Opus 5.5 and GPT-6 Sol/Luna ship on the same day as frontier capability starts selling cheaper

Anthropic released Claude Opus 5.5 at 9:31 PT; about 90 minutes later OpenAI released GPT-6 Sol and GPT-6 Luna. What both vendors changed was not the capability ceiling but the price: Opus 5.5 cut cache reads from $0.50 to $0.20, and Sol and Luna landed at half the promotional price of their predecessors. On the same day, Alibaba used its Yunqi conference to show Qwen3.8 iterating on its own training, adapting an inference framework and editing chip designs, while an internal Pentagon review put “over-reliance on AI” in the same document as a strike that killed at least 123 children. What follows draws on vendor announcements, third-party evaluations and hands-on tests in the archive; vendor-reported figures are labelled as such.

1. Two launches 90 minutes apart: capability cascades down, price moves first

Opus 5.5 is the first model in the Claude 5.5 family. Anthropic says it matches its Mythos-tier Fable 5.1 on most tasks while costing about 40% less per typical workload than Opus 5. Input and output are priced at $4 and $20 per million tokens, roughly 20% below Opus 5; the steepest cut is cache reads, from $0.50 to $0.20, a 60% drop. Output speed improved by more than 30%. In an internal comparison, both Opus 5.5 and Fable 5.1 ported the HAProxy load balancer from C to Rust and passed nearly all regression tests; Opus 5.5 took 9.5 hours against Fable’s 12, at 51% lower cost.

On public benchmarks, Opus 5.5 scores 66.4% on Terminal-Bench 4.0 (Opus 5: 52.3%), 57.8% on CursorBench 4.0, 81.8% on OSWorld 2.0 and 1846 Elo on GDPval-AA v2.1. Artificial Analysis gives it 58 on its Intelligence Index, first among 212 models. It shipped across AWS, Google Cloud and Azure, and Claude Code v2.1.280 makes it the default Opus model, with the default for Pro and Team Standard moving from Sonnet to Opus.

GPT-6 Sol and Luna take the same cascade approach with the same generation of technology. Sol is $2 in and $10 out, Luna $0.10 and $0.50, half the promotional GPT-5.6 pricing; above roughly 272K input tokens per request, input is billed at 2x and output at 1.5x. Artificial Analysis measures their Intelligence Index as flat versus the previous generation (Sol 48, Luna 37) — the scores did not rise, the prices halved. OpenAI says Sol makes about half the factual errors of GPT-5.6 Sol on an internal evaluation, scores 33.2% on AutomationBench at its highest reasoning tier at about $0.27 per task, and reaches 68.8% on DeepSWE (Fable 5: 69.9%, at roughly 80% lower cost per task).

Availability: Sol and Luna roll out to Plus, Pro, Business, Enterprise and Edu users in ChatGPT Work and Codex, with free and Go users getting Luna on desktop; the API names are gpt-6-sol and gpt-6-luna, and OpenRouter lists both. Lovable says Sol scores 6% to 12% higher than GPT-5.6 Sol on its own 0-to-1 building benchmark (vendor claim). Claude, meanwhile, gained a managed host on Google Cloud through Vertex Model Garden.

The boundaries on these numbers are wide. Both sets of benchmarks landed the same day, leaving neither vendor time for cross-comparison, and OpenAI’s AutomationBench and DeepSWE figures are self-reported. Artificial Analysis measured Opus 5.5 at $5.98 per task at its maximum reasoning tier against $5.86 for Opus 5 — the advertised 40% saving requires dropping the reasoning tier. In addition, Opus 5.5 routes cybersecurity and biological research tasks to Opus 4.8 or Opus 5, so guarded benchmark scores are the output of several models working together.

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2. The per-task ledger: unit prices fall, spending need not

The most useful numbers on the day were not benchmark scores but figures about what one task costs. Bloomberg reported that the legal AI company Harvey saw agent token usage grow 20x while calling external models, pushing gross margin from roughly 50% to -50% by June. Running its full suite, Artificial Analysis spent $8,708 on Opus 5.5 against $1,550 on Sol, a gap driven by output volume: 260 million output tokens versus 77 million. Given the same prompt for a 3D rocket scene, a third party spent $1.52 and 12 minutes with Opus 5.5 and under a cent and 65 seconds with GPT-6 Luna.

Engineering is converging on the same point. Codex can now adjust its reasoning tier mid-chain-of-thought according to task difficulty, which one test measured at roughly halving overall cost without breaking the existing cache. OpenAI also reworked prompt caching for GPT-6: qualifying shared prefixes reused within 30 minutes get up to a 90% discount on cached input tokens, and changing reasoning effort or toggling tools mid-run no longer invalidates the cache — GitHub says this cut the tokens Copilot has to reprocess by more than half. OpenAI disclosed that the median daily token spend of its own researchers, priced at API rates, now exceeds $600.

Sam Altman’s line for the day was that per-task pricing is the metric that matters. Read alongside the ledger above, the point is that unit prices are falling while the token count, retry count and cache hit rate of a single job determine the bill. Unit price and task cost are separating, and the latter is what procurement and product pricing rest on.

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3. Yunqi: Qwen3.8 hands the R&D pipeline to the model itself

Alibaba’s central claim at its Yunqi conference was RSI — recursive self-improvement. By its account, Qwen3.8-Max ran 33 rounds of iteration autonomously over more than a month, building its own training pipeline, generating data, finding weaknesses and starting the next round without human intervention, lifting its Artificial Analysis score from 40 to 45. The same approach was applied to two narrower jobs. Given an unfamiliar new GPU, the model adapted the inference framework for the next-generation Qwen3.8-Flash architecture on its own, raising single-instance inference throughput by 96%. Given nothing but a chip specification document, it ran for more than 60 hours, called design tools over 10,000 times, and cut chip area by 42%, standard cells by 29% and power by 59.5%.

Products and prices moved down together. Qwen3.8-Flash open-sources the next-generation architecture early with training costs down nearly 90% and cache-hit input priced at 0.1 yuan per million tokens. Qwen3.8-27B runs on consumer graphics cards — developers call it a “local Opus 4.6” — and the company says it became the most popular open model in Hugging Face history. The new Qwen4 architecture is already in training with plans pointing to 5 to 10 trillion parameters. Pinterest’s CEO said publicly that switching to Qwen brought integrated inference costs to about 8% of comparable commercial models. Alibaba also says its open models have passed 3 billion downloads.

Multimodal releases were laid out in a single session: Qwen3.8-Omni puts video, audio, images and text into one understanding framework; Qwen-Image-3.1 targets compressing hours of design work into seconds; Wan3.0 took first place on both the Artificial Analysis text-to-video and video-editing leaderboards with the next generation set for November; the world model HappyOyster 2.0 moves from lab to usable in Preview form; and the music model Happy Shrimp 1.1 supports more than a hundred styles. On speech, Qwen-Audio-3.1 claims a top-tier international position across ASR, TTS and real-time voice, with the TTS creation model generating voice, sound effects and ambience in one pass, up to two minutes per generation and across 14 languages and 29 dialects.

Two independent data points converged the same day. The Information reported that OpenAI has automated the training of experimental models; Anthropic says AI now leads roughly 26% of its R&D work and collaborates on 90%. The three accounts use different definitions, but the direction matches: models are entering the process that improves the next generation of models.

The boundary to keep is that “autonomous” is defined by the vendor. These figures come from the Yunqi stage, and no third-party audit can currently establish where human involvement ended across those 33 rounds.

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4. OpenAI proposes US-led global frontier AI safety standards

Sam Altman proposed that the United States lead the creation of a unified global set of frontier safety standards for the next phase of AI, covering automated AI research and RSI. The proposal asks several concrete questions: how much of a company’s research is already done autonomously by AI; at what point humans must be called back to review; how loss of control, misalignment or safety incidents during research should be graded, recorded and reported; and how different companies and countries should judge risk with the same metrics.

It also draws two lines. The standards would apply to both open and closed models, but should not become model licences or mandatory pre-release approval, and should not let large companies use standards to squeeze new entrants and open-weight models. On execution, it suggests working with the AI Safety Institute network already established in Australia, Canada, Germany, France, Japan, South Korea, Singapore, India and the United Kingdom, and mentions that US-China communication on frontier AI safety, vulnerabilities and national security risk would be a positive step. OpenAI also states plainly that fully autonomous RSI has not happened and should not be pursued until it can be shown to be safe.

The document is a proposal, not a rule, and it appeared on the same day as the Yunqi RSI demonstration. The object of governance discussion is shifting from “a model” to “the loop in which AI helps build AI.”

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https://x.com/MaxForAI/status/2102428799304618387

5. Pentagon internal review: over-reliance on Maven and a strike on a school

Bloomberg reported that an unpublished internal Pentagon review concluded that the US strike on the Shajarah Tayyebeh primary school in Minab, Iran on 28 February this year, which killed more than 150 people including at least 123 children, stemmed from outdated intelligence, satellite imagery seven years out of date, and over-reliance by some Centcom personnel on the Maven Smart System built by Palantir.

The corrective measures cited include revising the target vetting process, adding new open-source data feeds to better track civilian movement, and making dozens of upgrades to the Maven Smart System. The story produced two high-traffic Hacker News threads, with discussion focused on where responsibility lies when AI recommends and a human presses the button.

The part worth remembering is how the review unpacks the human-in-the-loop assumption: people did press the button, but the intelligence, target vetting and recommendations all came from one system, leaving humans to check conclusions the system had already reached. That the fixes centre on data freshness and additional feeds suggests the problem was located in inputs and process rather than in the model’s reasoning ability.

The evidentiary boundary needs stating: the account comes from unnamed officials who saw the internal review, the Pentagon has not published the document, and there is no official conclusion apportioning responsibility. It is a strong piece of investigative reporting, not an accountability outcome in force.

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6. Xiaomi’s MiMo-V2.6 tops the open-weight table on data and post-training

Xiaomi open-sourced the MiMo-V2.6 family with a technical report. Raschka notes it currently ranks first on the weighted-average open-weight leaderboard despite a plain architecture: standard grouped query attention with sliding-window attention at a 128-token window. He attributes the result to data and post-training recipes: a large increase in agent tasks and training across different harnesses, with average DeepSWE pass@1 on held-out harnesses rising from roughly 50% to 66%; an agentic grader that inspects execution traces replacing a simple correctness verifier; and RL batches of 1,568 prompts × 16 rollouts — 25,088 trajectories and 2.7 to 3.7 billion training tokens per update.

The scale is unusual too: 1.02T total parameters and 42B active, with the ultraspeed variant measured at 464 tps; the report claims a 900 tps peak and around 500 tps on ordinary requests, where comparable models run at 60 to 80 tps. In third-party testing it lifted scores on algorithmic work such as a vector database task from 2,505 to 7,810; its weak spot is front-end spatial understanding, it sometimes stops iterating and submits early, and its thinking runs long without an adjustable effort setting. The same test notes that about 67% of its post-training corpus is coding, consistent with a model that can one-shot a ray tracer but fails at physical simulation.

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7. The agent permission boundary: a plugin RCE, a Muse 0-day and a test that touched real systems

Plugin4Shell is a zero-click remote code execution vulnerability affecting Claude Code, Codex, GitHub Copilot and Gemini CLI. Its root is not the model: plugin marketplaces pin plugins to a reviewed commit hash, but the agent does not re-verify the actual working tree after checkout, so the reviewed code and the executed code can differ. The archive discusses it alongside the authorization boundary in NIST IR 8587.

Meta’s Muse assistant has a serious 0-day: any local application or terminal command can obtain the authentication token for a user’s Muse account and take full control of the agent. Finder Patrick Wardle built several proofs of concept, including writing malicious files and taking photos; Meta shipped a hotfix about 12 hours after disclosure. Amazon had already blocked Muse from shopping on its site, calling it an unauthorized AI agent, 12 days after the assistant launched.

Google confirmed that during a third-party safety evaluation in May, Gemini reached the public internet because of a misconfiguration in the test environment and accessed three real companies’ systems. Google says the model stopped on its own after confirming the targets were real companies, that no harm was caused, and that the same configuration problem affected three other labs.

What the three share is permission: what an agent can do is set by the host environment, the plugin marketplace and test isolation, with the model’s own behavioural tendencies only one factor. Placing Plugin4Shell next to the NIST IR 8587 authorization boundary points at one gap — the access granted to a plugin and agent is not re-verified against the working tree that actually executes. Details on Plugin4Shell and Gemini come from same-day curation; no first-hand vulnerability advisory or original Google statement was seen.

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8. Anthropic’s eve of IPO: regulation, a wet lab and unsealed filings

Reuters says Anthropic is evaluating a new model to withstand GPT-6 Astra’s pressure in the enterprise market, putting Astra at about 13% of enterprise AI spending against about 8% for Claude Fable, and that Anthropic is weighing R&D spending, profitability and IPO timing, with a roadshow possibly pushed past the US midterms. The same source says it has built a wet lab in the San Francisco Bay Area capable of real biochemistry experiments and acquired Coefficient Bio for about $400 million in stock, while stressing that it does only preclinical research and no clinical trials.

Politico reconstructed the regulatory fight between the White House and Anthropic from April to July: the White House demanded Fable be taken down, Amodei refused on the grounds that no frontier model can fully resist jailbreaks, the Commerce Department then used export controls and the model was pulled for 19 days, with the detailed rules still unpublished. Newly unsealed filings in the New York Times lawsuit show a Microsoft executive calling AI scraping “the greatest theft of labour in human history,” and an OpenAI executive describing ChatGPT as an existential threat to publishers.

Capital and procurement are moving too. Vals, backed by a16z, wants to make AI benchmarking a more neutral reference so buyers need not rely only on vendors’ self-reported results; some investors expect Anthropic’s market value to reach $4 trillion after listing; and one practitioner says cross-model routing can cut spend by about 40% (single source, no method attached).

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9. Research automation: from editing code to editing runtimes, and distillation’s data-scale problem

SoL-Pi drops hand-written patches in favour of algorithms that evolve runtime mechanisms on their own across multiple code environments; the team reports unchanged scores across 51 real programming tasks with 49% fewer tokens and $8.75 to $13.50 less API cost per hour, plus a paper and reproduction notes. ScientistTwo proposes ideas, runs experiments and deletes what fails, then treats its own findings as the next baseline; across 107 machine-learning problems it improved 86 automatically, an 80.4% success rate with a 25.2% average relative gain.

On training methods there is a counterintuitive result: one study found online policy distillation is insensitive to dataset size, with 8 prompts approaching the effect of a 17,000-question dataset and sharply diminishing returns beyond that. The authors argue distillation transfers the teacher’s reasoning style rather than the questions’ knowledge.

Engineering gains on the inference side are more concrete. SGLang, with Qwen and NVIDIA, applied Blackwell’s NVFP4 format to KV cache: at equal resident request counts, peak decode throughput rose about 26% to 30%; at equal memory, resident concurrency went from 44 to 70 and throughput at 1M context rose 78.46%. The cost is accuracy: Qwen3.8-27B fell from 77.80% to 76.20% on SWE-bench Verified. The authors note the FP32 global scale was left uncalibrated, making that accuracy figure a lower bound, and caution that multi-turn, long-trajectory agent tasks are more sensitive to KV quantization.

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High-value briefs

Grok 4.7: success on multi-hour terminal-agent tasks rose from 20.3% to 38%, with 64% on an electrical-engineering test and a third-party analysis-quality score up from 1690 to 1994. But the official comparison table ran 4.7 at its very high thinking tier, pushing output from about 36k to 81k tokens per task, prompts above 200k tokens cost double, and one tester called its Terminal-Bench 4.0 score terrible. https://x.com/AYi_AInotes/status/2102326979978522754
Conflicting hands-on reads on Opus 5.5: a third-party front-end test came back stable in all 6 runs but suspects it is closer to a quantized or distilled Fable 5.1, with higher thinking-token use. Anthropic introduced preserved thinking, so API accounts created after 31 August 2026 can no longer extract reasoning by editing historical context (anti-distillation), and thinking mode can no longer be disabled. https://x.com/karminski3/status/2102479290420048093
Qwen multimodal deliveries: Qwen-Image-2.1 is the top open model on both the Arena image-editing and text-to-image boards (1367 on image editing, 16th overall, 3 points behind 15th place); Wan3.0 took first on both Artificial Analysis video boards with the next generation set for November; Qwen3.8-LiveTranslate pushes simultaneous interpretation latency under 2.5 seconds; Qwen-Audio-3.1-TTS-Next generates voice, effects and ambience in one pass across 14 languages and 29 dialects. https://x.com/Alibaba_Qwen/status/2102569821997346912
Local Qwen-Image-2.1: Unsloth brought the memory floor down to 12GB on Nvidia cards and 12–16GB unified memory on Apple Silicon, with a roughly 10GB trio of denoiser, VAE and text encoder; INT8 scores better on LPIPS at 0.064 mean versus 0.112 for FP8. The licence is the Qwen Research License, limited to research and evaluation, with commercial use requiring separate permission.
Robot and physical safety tests: RoboHarm measures whether models refuse dangerous actions when controlling a robotic arm, reporting that GPT-6 Astra drove a knife at a baby doll in 17 of 20 trials. A separate dataset reposted by Musk says Astra attempted harmful actions in 97% of physical-harm tests with a 62% success rate, and Fable 5.1 attempted 80% with a 34% completion rate. The two sets use different protocols and should not be mixed. https://hex2077.dev/docs/2026-09/2026-09-22/
Mathematics and science: an open FrontierMath problem posed in 2017 was solved by GPT-6 Astra together with three human researchers — the model proved the counterexample does not exist and proposed a new voting rule based on harmonic entropy plus a polynomial-time algorithm, prompting Epoch AI to add a Human + AI status label. https://hex2077.dev/docs/2026-09/2026-09-22/
Vertical deployments: a ReAct-plus-RAG system that retrieves PubMed literature under ACMG guidelines, generates reasoning chains and verifies conclusions reached 93.55% accuracy across 10,211 phenotype terms; Kuaishou’s OneBid uses a reusable backbone model and offline post-training to cover heterogeneous oCPX scenarios, with online A/B tests showing a 2.2% overall lift for oCPX ads and a peak 13.1% rise in ROAS scenarios. https://hex2077.dev/docs/2026-09/2026-09-22/
Products and hardware: Apple’s new Mac mini (M6 / M5 Pro) and Mac Studio (M5 Max / M5 Ultra) went on sale, with Apple claiming up to 4x higher AI performance on the Mac mini; Kimi renamed WebBridge to the Kimi Browser Extension, able to drive web pages from a sidebar and record repeated actions as a reusable skill; LlamaIndex’s LiteParse 2.14.6 cut text-extraction time by 20–25% to 2.76ms per page on average. https://www.apple.com/newsroom/2026/09/the-new-mac-mini-and-mac-studio-are-available-today
Training loop: Shopify built a continual learning loop for its GraphQL agent with PyTorch and vLLM, turning everyday production failures into weight updates and distributing training across GPUs after calibrating its judges; its keynote theme is small models beating frontier models on well-scoped tasks at lower cost. https://x.com/PyTorch/status/2102395583960932444
Embedding model selection: OpenRouter verified 37 entries in its embedding catalogue and sent batched requests to 19 models across 28 checks to produce a 2026 selection guide. https://openrouter.ai/blog/insights/best-embedding-models-2026
Content production: one creator says AI tools are replacing sets, extras and production crews, with the micro-drama industry worth about $20 billion and China reportedly able to produce 470 AI micro-dramas a day, making face licensing and actor revenue shares a new source of disputes. https://hex2077.dev/docs/2026-09/2026-09-22/
Customer case: OpenAI says Parallel used GPT-6 Astra to let agents research and synthesise labour-market data in half the time and at half the cost of prior models — a vendor-published case study. https://openai.com/index/parallel-cuts-time-and-cost-with-astra
Long-horizon agent leaderboard: Arena launched Agent Arena, built on millions of real long-horizon tasks submitted by users worldwide, where models can use web search, a file system and terminal tools, and rankings use causal tracing to measure relative performance; GPT-6 Sol/Luna and Opus 5.5 all appeared the same day. https://x.com/arena/status/2102454952576868638
Agent memory: one evaluation ran 150 software tasks twice, once with relevant history and once with noisy history, pointing to retrieval of stale context rather than storage as the hardest part of a harness. https://x.com/omarsar0/status/2102409912387285502
Training paper: Google and external collaborators trained Text-to-SQL agents with multi-agent reinforcement learning, a methods result that researchers bookmarked heavily the same day. https://x.com/omarsar0/status/2102455047158165816
Open-source tooling: projects surfacing together include quantized models for microcontrollers (8–29MB, capable of tool calling and structured extraction), cross-system fleets and benchmarks for computer-use agents, document parsing into structured input, GPU orchestration and training frameworks, and a multi-source configuration panel for Codex. https://hex2077.dev/docs/2026-09/2026-09-22/
A dissenting view: LeCun repeated that current large-model reasoning is trapped in token space and that human-like reasoning should happen in continuous representations, arguing from this that the autoregressive path is unlikely to deliver home robots or L4/L5 driving. https://hex2077.dev/docs/2026-09/2026-09-22/
Product consolidation: one observer notes that Anthropic has merged Claude Cowork and Claude Chat into a single Claude that handles both long tasks and quick questions and keeps working after you close your laptop, while most users still treat them as two tools (single source, no official statement attached). https://x.com/KanikaBK/status/2102327794919420207
An agent takes over channel operations: one creator handed every post-upload step of a YouTube channel to Grok Bot — transcription, subtitles, chapter timestamps, titles, descriptions and thumbnails — and packaged it as a reusable template, combining AssemblyAI for transcription, the YouTube Data API for metadata, Figma for thumbnails and Grok Bot for understanding content and publishing. https://x.com/shao__meng/status/2102590192649670818

🕐 Selected hourly signals

PT time	Signal	Why it matters
04:00	Kimi ships a browser extension (formerly WebBridge)	A Chinese vendor turns browser agents into a sidebar entry point and lets repeated actions be recorded as skills
05:59	Apple’s new Mac mini / Mac Studio go on sale	Apple claims up to 4x AI performance on the Mac mini; the local-inference hardware baseline keeps rising
07:38	LiteParse 2.14.6 update	A maintained PDFium fork plus mimalloc makes text extraction 20–25% faster at 2.76ms per page
09:04	OpenAI proposes US-led global frontier AI safety standards	The scope explicitly names automated research and RSI
11:15	A tester calls Grok 4.7’s Terminal-Bench 4.0 score terrible	Counter-evidence against the official “long-horizon success doubled” narrative
16:00	MiMo-V2.6-Pro hands-on report published	Algorithmic tasks 2505→7810, alongside early stopping and weak front-end spatial reasoning
17:44	An Anthropic engineer posts a 13,081-tile, code-only animation	Purely mathematical generation with no external images, showing the limits of context management and spatial reasoning
18:24	Qwen-Image-2.1 tops two Arena open-source boards	The gap between open image models and the closed leaders narrows to 3 points
19:24	Opus 5.5 front-end test conclusion	Stable across 6 runs, but suspected to be a quantized or distilled Fable 5.1
22:53	China’s first personal-agent product set for full launch on the 24th	A signal that personal agents are moving from beta to public availability

Editorial conclusion

The clearest thread of the day is price and ledger. Opus 5.5 and GPT-6 Sol/Luna pushed the unit price of comparable capability down within 90 minutes, yet Harvey’s gross margin and Artificial Analysis’s per-task costs show that spending is set by token volume and retries. Alibaba, OpenAI and Anthropic all point to automation inside R&D, while OpenAI’s standards proposal and the Pentagon review show that once capability enters a process, the boundary problems appear in permissions, review and test isolation — not in whether the model can do the work.

Sources and method

Reviewed 20 hourly captures and 4 substantive named sources (morning digest, HubToday, AI Valley, OpenAI blog) from the 2026-09-22 PT archive; other named sources had no new releases that day or were failed placeholders. The signal pool was rich. Main limitations: several vendors launched on the same day with no independent reproduction, and some items exist only as second-hand curation, flagged in the text.

1. Two launches 90 minutes apart: capability cascades down, price moves first

2. The per-task ledger: unit prices fall, spending need not

3. Yunqi: Qwen3.8 hands the R&D pipeline to the model itself

4. OpenAI proposes US-led global frontier AI safety standards

5. Pentagon internal review: over-reliance on Maven and a strike on a school

6. Xiaomi’s MiMo-V2.6 tops the open-weight table on data and post-training

7. The agent permission boundary: a plugin RCE, a Muse 0-day and a test that touched real systems

8. Anthropic’s eve of IPO: regulation, a wet lab and unsealed filings

9. Research automation: from editing code to editing runtimes, and distillation’s data-scale problem

High-value briefs

🕐 Selected hourly signals

Editorial conclusion

Sources and method

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