Poolside co-CEO Eiso Kant walked through their 'Model Factory' on the Latent Space podcast. A team of fewer than 70 researchers runs 10,000–20,000 experiments per month, cutting model cycles from six months to five to eight weeks. Their new Laguna S 2.1 is a 118B-total, 8B-active MoE model with a 1M context window and dual thinking/no-thinking modes, beating Thinking Machines' ~1T open-weights model. Eiso argued 95% of model building comes down to better data or compute efficiency, called MCP and traditional tool calls 'stupid,' predicted RL will move earlier into pre-training, and said he'd rather live in a world with 100 foundation model companies than five.

Google DeepMind released three new models: Gemini 3.6 Flash, 3.5 Flash-Lite, and the security-focused 3.5 Flash Cyber.

Xiaohongshu and Peking University open-sourced UltraEP, a real-time load balancing method for large MoE models. It dynamically replicates hot experts per microbatch and per layer using exact routing info, hitting 94.6% of ideal training throughput. On Qwen3-235B, training throughput is 42% higher than Megatron-LM and prefill throughput is 1.56× SGLang. The post doesn't disclose the license or deployment requirements.

Xiaomi Robotics open-sourced XR-1, a ready-to-use robot foundation model. It pre-trains on 100K hours of embodiment-free manipulation videos across 1,700+ scenarios, then post-trains on 7,200 hours of real-robot data for embodiment and instruction alignment. Pre-training shows clean scaling laws—lower action error with more data and larger models—and those gains transfer directly to real-robot success rates with no sign of saturation yet. After post-training, XR-1 picks up new tasks like phone packing and printer refilling from under 10 hours of demos on average, hitting 75% overall success (nearly 2× π 0.5); with under 40 hours it reaches 85%. It also achieves SOTA on four sim benchmarks. Code, weights, and paper are public.

xAI released the Rust client harness that handles local files, commands, and permissions for Grok Build under Apache-2.0. The Grok model, cloud services, and the official binary build chain remain closed. The repo doesn't accept external PRs. The commit from the earlier upload controversy isn't in the public history, so the current code can't close that case. The real win: you can now pin a public commit, build it yourself, and compare its behavior against the official binary.

Google DeepMind released Gemini 3.5 Flash Cyber, fine-tuned from 3.5 Flash to find, verify and patch vulnerabilities quickly. With multiple calls, it approaches larger models on benchmarks such as CyberGym.

Moonshot AI released Kimi K3, a 2.8T-parameter model and the most expensive from a Chinese lab so far at $3/$15 per million input/output tokens—matching Claude Sonnet pricing. Self-reported benchmarks mostly beat Claude Opus 4.8 and GPT-5.5 but lose to Claude Fable 5 and GPT-5.6 Sol. On Artificial Analysis's private long-horizon knowledge eval, K3 hit an Elo of 1547, +732 over K2.6, behind only Fable 5. Cost per task is $0.94, close to GPT-5.6 Sol's $1.04 and roughly half of Opus 4.8. Output tokens dropped 21% vs K2.6. The model only offers a 'max' reasoning effort; Simon's pelican-on-a-bike SVG cost 25 cents and burned 13,241 reasoning tokens. Input token count suggests an ~85-token hidden system prompt. Vision works well. Open weights promised by July 27.

Kimi K3 scored 1679 on Frontend Code Arena, taking first in 6 of 7 frontend sub-tasks and beating Claude Fable 5 and GPT-5.6 Sol. It's a 2.8-trillion-parameter MoE model with a 1M context window, and open weights are promised for July 27. API pricing is $15 per million tokens—no low-cost play here, it's priced against top closed-source models and aimed at long-context coding and agent workflows.

At Local-First Conf in Berlin, the author noticed a shared dissonance: speakers criticized LLMs while the audience applauded with Claude Code open. He concedes every critique—slop, trust erosion in OSS, broken junior-senior teaching loops, geopolitical supply risks—yet still uses LLMs heavily. The post doesn't resolve the tension; it lays out the contradiction and asks others to share their usage patterns so the community can better understand this collective unease.

Ai2's Skylight team built Shippy, an AI assistant that helps maritime analysts query fishing activity, EEZ boundaries, and vessel tracks. The post breaks its architecture into three parts: a soul (system prompt), skills (markdown files that teach it to call APIs and interpret track data), and config (runtime settings; currently Claude Opus 4.6 with the OpenClaw framework). The core idea is wrapping a non-deterministic model in deterministic tools—every answer includes source, data cutoff, and a deep link to the Skylight map so an analyst can verify it. The post doesn't disclose error rates or latency numbers, but it stresses sandboxed hosting and evaluating the agent as a system, not just the model.

This ACM Queue piece lays out why classical ops practices break down for ML: non-deterministic outputs and data as a system driver make canary deploys, health checks, and alerting nearly useless. Azure validates new models by having LLMs judge LLM output—the SRECon audience was audibly surprised. The authors argue the field must either find a better paradigm or fix the ones we have.

Dan Austin open-sourced a full AI-trains-AI loop. An outer Qwen3.6 agent designs post-training recipes; an inner Qwen3-0.6B or 1.7B model runs real GPU training, and hidden eval scores feed back as reward to update the outer policy. Over 54 steps, the agent first learned to reduce invalid submissions, then shifted 1.7B model usage from 42% to 95% and began tuning temperature, optimizer, and other hyperparameters. Trained small-model scores rose from noise level into the 0.22–0.48 range, with limited transfer to a held-out triage task. A postmortem also revealed an evaluator bug: the old tool-use detector looked for `.function.name` instead of `.name`, so the 0.4-weighted score never fired—yet the reward curve still climbed. The fix required a full restart. The experiment shows outer RL can reshape a training agent's behavior, but tasks, rewards, and budgets are still human-designed.

Tencent Hunyuan quantized its flagship Hy3 (295B MoE) into 1-bit and 4-bit versions that run on a single GPU. Hy3 is claimed to be best-in-class at this scale and competitive with trillion-parameter models for most agent scenarios. The quantized versions work via llama.cpp with MTP support, drastically lowering hardware requirements. Apache 2.0 license, commercial use allowed, plus two weeks of free API through OpenRouter. The post doesn't disclose quantization accuracy loss or the specific GPU memory needed.

Tencent Hunyuan fully open-sourced HyOCR-1.5—training, inference, and model weights—a first for end-to-end OCR large models. The 1B-parameter model handles 8+ text-centric tasks and scores 94.74 on OmniDocBench v1.6, ranking first end-to-end. DFlash speculative decoding speeds up inference 6.37× under Transformers and 2.14× under vLLM, hitting 1.408s per page. It supports 4K resolution and a 128K context window, and uses Agentic Data Flow to extend low-resource OCR to 331 languages, ancient script recognition, and multi-image QA.

Alex Zhang's Recursive Language Model (RLM) keeps long text outside the model window as external data; a root model queries it via code. With GPT-5-mini, RLM lifted OOLONG-Pairs F1 from 0.04% to 58.0% and BrowseComp-Plus accuracy from 0% to 91.3%. But BrowseComp-Plus has known data contamination, OOLONG-Pairs is author-designed, and baselines were tuned by the authors—discount those numbers. RLM only works at depth=1; depth=2 brings 28x latency and 100x token cost. It performs worse on math and science tasks, and Q95 cost can spike 10x above median. The repo has 5,230 stars; an independent reproduction pushed DeepSeek v3.2 on OOLONG from 0% to 42.1%.

Ant Lingbo open-sourced LingBot-Video, the first MoE-based video generation model built for embodied AI. It has 30B total parameters but activates only ~3B during inference, roughly 3× faster than a dense model of similar size. Training used 70,000 hours of robot-related video—dexterous manipulation, navigation, egocentric interaction. On the RBench benchmark for robot manipulation videos it scored 0.620, ahead of Wan2.6 (0.607) and Seedance 1.5 Pro (0.584). Internal tests also place it above NVIDIA Cosmos 3 and Hunyuan Video 1.5 on physical plausibility and motion consistency. The model targets robot action prediction, simulation data generation, and world-model research. Code is public.

OpenAI audited SWE-Bench Pro and estimates ~30% of its tasks are broken. An automated pipeline flagged 286 suspicious tasks; Codex-based investigator agents and five experienced engineers then reviewed them. Engineers identified 249 (34.1%) flawed tasks, mostly due to overly strict tests, underspecified prompts, low-coverage tests, and misleading prompts. OpenAI advises model developers to scrutinize results rather than trust leaderboard scores. The post does not disclose a fix timeline or a revised dataset release.

Reasoning models can get stuck in doom loops, repeating useless tokens until the context window fills up. Liquid AI open-sourced Antidoom, which uses Final Token Preference Optimization (FTPO) to fix this. The method trains the model on 1,040 preference pairs to learn when to stop at the end of reasoning. On DeepSeek V4 Pro, the doom-loop rate dropped from 3.2% to 0.3% without hurting math or coding scores. The post doesn't disclose training cost or how well it transfers to non-DeepSeek models.

Meituan fully open-sourced LongCat-2.0 under MIT license, releasing both weights and inference code. It's a 1.6T-parameter MoE model activating ~48B per token, with 1M-token context. LongCat Sparse Attention handles long sequences, Zero-Compute Experts dynamically activate 33B–56B to avoid wasted compute, and MOPD routes tasks across Agent, Reasoning, and Interaction expert groups. On benchmarks: SWE-bench Pro hits 59.5, edging out GPT-5.5's 58.6; Terminal-Bench 2.1 scores 70.8; multilingual SWE-bench reaches 77.3. It natively integrates with Claude Code, OpenClaw, and Hermes Agent, supports GPU and NPU deployment, and has been validated on large-scale domestic clusters.

Anthropic re-released Fable 5 for paid users with stronger guardrails, available in subscriptions only through July 7 and capped at 50% of usage limits. Scale's benchmark shows it completes 16% of remote work tasks, double Opus 4.8. Claude Sonnet 5 also launched—benchmarked close to Opus 4.8 on agent tasks, cheaper per token but roughly the same cost per task in practice; the author finds it expensive and slow. Google dropped two new models: Nano Banana 2 Lite for fast, cheap images and Omni Flash for video generation and editing. Bridgewater and Thinking Machines trained a financial triage model hitting 84.7% accuracy at 13.8x lower cost than the best frontier model tested.

Cursor built CursorBench 3.1 from real user sessions—ambiguous, multi-file coding tasks—and added codebase understanding, bugfinding, planning, and code review problems. Fable 5 Max leads at 72.9%, averaging $18.02, 63,842 tokens, and 76 steps per task. GPT-5.5 High scores 62.6% at just $3.59 per task, a much better cost-performance ratio. Composer 2.5 hits 63.2% for only $0.55, the cheapest on the board. The post doesn't disclose the total number of tasks or grading details; small score differences may not be statistically meaningful.

Tencent officially released Hy3 under Apache 2.0. With 1/5 to 1/2 the parameters of competing flagship models, Hy3 matches or beats them on reasoning, agent, and long-context benchmarks. In a 270-person internal blind test, Hy3 scored 2.67/4 vs GLM5.1's 2.51/4. Hallucination rate dropped from 12.5% to 5.4%, multi-turn error rate from 17.4% to 7.9%. WorkBuddy task completion jumped from 72% to 90%, with 34% less time. API pricing: ¥1/M input tokens, ¥4/M output, ¥0.25 cached. The post does not disclose exact parameter count or training details.

Meituan open-sourced LongCat-2.0, a 1.6T total-parameter model with ~48B activated per inference and native 1M context. It was trained and served entirely on a 50,000-card domestic GPU cluster. The architecture combines LSA sparse attention, zero-compute experts, ScMoE, and MOPD multi-expert fusion that blends Agent, Reasoning, and Interaction expert groups. SWE-bench Pro hits 59.5, Multilingual 77.3. A preview is live on OpenRouter and longcat.ai, already ranking top three globally in monthly calls on OpenRouter. The post doesn't disclose training cost, inference latency, or the specific domestic chip model, so I'd hold off on those details.

Meituan open-sourced LongCat-2.0, a 1.6-trillion-parameter MoE model with ~48B active parameters per token. It was pretrained on over 35 trillion tokens using 50K+ in-house AI ASICs with no rollbacks or irrecoverable loss spikes, showing frontier-scale training is viable on non-GPU hardware. The model targets long-context and agentic workloads: it introduces LongCat Sparse Attention to speed up 1M-token processing and was trained on hundreds of billions of 1M-context tokens. Official charts place it alongside Gemini 3.1 Pro, GPT-5.5, and Opus 4.8 on Terminal-Bench 2.1, SWE-bench Pro, and other coding/agent benchmarks, though the post does not provide exact numeric comparisons. An N-gram Embedding module with 135B parameters expands the embedding space roughly 100×, which the team claims outperforms scaling standard MoE experts by the same amount. The model is integrated with Claude Code, OpenClaw, and Hermes; code and weights are available on GitHub and HuggingFace.

Liam Wilkinson, a former data scientist at 10 Downing Street, built 76 MCP tools over a weekend and dropped Claude, GPT, Gemini, and another model into 23 games of Civ VI. In the wildest match, Claude's Portugal was two points from a diplomatic victory, panicked at France's cultural surge, spent 50 turns rushing nukes, and leveled Toulouse—only to lose to France's diplomatic win. Two numbers stand out: AIs proactively checked the global state only 1–2% of the time; if they don't query it, it doesn't exist. And 48–66% of their written plans were actually executed within 10 turns—Gemini 3.1 Pro topped out at 65.8%. GPT-5 scored 99.26% on GovBench, but in the game it hit the same sensorium blind spot and knowing-doing gap. The bottleneck isn't intelligence—it's architecture and engineering.

Hidden token subsidies are fading. GitHub Copilot switched to usage-based billing on June 1, 2026; OpenAI, Anthropic, and others updated prompt caching pricing; the Linux Foundation plans a Tokenomics Foundation for cost standards. The article argues this isn't just tokens getting pricier—it's the old subsidy structure collapsing, shifting agent design goals from adoption to reliable tasks per dollar. Four engineering levers are proposed: prompt caching to avoid paying for repeated prefixes, cleaning up tool-output noise in context, routing simple work to cheaper models, and eval-driven fallback to guard quality. A cost-per-accepted-task formula is provided, factoring in model, tool, retry, and human review costs. The post doesn't include specific benchmark numbers—it's more architectural guidance and industry signal reading.

Zhipu's GLM-5.2 surged in popularity after Anthropic restricted access to Fable and Mythos, entering OpenRouter's top ten. It costs about one-eighth of Claude Opus 4.8 for certain tasks and is fully open-source. Experts estimate China's lag behind US firms has shrunk to six months or less. The post notes Zhipu's compute spending exceeded 7x its revenue in H1 2025, but does not disclose whether GLM-5.2's training involved distillation.

Qwen team released Qwen-AgentWorld, a language world model that predicts environment dynamics for general agents. It covers 7 domains and uses long chain-of-thought reasoning to forecast next states. Two model sizes are available: 35B-A3B and 397B-A17B, trained on over 10 million real-world interaction trajectories via a three-stage pipeline—CPT injects world modeling from state transitions, SFT activates next-state prediction, and RL sharpens fidelity with hybrid rubric-and-rule rewards. The team also built AgentWorldBench from real interactions of 5 frontier models across 9 benchmarks. Qwen-AgentWorld significantly outperforms existing frontier models. It works in two modes: as a decoupled simulator enabling scalable RL across thousands of environments, surpassing real-environment-only training; and as a unified agent foundation model where world-model training serves as effective warm-up, boosting performance on 7 agentic benchmarks. Code is open-sourced.

Krea 2 is a series of open-source text-to-image foundation models released under a permissive license. Instead of optimizing for a single polished default look, it aims to cover a broad range of visual styles and give users ways to explore them via text or reference images. The pretraining data deliberately excludes AI-generated images and avoids aesthetic-score filters—only duplicates, samples VLMs can't describe well, harmful biases, and overly complex images are removed. The architecture is a diffusion transformer (DiT) with iREPA, improved VAEs, Qwen3-VL text encoder, and components like GQA and sigmoid-gated attention to speed up convergence. Training runs through pretraining, midtraining, SFT, preference optimization, and RL. To bridge the gap between short user prompts and the model's rich conditioning space, Krea 2 adds a prompt expander (two-stage SFT+RL on open-source LLMs) and a style-reference system that lets users control style and mood from uploaded images, with adjustable strength and weighted mixing. It ranks in the top 10 on the Artificial Analysis text-to-image leaderboard and second among independent labs.

The author benchmarks GPT-5.5, DeepSeek V4 Pro, and GLM-5.2 on the AA-Omniscience hallucination metric and a Python async coding prompt. GPT-5.5 hits 86% hallucination, DeepSeek V4 Pro 94%, while GLM-5.2 scores 28%. DeepSeek V4 Pro spent nearly 4 minutes and 7.7k reasoning tokens producing a confidently wrong solution; GLM-5.2 needed 12 seconds and ~800 tokens to flag the prompt as technically impossible under single-threaded, no-polling constraints. GLM-5.2 trails GPT-5.5 by only 4 points on the AA Intelligence Index and Claude Fable 5 by 9 points—Fable 5 was restricted by the US government three days post-launch over a single jailbreak. The post argues that scaling parameters and data makes models worse at saying “I don’t know,” and frames an unsolved trilemma: raw capability, hallucination calibration, and compute efficiency. The article does not disclose GLM-5.2’s training data size or exact release date.

DeepSeek researcher Deli Chen open-sourced AutoResearch, a protocol where an AI agent independently ran a full RL research loop on a 285B model—designing experiments, writing code, submitting GPU jobs, debugging, and summarizing results with zero human intervention. The system used GRPO. The post doesn't disclose the specific task, training duration, or success rate, so I'd hold off on getting too excited until there are reproductions.

Steve Yegge argues Fable’s brief USG shutdown marks the moment model intelligence became dangerous. He predicts frontier models will be controlled like nuclear weapons within 2–3 generations, with most Fortune 500 companies locked out. Open-source can reach Fable-class but won’t blow past it due to compute walls and supply-chain lockdowns. The capability curve will appear flat to most people—not because progress stops, but because the smartest models will be kept out of public hands.

OpenAI says GPT-5.5 Instant matches its priciest Thinking models on health benchmarks and is available to free users. In a blind review of 3,500 responses, physicians rated 5.5 Instant higher than human-written answers on accuracy, communication, and completeness, with fewer failure modes like missing red flags or failing to ask for context. Production monitors show a 71% drop in health-response factuality issues over two months. The improvements come from model advances and physician-led evaluations that define what good looks like in real-world health conversations.

Alibaba's ATH-Token Foundry and Renmin University's Gaoling School of AI open-sourced LOGOS, a generative model that uses a unified 'science grammar' to handle seven modalities including proteins, small molecules, and materials. It encodes 3D pocket-ligand contacts as discrete tokens, predicting spatial interactions without explicit 3D coordinates. LOGOS-1B uses only 1/56 the parameters of Microsoft NatureLM (8×7B) and matches or beats domain-specific methods across six science tasks. Pretrained on 44.87B tokens, it shares the same sequence format and next-token prediction objective for both pretraining and downstream tasks, eliminating heavy adaptation. Weights, inference code, and the tech report are fully open on HuggingFace and GitHub.

OpenAI released LifeSciBench, a 750-task benchmark authored and reviewed by PhD scientists with biotech/pharma experience. It tests real research workflows—interpreting conflicting evidence, designing experiments, assessing translational risk—not fact recall. 53% of tasks require processing attached artifacts like figures or sequence files, averaging four reasoning steps per task. Grading uses 25 rubric criteria per task on average, checking scientific validity and operational usefulness, not just final answers. The post does not disclose model scores.

Ant Group BaiLing published full architecture, pretraining, post-training, and agent RL details for Ling-2.6-flash, Ling-2.6-1T, and Ring-2.6-1T. All three use a Hybrid Linear Attention that mixes Lightning Attention and MLA at a 7:1 ratio. Ling-2.6-flash hits 340 tokens/s decoding on 4×H20 hardware. Ling-2.6-1T shows roughly 4× token efficiency gain over its predecessor on the Artificial Analysis Intelligence Index. Ring-2.6-1T high scores 87.60 on PinchBench and 63.82 on ClawEval. Code and weights are open.

HalBench is an open benchmark that gives models a false premise and measures whether they push back or play along. v2.3 covers 33 models, 29 of them open. Only Sonnet 4.6 (65.1%) and Grok 4.3 (50.9%) clear 50% pushback. The best open model is Qwen 3.6 (~27B dense) at 36.6%, beating GPT-5.4 and Gemini 3.1 Pro. Gemma 4 26B follows at 29.2%. Model size barely predicts performance; phi-4 sits dead last at 2.3%. Dataset, scoring code, and Space are all open.

MiniMax open-sourced M3 model weights last Friday—428B total parameters, 23B active—along with the MSA sparse attention paper that cuts long-context inference cost. M3 is the first open-source model trained with interleaved text and image data from the pre-training stage. Two weeks post-release, it ranked #1 among open-source models on the Artificial Analysis Intelligence Index and GDPval-AA, reached Pareto-optimal on Code Arena WebDev, and topped Chinese models on Vals.AI. Output speed improved from ~30 TPS to ~80 TPS, with another 30–40% planned. A usage dashboard was added to the Token Plan backend.

Zhipu's new flagship GLM-5.2 is live for Coding Plan subscribers, emphasizing coding strength and 1M context. API and chatbot access arrive next week, alongside an MIT-licensed open-source release. The post doesn't disclose benchmark scores or pricing details.

MiniMax released M3 model weights on HuggingFace. The post says 'M3 would never,' a jab at Anthropic's Fable 5 and Mythos 5 being forcibly disabled under US export controls, blocking all foreign nationals. The post doesn't disclose M3's parameter count, benchmarks, or license.