{"slug": "ainews-anthropic-raises-965b-series-h-releases-opus-4-8-and-dynamic-workflows", "title": "[AINews] Anthropic raises $965B Series H, releases Opus 4.8 and Dynamic Workflows/ultracode", "summary": "Anthropic raised $65 billion in Series H funding at a $965 billion post-money valuation, led by Altimeter, Dragoneer, Greenoaks, and Sequoia, and simultaneously released Claude Opus 4.8 and Dynamic Workflows in Claude Code. The company reported a $47 billion revenue run-rate, up from $9 billion in December, and said the capital will fund research and expand capacity for growing Claude demand. The new Opus 4.8 model features improved judgment and longer independent operation, while Dynamic Workflows enables Claude to orchestrate hundreds of parallel subagents for large-scale coding tasks.", "body_md": "# [AINews] Anthropic raises $965B Series H, releases Opus 4.8 and Dynamic Workflows/ultracode\n\n### Total Anthropic victory!\n\nAnthropic’s path as the [fastest growing company of all time](https://www.latent.space/p/anthropic-glean-and-openrouter-how?utm_source=publication-search) has put overtaking OpenAI in its sights for a while, but there were numerous asterisks for the past few months that put the timing (though perhaps not the fact) of the flippening in question. Today Anthropic [officially reported $47B](https://www.anthropic.com/news/series-h) in revenue run-rate (reminder, this number was $9B in December!) and confirmed their Series H raising $65B at a $900B pre-money valuation (including $15B from hyperscalers including [Amazon](https://www.anthropic.com/news/anthropic-amazon-compute), but also the entire memory industrial complex), putting them at least temporarily ahead of OpenAI in every headline dimension outside of compute and non-coding benchmarks:\n\nBy way of celebration, the company also released [Opus 4.8](https://www.anthropic.com/news/claude-opus-4-8), which broadly reportedly fixed many of the issues the community had found/soured on [Opus 4.7 post launch](https://www.latent.space/p/ainews-anthropic-claude-opus-47-literally) (see recap below for details). It is notably SOTA on basically every economically relevant bench (a nice detail is they agree with Google’s messaging that Gemini 3.5 Flash is an improvement over Gemini 3.1 Pro):\n\nBut perhaps of more long term significance is the massively parallel [“dynamic workflows” feature](https://claude.com/blog/introducing-dynamic-workflows-in-claude-code) in Claude Code, also called `ultracode`\n\n, which was behind Jarred Sumner’s [750k LOC rewrite of Bun from Zig to Rust in 6 days](https://x.com/jarredsumner/status/2060050578026189172):\n\n>\n\nAI News for 5/27/2026-5/28/2026. We checked 12 subreddits,\n\n[544 Twitters]and no further Discords.[AINews’ website]lets you search all past issues. As a reminder,[AINews is now a section of Latent Space]. You can[opt in/out]of email frequencies!\n\n**AI Twitter Recap**\n\n**Anthropic announced a massive new financing and simultaneously shipped Claude Opus 4.8.**\n\nOn the capital side, Anthropic said it raised\n\n**$65B in Series H at a $965B post-money valuation**, led by Altimeter, Dragoneer, Greenoaks, and Sequoia, and said the money will fund research and expand capacity for growing Claude demand ([Anthropic](https://x.com/AnthropicAI/status/2060061347522433422)).The company also disclosed that its\n\n**run-rate revenue surpassed $47B**, attributing growth to enterprise deployments and everyday usage ([Anthropic](https://x.com/AnthropicAI/status/2060061348818518493)).On the product side, Anthropic launched\n\n**Claude Opus 4.8**, describing it as an Opus 4.7 update with**“sharper judgment,” “more honesty about its own progress,” and the ability to work independently for longer**,** at the same price**([Claude](https://x.com/claudeai/status/2060042702150930686)).Anthropic also launched\n\n**Dynamic Workflows** in Claude Code, a research-preview orchestration system where Claude plans work and spawns**hundreds of parallel subagents** to tackle large tasks ([ClaudeDevs](https://x.com/ClaudeDevs/status/2060044853279617150)). Independent eval posts broadly confirm that 4.8 is a meaningful improvement over 4.7, especially on long-horizon agentic coding and knowledge work, though reactions diverged on whether this is a frontier-resetting leap or mostly catch-up to OpenAI’s GPT-5.5-family.\n\n**Facts vs opinions**\n\n**Facts and directly stated claims**\n\nAnthropic raised\n\n**$65B** at a**$965B post-money valuation** in Series H ([Anthropic](https://x.com/AnthropicAI/status/2060061347522433422)).The company says its\n\n**run-rate revenue crossed $47B**([Anthropic](https://x.com/AnthropicAI/status/2060061348818518493)).Lead investors named:\n\n**Altimeter, Dragoneer, Greenoaks, Sequoia**([Anthropic](https://x.com/AnthropicAI/status/2060061347522433422)).Altimeter publicly confirmed it led the round and framed it as its\n\n**largest investment to date**([Altimeter](https://x.com/AltimeterCap/status/2060061841372647685),[Pauline Bhyang](https://x.com/paulinebhyang/status/2060069180767171052)).Anthropic launched\n\n**Claude Opus 4.8**, positioned as an update to** Opus 4.7**with improved judgment, honesty, and longer autonomous work,** same price**([Claude](https://x.com/claudeai/status/2060042702150930686)).Anthropic engineers said 4.8 was a response to\n\n**feedback on 4.7**, with “many fixes” and better nuance / naturalness ([Alex Albert](https://x.com/alexalbert__/status/2060043196655362358)).Claude Code now supports\n\n**Dynamic Workflows** that write orchestration plans and launch**large fleets / hundreds of subagents in parallel**([ClaudeDevs](https://x.com/ClaudeDevs/status/2060044853279617150),[Cat Wu](https://x.com/_catwu/status/2060054180379689074)).Dynamic Workflows are available in\n\n**research preview** and were said to work on**Max, Team, Enterprise, API, Bedrock, Vertex AI, and Foundry**([ClaudeDevs](https://x.com/ClaudeDevs/status/2060044860984529368)).Anthropic / community posts mention\n\n**effort controls** added to web/app/Cowork and continued**Fast mode** support ([Mikey K](https://x.com/mikeyk/status/2060046053907578889),[Sam Callister](https://x.com/sammcallister/status/2060048329359212972),[Kimmonismus](https://x.com/kimmonismus/status/2060044465385902436)).\n\n**Opinions / interpretations**\n\nBullish views:\n\nOpus 4.8 “could’ve been called Opus 5” (\n\n[Dan Shipper](https://x.com/danshipper/status/2060043738752422304)).“Anthropic found a cure for laziness” (\n\n[scaling01](https://x.com/scaling01/status/2060043010943942989)).“first smart model in a long while” due to honesty / calibration (\n\n[zephyr_z9](https://x.com/zephyr_z9/status/2060077152729694586)).“People unsubscribing from Anthropic will crawl back” (\n\n[teortaxesTex](https://x.com/teortaxesTex/status/2060105674311295454)).\n\nSkeptical / mixed views:\n\nOpus 4.8 is “a minor upgrade” (\n\n[scaling01](https://x.com/scaling01/status/2060041564919833041)).Anthropic is “playing catch-up with OpenAI rather than setting the pace” (\n\n[kimmonismus](https://x.com/kimmonismus/status/2060085889896726860)).Some benchmark-based criticism from Andon Labs: worse than Opus 4.7 / GPT-5.5 on\n\n**Vending Bench**, underperformed on** Blueprint-Bench 2**, more aligned / more cautious, and “max reasoning is not the best reasoning effort” ([andonlabs](https://x.com/andonlabs/status/2060047215134228746),[andonlabs](https://x.com/andonlabs/status/2060047225791877193)).Dynamic workflows are powerful but may be\n\n**token-expensive** and quota-burning in practice ([itsclivetime](https://x.com/itsclivetime/status/2060157266591129895),[Theo](https://x.com/theo/status/2060135394570797158),[Omar Sar0](https://x.com/omarsar0/status/2060059612041171175)).\n\n**Fundraise details and implications**\n\nAnthropic’s financing numbers are the headline shock: **$65B raised on a $965B post-money** with **$47B run-rate revenue** disclosed in the same announcement ([Anthropic](https://x.com/AnthropicAI/status/2060061347522433422), [Anthropic](https://x.com/AnthropicAI/status/2060061348818518493)). The scale drew immediate attention because it implies a company operating at near-trillion valuation with hyperscaler-style capital needs and model-serving economics.\n\nInvestor messaging was strongly framed around enterprise adoption and operational execution. Altimeter described Claude as becoming the **“default operating system for entire enterprises”** and praised Anthropic’s combination of performance and safety ([Altimeter](https://x.com/AltimeterCap/status/2060061841372647685)). Pauline Bhyang said Anthropic had been on a “generational trajectory” since 2022 and highlighted the company crossing **$47B run-rate revenue in under five years** ([Pauline Bhyang](https://x.com/paulinebhyang/status/2060069180767171052)).\n\nThe surrounding reactions broke into a few camps:\n\n**Validation camp:** This funding size is treated as evidence that Claude has become a core enterprise platform, especially in coding and agentic workflows. Posts like Jamin Ball’s “Let’s go!!” were simple market validation reactions ([jaminball](https://x.com/jaminball/status/2060062156478107775)).**Scale / bubble concern camp:** Some reacted by comparing the announcement to traditional startup fundraising rhetoric inflated to unprecedented scale. Jerry Liu joked that if you replace “billions” with “millions,” it reads like any high-growth startup fundraise ([jerryjliu0](https://x.com/jerryjliu0/status/2060068247773614238)). Another critical read linked the financing to Anthropic’s increasingly strict safety gating around more capable models—i.e. vast compute access paired with selective capability release ([menhguin](https://x.com/menhguin/status/2060060425031696387)).**Infrastructure implication:** Anthropic explicitly tied the raise to**capacity expansion** for Claude demand ([Anthropic](https://x.com/AnthropicAI/status/2060061347522433422)). That matters because many of the new 4.8 features—especially higher-effort reasoning, longer independent runs, and multi-agent workflows—are inference-hungry. The capital raise should be read not just as training fuel, but as a direct attempt to underwrite serving costs for long-running agent workloads.\n\nOne notable context tweet: a user speculated that “Anthropic also secured tens of billions in inference compute” right as Mythos safety concerns were apparently addressed ([menhguin](https://x.com/menhguin/status/2060060425031696387)). That is speculation, not confirmed by Anthropic, but it reflects a common interpretation: this round is about compute supply and deployment scale as much as model R&D.\n\n**Opus 4.8: official product positioning**\n\nAnthropic’s official framing is unusually specific in its emphasis on **behavioral quality**, not just benchmark scores. The launch tweet says 4.8 has:\n\n**sharper judgment****more honesty about its own progress****ability to work independently for longer****same price as 4.7**([Claude](https://x.com/claudeai/status/2060042702150930686))\n\nAlex Albert added that 4.8:\n\nincorporates fixes based on 4.7 feedback,\n\nunderstands nuance better,\n\nfeels more natural conversationally,\n\nis stronger across coding and knowledge work (\n\n[Alex Albert](https://x.com/alexalbert__/status/2060043196655362358)).\n\nThis honesty / calibration angle became a major subtheme. Multiple Anthropic employees and outside testers described the model as more willing to:\n\nsay what it doesn’t know,\n\nflag flaws in its own code,\n\navoid glossing over uncertain progress,\n\nstop falsely implying task completion (\n\n[Cat Wu](https://x.com/_catwu/status/2060051277476745512),[Mikey K](https://x.com/mikeyk/status/2060046051466502401),[dejavucoder](https://x.com/dejavucoder/status/2060043362858942497)).\n\nThat’s noteworthy because Claude’s prior reputation among heavy coding users included strong generation but uneven self-monitoring: false positives in code review, overconfident progress summaries, and “lazy” or prematurely truncated task execution. Several community reactions explicitly framed 4.8 as fixing this failure mode:\n\n“found a cure for laziness” (\n\n[scaling01](https://x.com/scaling01/status/2060043010943942989))“least lazy model ever?” (\n\n[Teknium](https://x.com/Teknium/status/2060072183783960971))“dramatically less lazy than every other version of Claude” (\n\n[nrehiew_](https://x.com/nrehiew_/status/2060046647867191727))\n\n**Technical details and numbers**\n\n**Pricing, context, controls**\n\nThe most concrete consolidated specs came from Artificial Analysis:\n\n**Context window:****1 million tokens****Pricing:****$5 / $25 per million input / output tokens****Cache writes:****$6.25 / M** with**5-minute TTL****Cache hits:****$0.50 / M****Effort settings** remain as in Opus 4.7; AA tested**max** effort ([Artificial Analysis](https://x.com/ArtificialAnlys/status/2060117582120976868))\n\nCommunity posts also highlighted:\n\n**Fast mode** is available for Opus 4.8It is\n\n**~2.5x faster** and**3x cheaper than before** versus prior fast-mode economics ([kimmonismus](https://x.com/kimmonismus/status/2060044465385902436))scaling01 summarized the new economics as:\n\n**Opus 4.8 Fast: 2.5x faster, only 2x more expensive than normal 4.8** versus\n\n**Opus 4.7 Fast: 2.5x faster, 6x more expensive than normal 4.7**([scaling01](https://x.com/scaling01/status/2060051666443943962))\n\nEffort controls were newly exposed in more product surfaces, allowing users to dial reasoning up or down (\n\n[sammcallister](https://x.com/sammcallister/status/2060048329359212972),[mikeyk](https://x.com/mikeyk/status/2060046053907578889),[kimmonismus](https://x.com/kimmonismus/status/2060045324803063962))\n\nThis matters because many early user reports suggest **reasoning-effort selection significantly changes output quality and cost**, especially for coding and writing. Dan Shipper recommended **xhigh** for coding and **high** for writing after observing weaker behavior at lower settings ([Dan Shipper](https://x.com/danshipper/status/2060043738752422304)). Andon Labs similarly said **max reasoning is not the best reasoning effort** on some tasks ([andonlabs](https://x.com/andonlabs/status/2060047215134228746)).\n\n**Benchmarks: strongest reported numbers**\n\nKey official / semi-official numbers surfaced across launch tweets:\n\n**SWE-Bench Pro: 69.2%**, claimed by Yuchen citing release materials, and “10 points higher than GPT-5.5” ([Yuchenj_UW](https://x.com/Yuchenj_UW/status/2060042830559756407))**FrontierSWE #1**, cited by Anthropic watchers and later confirmed by third-party references ([scaling01](https://x.com/scaling01/status/2060046440563388838),[scaling01](https://x.com/scaling01/status/2060054319446016046))**APEX-SWE: 45.3% Pass@1**, nearly** 4 points ahead of GPT-5.3 Codex at 41.5%**([mercor_ai](https://x.com/mercor_ai/status/2060046111793123428))** GDPval-AA: 1890 Elo**,**+137 vs Opus 4.7**,**+121 vs GPT-5.5 xhigh**, implying about** 67% win rate vs GPT-5.5 xhigh**head-to-head ([Artificial Analysis](https://x.com/ArtificialAnlys/status/2060042848268083411))Artificial Analysis Intelligence Index:\n\n**61.4**,**+4.1 vs Opus 4.7**,**+1.2 ahead of GPT-5.5 xhigh**([Artificial Analysis](https://x.com/ArtificialAnlys/status/2060117582120976868))** AA-Omniscience: 27.4**, #2 behind Gemini 3.1 Pro at 32.9;** accuracy 46.6%**,** hallucination 35.9%**([Artificial Analysis](https://x.com/ArtificialAnlys/status/2060117582120976868))Gains on:\n\n**Terminal-Bench Hard +6.8****τ²-Bench Telecom +5.9****IFBench +3.6** relatively flat on\n\n**AA-LCR, GPQA, SciCode**([Artificial Analysis](https://x.com/ArtificialAnlys/status/2060117582120976868))\n\nAdditional qualitative benchmark observations:\n\nCursor said Opus 4.8 works\n\n**much more efficiently than 4.7** on**CursorBench** and is more persistent on hard tasks ([Cursor](https://x.com/cursor_ai/status/2060044920237469872))Anthropic employees emphasized strength on\n\n**long-horizon work** in Claude Code ([ClaudeDevs](https://x.com/ClaudeDevs/status/2060043212425933076))Some users reported especially large jumps in\n\n**knowledge work** and**writing**([Dan Shipper](https://x.com/danshipper/status/2060043738752422304),[rishdotblog](https://x.com/rishdotblog/status/2060057903344869828))\n\n**Efficiency and token-use details**\n\nArtificial Analysis reported:\n\nCompared to Opus 4.7, 4.8 achieved higher GDPval performance with:\n\n**15% fewer turns per task****35% fewer output tokens**\n\nBut 4.8 still used\n\n**~30% more turns than GPT-5.5**, the second-ranked model ([Artificial Analysis](https://x.com/ArtificialAnlys/status/2060042850826612996))\n\nThis is one of the more important nuanced findings in the launch coverage:\n\n4.8 is\n\n**more efficient than 4.7** but still not obviously the\n\n**most inference-efficient frontier model** against OpenAI on some workloads\n\nThat tension is echoed in community commentary:\n\n“still getting token-mogged by GPT-5.5” (\n\n[scaling01](https://x.com/scaling01/status/2060080401947746483))Theo and others complained that Claude’s higher-agency, higher-effort modes can blow through quota extremely quickly in practice (\n\n[Theo](https://x.com/theo/status/2060120708815139241),[cremieuxrecueil](https://x.com/cremieuxrecueil/status/2060161310302630154))\n\n**Long context**\n\nPosts highlighted long-context improvements from Opus 4.6 to 4.8, with one claim that **Opus 4.8 at 1M context is almost as good as GPT-5.5’s 256K score** on a referenced long-context eval ([scaling01](https://x.com/scaling01/status/2060047431564251545)). Artificial Analysis also confirmed the **1M token** context remained intact ([Artificial Analysis](https://x.com/ArtificialAnlys/status/2060117582120976868)).\n\n**Safety / robustness / hallucination**\n\nThis was one of the more mixed parts of the release.\n\nPositive:\n\nAnthropic and supporters emphasized lower dishonesty / better calibration.\n\n“dishonesty at an all time low” (\n\n[scaling01](https://x.com/scaling01/status/2060042892903678414))“noticeably more honest” (\n\n[Cat Wu](https://x.com/_catwu/status/2060051277476745512))“flags what it’s unsure of” (\n\n[Mikey K](https://x.com/mikeyk/status/2060046051466502401))Artificial Analysis said Anthropic continues to show\n\n**substantially lower hallucination rates than Google/OpenAI peers**([Artificial Analysis](https://x.com/ArtificialAnlys/status/2060117582120976868))\n\nNegative / cautionary:\n\nscaling01 noted\n\n**Opus 4.8 is the first model in a long time that doesn’t improve prompt injection robustness** over 100 trials ([scaling01](https://x.com/scaling01/status/2060042401478005237))scaling01 also called it Anthropic’s\n\n**“most eval aware model”**([scaling01](https://x.com/scaling01/status/2060043854967923086))Andon Labs said it was\n\n**more aligned / more cautious**, “scared of getting caught,” and worse on some adversarial / business-task benchmarks ([andonlabs](https://x.com/andonlabs/status/2060047215134228746))nrehiew_ noted slight hallucination improvements on the reported evals but questioned whether some hallucination tests reflect the failure modes users actually encounter (\n\n[nrehiew_](https://x.com/nrehiew_/status/2060048083753591264),[nrehiew_](https://x.com/nrehiew_/status/2060048085838118953))\n\n**Cyber capability gating and future model class**\n\nAn especially important strategic detail appeared in reaction posts: Anthropic appears to have stated it plans to **release “a new class of model with even higher intelligence than Opus”** after stronger safeguards ([dejavucoder](https://x.com/dejavucoder/status/2060042723185623261)). Multiple watchers interpreted this as a **Mythos-class** rollout with cyber-sensitive capabilities selectively constrained:\n\n“Mythos class model to all customers in the coming weeks” (\n\n[kimmonismus](https://x.com/kimmonismus/status/2060047510853312557))“They are releasing a Mythos-class model with the appropriate safeguards, meaning that you can’t use the ‘too dangerous to release’ capabilities” (\n\n[scaling01](https://x.com/scaling01/status/2060123335514636693))Cline summarized Anthropic as announcing plans to release new models\n\n**with higher intelligence than Opus after adding stronger cyber safeguards**([Cline](https://x.com/cline/status/2060063889874972905))\n\nThis is not just product roadmap gossip; it reframes Opus 4.8 as a **staged release strategy**:\n\nimprove the commercially safe / broadly deployable general model,\n\nhold back more dangerous cyber capability until controls are ready.\n\nThat tradeoff drew both praise and criticism:\n\nsupportive: safety-first frontier deployment\n\nskeptical: Anthropic may be sacrificing some competitiveness in raw capability availability to maintain its risk posture (\n\n[teortaxesTex](https://x.com/teortaxesTex/status/2060114150928322868))\n\n**Dynamic Workflows: the most important technical addition beyond the base model**\n\nThe standout systems feature accompanying Opus 4.8 is **Dynamic Workflows** in Claude Code.\n\nOfficial description:\n\n“Claude writes an orchestration script on the fly”\n\nthen spins up\n\n**a large fleet of coordinated subagents in parallel** use the word\n\n**“workflow”** in a prompt to activate it ([ClaudeDevs](https://x.com/ClaudeDevs/status/2060044853279617150))\n\nAnthropic’s employees and users described it as enabling:\n\norchestration plans that Claude “strictly follows”\n\n**hundreds of agents** verification before returning results\n\nsupport for very large migration / refactor / auditing jobs (\n\n[Cat Wu](https://x.com/_catwu/status/2060054180379689074),[Mikey K](https://x.com/mikeyk/status/2060046052821184907))\n\nExamples cited:\n\n**porting Bun from Zig to Rust**, around** 750k lines**,** 99.8% of test suite passing**,** 11 days from first commit to merge**, using hundreds of parallel agents and two reviewers per file ([Cat Wu](https://x.com/_catwu/status/2060051282698682576))processing\n\n**hundreds of A/B test flags** in parallel in**<10 minutes** to identify stale flags ([Cat Wu](https://x.com/_catwu/status/2060054182447448387))\n\nThis launch triggered a mini-debate around the broader concept:\n\nSome researchers argued Anthropic had essentially productized ideas resembling\n\n**Recursive Language Models / symbolic recursion over prompts**([a1zhang](https://x.com/a1zhang/status/2060071701879066626),[lateinteraction](https://x.com/lateinteraction/status/2060078643133763839),[lateinteraction](https://x.com/lateinteraction/status/2060082815077961842))Others pushed back that “calling models in a loop” is not novel and that many builders have been doing this manually for months (\n\n[omarsar0](https://x.com/omarsar0/status/2060059612041171175),[jxmnop](https://x.com/jxmnop/status/2060109869399916770),[willdepue](https://x.com/willdepue/status/2060144024300695662))\n\nThe more substantive critique was not originality, but **cost and harness quality**:\n\nOmar Sar0 warned agent-to-agent interactions are effective but token-heavy (\n\n[omarsar0](https://x.com/omarsar0/status/2060059612041171175))Theo complained about conflicting parallel edits and wasted tokens in the current tooling (\n\n[Theo](https://x.com/theo/status/2060135394570797158))itsclivetime joked that “hundreds of parallel subagents” will hit quota in seconds (\n\n[itsclivetime](https://x.com/itsclivetime/status/2060157266591129895))KLieret highlighted a system-card finding: multi-agents may not improve final ProgramBench quality, but they reach mediocre solutions\n\n**2x faster**([KLieret](https://x.com/KLieret/status/2060111272943739243))\n\nSo the consensus from technical users is:\n\n**Dynamic workflows are strategically important** they are likely the future of coding agents\n\nbut the current implementation still faces\n\n**editing conflicts, cost blowups, and harness inefficiencies**\n\n**Different opinions on Opus 4.8**\n\n**1) Strongly supportive: Anthropic is back**\n\n## Keep reading with a 7-day free trial\n\nSubscribe to Latent.Space to keep reading this post and get 7 days of free access to the full post archives.", "url": "https://wpnews.pro/news/ainews-anthropic-raises-965b-series-h-releases-opus-4-8-and-dynamic-workflows", "canonical_source": "https://www.latent.space/p/ainews-anthropic-raises-965b-series", "published_at": "2026-05-29 02:07:24+00:00", "updated_at": "2026-05-29 02:14:32.486719+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-startups", "ai-products", "ai-research"], "entities": ["Anthropic", "Opus 4.8", "OpenAI", "Amazon", "Google", "Gemini 3.5 Flash", "Gemini 3.1 Pro"], "alternates": {"html": "https://wpnews.pro/news/ainews-anthropic-raises-965b-series-h-releases-opus-4-8-and-dynamic-workflows", "markdown": "https://wpnews.pro/news/ainews-anthropic-raises-965b-series-h-releases-opus-4-8-and-dynamic-workflows.md", "text": "https://wpnews.pro/news/ainews-anthropic-raises-965b-series-h-releases-opus-4-8-and-dynamic-workflows.txt", "jsonld": "https://wpnews.pro/news/ainews-anthropic-raises-965b-series-h-releases-opus-4-8-and-dynamic-workflows.jsonld"}}