. Retiring old endpoints July 2026 routes to V4, easing migration. V4 shrinks premium model premiums, prioritizing price\u002Fperformance for serious agent building.",{"title":47,"searchDepth":48,"depth":48,"links":4632},[4633,4634,4635,4636],{"id":4599,"depth":48,"text":4600},{"id":4609,"depth":48,"text":4610},{"id":4619,"depth":48,"text":4620},{"id":4626,"depth":48,"text":4627},[110],{"content_references":4639,"triage":4650},[4640,4642,4644,4646,4648],{"type":4548,"title":4641,"context":65},"arena.ai",{"type":4548,"title":4643,"context":65},"Vals.ai",{"type":4548,"title":4645,"context":65},"Hugging Face",{"type":62,"title":4647,"context":65},"DeepSeek V4 Technical Report",{"type":62,"title":4649,"context":75},"MIT Technology Review",{"relevance":81,"novelty":4571,"quality":81,"actionability":4571,"composite":4651,"reasoning":4652},3.6,"Category: AI & LLMs. The article discusses a new AI model, DeepSeek V4, that offers significant cost advantages and performance metrics relevant to developers building AI-powered products. It provides insights into its architecture and performance benchmarks, which can help developers evaluate its applicability in their projects.","\u002Fsummaries\u002Fdeepseek-v4-98-cheaper-rival-to-gpt-5-5-in-coding-summary","2026-04-25 23:08:29","2026-04-26 17:15:59",{"title":4589,"description":47},{"loc":4653},"3d1a124f7cdfcc62","AI Revolution","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=QhDzb16iFzs","summaries\u002Fdeepseek-v4-98-cheaper-rival-to-gpt-5-5-in-coding--summary",[95,98,96,97],"DeepSeek V4 Pro\u002FFlash deliver 1M token context, open MIT weights, and pricing 98% below GPT-5.5 Pro ($1.74\u002F$3.48 vs $30\u002F$180 per M tokens), topping open-source coding benchmarks while running on Nvidia or Huawei chips.",[],"5hRMffm52F2L3Mae9PcnM5ic3M8HGgI43EMt2vd3Xp8",{"id":4667,"title":4668,"ai":4669,"body":4674,"categories":4714,"created_at":56,"date_modified":56,"description":47,"extension":57,"faq":56,"featured":58,"kicker_label":56,"meta":4715,"navigation":84,"path":4733,"published_at":56,"question":56,"scraped_at":4734,"seo":4735,"sitemap":4736,"source_id":4737,"source_name":90,"source_type":91,"source_url":4738,"stem":4739,"tags":4740,"thumbnail_url":56,"tldr":4741,"tweet":56,"unknown_tags":4742,"__hash__":4743},"summaries\u002Fsummaries\u002Fglm-5-leads-open-source-in-coding-reasoning-agents-summary.md","GLM-5 Leads Open-Source in Coding, Reasoning, Agents",{"provider":7,"model":8,"input_tokens":4670,"output_tokens":4671,"processing_time_ms":4672,"cost_usd":4673},7657,1725,8371,0.00188705,{"type":14,"value":4675,"toc":4709},[4676,4680,4683,4686,4689,4693,4696,4699,4703,4706],[17,4677,4679],{"id":4678},"scale-pre-training-and-rl-for-superior-competence","Scale Pre-Training and RL for Superior Competence",[22,4681,4682],{},"GLM-5 advances from GLM-4.5's 355B params (32B active) and 23T tokens to 744B params (40B active) with 28.5T pre-training tokens, using DeepSeek Sparse Attention (DSA) to maintain long-context capacity at lower deployment costs. Post-training leverages slime, an asynchronous RL infrastructure (github.com\u002FTHUDM\u002Fslime), to boost throughput for fine-grained iterations, bridging pre-trained competence to excellence without RL's typical inefficiency.",[22,4684,4685],{},"This yields best-in-class open-source results: Humanity's Last Exam (30.5, 50.4 w\u002Ftools), AIME 2026 I (92.7%), HMMT Nov 2025 (96.9%), GPQA-Diamond (86.0%). Coding excels on SWE-bench Verified (77.8%), Multilingual (73.3%), Terminal-Bench 2.0 (56.2\u002F60.7 verified), CyberGym (43.2%). Agent benchmarks include BrowseComp (62.0\u002F75.9 w\u002Fcontext mgmt), τ²-Bench (89.7%), MCP-Atlas (67.8%), Tool-Decathlon (39.2), nearing Claude Opus 4.5 and Gemini 3.0 Pro.",[22,4687,4688],{},"On CC-Bench-V2 internal suite, GLM-5 outperforms GLM-4.7 in frontend, backend, long-horizon tasks, closing gap to Claude Opus 4.5. Vending Bench 2 simulates year-long vending machine ops; GLM-5 ends with $4,432 balance (#1 open-source, near Claude's $4,967).",[17,4690,4692],{"id":4691},"agentic-engineering-for-real-deliverables","Agentic Engineering for Real Deliverables",[22,4694,4695],{},"GLM-5 shifts from 'vibe coding' to agentic systems engineering, handling complex, multi-turn tasks like generating production-ready .docx\u002F.pdf\u002F.xlsx files (PRDs, spreadsheets, reports) from prompts. Example: Student council football sponsorship proposal includes structured sections (intro, event details, fund use, tiers, benefits), visual elements (image placeholders, tables), color palette (navy\u002Fgold), ensuring print-ready output without edits.",[22,4697,4698],{},"Z.ai's Agent mode supports PDF\u002FWord\u002FExcel creation, multi-turn collaboration for deliverables. Outperforms priors on long-horizon planning\u002Fresource mgmt, as in Vending Bench 2's operational simulation.",[17,4700,4702],{"id":4701},"deploy-anywhere-integrate-seamlessly","Deploy Anywhere, Integrate Seamlessly",[22,4704,4705],{},"Open-source (MIT) on HuggingFace (zai-org\u002FGLM-5), ModelScope, GitHub (zai-org\u002FGLM-5). API via api.z.ai, BigModel.cn, Z.ai chat\u002Fagent modes. Local inference: vLLM\u002FSGLang; non-NVIDIA chips (Ascend, Moore Threads) via quantization.",[22,4707,4708],{},"Coding agents: Claude Code\u002FOpenClaw (update to 'GLM-5', higher quota use), OpenCode\u002FKilo\u002FRoo\u002FCline\u002FDroid. Z Code IDE for multi-agent collab. Gradual rollout to GLM Coding Plan subs; free trial on Z.ai.",{"title":47,"searchDepth":48,"depth":48,"links":4710},[4711,4712,4713],{"id":4678,"depth":48,"text":4679},{"id":4691,"depth":48,"text":4692},{"id":4701,"depth":48,"text":4702},[],{"content_references":4716,"triage":4731},[4717,4721,4724,4728],{"type":4718,"title":4719,"url":4720,"context":65},"paper","GLM-5 Tech Report","https:\u002F\u002Farxiv.org\u002Fabs\u002F2602.15763",{"type":4548,"title":4722,"url":4723,"context":65},"slime","https:\u002F\u002Fgithub.com\u002FTHUDM\u002Fslime",{"type":4725,"title":4726,"url":4727,"context":65},"dataset","Terminal-Bench 2.0 Verified","https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002Fzai-org\u002Fterminal-bench-2-verified",{"type":62,"title":4729,"url":4730,"context":65},"Vending Bench 2","https:\u002F\u002Fandonlabs.com\u002Fevals\u002Fvending-bench-2",{"relevance":81,"novelty":4571,"quality":81,"actionability":4571,"composite":4651,"reasoning":4732},"Category: AI & LLMs. The article discusses GLM-5's advancements in coding and reasoning capabilities, which directly relates to AI engineering and the development of AI-powered products. It provides insights into the model's performance benchmarks and practical applications, such as generating production-ready documents, which can help developers understand how to leverage these capabilities in their projects.","\u002Fsummaries\u002Fglm-5-leads-open-source-in-coding-reasoning-agents-summary","2026-04-16 03:07:03",{"title":4668,"description":47},{"loc":4733},"661d1f4b66cfe71b","https:\u002F\u002Fz.ai\u002Fblog\u002Fglm-5","summaries\u002Fglm-5-leads-open-source-in-coding-reasoning-agents-summary",[95,97,96,98],"GLM-5 scales to 744B params (40B active) and 28.5T tokens, tops open-source benchmarks like SWE-bench (77.8%) and Vending Bench 2 ($4,432 balance), enabling complex engineering and long-horizon agents while cutting deployment costs via DSA.",[],"SVWFe-Sw3vvPQF8499Kkv_Wd-wGfbR0Xnq0KzhacPiM",{"id":4745,"title":4746,"ai":4747,"body":4752,"categories":4780,"created_at":56,"date_modified":56,"description":47,"extension":57,"faq":56,"featured":58,"kicker_label":56,"meta":4781,"navigation":84,"path":4791,"published_at":4792,"question":56,"scraped_at":4793,"seo":4794,"sitemap":4795,"source_id":4796,"source_name":4797,"source_type":91,"source_url":4798,"stem":4799,"tags":4800,"thumbnail_url":56,"tldr":4801,"tweet":56,"unknown_tags":4802,"__hash__":4803},"summaries\u002Fsummaries\u002Ftokenspeed-beats-tensorrt-llm-9-11-on-agentic-codi-summary.md","TokenSpeed Beats TensorRT-LLM 9-11% on Agentic Coding Inference",{"provider":7,"model":8,"input_tokens":4748,"output_tokens":4749,"processing_time_ms":4750,"cost_usd":4751},6300,1652,21300,0.00157915,{"type":14,"value":4753,"toc":4775},[4754,4758,4761,4765,4768,4772],[17,4755,4757],{"id":4756},"tackling-agentic-inference-bottlenecks","Tackling Agentic Inference Bottlenecks",[22,4759,4760],{},"Agentic coding systems like Claude Code, Codex, and Cursor push inference engines with contexts over 50K tokens across dozens of turns, stressing per-GPU tokens-per-minute (TPM) for multi-user scaling and per-user tokens-per-second (TPS) for responsiveness (target floor: 70 TPS, up to 200+ TPS). Public benchmarks miss this dual pressure, so TokenSpeed (MIT-licensed preview from LightSeek Foundation) prioritizes both metrics via specialized architecture, avoiding generic chat optimizations.",[17,4762,4764],{"id":4763},"architectural-edges-for-speed-and-safety","Architectural Edges for Speed and Safety",[22,4766,4767],{},"TokenSpeed builds on five subsystems: (1) Compiler-backed SPMD modeling auto-generates collective ops from I\u002FO annotations, skipping manual comms code. (2) Scheduler splits C++ control plane (FSM with type-enforced KV cache ownership\u002Ftransfers for compile-time safety) from Python execution plane (fast iteration). (3) Pluggable kernel layer with registry supports heterogeneous accelerators; its MLA kernel (grouping q_seqlen\u002Fnum_heads for Tensor Core fill, tuned binary prefill softmax) beats TensorRT-LLM decode\u002Fprefill, adopted by vLLM. (4) Safe KV reuse restrictions. (5) SMG for low-overhead CPU-GPU handoff. These cut KV errors (common pitfall) and enable modular accel support beyond NVIDIA.",[17,4769,4771],{"id":4770},"benchmark-dominance-on-real-workloads","Benchmark Dominance on Real Workloads",[22,4773,4774],{},"On NVIDIA B200 with SWE-smith traces (production-like coding agent traffic) and Kimi K2.5 model, TokenSpeed in Attention TP4 + MoE TP4 config tops TensorRT-LLM Pareto: 9% faster at batch=1 min-latency (>70 TPS\u002Fuser), 11% higher throughput at ~100 TPS\u002Fuser. Decode MLA folds query-seq into head axis for better BMM tile fill; binary prefill tunes softmax. With speculative decoding + long prefix KV at batches 4\u002F8\u002F16, latency nearly halves vs. TensorRT-LLM. Single-node only for now; PD disagg coming.",{"title":47,"searchDepth":48,"depth":48,"links":4776},[4777,4778,4779],{"id":4756,"depth":48,"text":4757},{"id":4763,"depth":48,"text":4764},{"id":4770,"depth":48,"text":4771},[55],{"content_references":4782,"triage":4789},[4783,4786],{"type":4548,"title":4784,"url":4785,"context":65},"TokenSpeed","https:\u002F\u002Fgithub.com\u002Flightseekorg\u002Ftokenspeed",{"type":62,"title":4787,"url":4788,"context":4557},"LightSeek TokenSpeed Technical Details","https:\u002F\u002Flightseek.org\u002Fblog\u002Flightseek-tokenspeed.html",{"relevance":81,"novelty":4571,"quality":81,"actionability":4571,"composite":4651,"reasoning":4790},"Category: AI & LLMs. The article discusses a new open-source LLM inference engine, TokenSpeed, which addresses specific performance issues in agentic workloads, directly relevant to AI engineers and developers. It provides insights into architectural improvements and benchmarks, but lacks detailed implementation guidance for practical application.","\u002Fsummaries\u002Ftokenspeed-beats-tensorrt-llm-9-11-on-agentic-codi-summary","2026-05-07 22:03:47","2026-05-08 11:28:23",{"title":4746,"description":47},{"loc":4791},"138f159d6a0dc547","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F07\u002Flightseek-foundation-releases-tokenspeed-an-open-source-llm-inference-engine-targeting-tensorrt-llm-level-performance-for-agentic-workloads\u002F","summaries\u002Ftokenspeed-beats-tensorrt-llm-9-11-on-agentic-codi-summary",[95,97,98],"TokenSpeed open-source engine optimizes agentic workloads with long contexts (>50K tokens) and multi-turn convos, delivering 9% lower latency and 11% higher throughput than TensorRT-LLM at 70-100 TPS\u002Fuser on NVIDIA B200.",[],"4kQU4vl3AcHUhD0wYvypv-tRHC9yz6_3gHGYW-lISUk"]